Category Archives: Transhuman News

The following article is an opinion piece written by Mark J. Kiel. The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position of Technology Networks.

The recent publication of the gapless, telomere-to-telomere human genome assembly serves as a real reminder of the significant work that remained to be completed from initial draft genome to the final sequence assembly. Similarly, while we have learned much about causative genetic variants in human disease over the past many years, there is much work that remains to complete this knowledge and to put it into practice.

To fully realize the promise of precision medicine, it is my contention that we must pre-curate every variant in the human genome. It is not scalable for analysts in clinical practice to spend as much as 90 minutes per variant to report the results of a molecular lab test. Pre-curation will make this analysis maximally efficient and reproducible.

A great example of where pre-curation will be beneficial is newborn screening by next-generation sequencing. This new method of screening demands a great deal of information about a multitude of diseases to ensure the utmost accuracy of resulting diagnoses. However, real-time assessment of this information for each patient and each variant will challenge the scalability of this initiative.

Conventional methods for variant curation are laborious, slow, incomplete and error-prone relying as they do on painstaking manual searches for evidence in the scientific and clinical literature. These manual processes too often miss key data and lead to inaccurate conclusions about the clinical significance of a patients variant. Collectively, all of this previous work has led to the pre-curation of just a fraction of human genetic variants. Moreover, these older pre-curations are often inadequate as new knowledge is being created every day requiring continuous updates to existing curations to ensure nothing is missed. If we continue to rely on outmoded techniques, it will not be possible to fully curate the human genome in our lifetimes.

Faster and more comprehensive variant curation will require a combinatorial approach merging the scalability and sensitivity of AI and the specificity and accuracy that can only come from the expert judgment of experienced human curators. AI-driven indexing of the scientific and clinical literature can ensure more complete information for each variant, while AI-informed expert curation delivers maximum specificity for results in a maximally efficient manner.

The primary bottleneck to achieving accurate and comprehensive variant curation is the need to manually locate, assess, annotate and document evidence from the scientific and clinical literature. In particular, the nuances of the genetic code and the idiosyncrasies of genetic nomenclature and other complexities of biology make it difficult to disambiguate terminology and ensure that curations are correct and complete.

AI is a natural solution to these challenges. Only computational approaches can meet the scale and sensitivity requirements of this ambitious genome-wide variant curation. There is precedent for using AI to index vast amounts of data often unstructured and poorly organized data so it is an excellent fit for indexing the scientific and clinical literature. Paying attention to and resolving genetic ambiguities and focusing on the most critical clinical information is paramount.

A team of highly trained experts is necessary to carefully assess the assembled evidence and make an informed judgment about its appropriateness and applicability, as well as ensure the utmost accuracy of all final interpretations. This review process can be further accelerated by AI-driven organization and annotation of the data.

This approach is already making it possible to deliver new insights about disease-causing variants in patients. In one example, scientists at the Rare Genomics Institute reanalyzed previously inconclusive exome results using an AI-powered tool and found a single scientific report that allowed them to classify a key variant as pathogenic and produce an evidence-based diagnosis and effective treatment for the patient.

With the speed and scale afforded by AI technology, it will be possible within the next several years to curate the entire human genome. With a combinatorial approach that brings together the best of automated AI and expert curation, we can firmly establish the foundation of genomic intelligence needed to make precision medicine a reality for all patients.

About the author:

Mark Kiel is chief scientific officer and co-founder of Genomenon, an AI genomics company. He has extensive experience in genome sequencing and clinical data analysis. Mark is a molecular genetic pathologist having received his MD/PhD in stem cell biology and cancer genomics from the University of Michigan.

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Curation of the Entire Human Genome Requires the Best of Both Human and Artificial Intelligence - Technology Networks

The release of the human reference genome in 2001 established a foundation to explore the evolution of the human genome and how it influences human health. A persons genome has over 6 billion base pairs (the chemical building blocks of DNA represented by the letters CGAT). Overall human genomes are extremely similar. However, small differences play a role in making each individual unique, including their health. A reference genome helps identify these differences by providing a map of where genes are on the genome and what many of the genetic differences mean.

The current reference genome is a composite created from the DNA sequences of about 20 individuals, with most of the sequence coming from one person. As a result, the reference genome does not reflect global genomic diversity. It is also incomplete. While some of the original gaps have been filled in, more than 150 million bases are missing. For example, a study of 910 people of African ancestry compared their genomes to the reference genome, and found that almost 10% of the information contained within their genomes was not present in the reference.

The current reliance on a single, and not quite complete, reference genome clearly needs addressing as sequences that differ considerably from the reference can be incorrectly interpreted leading to incomplete understanding of the genomic causes of disease and hindering improvements in clinical care. To tackle these issues global collaborative sequencing projects such as the Human Pangenome Project are using the latest developments in sequencing technology to fill in the missing gaps and create a human reference genome that better reflects global genomic diversity.

The gaps in the human genome exist in regions that are hard to sequence using current technologies. However, with recent advancements such as long-read sequencing technologies, scientists are now able to interrogate these regions. They can be highly repetitive sections of genetic sequence or structural variants, where large sections of DNA have moved position. It is important to understand these regions because they have been associated with several diseases.

Long-read sequencing reads longer sections of the genome in one go, making it easier to piece together these challenging genome sequences in the correct order, creating a more reliable and accurate reference. A major achievement has been the use of extremely long and highly accurate sequence reads to reconstruct entire human chromosomes from telomere to telomere (T2T). But, despite the success of the T2T assembly, it does not capture the diversity of sequences across populations.

Pangenome research aims to broaden the reference genome to represent genomic diversity within and across all human populations, vital to addressing the imbalance in population representation in genomic data. The Human Pangenome Project will utilise the latest advances in sequencing technologies including long-read, single-cell, and advanced imaging to meet its initial goals of creating 350 highly detailed genome sequences. In time, expanding to sequence thousands of genomes to capture as much human genetic diversity as possible.

The Project is a big science effort, requiring collaboration between multidisciplinary teams of scientists as well as policy experts and ethicists to navigate the technological and societal challenges that will be encountered when collecting data for this project.

The genomes of different individuals and populations harbour a wealth of information on humanitys responses to historical environmental and biological pressures. Some of these genetic differences have no effect on a persons health whilst others can have a profound effect. It is this molecular diversity that underlies genetic disorders, inherited traits and disease susceptibility. Diversity in genomic research has numerous benefits ranging from novel insights into health disparities, better understanding of human biology, improving clinical care, and informing genetic diagnosis.

In addition, therapies and drugs developed using genetic data from specific populations that share the same genetic ancestry will most likely work best in those populations. By examining previously underrepresented populations, new ancestry-specific associations for different diseases could be found, which also furthers the understanding of the genetic background of traits.

Apart from the scientific advancements, inclusion is a matter of justice; individuals benefit most from research conducted in those with a similar ancestral background to them. Including diverse populations in genomic research is the right thing to do for reasons of equity. It will ensure that all populations can benefit from genomic knowledge and its impact on healthcare.

To increase diversity in genomic datasets there needs to be an acknowledgment and understanding that many of the groups that are underrepresented suffer from health inequalities. Past events have significantly impacted on the publics perception of genomic research, particularly with abuses of genomic data from certain populations. For example, the Havasupai Tribe where DNA was donated for studies on type 2 diabetes, but was then used without their consent for studies on schizophrenia and migration. This resulted in a lawsuit and the Navajo Nation placing a moratorium on genetic research studies, which is now being reconsidered. Some communities have set out codes of conduct and guidelines of how the scientific community is expected to engage with them, for example the Global Code of Conduct for Research. There are also ongoing challenges around informed consent, privacy, and data sharing.

Population sampling for pangenome efforts require purposeful engagement with communities, fair representation, careful policy and ethical guidance which should ensure respectful partnerships with communities and participants. The Project is tackling this by having social ethicists embedded in the decision-making processes and their continuous vetting within the project. The Project is also encouraging scientists within Indigenous population to generate their own reference sequences. A number of countries have launched their own population-specific projects that aim to produce high-quality reference genomes using their own frameworks for sample collection and consent.

There are many challenges that the Human Pangenome Project will need to overcome, but when done, the release of the pangenome will be a major upgrade in the reference genome. It is expected to accelerate genotype-to-phenotype studies, drive technology innovation and enable a new era of human biomedical research. By being more inclusive and representative of the global population there will be better understanding of disease and how clinical care can be improved, for all. It will transform the way that basic and clinical research is done, while leading to improved standards for genomics research, data sharing, and reproducible workflows. The results of the project are highly anticipated and could create an important shift in how genomics research is done and used in healthcare.

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Bringing diversity to the reference genome - PHG Foundation

The Human Genome Project was an ambitious initiative to sequence every piece of human DNA. The project drew together collaborators from research institutions around the world, including MIT's Whitehead Institute for Biomedical Research, and was finally completed in 2003. Now, over two decades later, MIT Professor Jonathan Weissman and colleagues have gone beyond the sequence to present the first comprehensive functional map of genes that are expressed in human cells. The data from this project, published online June 9 in Cell, ties each gene to its job in the cell, and is the culmination of years of collaboration on the single-cell sequencing method Perturb-seq.

