What’s the Difference Between Prokaryotic and Eukaryotic Cells? – HowStuffWorks

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You know when you hear somebody start a sentence with, "There are two kinds of people..." and you think to yourself "Oh boy, here it comes." Because reducing the whole of humanity down to "two kinds of people" seems like an odious activity at best.

But what if I were to tell you that there are just two kinds of organisms?

According to scientists, the world is split into two kinds of organisms prokaryotes and eukaryotes which have two different types of cells. An organism can be made up of either one type or the other. Some organisms consist of only one measly cell, but even so, that cell will either be either prokaryotic or eukaryotic. It's just the way things are.

The difference between eukaryotic and prokaryotic cells has to do with the little stuff-doing parts of the cell, called organelles. Prokaryotic cells are simpler and lack the eukaryote's membrane-bound organelles and nucleus, which encapsulate the cell's DNA. Though more primitive than eukaryotes, prokaryotic bacteria are the most diverse and abundant group of organisms on Earth we humans are literally covered in prokaryotes, inside and out. On the other hand, all humans, animals, plants, fungi and protists (organisms made up of a single cell) are eukaryotes. And though some eukaryotes are single celled think amoebas and paramecium there are no prokaryotes that have more than one cell.

"I think of a prokaryote as a one-room efficiency apartment and a eukaryote as a $6 million mansion," says Erin Shanle, a professor in the Department of Biological and Environmental Sciences at Longwood University, in an email interview. "The size and separation of functional 'rooms,' or organelles, in eukaryotes is similar to the many rooms and complex organization of a mansion. Prokaryotes have to get similar jobs done in a single room without the luxury of organelles."

One reason this analogy is helpful is because all cells, both prokaryotes and eukaryotes, are surrounded by a selectively permeable membrane which allows only certain molecules to get in and out much like the windows and doors of our home. You can lock your doors and windows to keep out stray cats and burglars (the cellular equivalent to viruses or foreign materials), but you unlock the doors to bring in groceries and to take out the trash. In this way, all cells maintain internal homeostasis, or stability.

"Prokaryotes are much simpler with respect to structure," says Shanle. "They have a single 'room' to perform all the necessary functions of life, namely producing proteins from the instructions stored in DNA, which is the complete set of instructions for building a cell. Prokaryotes don't have separate compartments for energy production, protein packaging, waste processing or other key functions."

In contrast, eukaryotes have membrane-bound organelles that are used to separate all these processes, which means the kitchen is separate from the master bathroom there are dozens of walled-off rooms, all of which serve a different function in the cell.

For example, DNA is stored, replicated, and processed in the eukaryotic cell's nucleus, which is itself surrounded by a selectively permeable membrane. This protects the DNA and allows the cell to fine-tune the production of proteins necessary to do its job and keep the cell alive. Other key organelles include the mitochondria, which processes sugars to generate energy, the lysosome, which processes waste and the endoplasmic reticulum, which helps organize proteins for distribution around the cell. Prokaryotic cells have to do a lot of this same stuff, but they just don't have separate rooms to do it in. They're more of a two-bit operation in this sense.

"Many eukaryotic organisms are made up of multiple cell types, each containing the same set of DNA blueprints, but which perform different functions," says Shanle. "By separating the large DNA blueprints in the nucleus, certain parts of the blueprint can be utilized to create different cell types from the same set of instructions."

You might be wondering how organisms got to be divided in this way. Well, according to endosymbiotic theory, it all started about 2 billion years ago, when some large prokaryote managed to create a nucleus by folding its cell membrane in on itself.

"Over time, a smaller prokaryotic cell was engulfed by this larger cell," says Shanle. "The smaller prokaryote could perform aerobic respiration, or process sugars into energy using oxygen, similar to the mitochondria we see in eukaryotes that are living today. This smaller cell was maintained within the larger host cell, where it replicated and was passed on to subsequent generations. This endosymbiotic relationship ultimately led to the smaller cell becoming a part of the larger cell, eventually losing its autonomy and much of its original DNA."

However, the mitochondria of today's eukaryotes have their own DNA blueprints that replicate independently from the DNA in the nucleus, and mitochondrial DNA has some similarity to prokaryotic DNA, which supports the endosymbiotic theory. A similar model is thought to have led to the evolution of chloroplasts in plants, but the story begins with a eukaryotic cell containing a mitochondria engulfing a photosynthetic prokaryote.

Eukaryotes and prokaryotes they're different! But even though it can be hard to see the similarities between humans and bacteria, we are all made of the same stuff: DNA, proteins, sugars and lipids.

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What's the Difference Between Prokaryotic and Eukaryotic Cells? - HowStuffWorks

University of Hull Supercomputer Supporting Global COVID-19 Research – HPCwire

June 1, 2020 A multi-million-pound high-performance computer (HPC) at the University of Hull is playing a crucial role in global COVID-19 research. Known as Viper, the supercomputer became the fastest machine of any northern university when it arrived in Hull back in 2016.

Four years on, Viper is now helping researchers around the world better understand and tackle the spread of COVID-19. The University has partnered up with HPC specialist OCF to support global research into COVID-19 on a project called [emailprotected].

Chris Collins, Research Systems Manager at the University of Hull said: It has been humbling to see how the University has responded to the challenges posed by COVID-19. From a team producing face shields for the NHS, to helping re-train former NHS staff, the University is doing everything it can in this difficult time.

[emailprotected] is another example of this. Using spare compute capacity on Viper which is constantly supporting other research projects within the University is us doing our bit to help tackle COVID-19. Viper is able to download and process bitesize chunks of huge computer simulations, and the final results can then be accessed by researchers across the world.

OCF is helping the University of Hull and other research institutions to donate any spare capacity in their existing solutions to the COVID-19 sequencing effort through [emailprotected] Spare capacity can be utilised when users are not using all HPC resources and any donation of clock cycles doesnt need to impact on any current workloads that are being worked on.

HPC is one of the most powerful tools we have in the fight against disease, giving us detailed insight into the building blocks of viruses, said Russell Slack, managing director at OCF. This is an opportunity for anyone with an x86 Slurm cluster to get involved in combating COVID-19. GPU capacity is the most sought after at this time, but all donated resources help.

[emailprotected] is a distributed computing project for simulating protein dynamics, including the process of protein folding and the movements of proteins implicated in a variety of diseases, developed by Stanford University in California to focus on disease research. The project brings together personal computers, as well as those donated by larger companies and institutions from across the world and enables them to join together to run huge simulations to provide new opportunities for developing therapeutics and treatments for COVID-19.

Breaking up and distributing large tasks across personal computers is not a new concept, with projects using this approach since the 1990s, Collins said. Supercomputers like Viper are normally used to tackle the grand challenges of science and engineering on their own rather than as part of distributed projects like this, however COVID-19 has really brought computers like Viper to the forefront of the [emailprotected] project.

The Universitys HPC team is working hard to dedicate any resources not currently being used for University research to the project. Other OCF customers also joining the [emailprotected] effort include the University of Aberdeen, the University of East Anglia and Plymouth Marine Laboratory.

Source: OCF and Hull University

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University of Hull Supercomputer Supporting Global COVID-19 Research - HPCwire

QCI Achieves Best-in-Class Performance with its Mukai Quantum-Ready Application Platform – Quantaneo, the Quantum Computing Source

These performance benefits eliminate one of the greatest obstacles to the development and adoption of quantum-ready applications, since up until now they have been slower than traditional methods running classically. The results show that Mukai provides better results than currently used software to solve complex optimization problems faced by nearly every major company and government agency worldwide.

While future quantum computers are expected to deliver even greater performance benefits, Mukai delivers today the best-known quality of results, time-to-solution, and diversity of solutions in a commercially available service. This superior capability enables business and government organizations to become quantum-ready today and realize immediate benefits from improved performance.

Optimization problems can occur in logistics routing, where timely delivery, reduced fuel consumption, and driver safety all come into play. Optimization solutions can significantly mitigate the impact to revenue or business operations posed by events such as flooding or power outages. Companies can leverage the robust and diverse solutions offered by Mukai to minimize disruptive high-impact events in real-time.

Optimization can also be achieved in R&D contexts like drug design, where better predicted protein folding can speed the design process, increase the efficacy of drugs, and guide the search for patient cohorts who might benefit. Optimization of business processes generated by solvers like Mukai can result in savings of hundreds of billions of dollars annually.

The technical study used MITs MQlib, a well-established combinatorial optimization benchmark, to compare QCI qbsolv performance with those of a variety of solvers. QCI qbsolv delivered better quality or energy of results for most problems (27 of 45) and often ran more than four times faster than the best MQlib solver (21 of 45 problems).

In terms of diversity of resultsfinding, for example, logistics routes that are quite different from each otherQCI qbsolv often found dozens of binary results that were different in more than 350 different positions (i.e., route segments). Known also to researchers as Hamming distance, diversity of results is another important advantage expected of quantum computing.

The paper, QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation, describes the full results and discusses their impact, and is available at arxiv.org/abs/2005.11294

These results demonstrate that Mukai-powered applications can exploit quantum computing concepts to solve real-world problems effectively using classical computers, noted QCI CTO, Mike Booth. More importantly, the quality, speed, and diversity of solutions offered by Mukai means government and corporate organizations can use Mukai to adopt quantum-ready approaches today without sacrificing performance. Mukai is also hardware-agnostic, enabling adopters to exploit whichever hardware delivers the quantum advantage. Were confident that leading companies can leverage Mukai today to achieve a competitive advantage.

To be sure, we are very early in the quantum computing and software era, continued Booth. Just as the vectorizing compilers for Crays processors improved radically over time, we are planning to introduce further performance improvements to Mukai over the coming months. Some of these advancements will benefit application performance using classical computers as well as hybrid quantum-classical scenarios, but all will be essential to delivering the quantum advantage. We expect Mukai to play an integral role in the quantum computing landscape by enabling organizations to tap into quantum-inspired insights today to better answer their high-value problems.

The Mukai software execution platform for quantum computers enables users and application developers to solve complex discrete constrained-optimization problems that are at the heart of some of the most difficult computing challenges in industry, government and academia. This includes, for example, scheduling technicians, parts and tools for aircraft engine repair, or designing proteins for coronavirus vaccines and therapies.

QCI recently announced version 1.1 of Mukai, which introduced higher performance and greater ease-of-use for subject-matter experts who develop quantum-ready applications and need superior performance today. Local software connects users to the Mukai cloud service for solving extremely complex optimization problems. It enables developers to create and execute quantum-ready applications on classical computers today that are ready to run on the quantum computers of tomorrow when these systems achieve performance superiority.

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QCI Achieves Best-in-Class Performance with its Mukai Quantum-Ready Application Platform - Quantaneo, the Quantum Computing Source

AMD COVID-19 HPC Fund delivers supercomputing to researchers – Scientific Computing World

AMD and Penguin Computing

AMD and Penguin Computing have donated seven petaflops of compute power as part of the AMD HPC Fund for COVID-19 research. New York University (NYU), Massachusetts Institute of Technology (MIT) and Rice University are the first universities named to receive complete AMD-powered, high-performance computing systems.

Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing commented on the importance of computing resources in the fight against the current viral outbreak. Across MIT we are engaged in work to address the global COVID-19 pandemic, from that with immediate impact such as modelling, testing, and treatment, to that with medium and longer term impact such as discovery of new therapeutics and vaccines. Nearly all of this work involves computing, and much of it requires the kind of high performance computing that AMD is so generously providing with this gift of a Petaflop machine,

At the Center for Theoretical Biological Physics, Rice researcher Jos Onuchic is using his previous studies on influenza A as a guide to explore how the coronavirus's surface proteins facilitate entrance to human cells, the critical first step of infection. Another scientist, Peter Wolynes, is using principles from his foundational theories of protein folding to screen thousands of drug molecules and identify the best candidates for clinical tests based upon how well they bind to the virus's surface proteins.

Peter Rossky, dean of Rice's Wiess School of Natural Sciences said:The AMD gift will be truly transformational for Rice's computational attack on COVID-19.We have the methods to progress, but studies of large, complex systems are at the cutting-edge of computational feasibility. The AMD contribution of dedicated, state-of-the-art computational power will be a game changer in accelerating progress toward defeating this virus.

AMD also announced it will contribute a cloud-based system powered by AMD EPYC and AMD Radeon Instinct processors located on-site at Penguin Computing, providing remote supercomputing capabilities for selected researchers around the world.

Penguin Computing is looking forward to supporting and contributing to the COVID-19 research efforts through this AMD collaboration. We are committed to providing our applications and technology expertise in high performance computing, artificial intelligence and data analytics to both the University on-premises and our remote POD cloud environments, said Sid Mair, president of Penguin Computing.

Combined, the donated systems will collectively provide researchers with more than seven petaflops of compute power that can be applied to fight COVID-19.Contributions from Penguin Computing, NVIDIA, Gigabyte, and others are helping the AMD HPC Fund advance COVID-19 research.

Ultra-fast data speeds and smart data-processing are key to delivering insights that science demands, particularly in these challenging times, said Gilad Shainer, senior vice-president of marketing for Mellanox networking at NVIDIA. NVIDIA Mellanox HDR 200 gigabit InfiniBand solutions provide high data throughput, extremely low latency, and application offload engines that accelerate bio-science simulations and further the development of treatments against the coronavirus.

The AMD COVID-19 HPC fund was established to provide research institutions with computing resources to accelerate medical research on COVID-19 and other diseases. In addition to the initial donations of $15 million of high-performance computing systems, AMD has contributed technology and technical resources to nearly double the peak system of the Corona system at Lawrence Livermore National Laboratory which is being used to provide additional computing power for molecular modelling in support of COVID-19 research.

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AMD COVID-19 HPC Fund delivers supercomputing to researchers - Scientific Computing World

QCI Achieves Best-in-Class Performance with its Mukai Quantum-Ready Application Platform – GlobeNewswire

LEESBURG, Va., June 02, 2020 (GLOBE NEWSWIRE) -- Quantum Computing Inc. (OTCQB:QUBT) (QCI), a technology leader in quantum-ready applications and tools, reported in a newly released scientific paper that QCI qbsolv, a component of its Mukai software execution platform for quantum computers, has delivered on its promise of immediate performance benefits from quantum-ready methods running on classical computers.

These performance benefits eliminate one of the greatest obstacles to the development and adoption of quantum-ready applications, since up until now they have been slower than traditional methods running classically. The results show that Mukai provides better results than currently used software to solve complex optimization problems faced by nearly every major company and government agency worldwide.

While future quantum computers are expected to deliver even greater performance benefits, Mukai delivers today the best-known quality of results, time-to-solution, and diversity of solutions in a commercially available service. This superior capability enables business and government organizations to become quantum-ready today and realize immediate benefits from improved performance.

Optimization problems can occur in logistics routing, where timely delivery, reduced fuel consumption, and driver safety all come into play. Optimization solutions can significantly mitigate the impact to revenue or business operations posed by events such as flooding or power outages. Companies can leverage the robust and diverse solutions offered by Mukai to minimize disruptive high-impact events in real-time.

Optimization can also be achieved in R&D contexts like drug design, where better predicted protein folding can speed the design process, increase the efficacy of drugs, and guide the search for patient cohorts who might benefit. Optimization of business processes generated by solvers like Mukai can result in savings of hundreds of billions of dollars annually.

The technical study used MITs MQlib, a well-established combinatorial optimization benchmark, to compare QCI qbsolv performance with those of a variety of solvers. QCI qbsolv delivered better quality or energy of results for most problems (27 of 45) and often ran more than four times faster than the best MQlib solver (21 of 45 problems).

In terms of diversity of resultsfinding, for example, logistics routes that are quite different from each otherQCI qbsolv often found dozens of binary results that were different in more than 350 different positions (i.e., route segments). Known also to researchers as Hamming distance, diversity of results is another important advantage expected of quantum computing.

The paper, QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation, describes the full results and discusses their impact, and is available at arxiv.org/abs/2005.11294.

These results demonstrate that Mukai-powered applications can exploit quantum computing concepts to solve real-world problems effectively using classical computers, noted QCI CTO, Mike Booth. More importantly, the quality, speed, and diversity of solutions offered by Mukai means government and corporate organizations can use Mukai to adopt quantum-ready approaches today without sacrificing performance. Mukai is also hardware-agnostic, enabling adopters to exploit whichever hardware delivers the quantum advantage. Were confident that leading companies can leverage Mukai today to achieve a competitive advantage.

To be sure, we are very early in the quantum computing and software era, continued Booth. Just as the vectorizing compilers for Crays processors improved radically over time, we are planning to introduce further performance improvements to Mukai over the coming months. Some of these advancements will benefit application performance using classical computers as well as hybrid quantum-classical scenarios, but all will be essential to delivering the quantum advantage. We expect Mukai to play an integral role in the quantum computing landscape by enabling organizations to tap into quantum-inspired insights today to better answer their high-value problems.

The Mukai software execution platform for quantum computers enables users and application developers to solve complex discrete constrained-optimization problems that are at the heart of some of the most difficult computing challenges in industry, government and academia. This includes, for example, scheduling technicians, parts and tools for aircraft engine repair, or designing proteins for coronavirus vaccines and therapies.

QCI recently announced version 1.1 of Mukai, which introduced higher performance and greater ease-of-use for subject-matter experts who develop quantum-ready applications and need superior performance today. Local software connects users to the Mukai cloud service for solving extremely complex optimization problems. It enables developers to create and execute quantum-ready applications on classical computers today that are ready to run on the quantum computers of tomorrow when these systems achieve performance superiority.

Mukai addresses the fast-growing market for quantum computing, which isexpected to grow at a 23.2% CAGR to $9.1 billion by 2030, according to Tractica.

For more information about Mukai or a demonstration of the platform, contact John Dawson at (703) 436-2161 or info@quantumcomputinginc.com.

About Quantum Computing Inc.Quantum Computing Inc. (QCI) is focused on developing novel applications and solutions utilizing quantum and quantum-ready computing techniques to solve difficult problems in various industries. The company is leveraging its team of experts in finance, computing, security, mathematics and physics to develop commercial applications for industries and government agencies that will need quantum computing power to solve their most challenging problems. For more information about QCI, visit http://www.quantumcomputinginc.com.

Important Cautions Regarding Forward-Looking StatementsThis press release contains forward-looking statements as defined within Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. By their nature, forward-looking statements and forecasts involve risks and uncertainties because they relate to events and depend on circumstances that will occur in the near future. Those statements include statements regarding the intent, belief or current expectations of Quantum Computing (Company), and members of its management as well as the assumptions on which such statements are based. Prospective investors are cautioned that any such forward-looking statements are not guarantees of future performance and involve risks and uncertainties, and that actual results may differ materially from those contemplated by such forward-looking statements.

The Company undertakes no obligation to update or revise forward-looking statements to reflect changed conditions. Statements in this press release that are not descriptions of historical facts are forward-looking statements relating to future events, and as such all forward-looking statements are made pursuant to the Securities Litigation Reform Act of 1995. Statements may contain certain forward-looking statements pertaining to future anticipated or projected plans, performance and developments, as well as other statements relating to future operations and results. Any statements in this presentation that are not statements of historical fact may be considered to be forward-looking statements. Words such as "may," "will," "expect," "believe," "anticipate," "estimate," "intends," "goal," "objective," "seek," "attempt," aim to, or variations of these or similar words, identify forward-looking statements. These risks and uncertainties include, but are not limited to, those described in Item 1A in the Companys Annual Report on Form 10-K, which is expressly incorporated herein by reference, and other factors as may periodically be described in the Companys filings with the SEC.

Company ContactRobert Liscouski, CEOTel (703) 436-2161info@quantumcomputinginc.com

Investor & Media Relations ContactRon Both or Grant StudeCMA Investor RelationsTel (949) 432-7566Email Contact

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QCI Achieves Best-in-Class Performance with its Mukai Quantum-Ready Application Platform - GlobeNewswire

Global Lab Automation in Protein Engineering Market Industry Analysis and Forecast… – Azizsalon News

Global Lab Automation in Protein Engineering Market is expected to reach US$ 2,710Mn by 2026 from US$ 1,073.01 Mn in 2018 at CAGR of 14.15%.

Lab automation in protein engineering market report helps to cover the marketplace and internal & external factors which could impact the automation industry. The increasing demand for protein drugs over non-protein drugs along with high incidences of lifestyle diseases is one of the key drivers for automation in the protein engineering market globally. Other drivers such as positive regulation of government for protein engineering and the need for consistency in quality.

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Lack of planning for technology development, improperly trained personnel and high initial setup cost and low priority for lab automation among small and medium-sized laboratories hampering the Global Lab Automation in Protein Engineering Market.

Monoclonal antibodies segment is expected to grow at the highest XX% CAGR during the forecast period. Monoclonal antibodies are widely used as diagnostic and research reagents also in human therapy. This growth is attributed to the rise in adoption of them for various therapies such as cancer and autoimmune diseases.

Software and informatics segment is expected to grow at the highest XX% CAGR during the forecast period. The software can be used to improve electron density maps throughout a statistical approach in combining experimental X-ray diffraction data with information about the expected characteristics of an electron map. Automation of instrument helpful to understand and solve the mysteries of protein dysfunction, as well as mis-folding, aggregation, and abnormal movement.

On the basis of region Global Lab Automation in Protein Engineering Market divided into five regions such as Asia Pacific, North America, Europe, Latin America, and Middle East Africa. Among all the regions, North America had the XX% market share in 2018 and is projected to lead the market during the forecast period. Because of dominating the lab automation in protein engineering market globally and growing outsourcing pharmaceutical manufacturing to these regions due to the availability of cheaper labour and resources. Strict regulations imposed by the US government and the FDA, increasing demand in the diagnostic market, a growing emphasis on the drug discovery and research labs, and the rising presence of numerous diseases in North America have fueled the growth.

Key players operating in global automation in protein engineering market, Thermo Fisher Scientific, Danaher, Hudson Robotics, Becton Dickinson, Synchron Lab Automation, Agilent Technologies, Siemens Healthcare, Tecan Group Ltd, Perkinelmer, Honeywell International, Bio-Rad, Roche Holding AG, Eppendorf AG, Shimadzu, Aurora Biomed.

The objective of the report is to present comprehensive analysis of Global Lab Automation in Protein Engineering Market including all the stakeholders of the industry. The past and current status of the industry with forecasted market size and trends are presented in the report with the analysis of complicated data in simple language. The report covers all the aspects of industry with dedicated study of key players that includes market leaders, followers and new entrants by region. PORTER, SVOR, PESTEL analysis with the potential impact of micro-economic factors by region on the market have been presented in the report. External as well as internal factors that are supposed to affect the business positively or negatively have been analyzed, which will give clear futuristic view of the industry to the decision makers. The report also helps in understanding Global Lab Automation in Protein Engineering Market dynamics, structure by analyzing the market segments, and project the Global Lab Automation in Protein Engineering Market size. Clear representation of competitive analysis of key players by A Global Lab Automation in Protein Engineering Type, price, financial position, product portfolio, growth strategies, and regional presence in the Global Lab Automation in Protein Engineering Market make the report investors guide.

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Scope of Global Lab Automation in Protein Engineering Market

Global Lab Automation in Protein Engineering Market, by Software

Automated liquid handling Micro plate readers Standalone robots Software and informatics ASRSGlobal Lab Automation in Protein Engineering Market, by Protein Type

Monoclonal Antibodies Interferon Growth HormoneGlobal Lab Automation in Protein Engineering Market, by Application

Clinical diagnostics Drug discovery Genomics solutions Proteomics solutions Protein engineeringGlobal Lab Automation in Protein Engineering Market, by Type of automation

Modular automation Total lab automationGlobal Lab Automation in Protein Engineering Market, by Region

North America Europe Asia Pacific Middle East and Africa South AmericaKey Players Operating in Global Lab Automation in Protein Engineering Market

Thermo Fisher Scientific Danaher Hudson Robotics Becton Dickinson Synchron Lab Automation Agilent Technologies Siemens Healthcare Tecan Group Ltd Perkinelmer Honeywell International Bio-Rad Roche Holding AG Eppendorf AG Shimadzu Aurora Biomed

MAJOR TOC OF THE REPORT

Chapter One: Lab Automation in Protein Engineering Market Overview

Chapter Two: Manufacturers Profiles

Chapter Three: Global Lab Automation in Protein Engineering Market Competition, by Players

Chapter Four: Global Lab Automation in Protein Engineering Market Size by Regions

Chapter Five: North America Lab Automation in Protein Engineering Revenue by Countries

Chapter Six: Europe Lab Automation in Protein Engineering Revenue by Countries

Chapter Seven: Asia-Pacific Lab Automation in Protein Engineering Revenue by Countries

Chapter Eight: South America Lab Automation in Protein Engineering Revenue by Countries

Chapter Nine: Middle East and Africa Revenue Lab Automation in Protein Engineering by Countries

Chapter Ten: Global Lab Automation in Protein Engineering Market Segment by Type

Chapter Eleven: Global Lab Automation in Protein Engineering Market Segment by Application

Chapter Twelve: Global Lab Automation in Protein Engineering Market Size Forecast (2019-2026)

Browse Full Report with Facts and Figures of Lab Automation in Protein Engineering Market Report at: https://www.maximizemarketresearch.com/market-report/global-lab-automation-in-protein-engineering-market/22288/

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Global Lab Automation in Protein Engineering Market Industry Analysis and Forecast... - Azizsalon News

Monster or Machine? A Profile of the Coronavirus at 6 Months – Seattle Times

A virus, at heart, is information, a packet of data that benefits from being shared.

The information at stake is genetic: instructions to make more virus. Unlike a truly living organism, a virus cannot replicate on its own; it cannot move, grow, persist or perpetuate. It needs a host. The viral code breaks into a living cell, hijacks the genetic machinery and instructs it to produce new code new virus.

President Donald Trump has characterized the response to the pandemic as a medical war, and described the virus behind it as, by turns, genius, a hidden enemy and a monster. It would be more accurate to say that we find ourselves at odds with a microscopic photocopy machine. Not even that: an assembly manual for a photocopier, model SARS-CoV-2.

For at least six months now, the virus has replicated among us. The toll has been devastating. Officially, more than 6 million people worldwide have been infected so far, and 370,000 have died. (The actual numbers are certainly higher.) The United States, which has seen the largest share of cases and casualties, recently surpassed 100,000 deaths, one-quarter the number of all Americans who died in World War II. Businesses are shuttered in 10 weeks, some 40 million Americans have lost their jobs and food banks are overrun. The virus has fueled widespread frustration and exposed our deepest faults: of color, class and privilege, between the deliverers and the delivered to.

Still, summer summer! has all but arrived. We step out to look, breathe, vent. The pause is illusory. Cases are falling in New York, the epicenter in the United States, but firmly rising in Wisconsin, Virginia, Alabama, Arkansas, North and South Carolina, and other states. China, where the pandemic originated, and South Korea saw recent resurgences. Health officials fear another major wave of infections in the fall, and a possible wave train beyond.

We are really early in this disease, Dr. Ashish Jha, director of the Harvard Global Health Institute, told The New York Times recently. If this were a baseball game, it would be the second inning.

There may be trillions of species of virus in the world. They infect bacteria, mostly, but also abalone, bats, beans, beetles, blackberries, cassavas, cats, dogs, hermit crabs, mosquitoes, potatoes, pangolins, ticks and the Tasmanian devil. They give birds cancer and turn bananas black. Of the trillions, a few hundred thousand kinds of viruses are known, and fewer than 7,000 have names. Only about 250, including SARS-CoV-2, have the mechanics to infect us.

In our information age, we have grown familiar with computer viruses and with memes going viral; now here is the real thing to remind us what the metaphor means. A mere wisp of data has grounded more than half of the worlds commercial airplanes, sharply reduced global carbon emissions and doubled the stock price of Zoom. It has infiltrated our language social distancing, immunocompromised shoppers and our dreams. It has postponed sports, political conventions, and the premieres of the next Spider-Man, Black Widow, Wonder Woman and James Bond films. Because of the virus, the U.S. Supreme Court renders rulings by telephone, and wild boars roam the empty streets of Barcelona, Spain.

It also has prompted a collaborative response unlike any our species has seen. Teams of scientists, working across national boundaries, are racing to understand the viruss weaknesses, develop treatments and vaccine candidates, and to accurately forecast its next moves. Medical workers are risking their lives to tend to the sick. Those of us at home do what we can: share instructions for how to make a surgical mask from a pillowcase; sing and cheer from windows and doorsteps; send condolences; offer hope.

Were mounting a reaction against the virus that is truly unprecedented, said Dr. Melanie Ott, director of the Gladstone Institute of Virology in San Francisco.

So far the match is deadlocked. We gather, analyze, disseminate, probe: What is this thing? What must be done? When can life return to normal? And we hide while the latest iteration of an ancient biochemical cipher ticks on, advancing itself at our expense.

A Fearsome Envelope

Who knows when viruses first came about. Perhaps, as one theory holds, they began as free-living microbes that, through natural selection, were stripped down and became parasites. Maybe they began as genetic cogs within microbes, then gained the ability to venture out and invade other cells. Or maybe viruses came first, shuttling and replicating in the primordial protein soup, gaining shades of complexity enzymes, outer membranes that gave rise to cells and, eventually, us. They are sacks of code double- or single-stranded, DNA or RNA and sometimes called capsid-encoding organisms, or CEOs.

As viruses go, SARS-CoV-2 is big its genome is more than twice the size of that of the average flu virus and about one-half larger than Ebolas. But it is still tiny: 10,000 times smaller than a millimeter, barely one-thousandth the width of a human hair, smaller even than the wavelength of light from a germicidal lamp. If a person were the size of Earth, the virus would be the size of a person. Picture a human lung cell as a cramped office just big enough for a desk, a chair and a copy machine. SARS-CoV-2 is an oily envelope stuck to the door.

It was formally identified on Jan. 7 by scientists in China. For weeks beforehand, a mysterious respiratory ailment had been circulating in the city of Wuhan. Health officials were worried that it might be a reappearance of severe acute respiratory syndrome, or SARS, an alarming viral illness that emerged abruptly in 2002, infected more than 8,000 people and killed nearly 800 in the next several months, then was quarantined into oblivion.

The scientists had gathered fluid samples from three patients and, with nucleic-acid extractors and other tools, compared the genome of the pathogen with that of known ones. A transmission electron microscope revealed the culprit: spherical, with quite distinctive spikes reminiscent of a crown or the corona of the sun. It was a coronavirus, and a novel one.

In later colorized images, the virus resembles small garish orbs of lint or the papery eggs of certain spiders, adhering by the dozens to much larger cells. Recently a visual team, working closely with researchers, created the most accurate model of the SARS-CoV-2 viral particle currently available: a barbed, multicolored globe with the texture of fine moss, like something out of Dr. Seuss, or a sunken naval mine draped in algae and sponges.

Once upon a time, our pathogens were crudely named: Spanish flu, Asian flu, yellow fever, Black Death. Now we have H1N1, MERS (Middle East respiratory syndrome), HIV strings of letters as streamlined as the viruses themselves, codes for codes. The new coronavirus was temporarily named 2019-nCoV. On Feb. 11, the International Committee on Taxonomy of Viruses officially renamed it SARS-CoV-2, to indicate that it was very closely related to the SARS virus, another coronavirus.

Before the emergence of the original SARS, the study of coronaviruses was a professional backwater. There has been such a deluge of attention on we coronavirologists, said Susan R. Weiss, a virologist at the University of Pennsylvania. It is quite in contrast to previously being mostly ignored.

There are hundreds of kinds of coronaviruses. Two, SARS-CoV and MERS-CoV, can be deadly; four cause one-third of common colds. Many infect animals with which humans associate, including camels, cats, chickens and bats. All are RNA viruses. Our coronavirus, like the others, is a string of roughly 30,000 biochemical building blocks called nucleotides enclosed in a membrane of both protein and lipid.

Ive always been impressed by coronaviruses, said Anthony Fehr, a virologist at the University of Kansas. They are extremely complex in the way that they get around and start to take over a cell. They make more genes and more proteins than most other RNA viruses, which gives them more options to shut down the host cell.

The core code of SARS-CoV-2 contains genes for as many as 29 proteins: the instructions to replicate the code. One protein, S, provides the spikes on the surface of the virus and unlocks the door to the target cell. The others, on entry, separate and attend to their tasks: turning off the cells alarm system; commandeering the copier to make new viral proteins; folding viral envelopes, and helping new viruses bubble out of the cell by the thousands.

I usually picture it as an entity that comes into the cell and then it falls apart, Ott of the Gladstone Institute said. It has to fall apart to build some mini-factories in the cell to reproduce itself, and has to come together as an entity at the end to infect other cells.

For medical researchers, these proteins are key to understanding why the virus is so successful, and how it might be neutralized. For instance, to break into a cell, the S protein binds to a receptor called angiotensin converting enzyme 2, or ACE2, like a hand on a doorknob. The S protein on this coronavirus is nearly identical in structure to the one in the first SARS SARS Classic but some data suggests that it binds to the target enzyme far more strongly. Some researchers think this may partly explain why the new virus infects humans so efficiently.

Every pathogen evolves along a path between impact and stealth. Too mild and the illness does not spread from person to person; too visible and the carrier, unwell and aware, stays home or is avoided and the illness does not spread. SARS infected 8,000 people, and was contained quickly, in part because it didnt spread before symptoms appeared, Weiss noted.

By comparison, SARS-CoV-2 seems to have achieved an admirable balance. No aspect of the virus is extraordinary, said Dr. Pardis Sabeti, a computational geneticist at the Broad Institute who helped sequence the Ebola virus in 2014. Its the combination of things that makes it extraordinary.

SARS Classic settled quickly into human lung cells, causing a person to cough but also announcing its presence. In contrast, its successor tends to colonize first the nose and throat, sometimes causing few initial symptoms. Some cells there are thought to be rich in the surface enzyme ACE2 the doorknob that SARS-CoV-2 turns so readily. The virus replicates quietly, and quietly spreads: One study found that a person carrying SARS-CoV-2 is most contagious two to three days before they are aware that they might be ill.

From there, the virus can move into the lungs. The delicate alveoli, which gather oxygen essential to the body, become inflamed and struggle to do their job. The texture of the lungs turns from airy froth to gummy marshmallow. The patient may develop pneumonia; some, drowning internally and desperate for oxygen, go into acute respiratory distress and require a ventilator.

The virus can settle in still further: damaging the muscular walls of the heart; attacking the lining of the blood vessels and generating clots; inducing strokes, seizures and inflammation of the brain; and damaging the kidneys. Often the greatest damage is inflicted not by the virus but by the bodys attempt to fight it off with a dangerous cytokine storm of immune system molecules.

The result is an illness with a perplexing array of faces. A dry cough and a low fever at the outset, sometimes. Shortness of breath or difficulty breathing, sometimes. Maybe you lose your sense of smell or taste. Maybe your toes become red and inflamed, as if you had frostbite. For some patients it feels like a heart attack, or it causes delusion or disorientation.

Often it feels like nothing at all; according to the Centers for Disease Control and Prevention, 35% of people who contract the virus experience few to no symptoms, although they can continue to spread it. The virus acts like no pathogen humanity has ever seen, the journal Science notes.

More to the point, the pathogen has gone largely unseen. It has these perfect properties to spread throughout the entire human population, Fehr said. If we didnt know what a virus was and didnt take proper precautions this virus would infect virtually every human on the planet. It still might do that.

(BEGIN OPTIONAL TRIM.)

Data vs. Data

On Jan. 10, the Wuhan health commission in China reported that in the previous weeks, 41 people had contracted the illness caused by the coronavirus, and that one had died the first known casualty at the time.

That same day, Chinese scientists publicly released the complete genome of the virus. The blueprint, which could be simulated and synthesized in the lab, was almost as good as a physical sample, and easier for researchers worldwide to obtain. Analyses appeared in journals and on preprint servers like bioRxiv, on sites like nextstrain.org and virological.org: clues to the viruss origin, its errors and its weaknesses. From then on, the new coronavirus began to replicate not only physically in human cells but also figuratively, and likely to its own detriment, in the human mind.

Ott entered medicine in the 1980s, when AIDS was still new and terrifyingly unknown. Compare that time to today, there are a lot of similarities, she said. A new virus, a rush to understand, a rush to a cure or a vaccine. Whats fundamentally different now is that we have generated this community of collaboration and data-sharing. Its really mind-blowing.

Three hours after the viruss code was published, Inovio Pharmaceuticals, based in San Diego, began work on a vaccine against it one of more than 100 such efforts now underway around the world. Sabetis lab quickly got to work developing diagnostic tests. Ott and Weiss soon managed to obtain samples of live virus, which allowed them to actually look at whats going on when it infects cells in the lab, Ott said.

The cell is mounting a profound battle to prevent the virus from entering or, on entering, to alarm everyone around it so it cant spread, she said. The viruss intent is to overcome this initial surge of defense, to set up shop long enough to reproduce itself and to spread.

With so many proteins in its tool kit, the virus has many ways to counter our immune system; these also offer targets for potential vaccines and drugs. Researchers are working every angle. Most vaccine efforts are focused on disrupting the spike proteins, which allow entry into the cell. The drug remdesivir targets the viruss replication machinery. Fehr studies how the virus disables our immune system.

I use the analogy of Star Wars, he said. The virus is the Dark Side. We have a cellular defense system of hundreds of antiviral proteins Jedi knights to defend ourselves. Our lab is studying one specific Jedi that uses one particular weapon, and how the virus fights back.

These battles, fought on the field of biochemistry, strain the alphabet to describe. The Jedi in this analogy are particular enzymes (poly-ADP-ribose polymerases, or PARPS, if you must know) that are produced in infected cells and wield a molecule that attaches to certain invading proteins We dont know what these are yet, Fehr said and disrupts them. In response, the virus has an enzyme of its own that sweeps away our Jedi like dust from a sandcrawler.

Carolyn Machamer, a cell biologist at the Johns Hopkins School of Medicine, is studying the later stages of the process, to learn how the virus manages to navigate and assemble itself within a host cell and depart it. Among the research topics listed on her university webpage are coronaviruses but also intracellular protein trafficking and exocytosis of large cargo.

On entering the cell, components of the virus set up shop in a subregion, or organelle, called the Golgi complex, which resembles a stack of pancakes and serves as the cells mail-sorting center. Machamer has been working to understand how the virus commandeers the unit to route all the newly replicated viral bits, scattered throughout the cell, for final assembly.

The subject was poorly studied, she conceded. Most drug research has focused on the early stages, like blocking infection at the very outset or disrupting replication inside the cell. Like I said, it hasnt gotten a whole lot of attention, she said. But I think it will now, because I think we have some really interesting targets that could possibly yield new types of drugs.

The line of inquiry dates back to her postdoctoral days. She was studying the Golgi complex The organelle is really bizarre even then. Its following what you're interested in; thats what basic science is about. Its, like, you dont actually set out to cure the world or anything, but you follow your nose.

(END OPTIONAL TRIM.)

For all the attention the virus has received, it is still new to science and rich in unknowns. Im still very focused on the question: How does the virus get into the body? Ott said. Which cells does it infect in the upper airway? How does it get into the lower airway, and from there to other organs? Its absolutely not clear what the path is, or what the vulnerable path types are.

And most pressing: Why are so many of us asymptomatic? How does the virus manage to do this without leaving traces in some people, but in others theres a giant reaction? she said. Thats the biggest question currently, and the most urgent.

(STORY CAN END HERE. OPTIONAL MATERIAL FOLLOWS.)

Mistakes Are Made

Even a photocopier is imperfect, and SARS-CoV-2 is no exception. When the virus commandeers a host cell to copy itself, invariably mistakes are made, an incorrect nucleotide swapped for the right one, for instance. In theory, such mutations, or an accumulation of them, could make a virus more infectious or deadly, or less so, but in a vast majority of cases, they do not affect a viruss performance.

Whats important to note is that the process is random and incessant. Humans describe the contest between host and virus as a war, but the virus is not at war. Our enemy has no agency; it does not develop strategies for escaping our medicines or the activity of our immune systems.

Unlike some viruses, SARS-CoV-2 has a proofreading protein NSP14 that clips out mistakes. Even still, errors slip through. The virus acquires two mutations a month, on average, which is less than half the error rate of the flu and increases the possibility that a vaccine or drug treatment, once developed, will not be quickly outdated. So far its been relatively faithful, Ott said. Thats good for us.

By March, at least 1,388 variants of the coronavirus had been detected around the world, all functionally identical as far as scientists could tell. Arrayed as an ancestral tree, these lineages reveal where and when the virus spread. For instance, the first confirmed case of COVID-19 in New York was announced on March 1, but an analysis of samples revealed that the virus had begun to circulate in the region weeks earlier. Unlike early cases on the West Coast, which were seeded by people arriving from China, these cases were seeded from Europe, and in turn seeded cases throughout much of the country.

The roots can be traced back still further. The first known patient was hospitalized in Wuhan on Dec. 16, 2019, and first felt ill on Dec. 1; the first infection would have occurred still earlier. Sometime before that the virus, or its progenitor, was in a bat the genome is 96% similar to a bat virus. How long ago it made that jump, and acquired the mutations necessary to do so, is unclear. In any case, and contrary to certain conspiracy theories, SARS-CoV-2 was not engineered in a laboratory.

Those scenarios are so unlikely as to be impossible, said Dr. Robert Garry, a microbiologist at Tulane University and an expert on emerging diseases. In March, a team of researchers including Garry published a paper in Nature Medicine comparing the genome and protein structures of the novel virus with those of other coronaviruses. The novel distinctions were most likely the result of natural selection, they concluded. Our analyses clearly show that SARS-CoV-2 is not a laboratory construct or a purposefully manipulated virus.

In our species, the virus has found prime habitat. It seems to do most of its replicating in the upper respiratory tract, Garry noted: That makes it easier to spread with your voice, so there may be more opportunities for it to spread casually, and perhaps earlier in the course of the disease.

And there we have it: an organism, or whatever the right word is, ideally adapted to human conversation, the louder the better. Our communication is its transmission. Consider where so many outbreaks have begun: funerals, parties, call centers, sports arenas, meatpacking plants, dorm rooms, cruise ships, prisons. In February, a medical conference in Boston led to more than 70 cases in two weeks. In Arkansas, several cases were linked to a high school swim party that Im sure everybody thought was harmless, Gov. Asa Hutchinson said. After a choir rehearsal in Mount Vernon, Washington, 28 members of the choir fell ill. Not even song is safe anymore.

The virus has no trouble finding us. But we are still struggling to find it; a recent model by epidemiologists at Columbia University estimated that for every documented infection in the United States, 12 more go undetected. Who has it, or had it, and who does not? A firm grasp of the viruss whereabouts using diagnostic tests, antibody tests and contact tracing is essential to our bid to return to normal life. But humanitys immune response has been uneven.

In late May, in an open letter, a group of former White House science advisers warned that, to prepare for an anticipated resurgence of the pandemic later this year, the federal government needed to begin preparing immediately to avoid the extraordinary shortage of supplies that occurred this spring.

The virus is here, its everywhere, Dr. Rick Bright, former director of the Biomedical Advanced Research and Development Authority, told the U.S. Senate in mid-May. We need to unleash the voices of the scientists in our public health system in the United States, so they can be heard. Right now, he added, there is no master coordinated plan on how to respond to this outbreak.

SARS-CoV-2 virus has no plan. It doesnt need one; absent a vaccine, the virus is here to stay.

This is a pretty efficient pathogen, Garry said. Its very good at what it does.

The Next Wave

The virus spreads because of an intrinsic, latent quality in the culture, media theorist Douglas Rushkoff, who two decades ago coined the phrase going viral, wrote recently. Both biological and media viruses say less about themselves than they do about their hosts.

To know SARS-CoV-2 is to know ourselves in reflection. It is mechanical, unreflecting, consistently on-message the purest near-living expression of data management to be found on Earth. It is, and does, and is more. There is no I in a virus.

We are exactly its opposite: human, and everything that implies. Masters of information, suckers for misinformation; slaves to emotion, ego and wishful thinking. But also: inquiring, willful, optimistic. In our best moments, we strive to learn, and to advance more than our individual selves.

The best thing to come out of this pandemic is that everyone has become a virologist in some way, Ott said. She has a regular trivia night with her family in Germany, over Zoom. Lately, the topic has centered on viruses, and she has been impressed by how much they know. Theres so much more knowledge around, she said. A lot of wrong info around, also. But people have become so literate, because we all want it to go away.