The data are available for other scientists to use. Its a big resource in the way the human genome is a big resource, in that you can go in and do discovery-based research, says Weissman, who is also a member of the Whitehead Institute and an investigator with the Howard Hughes Medical Institute. Rather than defining ahead of time what biology you're going to be looking at, you have this map of the genotype-phenotype relationships and you can go in and screen the database without having to do any experiments.

The screen allowed the researchers to delve into diverse biological questions. They used it to explore the cellular effects of genes with unknown functions, to investigate the response of mitochondria to stress, and to screen for genes that cause chromosomes to be lost or gained, a phenotype that has proved difficult to study in the past. I think this dataset is going to enable all sorts of analyses that we haven't even thought up yet by people who come from other parts of biology, and suddenly they just have this available to draw on, says former Weissman Lab postdoc Tom Norman, a co-senior author of the paper.

Pioneering Perturb-seq

The project takes advantage of the Perturb-seq approach that makes it possible to follow the impact of turning on or off genes with unprecedented depth. This method was first published in 2016 by a group of researchers including Weissman and fellow MIT professor Aviv Regev, but could only be used on small sets of genes and at great expense.

The massive Perturb-seq map was made possible by foundational work from Joseph Replogle, an MD-PhD student in Weissmans lab and co-first author of the present paper. Replogle, in collaboration with Norman, who now leads a lab at Memorial Sloan Kettering Cancer Center; Britt Adamson, an assistant professor in the Department of Molecular Biology at Princeton University; and a group at 10x Genomics, set out to create a new version of Perturb-seq that could be scaled up. The researchers published a proof-of-concept paper in Nature Biotechnology in 2020.

The Perturb-seq method uses CRISPR-Cas9 genome editing to introduce genetic changes into cells, and then uses single-cell RNA sequencing to capture information about the RNAs that are expressed resulting from a given genetic change. Because RNAs control all aspects of how cells behave, this method can help decode the many cellular effects of genetic changes.

Since their initial proof-of-concept paper, Weissman, Regev, and others have used this sequencing method on smaller scales. For example, the researchers used Perturb-seq in 2021 to explore how human and viral genes interact over the course of an infection with HCMV, a common herpesvirus.

In the new study, Replogle and collaborators including Reuben Saunders, a graduate student in Weissmans lab and co-first author of the paper, scaled up the method to the entire genome. Using human blood cancer cell lines as well noncancerous cells derived from the retina, he performed Perturb-seq across more than 2.5 million cells, and used the data to build a comprehensive map tying genotypes to phenotypes.

Delving into the data

Upon completing the screen, the researchers decided to put their new dataset to use and examine a few biological questions. The advantage of Perturb-seq is it lets you get a big dataset in an unbiased way, says Tom Norman. No one knows entirely what the limits are of what you can get out of that kind of dataset. Now, the question is, what do you actually do with it?

The first, most obvious application was to look into genes with unknown functions. Because the screen also read out phenotypes of many known genes, the researchers could use the data to compare unknown genes to known ones and look for similar transcriptional outcomes, which could suggest the gene products worked together as part of a larger complex.

The mutation of one gene called C7orf26 in particular stood out. Researchers noticed that genes whose removal led to a similar phenotype were part of a protein complex called Integrator that played a role in creating small nuclear RNAs. The Integrator complex is made up of many smaller subunits previous studies had suggested 14 individual proteins and the researchers were able to confirm that C7orf26 made up a 15th component of the complex.

They also discovered that the 15 subunits worked together in smaller modules to perform specific functions within the Integrator complex. Absent this thousand-foot-high view of the situation, it was not so clear that these different modules were so functionally distinct, says Saunders.

Another perk of Perturb-seq is that because the assay focuses on single cells, the researchers could use the data to look at more complex phenotypes that become muddied when they are studied together with data from other cells. We often take all the cells where gene X is knocked down and average them together to look at how they changed, Weissman says. But sometimes when you knock down a gene, different cells that are losing that same gene behave differently, and that behavior may be missed by the average.

The researchers found that a subset of genes whose removal led to different outcomes from cell to cell were responsible for chromosome segregation. Their removal was causing cells to lose a chromosome or pick up an extra one, a condition known as aneuploidy. You couldn't predict what the transcriptional response to losing this gene was because it depended on the secondary effect of what chromosome you gained or lost, Weissman says. We realized we could then turn this around and create this composite phenotype looking for signatures of chromosomes being gained and lost. In this way, we've done the first genome-wide screen for factors that are required for the correct segregation of DNA.

I think the aneuploidy study is the most interesting application of this data so far, Norman says. It captures a phenotype that you can only get using a single-cell readout. You cant go after it any other way.

The researchers also used their dataset to study how mitochondria responded to stress. Mitochondria, which evolved from free-living bacteria, carry 13 genes in their genomes. Within the nuclear DNA, around 1,000 genes are somehow related to mitochondrial function. People have been interested for a long time in how nuclear and mitochondrial DNA are coordinated and regulated in different cellular conditions, especially when a cell is stressed, Replogle says.

The researchers found that when they perturbed different mitochondria-related genes, the nuclear genome responded similarly to many different genetic changes. However, the mitochondrial genome responses were much more variable.

Theres still an open question of why mitochondria still have their own DNA, said Replogle. A big-picture takeaway from our work is that one benefit of having a separate mitochondrial genome might be having localized or very specific genetic regulation in response to different stressors.

If you have one mitochondria thats broken, and another one that is broken in a different way, those mitochondria could be responding differentially, Weissman says.

In the future, the researchers hope to use Perturb-seq on different types of cells besides the cancer cell line they started in. They also hope to continue to explore their map of gene functions, and hope others will do the same. This really is the culmination of many years of work by the authors and other collaborators, and Im really pleased to see it continue to succeed and expand, says Norman.

Originally posted here:
New CRISPR-based map ties every human gene to its function - MIT News

We didn't see any indication of their drinking coming back to baseline, so we think that maybe this epigenetic editing will produce a long-lasting effect, Pandey says. I think a lot more work needs to be done in terms of how this can be translated into humans for a therapy, but I have high hopes.

To test that the Arc gene was truly responsible for this outcome, the researchers also designed a Crispr injection meant to decrease its expression. They tested it in rats that werent exposed to alcohol in adolescence. Following the injection, the rats had more anxiety and consumed more alcohol than they did before.

The study raises the possibility that our molecular memory could be revisedor even erased. I'm struck deeply by this work showcasing the feasibility of changing a gene's memory of its experience, says Fyodor Urnov, a professor of genetics at the UC Berkeley and scientific director at the Innovative Genomics Institute of UC Berkeley and UC San Francisco. But, he continues, rats arent humans, and we shouldnt leap to conclusions. The distance between curing a rat and injecting a human being with addiction to alcohol with an epigenetic editor is a formidable one, says Urnov. I think that we are quite a ways away from somebody who has developed a mild drinking problem becoming eligible for a quick injection into their amygdala.

That said, Urnov, who is also the cofounder of Tune Therapeutics, an epigenetic editing company, could see an experimental therapy like this being tested among people with alcohol addiction who have relapsed from treatment several times and have no other therapeutic options left.

Yet, as with directly editing genes, there could be unintended consequences of tweaking their expression. Because Arc is a regulator gene involved in brain plasticity, modifying its expression could have effects beyond alcohol addiction. We don't know what other behaviors are altered by this change, says Betsy Ferguson, a professor of genetics at Oregon Health and Science University who studies epigenetic mechanisms in addiction and other psychiatric disorders. Its a balance between finding something that's effective and something that's not disruptive to everyday life.

Another complicating factor is that the expression of dozens, perhaps hundreds, of genes are altered by alcohol use over time. In people, it may not be as simple as turning up the expression of Arc, which is only one of them. While it may seem like the solution would be to tweak all of those genes, manipulating the expression of many at once could cause problems. Knowing that behaviors, including alcohol use behaviors, are regulated by a number of genes, it's really a challenging problem to solve, Ferguson says.

And its not clear how long the effects of such editing might last. Epigenetic changes that occur naturally can be temporary or permanent, says Ferguson. Some can even be passed onto future generations. Overall, she finds the idea of using epigenetic editing to treat alcohol addiction fascinating, but shed want to see the results replicated and the Crispr treatment tried in larger animals that more closely mimic humans.

That day may not be too far off, as a handful of companies have recently launched to commercialize epigenetic editing. At Navega Therapeutics, which is based in San Diego, researchers are studying how to treat chronic pain by turning down the expression of a gene called SCN9A. When its highly expressed, it sends out lots of pain signals. But it would be a bad idea to simply delete this gene, because some amount of pain is useful; it signals when something is going wrong within the body. (In rare cases, people with an SCN9A mutation that effectively renders it inactive are immune to pain, which makes them vulnerable to injuries they arent able to sense.) In experiments at Navega, epigenetic editing in mice seemed to repress pain for several months.

Urnovs Tune Therapeutics, meanwhile, plans to use epigenetic editing for a broad range of conditions, including cancer and genetic diseases. Though Urnov doesnt see epigenetic editing as the antidote to binge drinking, he thinks this proof-of-concept study shows that it may be possible to rewire our genes experiences to reverse some of the damage of early alcohol abuse. It is empowering, frankly, to consider the fact that we now have genome editing to fight a drugs pernicious action right at the venue where the drug inscribes its memories onto the brain, he says.

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A New Kind of Genome Editing Is Here to Fine-Tune DNA - WIRED

Results provide foundational proof-of-concept for a unique target ID in glioblastoma, build further confidence in Mnemo's EnfiniT Discovery Engine

PARIS, June 9, 2022 /PRNewswire/ -- Mnemo Therapeutics, a biotechnology company developing transformational immunotherapies, today announced a publication in Cell Reportsthat demonstrates peptides derived from transposable elements (TEs) mobile regions of DNA that move to other areas of the genome may represent cancer-specific targets for immunotherapy. The article is authored by a team of researchers working under the leadership of Sebastian Amigorena, Ph.D., CNRS research director, scientific co-founder of Mnemo and head of the Immune Responses and Cancer Team at Institut Curie (Immunity and Cancer Unit Institut Curie/Inserm).

"Mnemo is committed to comprehensively tackling the challenges facing immunotherapies, including the lack of suitable cancer-specific targets," said Mnemo Chief Executive Officer, Robert LaCaze. "We are extremely encouraged by the data put forth in this publication which validates our unique target ID approach and informs the future of Mnemo's science."

Traditionally, the human genome has been broadly broken down into two main categories the 4%, known to encode for proteins, and the 96%, with elements either noncoding and/or poorly understood, also known as the "dark genome." The data demonstrated in this publication provides compelling evidence to suggest that novel cancer targets can be found outside of the 4%, necessitating the expansion of our definition to include the "grey genome." The "grey genome", which accounts for around 45% of the human genome, is composed of TEs, long non-coding RNAs and a few other non-coding transcripts. Increased transcription of TEs is found to occur in some tumor cells, suggesting they may encode for cancer antigens. The publication, titled, "Single-cell RNA-seq-based proteogenomics identifies glioblastoma-specific transposable elements encoding HLA-I-presented peptides," explored this connection, demonstrating that these DNA features may hold the key to unlocking new targets for immunotherapy.

"Known targets are encoded by a very small percentage of the human genome, leaving vast regions rich with potential tumor-specific targets uncharacterized and unexplored," said Dr. Amigorena. "The research in this publication provides foundational evidence that we can probe the grey genome to identify features with promising potential to encode for cancer-specific targets."

In this study, researchers leveraged a sophisticated proteogenomic approach to explore the grey genome, in a search to uncover TE-derived peptides that can act as suitable cancer targets. Additional results are outlined below.

"While initially tested in glioblastoma, this approach is applicable across cell-types, laying the groundwork for its application in other cancer models for indication-specific antigen identification," Mnemo Co-Founder and Chief Operating Officer, Alain Maiore. "This research further validates Mnemo's EnfiniT Discovery Engine, which aims to overcome the key challenges facing current immunotherapies by enhancing therapeutic precision, durability, and potency."

References:Single-cell RNA-seq-based proteogenomics identifies glioblastoma-specific transposable elements encoding HLA-I-presented peptides.Pierre-Emmanuel Bont, Yago A. Arribas, Antonela Merlotti, Montserrat Carrascal, Jiasi Vicky Zhang, Elina Zueva, Zev A. Binder, Ccile Alanio, Christel Goudot, Sebastian Amigorena.

About Mnemo Therapeutics

Mnemo is developing transformational immunotherapies to improve the body's ability to fight and overcome cancer. Integral to Mnemo's approach is the EnfiniT Discovery Engine, composed of key technologies that work to identify novel cancer-specific antigens and enhance immune cells' memory and persistence. Mnemo will harness these technologies with multiple modalities across a range of oncology indications, engineering the future of immunotherapies to transform the lives of people with cancer. Mnemo is headquartered in Paris with an office in New York City, and it maintains state of the art laboratories in Paris, New York, and Princeton, New Jersey. The company leveragesan international talent pool and global resources in its quest to create immunological cures.

To learn more, visit https://mnemo-tx.comand follow Mnemo Therapeutics on Twitter (@MnemoTx) and LinkedIn.

SOURCE Mnemo Therapeutics

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Landmark Publication Further Validates Approach to Expand the Potential Universe of Cancer-specific Targets by Probing the "Grey Genome" -...

A genome-wide view of antibiotic sensitivity

To obtain a genome-wide view of the genetic determinants that can modulate antibiotic stress in S. pneumoniae, Tn-Seq was employed in the presence of 20 antibiotics (ABXs), representing 9 different ABX groups and four classes including cell wall synthesis inhibitors (CWSIs), DNA synthesis inhibitors (DSIs), 30S and 50S protein synthesis inhibitors (PSIs) and an RNA synthesis inhibitor (RSI) (Fig.1a). Six independent transposon libraries were generated and grown for approximately 8 generations in the absence and presence of an antibiotic at a concentration that reduces growth by approximately 3050% (Supplementary Data1). Tn-mutant frequencies are determined through Illumina sequencing from the beginning and end of the experiment with high reproducibility between libraries (R2=0.700.90; Supplementary Fig.1) which is consistent with previous Tn-Seq experiments6,15,16,18,39,40,41,42. Combined with the population expansion during the experiment each mutants fitness (WMT) is calculated to represent their environment-specific relative growth rate, which means that a mutant with for instance a fitness of 0.5 (WMT=0.5) grows twice as slow as the wild type (WT)6,18,39,43,44. Each genes antibiotic-specific fitness is statistically compared to baseline fitness without ABXs, and is represented as W (WABX WnoABX) and categorized as: (1) Neutral, W=0, a mutants relative growth is similar in the absence and presence of an ABX; (2) Negative, W<0, a mutants fitness is significantly lower and thus grows relatively slower in the presence of an ABX; (3) Positive, W>0, a mutants fitness is significantly higher and thus grows relatively faster in the presence of an ABX. All antibiotics trigger both positive and negative fitness effects (Fig.1b, Supplementary Data2), which are distributed across 22 different gene categories (Fig.1c). Importantly, enrichment analysis shows there are multiple expected patterns, for instance genes involved in DNA repair are enriched in the presence of fluoroquinolones; cell-wall, peptidoglycan, and cell-division genes are enriched in -lactams and glycopeptides; membrane integrity genes in lipopeptides; and transcription and translation in PSIs (Fig.1d). Additionally, throughout the manuscript, we validate a total of 49 predicted genotype phenotype interactions, which indicates the Tn-Seq data is of high quality and in line with previously shown accuracy6,15,16,18,39,40,41,42 (Fig.1e, Supplementary Data8).

a Project setup and overview. Tn-Seq is performed with S. pneumoniae TIGR4, which is exposed to 20 antibiotics at a concentration that reduces growth by 3050%. Genome-wide fitness is determined for each condition, suggesting a multitude of options exists to increase as well as decrease antibiotic sensitivity. A co-fitness network is constructed by adding Tn-Seq data from 17 additional conditions, which through a SAFE analysis highlights functional clusters, and connects known and unknown processes. The genome-wide atlas and network are used to develop an antibiotic-antibody combination strategy, and to map out the wide-ranging options that can lead to decreased antibiotic sensitivity in vitro and in vivo and that are associated with a higher rate of stop codons in clinical samples. b There are a large number of genetic options that can modulate antibiotic sensitivity; with significant increased (W<0.15) and decreased sensitivity (W>0.15) split over all antibiotics almost equally likely. c Additionally, increased and decreased antibiotic sensitivity are distributed across a wide variety of functional categories. d Enrichment analysis shows that some pathways/processes such as glycolysis are relatively often involved in modulating responses to antibiotics, while other processes are more specific. e Validated growth experiments (n3 independent experiments) performed throughout the project highlight the Tn-Seq data is of high quality. SEM are shown for each data point. Source data are provided as a Source Data file.