Sabeti agreed, up to a point. She expressed a deep curiosity about viruses they are formidable opponents to understand but said that, this time around, she found herself less interested in the purely intellectual pursuit.

For me right now, the place that Im in, I really just most want to stop this virus, she said. Its so frustrating and disappointing, to say the least, to be in this position in which we have stopped the world, in which weve created social distancing, in which we have created mass amounts of human devastation and collateral damage because we just werent prepared.

I dont care to understand it, she said. For me, its I get up in the morning and my motivation is just: Stop this thing, and figure out how to never have this happen again.

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Monster or Machine? A Profile of the Coronavirus at 6 Months - Seattle Times

How to do PewDiePies workout for abs and biceps: Cheap equipment and other YouTubers to follow! – HITC – Football, Gaming, Movies, TV, Music

Although PewDiePie deleted his Twitter account awhile back, he was recently all over the social media space a couple of days ago thanks to a shredded picture posted to Instagram by his wife Marzia. This shredded image resulted in fans wanting to know his biceps and abs workout routine so they can do it as well, and thankfully the Swedish YouTube star has shared his routine and methods. Here youll discover how to do the workout as well what cheap equipment you can buy and other YouTubers you can follow to expand upon Pewds advice.

PewDiePie has been a controversial figure on YouTube over the years for some and he has explained some of his heated acts as him being pretty irresponsible in the past. However, he has lately been one of the more honest and straightforward celebrities on the mega platform and this has helped continue his popularity despite him no longer being the horror video game squealer he was before.

His transformation is shown by him acting a lot wiser and more mature, but his transformation is also now embodied by his ripped body. And here youll discover how to do his workout routine for abs and biceps with even cheap equipment.

PewDiePie has shared his five day dumbbell workout routine for abs, biceps, and more.

According to PewDiePie, his five day dumbbell workout routine for abs, biceps, and more begins on Monday with him focusing heavily on his chest and finishing on his shoulders.

Tuesday is a leg day where he does squats, dead-lifts, and lunges, meanwhile Wednesday is reserved for pull exercises.

Thursday is another leg day whereas Friday is a mix of both push and pull exercises.

Although the YouTuber didnt show himself performing any of the exercises, he did share a diagram of the moves he consistently performs.

Of course, anyone will tell you that to build muscle and to burn fat you need to do a lot more than just lift weights.

PewDiePie himself admitted this by stating that he is now eating a greater amount of protein. Not only that, but he has also largely quit alcohol with the exception of social gatherings.

You can buy workout equipment used by PewDiePie to follow his five day dumbbell workout routine.

His DTX Fitness Folding Weight Bench is currently unavailable on Amazon, but you can find other just as good benches for as cheap as 109.99.

PewDiePie says he uses a PowerBlock Sports Series Interchangeable Dumbbell that goes up to 90 pounds, and you can buy one of these from the Powerblock website.

If you really want to hone in on your abs, a machine you could buy is a wonder core for just 89.99. This is a great piece of equipment which allows you to do multiple ab exercises as well as even arms.

You could also instead buy an adjustable Power Tower for the same price. This is an extremely effective tool as it allows you to do ab exercises as well pull ones.

As for weights, you can do PewDiePies pull and lift five day dumbbell workout routine with dumbbells or his Power Blocks, but you may wish to invest in a barbell with a set of weight plates.

This is because it helps you become stronger and lift heavier thanks to both your arms sharing and lifting the load.

For squats and other leg exercises you may want to buy some resistance bands for extra tension.

Lastly, PewDiePie also states that he uses Wrist Wraps to help prevent injury when lifting and these can be bought for as cheap as 9.

If youre interested in changing your figure like PewDiePie there are other YouTubers you can watch for workout routines.

Athlean X is particularly good as he shares routines that can be done at home as well as in the gym, with expensive equipment or with just DIY resources such as a towel.

WWE wrestler Sheamus is also good as he showcases a wide variety of different workout routines from heavy lifting to crossfit. And yes, a lot of his can be performed at home too.

If you want to burn body fat, then youll also be interested in performing HIIT exercises as these burn more calories than lifting weights.

YouTubers/figures who are helpful in this area include Joe Wicks as well as crossfits Lauren Fisher who has her own virtual fitness classes.

In other news, TikTok: What is the Pause Challenge? And how can I do it?

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How to do PewDiePies workout for abs and biceps: Cheap equipment and other YouTubers to follow! - HITC - Football, Gaming, Movies, TV, Music

Oncoprotein SND1 hijacks nascent MHC-I heavy chain to ER-associated degradation, leading to impaired CD8+ T cell response in tumor – Science Advances

INTRODUCTION

Exploring the strategies for tumor immunotherapy is highly dependent on the discovery of molecular mechanisms of tumor immune escape. Tumor cells can escape immune response through loss of antigenicity and/or immunogenicity or by coordinating a suppressive immune microenvironment. Therefore, distinct therapeutic strategies may be required, depending on the mechanisms. Tumor immunotherapy strategies mediated by T cells rely on the functional competence of multiple immunological elements. For example, therapeutic monoclonal antibodies designed to disrupt inhibitory signals received by T cells through the Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) and Programmed cell death protein 1 (PD-1) have been demonstrating long-term survival benefits for some patients with metastatic melanoma (1, 2). However, not all tumors appear to respond effectively. The heterogeneity of cancer suggests the necessity to explore the additional immunoregulatory mechanisms. Defect in the surface expression of major histocompatibility complex class I (MHC-I) molecules is one of the most important reasons for tumor immune escape due to decreased recognition by CD8+ T cells, which has been found in approximately 20 to 60% of common solid cancers, including melanoma and lung, breast, renal, prostate, and bladder cancers, (3, 4). The molecular mechanisms underlying these changes vary according to the tumor type. These alterations can be genetic or regulatory at the transcriptional or posttranscriptional level (511).

Staphylococcal nuclease and tudor domain containing 1 (SND1) is a newly identified oncoprotein that is highly expressed in almost all the detected different tumor cells (1214). SND1 was first identified as a transcriptional coactivator for Epstein-Barr virus nuclear antigen 2. It is a ubiquitously expressed and highly conserved protein in mammals and plays important physiological roles in a variety of cellular processes (15). It comprises a tandem repeat of four staphylococcal nuclease (SN)like domains (referred to as SN domains) at the N terminus and a fusion of a Tudor domain with a partial SN domain at the C terminus (referred to as TSN domain) (16). Our current studies and others studies have demonstrated that SND1 regulates the differentiation and migration of different tumor cells via variant signal pathways at the cellular level (1719). For example, SND1 expression is up-regulated in breast cancer tissues, and it associates with transforming growth factor signaling pathway to promote epithelial-mesenchymal transition in breast cancer (20). SND1 regulates the cadherin switch for epithelial-mesenchymal transition in ovary SKOV3 cells (17). However, the fundamental impact of SND1 on the tumorigenesis in vivo is largely unknown. In the present study, we demonstrate that in tumor cells, SND1 is a novel endoplasmic reticulum (ER)associated protein hijacking the nascent heavy chain (HC) of MHC-I to ER-associated degradation (ERAD) process. With reduced expression of MHC-I on tumor cell membrane, it would be easy for tumor cells to orchestrate a cancer-favored immune microenvironment and escape immune response.

To investigate the fundamental role of oncoprotein SND1 in tumor proliferation, we performed affinity purification and mass spectrometry to identify SND1-associated proteins from cellular extracts of HeLa cells with stable expression of SND1-FLAG. As shown in Fig. 1A, a group of ER-related proteins were coprecipitated with SND1, including human leukocyte antigenA (HLA-A; the HC of human MHC-I), Valosin-containing protein (VCP), SEC61 translocon subunit alpha (SEC61A), and ribosomal protein L7a (RPL7A). As MHC-I molecule plays essential roles in antigen presentation, we therefore focused on investigating the relationship between SND1 and HLA-A. Coimmunoprecipitation (Co-IP) assay was performed to verify the association of SND1 and HLA-A in HeLa cells. As shown in Fig. 1B, the endogenous HLA-A was efficiently associated with ectopically overexpressed SND1-FLAG. In addition, anti-HC10 antibody that could specifically recognize immature (unfolded/partially folded) conformation of HLA-A, HLA-B, and HLA-C was used to detect the in vivo physical association of endogenous SND1 and HLA-A. The endogenous SND1 was efficiently coimmunoprecipitated with anti-HC10 antibody (Fig. 1C) and vice versa. HLA-A or HLA-B was coimmunoprecipitated with anti-SND1 antibody (Fig. 1D). Consistently, the Duolink assay [Fig. 1E, red dots indicate the proximity ligation assay (PLA) probe signal] and immunofluorescence assay (Fig. 1F) further confirmed the cellular colocalization of SND1 and HLA-A. We then mapped the interaction domain between SND1 and HLA-A by glutathione S-transferase (GST) pull-down assay. The bacterially produced GST-fusion protein containing full-length SND1 (GST-SND1), SN domain (GST-SN), TSN domain (GST-TSN) (as indicated in Fig. 1G), or GST alone was purified using glutathione agarose beads and used to incubate with HLA-A in vitrotranslated from rabbit reticulocytes. As shown in Fig. 1G, the full length of SND1 or SN domain, but not TSN domain, efficiently associated with HLA-AFLAG. Likewise, the GST-fusion proteins containing full-length HLA-A (GSTHLA-A), A1 domain (GSTHLA-AA1), A2 domain (GSTHLA-AA2), A3 domain (GSTHLA-AA3), C domain (GSTHLA-AC) (as indicated in Fig. 1H), or GST alone were used to incubate with recombinant histidine-tagged SND1 (His-SND1) purified from Escherichia coli. As shown in Fig. 1H, the full length of HLA-A, domain A1, domain A3, or domain C, but not domain A2, efficiently associated with His-SND1. To further consolidate the molecular interface required for the interaction between SND1 and HLA-A, FLAG-tagged different domain deletion mutants of SND1 were generated. Immunoprecipitation analysis in HeLa SND1-KO (knockout) cells demonstrated that the SN3 region of SND1 was required for the interaction of HLA-A (Fig. 1I). These data prompted us to interrogate the three-dimensional conformation for the complex of SND1 and HLA-A. Because the complex structure was not determined experimentally, we performed docking and molecular dynamics simulation to predict their associated conformation (2123). The resulting structure (Fig. 1J) showed that the interacting interface was located between the SN3 region of SND1 and domains A1 and A3 of HLA-A. The electrostatic interaction between the basic and acidic amino acids on SN3 region and A3 domain might play an important role in the association process. The key residues on the interface included K484, K496, K490, K401, K450, and R384 in SN3 domain and E232, E229, and D227 in A3 domain. The interaction between K401-E232, K496-E229, and K496-D227 were identified to be vital for the association of SND1 with HLA-A. These results indicated that SND1 could physically interact with the immature form of HC in partially folded/unfolded conformation, which raises the question about the functional association of SND1 with nascent HC of MHC-I.

(A) Immunopurification and mass spectrometry of SND1-containing protein complexes. Cellular extracts from HeLa cells stably expressing SND1-FLAG were immunopurified with anti-FLAG affinity beads and eluted with FLAG peptide. The elutes were resolved on SDS-PAGE and silver-stained. The protein bands on the gel were recovered by trypsinization and analyzed by mass spectrometry. (B) Co-IP analysis of the association between SND1 and HLA-A. Whole-cell extracts from HeLa cells with SND1-FLAG expression were immunoprecipitated with anti-FLAG beads, followed by Western blot with antibodies against the HLA-A. (C) Co-IP analysis of the association between SND1 and HC10. Whole-cell extracts from HeLa cells were immunoprecipitated with anti-HC10, followed by immunoblot (IB) with antibodies against the SND1. (D) Cellular extracts from HeLa cells were immunoprecipitated with anti-SND1 antibody, followed by Western blot with antibodies against the indicated proteins. (E) Duolink in situ PLA was adopted for detecting the association between SND1 and HLA-A. Two PLA probes were designed to respectively recognize either mouse or rabbit antibody against SND1 or HLA-A. Immunoglobulin G (IgG) was used as staining control. Scale bar, 20 m. (F) Immunostaining and confocal microscopic analysis of subcellular colocalization of SND1 and HLA-AFLAG (C terminus) in HeLa cells. HeLa cells were fixed and immunostained with antibodies against the indicated proteins. Scale bar, 10 m. (G) GST pull-down analysis of the bacterially produced GST-fusion protein containing full-length SND1 (GST-SND1), SN domain (GST-SN), and TSN domain (GST-TSN) involved in the interaction with in vitrotranslated HLA-A from rabbit reticulocytes. Coomassie blue staining for GST-fusion proteins refers to fig. S1A. aa, amino acid. (H) GST pull-down analysis of the different domains of HLA-A involved in the interaction with SND1. The His-SND1 and sample of GST-tagged different domains of HLA-A were purified from E. coli bacteria cells. Coomassie blue staining for GST-fusion proteins refers to fig. S1B. (I) Immunoprecipitation analysis of the domains involved in the interaction between SND1 and HLA-A with FLAG-tagged deletion mutants of SND1 purified from HeLa SND1-KO cells. The immunoprecipitation of FLAG refers to fig. S1C. (J) The spatial conformation of SND1-HLA-A complex predicted by the database of ZDOCK (http://zdock.umassmed.edu/) was further analyzed using the Gromacs package. The structural stability and binding energy refer to fig. S1 (E and F).

Since the nascent HC is synthesized on the ER membrane and matured in the ER lumen (9), it raises the question of where the interaction of SND1 and HC occurs. By analyzing and comparing our previous mass spectrometry data from Jurkat cells with the present data from HeLa cells, we found 221 proteins (fig. S2A) in the overlapped set of SND1-associated proteins, including ribosomal (RPLs or RPSs) or ER-associated proteins, such as HLA-A, SEC61A, ribosome binding protein 1 (RRBP1), VCP, signal recognition particle 72 (SRP72), etc. We then performed immunofluorescence to investigate the cellular colocalization of SND1 and ER-associated proteins. As shown in Fig. 2A, SND1 colocalized with RRBP1 (a ribosome receptor on ER; top) and SEC61A (a core component of ER translocation channel; middle). The colocalization of HLA-A and RRBP1 (bottom) was used as positive control. It indicated that SND1 was a potential ER-associated protein.

(A) Immunostaining for cellular colocalizations, followed by confocal microscopic analysis by using antibody against SND1, RRBP1, SEC61A, and HLA-AFLAG. Scale bar, 10 m. (B) HeLa cells were transfected with the ER reporter plasmids, GFG, HLA-SP-GFG, UGGT1-SP-GFG, GAPDH-NP-GFG, and SND1-NP-GFG, respectively. Western blot for molecular weight of these GFG-tagged fusion proteins expressed in HeLa cells. (C) Colocalizations of these GFG-tagged fusion proteins with SEC61A were detected by confocal microscopy. UGGT1 was used as a positive control for ER-associating protein, while GAPDH was used as a negative control. Scale bar, 20 m. Fluorescence intensity profiles of regions indicated by short lines are shown in the bottom. (D) Co-IP by antibody against SEC61A for interaction with SND1-GFP or SND1-NP/-GFP in HeLa cells transfected with either SND1-GFP (lane 3) or SND1-NP/-GFP vector (lane 4). (E) Co-IP by antibody against FLAG for interaction with SND1-NP in HeLa cells transfected with either GFG (lane 3) or SND1-NP-GFG vector (lane 4). (F) Ectopically increased expression of either SND1-GFP or SND1-NP/-GFP in SND1-KO HeLa cells followed by Western blot for SND1 and HLA-A expression. WT, wild type.

ER lumen or secretory proteins containing an N-terminal signaling peptide (SP) composed of hydrophobic amino, which is recognized by SRP and subsequently cleaved by signal peptidase in the ER lumen (24), such as HLA-A and UGGT1 (a glucosyltransferase in ER lumen) (as illustrated in fig. S2B). We noticed a hydrophobic amino acid sequence at N terminus (NP) of SND1 protein. To investigate whether it is an SP, we developed an ER reporter assay by constructing a pair of two ER luminal reporter vectors. One is named GFG containing green fluorescent protein (GFP)FLAGGFP sequences after the multiple cloning sites that could insert the sequence of designated peptide. The other one is GFG-KDEL with an additional sequence of peptide KDEL following GFP-FLAG-GFP sequences. KDEL is a specific peptide sequence at the C terminus of ER lumen proteins that keeps the protein in the ER lumen (25). Using these vectors, we constructed a series of plasmids containing the SP of HLA-A (HLA-ASPGFG and HLA-ASPGFG-KDEL) or UGGT1 (UGGT1SPGFG and UGGT1SPGFG-KDEL), respectively, as positive control of ER-associated proteins. The N-terminal sequence of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (GAPDH-NP-GFG and GAPDHNPGFG-KDEL) was used as negative control of ER-associated proteins. The N-terminal sequence of SND1 was inserted to construct SND1-NP-GFG and SND1NPGFG-KDEL (fig. S2B). The plasmids were transfected into HeLa cells, respectively, and Western blot was performed to observe whether the S(N)P could be cleaved from the S(N)P-GFG fusion protein by signal peptidase (Fig. 2B). The molecular mass of SND1-NP-GFG(-KDEL) (lanes 7 and 8) was heavier than that of the GFG(-KDEL) control (lanes 1 and 2) (molecular mass is about 55 kDa), HLA-ASPGFG(-KDEL) (lanes 3 and 4), or UGGT1-SP-GFG(-KDEL) (lanes 5 and 6) but at the same level as negative control GAPDH-NP-GFG(-KDEL) (lanes 9 and10). It demonstrated that the SP of HLA-ASPGFG(-KDEL) and UGGT1-SP-GFG(-KDEL) was successfully cleaved from the fusion protein by signal peptidase, but not the NP of SND1-NP-GFG(-KDEL) or GAPDH-NP-GFG(-KDEL). All these data indicate that SND1 is an ER-associated protein but not an ER lumen protein since the NP of SND1 is not an SP. Therefore, it is likely that SND1 associates with HLA-A on ER membrane.

To further clarify the localization of SND1, the above X-S(N)P-GFG plasmids were transfected into HeLa cells, respectively; then, the colocalization of the X-S(N)P-GFG with endogenous SEC61A and RRBP1 was detected by immunofluorescence assay. Fluorescence intensity profiles were used to quantify the degree of localization. As shown in Fig. 2C and fig. S2C, the GFG control protein did not colocalize with SEC61A (Fig. 2C, a) or RRBP1 (fig. S2C, a). HLA-ASPGFG or UGGT1-SP-GFG was well colocalized with SEC61A (Fig. 2C, b and c) and RRBP1 (fig. S2C, b and c). There was no obvious colocalization of GAPDH-NP-GFG with SEC61A (Fig. 2C, d) or RRBP1 (fig. S2C, d). SND1-NP-GFG was colocalized with SEC61A (Fig. 2C, e) or RRBP1 (fig. S2C, e) respectively, which is in accordance with the results of S(N)PGFG-KDEL (fig. S2D). It is understandable that the colocalization of HLA-ASPGFG or UGGT1-SP-GFG with SEC61A and RRBP1 is due to the distribution of the GFG in the ER lumen cleaved by SP. How to explain the colocalization of SND1-NP-GFG with SEC61A and RRBP1?

In eukaryotes, ER membranelocated SEC61 translocation complex is the core component of the translocon that transports proteins to the ER (26). We thus performed Co-IP assay to explore the potential interaction of SND1 with SEC61A. HeLa cells were transfected with the expression plasmid containing full-length SND1 tagged with GFP (SND1-GFP) or N-terminal peptidedeficient SND1 tagged with GFP (SND1-NP/-GFP), respectively. As shown in Fig. 2D, SEC61A coprecipitated with ectopically overexpressed SND1-GFP (lane 3) but not SND1-NP/-GFP (lane 4). In addition, the endogenous SEC61A was coprecipitated with anti-FLAG for SND1-NP-GFG (Fig. 2E, lane 4) but not the GFG alone (lane 3). All these data suggest that the NP of SND1 is required for the efficient interaction of SND1 and SEC61A. Consistently, fig. S2E showed that the full-length SND1-GFP, but not SND1-NP/-GFP, was well colocalized with endogenous SEC61A. In HeLa cells with deletion of endogenous SND1, the protein level of HLA-A was reduced with ectopically overexpressed SND1-GFP in a dose-dependent manner (Fig. 2F, lanes 3 and 4), while there was no obvious alteration with overexpressed SND1-NP/-GFP (lanes 5 and 6). Together, it is likely that SND1 is an ER-associated protein anchored on ER membrane by binding SEC61A upon where nascent MHC-I HC is caught.

To determine the relevance between SND1/HC association and the presentation of MHC-I molecules, we carried out flow cytometry to detect the protein level of HLA-A/B/C on the surface of tumor cells with deletion of SND1 (SND1-KO) by CRISPR-Cas9 system. As shown in Fig. 3A, the protein level of HLA-A/B/C molecules was increased in two different SND1-KO HeLa cell clones. Likewise, the similar changes were observed in two SKOV3 ovarian cancer cell clones with deletion of SND1 (shSND1#1 and shSND1#2) (fig. S3B). The Western blot further confirmed that the protein level of HLA-A was enhanced in the cells with deletion of SND1 (Fig. 3B, left), while it is reduced in the cells with ectopic overexpression of SND1 (Fig. 3B, right). However, there was no obvious alteration at the mRNA level (fig. S3A). The same results were observed in SKOV3 cells (fig. S3, B to F). We then detected the half-life of endogenous HLA-A in HeLa cells with treatment of cycloheximide (CHX). As shown in Fig. 3C, HLA-A was gradually degraded in parental HeLa cells [wild type (WT)]; however, the degradation of HLA-A was noticeably retarded in SND1-KO cells. In addition, the protein level of HLA-A was significantly increased in the cells pretreated with proteasome inhibitor MG132 (Fig. 3D, lane 2) but not in the cells pretreated with lysosomal inhibitor chloroquine (Fig. 3D, lane 3). Therefore, we examined the ubiquitylation of HLA-A to determine whether SND1-promoted HLA-A destabilization is via ubiquitin mediatedproteasome pathway. As shown in Fig. 3E, compared with the control cells (lanes 1 and 3), the ubiquitylation of ectopically overexpressed HLA-A was obviously decreased in SND1-KO cells (lane 4) but relatively increased in the cells with overexpression of SND1 (SND1-HA) (lane 2). On the basis of the aforementioned data, it is likely that SND1 leads to HLA-A degradation through the ubiquitin-proteasome pathway.

(A) Two clones of HeLa cells with stable depletion of SND1 by CRISPR-Cas9 system were analyzed by flow cytometry for human MHC-I using antibody simultaneously against HLA-A/B/C. (B) Two clones of HeLa cells with stable depletion of SND1 by CRISPR-Cas9 system and HeLa cells stably expressing SND1-FLAG were collected, followed by Western blot using antibodies against HLA-A. (C) The effect of KO SND1 on the half-life of HLA-A was evaluated in HeLa cells treated with CHX (50 g/ml) and harvested at the indicated time point, followed by Western blot. The protein half-life curves were obtained by quantifying relative intensities. (D) HeLa cells with ectopic HLA-A expression were pretreated with proteasome inhibitor MG132 (10 mM) or lysosomal inhibitor chloroquine (100 mM) for 8 hours and subjected to Western blot with antiHLA-A antibody. DMSO, dimethyl sulfoxide. (E) WT and SND1-KO HeLa cells were transfected with HLA-AFLAG and treated with MG132 (10 mM) for 8 hours. Cellular extracts were immunoprecipitated with anti-FLAG, followed by Western blot with anti-ubiquitin (Ub) antibody. (F) HeLa cells were cotransfected with control vector or SND1-HA and HLA-AFLAG or with control small interfering RNA (siRNA) or SND1 siRNA and HLA-AFLAG, and whole-cell lysates were collected and immunoprecipitated with anti-FLAG, followed by Western blot with indicating antibodies. (G) The Duolink in situ PLA was adopted for detecting the direct association between HLA-A and calnexin or 2-microglobulin (2m) in the presence of SND1-HA or in the absence of SND1. Scale bar, 20 m. The signal dots were calculated and plotted. *P < 0.05 and ****P < 0.0001, by unpaired t test.

In ER lumen, the nascent unfolded HLA-A can be initially retained by a key chaperone, calnexin, to ensure proper folding and quality control before ultimate assembly with 2-microglobulin (2m) to form mature MHC-I (27). The catching of nascent HLA-A by SND1 may have an impact on the association of calnexin and HLA-A and lead to a misfolding process of HLA-A. Immunoprecipitation assay revealed that the binding efficiency of HLA-A to calnexin, and HLA-A to 2m, was remarkably reduced in the presence of ectopically overexpressed SND1 (Fig. 3F, left) but was obviously increased in the absence of SND1 (right). According to the red dots of PLA probe signal from confocal images, Duolink assay (Fig. 3G) further validated that the binding efficiency of calnexin and 2m to HLA-A was decreased by overexpression of SND1 (SND1-HA), while it was significantly increased by deletion of SND1 (si-SND1). Moreover, glycosylation sitemutated HLA-A (N110Q) is not able to associate with both calnexin and 2m but sufficiently interacts with SND1 (fig. S3H). These observations suggest that SND1 hindering the normal assembly process of MHC-I in the ER lumen, consequently guiding the nascent HLA-A for degradation.

Misfolded or nascent HC, which fails to achieve the native conformation in complex with 2m, is dislocated from ER to cytosol and ubiquitinated for ERAD process (28). To investigate the underlying mechanisms of SND1-mediated HC degradation, we used affinity purification and mass spectrometry to identify HLA-Aassociated proteins in HeLa cells with stable expression of HLA-AFLAG. There were 278 affinity-purified proteins (fig. S4A) overlapping in both HLA-Aassociated and SND1-associated proteins. These overlapped proteins were further filtered by Kyoto Encyclopedia of Genes and Genomes analysis (fig. S4B). The top-ranking proteins include SND1, HLA-A, VCP, calnexin (Fig. 4A), SEC61A, etc. (fig. S4C).

(A) Immunopurification and mass spectrometry of HLA-Acontaining protein complexes. Cellular extracts from HeLa cells stably expressing HLA-AFLAG were immunopurified with anti-FLAG affinity beads and eluted with FLAG peptide. The elutes were resolved on SDSpolyacrylamide gel electrophoresis (SDS-PAGE) and silver-stained. The protein bands on the gel were recovered by trypsinization and analyzed by mass spectrometry. HLA-Ainteracted proteins were highlighted. (B) HeLa cells were coimmunoprecipitated by HLA-A antibody and subjected to Western blot by antibody against VCP. (C) HeLa cells were coimmunoprecipitated by SND1 antibody and subjected to Western blot by antibody against VCP. (D) Duolink assay followed by confocal microscopic analysis for direct molecular interactions among SND1, VCP, and HLA-A. IgG was used as a negative control. Scale bar, 20 m. (E) HeLa cells were cotransfected with control vector or SND1-HA and HLA-AFLAG or cotransfected with control siRNA or SND1 siRNA and HLA-AFLAG, and whole-cell lysates were collected and immunoprecipitated with anti-FLAG, followed by Western blot with anti-SND1, anti-VCP, anti-VIMP, and anti-HRD1 antibodies. Results of input were shown in fig. S4D. (F) HeLa cells were cotransfected with vector or HRD1-HA and HLA-AFLAG with the treatment of MG132. Cellular extracts were immunoprecipitated with anti-FLAG, followed by Western blot with anti-ubiquitin antibody.

As VCP plays essential roles in ERAD, we then validated the association of VCP, SND1, and HLA-A. Co-IP experiments revealed that VCP was able to physically interact with both HLA-A (Fig. 4B) and SND1 (Fig. 4C) in vivo. Moreover, Duolink assay (Fig. 4D) further demonstrated the association of these three proteins. Comparatively, the binding ability of SND1 and HLA-A (a) or HLA-A and VCP (b) was stronger than SND1 and VCP (c). It was reported that VIMP (the cofactor of VCP) and HRD1 (the E3 ligase) are the key participants in the degradation of HLA-A (29); meanwhile, the interaction of HRD1 with SND1 was found in our present study by Co-IP (fig. S4E). We therefore investigated the correlation of the SND1 expression and the association of VCP/VIMP/HRD1 with HLA-A. As shown in Fig. 4E, the interaction of VCP, VIMP, and HRD1 to HLA-A was remarkably increased in the presence of ectopically overexpressed SND1 (left) but was largely decreased in the absence of SND1 (right). Furthermore, compared with the control cells, the ubiquitylation of ectopically overexpressed HLA-A was obviously increased in the cells with overexpression of HRD1 (HRD1-HA) (Fig. 4F). These observations suggest that SND1 sequestrates the nascent HC of MHC-I and redirects it to the ERAD pathway for proteasomal degradation.

To explore the consequence of SND1-mediated HC degradation in vivo, we used murine syngeneic tumor models on the C57BL/6 background by using two murine cancer cell lines, B16F10 melanoma cells and MC38 colon adenocarcinoma cells. B16F10SND1-KO and MC38SND1-KO cell clones with deletion of SND1 were obtained by using CRISPR-Cas9 system. Consistent with previous results, both Western blot detection (Fig. 5A) and flow cytometry analysis (Fig. 5B) demonstrated that the protein level of H2Kb (HC of mouse MHC-I) was increased in different B16F10SND1-KO cells. The same results were observed in MC38SND1-KO cells (fig. S5, A and B). We then subcutaneously inoculated 5 105 parental or B16F10SND1-KO cells into the flank of C57BL/6 mice, and the tumor growth was monitored in the following days. Compared with the parental B16F10 tumor, the growth of B16F10SND1-KO cells with SND1 deficiency was markedly slow in terms of the developmental kinetics (Fig. 5C and fig. S5C). Consistently, the tumor size (Fig. 5D for B16F10 and fig. S5D for MC38) and weight (Fig. 5E for B16F10 and fig. S5E for MC38) were also smaller in SND1-KO cells with SND1 deletion than those in the control parental cells. As MHC-I molecules play essential roles in tumor antigen presentation for CD8+ T cellmediated immune response, we thus investigated the infiltration of CD8+ T cells in tumor tissues by immunofluorescence and flow cytometry. Compared with the parental tumor tissue, there were more CD8+ T cells (red) infiltrated in B16F10SND1-KO (Fig. 5F) and MC38SND1-KO tumor tissues (fig. S5F). The flow cytometry analysis further revealed that the infiltration of CD45.2+ leukocytes (Fig. 5, G and H) and CD8+ T cells (Fig. 5, G and I) was significantly increased in the B16F10SND1-KO tumor tissue and the MC38SND1-KO tumor tissue (fig. S5, G to I) compared with the parental tumor tissue. Specifically, the proportion of CD8+ T cells among CD45.2+ leukocytes was significantly increased in the B16F10SND1-KO tumors (Fig. 5J) and MC38SND1-KO tumors (fig. S5J). Besides, we also detected the potential exhaustion of CD8+ T cells in tumor tissues. Compared with the control parental, there was no apparent discrepancy in the percentage of PD-1+ CD8+ T cells with deletion of SND1 (Fig. 5K). Furthermore, we clarified that SND1 deficiency in mice melanoma (fig. S6, A to D) and colon carcinoma (fig. S6, E to H) resulted in no significant changes in regulating cell proliferation, apoptosis, or cell cycle in vitro. We also performed the experiments by using RAG-1 (recombination activating gene 1) KO mice (Rag-1/ mice) that lack mature T and B cells to investigate whether the absence of SND1 would affect tumor growth in vivo. We initially inoculated 5 105 WT or SND1-KO B16F10 cells subcutaneously into the flank of C57BL/6 WT mice and Rag-1/ mice. By comparison between WT cells and SND1-KO B16F10 cells inoculated in either C57BL/6 WT mice or Rag-1/ mice (fig. S6, I to K), we found that the size and weight of SND1-KO tumors were significantly decreased in the group of C57BL/6 WT mice, whereas in the group of Rag-1/ mice, the tumors were comparable between WT and SND1-KO B16F10 cells in terms of the tumor size and weight. Together, these observations imply that deletion of SND1 in tumor cells is likely to promote CD8+ T cellmediated cellular immune responses in the tumor microenvironment.

(A) Three clones of B16F10 cells with stable depletion of SND1 by CRISPR-Cas9 system were collected, followed by IB for murine MHC-I (H2Kb). (B) Two clones of B16F10 cells with stable depletion of SND1 by CRISPR-Cas9 system were analyzed by flow cytometry for murine MHC-I (H2Kb/H2Db) using antibody simultaneously against H2Kb/H2Db. (C to E) 5 105 of either WT or SND1-KO B16F10 cells were subcutaneously transplanted into C57BL/6 mice. The tumor growth was monitored at the indicated times. C57BL/6 mice were sacrificed at day 11. Tumors were removed and photographed. The tumor tissues were weighed and plotted. Data are presented as means SD; n = 5 tumors for each group. *P < 0.05, two-tailed t test. (F) Immunofluorescence images of CD4+ T and CD8+ T cells in B16F10 tumor sections (scale bar, 20 m). (G) C57BL/6 mice injected with equal numbers of WT or SND1-KO B16F10 cells were sacrificed at day 11. The digested tumor suspensions stained with antibodies against CD8 and CD45.2 (pan-leukocyte marker) were subjected to flow cytometry. (H to J) Percentages of infiltrating CD45.2+ cells and CD8+ T cells among total tumor tissuederived cells and the percentage of infiltrating CD8+ T cells among total CD45+ leucocytes. n = 5 tumors for each group. *P < 0.05 and **P < 0.01, by unpaired t test. The experiments were performed and repeated at least three times, independently. (K) The percentage of infiltrating PD-1+ CD8+ T cells among total CD8+ T cells. n = 5 tumors for each group. n.s., not significant.

Moreover, in light of our observation that SN domain of SND1 is responsible for the association of SND1 with MHC-I HC, it is tempting to speculate the crucial function of SN domain in vivo. Our supplementary data support that the rescue of SN domain of SND1 significantly increased the tumor growth through mobilizing less CD8+ T cells infiltrating in tumors [fig. S7, A to H (B16F10) and I to P (MC38)].

To further clarify the influence of high expression of SND1 on CD8+ T cellmediated cellular immune responses in tumor, we used transgenic OT-I mice. OT-I mice are ovalbumin (OVA)specific T cell receptor transgenic (OT-I) mice whose CD8+ T cells could recognize the specific peptides (257 to 264 SIINFEKL) of chicken OVA, a surrogate tumor antigen that can be conveniently used to investigate CD8+ T cellmediated immune responses directed against the OVA antigen (30). Meanwhile, B16F10-OVA cells and MC38-OVA cells were constructed by stably expressing the OVA antigen, which is able to be presented by MHC-I complex and specifically recognized by CD8+ T cells derived from OT-I mice. The identical expression of OVA was observed in both B16F10-OVA WT cells and B16F10-OVA SND1-KO cells with SND1 deletion (Fig. 6A), as well as in MC38-OVA WT and MC38-OVA SND1-KO cells (fig. S8A). The flow cytometry analysis demonstrated that higher level of MHC-I was detected in B16F10-OVA SND1-KO (Fig. 6B) or MC38-OVA SND1-KO cells (fig. S8B), which suggested that more OVA peptides might be presented in tumor cells in the absence of SND1. To interrogate the in vivo effect of antigen presentation, we subcutaneously inoculated equal numbers of B16F10-OVA WT or B16F10-OVA SND1-KO cells into OT-I mice. The tumor volume was monitored accordingly. The growth curve illustrated that SND1 deficiency in B16F10-OVA cells markedly inhibited the tumor growth in vivo (Fig. 6C). At day 19, the tumors were resected and the size and weight were measured. With deletion of SND1 (B16F10-OVA SND1-KO), the tumor size (Fig. 6D) and weight (Fig. 6E) were remarkably smaller than those of the control tumor (B16F10-OVA). The flow cytometry assay revealed that more CD45.2+ cells (Fig. 6F and G) and CD8+ T cells (Fig. 6F and H) were infiltrated in the tumor tissue of B16F10-OVA with SND1 deficiency (B16F10-OVA SND1-KO), and the ratio of CD8+ T cells to CD45.2+ leukocytes was significantly increased (Fig. 6I). These results further verified the impact of SND1 on MHC-I/antigenic peptide presenting in tumor cells and consequently affecting the infiltration of cytotoxic CD8+ T cells in tumor tissue.

(A) B16F10 cells with stable depletion of SND1 by CRISPR-Cas9 system were stably transfected with OVA vector, followed by IB. (B) B16F10-OVA with SND1 deficiency was analyzed by flow cytometry for murine MHC-I (H2Kb/H2Db). (C to E) OT-I mice were injected with equal numbers of WT or SND1-KO B16F10-OVA cells, and tumor growth was observed over time. Then tumors were removed, photographed, and weighted. *P < 0.05 and **P < 0.01. (F) Flow cytometry was used for the analysis of CD45.2+ leucocyte and CD8+ T cell infiltration in tumor tissues. (G to I) Percentages of infiltrating CD45.2+ leucocytes and CD8+ T cells among total tumor tissuederived cells and the percentage of infiltrating CD8+ T cells among total CD45.2+ leucocytes. n = 5 tumors for each group. **P < 0.01 and ***P < 0.001, by unpaired t test. (J) CD8+ T cells were purified from spleens of tumor-bearing OT-I mice and stimulated with 257 to 264 (SIINFEKL) peptide of OVA for 24 hours. Percentages of IFN+CD8+ T cells among total CD8+ T cells in the culture system were measured by flow cytometry. (n = 5, **P < 0.01). The experiments were repeated two times independently. (K) CD8+ T cells recognizing specific peptide of OVA (SIINFEKL) were purified from spleens of OT-I and then cocultured with WT or SND1-KO B16F10 cells stably expressing OVA (CD8+ T:B16F10-OVA, 10:1). Representative images were taken under a bright field at different time points. Scale bar, 20 m. (L) In vitro comparison of cytolysis rates against CD8+ T cells purified from spleens of OT-I mice between WT and SND1-KO B16F10-OVA cells at different cell rates of CD8+ T (effector cells) to B16F10 (target cells) with/without antiMHC class I antibodies (Ab) (E:T, 5:1, 10:1, 15:1, or 20:1). A lactate dehydrogenasereleasing cytotoxicity assay was performed to measure the cytolysis efficiency of CD8+ T cells on tumor cells. Each bar represents mean SD for biological triplicate experiments. ****P < 0.0001, two-way analysis of variance (ANOVA).

To further examine the contribution of SND1 to cytotoxic CD8+ T cellmediated immune response in tumor, we isolated CD8+ T cells from spleen of OT-I mice bearing B16F10-OVA-WT or B16F10OVASND1-KO tumor, respectively, and tested the cytotoxic CD8+ T cell population. As shown in Fig. 6J, the amount of interferon- (IFN)producing CD8+ splenic T cells was significantly increased in SND1-KO group compared to WT control, suggesting that SND1 deficiency in tumor cells promoted and activated the OVA-specific CD8+ T cell response in the peripheral immune organ. To examine whether the enhanced antigen presentation in the SND1-KO cells results in robust recruitment and activation of CD8+ T cellmediated cytotoxicity in the tumor environment, CD8+ T cells were isolated from OT-I mice and cocultured with tumor cells expressing OVA in the presence or absence of SND1. Representative time-lapse images showed that B16F10-OVA cells with SND1 deficiency were recognized and killed by more cytotoxic CD8+ T cells after 12 hours, while the parental B16F10-OVA cells survived after 12 hours with less aggregated CD8+ T cells (Fig. 6K). Furthermore, the amount of lactate dehydrogenase released from lysed target cells was used as indicator for cytolysis. Accordingly, the cytolysis value was higher in SND1-KO group in an E/T cell ratiodependent manner and significantly abolished with the treatment of MHC-blocking antibodies (AF6-88.5) (Fig. 6L, red), which indicated an efficient cytotoxic effect mediated by CD8+ T cells. Consistently, the same results were also observed in MC38-OVA cells (fig. S8, C and D). Collectively, these findings illustrated that SND1 impaired tumor antigen presentation to cytotoxic CD8+ T cells and sabotaged the CD8+ T cellmediated cellular immune response, supporting our previous data that deletion of SND1 in tumor cells promoted CD8+ T cellmediated cellular immune response, and, as a result, inhibited the tumor growth in vivo.

To illustrate the significant relevance of high SND1 expression with tumor immune response in human, we screened the Tumor Immune Estimation Resource (TIMER) database at http://cistrome.org/TIMER/, a comprehensive resource for systematic analysis of immune infiltrates across diverse cancer types from The Cancer Genome Atlas (31). We analyzed the correlation of SND1 expression and the infiltration of CD8+ T cells in melanoma and colon adenocarcinoma (fig. S9, A and C). The results showed that SND1 expression was moderately negatively correlated with infiltration level of CD8+ T cells in melanoma (r = 0.247, P = 1.68 107) and colon adenocarcinoma (r = 0.393, P = 2.10 1016).

In addition, we analyzed the correlation of SND1 with cancer patients prognosis by using PrognoScan database at http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html (32). Notably, SND1 expression significantly affects the prognosis in melanoma. The cohort of melanoma (GSE19234) included 38 samples at different stages of melanoma and showed that high SND1 expression was significantly associated with poorer prognosis [Overall survival (OS) hazard ratio (HR) = 7.39, 95% confidence interval (CI) = 1.51 to 36.30, Cox P = 0.000912] (fig. S9B). Consistently, similar trend between SND1 expression with prognosis was observed in colorectal cancer (GSE17536; OS HR = 1.49, 95% CI = 0.69 to 3.22, Cox P = 0.314186) (fig. S9D). Since SND1 was negatively associated with the infiltration of CD8+ T cells and the survival of patients, we speculated that targeting SND1 might be a potentially therapeutic approach to enhance immune response and suppress tumor growth.

In conclusion, as shown in the working model (fig. S9E), SND1, localized on the membrane of ER in tumor cells, is able to hijack MHC-I HC from normally assembling and physically associating with its chaperone calnexin in the ER lumen at an early stage of ER processing, thereby leading MHC-I HC to the proteasomal pathway of ERAD promoted by VCP, cofactor VIMP, and E3 ligase HRD1 and sensitizing tumor to the diminished immune surveillance with decreased cytotoxic CD8+ T cells. Thus, SND1 profoundly facilitates immune evasion from tumor immune microenvironment through inhibition of antigen presentation and that this effect is mediated by down-regulation of MHC-I HC molecule (left). On the other hand, the absence of SND1 in either melanoma or colon carcinoma from subcutaneously tumor-bearing WT mouse results in growth inhibition and promotes tumor inflammation with more cytotoxic CD8+ T cells (right). Genetic ablation of SND1 in OVA-expressed tumors on OT-I mice induces sufficient antigen presentation to cytotoxic CD8+ T cells and enhances antitumor immunity. Therefore, SND1 determines the fate of MHC-I HC maturation and orchestrates a cancer-favored immune microenvironment. This model proposes the blockade of SND1MHC-I HC axis in tumors as a viable option for immune system against cancer.

In the present study, we demonstrate that SND1 promotes immune escape of tumor cells through inhibition of MHC-I antigen presentation pathway, leading to impaired antitumor CD8+ T cell response in tumor microenvironment. Physiologically, the nascent unfolded HC of MHC-I would be stabilized by the chaperone calnexin before association with the 2m in ER. Here, we revealed that SND1 physically interacted with the nascent HC of MHC-I molecule in tumor cells. Instead of promoting the assembly of MHC-I molecule, SND1 recruits the nascent HC to VIMP/VCP complex for ERAD pathway. As a result, the MHC-I expression on tumor cell membrane is reduced, leading to impaired CD8+ T cell activation in tumor microenvironment.

SND1 is highly expressed in various cancers and is newly identified as a novel oncoprotein. We have previously reported that SND1 plays important physiological roles in a variety of cellular processes (1719, 33). By using various methodologies, we identified cytoplasmic SND1 as an ER-associated protein and physically interacting with the nascent HC on the ER membrane in tumor cells. The biogenesis of transmembrane proteins requires the activity of the SEC61 complex, in which the subunit SEC61A has been proposed to act as a gate for the membrane insertion of nascent polypeptides (34). The MHC-I HC is synthesized on membrane-associated ribosomes and inserted cotranslationally into the ER through the translocon composed of SEC61 complex. In ER lumen, the nascent HC associates with its chaperones and glycosylation-related enzymes to generate a properly folded glycoprotein, and the formation of HC-2m dimers indicates the maturity of MHC-I molecules (35). We have previously reported that SND1 protein is composed of SN and TSN domain (35). Here, we illustrated that SND1 protein is an ER-associated protein containing a functional N-terminal sequence (NP) that could associate with SEC61A on ER membrane. The predicted spatial conformation three-dimensional model of SND1-HLA-A complex is consistent with the mapping data, corroborating that SN domain of SND1 could physically interact with the A1 and A3 domain of HC. It indicates a fundamental and preceding role for SND1 at the early phase of HC assembly in the ER machinery. We further reveal that SND1 efficiently facilitates HC disassociation with calnexin and 2m, which interrupt the regular maturation and assembly of MHC-I molecule.