Screens such as Tn-Seq are geared toward highlighting the genetic regions and/or genes that are important under a specific screening condition. With increasing conditions, genes acquire profiles that reflect their involvement/importance in those conditions, where genes with similar profiles indicate having similar and/or shared tasks. Such profiles can thereby help fill gaps in pathways, and/or identify genes and gene clusters with similar roles. By building a correlation matrix based on each genes ABX fitness-profile patterns emerge along a similarity range; from genes with highly similar to contrasting profiles. Moreover, to increase statistical power (i.e., more conditions increases the ability to identify more and stronger associations) the ABX dataset was supplemented with previously collected Tn-Seq data from 17 additional non-antibiotic conditions18 (Supplementary Data3). This results in a 15191519 gene matrix where positive correlations between genes come from shared phenotypes (i.e., similar profiles), while negative correlations come from opposing phenotypic responses under the same condition (i.e., contrasting profiles; Supplementary Data4). By repeatedly hiding random parts of the data the stability and strength of each correlation is calculated and represented in a stability score (Supplementary Data5). The correlation matrix and stability score are turned into a network, where each node is a gene, and each edge is a correlation coefficient above a threshold (>0.75), which combined with the stability score indicates the strength of the relationship between two genes. (Fig.2a; Supplementary Data6). Spatial Analysis of Functional Enrichment (SAFE)45,46 is used to define local neighborhoods within the network, i.e., areas enriched for a specific attribute (e.g., a pathway or functional category), which identifies multiple clusters that represent specific pathways and processes including purine metabolism, cell-wall metabolism, cell division and DNA repair (Fig.2b; Supplementary Data7). Moreover, the network contains gene clusters of high connectivity identifying highly related genes including those within the same operon such as the ami-operon, an oligopeptide transporter, the dlt-operon which decorates wall and lipoteichoic acids with d-alanine, and the pst-operon a phosphate transporter (Fig.2c, IIII). Besides identifying known relationships, the network also uncovers interaction clusters between genes with known and unknown interactions and function. Several such clusters are highlighted in Fig.2c (IVVIII), including genes involved in purine metabolism (further explored below), threonine metabolism, and in secretion of serine-rich repeat proteins (SRRPs), which are important for biofilm formation and virulence47. Importantly, the identification of biologically relevant relationships among (clusters of) genes indicates the data is rich in known and new information.

a A 15191519 gene correlation matrix based on Tn-Seq data from 37 conditions generates a network with genes as nodes, and edges as interactions with a stability score and thresholded correlation >0.75. The network contains one large connected component and multiple smaller components placed underneath; b A SAFE analysis identifies at least 11 clusters within the network that represent specific pathways and processes; c The network contains highly connected clusters of smaller groups of genes for instance those within the same operon such as cluster: I. the ami-operon (unknown transport); II. the dlt-operon; and III. the pst-operon (phosphate transport). Several additional clusters are highlighted containing annotated and unannotated genes, connected through known and unknown interactions including cluster: IV. containing genes involved in purine metabolism and a putative deoxyribose transporter (boxed 1.); V. containing genes involved in threonine metabolism and several genes located as neighbors to SP_2066/thrC with unclear functions (boxed 2), including a regulator (SP_2062) and a transporter (SP_2065); VI. containing genes involved in secretion of serine-rich repeat proteins. Source data are provided as a Source Data file.

Two hundred and twenty-four genes with a known annotation are present in the data that have at least one significant phenotype in response to an antibiotic, which can be split over 21 functional groups according to a pathway or process they belong to (Fig.3a). Each group is characterized by having multiple instances of decreased fitness, indicating genes that upon disruption increase sensitivity to one or more antibiotics (negative phenotype). Additionally, each group, except for cell division, also has multiple instances that increase fitness, which is suggestive of genes that upon disruption decrease antibiotic sensitivity (Fig.3a; positive phenotype). Moreover, each antibiotic group triggers both negative and positive effects (Fig.3b). Where possible, the 21 functional groups are organized according to a pathway or process they belong to and each gene is combined with its antibiotic susceptibility profile. This results in an antibiotic susceptibility atlas, which shows on a fine-grained scale, how inhibiting a pathway or process can seemingly simultaneously lead to increased and decreased drug susceptibility in an antibiotic-specific manner (Fig.3c and Supplementary Figs.2 and 3). For instance, in the glycolysis group, knocking out any of the three genes involved in forming the phosphotransferase (PTS)-system (SP_0282-SP_0284) that imports glucose to generate glucose-6-phosphate (G-6P), has a negative effect on fitness in the presence of 30S and 50S PSIs as well as Synercid (a synergistic combination of two PSIs), while it increases fitness in the presence of all CWSIs (-lactams, glycopeptides, and daptomycin) and fluoroquinolones. Also, knocking out SP_0668 (gki, glucokinase), an enzyme that converts -D-Glucose into G-6P, has a positive effect on fitness in all CWSIs and a negative effect in 30S PSIs. In contrast, inhibiting SP_1498 (pgm, phosphoglucomutase), the major interconversion enzyme of G-6P and G-1P, has a negative effect on fitness with all antibiotics (Fig.3c). Additional detailed examples are highlighted in Fig.3c, for instance for pyruvate metabolism, where inhibiting lactate, or acetaldehyde production increases sensitivity to -lactams and glycopeptides and decreases sensitivity to 30S PSIs, inhibiting formate production decreases sensitivity to co-trimoxazole and 30S PSIs, and inhibiting acetyl-phosphate production decreases sensitivity to -lactams, glycopeptides, and co-trimoxazole. Within aspartate metabolism a range of changes can be triggered from increased sensitivity to -lactams, and glycopeptides, to decreased sensitivity to most other antibiotics. Moreover, the four genes involved in the production of threonine from L-aspartate trigger decreased sensitivity to fluoroquinolones and 30S and 50S PSIs. In the shikimate pathway inhibiting the production of chorismate from phosphoenolpyruvate (PEP) and erythrose-5-phosphate leads to increased sensitivity to -lactams, co-trimoxazole, and Synercid. Cell division is the only process that upon interference, only generates increased sensitivity, specifically to CWSIs and co-trimoxazole. Interfering with peptidoglycan synthesis also mostly leads to increased sensitivity to CWSIs, as well as to 30S PSIs, while changes to genes that are involved in anchoring proteins to the cell wall (SP_1218 [srtA], SP_1833) can decrease sensitivity to CWSIs. Lastly, interfering with protein turnover, for instance through the protease complex ClpCP (SP_2194, SP_0746) and the regulator CtsR (SP_2195), which are generally assumed to be fundamental for responding to stress48,49, leads to decreased CWSI sensitivity and increased sensitivity to 30S and 50S PSIs (Fig.3c and Supplementary Fig.2). Moreover, FtsH (SP_0013), important for clean-up of misfolded proteins from the cell wall, increases sensitivity to 30S PSIs and Synercid, indicating how important protein turnover is especially for surviving 30S PSIs, which can trigger the production of faulty proteins. Most importantly, these data show that, as expected, hundreds of options exist where disruption of a pathway or process leads to increased sensitivity to specific antibiotics. Remarkably, there seem to be almost as many options that can lead to decreased antibiotic sensitivity.

a The number of phenotypes scored for each pathway/process. Genes with at least one significant phenotype are split over 21 groups according to a pathway or process they belong to, which highlights how modulation of most pathways can lead to increased (negative phenotype) and decreased (positive phenotype) antibiotic sensitivity. b The number of phenotypes scored for each antibiotic group. While sensitivity to each antibiotic (group) can be increased by knocking out genes in the genome (negative phenotype), sensitivity can be decreased (positive phenotype) almost as often for most ABXs, except for Synercid, and to a lesser extent rifampicin, where most effects are negative. c Detailed view of 7 out of 21 groups/processes highlighting how modulation of specific targets within each process leads to changes in antibiotic sensitivity. Each group is indicated with a number that is the same as in a. Where possible, genes are ordered according to their place in a process/pathway, and gene numbers (SP_) are combined with gene names and annotation. Each indicated gene is combined with an antibiotic sensitivity bar indicating whether disruption leads to increased (red/negative fitness) or decreased (green/positive fitness) sensitivity to a specific or group of antibiotics. When phenotypic responses are the same, multiple genes are indicated with a single bar (e.g. SP0282/SP0283/SP0284 in glycolysis, or SP0413/SP1013/SP1361/SP1360 in Aspartate metabolism). Gene numbers in blue have no effect on growth in the absence of antibiotics when knocked out, while gene numbers in purple have a significant growth defect in the absence of ABXs (see for detailed fitness in the absence and presence of antibiotics Supplementary Data2). Essential genes are not indicated and genes with an asterisk have a partial or tentative annotation that has not been resolved. All 21 groups are listed in Supplementary Figs2 and 3. Source data are provided as a Source Data file.

By identifying targets that (re)sensitize bacteria against existing antibiotics, genome-wide antibiotic susceptibility data have the potential to guide the development of new antimicrobial strategies. One such strategy could be a combined therapeutic antibody-antibiotic approach; the antibody would target a gene product that is important for sensitivity to one or more antibiotics and ideally the product would be easily accessible for the antibody at the bacterial cell surface. To find suitable candidate targets, Tn-Seq data were filtered for gene products that, based on a known function or localization prediction, are likely to be present in the cell wall or membrane, and that when disrupted, increase sensitivity to one or more antibiotics. Moreover, it would likely be ideal if the gene is also important for survival in vivo. A strong candidate is SP_1505, which in the interaction network is most tightly linked to cell wall metabolism and cell-division genes (Fig.4a). After we previously hypothesized that it may play a role in cell wall integrity14, it was recently named cozEb, with a likely role in organizing peptidoglycan synthesis during cell division50, which fits its interaction profile (Fig.4a). Importantly, the antibiotic Tn-Seq data suggest that disruption creates increased sensitivity to vancomycin and rifampicin, while the product is critical in the presence of daptomycin, which was confirmed through individual growth curves (Fig.4b). The protein has eight predicted membrane-spanning domains (Fig.4c), and in vivo Tn-Seq predicts it is important for survival in both the nasopharynx and lungs (Fig.4a, Supplementary Data2). The gene was cloned into an expression plasmid generating an ~30kD product (Fig.4c), which was used to raise rabbit anti-CozEb antibodies, which were confirmed to be specific for the cozEb gene product (Fig.4c). Potential antibody in vitro activity was determined through a bacterial survival assay in the absence and presence of antibodies and either vancomycin or daptomycin. Incubating bacteria with antibodies or daptomycin has no significant effect on bacterial survival, while vancomycin alone at the concentration used slightly reduces the number of surviving bacteria. Moreover, combining the antibody with either vancomycin or daptomycin further reduces the number of surviving bacteria in vitro compared to any agent individually (Fig.4d). To assess whether the antibody-antibiotic approach works in vivo, mice were intranasally challenged with a bacterial inoculum either containing WT or cozEb. Two additional sets of mice were challenged with WT and 8h post infection they were either treated with daptomycin and control IgG antibody or with daptomycin and CozEb-specific antibody. Mice were sacrificed 24h post infection, and bacteria in the lungs were enumerated. As predicted by the in vivo Tn-Seq data the cozEb knockout has a significantly lower fitness in the lungs highlighted by an up to 2.5-log lower bacterial load compared to WT. Importantly, while the WT survives equally well in the presence of the low daptomycin concentration and the control IgG antibody, in the presence of daptomycin and the CozEb-targeting antibody, its survival in the lungs is significantly reduced and resembles that of the cozEb knockout (Fig.4e). This shows that by combining antibiotic and in vivo Tn-Seq with gene annotation information, a gene product can be selected that is central and critical to cell-wall synthesis and cell-division processes. Importantly, due to its presence in the membrane, it is directly targetable with an antibody, thereby sensitizing the bacterium to an antibiotic concentration it is normally not sensitive to.

a cozEb/SP_1505 is tightly clustered with cell division and cell wall metabolism genes, it is predicted to increase sensitivity to glycopeptides and the lipopeptide daptomycin, and has a decreased fitness in the mouse lung and nasopharynx. b Reduced relative growth of cozEb validates its increased sensitivity to daptomycin and vancomycin. c CozEb has 8 transmembrane domains, which generates a ~30Kd product (BSA is shown as a control). The cloned protein was used to raise antibodies, which proofed to be specific for a product in the WT membrane, but does not bind anything in cozEb, indicating the antibodies are specific for the membrane protein CozEb. d Incubation of WT for 2h with vancomycin (Vanco) or daptomycin (Dapto) and in the presence of CozEb antibody, slightly but significantly decreases bacterial survival. Mean values SEM are shown from n3 independent experiments. e An in vivo lung infection with WT or cozEb confirms the mutant is less fit in vivo. Challenging the WT with daptomycin and IgG does not affect bacterial survival. In contrast, challenging with daptomycin and CozEb-specific antibodies, significantly reduces the recovered CFUs 24h post infection. Mean valuesSEM are shown from n10 mice/experiment. Significance is measured through a one-way ANOVA with Dunnett correction for multiple testing: *p=0.03, **p=0.001, ***p<0.001. Source data are provided as a Source Data file.

The example above illustrates how negative fitness indicates increased antibiotic sensitivity reflected by reduced relative growth, which can guide the development of (re)sensitizing approaches. In contrast, the occurrences of increased fitness in the dataset indicate that a large number of options exist that could lead to reduced antibiotic sensitivity (Fig.3). With increased fitness to 3 out of 4 antibiotic classes, the ami-operon is among genes with the greatest number of positive fitness effects. The operon forms a tight cluster in the interaction network (Figs.3 and 5a) and it is annotated as an oligopeptide transporter with no clear function. Two separate knockouts for SP_1888 (amiE) and SP_1890 (amiC) confirm that increased fitness results in decreased drug sensitivity in the form of increased relative growth in the presence of ciprofloxacin, vancomycin and gentamicin, and increased sensitivity (i.e., decreased relative growth) to Synercid (Fig.5b). There is limited evidence that the ami-transporter may have (some) affinity for at least two different peptides (P1 and P2)51,52,53. These have been theorized to possibly function as signaling molecules and under certain circumstances may be generated by the bacterium itself51,52,53. Both peptides were synthesized and while neither peptide affects growth of the WT or knockout mutants in the absence of antibiotics (Supplementary Fig.4), the WT grows slightly better in the presence of gentamicin and peptide P2, but not P1 (Fig.5b). This shows that some peptides may, at least partially, inhibit or occupy the ami-transporter, and thereby trigger decreased antibiotic sensitivity, in a similar manner as a knockout does. Besides peptides, the ami-transporter may be (non-selectively) transporting antibiotics into the cell, which could explain its effect on antibiotic sensitivity. To explore this, bacteria were exposed to ciprofloxacin or kanamycin and the internalized antibiotic concentration was determined through mass spectrometry for WT and both ami knockout mutants. In both mutants the amount of internalized ciprofloxacin was significantly lower (~1.7 in amiE, and ~2.3 in amiC), while the kanamycin concentration was found to be significantly lower in amiC (~2; Fig.5c). This shows that a functional ami-transporter increases the concentration of fluoroquinolones and 30S PSIs, suggestively by transporting them into the cell, and thereby, due to a higher internal concentration, enhancing the antibiotics inhibitory effects on growth. There are multiple examples that transporters can contribute to tolerance54,55, which we recently showed is also the case for the ade transporter in Acinetobacter baumannii, which contributes to fluoroquinolone tolerance7. However, those examples are mostly based on efflux pumps that actively decrease the antibiotic concentration in the cell through upregulation of such pumps. In contrast, with respect to the ami-operon it would be the reverse, i.e., inhibition instead of upregulation would lead to tolerance. To explore this possible effect on tolerance, the WT and amiE were exposed to either 10xMIC of gentamicin or vancomycin over a period of 24h. Approximately 1% of the WT population survives 4h exposure to gentamicin, while none of the population survives exposure past 8h. The amiE population displays a slower decline in survival with 1% of the population surviving the first 8h (tolerant cells)25. At ~10h the decline ceases and the remaining population (~0.01%) survives at least up to 24h, which is representative of a persister fraction25. In contrast, the WT and amiE mutant populations decline at similar rates when exposed to vancomycin, showing that inhibition of the ami-transporter can lead to tolerance and persistence in an antibiotic-specific manner while MICs of gentamycin and vancomycin for WT and amiE are similar (Supplementary Data1). Importantly, these data show that increased fitness indeed leads to decreased ABX sensitivity, which can translate into at least two phenotypes: increased relative growth and increased survival (i.e., tolerance).

a The ami-operon forms a tight cluster, and upon knockout is predicted to decrease sensitivity to most antibiotics, and increase sensitivity to Synercid. b Growth curves of individual knockout mutants of amiE and amiC validate changes in antibiotic sensitivity; i.e., they show that positive fitness translates into decreased ABX sensitivity and increased relative growth, while negative fitness translates into increased ABX sensitivity and decreased relative growth. Additionally, growth curves suggest the transporter phenotypically responds to peptide P2. Mean valuesSEM are shown from n3 independent experiments. c Intracellular antibiotic accumulation analysis shows that the WT strain with an intact transporter reaches a higher intracellular antibiotic concentration, suggesting the transporter is involved in importing antibiotics, explaining why a knockout or occupation with a peptide such as P2, can lead to decreased antibiotic sensitivity. Mean valuesSEM are shown from n3 independent experiments. d Besides that modulation of the transporter leads to positive fitness, which translates into decreased ABX sensitivity and increased relative growth in the presence of gentamicin or vancomycin, it also leads to increased survival (i.e., tolerance) to gentamicin, but not vancomycin. Mean valuesSEM are shown from n=4 independent experiments. Significance is measured through a one-way ANOVA with Dunnett correction for multiple testing: *p=0.05, **p=0.01, ***p=0.001. Source data are provided as a Source Data file.