Previous studies have demonstrated that unfolded or misfolded HC that fails to form a proper structure can be recognized, dislocated, and degraded by the ERAD machinery (28). Accordingly, we, by far, are able to investigate how SND1 manages to hijack the nascent HLA-A for further degradation process on ER membrane. Although MHC-I HC is highly polymorphic and its potential ubiquitylation sites are variable (3), it is worthwhile to study the molecular mechanisms of how SND1 determines the fate of MHC-I HC precisely via the ubiquitin proteasome system. As a central element of the ubiquitin-proteasome system, VCP plays a key role in ERAD (36). During dislocation from the ER, misfolded or misassembled MHC-I HC as an ERAD substrate is ubiquitylated on the cytosolic side of the ER membrane and is degraded by the cytosolic proteasome (36). In the present study, we identified VCP, cofactor VIMP, and E3 ligase HRD1 of ERAD components as potential interactors of SND1, as revealed by mass spectrometric and coimmunoprecipitated analysis. Moreover, the results revealed that in the presence of SND1, more MHC-I HC was directed to the ERAD pathway for degradation.

We used mice model bearing tumors, especially transgenic OT-I mice, to illustrate the in vivo consequences of the tumor growth and immune response. With the absence of SND1 in either melanoma or colon carcinoma, the inoculated subcutaneous tumor growth was markedly inhibited but the amount of infiltrated CD8+ T cells in the tumor tissue was greatly increased. In accordance with the evidence from human database, we deduce that the highly expressed SND1 sabotages tumor antigen presentation to cytotoxic CD8+ T cell, thereby creating an immune niche with impaired surveillance that favors tumor growth.

Underlying the physiological relevance of our findings, the protein level of SND1 and MHC-I HC was negatively correlated with each other in human cervical and ovarian cancer cells, as well as in murine melanoma and colon cancerous cells. It is known that SND1 was robustly overexpressed in a variety of tumorigenic tissues and relatively highly expressed in normal tissues, and it was suggested that SND1 was an attractive target for anticancer therapy and a potent tumor marker (16). For more than a decade, it has been recognized that intact antigen presentation machinery, including MHC expression, in malignant cells is critical for T celldependent antitumor immunity because HLA-I antigen expression in tumors directly correlates with the degree of tumor T cell infiltration inside the tumor nests (3, 37). More recently, this knowledge has been underscored by findings showing that MHC class I molecule can be used as an independent prognostic factor for colorectal cancer and for predicting the efficacy of immunotherapy in bladder cancer and chemotherapy in ovarian cancer (1, 5, 38, 39). However, there is a long way to go before the different molecular mechanisms responsible for MHC-I alterations are precisely defined in different tumor types. For example, the mechanisms responsible for total MHC-I loss in about 60% of patients with breast cancer, in 50% of patients with prostate cancer, in 15% of patients with laryngeal cancer, or in 40% of patients with gastric cancer are yet to be identified (2, 6, 39, 40). However, it was proposed that during tumor development, tumors are heterogeneous with both HLA-positive and HLA-negative cells at early stages and are infiltrated by lymphocytes and M1 macrophages as a part of an active antitumor T helper 1 response (40). Thus, it is necessary to analyze tumor HLA expression and monitor HLA changes taking place during immunotherapy to understand how, when, and why the MHC/HLA alterations occur. In a way, our current study provides direct evidence for the idea of how the expression of MHC-I HC was regulated by an endogenously expressed protein SND1, the mechanism of which could be extendedly applied in the nonmalignant cells that may explain when and how MHC-I HC was altered during tumor initiation. Thus, SND1 could be a potential therapeutic target, at least for the treatment of malignancies with MHC-I defects in which the MHC-I is not genetically compromised.

To sum up, SND1 profoundly facilitates immune evasion from tumor immune microenvironment, and this effect is mediated by reducing the expression of MHC-I HC molecule. As a newly identified ER-associated protein, SND1 is able to hijack nascent MHC-I HC that is guided to ERAD-related proteasomal pathway, thereby impairing the proper assembling of HC with 2m in the ER lumen and sensitizing tumor cells to a diminished immune surveillance with abolished antigen presentation to cytotoxic CD8+ T cells. This novel finding may shed light on orchestrating the cancer-favored immune microenvironment via blockade of SND1MHC-I HC axis in tumors as a viable option for immune system against cancer.

HeLa, B16F10, and MC38 cells were obtained from the American Type Culture Collection (ATCC) and cultured using the standard conditions according to the ATCC instructions. B16F10 and HeLa cells were cultured in Dulbeccos modified Eagles medium [Biological Industries (BI)] supplemented with 10% fetal bovine serum (FBS; BI), and MC38 cells were cultured with RPMI 1640 (BI) supplemented with 10% FBS. The human SKOV3 cell line was purchased from China Infrastructure of Cell Line Resources (Beijing, China), and SKOV3 cells were cultured with McCoys 5A Medium (Sigma-Aldrich) supplemented with 10% FBS. All cell lines were cultured under an atmosphere of 5% CO2 at 37C. All of the cells were authenticated by examination of morphology and were confirmed to be mycoplasma-free.

Cells were transiently transfected with a Cas9 and single-guide RNA expression plasmid encoding puromycin resistance. The CRISPR-transfected cells will thus acquire transient resistance to puromycin, and the guide sequences were described as using the optimized CRISPR design at http://crispr.mit.edu. We confirmed that SND1-KO cells were not sensitive to puromycin after expansion, indicating a transient expression of CRISPR-Cas9 system in those cells.

Six- to 8-week-old male C57BL/6 mice were originally purchased from the Academy of Military Medical Sciences. OT-I transgenic mice, whose T cell receptor was designed to recognize OVA residues 257 to 264, were provided by Z. Dong from Tsinghua University. RAG-1 KO mice (Rag-1/ mice) that lack mature T and B cells were purchased from Nanjing Biomedical Research Institute of Nanjing University. The gene phenotype was routinely confirmed by polymerase chain reaction (PCR) using specific primers. All animal procedures were approved by the Committee on the Use and Care of Animals of Tianjin Medical University.

For xenograft experiments, B16F10 cells or MC38 cells (5 105) (WT and SND1-KO) were subcutaneously transplanted into the flank of C57BL/6 mice or Rag-1/ mice. Tumor height and width were measured with a caliper every 2 to 3 days to calculate tumor volume (= width2 height 0.523). Mice were sacrificed when tumors reached maximum allowed size (15 mm in diameter) or when signs of ulceration were evident. Likewise, 5 105 of B16-OVA or MC38-OVA cells were subcutaneously transplanted into the flank of OT-I mice. In all experiments, the initial implantation was conducted to animals at the age of 6 to 8 weeks.

Several plasmids were obtained from corporations including pCIpA102-G-HLA-A2-GFP (Addgene plasmid, no. 85162) and pcDNA3.1+ (Invitrogen, V79020), and several plasmids were gifts, including the following: pLV-IRES-Puro, pET-28a-c (+) vector from L. Shi (Tianjin Medical University), and CRISPR-Cas9 constructs px462 from X. Wu (Tianjin Medical University). Lentivirus plasmids expressing SND1 short hairpin RNA (shRNA) and the vector plasmid pLKO were purchased from MilliporeSigma (SHCLNG-NM_014390). The target sequences of shSND1 are shown as previously described (17). The FLAG-tagged or HA-tagged SND1 carried by pLV-IRES-Puro vector or pcDNA3.1+ were amplified from cDNA of HeLa cells or B16F10 cells with specific PCR primers. For exogenous HLA-A expression, the full length of HLA-A cloned from pCIpA102-G-HLA-A2-GFP with a FLAG tag at the C terminus was inserted into the lentiviral vector pLV-IRES-Puro. The HLA-A(N110Q)-FLAG mutant plasmid carried by pLV-IRES-Puro vector was constructed by the GENEWIZ Inc. The SN domain (SN-HA, 1 to 660 amino acids) or TSN domain (TSN-HA, 661 to 910 amino acids) of SND1 (mouse) protein with an HA tag at the C terminus was inserted into the lentiviral vector pLV-IRES-Puro. The FLAG-tagged full-length and truncation mutants of SND1 (human) were carried by pCMV-Blank vector for transient transfection. The pGEXT-4T-1 plasmids containing full length of SND1, SN domain, or TSN domain were generated as previously described (35). The pGEXT-4T-1 plasmids were inserted with full length of HLA-A(GST-HLA-A, 1 to 365 amino acids) and fragments (GSTHLA-AA1, 25 to 114 amino acids; GSTHLA-AA2, 115 to 206 amino acids; GSTHLA-AA3, 207 to 298 amino acids; and GSTHLA-AC, 299 to 365 amino acids) for GST pull-down assay.

An ER reporter plasmid was constructed with fusion protein (GFP-FLAG-GFP) and Eco RI/Xho I sites at the N terminus, which could be inserted with different signal peptides, such as HLA-A, UGGT, GAPDH, and SND1. KDEL, a target peptide sequence located on the C terminus, which prevents protein from secreting from the ER, was added at the N terminus of the fusion protein. Full length and deletion of N-terminal peptides (1 to 35 amino acids) of SND1 were inserted into the vector pcDNA3.1+ with a GFP tag at the C terminus.

Tumors were removed from sacrificed mice and digested by collagenase I (1.5 mg/ml) and deoxyribonuclease I (100 g/ml; Solarbio) in RPMI 1640 for 1 hour at 37C. The cell suspensions were passed through 70-m filters (Falcon) to remove undigested tumor tissues, and then, the erythrocytes were removed by ACK lysis buffer. Cell suspensions were incubated in mouse Fc block (anti-CD16/CD32, BioLegend) before staining. Fluorochrome-conjugated anti-mouse CD45.2 (clone 104, eBioscience), CD8a (clone 53-6.7, BD Biosciences), IFN (clone XMG1.2, BioLegend), and PD-1 (clone RMP1-30, BioLegend) antibodies were used following the manufacturers protocol. Flow cytometry results were analyzed using FlowJo software.

Cells were collected and lysed with radioimmunoprecipitation assay buffer on ice. Protein concentration was determined by the bicinchoninic acid assay. Equal amounts of protein from each cell lysate were subjected to 10% SDSpolyacrylamide gel electrophoresis (SDS-PAGE). The resolved proteins were transferred to polyvinylidene difluoride membranes and blotted with indicated antibodies. -Actin or GAPDH was used as an internal control.

Lysates from HeLa cells with or without SND1-FLAG expression were immunoprecipitated using Anti-FLAG M2 affinity beads (Sigma-Aldrich, A2220) for 12 hours at 4C with constant rotation. After extensive washing with phosphate-buffered saline (PBS) plus 0.1% Tween, the bound proteins were eluted with excess FLAG peptides for 12 hours at 4C with constant rotation, concentrated with Amicons (Ultra-0.5, 3 kDa, MilliporeSigma), and then visualized by silver staining on 8% SDS-PAGE. The distinct protein bands on the gel were recovered by trypsinization and analyzed by mass spectrometry.

Cellular lysates were prepared by incubating the cells in cell lysis buffer [50 mM tris-HCl (pH 8.0), 150 mM NaCl, 0.2% Nonidet P-40, and 2 mM EDTA] in the presence of protease inhibitor cocktail for 20 min at 4C with vortex every 5 min, followed by centrifugation at 13,000 rpm for 10 min at 4C. For immunoprecipitation, about 1 mg of protein was incubated with control [immunoglobulin G (IgG)] or specific antibodies (1 to 2 g) for 12 hours at 4C with constant rotation. A total of 50 l of 50% protein A/G agarose beads was then added, and the incubation was continued for an additional 6 hours. Beads were then washed five times using the PBS with 0.1% Tween. Between washes, the beads were collected by centrifugation at 2000 rpm for 5 min at 4C. The precipitated proteins were eluted from the beads by resuspending the beads in 2.5 SDS-PAGE loading buffer and boiling for 10 min. The boiled immune complexes were subjected to SDS-PAGE, followed by immunoblotting with appropriate antibodies.

Cells were fixed for 10 min at room temperature with 4% paraformaldehyde in PBS and permeabilized with 0.1% Triton X-100 in PBS for 10 min at room temperature. Samples were then blocked in 3% bovine serum albumin and incubated consecutively with primary antibodies to SND1/calnexin (ProteinTech, 60265-1-Ig/10427-2-AP) and the appropriate secondary antibodies coupled to Alexa Fluor 488 or 594 (Invitrogen). Cells were covered with a drop of 4,6-diamidino-2-phenylindole (DAPI) for 5 min. After washing with PBS, slides were mounted for observation. Confocal images were captured on Leica DMi8 using a 63 oil objective. The fluorescence intensity profiles of the targeted regions were obtained with a Leica DMi8 microscope and LAS X version 3.5.5.

Duolink assay was performed using the Duolink In Situ Red Starter Kit Mouse/Rabbit following the manufacturers instructions (Sigma-Aldrich, DUO92004), and its basic protocols of fixation, retrieval, and permeabilization are the same as immunofluorescence. Samples were incubated with blocking solution for 1 hour at 37C in a humidity chamber and then overnight at 4C with anti-SND1 mouse antibody and anti-calnexin rabbit antibody. Slides were then incubated for 1 hour at 37C with a mix of the MINUS (anti-mouse) and PLUS (anti-rabbit) PLA probes. Hybridized probes were ligated using the ligation-ligase solution for 30 min at 37C and then amplified using the amplification-polymerase solution for 100 min at 37C. Slides were covered with a drop of DAPI (Invitrogen) for 5 min and mounted for observation with a Leica DMi8 confocal microscope.

GST-fusion proteins that contained full length or truncations of SND1 and HLA-A were produced in BL21 E. coli and purified using glutathione agarose beads. Recombinant proteins (HLA-A) were expressed with rabbit reticulocyte lysate (TNT systems, Promega) according to the manufacturers recommendation. His-SND1 was purified in BL21(DE3) E. coli and purified using HIS-Select Nickel Affinity Gel (Sigma-Aldrich) according to the standard procedures.

In GST pull-down assays, the bead-bound GST-fusion proteins were incubated with in vitro transcribed/translated products at 4C for 10 hours. The beads were then washed five times with binding buffer containing 75 mM NaCl and detected by Western blotting.

OT-I mice were injected with WT or SND1-KO B16F10-OVA cells, and CD8+ T cells were collected by microbeads (no. 130-116-478, Miltenyi Biotec), restimulated ex vivo with SIINFEKL peptide (OVA-derived peptide being presented through the MHC class I molecules; no. S7951, Sigma-Aldrich) for 20 hours, and lastly cultured with brefeldin A (no. 420601, BioLegend) for 4 hours. Then, the cells were subjected to flow cytometric analysis, and the percentages of IFN+CD8+ T cells among total CD8+ T cells was measured by flow cytometry.

A cytotoxicity detection kit (R&D Systems) was used to measure the cytolysis rate elicited by CD8+ T cells against different tumor cells. B16F10-OVA or MC38-OVA (WT or SND1-KO) cells (2 104) were cocultured with CD8+ T cells in an E/T cell ratiodependent manner (1 105/2 105/3 105/4 105) isolated from OT-I mice with or without the treatment of MHC-blocking antibodies (AF6-88.5; 0.25 g for 1 105 CD8+ T cells) in sterile 96-well tissue culture plates for 12 hours. The microplate was centrifuged at 250g for 10 min, and supernatant was removed to incubate with reaction mixture. The absorbance of the samples was measured at 490 or 492 nm with an enzyme-linked immunosorbent assay reader.

The amount of lactate dehydrogenase released from lysed target cells was used as an indicator for cytolysis. Cytolysis rate (percentage) was calculated on the basis of the following equation: cytotoxicity (%) = (effector/target cell mix effector cell control low control)/(high control low control) 100. Under the same conditions, B16F10-OVA or MC38-OVA (WT or SND1-KO) cells (1 105) were cocultured with CD8+ T cells (1 106) in sterile 24-well culture plates, and images were taken under a bright field using a Leica microscope at different time points.

The three-dimensional structure of SND1 and HLA-A were generated using I-TASSER. Then, the ZDOCK website was used to predict the possible binding conformation between SND1 and HLA-A. The structure with the lowest binding free energy was chosen as the initial binding conformation, which was further optimized using molecular dynamics (MD) simulation. MD simulation was carried out with Gromacs package (version 5.0.1) and GROMOS96 54A7 force field. The binding complex of SND1 and HLA-A was first placed in a cube box with a minimum distance of 1.0 nm from the edge of box. Then, the complex was solvated with TIP3P water molecules and neutralized with the addition of Na+ and Cl ions. A total of 1000 steps steepest descent energy minimization was conducted to remove local contacts, and the equilibration was performed including 1-ns NVT (Constant volume) and 5-ns NPT (Constant pressure) relaxations. Last, 50-ns MD simulations were performed at 300 K and 1.0-atm pressure under periodic boundary conditions. In all simulation steps, the SHAKE algorithm was applied to constrain all bonds involving hydrogen atoms, Particle mesh Ewald method was used to treat long-range electrostatics, and a cutoff distance of 1.0 nm was used for short-range electrostatic and van der Waals. On the basis of the resulting MD trajectory, we can analyze the structural stability of the complex, calculate the binding free energy using Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA) method and identify key residues to the association process.

The correlation between SND1 and cancer immune infiltrates was investigated via TIMER (https://cistrome.shinyapps.io/timer/). The strength of correlations was evaluated using the Spearman correlation test; the Spearman coefficient was considered to indicate poor correlation if <0.2, moderate if <0.4, relatively strong if <0.6, strong if <0.8, and very strong if >0.8. P values <0.05 were considered statistically significant. The correlation between SND1 expression and survival in different cancers was analyzed by the PrognoScan database (http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html). The threshold was adjusted to a Cox P < 0.05.

Data from biological triplicate experiments are presented with error bars as means SD unless otherwise noted. Two-tailed unpaired t test was used to compare two groups of data, and analysis of variance (ANOVA) with Bonferronis correction was used to compare multiple groups of data used for statistical analysis. All of the statistical testing results were determined using GraphPad Prism 7.0 software.

Acknowledgments: We thank Z. Dong from Tsinghua University for sharing the OT-I mouse model. Funding: This work was supported by grant 31125012 from the National Science Foundation for Distinguished Young Scholars of China (to J.Y.); grant IRT13085 from the Innovation Team Development Plan of the Ministry of Education (to J.Y.); grant 31300709 (to X.W.), grants 31870747 and 31370749 (to J.Y.), and grant 81572882 (to Z.Y.) from the National Natural Science Foundation of China; and the High-Level Innovation and Entrepreneurship Team of Tianjin Talent Development Special Support Plan (to J.Y.). This work also received support from grant YJSCX201814 (to Y.W.) from the Postgraduate Innovation Fund of 13th Five-Year Comprehensive Investment, Tianjin Medical University. Author contributions: Y.W., X.W., and J.Y. generated the initial idea and conducted key experiments. Y.W., X.C., H.L., C.H., and L.X. performed the research. Y.W., X.W., X.C., and J.Y. analyzed the data and wrote the manuscript. Y.Z., T.Z., and K.Z. helped with the bioinformatics analysis. J.Y., X.Y., and Z.Y. critically revised the manuscript for important intellectual content. L.G., X.L., J.H., Y.R., W.Z., and X.S. provided administrative, technical, or material support. J.Y. supervised the study. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

Original post:
Oncoprotein SND1 hijacks nascent MHC-I heavy chain to ER-associated degradation, leading to impaired CD8+ T cell response in tumor - Science Advances

Inhibition of Hsp90 in the spinal cord enhances the antinociceptive effects of morphine by activating an ERK-RSK pathway – Science

Chaperones put the brakes on opioids

Until alternatives to opioids are developed, keeping opioid doses low but effective may be key to preventing their adverse effects. Duron et al. found that Hsp90 inhibitors injected into the spine of mice enhanced the efficacy of systemically administered opioids. In sensory neuronrich regions of the spine, the chaperone protein Hsp90 attenuated the activity of a kinase-to-protein synthesis pathway required for the antinociceptive effects of opioids. In mice, blocking Hsp90 in the spine, but not in the brain or periphery, made opioids more effective at dampening sensitivity to heat and touch, suggesting that this approach might be beneficial in patients. Additional observations further suggest that the role of Hsp90 in opioid signaling is tissue specific.

Morphine and other opioids are commonly used to treat pain despite their numerous adverse side effects. Modulating -opioid receptor (MOR) signaling is one way to potentially improve opioid therapy. In mice, the chaperone protein Hsp90 mediates MOR signaling within the brain. Here, we found that inhibiting Hsp90 specifically in the spinal cord enhanced the antinociceptive effects of morphine in mice. Intrathecal, but not systemic, administration of the Hsp90 inhibitors 17-AAG or KU-32 amplified the effects of morphine in suppressing sensitivity to both thermal and mechanical stimuli in mice. Hsp90 inhibition enabled opioid-induced phosphorylation of the kinase ERK and increased abundance of the kinase RSK in the dorsal horns of the spinal cord, which are heavily populated with primary afferent sensory neurons. The additive effects of Hsp90 inhibition were abolished upon intrathecal inhibition of ERK, RSK, or protein synthesis. This mechanism downstream of MOR, localized to the spinal cord and repressed by Hsp90, may potentially be used to enhance the efficacy and presumably decrease the side effects of opioid therapy.

Currently available therapeutics for the treatment of chronic pain are largely limited by their efficacy and undesired side effects. With more than 100 million individuals affected and an economic burden exceeding $600 billion in the United States alone, chronic pain remains an area of critical and growing medical need (1, 2). One of the more efficacious treatment options are opioid analgesics, such as morphine. Although these drugs can be very effective acutely, their side effects such as tolerance, addiction, and respiratory depression make them a high-risk choice when dealing with long-term medication regimens (3, 4). Accompanying these negative side effects is a growing social awareness of the potential dangers of opioids that have begun to negatively stigmatize their use, abetted by a growing opioid abuse and addiction crisis (5).

Intensive decades-long research has revealed a complex signaling network evoked by opioid treatment downstream of the -opioid receptor (MOR) (6). Increased understanding of the complexity of MOR signal transduction has resulted in new efforts for drug discovery and development, such as biased agonism to reduce the side effects of opioids (79). These efforts have produced new biased ligands, as well as additional drugs targeting key proteins, such as the kinase mammalian target of rapamycin, or MOR signalingrelevant receptors, such as protease-activated receptor 2, to either augment or reduce key behavioral outputs, such as antinociception (1014). These efforts illuminate the relevant MOR signaling cascades beyond the classical Gi cascade and show the value in elucidating key downstream signaling regulators.

Heat shock protein 90 (Hsp90) is a molecular chaperone protein that is up-regulated in response to stress. It regulates its client proteins through several molecular mechanisms, including protein folding, kinase modulation, protein complex formation, and subcellular localization (15, 16). Hsp90 makes up roughly 2% of the total protein pool in a given cell, highlighting its centrality to cell biology. Its functions have primarily been investigated in the context of cancer (1719), but Hsp90 has been shown to have a key role in regulating signal transduction at the receptor and downstream cascade levels in a number of different tissues and physiological contexts (20).

In earlier work, we tested the hypothesis that Hsp90 could play a key role in MOR signal transduction by selectively inhibiting Hsp90 in the brain using intracerebroventricular administration of 17-N-allylamino-17-demethoxygeldanamycin (17-AAG). In that study, we found that brain Hsp90 inhibition completely suppressed the antinociceptive effects of systemically administered morphine in various murine pain models (21). In addition, we showed that intracerebroventricular administration of 17-AAG blocked the phosphorylation of extracellular signalregulated kinase (ERK) in response to the selective MOR agonist and synthetic opioid peptide DAMGO ([d-Ala2, N-MePhe4, Gly-ol]-enkephalin) and that this loss of ERK phosphorylation was responsible for the loss of morphine antinociception. Those findings demonstrated that Hsp90 promotes MOR signaling in the brain and identified new signaling pathways to explore further. However, that work left many mechanistic details unknown, as well as the contribution of other regions of the central nervous system (CNS) like the spinal cord, which may be more applicable clinically because of the potential for intrathecal drug delivery.

Here, we investigated Hsp90 modulation of MOR signal transduction in the spinal cord in mice. Contrary to what was observed in our previous study in the brain, Hsp90 inhibition in the spinal cord amplified the antinociceptive effects of morphine. We further identified a molecular mechanism for this effect within the spinal cord dorsal horn. Our findings suggest a potential opioid dose reduction strategy via spinal Hsp90 inhibition to minimize the negative side effects of opioids while maintaining their analgesic benefits.

We previously showed that intracerebroventricularly administered Hsp90 inhibitors completely ablated morphine-induced antinociception in multiple pain models (21). In addition, Hsp90 has a considerable number of client proteins, which differ in various tissue, cellular, and environmental contexts (2224). This suggests the potential for context-specific roles for Hsp90 within downstream MOR signaling. We sought to test this role for Hsp90 in MOR signaling by considering the contribution of Hsp90 to morphine-induced antinociception within the spinal cord. CD-1 mice were treated with intrathecally administered 17-AAG, a geldanamycin derivative that competitively binds the N-terminal adenosine triphosphate (ATP) binding domain of Hsp90. Twenty-four hours after injection, mice were then treated with morphine [3.2 mg/kg, subcutaneously], and behavioral pain assays were performed.

Contrary to our previous report with intracerebroventricularly administered 17-AAG, we found that spinally inhibited Hsp90 resulted in an increased antinociceptive response (meaning, less pain-evoked sensitivity) due to morphine in both thermal tail-flick and mechanical postoperative paw incision pain models (Fig. 1, A and B). To verify the Hsp90 selectivity of these results, we used a C-terminal inhibitor of Hsp90, KU-32, that binds to an alternate site than 17-AAG and thus is unlikely to share off-target interactions (25, 26). KU-32 was administered to the spinal cord, followed by subcutaneous morphine 24 hours later. Enhanced morphine-induced antinociception was also observed with KU-32 treatment, confirming the Hsp90 selectivity of our results (Fig. 1C).

(A to E) Male and female CD-1 mice were treated as indicated in the labels with either 17-AAG (0.5 nmol) or KU-32 [0.01 nmol (C)] or vehicle (Veh) injected by the intracerebroventricular (icv) or intrathecal (it) route, followed by a 24-hour recovery, then injected subcutaneously with or without morphine [Mor; 3.2 mg/kg (A to D); 3.2 or 10 mg/kg (E)], and subjected to behavioral testing. BL. baseline response. Data are means SEM from N (number of mice per group noted on each graph); each experiment was performed with one (D), two (A to C), or three (E) independent technical replicates (meaning groups of mice performed on different days). *P < 0.05, ***P < 0.001, and ****P < 0.0001 versus same time point in Vehicle group, by two-way ANOVA with Sidaks post hoc test.

We next confirmed that these findings were not due to off-target motor or sedative effects using the rotarod test. Spinal 17-AAG treatment had no impact on rotarod performance in the mice, suggesting that our findings reflect bona fide changes to the opioid pain modulatory system (Fig. 1D). Last, in our previous report, we tested the brain role of Hsp90 in tail-flick pain using intracerebroventricular DAMGO instead of morphine, showing that inhibition of brain Hsp90 had no impact on tail-flick pain (21). To confirm that our tail-flick results here were due to changes in Hsp90 location (brain versus spinal cord) rather than drug and route (intracerebroventricular DAMGO versus subcutaneous morphine), we tested intracerebroventricular 17-AAG combined with subcutaneous morphine. We found that at both 3.2 and 10 mg/kg (subcutaneously), morphine had no impact on tail-flick response (Fig. 1E), the same as for intracerebroventricular DAMGO in our earlier study (21).

With behavioral differences identified in brain versus spinal cord Hsp90 inhibition, we next tested the interaction of these two regions using systemic Hsp90 inhibition, which would affect both brain and spinal cord. To do this, we injected mice with intraperitoneal 17-AAG and assessed its effects on morphine-induced antinociception after a 24-hour period. We tested for increased Hsp70 expression levels as a marker of Hsp90 inhibition within the periaqueductal gray (PAG) brain region and spinal cord tissue after 24 hours with intraperitoneal 17-AAG using Western blot (21). We found that Hsp70 levels were increased in the PAG as expected, validating our treatment regimen as effective in inhibiting CNS Hsp90 (Fig. 2, A and B). Unexpectedly, we were unable to detect an increase in spinal cord Hsp70 (Fig. 2, A and B). 17-AAG was likely reaching the spinal cord because we have shown that it can reach the PAG; thus, this result may represent different molecular mechanisms for Hsp90 in the brain versus the spinal cord. Now validated, we tested the impact of systemic 17-AAG on morphine antinociception. We observed that systemic delivery of 17-AAG had no impact on antinociception in the tail-flick pain model and markedly reduced antinociception in the paw incision pain model (Fig. 2, C and D); these results are very similar to what was observed with intracerebroventricular 17-AAG treatment previously (21) and here (Fig. 1E).

(A and B) Representative images (A) and analysis (B) of Western blotting for Hsp70 in the periaqueductal gray (PAG) and spinal cord (SC) from male and female CD-1 mice that received intraperitoneal (ip) injection with 17-AAG (50 mg/kg) or vehicle with a 24-hour recovery. Hsp70 densitometry was normalized to that of GAPDH (loading control) from each sample and was further normalized to the vehicle group within each tissue. Data are means SEM of N = 9 to 10 mice, performed with two technical replicates. **P < 0.01 versus same tissue in vehicle group, by unpaired two-tailed t test. (C and D) Tail-flick (C) and paw incision (D) pain behavior tests in mice injected with 17-AAG (50 mg/kg) or vehicle intraperitoneally, followed by a 24-hour recovery and then a subcutaneous injection of morphine (3.2 mg/kg). Data are means SEM of N (number of mice per group), noted on each graph, performed with four (C) or two (D) technical replicates. *P < 0.05 and ****P < 0.0001 versus same time point in the 17-AAG group, by two-way ANOVA with Sidaks post hoc test. (E and F) Tail-flick (E) and paw incision (F) pain behavior tests in mice that received both intracerebroventricular and intrathecal injections of 0.5 nmol of 17-AAG or vehicle, followed by a 24-hour recovery and then a subcutaneous injection of morphine (3.2 mg/kg). Data are means SEM of N (number of mice per group), noted on each graph, performed with two technical replicates. *P < 0.05 and ****P < 0.0001 versus same time point in the 17-AAG group, by two-way ANOVA with Sidaks post hoc test.

The effects on morphine antinociception seen with systemic Hsp90 inhibition suggest that the signaling events within the brain may override that of the spinal cord. To directly test this hypothesis and rule out peripheral mechanisms, we performed dual intracerebroventricular and intrathecal injections of 17-AAG. We found that dual brain and spinal cord injections recapitulated systemic injection in both the tail-flick and paw incision pain models (Fig. 2, E and F). These results suggest that the signaling events regulated by Hsp90 within the brain override MOR signaling within the spinal cord, which would otherwise allow for amplified pain relief in these models.

Our previous study within the brain demonstrated that blocked activation of ERK mitogen-activated protein kinase (MAPK) in the PAG by 17-AAG treatment is a mechanism for the reduction in morphine-induced antinociception (21). We thus tested ERK signaling activation within the spinal cord after intrathecal 17-AAG and DAMGO (selective MOR agonist) treatment using Western blot. DAMGO was used as a high-efficacy selective agonist, increasing our ability to observe kinase changes in tissue versus the partial agonist morphine; our results above (Fig. 1E) and experiments below validate this choice. DAMGO alone showed no ERK activation relative to vehicle treatment; in contrast, 17-AAG induced an elevated ERK baseline with a further increase in ERK phosphorylation when combined with DAMGO (Fig. 3, A and B). As in our systemic inhibition studies above, we also sought to confirm Hsp90 inhibition by 17-AAG by testing for Hsp70 up-regulation (21, 27, 28). We again found no Hsp70 up-regulation, even with direct intrathecal injection of inhibitor, confirming our systemic results above and further suggesting that Hsp90 molecular mechanisms may differ in the spinal cord versus the brain (Fig. 3, A and C).

(A to C) Representative images (A) and analysis (B and C) of Western blotting for phosphorylated ERK (pERK) and Hsp70 in the spinal cord from male and female CD-1 mice intrathecally injected with 0.5 nmol of 17-AAG or vehicle followed by a 24-hour recovery and then 0.1 nmol of DAMGO or vehicle intrathecally for 10 min. Densitometry of pERK was normalized to that of total ERK (tERK) within each sample, and the densitometry of Hsp70 was normalized to that of GAPDH; each was further normalized to that in the Vehicle/Vehicle group. Data are means SEM from N (the number of mice per group noted on the graphs), each performed as four technical replicates. In (B), *P < 0.05 and ****P < 0.0001 versus Vehicle/Vehicle group and ##P < 0.01 versus 17-AAG/Vehicle group (both by two-way ANOVA with Tukeys post hoc test). In (C), P > 0.05 by unpaired two-tailed t test. (D) Immunohistochemistry (IHC) for pERK (green) performed on L4-L6 region spinal cord tissue from mice treated as described in (A). Representative images from N = 9 to 10 mice per group are shown. (E and F) Assessment of colocalization (yellow staining; white arrow) of pERK (green) with neuronal markers NeuN or MAP2 (red) by IHC of the dorsal horn region from 17-AAG/DAMGO-treated mice. Representative images from N 3 individual spinal cords per target, each performed as two independent technical replicates. Higher-magnification images (63) are shown in (F). (G) Quantitation of the pERK signal in the dorsal horn region from all four groups in (D). Intensity values were normalized to the Vehicle/Vehicle group. N = 9 to 10 mice per group, each performed in four independent technical replicates. **P < 0.01 versus Vehicle/Vehicle group by two-way ANOVA with Tukeys post hoc test. (H and I) Tail-flick (H) and paw incision (I) pain behavior tests in mice intrathecally treated with 0.5 nmol of 17-AAG or vehicle for 24 hours, followed by 5 g of U0126 or vehicle intrathecally for 15 min, followed by morphine (3.2 mg/kg, subcutaneously). Data are means SEM from N (the number of mice per group noted on the graphs), each performed as four (H) or three (I) independent technical replicates. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001 versus same time point in the Veh/Veh group by two-way ANOVA with Sidaks post hoc test.

To localize the observed increases in ERK phosphorylation within the spinal cord, we performed immunohistochemical (IHC) analysis of spinal cord tissue from mice treated with intrathecal 17-AAG and DAMGO as for our Western studies. Our findings confirmed the Western results, with very low phosphorylated ERK (pERK) signal observed in vehicle-onlytreated mice (Vehicle/Vehicle; order of treatment denoted by /) and vehicle followed by DAMGO-treated mice (Vehicle/DAMGO); we observed some increase in signal in the 17-AAG/Vehicle group and a large increase in specific pERK signal in the 17-AAG/DAMGO group (Fig. 3D). We particularly noted an apparent increase in ERK phosphorylation in the lamina I/II region of the dorsal horn, a region rich in nociceptive input and opioid receptors (Fig. 3D, white arrows). We also performed colocalization studies with neuronal nuclei (NeuN), a marker for neuronal cell bodies, and microtubule-associated protein 2 (MAP2), a neuronal cytoskeletal protein enriched in dendrites; we found that the pERK signal colocalized with MAP2 but not NeuN, suggesting ERK activation in postsynaptic dendrites (Fig. 3E). This was confirmed using high-magnification imaging (Fig. 3F), in which substantial, but not complete, pERK/MAP2 overlap was detected. We also quantitated the pERK signal in the dorsal horn region, which confirmed a significant increase with 17-AAG and DAMGO cotreatment (Fig. 3G).

To investigate whether these differences in ERK signaling contribute to the enhanced morphine-induced antinociception observed, we performed behavioral analysis with cotreatment of intrathecal 17-AAG and intrathecal U0126, a MAPK kinase/ERK inhibitor. In both tail-flick and paw incision models, U0126 treatment brought the enhanced morphine-induced antinociceptive profile back to the baseline morphine response (Fig. 3, H and I). In addition, mice treated with U0126 alone without 17-AAG showed no difference in morphine-induced antinociception (Fig. 3, H and I). These results demonstrate that ERK phosphorylation within the spinal cord is necessary for increased morphine-induced antinociception via spinal cord Hsp90 inhibition. They also support our Western and IHC results, suggesting that ERK is not activated by opioids without Hsp90 inhibition.

Hsp90 and ERK MAPK signaling pathways have been previously connected to translational initiation (2932). To evaluate the possibility of these pathways altering translation and subsequently contributing to the behavioral differences observed here, we intrathecally administered the translational inhibitor cycloheximide (CX) in the context of our behavioral experiments. In a very similar pattern to the ERK inhibitor experiments above, we found that CX, 24 hours after 17AAG and 30 min before morphine, reduced the enhancement of morphine-induced antinociception back to baseline in the tail-flick model (Fig. 4A). CX alone without 17-AAG treatment did not change morphine-induced antinociception (Fig. 4A). These findings suggest that rapid translation within 30 min of opioid treatment is necessary for the enhanced morphine antinociception seen through spinally inhibited Hsp90.

(A) Tail-flick assay on male and female CD-1 mice intrathecally injected with 0.5 nmol of 17-AAG or vehicle for 24 hours, then 85 nmol of cycloheximide (CX) or vehicle intrathecally for 30 min, and then morphine (3.2 mg/kg, subcutaneously). *P < 0.05, **P < 0.01, and ***P < 0.001 versus corresponding Veh/Veh data; #P < 0.05 and ##P < 0.01 versus corresponding 17-AAG/CX data (by two-way ANOVA with Sidaks post hoc test). Data are means SEM from N (the number of mice per group as noted in the graph), each performed as four technical replicates. (B and C) Western blotting and densitometry analysis of pERK abundance in the spinal cords of mice intrathecally treated with 17-AAG then CX or vehicle as in (A), followed by 0.1 nmol of DAMGO or vehicle for 10 min. pERK density was normalized to tERK density in each sample and further normalized to the 17-AAG/Vehicle/Vehicle group. *P < 0.05 versus 17-AAG/Vehicle/Vehicle and #P < 0.05 versus 17-AAG/CX/Vehicle (both by two-way ANOVA with Tukeys post hoc test). Data are means SEM from N (the number of mice per group as noted in the graph), each performed as six technical replicates.

To identify the position of translation within the Hsp90/ERK molecular cascade, we performed Western blot analysis on spinal cord tissues harvested from mice treated with 17-AAG and combinations of CX and DAMGO. 17-AAG paired with DAMGO treatment stimulated ERK phosphorylation as above; CX treatment 30 min before DAMGO had no effect on this stimulation (Fig. 4, B and C). These results suggest that translational initiation is a downstream event from ERK phosphorylation after Hsp90 inhibition.

Our results above suggest that protein translation is altered by spinal Hsp90 inhibition; these changes should thus, in principle, be measurable by quantitative proteomics. We treated mice with intrathecal 17-AAG or Vehicle control as above for 24 hours and removed their spinal cords for analysis. We followed a protocol of protein extraction, SDSpolyacrylamide gel electrophoresis (PAGE) gel separation with six equal bands excised, tryptic digest, and a tandem mass spectrometry (MS/MS) analysis workflow (Fig. 5A). We detected 116 proteins significantly down-regulated by 17-AAG treatment and 69 proteins significantly up-regulated; unbiased hierarchical clustering analysis showed that the individual mice in each sample group (Vehicle versus 17-AAG) clustered together, validating a consistent effect of 17-AAG treatment (Fig. 5B). The full datasets for the significantly altered proteins in the whole analysis and in the subanalyses shown in this figure are available in data files S1 and S2. Of the proteins in this dataset, we noted that the abundance of the kinase ribosomal protein S6 kinase 2 (RSK2) was significantly up-regulated by 17-AAG treatment (Fig. 5C). RSK2 has been shown to promote acute opioid antinociception, highlighting this protein as a potential mechanism for spinal Hsp90 inhibition affecting opioid antinociception (33).

(A) Protein sample preparation and proteomic analysis workflow, as detailed in Materials and Methods. The samples were prepared using female CD-1 mice (N = 3 per group), which were intrathecally injected with 0.5 nmol of 17-AAG or vehicle for 24 hours. Spinal cords were removed for proteomic analysis, and protein was extracted as for Western blotting (detailed in Materials and Methods). These samples were used for all subsequent analysis in this figure. (B) Unbiased hierarchical clustering and heat map analysis of proteins significantly altered by 17-AAG treatment (P < 0.05). Red, increased; green, decreased; rows, individual proteins; columns, individual samples. Protein quantity traces for all proteins in each sample are shown (right insets). (C) Protein quantity data for the protein kinase RSK2, shown as means SEM of N = 3 per group. *P < 0.05 by unpaired two-tailed t test. (D) Principal components analysis of the full proteomic dataset was performed. Both treatment groups cluster together and are well separated along component 1, accounting for 75.1% of the variance. Within-group variance only occurs along component 2, accounting for only 8.5% of the variance. (E) Volcano plot of all detected proteins from the full proteomic dataset, plotting P value versus fold change. Red, significantly down-regulated; blue, significantly up-regulated; gray, not significant. (F) Gene ontology (GO) and KEGG pathway analysis of significantly altered proteins from (B) (see Materials and Methods for details). Data are plotted as significance versus fold enrichment. JNK, c-Jun N-terminal kinase.

We next performed additional analyses to validate the proteomic dataset and explore the network of protein changes evoked by Hsp90 inhibition. Principal components analysis showed that the individual mice in each treatment group (vehicle versus 17-AAG) clustered together; the groups were strongly separated from each other on component 1, accounting for 75.1% of the variance, whereas within the same treatment, the samples were much closer together along component 2, accounting for only 8.5% of the variance (Fig. 5D). We also represented our data in a volcano plot, permitting an overall visualization of significance and fold change (Fig. 5E). Together, these analyses further confirm the quality of our data and analysis.

Last, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the statistically significantly changed proteins using Database for Annotation, Visualization, and Integrated Discovery (DAVID) to identify broad themes in functions and processes altered by Hsp90 inhibition. We identified proteins heavily represented in molecular functions such as kinase activity, protein kinase binding, and protein phosphatase binding; pathways including metabolic pathways and oxytocin signaling; processes such as phosphorylation, cell proliferation, lipid metabolism, and synaptic plasticity; and cell components including synapse, exosome, focal adhesion, and postsynaptic density (Fig. 5F and data file S2). This network analysis begins to identify an overall role for Hsp90 in regulating protein networks in the spinal cord, which has not been previously reported.

Cytosolic RSK1 and RSK2 have both been implicated in translational initiation through several substrates, suggesting a potential link to our translation findings above (3440). RSK2 has also been implicated in acute morphine-induced analgesia within the medial habenula (33). Our proteomic analysis demonstrated altered expression levels of RSK2 within the spinal cord due to Hsp90 inhibition. Therefore, we aimed to probe both RSK1 and RSK2 as a potential mechanism within this molecular pathway.

To evaluate the necessity of RSK activation within our behavioral model, we used the irreversible RSK1/2 inhibitor 1-[4-amino-7-(3-hydroxypropyl)-5-(4-methylphenyl)-7H-pyrrolo[2,3-d] pyrimidin-6-yl]-2-fluoroethanone (Fmk). In a similar design to the U0126 and CX experiments above, 24-hour intrathecal 17-AAG was combined with intrathecal Fmk 30 min before morphine treatment in the tail-flick model. Fmk treatment returned the enhanced morphine antinociception caused by 17-AAG treatment back to baseline, whereas Fmk alone without 17-AAG treatment had no effect on morphine antinociception (Fig. 6A). These results show the same pattern as the U0126 and CX experiments above and strongly suggest that RSK promotes morphine antinociception after spinal Hsp90 inhibition. Fmk is nonselective between RSK1 and RSK2, so either or both isoforms could promote antinociception.

(A) Tail-flick assay in male and female CD-1 mice intrathecally injected with 0.5 nmol of 17-AAG or vehicle for 24 hours, followed by 10 nmol of Fmk or vehicle intrathecally for 30 min and then by morphine (3.2 mg/kg, subcutaneously). Data are means SEM from N (number of mice per group noted in the graph), each as three technical replicates. **P < 0.01, ***P < 0.001, and ****P < 0.0001 versus same time point Vehicle/Vehicle group by two-way ANOVA with Sidaks post hoc test. (B to D) Western blotting for phosphorylated (p) and total (t) RSK1 and RSK2 in the spinal cords from mice intrathecally injected with 0.5 nmol of 17-AAG or Vehicle for 24 hours, followed by 0.1 nmol of DAMGO or Vehicle intrathecally for 10 min. Densitometry of pRSK1 (C) and pRSK2 (D) was normalized to the corresponding tRSK within each sample and further normalized to the Vehicle/Vehicle group. Data are means SEM from N (number of mice per group noted in the graph), each as three technical replicates. *P < 0.05, ***P < 0.001, and ****P < 0.0001 versus Vehicle/Vehicle group and ##P < 0.01 versus 17-AAG/Vehicle group (both by two-way ANOVA with Tukeys post hoc test).

To confirm and extend these findings, we evaluated phosphorylation levels of both isoforms by Western blot in treated spinal cords as above. We found that both RSK1 and RSK2 demonstrate a similar phosphorylation pattern to that of ERK. 17-AAG treatment alone elicits increases in both RSK1 and RSK2 phosphorylation that rises to the level of significance for RSK2; 17-AAG and DAMGO cotreatment significantly increases phosphorylation of both proteins versus the Vehicle/Vehicle control group and over the 17-AAG/Vehicle group for RSK2 (Fig. 6, B to D). These results show that both RSK1 and RSK2 are activated by 17-AAG and DAMGO cotreatment and may both promote morphine antinociception after spinal cord Hsp90 inhibition.

In this study, we have identified a previously unknown molecular ERK-RSK signaling circuit in the spinal cord that can promote acute opioid-induced antinociception; this circuit is normally suppressed by Hsp90 and is only uncovered by spinal Hsp90 inhibition. Our results place rapid protein translation as a downstream event of ERK activation. Given the extensive literature that has shown an ERK-RSK-translation cascade (3440), we propose a model by which Hsp90 inhibition relieves the repression of ERK activation by MOR, resulting in an ERK-RSK-translationmediated cascade facilitating opioid-induced antinociception (Fig. 7).

Our data suggest that phosphorylation of ERK-MAPK proteins in the spinal cord by the MOR in response to opioids is blocked by Hsp90. Thus, Hsp90 inhibition (by 17-AAG or KU-32) enables ERK MAPK phosphorylation by the MOR with opioid treatment, leading to an ERK-RSK-translation cascade that promotes opioid antinociception.

Our results provide strong support that spinal ERK, RSK, and translation are not active at baseline for acute opioid antinociception. The inhibitors U0126, Fmk, and CX all had no effect on their own without 17-AAG treatment; we also showed that neither ERK nor RSK phosphorylation was stimulated by opioid treatment in vehicle-treated control mice using both Western blot and IHC methods. We could not find any literature reports showing acute activation of these kinases by opioids in the spinal cord. This is in sharp contrast to the brain, where our results and others show that ERK and RSK are phosphorylated by baseline opioid treatment and contribute to opioid antinociception (21, 33, 4144). This is not to say that ERK can have no impact on the opioid system in the spinal cord. Spinal ERK has been shown to have a role in mediating chronic opioid treatment side effects, particularly tolerance (45). ERK also has a well-established role in promoting chronic pain states after activation in the dorsal horn by strong and chronic pain stimuli (46). These contrasting findings show the importance of context in the function of signaling kinases. ERK is downstream of numerous receptor systems in the same cell and must be able to carry out diverse functions in the same cell when stimulated by these different systems. We propose that ERK is organized uniquely within the spinal cord so that it does not respond to acute MOR activation but is free to act in response to chronic MOR activation and in response to other receptor systems; our results suggest that Hsp90 could be this organizing factor preventing acute activation by the MOR. Removing this blockade enables ERK activation, leading to RSK activation, translation of new proteins, and enhanced antinociception. Uncovering these additional mechanisms will lend great insight into how MOR signaling is organized in the spinal cord.

One potential clue to the unique organization of Hsp90 in the spinal cord is that we found that spinal Hsp90 inhibition does not result in Hsp70 up-regulation, confirmed in multiple experiments. Hsp70 up-regulation in response to Hsp90 inhibition has long been considered a canonical response, caused by the release of heat shock factor-1 when Hsp90 is inhibited; we and many others have shown in this paper and elsewhere that Hsp70 is up-regulated in response to Hsp90 inhibition in numerous cell lines and brain tissue (21, 47). However, we cannot find any reports of Hsp70 up-regulation in the wild-type spinal cord in vivo after Hsp90 inhibitor treatment. Others have pointed out that Hsp90 inhibition does not always result in a heat shock response leading to Hsp70 up-regulation (48). It may be that Hsp90 in the spinal cord is organized differently at the molecular level than in the brain; perhaps it does not interact with heat shock factor-1 or similar proteins in the spinal cord. These differences may point to the mechanism by which Hsp90 has different signaling roles in brain versus spinal cord.

Our observations are consistent with the ERK/RSK cascade enhancing opioid activation via rapid protein translation. Hsp90 and ERK have both been linked to the initiation of protein translation (2932). RSK phosphorylation by ERK has been shown to activate translation through a variety of substrates including eukaryotic translation initiation factor-4B, tuberous sclerosis complex-1/2, the 40S ribosomal subunit protein S6, glycogen synthase kinase-3, and elongation factor-2 kinase (3440). These studies provide plausible targets linking ERK/RSK to protein translation but do not provide a potential mechanism for how protein translation enhances antinociception. Among the full list of proteins altered by 17-AAG treatment in our proteomic analysis were candidate proteins for this mechanism (data file S1). These include ion channels, such as potassium voltage-gated channel subfamily A member 4 and the calcium voltage-gated channel auxiliary subunit a2d1 subunit of the voltage-gated calcium channel, and numerous signaling proteins and signaling protein regulators (such as phospholipase Cd3, protein phosphatase 1, regulator of G protein signaling 12, and G protein-coupled receptor 162); these provide plausible future candidates to be investigated that could link the protein translation that we observed to enhanced antinociception. One finding that will guide such a search is that any candidate protein must have a rapid turnover half-life, given our observation that inhibiting translation within 30 min of opioid treatment abolished the response, suggesting that the protein must be degraded sufficiently within that time frame.

We also observed interesting systemic interactions above the level of molecular circuitry when investigating how Hsp90 inhibition in the brain and spinal cord interact. We found that Hsp90 inhibition in the brain had a dominant effect over that in the spinal cord in terms of the overall behavioral output, with either systemic or combined intracerebroventricular/intrathecal inhibition. For example, in the tail-flick pain model, brain Hsp90 inhibition had no notable effect on opioid-induced antinociception but nonetheless repressed the effects of spinal inhibition. The brain has a well-established circuit of opioidergic descending modulation with cell bodies in the rostroventral medulla and other regions and synapsing on nociceptive modulatory circuits in the spinal cord (49). It may be that descending modulatory neurons in the brain can override the spinal circuits when Hsp90 is inhibited in the brain. Lending some support to this hypothesis is our finding that spinal Hsp90 inhibition leads to enhanced ERK phosphorylation in lamina I/II of the dorsal horn of the spinal cord, which is a key target region for these descending neurons (50). Investigating the circuit context in which Hsp90 regulates antinociception will provide key insights into how the molecular circuitry translates into a whole animal behavioral response.

In this study, we demonstrated a spinal cordspecific role for Hsp90 within MOR downstream signaling and, in doing so, have begun to elucidate MOR-dependent downstream mechanisms of ERK phosphorylation within the spinal cord that can affect systemic morphine-induced antinociception. We propose a mechanism in which Hsp90 serves as a brake on ERK phosphorylation within neurons in the spinal cord dorsal horn. Once the brake is removed by a spinal Hsp90 inhibitor, ERK phosphorylation is unchained and can contribute to MOR agonistinduced antinociception through RSK activation and rapid translation. This translation event must up-regulate proteins that contribute to either hyperpolarization or the prevention of neurotransmitter release in primary or secondary nociceptive afferents within the spinal cord, further preventing the transmission of pain signals. Not only is this mechanism important in the context of molecular signaling, but there is also the potential to capitalize on these findings clinically by developing an opioid dose reduction strategy. Hsp90 inhibitors could be used to amplify morphine analgesia through the spinal cord without altering unwanted morphine side effects, many of which are evoked through brain regions such as the striatum (reward) or through the gut (constipation) and would not be affected by spinal cord treatment.

17-AAG (#AAJ66960MC), DAMGO (#11711), Fmk (#46-901-0), CX (#AC357420010), and U0126 (#11-445) were all purchased from Fisher Scientific (Waltham, MA). Morphine sulfate pentahydrate was obtained through the National Institute on Drug Abuse Drug Supply Program and distributed through the Research Triangle Institute. KU-32 was synthesized using published protocols, and purity (>95%) and identity were confirmed by high-performance liquid chromatography (HPLC) and mass spectrometry (25). 17-AAG, U0126, Fmk, KU-32, and CX were prepared as stock solutions in dimethyl sulfoxide (DMSO), and DAMGO was prepared as a stock solution in water. Morphine was prepared fresh for each experiment in United States Pharmacopeia (USP) saline. Powders were stored as recommended by the manufacturer, and stock solutions were stored at 20C. Appropriate vehicle controls were used for each experiment: 10% DMSO in water for KU-32, Fmk, and CX intrathecal injections; water for DAMGO intrathecal injections; USP saline for systemic morphine injections; and 10% DMSO, 10% Tween 80, and 80% USP saline for the 17-AAG and U0126 intrathecal, intracerebroventricular, and intraperitoneal injections.

Male and female CD-1 mice in age-matched controlled cohorts from 4 to 8 weeks of age were used for all experiments and were obtained from Charles River Laboratories (Wilmington, MA). Male and female mice were used in about equal numbers in each experiment; no sex differences were observed, so the male and female cohorts were combined for all data shown. CD-1 [also known as Institute for Cancer Research (ICR)] mice are commonly used in opioid research and in our previous work as a line with a strong response to opioid drugs [as in (21, 51)]. Mice were allowed to recover for at least 5 days after shipment before being used in experiments. The mice were kept in an Association for Assessment and Accreditation of Laboratory Animal Careaccredited vivarium at the University of Arizona under temperature control and 12-hour light/dark cycles with food (standard laboratory chow) and water available ad libitum. No more than five mice were kept in a cage. The animals were monitored daily, including after surgical procedures, by trained veterinary staff. All experiments performed were in accordance with Institutional Animal Care and Use Committeeapproved protocols at the University of Arizona and according to the guidelines of the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals handbook.

Before any behavioral experiment or testing, the animals were brought to the testing room in their home cages for at least 1 hour for acclimation. Testing always occurred within the same approximate time of day between experiments, and environmental factors (noise, personnel, and scents) were minimized. All testing apparatus (cylinders, grid boxes, etc.) were cleaned between uses. The experimenter was blinded to treatment group by another laboratory member delivering coded drug vials, which were then decoded after collection of all data.

Mechanical thresholds were determined before surgery using calibrated Von Frey filaments (Ugo Basile, Varese, Italy) with the up-down method and four measurements after the first response per mouse (21, 52). The mice were housed in a homemade apparatus with Plexiglas walls and ceiling and a wire mesh floor (3 inches by 4 inches by 3 inches with 0.25-inch wire mesh). The surgery was then performed by anesthesia with ~2% isoflurane in standard air, preparation of the left plantar hindpaw with iodine and 70% ethanol, and a 5-mm incision made through the skin and fascia with a no. 11 scalpel. The muscle was elevated with curved forceps, leaving the origin and insertion intact, and the muscle was split lengthwise using the scalpel. The wound was then closed with 5-0 polyglycolic acid sutures. All intracerebroventricular and intrathecal injections were performed as described in our previous work (21). For the 17-AAG/KU-32 experiments, the mice were then intrathecally injected and left to recover for 24 hours. The next day, the mechanical threshold was again determined as described above, and intrathecal injections took place for the U0126 experiments with a 15-min treatment time. Both the 17-AAG and the U0126 mice were then injected with morphine (3.2 mg/kg, subcutaneously), and mechanical thresholds were determined over a 3-hour time course. No animals were excluded from these studies.

Preinjection tail-flick baselines were determined in a 52C warm water tail-flick assay with a 10-s cutoff time (21). The mice were then intrathecally injected with 17-AAG, KU-32, CX, Fmk, or U0126 with a 24-hour (17-AAG and KU-32), 30-min (CX and Fmk), or 15-min (U0126) treatment time. Twenty-four hours after injection, baselines were determined for the 17-AAG experiments. The mice were then injected with morphine (3.2 mg/kg, subcutaneously), and tail-flick latencies were determined over a 2-hour time course. No animals were excluded from these studies.

Mice were subjected to three training trials of 3 min each on a rotarod device, with the machine off for trial 1, the machine on but not rotating for trial 2, and the machine rotating at 4 rpm for trial 3 (21). An automatic timer in the unit was used to record fall latencies with a 3-min maximum time. The mice were then intrathecally injected with 17-AAG or vehicle and allowed to recover for 24 hours, and another 3-min rotarod trial was performed without additional treatments or interventions. This trial was done with an accelerating 4- to 16-rpm task over the 3-min trial time. No mice were excluded from these studies.

Mouse spinal cord or PAG protein lysates were prepared using our previously published protocol (21) and quantified with a bicinchoninic acid protein quantitation assay using the manufacturers protocol (Bio-Rad). The protein was run on precast 10% bis-tris Bolt gels (Fisher Scientific, #NW00100BOX) using the Bolt gel apparatus and following the manufacturers instructions. The gels were transferred to nitrocellulose membrane (Bio-Rad) using a wet transfer system (30 V, minimum of 1 hour on ice). The blots were blocked with 5% nonfat dry milk in tris-buffered saline (TBS) and incubated with primary antibody in 5% bovine serum albumin (BSA) in TBS + 0.1% Tween 20 (TBST) overnight rocking at 4C. The blots were then washed three times for 5 min in TBST, incubated with secondary antibody (see below) in 5% milk in TBST for 1 hour of rocking at room temperature, washed again, and imaged with a LI-COR Odyssey infrared imaging system (LI-COR, Lincoln, NE). Some blots were then stripped with 25 mM glycine-HCl and 1% SDS (pH 2.0) for 30 to 60 min of rocking at room temperature before being washed and reexposed to primary antibody. The resulting image bands were quantified using Scion Image (based on NIH Image). All images were quantified in the linear signal range, which is easier to ensure because the Odyssey imager is a dynamic imager that allows for fine control of exposure. The pERK signal was normalized to the total ERK (tERK) signal, and pRSK1 and pRSK2 were normalized to tRSK1 and tRSK2, respectively, with both measured from the same blot as the primary target. The normalized intensities were further normalized to a vehicle control present on the same blot.

Perfusions were performed on drug-treated mice with cold phosphate-buffered saline (PBS), followed by cold 4% paraformaldehyde in PBS. Shortly after the perfusions were complete, fixed spinal cords were extracted and immediately placed in cold 4% paraformaldehyde for ~6 hours. Spinal cords were then placed in 15% sucrose in PBS overnight, followed by 30% sucrose in PBS overnight. Spinal cords were then flash-frozen in optimal cutting temperature compound using liquid nitrogen and sectioned with a Microm HM 525 cryostat at a thickness of 20 m between the L5 and L6 vertebrae and mounted on Surgipath X-tra microscope slides. Spinal cord sections were rehydrated in PBS in preparation for free-float staining. Samples were incubated in an endogenous peroxidase blocking buffer consisting of 60% methanol and 0.3% H2O2 in PBS at room temperature for 30 min and then washed with PBS + 0.1% Tween 20 (PBST). They were then incubated in 5% goat serum and 1% BSA in PBST at room temperature for 1 hour. Samples were then incubated with 1:5000 primary pERK antibody in 1.5% goat serum and 1% BSA in PBST at 4C overnight. Samples were then washed with PBST and then incubated with a 1:400 biotinylated secondary goat anti-rabbit immunoglobulin G (IgG) antibody in 1.5% goat serum and 1% BSA in PBST at room temperature for 1 hour. Samples were prepared as instructed using the VECTASTAIN Elite ABC horseradish peroxidase kit (#PK-6101) and TSA Plus Fluorescein evaluation kit (#NEL741E001KT), both from PerkinElmer. NeuN and MAP2 primary antibodies were used at 1:1000 and 1:500, respectively, during the pERK primary incubation. The secondary for NeuN and MAP2 was Alexa Flour goat anti-mouse IgG 594, which was used at 1:500 for both which was added during the pERK secondary incubation mentioned above. Stained spinal cord sections were then mounted onto slides with Novus FluorEver. Sections were imaged at 4, 10, and 63 using an Olympus BX51 microscope equipped with a Hamamatsu C8484 digital camera. Images were analyzed using ImageJ. Dorsal horn regions were selected, and average mean intensities were measured and normalized to no primary antibody and vehicle controls within experimental groups.

The antibodies used were as follows: Hsp70 (1:1000; Cell Signaling Technology, 4872S, lot 4, rabbit), glyceraldehyde phosphate dehydrogenase (GAPDH) (1:1000; Thermo Fisher Scientific, MA5-15738, lot PI209504, mouse), pERK (1:1000 for Westerns and 1:5000 for IHC; Cell Signaling Technology, 4370S, lot 12, rabbit), tERK (1:1000; Cell Signaling Technology, 4696S, lot 16, mouse), pRSK1 (1:1000; Cell Signaling Technology, 11989S, lot 4, rabbit), tRSK1 (1:1000; Cell Signaling Technology, 8408S, lot 5, rabbit), pRSK2 (1:1000; Cell Signaling Technology, 3556S, lot 4, rabbit), tRSK2 (1:1000; Cell Signaling Technology, 5528S, lot 1, rabbit), MAP2 (1:500; Invitrogen, 13-1500, lot TJ275359, mouse), NeuN (1:1000; Abcam, ab104224, lot GR3247200-1, mouse), secondary GM680 (1:10,000 to 1:20,000; LI-COR, 926-68020, lot C50721-02, goat), secondary GR800 (1:10,000 to 1:20,000; LI-COR, 926-32211, lot C50602-05, goat), and secondary Alexa Fluor goat anti-mouse IgG 594 (1:500; Invitrogen, A11032, lot 1985396, mouse).

Mouse spinal cord protein lysates (100 g) were prepared as for Western blot from animals that were treated with either 17-AAG or vehicle (N = 3 each) and were separated on a 10% SDS-PAGE gel and stained with Bio-Safe Coomassie G-250 Stain. Each lane of the SDS-PAGE gel was cut into six slices, and the gel slices were subjected to trypsin digestion. The resulting peptides were purified by C18-based desalting exactly as previously described (53, 54).

HPLCelectrospray ionizationMS/MS was performed in positive ion mode on a Thermo Scientific Orbitrap Fusion Lumos Tribrid mass spectrometer fitted with an EASY-Spray Source (Thermo Scientific, San Jose, CA). NanoLC was performed exactly as previously described (53, 54). Tandem mass spectra were extracted from Xcalibur RAW files, and charge states were assigned using the ProteoWizard 3.0 msConvert script using the default parameters. The fragment mass spectra were searched against the Mus musculus SwissProt_2018_01 database (16,965 entries) using Mascot (version 2.6.0; Matrix Science, London, UK) using the default probability cutoff score. The search variables that were used were as follows: 10parts per million mass tolerance for precursor ion masses and 0.5 Da for product ion masses; digestion with trypsin; a maximum of two missed tryptic cleavages; and variable modifications of oxidation of methionine and phosphorylation of serine, threonine, and tyrosine. Cross-correlation of Mascot search results with X! Tandem was accomplished with Scaffold (version Scaffold_4.8.7; Proteome Software, Portland, OR). Probability assessment of peptide assignments and protein identifications were made using Scaffold. Only peptides with 95% probability were considered. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the Proteomics Identifications Database (PRIDE) (55, 56) partner repository with the dataset identifier PXD015060 and 10.6019/PXD015060. The reviewer account details are as follows: username, reviewer97855{at}ebi.ac.uk and password, 8AM00kfd.

Progenesis QI for proteomics software (version 2.4; Nonlinear Dynamics Ltd., Newcastle upon Tyne, UK) was used to perform ion intensitybased label-free quantification as previously described (54). In an automated format, .RAW files were imported and converted into two-dimensional maps (y axis, time and x axis, m/z), followed by selection of a reference run for alignment purposes. An aggregate dataset containing all peak information from all samples was created from the aligned runs, which was then further narrowed down by selecting only +2, +3, and +4 charged ions for further analysis. The samples were then grouped, and a peak list of fragment ion spectra from only the top eight most intense precursors of a feature was exported to a Mascot generic file (.MGF) format and searched using Mascot (version 2.4; Matrix Science, London, UK) with the same search variables as described above. The resulting Mascot .XML file was then imported into Progenesis, allowing for peptide/protein assignment, whereas peptides with a Mascot ion score of <25 were not considered for further analysis. Protein quantification was performed using only nonconflicting peptides, and precursor ion abundance values were normalized in a run to those in a reference run (not necessarily the same as the alignment reference run).

All data were reported as means SEM and normalized where appropriate as described above to total protein and/or Vehicle control groups. The behavioral data were reported raw without maximum possible effect or other normalization. Biological and technical replicates are described in the figure legends. Comparisons between two groups (Hsp70 protein expression) were performed by unpaired two-tailed t tests. Comparisons of more than two groups (ERK and RSK signaling, paw incision, tail flick, and rotarod) were performed by two-way analysis of variance (ANOVA) with Sidaks (behavior) or Tukeys (Western) post hoc tests. In all cases, significance was defined as P < 0.05. All graphing and statistical analyses were performed using GraphPad Prism 8.1 (San Diego, CA).

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Inhibition of Hsp90 in the spinal cord enhances the antinociceptive effects of morphine by activating an ERK-RSK pathway - Science

Online plagues, protein folding and spotting fake news: what games can teach us during the coronavirus pandemic – The Conversation AU

Most of us dont take games too seriously. They are a way to unwind, or these days to maybe escape from the world of COVID-19 for a little while.

But games are also simulations in which real people play, make decisions and interact. This makes games powerful tools for learning and understanding complex situations, such as how diseases spread and even how to treat them.

One of the first incidents that showed epidemiologists and health researchers that games could give them insight into the spread of infectious disease occurred in 2005. A bug in World of Warcraft unleashed an infectious disease among the games large community of online players.

It started with a new raid encounter in the game designed to allowed a small team of players to fight an enemy that could infect characters with a curse called corrupted blood. The curse would reduce their health over time, and spread from player to player in close proximity.

Normally, when the character either won or lost the battle, the curse would be lifted as they left the zone in which it took place. But a bug allowed players pets and minions to carry the curse into the games wider virtual world.

Suddenly the curse was spreading across nearly 4 million players, and the people who ran the game had little control. Is this starting to sound eerily familiar?

Epidemiologists had used models and simulations in their work before, but the World of Warcraft incident was unique because each avatar in the simulation was controlled by a human player. Whats more, players in the game exhibited the same behaviours that people do in response to a real-life pandemic.

Some players followed the advice of the publisher of the game to avoid infected areas. Others rebelled, some didnt care, and some camped out in remote areas away from everyone else. These behaviours were studied in detail by Rutgers University epidemiologists Nina Fefferman and Eric Lofgren, who then published a paper on the potential using games as learning tools.

There are also many other ways in which we can use games as simulations to develop our understanding of global health.

Entertainment games such as Pandemic very directly refer to what we are all experiencing right now. In this game, players collaborate in order to fight a virus - and in its simplicity, it can illustrate why social cohesion is so vital in our global fight against the disease yet is also very difficult. The game teaches communication, collaboration, and decision-making skills in the context of crisis.

Read more: Playing Pandemic - the hit board game about the very thing we're trying to avoid

Modelling and simulation the use of formal, mathematical and often computerised calculations support policy makers and world leaders to make the right decisions. These models serve as tools for critical choices such as closing borders and national lockdowns. It takes huge amounts of trustworthy data and deep expertise to develop and interpret such models.

A game like Pandemic, or World of Warcraft, lets players engage in simplified versions of such crisis situations and can offer insight into human behaviour in these conditions. The simplified yet realistic scenarios allow for interaction, and learning by doing, without the risk of real-world consequences.

Games can also help us to develop new medical solutions. In Foldit, players can individually interact with protein folding, an important process in molecular biology. It is difficult to simulate with computers, but it plays a role in drug discovery and understanding certain types of diseases. The game uses a large number of individual players and the highest scoring solutions are reviewed by scientists as potential new solutions.

The game takes a distributed computational approach similar to the SETI@Home project that let people lend their computers to the search for extraterrestrial intelligence. The twist is that each node is again a human mind considering solutions, and this model outperforms computational algorithms attempting the same task.

In one example, players were able to find an elusive HIV enzyme in just 3 weeks. The game is now being applied directly to searching for solutions to the coronavirus.

Games are also helping researchers better understand the spread of misinformation about COVID-19, in a project of the American University Game Lab. (One of the authors of this article is affiliated with the Game Lab.)

Their game, Factitious, is a simple game that asks players to read a small article and then decide if it is real or fake news, with points awarded for each correct response. With over a million plays, the recorded dataset offers key insights into how players view and categorise information.

The new pandemic edition of the game is already informing us of dangerous trends in rumours and misleading information at this difficult time.

Read more: Gaming fosters social connection at a time of physical distance

Games may offer some much-needed escape and social connection in this time of physical distancing, but they are also incredible tools for learning more about the real world. In areas such as global health, they can act as very human simulations that help us plan for incredible situations and test multiple competing ideas to come up with the best way forward.

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Online plagues, protein folding and spotting fake news: what games can teach us during the coronavirus pandemic - The Conversation AU

EVE Online Reflects on 17th Anniversary, While Providing Updates on PLEX for GOOD, Quadrant 2, More – MMORPG.com

Happy birthday, EVE

By Poorna Shankar on May 06, 2020 | News | 0

In a letter by CCP Hellmar to the EVE Online community, the CEO looks to the 17th anniversary of the game in addition to sharing updates on PLEX for GOOD and other news.

Hellmar begins by recounting the launch of EVE Online and his memories of that day,

I remember this day very well despite weeks of sleep deprivation. It was a bit of an unassuming moment after the armageddon that was the end of the open beta. At 17 years of age, if EVE was a teenager in Iceland wed be celebrating it having achieved its pilots license!

As for the other news, PLEX for GOOD is set to run through the month of June, providing players and the community to continue aiding the fight against COVID-19 amidst the pandemic. As of this writing over $100,000 has been raised. Another donation option will be made available for Johns Hopkins Center for Health Security.

Project Discovery was also discussed, with the goal of protein folding for the Human Protein Atlas apparently going well. Phase 3 of this effort involves the fight against COVID-19. The team will share additional details as and when they become ready.

Hellmar also touched on the game itself, citing the influx of new players, the continued engagement of veteran players, and the relaunch of EVE China. Apparently, the latter is growing quite quickly so as both servers there are approaching equal size.

The post also touched on Quadrant 2 and Eclipse, with Quadrant 3 planning currently in the works. Hellmar mentioned that the team is working on addressing the botting, while issuing a warning to those who illegally buy ISK.

The conversion to 64-bit was also discussed. The team is working on recompiling the entire codebase it seems,

We are now working on recompiling the whole codebase of EVE, upgrading to Visual Studio 2019, and we are adding more hardware to increase the power of our servers.

The post is quite lengthy and touches on a myriad of topics as summarized above. If you wish to read the full thing, you can do so here.

A highly opinionated avid PC gamer, Poorna blindly panics with his friends in various multiplayer games, much to the detriment of his team. Constantly questioning industry practices and a passion for technological progress drive his love for the video game industry. He pulls no punches and tells it like he sees it. He runs a podcast, Gaming The Industry, with fellow writer, Joseph Bradford, discussing industry practices and their effects on consumers.

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EVE Online Reflects on 17th Anniversary, While Providing Updates on PLEX for GOOD, Quadrant 2, More - MMORPG.com

It’s game on in India, but where are the consumer brands? – Warc

Gaming has seen an incredible upswing in usage as India continues its COVID-19 lockdown measures, but why aren't consumer brands embracing this interactive medium to engage with audiences, asks Kunal Sinha.

Around the world, as people get used to life under lockdown, they are picking up or returning to games as a form of escape and connection. While many industries face challenges, gaming is certainly thriving.

With its inbuilt infrastructure supporting widespread socialization, gaming provides enthusiasts the closest live experience as they possibly could get. It is known that almost a quarter of gamers play video games to socialise with other gamers.

Nowhere is this more apparent than in India. According to the mobile marketing platform provider InMobi, usage of gaming apps rose by 110% between 1 January and 11 March this year.

Games like Psych and Houseparty are amongst the most-played games which people are played to relieve their quarantine ennui. According to Paavan Nanda, co-founder of WinZo Games, a popular vernacular gaming platform offering monetary benefits, they experienced a tripling of games plays and 30% higher traffic very soon after the lockdown was announced in mid-March. As the lockdown continued, traffic and engagement grew by the hour. For WinZo, primetime for gaming has changed from 7pm to 11pm in the evening to 11am to 5pm in the day, and multiplayer modes on the platform have grown threefold.

The experience of Paytm First Games has been no different. The platform, which allows users to choose from more than 100 games like Fantasy Cricket, Rummy and Ludo, has seen the number of Rummy games being played double every week. The rewards on offer - totalling over Rs 100 million - attract enthusiasts not only from Indias metros but also Tier 2 and 3 cities. New users who have joined the platform are mostly between the ages 18-45, the prime working age and student population.

This article is part of a special WARC Snapshot focused on enabling brand marketers to re-strategise amid the unprecedented disruption caused by the novel coronavirus outbreak.

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As against the hourly tournaments it used to conduct in the pre-Covid 19 days, the platform Gamerji now holds tournaments every 15 minutes. According to its founder Soham Thacker, gamers who would play two to three tournaments every day now play seven to nine tournaments.

Pokkt Mobile Ads reported a 39% increase in mobile game downloads in March 2020, and a 31% increase in mobile game ad requests globally. In response to the upsurge in gaming, Facebook Gaming released a feature that allows users to play tournaments against the general public, or friends. Gamers can create Fortnite or Call of Duty: Warzone tournaments, update leader boards and raise money for COVID-19 relief.

The penchant for gaming amongst technology users and consumers provides businesses, clinical services and civic authorities plenty of opportunities to influence behaviour. Gaming unlocks our instinct to compete and win. It doesnt matter if the prizes may be big or small often just having the opportunity to socialise a win could be motivation enough to play.

The World Health Organization (WHO) which only last year had declared gaming as a disorder, realized its immense potential in capturing attention and keeping millions entertained.

It teamed up with leading technology and gaming companies Microsoft, Sega, YouTube, Twitch, Riot Games and Activision Blizzard, to run a campaign Play Apart Together. With celebs like Kim Kardashian on board, the campaign urges gamers to stay home and adhere to social distancing, offering rewards, free special events and exclusives for their favourite games. The campaign has already racked up 4.7 billion consumer media impressions globally.

Location sharing app Zenly, owned by Snapchat, has gamified shelter-in-place. Typically, the app would encourage its users to go out and explore the world, and it might seem to lose relevance as most people went into self-quarantine. Instead, Zenly chose to launch a Stay At Home Challenge, which shows a leader board of which friends spent the most percentage of their time at home. It allows users to view who is embracing social distancing the best, and share stickers of the scoreboard to Snapchat, Instagram and other social networking sites, along with tips on keeping safe from coronavirus. Zenly essentially built a game around isolation, making it cool when people are not visiting friends, grabbing coffee or a drink, or taking a run.

For civic authorities, one of the key challenges which COVID-19 has thrown is to keep businesses open and encourage local consumption. Israel-based Colu Technologies launched a campaign in partnership with the Tel Aviv Foundation, to help local business and communities. Residents received a digital punch card, which they could use four times at local establishments for a minimum transaction of approximately US$6. Once they completed the entire challenge four qualifying transactions they win a one-time reward of US$10. The challenge revolved around the proven gamification concept of setting a goal, a reward and keeping score until the resident completes all four purchases. In just two weeks, by gaming the residents behaviour, the campaign was able to generate US$145,000 worth of economic activity.

Gaming is also being used to identify potential treatment for COVID-19. Researchers at the University of Washingtons Institute for Protein Design have designed a protein-folding puzzle called Foldit, whose players look for ways to twist virtual protein structures into different shapes and contortions. Some of these may have therapeutic value, which increases the players score. The game has so far produced 99 chances to win. The killer payoff all this has real-world implications in terms of countering COVID-19.

There are businesses that are looking at gaming as a way of building employee engagement. The employees of Gamify competed to create a game that was unrelated to any campaigns but would keep morale up. The winning game Mad Shopper turned out to be a commentary on the panic buying of toilet paper. Players were required to manoeuvre a shopping cart, grab as much toilet paper as they possibly could while dodging clouds of coronavirus.

It is a real irony that, at a time when change in peoples engagement with technology is dramatically undergoing transformation, consumer brands are choosing to adopt a policy of wait-and-watch. They are waiting for consumer demand to return. They are waiting for shops to reopen, for goods and people to start moving again.

The same might be said of media companies and entertainment studios, who cannot shoot through the lockdown. Could they not possibly create gamified experiences around their content and characters? What a wasted opportunity to keep consumers and viewers engaged when they have time on their hands and are looking for ways to relieve their boredom!

Isnt it time for them to game the slowdown?

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It's game on in India, but where are the consumer brands? - Warc

Here’s My Top Stock to Buy in May – The Motley Fool

I bought quite a few stocks in March and April. Am I now going to follow the old investing adage "sell in May and go away"? Absolutely not.

However, there are still a lot of stocks that I like. Several tech stocks, dividend stocks, and financial stocks are on my watchlist. But my top pick doesn't fall into any of these categories. Instead, it's a biotech stock that's already up by a solid double-digit percentage this year and has plenty of room to run. Here are three reasons my top stock to buy in May is...Vertex Pharmaceuticals (NASDAQ:VRTX).

Image source: Getty Images.

Some investors are concerned that the recent stock market resurgence could fizzle. Although I don't pretend to know what will happen next with the market, I do share those concerns. There's a lot of pent-up desire among many to return to normal life quickly. Count me in that group. But I also realize that the COVID-19 outbreak could continue to be a problem for longer than any of us would like. And the economic toll could be greater and last longer than some expect as well.

These factors weighed on my decision in picking Vertex as my top stock to buy in May. I think that Vertex is practically coronavirus-proof and recession-proof. The company markets drugs that treat the underlying genetic cause of cystic fibrosis (CF).

Because CF can impact lung function, patients really don't want to be diagnosed with COVID-19, which also affects the lungs. The likelihood that Vertex's product sales will fall off significantly because of the pandemic is therefore quite small.

An economic recession won't hurt the biotech's revenue much, either. Insurers will continue to pay for Vertex's drugs and patients will continue taking the drugs regardless of whether the economy is good or bad.

Vertex reported its first-quarter results on Wednesday. Those results included $895 million in sales for a new CF drug, Trikafta.

There are two things that are remarkable about that sales figure. First, the huge sales were generated in the drug's first full quarter on the market. Second, the FDA's approval decision was originally scheduled for March 20, 2020, which would have meant only minimal sales in Q1. But the FDA instead approved Trikafta five months early on Oct. 21, 2019 -- something that's nearly unheard of in the drug industry.

Image source: Getty Images.

Trikafta's commercial success is another key reason why I think so highly of Vertex's prospects. I'm not expecting massive near-term sales growth in the U.S. after the fantastic launch. Vertex CEO ReshmaKewalramani noted in the company's Q1 call that the majority of eligible patients in the U.S. are already taking Trikafta. However, Vertex should win European approval for the drug later this year -- and that will pave the way for tremendous growth.

Also, Vertex plans to file for FDA approval of Trikafta in treating younger CF patients between the ages of six and 11 in the second half of this year. Although this indication won't likely be approved until 2021, it sets the stage for even more growth.

While I think Vertex is a great stock to buy in May, it's not just because of what will happen for the biotech in the coming months or even the next year or two. My view is that Vertex has several exciting longer-term potential catalysts.

The company is leveraging its expertise in CF to target other genetic diseases that are also caused by protein folding issues. Its most promising pipeline candidate in this category is VX-814, which is being evaluated in a phase 2 study for treating alpha-1 antitrypsin deficiency (AATD), a rare genetic disease that affects a similar number of patients worldwide that CF does.

Another of Vertex's clinical programs that appears to be really promising is its collaboration with CRISPR Therapeutics on CTX001. The gene-editing therapy is currently in an early stage study targeting rare blood diseases beta-thalassemia and sickle cell disease. If CTX001 is successful, it could potentially cure both of these diseases.

Speaking of potential cures, Vertex also hopes to advance into clinical testing later this year or at least by early 2021 a gene-editing therapy just might cure type 1 diabetes. Vertex picked up the program with its acquisition of Semma Therapeutics last year. It's still really early, but this is a candidate that couldbe a game-changer.

Image source: Getty Images.

I'm unabashedly a fan of Vertex, but I'll admit that there are two main knocks against the stock. One is its valuation. Shares trade at more than 33 times expected earnings. The other is that the biotech doesn't have any late-stage pipeline candidates. Neither of these issues concerns me all that much, though.

Although Vertex's forward earnings multiple is high, my view is that the company's longer-term growth prospects need to be taken into consideration. The stock's price-to-earnings-to-growth (PEG) ratio, which factors in five years of projected growth, stands at only 0.59. A PEG ratio below 1.0 is usually considered to be an attractive valuation.

Vertex's lack of late-stage programs might be worrisome if the company didn't have a clear pathway to growth over the next several years. But it does have such a pathway. As Trikafta wins additional approvals around the world and for treating younger patients, it should enable Vertex to treat roughly 90% of all CF patients. Winning these approvals and launching the drug for new approved indications will give Vertex plenty of room for growth while its pipeline programs advance.

It's also important to note that Vertex sits on a cash stockpile of $4.2 billion. The company's management team has stated repeatedly that more business development deals are on the way to bolster the pipeline. I'm confident that Vertex will have more growth drivers in place when the time comes that its CF franchise revenue growth begins to taper off.

See the article here:
Here's My Top Stock to Buy in May - The Motley Fool

Cation-induced shape programming and morphing in protein-based hydrogels – Science Advances

Abstract

Smart materials that are capable of memorizing a temporary shape, and morph in response to a stimulus, have the potential to revolutionize medicine and robotics. Here, we introduce an innovative method to program protein hydrogels and to induce shape changes in aqueous solutions at room temperature. We demonstrate our approach using hydrogels made from serum albumin, the most abundant protein in the blood plasma, which are synthesized in a cylindrical or flower shape. These gels are then programmed into a spring or a ring shape, respectively. The programming is performed through a marked change in stiffness (of up to 17-fold), induced by adsorption of Zn2+ or Cu2+ cations. We show that these programmed biomaterials can then morph back into their original shape, as the cations diffuse outside the hydrogel material. The approach demonstrated here represents an innovative strategy to program protein-based hydrogels to behave as actuators.

Dynamic biomaterials that undergo conformational changes can enable artificial tissue structures, which could experience morphological transformations, and soft robotics, which could react and change in response to their environment. Currently, most common shape-morphing materials are based on polymers and require switching between a stiff and a soft phase (1). These materials typically rely on two or more network skeletons, sharing the same three-dimensional (3D) space (2), or have a chemical response to small ions (3, 4). Programming, defined as the capability of fixing a temporary shape in a material, requires a marked increase in stiffness, in a reversible manner (5). The initial shape recovery entails a switch from a stiff to a soft phase and is typically realized by compromising the integrity of the secondary network, by changing the temperature (1, 6), pH (7), or solvent (8, 9), or through photoswitching (10, 11).

Protein-based hydrogels use a protein as their primary network, in a water-rich environment (12). These hydrogels retain many of the characteristics displayed by the polymer-based materials but can harvest from a much more diverse biofunctional library. Proteins accomplish many life-supporting functions, from structural role to enzymatic reactivity, and their function is, in most cases, directly related to their folded 3D structure. While there is a substantial diversity for the starting material, when compared to polymers, proteins are stable and functional in a much narrower range of temperatures, pH, or salt conditions and require a water-based environment. Since the mechanical response of a material depends directly on the concentration of its constituent network nodes (1315), the range of obtainable stiffness for protein-based hydrogels is extremely limited: the protein needs to be above the critical gelation concentration to be turned into a biomaterial and below its specific solubility limit (1517). This narrow range allows for a change in a stiffness of only ~10 to 30%, depending on the starting protein. Furthermore, well-defined cross-linked network connections are critical to ensure a high shape recovery ratio (18). Unlike most linear polymer molecules, which can entangle like spaghetti in a bowl, globular proteins have well-defined 3D structures, acting as hard spheres. This structural integrity provides an excellent control over the cross-linking points and density, while preserving the tertiary structure of the network nodes.

Recently, we have introduced a new method to obtain shape memory in protein-based hydrogels by stiffening them through adsorbed polyelectrolytes (19). Our approach relies on producing protein hydrogels from bovine serum albumin (BSA), which is homologous to human serum albumin, the most abundant protein in blood plasma. BSA solutions can be turned into completely covalently cross-linked hydrogel biomaterials when starting from solutions with protein concentrations above 1 mM (17, 20). Below 1 mM, BSA hydrogels show irreversible plastic deformation under force, indicative of incomplete cross-linking (17). Because of the overall charge of BSA, these protein hydrogels have been programmed by stiffening, induced by the secondary network made from positively charged polyelectrolytes (19). The shape change was induced in this case via the unfolding response of the protein domains in chemical denaturants with complete recovery upon removal of the denaturing solution. In the unfolding phase transition responsible for shape memory, the proteins lose their tertiary structure and show a remarkable decrease in stiffness. This transition is highly repeatable, as protein folding has been tightly controlled by billions of years of evolution. However, the polyelectrolytes adsorption is irreversible, and because of their large size, the loading capacity is relatively limited, resulting in a change in stiffness of up to ~6.5-fold. Here, we explore the viability of using divalent cations to stiffen protein-based hydrogels toward programming them in various shapes. We then manage to morph back the protein materials from the temporary programmed shape to the initial permanent shape through simple diffusion. It has been previously shown, for other protein hydrogels (21) and peptide-based hydrogels (22), that divalent cations can reinforce their network. It is also well known that BSA can bind cations at various exposed amino acids (23, 24). For example, divalent cations such as Ni2+, Cu2+, and Zn2+ were shown to bind histidine (25, 26) and tryptophan (24), and to bridge cysteine amino acids (27). Adsorption of cations was also associated with an increase in the mechanical stability of proteins at the molecular level (28, 29). Here, we explore this mechanical change to induce the increase in stiffness needed to program protein-based biomaterials in various shapes (Fig. 1). The small ions have the advantage of allowing much higher loading of positive charge, leading to a 17-fold stiffening, and the capacity to diffuse outside the programmed material. This novel implementation of programming protein-based hydrogels with small ions is an important step toward obtaining biocompatible materials that can adjust their shape.

(Left) BSA-based protein hydrogels are fabricated using a light-activated reaction, in the presence of ammonium persulfate (APS) and tris(bipyridine) ruthenium(II) chloride [Ru(bpy)3]2+. (Right) Following synthesis, the protein hydrogels are exposed to Zn2+ or Cu2+, which reversibly increases their stiffness by up to 17-fold. This stiffening effect can be used for shape programming.

Protein-based hydrogels can be obtained using various cross-linking strategies, such as treatment with glutaraldehyde (20) and enzymatic reactions (30), using protein-based lock-and-key ends (31, 32) or photoactivation (12). Here, we use photoactivation via tris(bipyridine) ruthenium(II) chloride ([Ru(bpy)3]2+) to obtain protein-based hydrogels made from BSA (Fig. 1). This reaction was shown to produce covalent carboncarbon bonds at the exposed tyrosine amino acid sites between adjacent protein domains (12). The advantage of using light to trigger the start of the cross-linking reaction is that it allows us to load the reaction mixture in the desirable shape, without any change in viscosity before light exposure.