Among the 21 functional groups, purine metabolism has some of the largest number of positive fitness effects, mostly with -lactams and glycopeptides (Figs.3a and 6a). Moreover, two regulators (SP_1821/1979) associated with this pathway decrease sensitivity to -lactams and/or glycopeptides and two neighboring genes with unknown function have either the same (SP_0830), or the opposite effect (SP_1446) on antibiotic sensitivity as their defined neighbor, suggesting they may be involved in the same process as their neighbor (Fig.6a). Furthermore, the global interaction network positively links an ABC transporter (SP_0845-0848, Figs.2c, 6a) with multiple genes in this pathway due to their similar profiles. This operon is annotated as a putative deoxyribose transporter, and to verify whether an interaction exists with purine metabolism, single and double knockouts were created between SP_0846 (the transporters ATP binding protein) and SP_0829/deoB. Their profiles suggest they do not affect growth in the absence of ABXs and have increased sensitivity to Synercid, which was confirmed with individual growth curves (Fig.6b). However, when both knockouts are in the same background, their increased sensitivity to Synercid is masked. Thus, as indicated by the network, these results show that the ABC transporter indeed has a genetic interaction with purine metabolism/salvage, but plays an unknown role. Importantly, this confirms that the global network includes valuable interactions that can be explored to uncover functional relationships.

a Key steps in purine metabolism with the same color coding as in Fig.3. SP_1097 is listed as well, for which we found no change in ABX sensitivity, which is denoted with np for no phenotype. The putative deoxyribose transporter (SP_0845-0848), a high-connectivity cluster in Fig.2, is also shown. b Single knockouts for deoB/SP_0829 and SP_0846, as well as a double knockout show that mutants and WT grow equally well in the absence of antibiotics. In the presence of Synercid, as predicted and indicated by their ABX sensitivity bar, the single knockouts display a higher sensitivity to the drug then the WT. The double mutant suppresses the increased Synercid sensitivity phenotype of the single mutants, indicating that the positive interaction that is found in the co-fitness network leads to a positive genetic interaction between these genes. c Single and double knockouts of SP_1097 and SP_1645/relA grow just as well as WT in the absence of antibiotics. As predicted SP_1097 is equally sensitive to cefepime as the WT, while relA has decreased sensitivity as indicated by its ABX sensitivity bar in a. Additionally, the double knockout has decreased sensitivity to cefepime, indicating the dominant phenotype of relA. d The phenotype of SP_0831 was validated in growth as well, showing no change in growth in the absence of ABX, and decreased sensitivity (i.e., increased relative growth) in the presence of cefepime (FEP). e The alarmone (p)ppGpp is below the limit of detection (b.l.d.) in the absence of stress, upon induction with mupirocin it is synthesized in equal amounts in WT, SP_0831 and SP_1097, while it cannot be synthesized when relA is absent. f Synthesis of di- and trinucleotides is significantly affected in the different mutants upon mupirocin exposure. Mean valuesSEM are shown from n3 independent experiments. Significance is measured through a paired t-test with an FDR adjusted p value for multiple comparisons: *p<0.05, **p<0.01, ***p<0.001, ns not significant. Source data are provided as a Source Data file.

Furthermore, within purine metabolism the alarmone (p)ppGpp is synthesized from GTP and/or GDP. Like other bacterial species, S. pneumoniae likely responds to (some) ABXs via induction of the stringent response pathway56, in which relA (SP_1645) is the key player with both synthetase and hydrolase activity57. Additionally, SP_1097 is annotated as a GTP diphosphokinase and may be involved in the synthesis of pppGpp from GTP (Fig.6a). Our data suggests, and we confirmed for the -lactam cefepime (Fig.6c), that when synthesis of the alarmone is inhibited by deletion of relA, similar to many other interactions in purine metabolism, this leads to reduced -lactam and glycopeptide sensitivity manifested by increased relative growth (Fig.6c). Moreover, while SP_1097, as predicted, does not change ABX sensitivity (Supplementary Data2, Fig.6), a double knockout of relA-SP_1097 seems to further decrease sensitivity to cefepime by further increasing relative growth (Figs.6c and7a). Additionally, besides a change in growth, the single relA and double knockout (relA-SP_1097), also increases tolerance to cefepime by ~1000-fold at 24h (Fig.7b), without changing the MIC (Supplementary Data1). To understand how relA and SP_1097 affect purine metabolism, we used LC/MS to measure (p)ppGpp, ADP, ATP, GDP, and GTP. Additionally, we included SP_0831 a purine nucleoside phosphorylase involved in nucleotide salvage, which has the same ABX profile as relA (Fig.6a, d), but should not directly affect (p)ppGpp synthesis. While (p)ppGpp is below the limit of detection during normal growth in any of the strains, as expected relA and the double mutant relA-SP_1097 are unable to synthesize the alarmone when exposed to mupirocin, a strong activator of the stringent response (Fig.6e, Supplementary Data9). In contrast, WT, SP_0831, and SP_1097 synthesize (p)ppGpp upon mupirocin exposure to a similar extent (Fig.6e). Concerning the di- and trinucleotides in the pathway, upon mupirocin exposure GTP and GDP are significantly reduced in WT, SP_0831, and SP_1097, likely because they are used for (p)ppGpp synthesis (Fig.6f, Supplementary Data9). In contrast, while ATP and ADP again remain constant for the relA mutants, ATP and ADP synthesis are significantly increased upon mupirocin exposure, especially for WT and SP_1097. This suggests that during activation of the stringent response, synthesis from IMP is directed toward AMP, and not necessarily GMP, at least not enough to replenish GTP and GDP. Additionally, upon mupirocin exposure, ATP only minimally increases for SP_0831, while it increases over twofold for WT and SP_1097 (Fig.6f). It has been shown for bacteria including Escherichia coli and Staphylococcus aureus that a decreased ATP concentration can decrease sensitivity to ABXs such as ciprofloxacin58. Additionally, in S. aureus (p)ppGpp overexpression has been associated with decreased sensitivity to linezolid59. Our data suggest that (p)ppGpp and ATP synthesis may be intrinsically linked, i.e., at least in S. pneumoniae the inability to produce the alarmone also results in lowered ATP synthesis, which is associated with a lowered ABX sensitivity to -lactams and glycopeptides. However, SP_0831 shows that even if (p)ppGpp can be synthesized, modulation of purine metabolism, for instance through the salvage pathway, can result in decreased ATP synthesis, and can lead to lowered ABX sensitivity (i.e., increased relative growth). Importantly, in many bacterial species, alarmone production is generally assumed to be triggered in response to different types of stress and has been shown to affect a large variety of processes including nucleotide synthesis, lipid metabolism, and translation. (p)ppGpp is thereby a ubiquitous stress-signaling molecule that enables bacteria to generate a response that is geared toward overcoming the encountered stress. However, contradictory results between species indicate a possible non-uniformity across bacteria, leaving much to be learned about how the alarmone and the processes it can control fit into the entire organismal (response) network56. Our data suggest that the inability (i.e., due to mutations) to generate the alarmone in S. pneumoniae in response to -lactams and glycopeptides is linked to reduced ATP, which under specific circumstances may be an optimal response, as it results in decreased ABX sensitivity translating into increased relative growth and tolerance, and thereby a higher probability to survive the insult (Figs.6c and 7a, b).

a Relative growth rates (i.e., fitness) of 16 knockout mutants involved in 7 processes measured in the presence of 7 antibiotics, validate that decreased ABX sensitivity (i.e., increased relative growth) can be achieved by modulating a wide variety of processes. Mean valuesSEM are shown from n3 independent experiments. b Significantly increased survival during exposure to 510xMIC of an ABX over a 24h period is observed for 9 out of 12 knockouts. Significance is measured with an ANOVA with Dunnett correction for multiple comparisons: **p<0.01, ***p<0.001. Mean valuesSEM are shown from n=4 independent experiments. c Tn-Seq data with a positive fitness in the presence of at least one antibiotic (y-axis) is plotted against in vivo Tn-Seq data (x-axis). Note that only in vivo data is shown that is predicted to have no more than a small fitness defect, no fitness defect or an increased predicted in vivo fitness, either during nasopharynx colonization or lung infection. Circled and indicated with arrows are SP_0829 in red and SP_1396 in black. d In vitro growth curves validate decreased sensitivity (i.e., increased relative growth) to cefepime (SP_0829) and meropenem (SP_1396). Mean valuesSEM are shown from n=3 independent experiments. e Mice were challenged with WT and MT in a 1:1 ratio of which half received ABX 16h post infection (p.i.), and all were sacrificed 24h p.i. Displayed are the MTs competitive index (CI) in the nasopharynx and lung, and in the presence and absence of cefepime (SP_0829) or meropenem (SP_1396). In all instances, the addition of ABX significantly increases the CIof the mutant. Significance is measured with a MannWhitney test **p<0.01, ***p<0.001. Mean valuesSEM are shown from n7 mice/experiment. Source data are provided as a Source Data file.