Our first step was to explore the range of concentrations of two positively charged ions, which can potentially increase the stiffness of protein hydrogels and allow for shape programming. The change in stiffness of protein-based hydrogels in the presence of positively charged ions was quantified using our force-clamp rheometry apparatus (17, 33). We first produced cylindrical-shaped hydrogels starting from 2 mM BSA, using polytetrafluoroethylene (PTFE) tubes with an inner diameter of 0.56 mm. We chose 2 mM as starting concentration, as our polymerization method produces complete cross-linking for BSA and the hydrogels show reversible behavior without any plastic deformations in the sampled force range (17). Using surgical thread, these gels were then attached in our force-clamp rheometer via two metal hooks connected to a voice-coil motor and force sensor, respectively (Fig. 2, inset, and see also Materials and Methods). Following incubation for 30 min in a phosphate buffer saline with the desired cation concentration, the mechanical response of treated BSA hydrogel was measured in the 0- to 4-kPa range (Fig. 2). The change in stress as a function of strain, as the applied force is increased linearly with time, can be used to assess the stiffness of the material, as the slope in the rising part of the trace directly reports on the dynamic Youngs modulus. Typically, hydrogels made from globular proteins such as BSA also show hysteresis in the stress-stain curves (Fig. 2A). This hysteresis disappears for BSA hydrogels when exposed to chemical denaturants, which break down the tertiary structure of the protein domains forming the hydrogel network (12, 17). This hysteresis was related to the imbalance between the forces where unfolding and refolding take place, with the unfolding transition occurring at much higher forces than the refolding (34), and can allow for large energy dissipation before failure (35, 36). BSA hydrogels show an up to 5-fold increase in stiffness when treated with Cu2+ (50 kPa in 1.5 M Cu2+ from 11 kPa) and a 17-fold increase in stiffness in the presence of Zn2+ (191 kPa in 2 M Zn2+) (Fig. 2B). The stiffening of BSA hydrogels in 2 M Zn2+ is several orders of magnitude greater than that reported for the same gels when treated with polyelectrolytes (19) and should allow for more complex programmed shapes. The stiffening effect seems to depend more on the solution concentration of cations rather than their nature (Fig. 2B). The main advantage of Zn2+ over Cu2+ is its higher solubility in water, which allows us to prepare solutions with higher concentrations and observe greater stiffening.

(A) Chemomechanical changes induced by adsorption of various concentrations of Zn2+ (left) and Cu2+ (right) by protein hydrogels made from 2 mM BSA. The mesh highlights the force-loading part, used to assess the change in stiffness, and the thick, black curve follows the final strain at 4-kPa stress. Inset: Schematics of a hydrogel tube pulled under a feedback-controlled force, where the set point (SP) was increased and decreased linearly with 40 Pa/s. (B) Change in measured Youngs modulus as a function of cation concentrations. Both Zn2+ and Cu2+ induce stiffening when adsorbing to BSA-based hydrogels. Lines between points are eye guides. Error bars are SD (n = 3).

Apart from the stiffening effect, incubation of protein hydrogels in solutions with high concentrations of cations improves their mechanical failure properties (22). For these tests, we use a typical bone shape, where the BSA hydrogels were extended until failure or maximum force range of our force sensor (Fig. 3A). BSA-based hydrogels show an increase in both toughness and failure stress with increasing cation concentration. The measured toughness, which represents the ability of a material to absorb energy and deform without fracturing and is derived from the area under the pulling stress-strain curve (Fig. 3B and 3C, left), increased from 1 to 2.8 kJ/mol with cation concentration. The failure stress increased from 15 to 33 kPa. The maximum elongation did not show a substantial variation with cation concentration. This behavior suggests that, while the stiffness is probably given by the increase in the mechanical stability of protein domains (27) and noncovalent bridging (22) at ~120%, the primary network of the BSA hydrogels is the one starting to fail, experiencing irreversible breaking of covalent bonds. Hence, the cross-linking geometry is the limiting factor for extensions, and for further improvements in maximum elongation, the primary hydrogel network would require refinement.

(A) Picture of a gel casted using a bone-shaped silicone mold (left and middle) and attached in the force-clamp rheometer (right). (B) Stress versus strain of BSA hydrogels immersed in Zn2+ (left) and Cu2+ (right) and pulled until breaking. (C) Toughness (left), failure stress (middle), and maximum elongation (right) of BSA hydrogels incubated with increasing concentrations of Zn2+ (magenta) and Cu2+(blue). Toughness increased from 1.0 0.1 to 2.8 0.7 kJ/mol in the presence of 1.5 M Zn2+ and to 2.9 0.7 kJ/mol in the presence of 1.5 M Cu2+. The breaking stress increases from 15 2 to 33 5 kPa when BSA hydrogels were treated with 1.5 M Zn2+ and to 36 5 kPa when treated with 1.5 M Cu2+. The failing strain shows little variation (106 18% versus 107 15% in 1.5 M Zn2+ and 146 21% in 1.5 M Cu2+). Error bars are SD (n = 3). (Photo credit: Luai R. Khoury, UWM; Marina Slawinski, UWM).

While stiffening through a secondary network is necessary for programing in-shape of the hydrogel material, the dynamics of morphing from the programmed to the initial shape will directly depend on the diffusion of the cations outside the hydrogel. To monitor this effect, we first programmed a cylindrical hydrogel in a U-shape (Fig. 4). BSA hydrogels were mounted in a U-shape configuration, corresponding to a bending angle of 180 (as defined in Fig. 4, inset), and incubated for 30 min in three different concentrations of Zn2+ (8, 37). The fixity, Rf, which reports on the degree of the programmed hydrogel to maintain its shape when taken out of the mold used during the programming phase, varied from 96 3% in 2 M Zn2+ to 58 15% in 1 M Zn2+ (fig. S2). The fixity of protein hydrogels depends on the amount of stiffening that can be induced when dosing with cations. Hence, the ~17-fold increase in stiffness when incubating BSA hydrogels with 2 M Zn2+ produces a programmed bending degree closer to the U-shape mold than the equivalent ~2-fold stiffening in 1 M Zn2+ (Fig. 4 and fig. S2). These values are comparable with those measured for BSA hydrogels programmed with polyethylenimine (19) and with other polymeric materials using double network or polymer-ion interactions (37). As shown in Fig. 4, when a programmed hydrogel is immersed in regular Tris buffer, the bending angle decreases to a final value of ~45 in ~3 min. The mechanism behind obtaining the temporary shape involves a combination of ionic cross-linking (4) and the stabilization due to divalent cations of BSA domains (38). The shape morphing results from the diffusion of these divalent ions in the surrounding medium (Fig. 4).

Measured programmed angle of a U-shape gel, , as a function of time, upon immersion from Zn2+ into regular Tris buffer. Inset: Pictures of the hydrogel recovering from a U-shape at four different time points. Second inset from the left shows how the angle is measured. The error bars represent SD (n = 3). Movie S3 accompanies this figure. (Photo credit: Luai R. Khoury, UWM; Marina Slawinski, UWM).

Using the large change in stiffness of BSA hydrogels, induced by immersion in Zn2+ and Cu2+ solutions, we programmed cylindrically casted biomaterials into a spring shape and flower-casted materials into a ring shape (Fig. 5, top, and fig. S3). As shown with polyelectrolytes, a ~6.5-fold increase in stiffness already suffices to program BSA hydrogels into a spring shape, and both Zn2+ and Cu2+ induce a strong enough stiffening (up to ~17-fold). The main advantage of the small ions over polyelectrolytes is their diffusion, which happens relatively fast (<5 min). Furthermore, in the present case, the shape morphing is driven by simple diffusion and does not require compromising the primary protein network with the help of a chemical denaturant. Another advantage of the increase in Youngs modulus of BSA hydrogels in the presence of cations is that more complex shapes can be obtained. For example, we demonstrate the morphing from a ring to a flower shape (Fig. 5, bottom, and fig. S3). Compared to the spring shape, in this case, there are no free ends that can release any torsional tension. To obtain this complex shape morphing, we first casted the hydrogel in a flower-like shape using a silicone mold. Following the light-activated cross-linking reaction, we then programmed the hydrogel into a ring shape by mounting it onto a plastic tube, which was then immersed in the cation solution for 30 min. When removed from the plastic tube into a Zn2+ solution with the same concentration, the hydrogel maintains the ring shape (Fig. 5B, bottom left). However, when immersed into a regular Tris buffer, the ring shape quickly morphs into the original flower shape (Fig. 5B, bottom left, and movie S1).

(A) BSA hydrogels were casted in cylindrical shape in PTFE tubes (top left) and programmed in a spring shape using a negative cast, by immersion in 2 M Zn2+ solution (top right) or 1.5 M Cu2+ solution (middle right) for 30 min; BSA hydrogels were produced in a flower shape using a silicone mold (bottom left) and programmed into a ring, by immersion in 2 M Zn2+ solution for 30 min (bottom right). (B) Morphing from the programmed shape into the casted shape of BSA hydrogel upon immersion in regular Tris buffer at time 0 (left) and 5 min (right) for the hydrogels from (A). Movies S1 and S2 accompany this figure. (Photo credit: Luai R. Khoury, UWM; Marina Slawinski, UWM).

Polymer-based hydrogels have found various applications for shape memory and shape morphing. While these approaches can be made biocompatible, polymers cannot reach the same diversity and control over the sequence and structure as proteins. The approach demonstrated here enables shape morphing in protein-based hydrogels, which could harvest the best of both worlds. This approach relies on the stiffening induced by Zn2+ and Cu2+ to program a permanent shape into a new temporary configuration, and the diffusion of these ions outside the material enables the recovery of the original shape. While we demonstrate this approach with both Zn2+ and Cu2+, we envision that Zn2+ will enable more biologically relevant applications, as it is substantially less toxic than Cu2+.The main advantages of cation-programmed protein hydrogels are that the attainable stiffness is much higher than in regular buffer (~17-fold), enabling programming in complex shapes, and that the fast diffusion of the small ions leads to fast irreversible morphing (<5 min). Furthermore, the shape change is taking place in an aqueous environment at room temperature, which is compatible with conditions present in the human body. Permanent shape morphing based on protein hydrogels and cations could find applications for various implants (39) and injectable hydrogels (40). In addition, the shape morphing method demonstrated here, from a temporary to a permanent profile, does not require denaturation of the tertiary structure of protein domains inside the hydrogels. Hence, this approach allows the preservation of the functionality of proteins forming the skeleton of the hydrogel. In conclusion, the approach presented here provides a remarkable combination of biological diversity and programming capability.

BSA [molecular weight (MW), ~66.5 kDa) was purchased from Rocky Mountain Biologicals. Sodium phosphate monobasic anhydrous (NaHPO4) (MW, 119.98 g/mol) was obtained from Research Products International. Sodium chloride (NaCl) (MW, 58.44 g/mol) was purchased from Thermo Fisher Scientific. Ammonium persulfate (APS) [(NH4)2S2O8] (MW, 228.20 g/mol; 1 M) solution, tris(bipyridine) ruthenium(II) chloride {[Ru(bpy)3]2+} (MW, 748.62 g/mol; 6.67 mM) solution, Trizma base NH2C(CH2OH)3 (MW, 121.14 g/mol), hydrochloric acid (HCl) (37%), zinc sulfate monohydrate (ZnSO4H2O; purity, 99.9%) (MW, 179.47 g/mol), copper(ii) chloride dihydrate (CuCl22H2O; purity, 99.99%) (MW, 170.48 g/mol), and Sigmacote were purchased from Sigma-Aldrich. Tris [20 mM Tris-NaCl and 150 mM NaCl (pH ~7.4)] and phosphate-buffered saline [10 mM NaHPO4 and 150 mM NaCl (pH ~3)] were used as buffers. Double distilled H2O was used in all solution preparations.

Three different shapes of BSA-based hydrogels were prepared in this study. First, a BSA-based hydrogel mixture was prepared by mixing 2 mM BSA solution with 1 M APS and 1 M [Ru(bpy)3]2+ in a volume ratio of 15:1:1. To prepare the hydrogels with the cylindrical shape, we followed the same procedure as described in our previous studies (17, 33). Briefly, PTFE tubes (inner diameter, 0.56 mm; Cole-Parmer) were passivated with Sigmacote for 5 to 10 min and dried thoroughly. The solution mix was then injected, and the tube was placed under a light source for 30 min. The bone- and flower-like shape hydrogels were prepared starting from a custom-made silicone rubber mold made of Dragon Skin 30 (purchased from Smooth-On) (fig. S1). The bone-shape hydrogel samples had an overall length of 9 mm. The gauge length and width are 5 and 1 mm, respectively, and the thickness is 1 mm (fig. S1A). The flower-like shape sample has a width of 0.8 mm and a thickness of 1 mm (fig. S1B). Thereafter, the molds were passivated with Sigmacote for 10 min. The hydrogel mixture was casted into the slot and covered with a glass coverslip to reduce evaporation. The loaded molds were then placed under a 100-W mercury lamp for 30 min, after which the hydrogel samples were removed from the molds and immersed in Tris solution.

The mechanical investigation of hydrogel samples was carried out using a force-clamp machine, as described in previous studies (17, 33). A force-ramp protocol with a rate of 0.04 kPa/s was applied on the hydrogel sample while immersing in Tris solution at room temperature. Afterward, the hydrogel sample was immersed for 30 min at room temperature into one of two cations solutions: Zn2+ and Cu2+, which were dissolved in phosphate buffer saline at different concentrations. We chose a 30-min incubation time, as for the gels used here (with cylindrical shape and a diameter of 0.56 mm or with a square cross section of 0.5 mm by 0.5 mm), the material fully equilibrates with the solvent environment. Then, same force protocol was applied on the treated hydrogel sample. The Youngs modulus, toughness, and breaking stress and strain were calculated from stress-strain curves.

The shape programming and morphing of the BSA-based hydrogel samples were performed by starting from the tube or the flower shapes. First, a 2 mM BSA-based hydrogel was synthesized inside the plastic tube or the silicone mold. Then, the hydrogels obtained in the tubes were fixed in a 3D spiral-like shape or U-shape, while the flower-shaped hydrogels were fixed in a circular shape around a 5-ml plastic tube. Following treatment with Zn2+ or Cu2+ for 30 min at room temperature, at various concentrations, the gels were removed from the fixing mold and placed into the same solution buffer. The shape morphing was then induced by transferring the fixed gels from the Zn2+ or Cu2+ solution into a regular tris buffer. Recordings were performed with a Panasonic digital camera, in the time-lapse video mode, with a frame saved every 3 s.

All data acquisition and analysis of the mechanical behavior of hydrogels was performed in Igor Pro (WaveMetrics). All image processing for measuring bending angle of the U-shape gels was accomplished in ImageJ. The stress values were calculated by dividing the measured force (millinewtons) to the cross-section area, while the strain values were obtained from the measured extension, in respect to the tethered gel length.

To quantify the ability of our protein hydrogels to memorize their temporary shape and the dynamics of recovery from the temporary programmed shape into the initial permanent shape, we used the U-shape recovery method (37, 41). In this approach, the hydrogel is programmed into a U-shape, and a bending angle is defined as the difference between the original orientation (in our case, 180) and the programmed orientation (the angle between the arms of the programmed shape). The shape fixity, Rf, reports on how well the temporary programmed shape can memorize the geometry of the casting mold in the presence of cations and was calculated asRf=0p100where 0 was the measured bending angle at time zero, right after the gel was removed from the programming process. p is the expected programmed angle (which, for the U-shape, is 180). The dynamics of switching from the temporary programmed shape into the initial permanent shape was also quantified by monitoring (t), the bending angle of the U-shape hydrogel in tris buffer, at various times t.

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

Acknowledgments: Funding: This research was funded by the National Science Foundation (grant numbers MCB-1846143 and DBI-1919670), the Greater Milwaukee Foundation (Shaw Award), and the University of Wisconsin system (RGI 101X396). M.S. and D.R.C. also acknowledge support from SURF and UR@UWM. Author contributions: L.R.K. and I.P. designed the research. L.R.K., M.S., and D.R.C. performed the research. L.R.K. and I.P. analyzed the data. I.P. wrote the manuscript with input from all authors. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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Cation-induced shape programming and morphing in protein-based hydrogels - Science Advances

Beyond the cell factory: Homeostatic regulation of and by the UPRER – Science Advances

Abstract

The endoplasmic reticulum (ER) is commonly referred to as the factory of the cell, as it is responsible for a large amount of protein and lipid synthesis. As a membrane-bound organelle, the ER has a distinct environment that is ideal for its functions in synthesizing these primary cellular components. Many different quality control machineries exist to maintain ER stability under the stresses associated with synthesizing, folding, and modifying complex proteins and lipids. The best understood of these mechanisms is the unfolded protein response of the ER (UPRER), in which transmembrane proteins serve as sensors, which trigger a coordinated transcriptional response of genes dedicated for mitigating the stress. As the name suggests, the UPRER is most well described as a functional response to protein misfolding stress. Here, we focus on recent findings and emerging themes in additional roles of the UPRER outside of protein homeostasis, including lipid homeostasis, autophagy, apoptosis, and immunity.

Multicellular organisms face a constant barrage of stresses that warrant an effective response, coordinated across diverse tissues. Each cell or tissue must thus be capable of perceiving stresses and signaling distal cells to respond accordingly to mitigate perturbations in cellular function and homeostasis. Furthermore, the distinct membrane-bound environments of the cell require these stress responses to be compartment specific. To maintain homeostasis of these microenvironments, cells have evolved several subcellular stress responses, including the cytoplasmic heat shock response (HSR), the endoplasmic reticulum (ER) unfolded protein response (UPRER), and the mitochondrial unfolded protein response (UPRmt) (13). Of these responses, the ERs central function in biosynthesis, folding, and modification of membrane-bound and secreted proteins and its major role in lipid synthesis place particular interest on the UPRER. This interest is highlighted by the fact that defects in ER function are significantly associated with obesity, diabetes, cancer, and age-onset neurodegenerative disease (4, 5).

There are three primary branches of the UPRER, which enable the ER to maintain normal levels of protein folding, protein secretion, and lipid homeostasis. Each arm of the UPRER consists of a transmembrane protein containing a luminal-facing domain and transmembrane helix, which act as sensors for induction of a nuclear signal upon detection of ER stress (Fig. 1). The best characterized of the three UPRER branches involves an endonuclease, inositol-requiring protein 1 (IRE1 in mammals, IRE-1 in Caenorhabditis elegans, and Ire1p in Saccharomyces cerevisiae. Note: All gene and protein names will use nomenclature pertinent to the organism, and human nomenclature is used as a general terminology when no organism is specified), and a transcription factor, X-box binding protein 1 (XBP1 in mammals, XBP-1 in C. elegans, and Hac1p in S. cerevisiae). In this branch, unfolded protein stress or lipid disequilibrium is sensed from the ER-localized IRE1, which then undergoes homodimerization and autophosphorylation. This activates IRE1s cytosolic endonuclease domain to splice a specific intron from the mRNA of XBP1u to create XBP1s. The spliced mRNA is translated into XBP1s, which translocates into the nucleus to mediate expression of protein degradation, protein folding, and lipid metabolism gene targets (2, 6). IRE1 also plays an important role in regulating mRNA levels through regulated IRE1-dependent decay (RIDD). A majority of the identified RIDD mRNA targets encode proteins with signal peptides and transmembrane domains, including several secreted components of the insulin secretory pathway in cells and mucin 2 in secretory goblet cells, whose reduced translation is expected to reduce the protein-folding load on the ER under conditions of ER stress or damage (79).

There are three branches of UPRER, each consisting of a transmembrane protein with a luminal-facing sensor for damage, which then signals to the nucleus through a unique transcription factor. When IRE1 senses misfolded protein or lipid stress in the ER, it homodimerizes, is autophosphorylated, and promotes splicing of XBP1u mRNA to XBP1s which is translated into functional XBP1s, acting as a transcription factor to turn on genes important for restoring ER homeostasis. Similarly, PERK and ATF6 are activated under ER stress. When PERK is activated, it also oligomerizes, causing phosphorylation of eIF2 to inhibit global translation. There is also downstream activation of ATF4, which promotes the expression of ER-restoring genes that escape down-regulation via eIF2. Unlike the other two ER stress sensors, ATF6 is proteolytically cleaved under ER stress, which causes translocation to the Golgi for further processing, allowing ATF6 to function as a transcription factor.

The other branches of the UPRER have different mechanisms of action, namely, the (i) global reduction of protein translation via eIF2 downstream of protein kinase RNA-like ER kinase (PERK in mammals and PEK-1 in C. elegans) and (ii) the proteolytic cleavage of an ER-resident protein, which translocates to the Golgi under stress to become a proteostasis-promoting transcription factor, activating transcription factor 6 (ATF6 in mammals and ATF-6 in C. elegans) (2, 6). Similar to IRE1, PERK undergoes homodimerization and phosphorylation in response to unfolded proteins and lipid disequilibrium in the lumen. This leads to phosphorylation of eIF2, which induces a global down-regulation of translation. However, critical mRNA species escape this translational down-regulation, including the activation of transcription factor 4 (ATF4 in mammals and ATF-4 in C. elegans), which is up-regulated during ER stress to promote the integrated stress response through remodeling of metabolic and translational programs (10). In addition, ATF4 can promote apoptosis during sustained ER stress by up-regulating CCAAT enhancer binding protein (C/EBP) homologous protein (CHOP).

The third arm of the UPR is initiated by ATF6, a type II ER transmembrane protein that translocates to the Golgi upon activation. During stress, the luminal domain of ATF6 loses its association with BiP/GRP78 (HSP-4 in C. elegans), which causes translocation of ATF6 into the Golgi. Once in the Golgi, Golgi-resident site 1 protease (S1P) and site 2 protease (S2P) cleave ATF6, allowing the N-terminal cytosolic fragment to translocate into the nucleus and act as a transcription factor to up-regulate target genes, including protein disulfide isomerase (PDI), XBP1, and CHOP (1113).

Dysregulation of the UPRER is a common feature of many diseases, including neurodegeneration, metabolic disease, and cancer. During the aging process, UPRER activation also becomes dysregulated across multiple organisms. For example, in C. elegans, the capacity to activate XBP-1mediated UPRER in response to protein misfolding stress declines sharply during the aging process (14). Similarly, in aged mice, expression of genes involved in ER quality control show marked decline in the brain (15, 16). The decreased function of the UPRER during aging can lead to the accumulation of damaged and aggregated proteins, which contribute to proteotoxicity and eventual cell death (17). Conversely, up-regulation of ER chaperones can protect cells during stress (18, 19), and hyperactivation of the UPRER can have direct impacts on life span and healthspan: Overexpression of xbp-1s in C. elegans extends life span and stress resistance (14), and increased PERK-eIF2 signaling protects neurons from stress associated with misfolded proteins (20, 21). Many of these studies focus primarily on chaperones and other mechanisms involved in restoring protein homeostasis. However, it is clear that there are other critical downstream targets of the transcription factors involved in up-regulating UPRER. This review touches on these core machineries outside of protein homeostasis and highlights the open-ended questions involved in how stress affects other functions of the ER, such as lipid and redox homeostasis.

Beyond the UPRER, there are several other mechanisms involved in maintaining ER homeostasis. Given the major role of the ER in protein synthesis, there are limited proteases that function within the ER. Therefore, proteins that are beyond repair, such as terminally misfolded proteins, are first extracted from the ER by adenosine triphosphatedriven motors and targeted for proteasomal degradation through ER-associated degradation (ERAD). In yeast, where most of the ERAD components have been originally described, transmembrane protein complex including the ubiquitin ligases Hrd1p and Doa10p recognize misfolded proteins and tag them for degradation (22, 23). Upon poly-ubiquitylation via the ERAD machinery, the AAA+ adenosine triphosphatase (ATPase) Cdc48p (p97 or valosin-containing protein in humans) drives extraction of the proteins from the ER into the cytosol, where it is subsequently degraded by the proteasome (24). ERAD also plays an important role in maintaining protein quantity control by tagging excess or unnecessary proteins for degradation through similar mechanisms (25, 26). When accumulation of damaged proteins in the ER has exceeded the repair capacity of ERAD, portions of the organelle can be specifically targeted for large-scale degradation through autophagy (ER-phagy). ER-phagy is capable of clearing ERAD-resistant proteins or other ER components, such as lipids, which cannot be cleared by conventional quality control machineries but are generally subject to autophagy through Vps34p/beclin-1dependent machinery (27). It would be of great interest to understand whether ERAD and ER-phagy are critical for maintaining ER function outside of its proteome. It is possible to imagine that eliminating damaged ER via autophagy will also remove toxic lipid species, but can ERAD impose a similar benefit to lipids and other nonprotein components of the ER?

Here, we focus primarily on the UPRER with specific emphasis on noncanonical roles of UPRER outside of protein quality control. For a more thorough review on ER quality control machineries outside of UPRER, refer to (1, 28, 29).

Lipids are synthesized and metabolized within multiple organelles; however, specific functions are compartmentalized within organelles to maintain lipid homeostasis. For example, initial fatty acid synthesis primarily occurs in the mitochondria and cytoplasm. Subsequent fatty acid elongation then occurs within the mitochondria, cytoplasm, and ER (30, 31). More complex lipids such as ether lipids are produced by the peroxisome, while sterols, phospholipids, and neutral lipids are synthesized by the ER. Thus, many critical enzymes for lipid metabolism reside in the ER, making the ER a critical hub for lipid homeostasis and a primary source of membrane lipids for all other organelles (32, 33).

Since the ER serves as a critical organelle in regulation of lipid homeostasis, key sensors monitor lipid quality within the ER. These sensors are the same UPRER transmembrane proteins involved in protein homeostasis: IRE1, PERK, and ATF6. Adjacent to their transmembrane helices, IRE1 and PERK contain an amphipathic helix capable of sensing general ER membrane imbalances and can activate the UPRER independent of their luminal unfolded protein-sensing domains (34, 35). Within the transmembrane domain of ATF6, a sphingolipid-sensing motif is able to trigger ATF6 activation upon accumulation of dihydrosphingosine or dihydroceramide, also independent of proteotoxic stress (36). In combination with basal lipid metabolism transcription factors, these proteins play an integral role in maintaining lipid homeostasis. Activation of UPRER alters the expression of many lipid metabolism genes. For example, PERK/eIF2 phosphorylation activates sterol regulatory elementbinding protein-1c (SREBP-1c) and SREBP-2, master transcription factors that regulate enzymes of lipogenic pathways (37). Mice with compromised eIF2 signaling down-regulate lipogenesis and displayed reduced high-fat diet (HFD)induced fatty livers (38). Furthermore, XBP1s directly up-regulates lipogenic genes, including Dgat2, Scf1, and Acc2, while deletion of Xbp1 results in hypocholesterolemia and hypotriglyceridemia of the liver (39). Last, large-scale sequencing studies in C. elegans found that a large subset of genes induced by IRE-1, XBP-1, PEK-1, and ATF-6 under conditions of ER stress were involved in lipid and phospholipid metabolism (40).

Two recent, complementary studies found that constitutive activation of UPRER downstream of xbp-1s resulted in notable lipid depletion in C. elegans. The original study from our laboratory describing xbp-1s overexpression in C. elegans identified that overexpression of xbp-1s in neurons was sufficient to elicit nonautonomous UPRER activation in peripheral tissue to promote whole-organism life-span extension (14). However, overexpression in other tissues either failed to elicit the same response or was detrimental in some other cases, suggesting that neurons were specialized in sending a specific and beneficial stress signal to other cells. Another unexpected study from our laboratory found that glia could signal a similar beneficial signal to the periphery (41).

Following this work, neuron-specific overexpression of xbp-1s was found to result in whole-animal depletion of lipids via two mechanisms: (i) up-regulation of lysosomal lipases and desaturases, which resulted in decreased triglycerides and increased oleic acid levels (42), and (ii) activation of lipophagy via a conserved RME-1/RAB-10/EHBP-1 (receptor mediated endocytosis-1/ras- related GTP binding protein-10/EH domain binding protein-1) complex, which depletes neutral lipids and decreases lipid droplet size and number, a phenomenon described by our work (Fig. 2, left) (43). When xbp-1s is overexpressed in neurons, both protein homeostasis and lipid metabolism are activated in peripheral tissue (14, 43). Perturbations of either protein homeostasis or lipid metabolism suppress the beneficial effects of neuronal xbp-1s overexpression on life span and ER stress resistance, suggesting that both are essential components downstream of xbp-1s to promote ER quality control and organismal health. However, the most notable finding in the latter study is that the beneficial effects of lipid depletion on animal physiology can be uncoupled from protein homeostasis. Overexpression of ehbp-1 is sufficient to drive lipid depletion and life-span extension but does not promote chaperone induction, suggesting that these two mechanisms can be uncoupled. In the former study, changes in lipid profiles caused by xbp-1s overexpression in neurons were sufficient to drive improvements in protein homeostasis. Specifically, supplementation with oleic acid decreased toxicity associated with ectopic polyQ40 expression, suggesting that changes in lipid homeostasis are sufficient to improve protein quality control even in the absence of chaperone induction. Since the ER is composed of both integral lipids and proteins, it is likely that promoting overall ER quality drives global organelle homeostasis, although further studies are required to understand the cross communication of lipid and protein quality control machineries within the ER. Whether this is indirect (i.e., the decreased burden of maintaining lipid homeostasis allows the ER to divert all its energy to protein quality control machineries) or direct (i.e., ER lipid health can directly alter protein folding via a still unknown molecular pathway) is still under investigation. In addition, the specific signal originating from neurons to drive these seemingly separable changes in the periphery also remains to be discovered.

In C. elegans (left), overexpression of xbp-1s in neurons promotes two distinct changes to ER homeostasis in peripheral tissue (intestine): increased protein homeostasis by up-regulation of chaperones and increased lipid metabolism through mobilization of lipids via lipases, desaturases, and increased lipophagy. Both the increase in protein folding and decreased lipids are essential for the life-span extension found in this paradigm. Ectopic expression of xbp-1s in glia has also been shown to promote peripheral protein homeostasis and extend life span, although a role in glial signaling in lipid homeostasis has yet to be described. A similar phenomenon was also found in mice (right), where overexpression of Xbp1s in Pomc neurons (or simply activating Pomc neurons via olfactory exposure to food) is sufficient to drive UPRER in peripheral tissue. Specifically, XBP1s in POMC neurons promotes XBP1s and mTOR signaling in hepatocytes and adipose tissue, resulting in increased metabolic health, including resistance to diabetes and obesity. As UPRER has been shown to be critical in proper muscle and B cell function, it would be of great interest to investigate whether neuronal XBP1s can signal to elicit a beneficial effect in these and other cell types.

A similar communication from neurons to peripheral tissue is observed in vertebrates. When Xbp1s is overexpressed in Pomc neurons of the hypothalamus of mice, the UPRER is up-regulated and has beneficial impacts on metabolic physiology (e.g., improved glucose levels, improved insulin sensitivity, and protection against HFD-induced obesity) (Fig. 2, right) (44). In this model, Xbp1s increases Pomc neuronal activity, which in turn increases energy expenditure by promoting brown adipose tissue thermogenesis and browning of white adipose tissue, which results in an overall decrease in fat mass and body weight, consistent with the findings in C. elegans. Conversely, mice with Xbp1 deleted only in neurons or glia are more susceptible to diet-induced obesity and exhibit elevated levels of insulin and leptin in response to HFD (45). In mice, food perception (i.e., smelling of food) was sufficient to drive a Pomc neuron response to activate hepatic mammalian target of rapamycin (mTOR) and XBP1 signaling to promote metabolic homeostasis (46). Mice with olfactory exposure to food were able to phenocopy Xbp1s overexpression in Pomc neurons, driving peripheral Xbp1 activation and its downstream beneficial effects on animal physiology. Both protein homeostasis and lipid homeostasis are activated via peripheral Xbp1 activation (e.g., hepatic tissue activation upon receiving cues from Pomc neurons), and it is unclear whether these two mechanistic pathways can be uncoupled in mammalian models as was found in C. elegans.

Determining whether promoting chaperones and overall protein handling in the ER can alter lipid homeostasis and vice versa would be of great interest to understanding the independent roles that lipids and proteins have on mammalian organismal health. Is enhancing lipophagy through EHBP1 sufficient to drive ER stress resistance and organismal healthspan and life span in mammals similar to C. elegans? Do there exist divergent nodes of protein and lipid homeostasis downstream of XBP1s, or are these downstream mechanisms overlapping in higher eukaryotes? Under disease conditions, is loss of a single node of XBP1s signaling sufficient to drive pathogenesis? These questions are critical to develop novel therapeutic intervention for diseases that cause dysregulation of UPRER.

While the activation of the UPRER has many implications in organismal health and life span, persistent activation of the UPRER is associated with several metabolic diseases. Chronic UPRER activation is often observed in the liver or adipose tissue of models of obesity, nonalcoholic fatty liver disease, and diabetes (47). Moreover, ER stress within the brains metabolic control center, the hypothalamus, has been shown to contribute to metabolic changes that promote weight gain and insulin resistance in mice, hallmark symptoms of obesity (6, 48). A major feature of obesity is increased free fatty acids in circulation, which have been linked to UPRER activation in several models (49, 50). Excessive accumulation of lipids can cause metabolic abnormalities and initiate cell death in response to lipotoxicity, often linked to chronic ER stress and defects in UPRER signaling. Specifically, saturated fatty acids, such as palmitate, activate the UPRER and cause detrimental effects in pancreatic , liver, adipose, and muscle cells.

In primary rat cells, exposure to palmitate results in increased phosphorylation of eIF2 through PERK activation, increased Xbp1s splicing, and increased ATF4 activity (5153). Elevated levels of palmitate can result in excessive palmitoylation of proteins, which induce ER stress and activate caspase activity, causing cell death. In addition, excess palmitate can also cause lipotoxicity and ER dysfunction by altering the composition and membrane fluidity of the ER by changing phospholipid composition (54), promoting ceramide accumulation (55), and altering sphingolipid metabolism (56). Regardless of the mechanism, the chronic activation of the ER stress response promotes cell death through the induction of apoptosis, which often includes the hyperactivation of cytokines, including interleukin-1 (IL-1), interferon-, tumor necrosis factor (TNF), and nuclear factor B (NF-B) [reviewed in (57)].

Similarly, ER stress through exposure to saturated fatty acids is a major contributing factor in liver lipotoxicity. In several liver cell lines, including HepG2 hepatoma and L02 immortalized liver cells, exposure to saturated fatty acids resulted in activation of PERK and up-regulation of its downstream targets such as ATF4 and CHOP (58). Suppression of PERK activation or reducing ER stress load via overexpression of BiP was sufficient to reduce palmitate-induced death (58, 59). Liver cell exposure to palmitic acid results in aberrant phospholipid metabolism and increased membrane saturation (60). Alterations in the ER lipid composition and fluidity inhibit ER Ca++ signaling (61), which can result in aberrant mitochondrial metabolism and increased reactive oxygen species (ROS) production, causing further cellular toxicity (62). Restoring ER lipid composition through conversion of saturated lipid species into unsaturated fatty acylcoenzyme As (CoAs) by overexpressing catalytic enzymes, such as Lpcat3, or restoring Ca++ homeostasis by overexpression of sarco-ER calcium ATPase reduces lipotoxicity in liver cells and can improve hepatic function in obese individuals (61, 63). Last, lipid overload impairs autophagic flux in murine models and human patients with nonalcoholic fatty liver disease, suggesting a functional role for autophagy in preventing ER stressmediated apoptosis (64).

Although less understood, muscle cells are also sensitive to lipid-induced ER stress. Mice fed an HFD showed up-regulation of Xbp1 splicing, BiP, and ATF4/CHOP in skeletal muscle (65), while myotubes exposed to high levels of palmitate induced ATF4 and XBP1 activity (66). Prolonged lipotoxicity in muscle cells results in increased inflammation and ER stress, which can promote insulin resistance. Overexpression of stearoyl-CoA desaturase 1 (SCD1), a key regulator in lipid metabolism, can restore lipid homeostasis and reduce inflammatory cytokine expression, ultimately preventing insulin resistance in myotubes (66). However, a separate study in human and mouse cells showed that restoring ER homeostasis in palmitate-treated muscle cells did not restore insulin signaling, suggesting that palmitate-induced ER stress may not be the cause of reduced insulin signaling (67). Another study in human patients on a high-fat, hypercaloric diet showed similar contradicting results. While patients on HFD exhibited glucose intolerance, skeletal muscle biopsies failed to show an increase in ER stress markers, including XBP1, BiP, or PERK (68). Thus, further research is necessary to elucidate the connection between lipotoxicity and ER homeostasis in skeletal muscle cells.

Despite these controversies, a recent study in mice showed an interesting role for skeletal muscle in signaling lipotoxicity to other cells. Here, muscle-specific knockout of the lipid dropletassociated protein, perilipin 5, caused an increase in fatty acid oxidation and reduced ER stress in muscle cells. This resulted in whole-body glucose intolerance and insulin resistance due to reduced secretion of fibroblast growth factor 21 from both skeletal and liver cells, highlighting a critical cross-talk between muscle and liver in ER lipid homeostasis (69).

Overall, it is clear that the UPRER plays a critical role in regulation of lipid homeostasis and metabolic state of the organism. Still to be investigated is whether the impact of UPRER activity serves to be beneficial or detrimental to organismal health. While many studies have highlighted a beneficial effect of UPRER activation in neurons (14, 41, 42, 44), whole-organism xbp-1s overexpression has no beneficial effect on life span in C. elegans (14). Thus, it is possible that increased UPRER signaling can be detrimental in some tissue. Next, we describe the potential detrimental impacts of a sustained UPRER.

Despite many studies providing evidence for UPRER providing a beneficial role in clearing damage, sustained and unresolved ER stress can result in activation of apoptosis. Hence, chronic and irreversible UPRER induction can contribute to pathophysiological processes involved in a number of diseases, including neurodegeneration. In unresolved ER stress, the PERK-ATF4 axis of the UPRER induces the transcriptional activation of proapoptotic machinery, including C/EBP-homologous protein CHOP. CHOP then promotes the down-regulation of the antiapoptotic factor, B cell lymphoma 2 (BCL2), and activation of proapoptotic genes, thus inducing the core mitochondrial apoptosis machinery through BCL2-associated X protein (BAX) and BCL2-antagonist/killer 1 (BAK) (70).

Under certain conditions, chronic ER stress can also regulate cell death decisions by influencing several mitogen-activated protein kinase (MAPK)signaling components, including extracellular signalregulated kinase (ERK), p38 MAPK, and JUN N-terminal kinase (JNK) (Fig. 3) (71, 72). For example, ER stressinduced JNK activation is thought to initiate a proapoptotic pathway. Under ER stress, IRE oligomerizes, activating its kinase domain and increases interaction with TNF receptorassociated factor 2 (TRAF2), which activates JNK via induction of apoptosis signalregulating kinase 1 (ASK1). IRE1-TRAF2 promotes ASK1 oligomerization and autophosphorylation, which is required for its kinase activity to promote JNK signaling (73). Activation of JNK signaling can promote cell death by promoting de novo synthesis of death receptors and their ligands and by targeting components of the BCL2 family to initiate apoptosis (74). Inhibiting the downstream activation of JNK has been shown to promote resistance to ER stressinduced cell death: In human pancreatic cells, inhibition of JNK significantly decreased eIF2 activity and promoted cell viability under ER stress (75); Ask1/ mice showed reduction in JNK activation and decreased apoptosis under ER stress (76), and phosphorylation of ASK1 on Ser83 decreased its activity, promoting prosurvival by reducing apoptosis (77). In addition to the IRE1-TRAF2-ASK1 pathway, JNK can also be activated by the PERK axis of UPRER through CHOP. CHOP expression promotes the release of Ca++ from the ER, which also activates ASK1 through Ca++/calmodulin-dependent protein kinase II (CaMKII) (78). JNK activation through CaMKII-ASK1 promotes apoptosis through increased cell surface localization of the death receptor Fas, and in vivo knockout of CaMKII can suppress apoptosis induced via ER stress (79).

Functionally, the UPRER serves as a quality control mechanism to restore ER form and function under conditions of stress. However, under sustained and unresolved ER stress, UPRER can actually promote cell death through apoptosis. For example, sustained PERK signaling can promote the activation of CHOP through ATF4, which activates proapoptotic signals. The other branches of UPRER can also modulate MAPK signaling, which feeds into cell survival or apoptotic cues in various ways. For example, IRE-1 can activate both prosurvival signals through activation of ERK1/2 and proapoptotic signals through JNK depending on the ER stress conditions. Beyond the UPRER, extracellular cues can promote cell survival under ER stress. Specifically, the cell surface hyaluronidase, TMEM2, cleaves highmolecular weight hyaluronic acid (HMW HA) into lowmolecular weight hyaluronic acid (LMW HA), which acts as a ligand to the CD44 receptor and activates downstream p38 and ERK1/2 prosurvival signals.

In contrast to JNK signaling, activation of ERK1/2 signaling serves as a prosurvival cue under ER stress. As a primary signaling molecule downstream of almost all growth factors, ERK1/2 promotes cell survival under numerous stress stimuli by promoting transcriptional activation of several prosurvival proteins, including BCL2 (80). Moreover, ERK1/2 activation under ER stress is dependent on IRE1. In gastric cancer cells, IRE1 knockdown decreased ERK1/2 signaling under ER stress, which results in decreased BiP levels and subsequent induction of cell death (81). In mouse embryonic fibroblasts, IRE1 also regulates ERK1/2 signaling by regulating the pool of the Src homology 2/Src homology 3 domaincontaining adaptor Nck. Under basal conditions, ER-associated Nck suppresses ERK1 signaling, but upon exposure to ER stress, Nck dissociates from the ER membrane, eliciting IRE1-dependent ERK1 activation to promote cell survival (82). However, how IRE1 promotes the activation of ERK1 is still unclear.

ERK1/2 hyperactivation is also found in numerous cancers and is a target for therapeutic intervention (83). Several human melanoma cell lines have been shown to be protected from therapeutic interventions that promote ER stressinduced apoptosis due to increased ERK1/2 signaling in these cancers. In some cases, inhibition of ERK1/2 signaling increased sensitivity of cancer cells to ER stressinduced cell death, introducing combined ERK1/2 inhibition and ER stress as a potential therapeutic intervention for these cancers, including melanoma (84).

MAPK signaling does not only function downstream of UPR activation but can also promote UPRER signaling. For example, p38 MAPK can phosphorylate two serine residues found in CHOP, increasing the activity of its transactivation domain (85). While the phosphorylation of these serine residues by p38 was not critical for the DNA binding activity of CHOP, they had notable implications in its association with binding partners required to promote cell death machinery (86). In cardiomyocytes, ATF6 has also been shown to be a direct substrate for phosphorylation by p38 (87). Sustained p38 activity increased ATF6 phosphorylation and promotes its downstream signaling, including the induction of BiP (88, 89).

A recent study from our laboratory elucidated a role for MAPK signaling in maintaining ER stress resistance independent of the UPRER (90). Through whole-genome CRISPR-Cas9 screening in karyotypically normal fibroblasts, the cell surface hyaluronidase transmembrane protein 2 (TMEM2) was identified as a novel regulator of ER homeostasis. Specifically, overexpression of TMEM2 increased resistance to ER stress through ERK and p38 MAPK signaling. While the exact signaling cascade is unknown, it is proposed that the lowmolecular weight product of hyaluronic acid produced by TMEM2 converges on the CD44 receptor to activate ERK and p38-dependent cell survival under ER stress. Intriguingly, overexpression of human TMEM2 in C. elegans was sufficient to extend life span by more than 20% by preventing the age-associated decline in innate immunity (immunosenescence), similarly dependent on ERK/p38 (PMK-1/MPK-1 in C. elegans). Most of the cells in the adult nematode are postmitotic, and MAPK signaling does not play a role in regulating apoptosis in the adult. Rather, the central role of MAPK signaling is in regulating innate immunity (91). Perhaps, most notable in the study was that the beneficial effects of TMEM2 were completely independent of all three branches of UPRER. Therefore, despite numerous studies highlighting notable overlap between UPRER and MAPK signaling modalities, it is clear that there exist mutually exclusive mechanisms of modulating cell survival under conditions of ER stress.