For instance, in the glycolysisTo further confirm that antibiotic sensitivity can be decreased by inhibiting a variety of processes, knockouts (KOs) were generated for fourteen mutants from 8 different processes. Moreover, an additional goal was to determine what increased fitness (i.e., decreased ABX sensitivity) would look like phenotypically, and thus whether it would translate into increased relative growth and/or tolerance. Of the 14 mutants with a Tn-Seq predicted increased fitness, 13 display an increased ability to grow in the presence of an ABX compared to the WT. Moreover, eight mutants, which inhibit several different processes including different metabolic pathways, transport, and transcription and translation, displayed tolerance, while retaining a similar MIC, and thereby have an increased ability to survive high-level exposure to an ABX (510xMIC) for at least 24h (Fig.7a, b, Supplementary Data1,8). Note that we validated 49 single KO genotype phenotype associations in this study, with an equal distribution across the entire spectrum of ABX sensitivity (Fig.1e, Supplementary Data8). This highlights that our approach uncovered a detailed genome-wide ABX sensitivity atlas composed of a multitude of genes, pathways and processes that when modulated can increase and/or decrease ABX sensitivity. The validation experiments highlight that the resulting fitness accurately predicts the relative growth rate of a mutant, which we have previously shown for hundreds of other negative fitness phenotypes6,14,15,16,18,39,40,41,42,44,60,61,62. Moreover, it turns out that in the majority of cases, increased fitness not only results in increased relative growth in the presence of an antibiotic, but also tolerance. Thereby, the part of the atlas that depicts decreased ABX sensitivity (i.e., increased fitness) includes a genome-wide tolerome, composed of a wide variety of pathways and processes that when modulated trigger tolerance in vitro in an ABX dependent manner.

Obviously, the selection regime in vivo is far more complex and stricter than in a test tube, which raises the question whether many of the options that decrease ABX sensitivity in vitro, including those that increase tolerance, would be available in vivo as well. To explore this, all the Tn-Seq data with a positive fitness in the presence of at least one antibiotic was combined with in vivo Tn-Seq data and filtered for those genes with no or only a small fitness defect predicted in vivo during nasopharynx colonization or lung infection (Fig.7c, Supplementary Data2). Two genes were selected that we had confirmed for decreased ABX sensitivity in vitro: (1) SP_0829/deoB synthesizes Ribose-1P and is involved in purine metabolism (Fig.6a). deoB has no effect on in vitro growth in the absence of ABX (Fig.7a, d), as predicted it grows better in the presence of cefepime (Fig.7a, d), but it does not affect survival/tolerance (Fig.7b); (2) SP_1396/pstA is the ATP binding protein of a phosphate ABC transporter (Supplementary Fig.3). pstA has no effect on in vitro growth (Fig.7a, d), it has a higher relative growth rate in the presence of meropenem (Fig.7a, d), and it also increases survival/tolerance (Fig.7b). Both mutants were mixed with WT in a 1:1 ratio and used in an in vivo mouse infection competition model as we have done previously18. Of the infected mice, half were administered antibiotics at 16h post infection, and were sacrificed 6h later to determine the strains competitive index (CI) (Fig.7e). Importantly, while both mutants may have a slight disadvantage compared to the WT when colonizing the lung or nasopharynx, their CI increases significantly in the presence of ABXs, leading to increased survival compared to the WT (Fig.7e, Supplementary Data10). Combining antibiotic- with in vivo Tn-Seq highlights the ability to predict the existence of a wide array of possible alterations of specific genes, pathways and processes that can have a beneficial effect in vivo in the presence of antibiotics. Such changes could thereby contribute to escape from antibiotic pressure and even create a path toward the emergence of antibiotic resistance.

There is likely significant overlap in the selective pressures a bacterial pathogen would experience in a mouse infection model compared to the human host. This raises the possibility that those gene disruptions that are predicted by Tn-Seq to lead to decreased antibiotic sensitivity and that simultaneously have no more than a minimal defect in vivo, could also have an advantage in the human host in the presence of ABXs and thereby contribute to ABX escape and/or the emergence of resistance. A premature stop codon most closely reflects the effect a transposon insertion has on a gene; i.e., it disables a gene. We thereby hypothesized that stop codons in certain gene sets predicted by Tn-Seq could be enriched for in antibiotic-resistant clinical isolates. To test this hypothesis 4 gene sets were compiled consisting of those that upon disruption: (1) decrease antibiotic sensitivity in at least 1 antibiotic and have no strong defect in vivo; (2) decrease antibiotic sensitivity in at least 1 antibiotic and have a defect in vivo; (3) have little to no effect on antibiotic sensitivity and in vivo; (4) have no effect or increase antibiotic sensitivity and have a defect in vivo (Fig.8a, b; Supplementary Fig.5). Thousands of strains were selected from the PATRIC63,64 database that could be split into a group of co-trimoxazole (SXT) resistant and a group of -lactam resistant strains, and each group was matched with an equal number of sensitive strains from the database. In all strains in the SXT and -lactam groups, irrespective of resistant or sensitive status, the number of stop codons in gene sets 1 and 3 are highest, which reflects the Tn-Seq predicted in vivo effects, i.e., while gene sets 1 and 3 contain mostly genes with potentially neutral effects, gene sets 2 and 4 contain many genes that are suggested to have a defect in vivo when disabled (e.g. with a stop codon) (Fig.8c). Moreover, SXT resistant isolates in gene set 1 more often contain a stop codon compared to sensitive strains, and in -lactam resistant isolates this is true for gene sets 13 (Fig.8d). While these are not ideal comparisons, for instance the entire ABX profile is not clear for many strains, different changes than premature stops could have ABX/in vivo modulating effects, strains could have experienced different ABX and/or in vivo selective pressures, and genetic changes can be strain-background dependent, it shows that genetic changes that can affect ABX and/or in vivo sensitivity, which are predictable with Tn-Seq, readily occur in clinical samples. This in turn underscores that ongoing infections may consist of variants that enable different paths to adjusting to, or overcoming a challenging host/ABX environment.

a Based on in vivo and ABX Tn-Seq data, four gene sets consisting of 34 genes each were compiled with specific fitness profiles in the presence of antibiotics and in vivo. Shown are the in vivo effects for nasopharynx, while lung data are depicted in Supplementary Fig.5. W represents the fitness difference of a gene in a specific condition (e.g., an antibiotic, in vivo) minus its fitness in vitro in rich medium. Dashed lines indicate significance cut-offs, grayed-out dots indicate genes with no significant change in fitness in the presence of antibiotics, colors represent antibiotics and are the same as in Fig.1. b Detailed distributions for each gene set highlight whether effects in the presence of antibiotics, in the nasopharynx and lungs increase (+), do not affect (0) or decrease () relative fitness. Gene set rationales are described in the text. c The total number of stop codons in each gene set for 2296 co-trimoxazole and 1166 -lactam resistant and sensitive strains. d The number of sensitive and resistant strains with at least one stop codon in a gene in each gene set. Significance is measured through a Fishers exact test: **p<0.01, ***p<0.001, ****p<0.0001. Source data are provided as a Source Data file.

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A genome-wide atlas of antibiotic susceptibility targets and pathways to tolerance - Nature.com

CAMBRIDGE, Mass. & SCOTTS VALLEY, Calif.--(BUSINESS WIRE)--Cantata Bio launched today, with the mission of enabling researchers to address the worlds most challenging scientific questions, from human disease to agricultural sustainability, using leading-edge multi-dimensional NGS technologies. The company is a result of the merger between Dovetail Genomics, the industry leader in advanced proximity ligation genomic solutions, and Arc Bio, which develops novel, proprietary metagenomic tools for accurate and sensitive microbial profiling. Committed to delivering the most innovative NGS-based solutions, Cantata Bio comprises three business units, Epigenetics & Genome Structure, Microbial Profiling, and Genetic Analysis Solutions.

The benefits of this merger to both our customers and the companies were clear - accelerated innovation, the potential to aggregate multimodal data to better service our partners, and streamlined operations, said Todd Dickinson, CEO of Cantata Bio. Cantata Bio aims to dramatically accelerate advances in the life sciences industry with key competencies, including integrated metagenomics workflows for understanding the interplay between microbes and disease, and unique NGS sequencing assays that deliver industry-leading long-range data empowering genome assembly, haplotype phasing, chromatin structure and epigenomic applications.

Serving on the Board of Directors for former Dovetail and Arc Bios parent company, EdenRoc Sciences, Todd Dickinson continues in the role of CEO for Cantata Bio. A life sciences executive with more than 20 years of genomics experience, Todd was a founding scientist of Illumina, holding a variety of technical and commercial executive roles both at Illumina and subsequently at Bionano Genomics.

Along with the company launch, Cantata Bio announced today its Dovetail TopoLink Kit, a revolutionary new assay delivering genomic interactions at unparalleled speed. With genomic interactions critical to understanding functional biology, the TopoLink Assay removes the bias introduced from motif-based assays, captures chromatin topology features with a higher rate of discovery, and offers superior accuracy in detecting chromatin loops, all in an unprecedented six hour sample-to-sequencing library workflow - less than half the time of traditional Hi-C approaches.