Beyond apoptosis, chronic activation of PERK signaling can result in sustained repression of translation through eIF2, which can also be detrimental. For example, in animal models, hyperactivation of PERK promotes synaptic failure and neuronal death in prion disease mouse models, which suggests that decreasing UPRER activity could be a potential therapeutic intervention by restoring protein synthesis in neurons (58). In triple-negative breast cancers, hyperactivation of XBP1 can also promote tumor growth, and inhibition of IRE1/XBP1 was shown to be beneficial (59). Thus, it is clear that UPRER signaling is complex and context specific, highlighting the importance of dissecting the molecular mechanisms downstream of UPRER activation for therapeutic intervention.

ER stress is commonly found in inflammatory diseases, such as diabetes, atherosclerosis, and inflammatory bowel disease (92). Accumulating evidence links the activation of the UPRER in inflammatory signaling cascades, including the activation of cytokine release (93). In addition, several studies indicate that inflammation itself augments ER stress responses (Fig. 4). For example, exposure to proinflammatory cytokines, such as TNF, IL-1, and IL-6, induced ER stress, promoted XBP1s expression, and activated UPR in mouse livers and fibrosarcoma cells (94, 95). In addition, lipopolysaccharide (LPS) stimulation resulted in the activation of XBP1s, ATF4, and CHOP in mice (96). These studies strongly link the connection between ER stress and immunity.

The immune response and the UPRER have both been shown to affect the other. Mounting an immune response requires the synthesis of many proteins, including several secreted factors, which makes a functional ER imperative during pathogenic infection. Thus, under exposure to pathogens, UPRER is activated to promote protein homeostasis. In addition, to avoid cell death, immune signals may dampen the PERK arm to inhibit apoptosis. UPRER components can also alter immunity through IRE1-mediated activation of TRAF2, which can promote cytokine signaling through NF-B or directly alter transcription of immune response genes through p38 MAPK signaling.

Perhaps the first identified role of UPRER in the immune system was in the development of specific immune cells. For example, XBP1 is critical for the development of immunoglobulin-secreting plasma cells, such that mice lacking Xbp1 fail to mount antibody responses, have decreased levels of all immunoglobulins, and are more susceptible to infections that are normally cleared by antibody-mediated immune responses (97). Subsequent studies have shown that functional B cells splice Xbp1 mRNA and up-regulate UPR target genes, including BiP, upon exposure to LPS (98, 99). It is likely that the massive induction of UPR in B cells is critical to expand the ER and promote protein synthesis to meet the new secretory demands of a mature B cell (100). Both XBP1 activity and ATF6 activity reach maximal levels once Ig synthesis and secretion are induced in B lymphocytes (101). PERK is not activated upon LPS stimulation, and B cells lacking Perk develop normally and are fully capable of Ig synthesis and antibody secretion, providing further evidence that the purpose of UPRER activation in B cells is primarily to meet the increased secretory demands of these cells (102).

Similar to B cells, T cell differentiation is also highly dependent on a functional UPR. During viral or bacterial infection, expansion of antigen-presenting CD8+ T cells requires splicing of Xbp1 mRNA downstream of IL-2 signals. Unlike B cells, T cells exhibit increased Atf4 mRNA, suggesting that the PERK/eIF2 pathway is also activated during T cell differentiation (103). Xbp1 splicing is also critical in maintaining dendritic cells (professional antigen-presenting cells), as loss of XBP1 leads to reduced numbers due to increased apoptosis of dendritic cells, whereas overexpression of Xbp1s promotes their survival (104). In addition to promoting survival in these cell types, ER stress also plays a critical role in antigen presentation, although the exact mechanism is not yet understood (105, 106). Increased levels of triglycerides have been found in dendritic cells in both mice and human patients with tumors (107, 108). Lipid accumulation occurs in dendritic cells due to up-regulation of receptors involved in extracellular lipid uptake, which has detrimental effects in dendritic cell function (109). It would be of particular interest to determine whether hyperactivation of XBP1 can promote lipid depletion in dendritic cells similar to the neuronal XBP1 signaling paradigms described in mice and nematodes. Can Xbp1 overexpression promote dendritic cell survival and function by preventing accumulation of triglycerides? Pharmacological normalization of lipid levels on dendritic cells restored their functional activity and promoted immune response (109).

UPRER also affects innate immunity. Exposure to ER stress activates many inflammatory signaling cascades, including NF-B, which is considered a major mechanism for inducing the innate immune response. Under ER stress, IRE1 interacts with inhibitor of nuclear factor B (IB) kinase through TRAF2, which enhances TNF and NF-B activation (110). NF-B can also be activated via PERK, which promotes NF-B by translational inhibition of IB via eIF2 (111). UPRER activation also occurs in macrophages, one of the primary immune cell types involved in innate immunity through phagocytosis of infectious agents. Upon exposure to pathogens, Toll-like receptors (TLRs) detect microbes to activate immune responses in macrophages. TLR2 and TLR4 specifically activate IRE1/XBP1, which are critical for sustained production of inflammatory cytokines in macrophages. IRE1 is activated upon TLR ligation via interaction with TRAF6, which promotes its phosphorylation to sustain IRE1 function (112). Mice lacking XBP1 in macrophages display increased sensitivity to infection due to impaired production of IL-6 and TNF (113). In addition to activating the IRE1/XBP1 branch of UPR, TLR activation promotes suppression of the ATF4/CHOP branch of UPR downstream of PERK. Prolonged PERK activation triggers cell death through CHOP as described above, and thus, TLRs play a critical role in suppressing ATF4/CHOP-mediated apoptosis to promote survival of macrophages (114).

Since C. elegans lack an adaptive immune system, resistance to pathogenic infection is dependent on PMK-1 (MAPK)mediated innate immunity responses, which potentially induce ER stress in the organism because of the increased secretory demand of the response (91). It has been shown that XBP-1 plays an essential role in protecting nematodes during pathogenic infection. For example, animals lacking xbp-1 exhibit major defects in ER morphology and larval lethality when exposed to Pseudomonas aeruginosa infection (115). Moreover, the increased sensitivity of xbp-1 mutants to P. aeruginosa exposure was exacerbated with simultaneous loss of pek-1 both in larval stages and during adulthood, suggesting that PEK-1 and XBP-1 function together to protect against immune activation (116). Similarly, exposure to pore-forming toxins, the most common proteinaceous exotoxin produced by bacteria, activates the IRE-1/XBP-1 pathway in a p38/MAPK-dependent manner. Loss of ire-1, xbp-1, and, to a lesser extent, atf-6 resulted in severe sensitivity of animals to pore-forming toxins (117). UPRER activation during pathogenic infection is controlled by neuronal G proteincoupled receptors (GPCRs). Specifically, the octopamine GPCR, OCTR-1, expressed in sensory neurons serves as a negative regulator of UPR, such that mutations in octr-1 increases UPR activation and promotes immunity (118, 119). Therefore, UPRER serves as a critical means to maintain ER homeostasis during pathogen infection in nematodes.

Similar to other stress responses, the innate immune response declines in function during the aging process in C. elegans. Termed immunosenescence, a decline in p38/MAPK signaling occurs during intestinal aging, allowing bacterial proliferation in the gut, which is the leading cause of death (91). As described above, promoting p38/MAPK signaling can prevent immunosenescence and extend life span independent of the UPRER. However, it is also likely that promoting canonical UPRER can promote resistance to pathogenic invasion and prevent immunosenescence. A forward genetic screen in C. elegans identified that dominant mutants of vitellogenin proteins (homologs of human apolipoprotein B-100) caused ER stress and increased sensitivity to pathogenic infection. Specifically, accumulation of mutant vitellogenins in the intestine caused collapse of the proteome and caused massive ER stress, decreasing the secretory capacity of the intestine, which is essential for mounting an efficient innate immune response. An up-regulated UPR counteracts the toxic effects of the ER stress associated with the accumulation of lipoproteins, while inhibition of UPRER via xbp-1 or ire-1 knockdown resulted in a notable increase in sensitivity to pathogens in this model (120). Moreover, another study found that overexpression of xbp-1s was sufficient to drive increased secretion of vitellogenins from the intestine, which suggests that these animals would perform better against infection (43).

The matrix of the ER is under highly oxidizing conditions in comparison to the cytosol to allow for oxidation of cysteine residues required to form intramolecular disulfide bonds during protein folding. Moreover, many enzymes that catalyze the formation of these disulfide bonds, including phosphodiesterases (PDIs), become reduced during their activity and need to be reoxidized to promote further reactions. Thus, additional enzymes, such as endoplasmic reticulum oxidoreductin 1 (ERO1), exist to provide oxidizing environments within the ER [reviewed in (121, 122)]. Ultimately, the primary functions of protein folding in the ER itself can serve as a major source of ROS and oxidative stress, especially under ER stress. Thus, under conditions of ER stress, global down-regulation of protein translation can mitigate ER oxidation and promote resistance to ER stress. In contrast, cells lacking Perk fail to down-regulate global translation through eIF2 and accumulate endogenous peroxides within the ER and experience increased oxidative stress (123).

In metazoans, the nuclear factor erythroid 2related factor 2 basic leucine zipper (NRF bZIP)family transcription factors (NRF1/2/3 in mammals and SKN-1 in C. elegans) serve to promote activation of oxidative stress defense genes. Under basal conditions, NRF2 remains in the cytosol via association with Keap1. Upon exposure to ER stress, PERK-dependent phosphorylation of NRF2 promotes NRF2 dissociation from Keap1, allowing subsequent nuclear transport and activation of NRF2 targets, including glutathione (GSH) synthesis genes responsible for buffering ROS from the ER (124, 125). While these studies highlight a clear connection between UPRER and oxidative stress response, it is unclear whether NRF2 can directly affect quality control of the ER or simply serves as a means to clear ER-induced oxidative stress. A comprehensive analysis of SKN-1 targets in C. elegans identified several UPRER targets activated directly by SKN-1. Specifically, in animals lacking functional SKN-1, ER stress failed to increase the expression of major UPRER targets, including chaperones, autophagy, calcium homeostasis, lipid homeostasis, and even UPR transcription factors themselves. Due to the failure to mount an appropriate UPRER, skn-1 mutants also exhibited increased sensitivity to multiple forms of ER stress, providing direct evidence that SKN-1 can affect ER quality control beyond its indirect roles in redox buffering (126). Perhaps most surprising in this study is that the core UPR machinery was also required for SKN-1mediated oxidative stress response. All three branches of the UPR were shown to affect skn-1 transcriptional expression, and functional IRE-1 was required for nuclear localization of SKN-1 under arsenite-induced oxidative stress (126).

Similar findings in human cells and Drosophila suggest that the integrated signaling of UPRER and oxidative stress are conserved across eukaryotes. In Drosophila, increased ER folding capacity by UPRER promotes long-term tissue homeostasis by enhancing redox response through JNK and the Nrf2 homolog CncC (127). In human HepG2 cells, NRF1 and NRF2 were shown to be required to promote the activation of ER stress signaling in response to ER stress. Specifically, NRF1 knockout cells had a diminished response to tunicamycin by ATF6, IRE1, and PERK, and partial loss of all three UPRER responses was found in NRF2 knockout cells (128).

Beyond the regulation of NRF2, UPRER components have also been shown to directly affect the transcriptional output of redox homeostasis genes. For example, ATF4 is essential for GSH synthesis to maintain redox balance of the ER (123). Moreover, XBP1 can stimulate the hexosamine biosynthesis pathway (HBP), which promotes synthesis of glycosylation products that can increase defense against ROS (129). Through these studies, it is clear that oxidative stress response and UPRER are tightly linked (Fig. 5), which begs the question of why such an extensive overlap between two distinct processes would have evolved. Perhaps the simplest explanation is that the ER serves as a major source of ROS production through its protein-folding capacity and the requirement to maintain a highly oxidative environment within its matrix, and thus, modulating NRF2 activity is critical. Beyond this, it is possible that the NRF2-UPR axis serves as a bidirectional signal between the ER and cytoplasm about its homeostatic state. As a hypothetical example, under ER stress, the UPR activates NRF2 to prepare the cytoplasm for the potential toxic effects downstream of ROS production under protein misfolding condition. Similarly, when cytoplasmic stress is high, it would be advantageous to activate a robust UPR response to promote protein folding of essential homeostatic regulators (e.g., chaperones) while also down-regulating global protein translation through eIF2.

It is becoming increasingly clear that cellular stress responses are not completely separate, and there exist notable cross communication and interdependent regulation. The UPRER and oxidative stress response (OxSR) have been shown to functionally affect the other, such that targets of XBP1s affect redox homeostasis and targets of NRF2 affect ER homeostasis. One study in C. elegans showed that transcriptional output of SKN-1 was, to a certain extent, dependent on XBP-1s function and vice versa. There are also some studies in mammalian systems that hint to similar signaling pathways, where NRF2 promotes ER quality control genes and XBP1s promotes genes involved in redox homeostasis. Another study found that glutathione synthesis genes (GSH) were potentially downstream of ATF6 signaling.

The UPRER and autophagy are two cellular processes that respond to both intra- and extracellular stressors. Both of these processes work to maintain organellar and cellular homeostasis. While it is clear that autophagy can play a role in regulating ER homeostasis by mediating lysosomal degradation of damaged ER through ER-phagy, the interplay and cross-talk between UPRER and autophagy remain poorly understood.

Autophagy is a cellular degradative process that removes damaged or unnecessary proteins and organelles to recycle macromolecules such as amino acids and lipids. Autophagy requires the coordination of more than 30 autophagy-related genes, which are involved in the formation of the autophagasome, generation of the autophagic vesicle, and fusion with the lysosome (130). Autophagy is activated under times of nutrient deprivation, mitochondrial and ER stress, cell fate and lineage decisions, and pathogen infection (131). Under conditions of ER stress, misfolded proteins accumulate in the ER and can lead to the activation of autophagy to reestablish cellular homeostasis. For example, aggregated polyglutamine in the cytosol can cause ER stressinduced activation of PERK, which induces conversion of microtubule-associated protein light chain 1 (LC1) to LC3, inducing apoptosis in an eIF2-dependent manner (132). Recent studies have shown that under conditions of ER stress, PERK can actually mobilize the major autophagy transcription factors, transcription factor EB (TFEB) and transcription factor E3 (TFE3), to translocate to the nucleus. TFEB/TFE3 activation not only leads to the induction of autophagy and lysosomal genes but also induces ATF4 and CHOP, making them more resistant to ER stressinduced apoptosis (133).

In addition, the IRE1/XBP1 pathway has been implicated in the activation of autophagy (Fig. 6). In cancer cells, XBP1s has been shown to induce autophagy through regulation of expression of Beclin2, an antiapoptotic protein, which interacts with Beclin1 to inhibit the nucleation of autophagy (134, 135). Similarly, sustained XBP1s activation in endothelial cells can promote autophagic vesicle formation, conversion of microtubule-associated protein LC1 to LC3, and expression of Beclin1. Conversely, XBP1 deficiency in mouse endothelial cells reduces LC3 expression and decreases autophagosome formation (136). IRE1 can also induce autophagy via a TRAF2-mediated pathway similar to the apoptosis machinery by inducing JNK activation and downstream Beclin1 transcription by c-Jun (137). In contrast to these studies, depletion of IRE1/XBP1 activity has also been shown to enhance autophagy and promote viability in cells obtained from patients with amyotrophic lateral sclerosis (ALS). XBP1s deficiency leads to increased forkhead Box O1 (FOXO1) expression and increased autophagy in neurons, and neuron-specific XBP1 ablation is sufficient to drive disease resistance in mice (138). These contrasting effects of the IRE1/XBP1s branch on autophagy indicate the complex interplay between the two mechanisms and highlight the importance of further research to consider targeting UPRER-autophagy cross communication as a potential avenue of therapeutic intervention.

The IRE1/XBP1 pathway has been shown to regulate autophagy both through direct transcriptional regulation of autophagic genes downstream of XBP1s and indirectly through other signaling molecules, including FOXO1 and JNK. IRE1 can promote JNK signaling through TRAF2-mediated pathways similar to the apoptosis machinery and thus activate BCL1/2 to promote autophagy. XBP1s can also activate autophagy either by inhibiting FOXO1 signaling, which releases its inhibitory effect on autophagy, or by promoting conversion of LC3 I to LC3 II.

Recent work in C. elegans has shown that activation of lysosomal activity downstream of constitutive UPRER activation via xbp-1s overexpression in neurons is crucial for xbp-1smediated longevity (139). Both cell autonomous, via intestinal xbp-1s overexpression, and cell nonautonomous, via neuronal xbp-1s overexpression, activation of UPRER induce lysosomal gene expression. In addition, xbp-1s overexpression leads to increased lysosomal activity and acidity within the intestine, which is necessary for the enhanced life span and proteostasis found in this long-lived paradigm. These processes may be mediated by HLH-30, the C. elegans homolog to mammalian TFEB, as hlh-30 knockdown is sufficient to suppress the life-span extension of neuronal xbp-1s animals. Another study has found that HPL-2, a chromatin-modifying protein, plays a critical role in ER homeostasis through autophagy. Specifically, knockdown of hpl-2 increases resistance to ER stress by promoting autophagy (140). Further, transcriptional profiling of worms deficient in phosphatidylcholine (PC) synthesis, which causes ER stress through lipid dysregulation, also induced autophagy in an IRE-1/XB-1dependent manner (141). This is highly similar to a process previously described in yeast, where inhibition of PC biosynthesis activates microlipophagy downstream of UPRER (142). These studies highlight the critical impact of UPRER on autophagy beyond canonical protein misfolding stress in the ER.

Besides the well-characterized ER chaperones and ER quality control genes, XBP1s can also transcriptionally up-regulate genes involved in N-glycan biosynthesis (143, 144) and the HBP, which generates uridine diphosphate (UDP)N-acetylglucosamine (UDP-GlcNAc), an essential substrate for both N- and O-linked glycosylation (145, 146). N-linked glycosylation begins in the ER, in which a preassembled oligosaccharide is transferred to selective asparagine residues on newly synthesized polypeptides. These oligosaccharides are essential for protein folding and maturation through the secretory pathway, and blockage of ER N-glycosylation leads to ER stress [for a detailed review, see (147, 148)]. Intriguingly, activation of XBP1s up-regulates not only genes required for ER N-glycosylation but also glycotransferases and sugar transporters in the ER and Golgi that modulate N-glycan maturation, resulting in remodeling of N-glycan structures on cell surface and secreted proteins (149). While the functional role of XBP1s-induced glycoproteome remodeling is unclear, it likely influences how cells interact with the extracellular environment and may be used to communicate ER stress between cells.

Glycosylation also regulates cytosolic and nuclear proteins via O-linked GlcNAc modifications, a dynamic posttranslational modification analogous to phosphorylation. Activation of HBP, either by XBP1s induction or by increased expression of HBP rate-limiting enzymes, enhances cellular O-GlcNAc modifications and has been shown to protect cardiomyocytes from ischemia/reperfusion injury in mice and promote proteostasis in C. elegans (145, 146). However, the specific O-GlcNAcmodified proteins that mediate such protective effects are yet to be identified. In contrast, O-GlcNAc modification on eIF2 inhibits downstream activation of UPRER, preventing ER stressinduced apoptosis (150). Additional studies will be required to understand how glycosylation changes on specific proteins during ER stress may modulate UPRER and intertissue ER stress signaling.

Peroxisomes are organelles that aid in lipid metabolism and neutralizing or using hydrogen peroxide to oxidize substrates. These functions often overlap with other cellular compartments, such as the cytosol and mitochondria, because of their overlap in metabolic processes. For example, the cytosol houses several ROS scavengers, while the mitochondria contain critical enzymes in -oxidation of fatty acids and fatty acid derivatives (87). Peroxisomes also communicate with other organelles to mediate these processes through cellular signaling pathways, vesicular trafficking, and membrane-membrane interactions. Through these complex interorganellar communications, peroxisomes regulate cellular aging in multiple ways: maintenance of the lipid bodies within the cell, exchange of metabolites between peroxisomes and other organelles, maintenance of ROS homeostasis and oxidative stress, and recycling of tricarboxylic acid cycle intermediates [refer to (151) for a more comprehensive review]. Similar to all other membrane-bound organelles, the peroxisome has a tight link with the ER, as the ER serves as the primary site for lipid and protein biogenesis of the organelle.

While there are numerous studies highlighting the importance of the ER and functional ER in maintaining peroxisomal function and biogenesis [reviewed in (152)], much less is known about the function of the peroxisome under ER stress and how UPRER affects this organelle. One study found that peroxisome deficiency can activate ER stress signaling, primarily through PERK and ATF4 signaling, which can lead to lipid dysregulation and dysfunction in cholesterol homeostasis. Specifically, peroxisome-deficient PEX2 knockout mice exhibited UPRER activation, which results in dysregulation of the endogenous sterol pathways through SREBP-2 (153). In addition, peroxisome-deficient mice showed increased peroxisome proliferatoractivated receptor (PPAR), which can cause increased expression of both SREBP-2 and the transcriptional regulator p8, leading to increased ER stress. Sustained p8 and UPRER activity can contribute to the development of hepatocarcinogenesis (154). Despite these studies highlighting a link between ER and peroxisomes, it is still unclear how peroxisome dysfunction leads to ER stress. Are the effects simply indirect where lipid dysregulation upon peroxisome dysfunction leads to ER stress? Or is there a causative link between ER and peroxisome function?

The current state of the literature has made it evident that the ER serves numerous critical functions outside of protein homeostasis. As such, the quality control machineries dedicated to preserving ER form and function, such as the UPRER, are essential in homeostatic regulation of these alternative functions, including lipid metabolism, autophagy, apoptosis, redox homeostasis, and glycosylation. Here, we briefly discussed how the UPRER affects these other functional roles of the ER independently. However, a critical question is how these functional roles overlap and whether the homeostatic regulation of these pathways can be separated. It is clear that when the UPRER is activated, many downstream targets are simultaneously regulated. For example, under conditions of protein misfolding stress, lipid homeostasis genes downstream of IRE1/XBP1 are activated in addition to chaperones and protein repair machinery. Thus, is it sufficient to promote a single component downstream of UPRER, or is it essential to simultaneously maintain all functions of the UPRER? Alternatively, if lipid homeostasis of the ER is enhanced in the absence of protein quality control machinery, would that be detrimental? Is there an essential balancing act that occurs between all the functional roles of the ER? And if so, how does the cell modulate this balance?

Beyond the beneficial roles of the UPRER, we also discussed how sustained and unresolved UPRER signaling can be detrimental. However, often the detrimental effects of the UPRER are described under conditions where there is unresolved ER stress. Hyperactivation of the UPRER in the absence of stress is generally a beneficial phenomenon and promotes metabolism, organismal health, and life span [reviewed in (6)]. Note that there do exist some specific circumstances where even UPRER hyperactivation in the absence of stress can also be detrimental. For example, overexpression of xbp-1s in the muscle of C. elegans decreases life span (14), and overexpression of HAC1s (the S. cerevisiae homolog of XBP1) can perturb cell cycle progression (155). Therefore, how does a cell differentiate between a beneficial and detrimental UPRER signature? Do there exist other transcriptional regulators that function with canonical UPRER transcription factors to alter the downstream signaling cascade? We briefly discussed the interplay between SKN-1 and XBP-1 in C. elegans. What are the other transcriptional cofactors of the canonical UPRER transcription factors, and how do they serve as sensors to inform the cell of when UPRER activation is beneficial or damaging?

An additional concern in studying quality control mechanisms is that, historically, research is generally focused on a single, organelle-specific machinery. However, current research has made it apparent that communication between homeostatic and stress response machineries is not only common but also critical. For example, we described the complex interplay between the oxidative stress response and the UPRER that is impossible to disconnect. Moreover, as the ER is not the only organelle responsible for producing ROS, it comes as no surprise that mitochondrial quality control machineries are also highly interconnected to the oxidative stress response (156). How then do all these quality control machineries communicate with one another? Under conditions of competing needs, such as through general stress where several organelles are damaged, which stress response pathway is preferentially activated? Can all cellular stress responses be mutually activated in a way that is beneficial to the cell? Hyperactivation of a single stress response is generally sufficient to promote organismal healthspan and life span [reviewed in (1)]. In these models, is it possible that other quality control machineries are also activated? Or would hyperactivating multiple stress response pathways simultaneously have a compounded effect and create a super long-lived organism? Conversely, is it possible that hyperactivating too many stress response pathways would be detrimental for an organism?

Last, we still know relatively little about cross communication of the stress signals identified here across cell and tissue types. While cell nonautonomous signaling has generally been heavily studied in the realm of the UPRER, most of these studies focused primarily on the canonical role of the UPRER in protein homeostasis. Very recent studies have now emerged in how nonautonomous communication of UPRER from the nervous system to the periphery can promote lipid homeostasis in distal tissues, as described above. Even in these studies, the actual signaling events that happen across tissues are still poorly understood. Do there exist similar cell-to-cell communication events for regulation of autophagy, immunity, oxidative stress response, etc.? If so, are the signaling molecules and receptors involved similar to or distinct from those already identified? Answering these questions described above is critical in furthering our understanding of the impacts of manipulating the UPRER for therapeutic intervention. Because of the pleiotropic effects of the UPRER described here, it is clear that targeting the master regulators of UPRER activation is unwise. However, downstream targets of UPRER can be targeted for specific diseases, ideally in specific tissue types of interest.

Acknowledgments: We would like to thank all members of the Dillin laboratory for feedback and technical/scientific support, with special thanks to R. Bar-Ziv and A. Frakes for careful review of the manuscript. Funding: M.G.M. was supported by 1F31AG060660-01 through the National Institute of Aging (NIA), R.H.-S. was supported by the Glenn Foundation for Aging Postdoctoral Fellowship and grant 1K99AG065200-01A1 from the NIA, and A.D. was supported by 4R01AG042679-04 through the NIA and the Howard Hughes Medical Institute. Author contributions: M.G.M. prepared all figures and wrote the autophagy and peroxisome sections. R.H.-S. wrote the abstract, introduction, apoptosis, immunity, oxidative stress response, and concluding remarks sections. G.G. and R.H.-S. wrote the lipid homeostasis section. C.K.T. wrote the glycosylation section. A.D. provided intellectual contributions. All authors edited the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: No data were produced in this manuscript.

Original post:
Beyond the cell factory: Homeostatic regulation of and by the UPRER - Science Advances

Extrinsic noise prevents the independent tuning of gene expression noise and protein mean abundance in bacteria – Science Advances

Abstract

It is generally accepted that prokaryotes can tune gene expression noise independently of protein mean abundance by varying the relative levels of transcription and translation. Here, we address this question quantitatively, using a custom-made library of 40 Bacillus subtilis strains expressing a fluorescent protein under the control of different transcription and translation control elements. We quantify noise and mean protein abundance by fluorescence microscopy and show that for most of the natural transcription range of B. subtilis, expression noise is equally sensitive to variations in the transcription or translation rate because of the prevalence of extrinsic noise. In agreement, analysis of whole-genome transcriptomic and proteomic datasets suggests that noise optimization through transcription and translation tuning during evolution may only occur in a regime of weak transcription. Therefore, independent control of mean abundance and noise can rarely be achieved, which has strong implications for both genome evolution and biological engineering.

Understanding the sources of diversity among individuals in a population has been a long-standing problem in biology. Genetic variability and environment account for most of this diversity. However, genetically identical individuals sharing the same environment still exhibit some phenotypic variability. This variability has been observed for more than half a century (13), and its mechanistic origins and evolutionary consequences have been intensively studied in the past decades (46).

Phenotypic variability stems from the stochastic nature of intracellular biochemical processes, in particular, gene expression. Gene expression involves many molecular events requiring the random encounter of chemical species that are present in small numbers inside the cell, leading to stochastic births and deaths of mRNAs and proteins (intrinsic noise) (4, 7). In addition, gene expression relies on many molecules such as polymerases, ribosomes, nucleotides, or amino acids, whose concentration can fluctuate inside the cell, creating a stochastic environment for the protein production process (extrinsic noise) (4, 7). The intrinsic and extrinsic components of noise can be assessed using the dual-reporter method developed by Elowitz et al. (4), who found that both extrinsic and intrinsic sources can substantially contribute to noise in prokaryotic gene expression.

Gene expression can be divided into two main steps, namely, transcription and translation. The relative contribution of these two processes to noise generation has been investigated both theoretically and experimentally (811). The classical two-stage model of gene expression, which describes the temporal evolution of the number of mRNA molecules and the number of proteins as two Markovian birth and death processes (12), predicts a different impact of transcription and translation on gene expression noise (6, 8, 12, 13). In particular, in this model, the Fano factor of the protein copy number distribution, i.e., the variance divided by the mean, increases linearly with the rate of translation but is independent of the rate of transcription (8). This differential effect of translation and transcription on noise reflects the importance of mRNA fluctuations in protein expression noise. mRNAs are present in small numbers in the cells and are therefore subject to strong fluctuations. mRNA fluctuations generate fluctuations in protein abundance, whose amplitude depends on the efficiency of translation, a phenomenon called translational bursting (6, 13, 14). The translational bursting mechanism was experimentally tested by Ozbudak et al. (9), who constructed four Bacillus subtilis strains expressing the green fluorescent protein (GFP) under an inducible promoter but four different translation control elements. Measuring GFP abundance in single cells by flow cytometry, for the four different strains under different induction conditions, Ozbudak et al. (9) concluded that the Fano factor (variance divided by the mean), also called the noise strength, linearly depends on the translation rate but is largely independent of the transcription rate, confirming the translational bursting mechanism predicted by the two-stage model. As a result, the mean abundance of a protein and the expression noise have been deemed to be independently controllable through combinations of transcription and translation control elements.

Noise in intracellular processes can limit the performance of the cell by driving it away from the optimal concentration of its molecular components (1518). In contrast, it can also be used to create diversity in a clonal population. This diversity can be the basis for bet-hedging strategies in case of fluctuating environments (1922), and it allows division of labor (23). Consequently, noise optimization can lead to substantial selective forces acting on genome evolution (5). It has, for instance, been shown that some regulatory motifs, such as negative feedback loops, can decrease the level of noise (24). These motifs can therefore be selected for during evolution on the basis of their noise reduction property. Likewise, the position of the gene in the genome can affect its expression noise (25, 26), and noise optimization has thus been proposed to exert a selective force on genome organization. The independent control of mean abundance and noise by translation and transcription control elements, such as that described by Ozbudak et al. (9), offers a particularly simple way to modulate the level of noise in the expression of a given gene. In other words, a given mean expression level can then be achieved through different strategies leading to different noise levels: with a strong transcription and weak translation, leading to low noise levels, or with a weak transcription and strong translation, leading to high noise levels (6, 9). This would have important implications both for genome evolution and for synthetic biology and biological engineering, where the genetic elements controlling transcription and translation could be tuned to reduce noise and optimize a bioproduction process (27).

The translational bursting mechanism predicted by the two-stage model and evidenced in B. subtilis (9) is in agreement with later system-wide analysis in yeasts. A large number of naturally expressed proteins in Saccharomyces cerevisiae showed a scaling between protein abundance and noise, where the squared coefficient of variation is proportional to the inverse of the mean (28, 29). This scaling was interpreted as the result of mRNA fluctuations (28, 29). Similar system-wide analysis in the model bacterium Escherichia coli revealed that a similar scaling exists for very weakly expressed genes (30). However, it does not hold for most of the proteome (30), questioning the generality of translational bursting. Although translational bursting is generally assumed to be the main mechanism underlying noise generation in prokaryotes, the experimental evidence is still scarce. To our knowledge, the study of Ozbudak et al. (9) is the only one where the effects of transcription and translation on noise were independently measured. Although the results of this study are coherent with theoretical predictions, this simple picture is clouded by several issues. First, the two-stage model is based on several questionable assumptions, such as the Poissonian production of mRNAs (31, 32), and it only describes intrinsic noise. Second, the experimental data of Ozbudak et al. (9) are based on only four strains with different translation control elements, and transcription is varied using an inducible promoter, whereas noise at intermediate induction levels are known to be strongly affected by extrinsic fluctuations in the activity of the regulatory protein mediating induction (4).

Translational bursting and the associated differential effect of transcription and translation rates on noise are often evoked as the basis for noise optimization strategies. Given the discrepancy between the importance of the result and the scarcity of experimental evidence, we decided to revisit the relative contributions of transcription and translation in prokaryotic gene expression noise. To that end, we implemented a strategy similar to the one developed by Ozbudak et al. (9), allowing us to test independently the effect of translation and transcription. We designed a library of 40 strains of B. subtilis, where the chromosomally inserted gene of GFPmut3 is expressed under the control of a combination of different translation and transcription control elements. As a result, the fluorescence of the strains covers a wide range of expression that is representative of the entire natural range of expression in B. subtilis. For each strain, the fluorescence was quantified at the single-cell level using fluorescence microscopy and flow cytometry. We showed that in contrast to the prediction of the two-stage model and to previous experimental findings in B. subtilis (9), the noise strength (or Fano factor) increases linearly with both transcription and translation rates. Using the dual-reporter method designed by Elowitz et al. (4), we showed that this unexpected result can be explained by extrinsic noise.

We designed a library of 40 B. subtilis strains in which the gene of the GFPmut3 protein is inserted into the chromosome and expressed under the control of a combination of eight different transcription control elements (transcription modules) and five different translation control elements (translation modules) (Fig. 1A). The different strains and their control elements are listed in table S1. The translation modules consist of natural (fbaA, gtlX, and tufA) or synthetic (fbaAhs and fbaAshort) translation initiation regions (TIRs), defined as the 5 untranslated region deprived of the first eight nucleotides. Our transcription modules contain natural promoters, defined as the 50 base pairs (bp) preceding the first transcribed nucleotide and including the 35 and 10 boxes. The transcription start site (TSS), i.e., the first transcribed nucleotide, is known to affect the efficiency of initiation, and the site of initiation can vary by a few bases between several initiation events (33). Therefore, we decided to extend our transcription module beyond the promoter and include the extended TSS (eTSS), defined as the first eight transcribed nucleotides (34). The different promoters and TIRs were chosen to ensure a wide range of expression on the basis of data from Nicolas et al. (35) and Borkowski et al. (36). We constructed 37 of the 40 designed strains. For the three remaining strains, repeated failures in the construction suggest that for some uncharacterized reasons, the designed sequences impose a strong burden to the cells. Further details on the design and construction of the library can be found in the Supplementary Materials.

(A) Synthetic sequences are made of a combination of eight transcription modules (promoters and eTSS) exhibiting different transcription strengths (yellow intensity) and five translation modules (TIRs) exhibiting different translation efficiencies (blue intensity). Combined modules are cloned upstream of the GFPmut3 coding sequence, resulting in a library of 40 synthetic sequences, which allow a wide range of GFPmut3 expression, that is representative of the natural range of protein expression in B. subtilis (fig. S2). (B and C) Mean protein abundance (B) and protein concentration noise strength (C) of all the strains of the library. To facilitate the interpretation, the protein concentration is expressed in number of proteins in 1 fl, which is the average cell volume. Therefore, the mean concentration corresponds to the mean number of proteins per cell (mean protein abundance). The noise strength is defined as the variance of the single-cell protein concentration divided by the mean. For each strain, at least two replicate experiments were performed. Each dot represents a single experiment. Experiments using the same strains are represented with vertically aligned dots of identical color. (D and E) The strains are ordered in a two-dimensional map according to their transcription (x axis) and translation (y axis) modules. Translation modules (1, fbaAhs; 2, fbaA; 3, fbaAshort; 4, gtlX; and 5, tufA) and transcription modules (1, ykwB; 2, yufK; 3, yqzM; 4, zwf; 5, ykpA; 6, fbaA; 7, rrnJP2; and 8, ylxM) are ordered according to their strength. The color of the pixels represents the log-transformed mean protein abundance (D) and log-transformed noise strength (E). White pixels correspond to the strains that could not be constructed or measured. Crossed-out pixels correspond to strains with an unexpected mean fluorescence, suggesting specific interactions between the transcription and translation modules.

For all the strains in the library, we quantified the fluorescence at the single-cell level using both epifluorescence microscopy and flow cytometry. Flow cytometry allows fast, high-throughput data acquisition but is less accurate and sensitive than fluorescence microscopy. In consequence, only 21 of the 37 strains of the library produced a quantifiable signal in cytometry. In contrast, the fluorescence of all the strains was quantified using microscopy, except the S27 strain, which had an unexpectedly low fluorescence that was indistinguishable from the natural autofluorescence of B. subtilis. In addition, for our analysis, the fluorescence signal has to be normalized by cell size to eliminate the variability coming from the cell cycle. Cell size can be directly measured from microscopy images, whereas it can only be coarsely estimated from cytometry measurements on the basis of the forward scatter signal (FSC). Therefore, we focused here on microscopy measurements and used flow cytometry as a control, ensuring that our conclusions are supported by data obtained using two independent measurement methods. The mean fluorescence and noise strength of the strains measured using cytometry are in agreement with microscopy measurements (fig. S1), and all the conclusions presented thereafter are supported by both cytometry and microscopy measurements.

Translation and transcription rates can vary substantially with the rate of growth, in a way that is dependent on the sequences controlling expression (35, 36). As a consequence, to characterize gene expression noise in our library, the growth rate has to be reproducibly controlled between experiments. We therefore performed fluorescence measurements on cells that are in a steady state of balanced growth (37). More precisely, we plated diluted cell precultures on agarose pads and let single cells grow into microcolonies. We monitored microcolony growth, waited six to eight generations, allowing the growth rate to reach its steady-state value, and imaged ca. 30 microcolonies, in phase contrast and fluorescence. Analyzing microcolony growth rates, we found that their variations were limited (coefficient of variation, ~14%), were mainly due to interexperiment variability, and did not significantly affect the fluorescence measurements (text S1). Single cells within microcolonies were segmented from phase contrast images, and their fluorescence was measured and normalized by the segmented cell area. Fluorescence values were then normalized to actual protein concentrations based on fluorescence measurements performed on strains with known protein abundances (see Materials and Methods).

For each strain, we performed at least two replicate experiments. Figure 1 (B and C) shows that fluorescence measurements were reproducible between replicate experiments. This can be more quantitatively addressed using a partition of variance such as that performed in a one-way analysis of variance (ANOVA). This analysis shows that for both mean fluorescence and noise strength, >95% of the variance observed between experiments is explained by the different strains used, whereas the residual variance corresponding to replicate experiments is <5% (see text S1).

As shown in Fig. 1B, the library covers a 200-fold range of expression levels, which is representative of natural expression levels in B. subtilis (see fig. S2). Figure 1D shows the mean fluorescence of all the strains, ordered along the x axis according to the strength of their transcription module and along the y axis according to the strength of their translation module. As expected, the mean expression strongly depends on both the transcription and translation modules. Figure 1D also shows that, except for three strains that exhibit unexpected behaviors (S04, S07, and S27; crossed-out pixels in Fig. 1D), the transcription modules can be ranked according to their strength independently of the translation module and reciprocally, suggesting that transcription and translation modules generally have independent effects on mean expression. For S04, S07, and S27, the mean fluorescence is not coherent with the rankings of the modules, suggesting a specific interaction, such as an effect of the eTSS on mRNA folding. In the simple two-stage model of gene expression, the mean expression of a gene is proportional to the product of the transcription rate and the translation rate. Therefore, according to this model, transcription and translation modules are expected to have independent effects on the log-transformed mean expression. This assumption can be tested using a partition of variance, such as that performed in a two-way ANOVA. Using two-way ANOVA, the variance can be partitioned between the independent effects of the two factors, as well as an interaction term and a residual unexplained variance. The underlying model implies additive effects of the two factors, so here, we performed the ANOVA on the log-transformed mean fluorescence, using transcription and translation modules as factors. This analysis demonstrated that >90% of the total variance is explained by the independent effects of the transcription and translation modules (text S2). Our microscopy dataset contains only two replicate experiments per strain, which limits the precision of the ANOVA. Therefore, to further check the independence of the effects of the transcription and translation modules, we also measured the fluorescence of all the strains at the population level during exponential growth in 96-well microplates, performing five independent measurements for each strain. A two-way ANOVA confirmed the results obtained with our microscopy data (text S2). Therefore, the effects of transcription and translation modules on mean expression are mostly independent, except on some rare instances where substantial interaction can occur, such as for the strains S04, S07, and S27, which were not used in the following analyses. These results are in agreement with previous results obtained in E. coli with a similar approach by Mutalik et al. (38) and with a larger library by Kosuri et al. (39).

We then analyzed how expression variability depends on the transcription and translation modules. Here, we used the Fano factor or noise strength, i.e., the variance divided by the mean, as a measure of expression variability. In the work of Ozbudak et al. (9), the noise strength was found to vary substantially with the translation rate, whereas the effect of the transcription rate was much weaker, as predicted by the two-stage model. In contrast, Fig. 1E shows that in our library, the noise strength depends substantially on both the transcription and translation modules.

We analyzed the dependence of the noise strength on the translation module for each transcription module, as shown in Fig. 2 and fig. S3. For each transcription module, increases linearly with the mean expression when the translation module changes, which is in agreement with previous work in B. subtilis (9). The two-stage model predicts that increases linearly with the rate of translation and therefore increases linearly with ( ab, with a being the transcription rate and b being the translation rate) with a slope that depends on the strength of the transcription modules, i.e., the slope should be smaller for modules eliciting a higher transcription rate. We performed linear regressions of versus when the translation module is changed for each transcription module. The estimated slopes are given in table S3. As predicted by the model, the slope decreases with the strength of the transcription module.

Each subplot corresponds to a group of strains with the same transcription module: (A) fbaA, strains S1 to S3 and S5; (B) rrnJP2, strains S7 to S9; (C) ykpA, strains S11 to S15; (D) ykwB, strains S16 to S20; (E) ylxM, strains S21 to S24; (F) yqzM, strains S26 and S30; (G) yufK, strains S31 to S35; and (H) zwf, strains S36 to S40. In each subplot, the different colors correspond to different translation modules (blue, fbaAhs; cyan, fbaA; green, fbaAshort; magenta, gtlX; and red, tufA). Black lines are linear regressions (parameters are given in table S3). To facilitate the interpretation, the protein concentration is expressed in number of proteins in 1 fl, which is the average cell volume. Therefore, the mean concentration corresponds to the mean number of proteins per cell (mean abundance).

Likewise, we analyzed the dependence of on the transcription module for each translation module. In contrast to previous experimental work in B. subtilis (9) and model predictions, we found that for all the translation modules, increases linearly with when the transcription rate changes. This is shown in Fig. 3 and fig. S4. We performed linear regressions and found that the slope is quite similar for all the translation modules (see table S4). The slopes are on the same order of magnitude than the slopes obtained when translation is modulated and transcription is constant (see tables S3 and S4). Consequently, for many strains in the library, increasing the mean expression by changing transcription or translation modules leads to similar noise strength (Fig. 4A).

Each subplot corresponds to a group of strains with the same translation module: (A) fbaA, (B) fbaAhs, (C) fbaAshort, (D) gtlX, and (E) tufA. In each subplot, the different colors correspond to different transcription modules (blue, yufK; cyan, yqzM; green, ykpA; yellow, zwf; magenta, ykwB; orange, fbaA; red, rrnJP2; and brown, ylxM). Black lines are linear regressions (parameters are given in table S4). To facilitate the interpretation, the protein concentration is expressed in number of proteins in 1 fl, which is the average cell volume. Therefore, the mean concentration corresponds to the mean number of proteins per cell (mean abundance).

To facilitate the interpretation, the protein concentration is expressed in number of proteins in 1 fl, which is the average cell volume. Therefore, the mean concentration corresponds to the mean number of proteins per cell (mean abundance). (A) The mean protein abundance is modulated by changing the transcription (red) or the translation (green) module. The green dots correspond to the strains with the ylxM transcription module (and different translation modules, strains S21 to S24), and the red diamonds corresponds to the strains with the fbaAshort translation module (and different transcription modules, strains S03, S08, S13, S18, S23, S33, S38, A1 to A7, and B1 to B7). The superimposed green dot and red diamond correspond to the S23 strain (transcription module, ylxM and translation module, fbaAshort). Straight lines are linear regressions. (B) The mean protein abundance is modulated by changing only the promoter. The red squares correspond to different strains with the same eTSS and translation module (strains S03 and A1 to A7), and the black straight line is a linear regression. (C) The mean protein abundance is modulated by changing either the promoter [red squares, strains S03 and A1 to A7 as in (B)], the eTSS (blue circles, strains S8 and B1 to B7), or both (green diamonds, strains S13, S18, S23, S33, and S38).

When the translation rate increases, increases with , with a slope that depends on the strength of the transcription module (table S3). In contrast, when the transcription rate increases, increases linearly with with a slope that is independent of the translation module, but the intercept depends on the strength of the translation module (table S4). These relations impose a mathematical relationship between and the rate of transcription (a) and translation (b) of the form = C1 + C2b + C3ab (Eq. 1) (see text S4 for details), with C1, C2, and C3 constants. In previous works, relations between and the rate of translation (b) were derived from a modeling approach on the basis of assumptions on the underlying biological mechanisms (8, 12, 13). In contrast, here, Eq. 1 is derived directly from the data, with no modeling assumptions. Equation 1 can be rewritten to show the dependence of on : = C1 + C2/a + C3. This equation shows that when the mean abundance is varied through the translation rate, the slope of versus (Stranslation) is the sum of a transcription-dependent term (C2/a) and a constant term (C3). This constant C3 is the slope of versus when the transcription rate varies (Stranscription). Therefore, if C2/a is small compared to C3, then modulating transcription or translation has a similar effect on noise (Stranslation ~ Stranscription). In contrast, if C2/a is large compared to C3, then translational bursting dominates and translation has a stronger impact than transcription on noise (Stranslation >> Stranscription). Thus, comparing C2/a and C3 allows defining a regime of weak transcription where translational bursting dominates noise production.

Comparing the slopes of Figs. 2 and 3 (see tables S3 and S4), we see that only the three weakest transcription modules of our library (ykwB, yufK, and yqzM) belong to this translational bursting regime. For these modules, C2/a is approximately twice as large as C3, i.e., Stranslation ~ 3.Stranscription. We analyzed genome-wide transcriptomic data from the work of Nicolas et al. (35) and found that only ca. 30% of B. subtilis proteome corresponds to a transcription rate weaker than the one of yqzM (text S5 and fig. S6) and should therefore belong to the translational bursting regime. On the basis of Eq. 1 and the genome-wide transcriptomic data, we can also compute a theoretical value for Stranslation for the whole proteome (text S5 and fig. S7). Although this approach is unlikely to give precise predictions at the single-gene level, it allows estimating the fraction of the proteome that is in the translational bursting regime. For instance, we estimated that Stranslation is 10-fold (respectively 2-fold) higher than Stranscription for only 1% (respectively 35%) of B. subtilis native promoters.

In the work of Ozbudak et al. (9), the transcription rate was modulated by using an inducible promoter. In contrast, we used different transcription modules, all leading to constitutive gene expression. As explained in the first section, we decided to consider the first eight transcribed nucleotides, namely, the eTSS, as part of our transcription modules because of its substantial effect on the transcription rate. However, these nucleotides are part of the mRNA sequence and therefore may also have an impact on its folding, degradation, and/or translation (40). Therefore, our unexpected results could stem from the design of the library, the different transcription modules potentially having an artifactual impact on translation through their different eTSS (40). To rule out any bias due to the effect of the eTSS on mRNA translation and degradation, we constructed seven new strains where only the promoter varies, while the eTSS and TIRs are identical (strains A1 to A7; see table S1). Figure 4B shows that in these strains, the noise strength also increases with the mean expression level. Therefore, the effect of the transcription modules on noise strength in the whole library is not due to a bias caused by eTSS modifications. We also constructed six strains that have identical TIRs and promoters but different eTSS regions (strains B1 to B7; see table S1). We found that changing the eTSS, the promoter, or both gives rise to a similar effect on noise strength. This is illustrated in Fig. 4C.

Equation 1 ( = C1 + C2b + C3ab), which is derived directly from the linear relations observed in Figs. 2 and 3 and describes the dependence of the noise strength on the transcription and translation rates, is reminiscent of the formula established by Taniguchi et al. (30) to take into account extrinsic noise. In the work of Taniguchi et al. (30), the two-stage model is generalized by introducing temporal fluctuations of the translation and transcription rates. The formula obtained for the noise strength is of the form of Eq. 1, with a and b being the average rates of transcription and translation and C1, C2, and C3 depending on the level of extrinsic noise. This suggests that our unexpected results could be due to a strong extrinsic noise component. In E. coli, the noise was shown to scale with protein abundance for very low expression levels and to reach a plateau when the mean abundance increases above ~10 proteins per cell (30). This plateau was suggested to be the consequence of extrinsic noise (30). Our data also show a plateau for the noise, which is reached when the mean abundance increases above ~100 proteins per average cell volume (Fig. 5A). This global analysis is therefore also in agreement with a strong extrinsic noise.

(A) The noise (squared coefficient of variation: CV2, y) of the protein concentration as a function of the mean protein abundance (x) for all the strains. Each blue circle corresponds to a single experiment with a single strain. The red line corresponds to a fit y = C/x for all the experiments for which x < 50 (left part of the graph). (B) The total noise (blue), extrinsic noise (green), and intrinsic noise (red; y) as a function of the mean (x), for the two-colored strains (same eTSS and translation module and different promoters). The red line is a fit y = k1/x + k2, as in (4). (C) The total (blue dots), extrinsic (green dots), and intrinsic (red dots) noise strength as a function of the mean, for the two-colored strains. Straight lines are linear regressions. To facilitate the interpretation, the protein concentration is expressed in number of proteins in 1 fl, which is the average cell volume. Therefore, the mean concentration corresponds to the mean number of proteins per cell (mean abundance).

To further assess the role of extrinsic noise, we used the dual-reporter method developed by Elowitz et al. (4). For the eight strains that have identical eTSS and translation module but variable promoters (strains S03 and A1 to A7; see table S1), we introduced the gene of the mKate2 red fluorescent protein into the genome, with the same control elements as for the GFPmut3 (table S2). The mKate2 gene was introduced directly downstream of the GFPmut3 gene, thus limiting difference in gene copy number during the cell cycle. Quantification of both red and green fluorescence in single cells showed that the expression of mKate2 and GFPmut3 are strongly correlated in all strains (Spearmans rank correlation between 0.6 and 0.9; P < 1010; see fig. S5). The noise and noise strength can be decomposed into their extrinsic and intrinsic components, as explicated by Elowitz et al. (4). The decomposition of noise shown in Fig. 5B shows that it is dominated by the extrinsic component, which accounts for ca. 60% of the noise at the lowest expression levels and up to ca. 90% at the highest expression levels. Figure 5C shows that the increase of noise strength when transcription increases can be fully explained by the strong extrinsic component, which increases with transcription rate.

Genome-wide analysis of transcription and translation levels in the yeast S. cerevisiae revealed that essential genes are more transcribed and less translated than nonessential genes with the same protein expression level (17). This was interpreted as the signature of a selection pressure toward noise reduction, which is likely to be stronger for essential genes. This conclusion relies on the assumption that tuning the relative levels of transcription and translation allows tuning the expression noise. In this work, we show that this strategy is less effective than previously thought in bacteria and only concerns very low expression levels for which intrinsic noise is stronger. Therefore, we investigated whether the different expression strategies observed for essential and nonessential genes in yeast also exist in B. subtilis. To that end, we performed a genome-wide analysis that allows comparing the levels of transcription and translation of essential and nonessential genes.

We used the transcriptomic and proteomic data presented by Borkowski et al. (36) and Goelzer et al. (41) and the list of essential genes from SubtiWiki (42). Protein abundance is, on average, higher for essential than nonessential genes. Therefore, to control for this effect, we grouped the genes according to their protein abundances. Then, for each group of similarly expressed genes, we divided the genes into three subgroups of identical size, according to their transcription rate: the third of the genes that have the highest transcription rate, the third that has the lowest transcription rate, and the remaining third. We then computed the number of essential genes in the two extreme subgroups (lowest and highest transcription rates), as performed by Fraser et al. (17). These subgroups a priori contain essential and nonessential genes, and if essential and nonessential genes have similar expression strategies, then the number of essential genes in the different subgroups should be similar. In S. cerevisiae, the number of essential genes was shown to be 2- to 10-fold higher in the high-transcription subgroups for all the protein expression levels (17). In contrast, Fig. 6 shows that in B. subtilis, the number of essential genes in the highly transcribed (red) and weakly transcribed (blue) subgroups are not markedly different. Thus, B. subtilis does not use markedly different expression strategies for essential and nonessential genes. However, note that there is a significant enrichment of essential genes in the high-transcription subgroups for genes with low expression levels (typically <300 proteins per cell). Note that the genome-wide data used here contain genes that are transcriptionally regulated, and low expression levels may correspond to transcriptional repression. However, removing genes that are likely to be transcriptionally regulated does not change the results shown in Fig. 6A (as Fig. 6A, where all genes are included, is similar to fig. S8, where regulated genes are excluded), suggesting that the different expression strategies of essential and nonessential genes at low expression levels are not due to different transcriptional regulation. In contrast, it may reflect a selection pressure for noise reduction of poorly expressed essential genes.

(A to C) Genes are grouped according to the protein abundance, and each group is divided into three subgroups of identical size according to the transcription rate. The subgroups are formed with the third of the genes that have the highest transcription rate, the third that has the lowest transcription rate, and the remaining third. Then, the number of essential genes in each subgroup is computed. Red circles, number of essential genes in the high-transcription subgroup; blue circles, number of essential genes in the low-transcription subgroup. The filled circles indicate significant differences based on Fishers exact test (P < 0.05). (A) The analysis is performed on all genes in the genome. (B) The analysis is performed on a subset of genes that are weakly transcribed (less transcribed than yqzM). (C) The analysis is performed on the rest of the genes (i.e., those more transcribed than yqzM). In (A) to (C), the procedure to group the genes of identical protein abundance is not a simple binning and creates groups of genes whose levels of expression are not significantly based on an ANOVA (see Materials and Methods for details). The different groups therefore do not contain the same number of genes, and the number of groups is different in (A) to (C).

As we presented above, translational bursting dominates noise production only in a regime of weak transcription, which represents only a small fraction of the natural proteome. For instance, we showed that only ca. 30% of natural promoters should lead to Stranslation > 3 Stranscription. Restricting the analysis shown in Fig. 6A to this group of weakly transcribed genes gives identical results, as shown in Fig. 6B. In contrast, if we use the 70% most transcribed genes, then the effect at low expression levels disappears and no difference can be detected between essential and nonessential genes (Fig. 6C).

It is generally assumed that translational bursting is the dominant source of noise in prokaryotic gene expression and that translation therefore has a stronger impact on noise than transcription. In this work, we show that translational bursting dominates noise production only in a regime of weak transcription, which corresponds to a small fraction of the natural transcription range of bacteria. In contrast, for most of the natural expression range, translation and transcription modulations have similar effects on noise. We show here that this phenomenon can be explained by the prevalence of extrinsic noise.

As previously demonstrated, very weak promoters associated with strong translation control elements can promote noisy expression (43). Such an expression strategy could therefore be selected for by evolution or implemented in synthetic biology approaches to increase population diversity and/or implement bet-hedging strategies. However, our results show that for most of B. subtilis natural transcription range, noise cannot be tuned independently of mean abundance by varying the ratio of transcription and translation rates. This strategy is therefore less general than previously thought (6, 8, 9), which has important implications both for synthetic biology and engineering and for genome evolution. In bioengineering, the control of gene expression noise is an essential component of system design. Until now, strong promoters and weak RBS (ribosome binding site) sequences were favored when assembling robust, i.e., low-noise gene circuits (27). Our results indicate that the future of bioengineering will require the elaboration of a novel framework for engineering noise in various living systems.

Our analysis of genome-wide transcriptomic and proteomic data in B. subtilis shows that at low expression levels, essential genes are transcribed more and translated less than nonessential genes of identical protein abundance. As previously proposed for yeasts, this difference may reflect a selection pressure for noise reduction, which is assumed to be stronger for essential genes. Notably, the difference in expression strategies between essential and nonessential genes is restricted to a fraction of the genome, which corresponds to weakly transcribed genes. Therefore, our experimental results and our genome-wide analysis offer a coherent picture. In the weak transcription regime, noise can be tuned independently of mean abundance by varying the ratio of transcription and translation, leading to a selection force acting on genome evolution. However, this force is negligible in the evolution of most of the genome.

Translational bursting is expected to have a different impact on noise for different functional categories of genes. In particular, transcription factors are known to be present at low copy number in the cell compared to enzymes or structural proteins (44). In addition, among transcription factors, those that act specifically on a few genes, such as E. coli Lac repressor, are usually present at lower concentrations than global regulators that act on many genes. Low copy number transcription factors are therefore expected to be in the weak transcription regime, where noise can be tuned independently of mean expression. This noise tuning can lead to strong phenotypic effects and provide a basis for specific bet-hedging strategies (22, 43). In contrast, for enzymes that are present in high copy number, expression noise cannot be tuned by varying the ratio of transcription and translation. The cell therefore often implements alternative strategies to minimize the fluctuations in biochemical pathways, such as the negative regulation of a biosynthesis pathway by its end product.

The existence of two regimes of noise production, dominated either by translational bursting or extrinsic noise depending on the strength of transcription, is likely to hold for other organisms. Different organisms may be in different regimes depending on their natural transcription range and the source and intensity of the extrinsic noise. In yeasts, markedly different expression strategies between essential and nonessential genes suggest that noise can generally be tuned by varying the ratio of transcription and translation, thus suggesting that at the whole-genome scale, noise production is mainly in the regime where translational bursting prevails. This pattern may be related to the level of extrinsic noise, which was reported to be lower in yeasts than in bacteria (28, 29). Note that in the case of transcriptional bursting, i.e., when promoters can stochastically switch between inactive and active states, different regimes of noise production can also be defined, by comparing the transcription rate to the activation and inactivation rates of the promoter (11). Therefore, both extrinsic noise and transcriptional bursting can prevail over translational bursting, restricting the regime in which noise can be tuned independently of the mean abundance by varying the ratio of transcription and translation.

E. coli Mach1T1 and TG1 were used for plasmid construction and amplification, respectively, using standard techniques (45). B. subtilis strains were obtained by integration of the plasmid by single crossing-over in a tryptophan prototrophic 168 strain (BSB168) (46), using standard procedures.

When required, DNA fragments were purified using the QIAquick PCR Purification Kit or QIAquick Gel Extraction Kit (QIAGEN, Hilden, Germany). Plasmids were purified from E. coli cultures using the QIAprep Spin Miniprep Kit (QIAGEN).

The vectors used to generate the strain collection were made as follows: The vector pBaSysBioII (46) was linearized by Eco RV and recircularized by ligation of a 714-bp PCR (polymerase chain reaction) product to obtain the plasmid PL1. The PCR fragment was obtained by amplification of B. subtilis chromosomal DNA between the coordinates 213.017 and 213.757 according to the version AL009126 of the complete genome of B. subtilis deposited in GenBank.

The synthetic sequences used to control expression of GFPmut3 in the strains S01 to S40 (table S1) have been chemically synthesized by GeneArt. Briefly, each of the synthetic sequence is made of the association of a given promoter, an eTSS, and a TIR. These DNA sequences are preceded by a 29-bp sequence identical to the 29 bp upstream of the promoter PfbaA in the original PL1 and followed by 29 bp identical to the first 29 bp of the GFPmut3 coding sequence.

Plasmids PL1S01 to PL1S40 had been built as follows: The plasmid PL1 was PCR-amplified using primers P-PS-AM and P-PS-AV, resulting in a linear DNA sequence of 5243 bp made of the whole PL1 plasmid devoid of any promoter and RBS upstream of the GFPmut3 coding sequence (CDS). Synthetic sequences were PCR-amplified using the universal primers PS-F and PS-R, purified, and cloned in the plasmid by Gibson assembly using a NEBuilder HiFi DNA Assembly kit according to the manufacturers instructions (New England Biolabs, Ipswich, MA, USA). Each Gibson assembly mix has been used to transform chemically competent Mach1T1 E. coli cells. Once sequenced (GATC Biotech, Cologne, Germany), recombinant plasmids were transformed and multiplied in chemically competent TG1 cells before transformation in BSB168.

The following protocol is used to ensure a steady state of balanced growth. All incubation steps are performed at 37C under agitation. Cultures are inoculated in LB supplemented with spectinomycin (100 g/ml) and incubated overnight. They are then diluted 100-fold in LB, incubated for 2 hours, and then diluted 50-fold in S Medium [0.2% (NH4)2SO4, 1.4% K2HPO4, 0.6% KH2PO4, 0.1% sodium citrate, 0.0096% MgSO4, 104% MnSO4, 0.5% glucose, and 0.00135% FeCl3] and incubated for 2 hours. The culture is then diluted 8-fold in S medium, incubated for 3 hours, diluted again 70,000-fold in S medium, and incubated until the optical density at 600 nm reaches 0.2. The culture is then analyzed by flow cytometry and/or fluorescence microscopy.

Single-cell fluorescence, FSC, and side scatter measurements were carried out on a Becton Dickinson FACSCalibur flow cytometer, equipped with a 488-nm excitation laser and a 530/30-nm emission filter, and controlled by the CellQuest software. For all the strains, measurements were performed with the same laser power and voltage settings. The exponentially growing cultures were diluted 40-fold, and measurements were performed on 104 to 105 cells.

Microcolony growth monitoring and single-cell fluorescence measurements were performed using an inverted DeltaVision Elite microscope equipped with the Ultimate Focus system for automatic focalization, a 100 oil immersion objective (numerical aperture 1.4), a temperature-controlled chamber (37C), and the DV Elite sCMOS Camera. Bright-field illumination was provided by a white light-emitting diode (LED), and fluorescence illumination was provided by the DV Light Solid State Illuminator 7 Colors (475-nm LED for GFP and 575-nm LED for mKate2). Our microscope can perform two different illumination techniques: Khler illumination and critical illumination. We used critical illumination to improve evenness of illumination.

A liquid solution of 1.5% high-resolution low-gelling temperature agarose (Sigma-Aldrich) in S medium is prepared. To that end, agarose is first dissolved in water, heated, and allowed to cool down to 50C. The components of the S medium are then added to the agarose solution. A Gene Frame (125 l, 1.7 cm by 2.8 cm; Thermo Fisher Scientific) is stuck on a clean glass slide (Knittel Glass; 76 mm by 26 mm); the resulting cavity is filled with S-agarose, covered with a microscope slide, and cooled for 1 hour at 4C. Then, the microscope slide is removed, and stripes of S-agarose are removed using a surgical scalpel to leave three small stripes of agarose (~4 mm wide, with ~4 mm spacing), separated by air cavities ensuring oxygenation. Three different strains are then loaded on the three agarose stripes. To that end, the exponentially growing cultures are diluted 300-fold, and cca. 2 ml is deposited on each agarose stripe. Once the liquid is absorbed, the cavity is sealed with a clean coverslip (Knittel Glass Cover Slips; 24 mm by 60 mm), and the slide is placed in the temperature-controlled chamber set at 37C for 1 hour before acquisition begins.

We first follow the growth of microcolonies from single cells using phase contrast microscopy. Images are acquired using 50-ms exposure with 32% of the maximum intensity of the white LED. For each strain, we image ~30 microcolonies, every 5 or 10 min, for cca. 4 hours. After 4 hours of growth, the cells are in a steady state of growth, and the microcolonies are still in monolayers. We then image ca. 30 microcolonies, using both phase contrast and fluorescence. Depending on their fluorescence levels, the strains are imaged with different illumination intensities and/or exposure times.

To convert the fluorescence levels into protein concentrations, we quantified the fluorescence of two B. subtilis strains that express GFPmut3 and for which the concentration of proteins was previously quantified by two-photon fluorescence fluctuation microscopy (47). More precisely, we used two strains where GFPmut3 is under the control of the gapB or the cggR promoter, and we measured the fluorescence during exponential growth in 96-well microplates in S medium with glucose or malate as carbon sources, leading to different induction levels of the gapB or cggR promoters [see (47)]. We simultaneously measured the fluorescence of the S5, S9, and S13 strains in glucose-S medium, to allow determining the average concentration of proteins for those strains. The single-cell fluorescence data are then normalized accordingly for the whole library.

The fluorescence images are first corrected for inhomogeneous illumination. To estimate the illumination profile [b(x,y): the illumination intensity at (x,y) coordinate], we averaged ~40 images of agarose pads supplemented with fluorescein. For an image I0(x,y), we perform the following normalization to get the corrected image I1(x,y): I1(x,y) = I0(x,y) /b(x,y), where is the mean intensity averaged over every pixel. We also correct for the autofluorescence of the agarose gel by subtracting to the fluorescence image the average background intensity (pixels outside of the microcolony). We also normalize the fluorescence signal by the excitation energy to take into account the different illumination settings used for different strains. The corrected images are then analyzed using Schnitzcells software (48). Bacteria are segmented using the phase contrast images, and their fluorescence intensity is measured, i.e., the total fluorescence of the cell normalized by the cell area.

All data analysis is performed using MATLAB. One-way and two-way ANOVAs are performed using MATLABs functions anova1 and anova2.

For both microscopy and flow cytometry data, autofluorescence was estimated from measurements of the wild-type BSB168 strain, which does not contain any fluorescent protein. The single-cell fluorescence of cells expressing GFP and/or mKate2 is the sum of the contribution from the fluorescent proteins and the autofluorescence. Therefore, to reflect only the number of fluorescent proteins, the mean fluorescence is corrected by subtracting the mean autofluorescence. The single-cell autofluorescence is assumed to be independent of the number of fluorescent proteins in cells expressing GFP and/or mKate2. Therefore, the variance of the fluorescence can be corrected by subtracting the variance of the autofluorescence.

Analysis of the microscopy data shows that the autofluorescence is Gaussian. In the flow cytometry data, the distribution of autofluorescence is truncated on the left of a threshold that corresponds to the sensitivity of the cytometer. We therefore reconstruct the whole distribution as follows: The sensitivity threshold is lower than the mode of the distribution (i.e., the maximum of the density). Therefore, the right half of the distribution can be estimated. The whole Gaussian distribution is then reconstructed by symmetry, and the average and variance can be estimated.

For single-cell fluorescence measurements with the flow cytometer, we eliminated all the strains for which the fluorescence distribution was truncated by the sensitivity threshold. To reduce the fluctuations originating from cell size variations in the cytometry data, we kept only the cells whose FSC signal was within 3% of the mode of the FSC signal distribution.

The transcriptomic and proteomic data are taken from the works of Borkowski et al. (36) and Goelzer et al. (41), respectively, and the list of essential genes is taken from SubtiWiki (42). For each gene, the dataset contains several independent proteomic measures (up to nine replicates) and several independent transcriptomic measures (up to four replicates). Genes were binned according to their protein expression as follows: First, protein expression was estimated for each gene as the average of the proteomic replicates, and the genes were ranked according to this averaged measure. Then, we use all the replicates to take into account the level of confidence of the proteomic measure for each gene and to group the genes whose levels of expression are not significantly different. Starting with the first gene, we add the next genes one by one, performing a one-way ANOVA at each step. If the P value of the ANOVA is larger than a fixed threshold (0.05), then the gene is added to the group. Otherwise, it is used to start a new group, where genes are added one by one similarly. In contrast to a simple binning, this procedure takes into account the level of confidence of the measurements and produces groups of genes whose levels of expression are not significantly different.

Acknowledgments: We thank M. Calabre (Micalis, Jouy-en-Josas, France) for technical assistance in constructing the library. We thank A. Amir and J. Lin for useful comments on the manuscript. Funding: This work was supported by the French National Research Agency (ANR-18-CE44-0003) and the European Commission (FP7-244093). A.D. acknowledges a 3-year Ph.D. grant from the Interface Pour le Vivant(IPV) doctoral program of Sorbonne Universit. Author contributions: L.R., M.J., J.R., and S.A. conceived the project and designed the experimental plan. L.R. and J.R. designed the experimental setup. A.D. performed the experiments. V.S., M.J., and S.A. designed the strain library. V.S. constructed the library. A.D. and L.R. analyzed the data. L.R. wrote the manuscript with contributions from all authors. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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Extrinsic noise prevents the independent tuning of gene expression noise and protein mean abundance in bacteria - Science Advances

Nanopores can identify the amino acids in proteins, the first step to sequencing – University of Illinois News

CHAMPAIGN, Ill. While DNA sequencing is a useful tool for determining whats going on in a cell or a persons body, it only tells part of the story. Protein sequencing could soon give researchers a wider window into a cells workings. A new study demonstrates that nanopores can be used to identify all 20 amino acids in proteins, a major step toward protein sequencing.

Researchers at the University of Illinois at Urbana-Champaign, Cergy-Pontoise University in France and the University of Freiburg in Germany published the findings in the journal Nature Biotechnology.

Graduate student Kumar Sarthak and physics professor Aleksei Aksimentiev were part of a research team that demonstrated that nanopores could sequence proteins, giving reserachers and clinicians insight into activity within a cell.

Photo by L. Brian Stauffer

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DNA codes for many things that can happen; it tells us what is potentially possible. The actual product that comes out the proteins that do the work in the cell you cant tell from the DNA alone, said Illinois physics professor Aleksei Aksimentiev, a co-leader of the study. Many modifications happen along the way during the process of making protein from DNA. The proteins are spliced, chemically modified, folded, and more.

A DNA molecule is itself a template designed for replication, so making copies for sequencing is relatively easy. For proteins, there is no such natural machinery by which to make copies or to read them. Adding to the difficulty, 20 amino acids make up proteins, as compared with the four bases in DNA, and numerous small modifications can be made to each amino acid during protein production and folding.

Many amino acids are very similar, Aksimentiev said. For example, if you look at leucine and isoleucine, they have the same atoms, the same molecular weight, and the only difference is that the atoms are connected in a slightly different order.

Nanopores, small protein channels embedded in a membrane, are a popular tool for DNA sequencing. Previously, scientists thought that the differences in amino acids were too small to register with nanopore technology. The new study shows otherwise.

The researchers used a membrane channel naturally made by bacteria, called aerolysin, as their nanopore. In both computer modeling and experimental work, they chopped up proteins and used a chemical carrier to drive the amino acids into the nanopore. The carrier molecule also kept the amino acids inside the pore long enough for it to register a measurable difference in the electrical signature of each amino acid even leucine and isoleucine, the near-identical twins.

This work builds confidence and reassures the nanopore community that protein sequencing is indeed possible, said Abdelghani Oukhaled, a professor of biophysics at Cergy-Pontoise whose team carried out much of the experimental work.

The researchers found they could further differentiate modified forms of amino acids by using a more sensitive measurement apparatus or by treating the protein with a chemical to improve differentiation. The measurements are precise enough to potentially identify hundreds of modifications, Aksimentiev said, and even more may be recognized by tweaking the pore.

This is a proof-of-concept study showing that we can identify the different amino acids, he said. The current method for protein characterization is mass spectrometry, but that does not determine the sequence; it compares a sample to whats already in the database. Its ability to characterize new variations or mutations is limited. With nanopores, we finally could look at those modifications which have not yet been studied.

The aerolysin nanopore could be integrated into standard nanopore setups, Aksimentiev said, making it accessible to other scientists. The researchers are now exploring approaches to read the amino acids in sequential order as they are cut from the protein. They also are considering other applications for the system.

One potential application would be to combine this with immunoassays to fish out proteins of interest and then sequence them. Sequencing them will tell us whether theyre modified or not, and that could lead to a clinical diagnostic tool, Aksimentiev said.

This work shows that theres really no limit to how precisely we can characterize biological molecules, he said. Very likely, one day we will be able to tell the molecular makeup of the cell what we are made of, down to the level of individual atoms.

The National Institutes of Health and the National Science Foundation supported this work. Computer modeling was done on the Blue Waters supercomputer at the National Center for Supercomputing Applications at the U. of I.

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Nanopores can identify the amino acids in proteins, the first step to sequencing - University of Illinois News

Origami Therapeutics, Inc. selected as a CONNECT 2020 Cool Company – thepress.net

SAN DIEGO, June 8, 2020 /PRNewswire/ -- Origami Therapeutics http://origamitherapeutics.com, an early stage biotech company taking a precision medicine approach to find disease-modifying treatments for neurodegenerative diseases caused by protein folding, announced today it has been selected as one of the 60 "Cool Companies" for 2020 by Connect with San Diego Venture Group. Origami was selected from a pool of over 300 tech and life science applicants.

Cool Companies is an annual capital program designed to match San Diego's best technology and life sciences startups ready to raise Series A with quality venture capital. The program selects top tier, local entrepreneurs raising institutional funding, and grants them opportunities for direct access to capital providers. The program regularly attracts over 200 VCs to the region annually. Since 2016, Cool Companies have raised over $400M, in just Series A institutional funding.

"We received a record number of applications from extraordinary companies for the 'Cool Companies' program this year," said Mike Krenn, CEO of Connect.

"We are very excited to be included in such a stellar group of new, innovative companies," said Beth Hoffman, Founder, President & CEO of Origami. "We are happy to be part of the vibrant San Diego biotech ecosystem and look forward to showcasing our novel therapeutics to investors."

Leveraging the Founder's experience in discovering transformational therapies for Cystic Fibrosis that modulate CFTR conformation, Origami's focus is to treat neurodegeneration by directly modulating the pathogenic proteins that cause disease. Their platform enables discovery of both protein degraders and conformation correctors, allowing them to match the best drug to treat each disease by using patient-derived disease models to ensure success in clinical trials.

About Connect

Connect is a community nonprofit organization passionate about helping tech and lifesci entrepreneurs build great companies. Connect serves entrepreneurs throughout their growth journey with a suite of curated programs aimed to help companies grow, gain access to capital, and scale. Connect helps innovative companies thrive so they can make a meaningful impact on the economic development of the region, and together create a world-class tech ecosystem.

About Origami Therapeutics

Origami is generating a pipeline of small molecule therapeutics that prevent or delay the onset and the progression of neurodegenerative diseases by targeting the underlying genetic cause of disease. Currently, they are selecting the optimal protein degrader molecule to advance into preclinical testing for Huntington's disease, a devastating fatal disease that strikes at the prime of life. Origami's core technology should be applicable to multiple neurological disorders where the proximate cause of the disease is a misfolded protein. These include Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, frontotemporal dementia, Lewy body diseases, and other polyglutamine diseases. For more information, please visit http://www.origamitherapeutics.com

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Origami Therapeutics, Inc. selected as a CONNECT 2020 Cool Company - thepress.net

A novel system to map protein interactions reveals evolutionarily conserved immune evasion pathways on transmissible cancers – Science Advances

INTRODUCTION

Metastatic cancer affects most mammals, but the cancer incidence can vary widely across phylogenetic groups and species (Fig. 1 and table S1) (13). In humans, the lifetime risk of developing cancer is around 40% (4). This figure is in stark contrast to a general cancer incidence of 3% for mammals, 2% for birds, and 2% for reptiles reported by the San Diego Zoo (N = 10,317) (2, 5). A more recent study at the Taipei Zoo reported cancer incidence of 8, 4, and 1% for mammals, birds, and reptiles, respectively (N = 2657) (6). Cancer incidence in domestic animals is generally less than 10% (N = 202,277) (3). However, two studies performed 40 years apart reported that greater than 40% of Tasmanian devils develop spontaneous, often severe neoplasia in their lifetime (5, 7). Devils are also unique because they are affected by two of the three known naturally occurring transmissible cancers in vertebrate species (8, 9). Transmissible cancers are a distinct form of cancer in which the tumor cells function as an infectious pathogen and an allograft. Dogs (Canis lupus familiaris) are the only other vertebrate species affected by a transmissible cancer (10), and interestingly, some breeds of dogs also have high cancer incidence (3, 11).

Metastatic cancer has been reported in nearly all mammalian orders, and MHCs have been the most intensely studied molecules in most orders. In the past decade, studies of immune checkpoint molecules (PD1, PDL1, and CTLA4) have become a primary focus in humans and rodents. However, immune checkpoint studies in other species are limited, particularly at the protein level, because of the lack of species-specific reagents. This creates a vast gap in our understanding of the evolution of the mammalian immune system. The numbers in the columns represent the number studies matching Web of Science search results between 2009 and 2019. See table S1 for search terms.

The devil facial tumor (DFT) disease was first detected in Northwest Tasmania and has been a primary driver of an 80% decline in the wild Tasmanian devil population (8, 12). The clonal DFT (DFT1) cells have been continually transmitted among devils and are estimated to have killed at least 10,000 individuals since at least 1996. In 2014, a second independent transmissible Tasmanian DFT (DFT2) was found in wild devils (9), and 23 cases have been reported to date (13). Genetic mismatches, particularly in the major histocompatibility complex (MHC) genes, should lead to rejection of these transmissible tumors. Consequently, the role of devil MHC has been a focus of numerous studies (Fig. 1 and table S1) to understand the lack of rejection of the transmissible tumors. These studies have revealed that the DFT1 cells down-regulate MHC class I (MHC-I) expression (14), a phenomenon observed in many human cancers. In contrast to DFT1 cells, the DFT2 cells do express MHC-I (15). DFT1 and DFT2 cells have 2884 and 3591 single-nucleotide variants, respectively, that are not present in 46 normal devil genomes (16). The continual transmission of DFT1 and DFT2, despite MHC-I expression by DFT2 cells and genetic mismatches between host and tumor, suggests that additional pathways are likely involved in immune evasion.

Human cancer treatment has been transformed in the past decade by manipulating interactions among immune checkpoint molecules. These have proven broadly effective in part because they function across many different MHC types and tumor mutational patterns. However, these pathways have received little attention in transmissible cancers and other naturally occurring cancers in nonmodel species (Fig. 1 and table S1) (1719). We have previously shown that the inhibitory immune checkpoint molecule programmed death ligand 1 (PDL1) is expressed in the DFT microenvironment and is up-regulated by interferon- (IFN-) in vitro (17). This finding led us to question which other immune checkpoint molecules play a role in immune evasion by the transmissible cancers and the devils high spontaneous cancer incidence. Understanding immune evasion in a natural environment will support DFT vaccine development to help protect this endangered species (20) and has the potential to identify protein interactions that are conserved across divergent species to improve translational success of animal models (19). Unfortunately, a persistent limitation for immunology in nontraditional study species is a lack of species-specific reagents. Wildlife biologists and veterinarians are at the front lines of emerging infectious disease outbreaks, but they often lack species-specific reagents to fulfill the World Health Organizations call for cross-cutting R&D preparedness and perform mechanistic immunological investigations.

To solve the paucity of reagents available for Tasmanian devils and address ongoing limitations for nontraditional study species, we developed a Fluorescent Adaptable Simple Theranostic (FAST) protein system that builds on the diverse uses of fluorescent proteins previously reported (2123). This simple system can be used for rapid development of diagnostic and therapeutic (i.e., theranostic) immunological toolkits for any animal species (Fig. 2). We demonstrate the impact of the FAST system by using it to confirm seven receptor-ligand interactions among 12 checkpoint proteins in devils. We demonstrate the versatility of the system across species by fusing a fluorescent reporter to a well-characterized camelid-derived nanobody that binds human PDL1 (24).

(A) Schematic diagram of FAST protein therapeutic and diagnostic (i.e., theranostic) features. POI, protein of interest. (B) Graphic overview of FAST protein system including key steps: (i) characterize gene of interest (GOI) in silico; (ii) design expression vectors; (iii) digest FAST base vectors and insert alternative genes of interest or colors; (iv) transfect FAST vectors into mammalian cells and monitor using fluorescent microscopy or flow cytometry; (iv) purify the protein using 6xHis tag, visualize fluorescent color to show that protein is in frame and correctly folded. Image of microfuge tubes shows 100 l of mCitrine, mOrange, and mCherry FAST proteins (1 mg/ml) excited with blue light with an amber filter. Full protocols for vector construction and protein testing are available in the Supplementary Materials. (C) Results of flow cytometry binding assay with devil 41BB FAST proteins. The colored lines in the histograms show binding of devil 41BB fused to mTagBFP, mCerulean3, mAzurite, mCitrine, mOrange, mCherry, or mNeptune2 to CHO cells transfected with devil 41BBL, and the black lines show binding to untransfected CHO cells. FSC, forward scatter; SSC, side scatter.

In humans, checkpoint proteins have been targets of immunotherapy in clinical trials, but the functional role and binding patterns of these proteins are unknown for most other species. We have used the FAST system to show that the inhibitory checkpoint protein CD200 is highly expressed on DFT cells, opening the door to single-cell phenotyping of circulating tumor cells (CTCs) in devil blood. Furthermore, we are the first to report that coexpression of CD200R1 can block surface expression of CD200 in any species. Understanding how clonal tumor cells graft onto new hosts, evade immune defenses and metastasize within a host will identify evolutionarily conserved immunological mechanisms to help improve cancer, infectious disease, and transplant outcomes for human and veterinary medicine.

Initially, we developed FAST proteins to determine whether monomeric fluorescent proteins could be fused to devil proteins and secreted from mammalian cells (Fig. 2A and table S2). We used 41BB (TNFRSF9) for proof-of-concept studies by fusing the extracellular domain of devil 41BB checkpoint molecule to monomeric fluorescent proteins (Fig. 2, A and B, and fig. S1). We used wild-type Chinese hamster ovary (CHO) cells and CHO cells transfected with 41BBL (TNFSF9) to confirm specificity of the 41BB FAST proteins and demonstrate that the fluorescent proteins [mTag-blue fluorescent protein (BFP), mCerulean3, mAzurite, mCitrine, mOrange, mCherry, and mNeptune2] remained fluorescent when secreted from mammalian cells (Fig. 2C).

We chose mCherry, mCitrine, mOrange, and mBFP for ongoing FAST protein development. Initial batches of FAST proteins were purified using the 6xHis tag and eluted with imidazole. Following purification of FAST protein the color can be immediately observed with blue light and an amber filter unit, allowing confirmation that the fluorescent protein DNA coding sequences were in frame and the proteins were properly folded. After combining, concentrating, and sterile filtering the eluted fractions, 100 l at 1 mg/ml was aliquoted and visualized again using blue light to confirm fluorescent signal (Fig. 2B). A full step-by-step protocol and set of experimental templates for creating and testing FAST proteins for any species are available online in the Supplementary Materials.

We chose candidate immune checkpoint molecules for FAST protein development (Fig. 3A and table S2) based on targets of human clinical trials and then selected devil genes for which a reliable sequence was available either in the published devil genome or transcriptomes (19, 25, 26). We transfected the FAST protein expression vectors (table S3) into CHO cells and tested the supernatant against CHO cell lines expressing full-length receptors. 41BB FAST proteins in supernatant exhibited strong binding to 41BBL cell lines, but the fluorescent signals from most other FAST proteins were too weak to confirm binding to the expected receptors (fig. S2). As FAST proteins do not require secondary reagents, we next incubated target cells with purified FAST proteins and added chloroquine to block the lysosomal protein degradation pathway. This allowed us to take advantage of receptor-mediated endocytosis, which can allow accumulation of captured fluorescent signals inside the target cells (27). This protocol adjustment allowed confirmation that CD47-mCherry, CD200-mBFP, CD200-mOrange, CD200R1-mBFP, and CD200R1-mOrange, and PD1-mCitrine bound to their expected receptors (Fig. 3B). We also demonstrated the flexibility of the FAST proteins by showing that alternative fusion conformations (fig. S1, C and D), such as type II proteins (e.g., mCherry-41BBL) and a devil Fc tag (e.g., CD80-Fc-mCherry) bound to their expected ligands (Fig. 3B). The stability of the fusion proteins was demonstrated using supernatants that were stored at 4C for 2 months before use in a 1-hour live-culture assay with chloroquine (fig. S3).

(A) Diagram of soluble FAST proteins and full-length proteins used for testing of FAST proteins. 41BBL is a type II transmembrane protein; all other proteins are type I. CD80 and CTLA4 soluble FAST proteins included a devil immunoglobulin G (IgG) Fc tag. Arrows indicate interactions confirmed in this study. TNF, tumor necrosis factor. (B) Histograms showing binding of FAST proteins to CHO cells expressing full-length devil proteins. Target CHO cells were cultured with chloroquine to block lysosomal degradation of FAST proteins and maintain fluorescent signal during live-culture binding assays with purified FAST proteins (2 g per well) for 30 min or 18 hours to assess receptor-ligand binding (N = 1 per time point).

To further streamline the reagent development process, we next took advantage of the single-step nature of FAST proteins (i.e., no secondary antibodies or labels needed) in live-cell coculture assays (Fig. 4A). Cell lines secreting 41BB-mCherry, 41BBL-mCherry, or CD80-Fc-mCherry FAST proteins were mixed with cell lines expressing full-length 41BB, 41BBL, or CTLA4-mCitrine and cocultured at a 1:1 ratio overnight with chloroquine. Singlet cells were gated (Fig. 4B) and binding of mCherry FAST proteins to carboxyfluorescein diacetate succinimidyl ester (CFSE) or mCitrine-labeled target cells was analyzed (Fig. 4C). The strongest fluorescent signal from 41BB-mCherry, 41BBL-mCherr, and CD80-Fc-mCherry was detected when cocultured with their predicted receptors, 41BBL, 41BB, and CTLA4, respectively.

(A) Schematic of coculture assays to assess checkpoint molecule interactions (absent, weak, and strong). Cells were mixed and cultured overnight with chloroquine. Protein binding and/or transfer were assessed using flow cytometry. (B) Gating strategy for coculture assays. (C) CHO cells that secrete 41BBL-mCherry, 41BB-mCherry, or CD80-Fc-mCherry were cocultured overnight with target CHO cells that express full-length 41BB, 41BBL, or CTLA4. 41BB and 41BB-L were labeled with CFSE, whereas full-length CTLA4 was directly fused to mCitrine. Cells that secrete mCherry FAST proteins appear in the upper left quadrant. Cells expressing full-length proteins and labeled with CFSE or mCitrine appear in the lower right quadrant. Cells in the upper right quadrant represent binding of mCherry FAST proteins to full-length proteins on carboxyfluorescein diacetate succinimidyl ester (CFSE) or mCitrine-labeled cells. Results shown are representative of n = 3 per treatment. (D) CTLA4-Fc-mCherry FAST protein binding to DFT cells. DFT1 C5065 cells transfected with control vector (black), 41BB (gray), CD80 (red), or CD86 (blue) were stained with CTLA4-Fc-mCherry supernatant with chloroquine. Results are representative of N = 2 replicates per treatment.

The fluorescent binding signal of CD80-Fc-mCherry was lower than expected, so we next reexamined our Fc tag construct. In humans and all other mammals examined to date, the immunoglobulin G (IgG) heavy chain has glycine-lysine (Gly-Lys) residues at the C terminus; the initial devil IgG constant region sequence available to us had an incomplete C terminus, and thus, our initial CD80-Fc-mCherry vector did not have the C-terminal Gly-Lys. We subsequently made a new FAST-Fc construct with CTLA4-Fc-mCherry, which exhibited strong binding to both CD80 and CD86 transfected DFT cells (Fig. 4D).

Analysis of previously published devil and DFT cell transcriptomes suggested that CD200 mRNA is highly expressed in DFT2 cells and peripheral nerves, moderately expressed in DFT1 cells, and lower in other healthy devil tissues (Fig. 5A) (25, 26, 28). As CD200 is an inhibitory molecule expressed on most human neuroendocrine neoplasms (29), and both DFT1 and DFT2 originated from Schwann cells (26, 30), we sought to investigate CD200 expression on DFT cells at the protein level. Staining of wild-type DFT1 and DFT2 cells with CD200R1-mOrange FAST protein showed minimal fluorescent signal (Fig. 5B). However, overexpression of CD200 using a human EF1 promoter yielded a detectable signal with CD200R1-mOrange binding to CD200 on DFT1 cells. A weak signal from CD200-mOrange was detected on DFT1 cells overexpressing CD200R1 (Fig. 5B). To confirm naturally expressed CD200 on DFT cells, we digested CD200 and 41BB FAST proteins using tobacco etch virus (TEV) protease to remove the linker and fluorescent reporter. The digested proteins were then used to immunize mice for polyclonal serum production. We stained target CHO cell lines with preimmune or immune mouse sera collected after three-times immunizations. Only the immune sera showed strong binding to the respective CD200 and 41BB target cell lines (Fig. 5C). After the final immunization (four times), we collected another batch of sera and tested it on DFT1 and DFT2 cells (Fig. 5D). In agreement with the transcriptomic data for DFT cells (25), the polyclonal sera revealed high levels of CD200 on DFT cells, but low levels of 41BB.

(A) GOIs for this study are plotted as a log2-transformed transcripts per million (TPM) heat map with dark blue indicating the most highly expressed genes. Technical replicates (N = 2) from separate flasks were used for the cell lines (C5065, RV) and biological replicates (N = 2) were used for primary tissues, except peripheral nerve (PN) (N = 1). (B) Wild-type DFT1.C5065, DFT2.JV, DFT2.SN, and DFT1.C5065 transfected to overexpress CD200 or CD200R1 were stained with either CD200R1-mOrange or CD200-mOrange FAST protein. Histograms filled with blue or red highlight expected strong binding interactions. The percentage of events that falls within the marker is shown. Results are representative of N = 2 replicates per treatment. (C) Mice were immunized with 41BB or CD200 FAST proteins. Black, preimmune; gray, immune sera from a mouse immunized with 41BB; red, preimmune; blue, immune sera from a mouse immunized with CD200. CHO cells transfected with either full-length 41BB or CD200 were stained with sera and then anti-mouse AF647. Results are representative of N = 2 per treatment. (D) Sera were used to screen two strains each of DFT1 and DFT2 cells for 41BB and CD200 expression. Results are representative of N = 3 per treatment. (E) DFT1 C5065 transfected with either vector control, CD200, or CD200R1 was stained with purified polyclonal anti-CD200 and anti-mouse IgG AF647 (black, no antibodies; red, secondary antibody only; blue, primary and secondary antibody). Results are representative of N = 2 per treatment.

In humans, overexpression of some checkpoint proteins can block surface expression of heterophilic binding partners in cis (e.g., CD80 and PDL1) (31). As a potential route for disrupting the inhibitory effects of CD200 on antitumor immunity, we tested whether overexpression of CD200R1 on DFT cells could reduce CD200 surface expression. We stained a DFT1 strain, C5065, and DFT1 C5065 cells transfected to overexpress CD200 or CD200R1 with polyclonal anti-CD200 sera and secondary anti-mouse IgG Alexa Fluor 647 (AF647). We detected no surface protein expression of CD200 DFT1 cells overexpressing CD200R1 (Fig. 5E).

In addition to high expression of CD200 on neuroendocrine neoplasms (29), CD200 is used as a diagnostic marker for several human blood cancers (32). DFT cells metastasize in the majority of cases (33), and our transcriptome results (Fig. 5A) suggest that CD200 mRNA is more highly expressed in DFT cells than in peripheral blood mononuclear cells (PBMCs) (25, 26). As a result, we tested whether CD200 could be used to identify DFT cells in blood. We stained PBMCs and DFT2 cells separately with polyclonal anti-CD200 sera and anti-mouse AF647 and then analyzed CD200 expression by flow cytometry (fig. S4A). We then mixed the stained PBMCs and DFT2 cells at ratios of 1:10 (fig. S4A) and 1:5 (fig. S4B) and analyzed the mixed populations. PBMCs showed minimal CD200 expression and background staining (fig. S4), whereas CD200 was highly expressed on DFT2 cells. CD200+ DFT2 cells were readily distinguishable from PBMCs.

As our RNA sequencing (RNA-seq) results only included mononuclear cells, we next performed a pilot test to determine whether DFT cells could be spiked into whole devil blood and identified via flow cytometry using CD200 staining. DFT1 and DFT2 cells were labeled with CellTrace violet (CTV), and 10,000 cells were diluted directly into 100 l of whole blood from a healthy devil (N = 1 per treatment; n = 1 devil). The cells were then stained with purified polyclonal anti-CD200 with and without secondary anti-mouse IgG AF647 before red blood cell (RBC) lysis. Initial results showed that DFT2 cells expressed CD200 above the leukocyte background but that DFT1 cells could not be distinguished from leukocytes (fig. S5). To eliminate the secondary antibody step from the whole blood staining protocol, we next labeled the polyclonal anti-CD200 and normal mouse serum (NMS) with a no-wash Zenon mouse IgG AF647 labeling reagent (n = 1 per treatment; n = 2 devils). This system again showed that CD200 expression could be used to identify DFT2 cells in blood (Fig. 6, A to E), suggesting that CD200 is a candidate marker for identification of metastasizing DFT2 cells.

Color dot plots showing DFT cells in green (CFSE), PBMCs in black, DFT Alexa Fluor 647+ (AF647+) cells in red, and PBMC AF647+ in blue. Forward- and side-scatter plot of DFT2.JV cells alone (A) and DFT2.JV cells mixed with PBMCs (B). (C) Color dot plot showing dead cells stained with 4,6-diamidino-2-phenylindole (DAPI) (right quadrants) and CFSE-labeled DFT cells (upper quadrants). (D) The top row shows unmixed PBMCs. The middle row and bottom row show PBMCs mixed with DFT1.C5065 (middle) and DFT2.JV (bottom) cells. Cell mixtures were either untreated or incubated with Zenon AF647labeled NMS or Zenon AF647labeled -CD200 serum. AF647+ DFT (red) and PBMC (blue) are in the right quadrants. (E) Histogram overlays to highlight AF647+ (right quadrants) from DFT1-PBMC and DFT2-PBMC mixtures. Cells were analyzed on the Beckman Coulter MoFlo Astrios. (F) FAST nanobody proof of concept was accomplished using supernatant from untransfected ExpiCHO cells or ExpiCHO cells secreting human antiPDL1-mCitrine nanobody. Nanobody supernatant was used undiluted or at 1:10 or 1:100 dilutions in media and used to stain CHO cells that express either human PDL1 or human CD80. Results are representative of N = 2 per treatment.

Last, to test whether the FAST system could be applied to other species (e.g., camelid-derived nanobody) and applications (FAST nanobody), we reverse-translated the protein sequence for an anti-human PDL1 nanobody (24) and inserted the codon-optimized DNA sequence into a FAST mCitrine vector. The assembled plasmid was transfected into ExpiCHO cells, and the supernatant was tested for binding to CHO cells stably transfected with either full-length human PDL1 or human CD80; the human proteins were fused to miRFP670 (Addgene no. 79987) in a FAST vector. The nanobody supernatant was used undiluted or at 1:10 or 1:100 dilutions. The nanobody showed strong binding to PDL1-expressing cells, but not CD80-expressing cells (Fig. 6F).

Naturally occurring cancers provide a unique opportunity to study immune evasion and the metastatic process across diverse hosts and environments. The exceptionally high cancer rate in Tasmanian devils coupled with the two transmissible tumors currently circulating in the wild warrants a thorough investigation of the devil immune system. However, taking advantage of these natural disease models has been out of reach for most species because of a lack of reagents. The FAST protein system that we developed here is well suited to discovering additional DFT markers and, more generally, to filling the reagent gap for nontraditional species. For proteins like 41BB that have high affinity for 41BBL, FAST proteins can be used as detection reagents directly from supernatant. For other molecules with lower receptor-ligand affinity, the FAST proteins can be purified, digested with a protease to remove the nontarget proteins, and used for production of higher-affinity binding proteins (e.g., antibodies, aptamers, and nanobodies).

The versatility of the FAST system was demonstrated by fusing a validated human anti-PDL1 nanobody derived from a camel (Camelus bactrianus) heavy-chain variable region to mCitrine. The nanobody-reporter fusion allowed direct testing of the nanobody from supernatant without the need for purification or secondary labeling and provided a 1:1 ratio of nanobody and reporter to allow quantification of target proteins. In addition to fusing nanobodies to fluorescent proteins, fluorescently labeled target proteins could be used with nanobody display libraries to pull down or sort nanobodies that bind the target protein.

The simple cut-and-paste methods for vector assembly lend the FAST protein system to entry-level immunology and molecular biology skill sets. In addition, the ability of FAST proteins to be used in live coculture assays and with elimination of secondary reagents will increase efficiency and reduce experimental error for advanced human and mouse cancer immunology studies. For example, previous high-throughput studies have used a two-step staining process (i.e., recombinant protein and secondary antibody) to screen more than 2000 protein interactions (34); this type of assay can be streamlined using FAST proteins to eliminate the need for secondary antibodies. Fc tags or other homodimerization domains can be incorporated into FAST proteins to increase binding for low-affinity interactions and to assess potential Fc receptormediated functions.

Production of recombinant proteins in cell lines that closely resemble the physiological conditions of the native cell type (i.e., mammalian proteins produced in mammalian cell lines) is more likely to yield correct protein folding, glycosylation, and function than proteins produced using evolutionarily distant cell lines. The fluorescent fusion proteins developed here take advantage of natural receptor expression and cycling processes (e.g., CTLA4 transendocytosis) in eukaryotic target cells; bacterial protein production methods are not amenable to coculture with eukaryotic target cells in immunological assays. Our demonstration of the FAST protein system in CHO cells suggest that this method can be efficiently integrated into existing research and development pipelines for humans and other vertebrate species.

A primary question in transmissible tumor research is why genetically mismatched cells are not rejected by the host. Successful infection of devils with DFT cells relies on the ability of the tumor allograft to evade and manipulate host defenses. The missing-self hypothesis suggests that the lack of constitutive MHC-I expression on DFT1 cells should lead to natural killer (NK) cellmediated killing of the allograft tumor cells. Here, we used the FAST protein system to develop a tool set to address this question and show that DFT1 and DFT2 cells express CD200 at higher levels than most other devil tissues examined to date. CD200 has been shown to directly inhibit NK cells in other species (35), so overexpression of CD200 is a potential mechanism of immune evasion of NK responses by DFT cells.

We hypothesize that CD200 could be particularly important in DFT transmission as the CD200-CD200R pathway is critical to the initial stages of establishing transplant and allograft tolerance in other species (36). In line with this hypothesis, a recent study reported that overexpressing several checkpoint molecules, including CD200, PDL1, and CD47, in mouse embryonic stem cells could be used to generate teratomas that could establish long-term allograft tolerance in fully immunocompetent hosts (37). We have previously reported that PDL1 mRNA and protein are up-regulated on DFT2 cells in response to IFN- (17), and our transcriptome results show that CD47 is expressed at moderate to high levels in DFT cells. Here, we show that overexpression of CD200R1 on DFT1 eliminates binding of our polyclonal anti-CD200 antibodies, suggesting that DFT cells overexpressing CD200R1 could be used to test the role of CD200 in allograft tolerance. Alternatively, genetic ablation of CD200 in DFT cells could be used as a complementary approach to examine the role of immune checkpoint molecules in DFT allograft tolerance. Low MHC-I expression is a primary means of immune evasion by DFT1 cells, and disrupting the CD200-CD200R1 pathway could facilitate improved recognition of DFT1 cells by CD8 T cells by enhancing IFN-mediated MHC-I up-regulation. Recent work in mice has identified immunosuppressive natural regulatory plasma cells that express CD200, LAG3, PDL1, and PDL2; we have previously identified PDL1+ cells with plasma cell morphology near or within the DFT microenvironment (17).

Previous DFT vaccine efforts have used killed DFT cells with adjuvants (38, 39). A similar approach to treat gliomas in dogs reported that tumor lysate with CD200 peptides nearly doubled progression-free survival compared to tumor lysate alone (40). Like devils, several breeds of dog are prone to cancer, and these genetically outbred large animal models provide a fertile ground for testing cancer therapies. The CD200 peptides are reported to provide agonistic function through CD200-like activation receptors (CD200R4) rather than by blocking CD200R1 (40). The functional role of CD200-CD200R pathway in devils remains to be elucidated, but the CD200R1NPLY inhibitory motif and key tyrosine residues are conserved in devil CD200R (19, 41, 42), demonstrating that this motif is conserved over 160 million years of evolutionary history (43). In addition to agonistic peptides, several other options for countering CD200-CD200R immune inhibition are possible. Human chronic lymphocytic leukemia cells often express high levels of CD200, which can be down-regulated in response to imiquimod (44). Likewise, we have previously shown that DFT1 cells down-regulate expression of CD200 mRNA in vitro in response to imiquimod treatment (25). In one of the longest running and most in-depth studies of host-pathogen coevolution, CD200R was shown to be under selection in rabbits in response to a myxoma virus biocontrol agent (45). As DFT1 and DFT2 have been circulating in devils for more than 20 and 5 years, respectively, it will be important to monitor CD200/R expression and the potential evolution of paired activating and inhibitory receptors in these natural disease models.

Immunophenotyping and single-cell RNA-seq of CTCs have a potential to identify key gene expression patterns associated with metastasis and tissue invasion. CD200 is a potential marker for the identification of CTCs from devil blood. As proof of concept, DFT2 cells could be identified in devil blood spiked with DFT2 cells. As CTCs are likely to be rare in the blood of most infected devils, CD200 alone would be insufficient for identifying DFT1 cells. Additional surface DFT markers would be required to purify CTCs for metastases and tissue invasion analyses. The FAST protein system provides a simple procedure to facilitate the production of a panel of DFT markers to help identify key proteins in the metastatic process.

In summary, the simple cut-and-paste production of the vectors and single-step testing pipeline of the FAST system provided multiple benefits. The FAST system allowed us to characterize receptor-ligand interactions and to identify evolutionarily conserved immune evasion pathways in naturally occurring transmissible cancers. Our initial implementation of the system confirmed numerous predicted protein interactions in a marsupial species and documented high expression of the inhibitory molecule CD200 on DFT cells. The high expression of CD200 in devil nervous tissues and neuroendocrine tumors, down-regulation of CD200 in response to imiquimod, and binding of CD200 to CD200R1 are consistent with results from human and mouse studies. Consequently, the CD200/R pathway provides a promising immunotherapy and vaccine target for DFTs (20). Beyond this study, FAST proteins meet the key attributes needed for reagent development, such as being straightforward to make, stable, versatile, renewable, cheap, and amenable to high-throughput testing. The direct fusion of the reporter protein to the protein of interest allows for immediate feedback during transfection, supernatant testing, and protein purification; proteins with frameshifts, introduced stop codons, or folded improperly will not fluoresce and can be discarded after a simple visualization, rather than only after extensive downstream testing. Efficient mapping of immune checkpoint interactions across species can identify evolutionarily conserved immune evasion pathways and appropriate large-animal models with naturally occurring cancer. This knowledge could inform veterinary and human medicine in the fields of immunological tolerance to tissue transplants, infectious disease, and cancer.

The objectives of this study were to fill a major gap in our understanding of the mammalian immune system and to understand how genetically mismatched transmissible tumors evade host immunity. To achieve this goal, we developed a recombinant protein system that directly fuses proteins of interest to a fluorescent reporter protein. The first phase was to determine whether the fluorescent protein remained fluorescent after secretion from mammalian cells and to confirm that proteins bound to their predicted receptors (i.e., ligands). Initial testing was performed in CHO cells and follow-up assays used devil cells. To reduce the risk of false positives in binding assays, we tested each FAST protein against the expected target protein and additional nontarget proteins. To further demonstrate the functionality of this system for antibody development, mice were immunized with either 41BB or CD200 proteins. Pre- and postimmunization polyclonal sera were used to confirm that the proteins used for immunization induced antibodies that specifically bound to surface-expressed recombinant proteins and native proteins on DFT cells. Last, to demonstrate the flexibility of the system, we replicated a known anti-human PDL1 nanobody that we fused to mCitrine. This shows that the FAST system can be used to target human proteins, to produce recombinant proteins derived from other species (e.g., camelid-derived nanobody), and for functions other than receptor-ligand interactions.

Target gene DNA sequences for vector construction were retrieved from Genbank, Ensembl, or de novo transcriptome assemblies (table S2). Target DNA was amplified from a complementary DNA template or existing plasmids using primers and polymerase chain reaction (PCR) conditions shown in tables S2 and S4 using Q5 High-Fidelity 2X Master Mix (New England Biolabs no. M0494L). Primers were ordered with 5 base extensions that overlapped expression vectors on either side of the restriction sites. The amplified products were identified by gel electrophoresis and purified using the NucleoSpin PCR and Gel Clean Up Kit (Macherey-Nagel no. 740609.5). Alternatively, DNA sequences were purchased as double-stranded DNA gBlocks (table S5) (Integrated DNA Technologies) for direct assembly into expression vectors.

All new plasmids were assembled using the NEBuilder kit (New England Biolabs; NEB no. E5520S) following the manufacturers recommendations unless otherwise noted. DNA inserts, digested plasmids, and NEBuilder master mix were incubated for 60 min at 50C and then transformed into DH5 included with the NEBuilder kit. Plasmid digestions were performed following manufacturer recommendations and generally subjected to Antarctic phosphatase (New England Biolabs no. M0289S) treatment to prevent potential reannealing. Sleeping Beauty transposon vectors pSBbi-Hyg (Addgene no. 60524), pSBbi-BH (Addgene no. 60515), pSBtet-Hyg (Addgene no. 60508), and pSBtet-RH (Addgene no. 60500) were gifts to Addgene from E. Kowarz (46). The pCMV(CAT)T7-SB100 containing the cytomegalovirus (CMV) promoter and SB100X transposase was a gift to Addgene from Z. Izsvak (Addgene no. 34879) (47). We first constructed an all-in-one Sleeping Beauty vector by inserting a CMV promoter and SB100X transposase from pCMV(CAT)T7-SB100 (47) into pSBi-BH (46) (tables S3 and S4). This was accomplished by using pAF111-vec.FOR and pAF111.1.REV primers to amplify an overlap region from pSBbi-BH (insert 1) and pAF111-2.FOR and pAF111-2.REV to amplify the CMV-SB100X region from pCMV(CAT)T7-SB100 (insert 2). The purified amplicons were then used for NEBuilder assembly of pAF111. The final all-in-one vectors pAF112 (hygromycin resistance and luciferase) and pAF123 (hygromycin resistance) were assembled from the pAF111 components. pAF112 was assembled by amplifying the Luc2 luciferase gene (insert 1) from pSBtet-Hyg and the P2A-hygromycin resistance gene (insert 2) from pSBbi-BH and inserting into the pAF111 Bsu36 I digest using NEBuilder. pSBbi-Hyg was Bsu36 Idigested to obtain the hygromycin resistance gene, and this fragment was inserted into Bsu36 Idigested pAF111 using T4 ligase cloning to replace the BFP-P2A-hygromycin segment in pAF111.

All full-length gene coding sequences except CTLA4 were cloned into the pAF112 Sfi I digest (table S2). All full-length vectors also contain luciferase with T2A peptide linked to the hygromycin resistance protein; luciferase was included for use in downstream functional testing that was not part of this study. Tasmanian devil CTLA4 was cloned into a NotI-HF and Xma I digest of pAF100 that was used in a different study but is derived from vectors pAF112 and pAF138. In addition, we also used devil PDL1 (CHO.pAF48) and 41BBL (CHO.pAF56) cell lines developed using a vector system described previously (17).

Plasmids containing fluorescent protein coding sequences mCerulean3-N1 (Addgene no. 54730), mAzurite-N1 (Addgene no. 54617), mOrange-N1 (Addgene no. 54499), and mNeptune2-N1 (Addgene no. 54837) were gifts to Addgene from M. Davidson. mTag-BFP was amplified from pSBbi-BH, mCitrine was amplified from pAF71, and mCherry was amplified from pTRE-Dual2 (Clontech no. PT5038-5). pAF137 was constructed by amplifying the devil 41BB extracellular domain with primers pAF137-1.FOR and pAF137-1.REV and amplifying mCherry with pAF137-2a.FOR and pAF137-2.REV (tables S3 and S4). 5 extensions on pAF137-1.FOR and pAF137-2.REV were used to create overlaps for NEBuilder assembly of pAF137 from a pAF123 Sfi Idigested base vector. 3 extensions on pAF137-1.REV and pAF137-2a.FOR were used to create the linker that included an Xma I/Sma I restriction site, TEV cleavage tag, GSAGSAAGSGEF linker peptide, and 6xHis tag between the gene of interest and fluorescent reporter. The GSAGSAAGSGEF was chosen because of the low number of large hydrophobic residues and less repeated nucleic acids than are needed with other flexible linkers such as (GGGS)4. The pAF137 primer extensions also created 5 Not I and 3 Nhe I sites in the FAST vector to facilitate downstream swapping of functional genes and to create a Kozak sequence (GCCGCCACC) upstream of the FAST protein open-reading frame. Following confirmation of correct assembly via DNA sequencing, the FAST 41BB-mCherry (pAF137) was digested and used as the base vector (Fig. 2B and fig. S1, A and B) for development of FAST vectors with alternative fluorescent proteins. This was accomplished by digestion of pAF137 with Sal I and Nhe I and then inserting PCR-amplified coding sequences for other fluorescent proteins using NEBuilder (tables S3 and S4).

Type I FAST (extracellular N terminus and cytoplasmic C terminus) protein vectors were constructed by digestion of 41BB FAST vectors with Not I and either Xma I or Sma I (Fig. 2B and fig. S1, A and B) and then inserting genes of interest (tables S2 to S4). To create an Fc-tagged FAST protein, we fused the extracellular domain of devil CD80 to the Fc region of the devil IgG (fig. S1C). The Fc region was amplified from a devil IgG plasmid provided by L. Corcoran (Walter and Eliza Hall Institute of Medical Research). All secreted FAST proteins in this study used their native signal peptides, except for 41BBL. 41BBL is a type II transmembrane protein in which the signal peptide directly precedes the cytoplasmic and transmembrane domains of the protein (cytoplasmic N terminus and extracellular C terminus). As type I FAST vectors cannot accommodate this domain architecture, we developed an alternative base vector for type II transmembrane FAST proteins (fig. S1D). To increase the probability of efficient secretion of type II FAST proteins from CHO cells, we used the hamster interleukin-2 (IL-2) signal peptide (accession no. NM_001281629.1) at the N terminus of the protein, followed by a Sal I restriction site, mCherry, an Nhe I restriction site, 6xHis tag, GSAGSAAGSGEF linker, TEV cleavage site, Xma I/Sma I restriction site, the gene of interest, and a Pme I restriction site following the stop codon.

Following transformation of assembled plasmids, colony PCR was performed as an initial test of the candidate plasmids. Single colonies were inoculated directly into a OneTaq Hot Start Quick-Load 2X Master Mix (NEB no. M0488) with primers pSB_EF1a_seq.FOR (atcttggttcattctcaagcctcag) and pSB_bGH_seq.REV (aggcacagtcgaggctgat). PCR was performed with 60C annealing temperature for 25 to 35 cycles. Colonies yielding appropriate band sizes were used to inoculate Luria broth with ampicillin (100 g/ml) for bacterial outgrowth overnight at 37C and 200 rpm. The plasmids were purified using standard plasmid kits and prepared for Sanger sequencing using the BigDye Terminator v3.1 Cycle Sequencing Kit (Thermo Fisher Scientific no. 4337455) with pSB_EF1a_seq.FOR and pSB_bGH_seq.REV primers. The BigDye Terminator was removed using Agencourt CleanSEQ (Beckman Coulter no. A29151) before loading samples to a 3500xL Genetic Analyzer (Applied Biosystems) for sequencing by fluorescence-based capillary electrophoresis.

DFT1 cell line C5065 and DFT2 cell line JV were cultured at 35C with 5% CO2 in cRF10 [10% complete RPMI (Gibco no. 11875-093) with 2 mM l-glutamine, supplemented with 10% heat-inactivated fetal bovine serum, and 1% antibiotic-antimycotic (Thermo Fisher Scientific no. 15240062)]. RPMI without phenol red (Sigma-Aldrich no. R7509) was used to culture FAST protein cell lines when supernatants were collected for downstream flow cytometry assays. Devil peripheral blood cells were cultured in cRF10 at 35C with 5% CO2. CHO cells were cultured at 37C in cRF10 during transfections and drug selection but were otherwise cultured at 35C in cRF5 (5% complete RPMI). For production of purified recombinant proteins, stably transfected CHO cells were cultured in suspension in spinner flasks in chemically defined, serum-free CHO EX-CELL (Sigma-Aldrich no. 14361C) media supplemented with 8 mM l-glutamine, 10 mM Hepes, 50 M 2-ME, 1% (v/v) antibiotic-antimycotic, and 1 mM sodium pyruvate and without hygromycin.

Stable transfections of CHO and DFT cells were accomplished by adding 3 105 cells to each well in six-well plates in cRF10 and allowing the cells to adhere overnight. The next day, 2 g of plasmid DNA was added to 100 l of phosphate-buffered saline (PBS) in microfuge tubes. Polyethylenimine (PEI) (linear, molecular weight, 25,000; Polysciences no. 23966-2) was diluted to 60 g/ml in PBS and incubated for at least 2 min. The PEI solution (100 l) was added to the 100 l of plasmid DNA in each tube to achieve a 3:1 ratio of PEI:DNA. The solution was mixed by gentle pipetting and incubated at room temperature for 15 min. While the solution was incubating, the media on the CHO cells were replaced with fresh cRF10. All 200 l from each DNA:PEI mix was then added dropwise to the CHO cells and gently rocked side to side and front to back to evenly spread the solution throughout the well. The plates were then incubated overnight at 37C with 5% CO2. The next day, the plates were inspected for fluorescence, and then the media were removed and replaced with cRF10 containing hygromycin (1 mg/ml) (Sigma-Aldrich no. H0654). The media were replaced with fresh cRF10 hygromycin (1 mg/ml) every 2 to 3 days for the next 7 days until selection was complete. The cells were then maintained in hygromycin (0.2 mg/ml) in cRF5 at 35C with 5% CO2. Supernatant was collected 2 to 3 weeks after transfection and stored at 4C for 2 months to assess stability of secreted FAST proteins.

Sixteen days after transfection, the first batch of FAST protein cell lines was adapted to a 1:1 mix of cRF5 and chemically defined, serum-free CHO EX-CELL media for 1 to 2 days to facilitate adaptation of the adherent CHO cells to suspension culture in serum-free media. At least 5 107 cells were then transferred to ProCulture spinner flasks (Sigma-Aldrich no. CLS45001L and no. CLS4500250) and stirred at 75 rpm at 35C in 5% CO2 on magnetic stirring platforms (Integra Bioscience no. 183001). Cells were maintained at a density ranging from 5 105 to 2 106 cells/ml for 8 to 14 days. Supernatant was collected every 2 to 3 days, centrifuged at 3200 relative centrifugal force (RCF) for 10 min, stored at 4C, and then purified using the KTA start protein purification system (GE Life Sciences no. 29022094). The supernatant was diluted 1:1 with 20 mM sodium phosphate (pH 7.4) and then purified using HisTrap Excel columns (GE Life Sciences no. 17-3712-05) according to the manufacturers instructions. Samples were passed through the columns using a flow rate of 2 ml/min at 4C; all wash and elution steps were done at 1 ml/min. Elution from HisTrap columns (GE Life Sciences no. 17-3712-05) was accomplished using 0.5 M imidazole and fractionated into 1-ml aliquots using the Frac30 fraction collector (GE Life Sciences no. 29023051). Fluorescence of FAST proteins was checked via brief excitation (Fig. 2) on a blue light transilluminator with an amber filter unit. In the case of mCherry, chromogenic color was visible without excitation. Fractions containing target proteins were combined and diluted to 15 ml with cold PBS, dialyzed (Sigma-Aldrich no. PURX60005) in PBS at 4C, 0.22-m sterile-filtered (Millipore no. SLGV033RS), and concentrated using Amicon Ultra centrifugal filter units (Sigma-Aldrich no. Z706345). The protein concentration was quantified using the 280-nm absorbance on a NanoDrop spectrophotometer. Extinction coefficients using for each protein were calculated using the ProtParam algorithm (48). The proteins were then aliquoted into microfuge tubes and frozen at 80C until further use. The CTLA4-Fc-mCherry protein was designed, assembled, and tested separately from the other FAST proteins and was tested directly in supernatant without purification.

CHO cells expressing full-length proteins were thawed in cRF10 and then maintained in cRF5 with hygromycin (0.2 mg/ml). The adherent CHO cells were washed with PBS and incubated with trypsin for 5 min at 37C to remove cells from the culture flask. Trypsin was diluted five times with cRF5 and centrifuged at 200 RCF for 5 min. Cells were resuspended in cRF5, counted (viability >95% in all cases), and resuspended and aliquoted for assays as described below.

Supernatants (cRF5) were collected from CHO cells expressing devil 41BB extracellular domain fused to either mCherry (pAF137), mCitrine (pAF138), mOrange (pAF164), mBFP (pAF139), mAzurite (pAF160), mCerulean3 (pAF161), or mNeptune2 (pAF163) (tables S2 to S4). The supernatant was spun for 10 min at 3200 RCF to remove cells and cellular debris and then stored at 4C until further use. CHO cells expressing devil 41BBL (CHO.pAF56) and untransfected CHO cells were prepared as described above. Flow cytometry tubes were loaded with 5 104 target CHO cells per well in cRF5, centrifuged 500 RCF for 3 min, and then resuspended in 200 l of supernatant from the 41BB FAST cell lines (N = 1 per treatment). The tubes were then incubated for 15 min at 4C, centrifuged at 500 RCF for 3 min, resuspended in 400 l of cold fluorescence-activated cell sorting (FACS) buffer, and stored on ice until the data were acquired on a Beckman Coulter Astrios flow cytometer (Fig. 2C). All flow cytometry data were analyzed using FCS Express 6 Flow Cytometry Software version 6 (De Novo Software).

U-bottom 96-well plates were loaded with 1 105 target CHO cells per well in cRF5, centrifuged 500 RCF for 3 min, and then resuspended in 175 l of cRF5 supernatant from FAST cell lines collected 11 days after transfection (N = 1 per treatment). The plates were then incubated for 30 min at room temperature, centrifuged at 500 RCF for 3 min, resuspended in 200 l of cold FACS buffer, centrifuged again, and fixed with FACS fix buffer [PBS, 0.02% NaN3, 0.4% formalin, glucose (10 g/liter)]. The cells were transferred to tubes, diluted with FACS buffer, and analyzed on a Beckman Coulter Astrios flow cytometer (fig. S2).

Purified FAST proteins were diluted to 20 g/ml in cRF5, aliquoted into V-bottom 96-well transfer plates, and then stored at 37C until target cells were ready for staining. Target cells were resuspended in cRF5 with 100 M chloroquine, and 100,000 cells per well were aliquoted into U-bottom 96-well plates. One hundred microliters of the diluted FAST proteins (N = 1 per treatment, two time points per treatment) was then transferred from the V-bottom plates into the U-bottom 96-well plates containing target cells. The final volumes and concentrations were 200 l per well in cRF5 with 50 M chloroquine and 2 g per well of FAST proteins. One set of plates was incubated at 37C for 30 min, and another set of plates was incubated at 37C overnight. The cells were then centrifuged 500 RCF for 3 min, the media decanted, and incubated for 5 min with 100 l of trypsin to dislodge adherent cells. The cells were then washed with 200 l of cold FACS buffer, fixed, resuspended in cold FACS buffer, and transferred to tubes for analysis on the Astrios flow cytometer (Fig. 3B).

The protocol for using FAST protein supernatants was the same above as the preceding experiment except for the modifications described here. Supernatants were collected 2 to 3 weeks after transfection, centrifuged at 3200 RCF for 10 min, and stored at 4C for 2 months. Before staining for flow cytometry, the supernatant was 0.22-m filtered. Supernatant was then loaded into V-bottom 96-well plates to facilitate rapid transfer to staining plates and stored at 37C until target cells were ready for staining. Target cells were prepared as described above except for being diluted in cRF5 with 100 M chloroquine. A total of 2 105 cells per well (100 l) were then loaded into U-bottom 96-well plates. One hundred microliters of FAST protein supernatant (N = 1 per treatment) was then transferred from the V-bottom plates to achieve 50 M chloroquine, and the cells were then incubated at 37C for 60 min. The plates were then washed, fixed, and analyzed on the Astrios flow cytometer (fig. S3). A similar procedure was used for staining stably transfected DFT cells with CTLA4-Fc-mCherry, except that the supernatant was used fresh (Fig. 4D).

CHO cells expressing full-length CTLA4 with a C-terminal mCitrine and CHO cells expressing full-length 41BB or 41BBL were labeled with 5 M CFSE; CFSE and mCitrine were analyzed using the same excitation laser (488 nm) and emission filters (513/26 nm). A total of 1 105 FAST proteinsecreting cells were mixed with 1 105 target cells in cRF5 with 50 M chloroquine and incubated overnight at 37C in 96-well U-bottom plates (Fig. 4A). The next day, the cells were rinsed with PBS, trypsinized, washed, fixed, and resuspended in FACS buffer before running flow cytometry. Cells were gated on forward and side scatter (FSC SSC) and for singlets (FSC-H FSC-A) (Fig. 4B). Data shown in Fig. 4C are representative of N = 3 technical replicates per treatment. Data were collected using a Beckman Coulter MoFlo Astrios and analyzed using FCS Express.

RNA-seq data were generated during previous experiments, aligned against the reference Tasmanian devil genome Devil_ref v7.0 (GCA_000189315.1), and summarized into normalized read counts as previously described (25, 26). Transcripts per millionnormalized read counts were calculated in R, and a heat map was produced from log2-converted values using the heatmap.2 function of gplots.

A total of 50,000 DFT cells per well were aliquoted into U-bottom 96-well plates, washed with 150 l of cRF10, and resuspended in 100 l of warm cRF10 containing 100 M chloroquine. Five micrograms of FAST protein per well was then added and mixed by pipetting. The plates were then incubated at 37C for 30 min. The cells were then transferred to microfuge tubes without washing, stored on ice, and analyzed on a Beckman Coulter MoFlo Astrios (N = 2 per treatment).

CD200 and 41BB FAST proteins were digested overnight with TEV protease (Sigma-Aldrich no. T4455) at 4C in PBS. The cleaved linker and 6xHis tag were then removed using a His SpinTrap kit (GE Healthcare no. 28-9321-71). Digested proteins in PBS were diluted 1:1 in Squalvax (OZ Biosciences no. SQ0010) to a final concentration of 0.1 g/l and were mixed using interlocked syringes to form an emulsion. Immunization of BALB/c mice for antibody production was approved by the University of Tasmania Animal Ethics Committee (no. A0014680). Preimmune sera were collected before subcutaneous immunization with at least 50 l of the emulsion. On day 14 after immunization, the mice were boosted using a similar procedure. On day 50, the mice received a booster with proteins in IFAVax (OZ Biosciences no. IFA0050); mice immunized with CD200 again received subcutaneous injections, whereas 41BB mice received subcutaneous and intraperitoneal injections. Preimmune and sera collected after three-times immunizations were then tested by flow cytometry against CHO cells expressing either 41BB or CD200. CHO cells were prepared as described above, and 2 105 cells were incubated with mouse serum diluted 1:200 in PBS for 30 min at 4C. The cells were then washed two times and stained with 50 l of anti-mouse IgG AF647 diluted 1:1000 in FACS buffer. The cells were then washed two times, stained with 4,6-diamidino-2-phenylindole (DAPI) to identify live cells, and analyzed on a CyAn ADP flow cytometer (Fig. 5C). CD200 and 41BB expression on DFT cells was tested using a procedure similar to the CHO cell staining, except that the sera used were collected after four-times immunizations and was diluted 1:500 and analyzed on the BD FACSCanto II (Fig. 5D).

Approximately, 200 l of NMS or anti-CD200 serum day 157 (after four-times immunizations) was purified using an Ab SpinTrap (GE Healthcare no. 28-4083-47) according to the manufacturers instructions. Serum was diluted 1:1 with 20 mM sodium phosphate and binding buffer (pH 7.0) and eluted with 0.1 M glycine-HCl (pH 2.7), and the pH was neutralized with 0.1 M glycine-HCl (pH 2.7). The eluted antibodies were then concentrated using an Amicon Ultra 0.5 centrifugal unit (Merck no. UFC500308) by centrifuging at 14,000 RCF for 30 min at 4C and then washing the antibodies with 400 l of PBS twice. The protein concentration was then quantified on a NanoDrop spectrophotometer at 280 nm using the extinction coefficients for IgG.

A total of 50,000 DFT cells per well were aliquoted into U-bottom 96-well plates and washed with 200 l of cold FACS buffer. Purified polyclonal anti-CD200 was diluted to 2.5 g/ml in cold FACS buffer, and the cells in appropriate wells were resuspend in 100 l per well (0.25 g per well) diluted antibody; wells that did not receive antibody were resuspended in 100 l of FACS buffer. The cells were incubated on ice for 20 min and then washed with 200 l of FACS buffer. While incubating, anti-mouse IgG AF647 was diluted to 1 g/ml in cold FACS buffer and then used to resuspend cells in the appropriate wells. The plates were incubated on ice for 20 min and then washed with 100 l of cold FACS buffer. The cells were then resuspended in 200 l of FACS fix and incubated on a rocking platform at room temperature for 15 min. The cells were then centrifuged 500 RCF for 3 min at 4C, resuspended in 200 l of FACS buffer, and stored at 4C until they were analyzed on a FACSCanto II (N = 2 per treatment) (Fig. 5E).

Blood collection from Tasmanian devils was approved by the University of Tasmania Animal Ethics Committee (permit no. A0014599) and the Tasmanian Department of Primary Industries, Parks, Water and Environment. Blood was collected from the jugular vein and stored in EDTA tubes for transport to the laboratory. Blood was processed within 3 hours by diluting 1:1 with serum-free RPMI and then layering onto Histopaque (Sigma-Aldrich no. 10771) before centrifuging at 400 RCF for 30 min. The interface containing the PBMCs was then collected using a transfer pipette, diluted with 50 ml of serum-free RPMI, and centrifuged for 5 min at 500 RCF. Cells were washed with again with cRF10 and then either used fresh or stored at 80C until further use.

Frozen devil PBMC was thawed and cultured in cRF10 at 35C with 5% CO2 for 2 hours; cells were then washed in FACS buffer and counted, and 3 105 PBMCs were used per sample. DFT2.JV cells were removed from culture flasks and counted, and 2 105 cells were used per sample. Samples were incubated with 50 l of normal goat serum (Thermo Fisher Scientific, catalog no. 01-6201) diluted 1:200 in FACS buffer for 15 min at 4C, and 50 l of anti-CD200 serum diluted 1:100 was added (1:200 final) for 30 min at 4C. Cells were then washed two times and stained with 50 l of anti-mouse IgG AF647 diluted to 1 g/ml in FACS buffer for 30 min at 4C. The cells were then washed two times, stained with DAPI (Sigma-Aldrich, catalog no. D9542) to identify live cells, and analyzed on the BD FACSCanto II. PBMC and DFT cells were run separately, and then PBMC and DFT2 were mixed at a ratio of 10:1 by volume for the combined samples (N = 1 per treatment) (fig. S4A). The experiment was repeated (N = 1 per treatment), except that PBMCs and DFT cells were mixed at a 5:1 ratio (fig. S4B).

DFT1.C5065 and DFT2.JV cells were labeled with 5 M CTV and cultured for 3 days at 37C. On the day of the assays, peripheral blood from one devil was collected and stored at ambient temperature for less than 3 hours. One hundred microliters of whole blood was aliquoted into 15-ml tubes and stored at ambient temperature while DFT cells were prepared. The media on CTV-labeled DFT cells were decanted, and the cells were detached from the flask by incubating in 2.5 ml of TrypLE Select for 5 min at 37C. The cells were washed with cRF10, resuspended in cRF10, and counted. DFT cells were then diluted to 1 104 cells/ml in cRF10, and 100 l was aliquoted into appropriate 15-ml tubes containing 100 l of whole blood. One microliter of purified anti-CD200 (0.5 g per tube) was diluted into the appropriate tubes and incubated for 15 min at ambient temperature. Next, anti-mouse IgG AF647 (0.5 g per tube) was added to each tube. Note: 0.5 l (0.5 g) of concentrated secondary antibody was accidentally added directly to the tube for the data shown in the top row, middle column of fig. S5A; for all other tubes, the secondary antibody was diluted 1:20 in PBS and 10 l was added to each tube. The cells were then incubated for 15 min at ambient temperature. The cells were then diluted in 1 ml of ammonium chloride RBC lysis buffer [150 mM NH4Cl, 10 mM KHCO3, and 0.1 mM EDTA disodium (Na2-2H2O)] and mixed immediately gently pipetting five times. The cells were incubated at ambient temperature for 10 min, then diluted with 5 ml of PBS, and centrifuged 500 RCF for 3 min. Some tubes contained residual RBCs, so the pellet was vigorously resuspended in 5 ml of RBC lysis buffer, incubated for 5 min, diluted with 5 ml of cold FACS buffer, and centrifuged 500 RCF for 3 min. The cells were then resuspended in 250 l of FACS buffer and stored on ice until analysis on a Beckman Coulter MoFlo Astrios (N = 1 per treatment). Data were analyzed in FCS Express version 6 (fig. S5).

The experiment above was repeated with the following modifications. DFT cells were labeled with 5 M CFSE and incubated for 2 days at 37C. On the day of the assays, fresh blood was collected from two devils. Purified anti-CD200 and NMS were labeled with Zenon mouse IgG AF647 (Thermo Fisher Scientific no. Z25008) and blocked with the Zenon blocking agent. A total of 1 104 CFSE-labeled DFT cells were diluted directly into 100 l of whole blood in 15-ml tubes, and 12 l (2-l antibody, 5-l labeling agent, and 5-l blocking agent) of Zenon AF647labeled purified NMS or anti-CD200 was added directly to the cells. The cells were incubated for 30 min at ambient temperature. The cells were then gently resuspended in 2.5 ml of RBC lysis buffer and incubated for 10 min at ambient temperature. The cells were diluted with 10 ml of PBS and centrifuged 500 RCF for 3 min. The cells were resuspended in 1.5 ml of RBC lysis buffer and incubated for another 10 min to lyse residual RBCs. The tubes were then resuspended in 9 ml of cRF10 and centrifuged 500 RCF for 3 min. The cells were resuspended in 350 l of cold FACS buffer containing DAPI (200 ng/ml) and stored on ice until analysis on a Beckman Coulter MoFlo Astrios (N = 1 per treatment for n = 2 devils) (Fig. 6, A to D).

The anti-human PD-L1 nanobody (KN035) (24) protein sequence was reverse-translated and as a double-stranded DNA gBlock (Integrated DNA Technologies) (table S5). The sequence was modified to include DNA extension that overlaps FAST vectors. The signal peptide from hamster IL-2 (also in pAF92) was added to the nanobody to increase secretion efficiency in CHO cells. The gBlock was inserted into a NotI-HF and Sma Idigested mCitrine FAST vector with NEB HiFi DNA Assembly Master Mix (NEB no. E2621). Transformation, purification of plasmid DNA, and sequencing were performed as described above.

ExpiCHO cells (Thermo Fisher Scientific no. A29127) for high-yield protein production were maintained at 37C with 8% CO2 with constant shaking at 200 rpm in ExpiCHO Stable Production Medium (SPM) (Thermo Fisher Scientific no. A3711001). ExpiCHO cells were added to a six-well plate at 3 105 cells per well in ExpiCHO SPM and cultured overnight. The next day, 2-g plasmid DNA was added to 100 l of PBS in a microcentrifuge tube. PEI was diluted to 60 g/ml in PBS and incubated at room temperature for 5 min. Diluted plasmid DNA was added to 100 l of PEI solution to achieve a 3:1 PEI:DNA ratio and incubated at room temperature for 15 min. During this time, ExpiCHO cells were transferred to 15-ml centrifuge tubes, washed with PBS at 300g for min, resuspended in 3-ml OptiPRO serum-free media (Thermo Fisher Scientific no. 12309019), and returned to the six-well plate. The PEI:DNA solution was then added directly to cells and incubated overnight. The next day, plates were inspected for fluorescence, and the media were removed and replaced with ExpiCHO SPM supplemented with hygromycin (1 mg/ml). Media were changed every second day until selection was complete. Once selection was complete, the cells were moved to 50-ml TPP TubeSpin bioreactor tubes (Sigma-Aldrich no. Z761028) and maintained at 4 106 to 6 106 cells/ml in ExpiCHO SPM with hygromycin (0.2 mg/ml). Supernatant was collected 2 weeks after transfection and stored at 4C.

CHO cells expressing either human PD-L1 or human CD80 fused to miRFP670 (table S3) were plated at 100,000 cells per well into a U-bottom 96-well plate and centrifuged at 300g for 5 min, and the supernatant was discarded. Two hundredmicroliter supernatant containing secreted PD-L1 nanobody was added to CHO cell lines either neat or diluted in 1:10 and 1:100 in FACS buffer. Cells were incubated at 4C for 30 min before being washed in FACS buffer for analysis on a Beckman Coulter FACSCanto II (Fig. 6F).

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