Cantata Bio has seen early demand for TopoLink, having already allocated the first several kits to members of its First Adopters Circle. These include Chris Mason, Professor of Genomics, Physiology, & Biophysics, and Director of Emerging Genome Technologies at Tempus Labs, David Sinclair, Professor in the Department of Genetics and co-Director of the Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Melissa Fullwood, Nanyang Assistant Professor at SBS, NTU and Junior Principal Investigator at CSI Singapore, and Emily Bernstein, Professor, and Dan Hasson, Associate Professor, at the Tisch Cancer Institute of the Icahn School of Medicine at Mount Sinai.

Cantata Bio and its newest product, the Dovetail TopoLink Kit, were announced today at Advances in Genome Biology and Technology (AGBT), where Cantata Bio is an official sponsor. To learn more about the Dovetail TopoLink Kit or Cantata Bio, visit suite 1825 throughout the conference, and join us for our launch party on the evening of June 7th during and after the Passport prizes/CLICK2WIN event. For more information, visit http://www.cantatabio.com.

About Cantata Bio

Cantata Bio is enabling researchers to solve tomorrows most challenging scientific problems through novel, multi-dimensional approaches that unlock access to genomic, epigenomic and metagenomic information at unprecedented levels. Combining proprietary technologies with platform solutions, services, and cutting-edge bioinformatics and software, our unique approaches are solving complex problems, including chromatin topology analysis, small and large structural variant detection, de novo chromosome assembly, haplotype phasing, metagenomics, and microbiome analysis. Our customers are positively impacting the fields of epigenetics, developmental biology, cancer research, evolutionary biology, infectious disease, and more. Cantata Bio is based in Scotts Valley, California and Cambridge, Massachusetts. For more information on Cantata Bio, its technology, and offerings, visit http://www.cantatabio.com.

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Introducing Cantata Bio, Inventive Multimodal Solutions for Accelerating Genome-based R&D - Business Wire

Bionano Genomics

Automated UHMW DNA extraction can consistently yield ultra-long DNA of outstanding quality and at increased throughput without user intervention

SAN DIEGO, June 09, 2022 (GLOBE NEWSWIRE) -- Hamilton and Bionano Genomics, Inc.(Nasdaq: BNGO) today announced the collaborative development of the Long String VANTAGE for the isolation of Ultra High Molecular Weight (UHMW) DNA for use in optical genome mapping (OGM).

Extraction of UHMW DNA of high quality and quantity is an important prerequisite and first step in Bionanos sample preparation workflow. UHMW DNA extraction is generally performed manually by highly trained and experienced laboratory personnel, limiting volume and application in routine sample preparation.

By combining Hamiltons long-standing expertise in automating genomic workflows through precision engineering of automated liquid handling solutions and consumables, and Bionanos knowledge in extraction, enzymatic treatment and analysis of UHMW DNA molecules, the companies have developed the worlds first walk-away automation solution for UHMW DNA extraction.

Hamiltons Long String VANTAGE is the first Assay Ready Workstation solution in Hamiltons Long String Genomics product program and supports extraction of UHMW DNA at increased scale. Running the Bionano Prep SP Blood and Cell Culture DNA Isolation Kit, customers can obtain up to 12 UHMW DNA samples in less than 4 hours with high consistency and reproducibility. This workflow has the potential to double manual output, with increased confidence in sample yields and DNA quality.

The companies plan to collaborate with select clinical research laboratories to test and further develop applications for the use of the Long String VANTAGE and the Bionano Prep SP kits and anticipate commercial release of some of these workflows by the end of 2022.

Hamilton is highly committed to empowering genomic sciences and diagnostics with innovative products and applications by collaborating with leading kit and technology providers, Dr. Martin Frey, CEO Hamilton Bonaduz AG said in a statement. Analyzing ultra-long DNA molecules by Optical Genome Mapping or genome sequencing technologies is providing insight into previously unresolved questions about genome biology and structural aberrations of the genome with clinical relevance. We are extremely pleased to partner with Bionano Genomics in addressing this innovation area.

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The outcomes of this partnership with Hamilton are outstanding. They have shown an exceptional ability to improve workflows through the automation of UHMW DNA isolation, which consistently results in extremely long and pure DNA molecules that are suitable for use in OGM. We expect this innovation will significantly reduce time to results, reduce hands-on time and improve OGM performance by standardizing the process of UHMW DNA isolation. Bionano and Hamilton look forward to seeing customers adopt the Long String VANTAGE solution, commentedErik Holmlin, PhD, president and chief executive officer ofBionano Genomics.

About HamiltonHamilton is a leading global manufacturer, providing automated liquid handling workstations and laboratory automation technology to the scientific community. With a focus on innovative design, Hamilton products incorporate patented liquid handling technologies into a portfolio that includes liquid handling platforms, standard application-based solutions, small devices, consumables, and OEM liquid handling solutions. Known for advancing life science, clinical diagnostics, forensics and biotechnology industries, Hamilton products offer reliability, performance, and flexibility. Ensuring a continuous commitment to quality, Hamilton utilizes state-of-the-art manufacturing at production facilities in Reno, Nevada and Bonaduz, Switzerland and has earned a global ISO 9001 certification. Privately held, Hamilton maintains headquarters in Reno, Nevada; Franklin, Massachusetts; and Bonaduz, Switzerland, along with subsidiary offices throughout the world. http://www.hamiltoncompany.com/robotics

About Bionano Genomics, Inc.Bionano Genomicsis a provider of genome analysis solutions that can enable researchers and clinicians to reveal answers to challenging questions in biology and medicine. The Companys mission is to transform the way the world sees the genome through OGM solutions, diagnostic services and software. The Company offers OGM solutions for applications across basic, translational and clinical research. Through itsLineagenbusiness, the Company also provides diagnostic testing for patients with clinical presentations consistent with autism spectrum disorder and other neurodevelopmental disabilities. Through its BioDiscovery business, the Company also offers an industry-leading, platform-agnostic software solution, which integrates next-generation sequencing and microarray data designed to provide analysis, visualization, interpretation and reporting of copy number variants, single-nucleotide variants and absence of heterozygosity across the genome in one consolidated view. For more information, visitwww.bionanogenomics.com,www.lineagen.comorwww.biodiscovery.com

Forward-Looking Statements of Bionano Genomics

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Words such as can, potential, plan, expect, and similar expressions (as well as other words or expressions referencing future events, conditions or circumstances) convey uncertainty of future events or outcomes and are intended to identify these forward-looking statements. Forward-looking statements include statements regarding our intentions, beliefs, projections, outlook, analyses or current expectations concerning, among other things, the anticipated benefits and improvements resulting from the use of Hamiltons Long String VANTAGE, or the ability of that system to reliably and consistently isolate high quality and sufficient quantity of UHMW DNA for use with optical genome mapping. Each of these forward-looking statements involves risks and uncertainties. Actual results or developments may differ materially from those projected or implied in these forward-looking statements. Factors that may cause such a difference include the risks and uncertainties associated with: the impact of the COVID-19 pandemic on our business and the global economy; general market conditions; changes in the competitive landscape and the introduction of competitive technologies or improvements in existing technologies; changes in our strategic and commercial plans; our ability to obtain sufficient financing to fund our strategic plans and commercialization efforts; the ability of medical and research institutions to obtain funding to support adoption or continued use of our technologies; and the risks and uncertainties associated with our business and financial condition in general, including the risks and uncertainties described in our filings with the Securities and Exchange Commission, including, without limitation, our Annual Report on Form 10-K for the year ended December 31, 2021 and in other filings subsequently made by us with the Securities and Exchange Commission. All forward-looking statements contained in this press release speak only as of the date on which they were made and are based on managements assumptions and estimates as of such date. We do not undertake any obligation to publicly update any forward-looking statements, whether as a result of the receipt of new information, the occurrence of future events or otherwise.

CONTACTSCompany Contact:Erik Holmlin, CEOBionano Genomics, Inc.+1 (858) 888-7610eholmlin@bionanogenomics.com

Investor Relations:Amy ConradJuniper Point+1 (858) 366-3243amy@juniper-point.com

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Hamilton and Bionano Genomics Announce Worlds First Ultra High Molecular Weight DNA Extraction Automation Solution for OGM - Yahoo Finance

As the vortex of the COVID-19 pandemic consumed the world in 2020, scientists worked at a frantic pace to understand the new virus sweeping the globe. The discoveries surrounding SARS-CoV-2 were impressive not only for the speed in which they took place, but also for the new pathways of research they opened.

To the average person, it looked as though scientists were making daily breakthroughs as spike proteins, antibodies and messenger RNA vaccines became topics of everyday conversation. But revolutionary discoveries are rarely Eureka! moments. Instead, scientific advances are almost always the culmination of research that occurs outside of the spotlight. In the realm of immunology, decades of research on the immune system, the human genome and a multitude of other viruses laid the foundation to quickly unravel the mysteries of SARS-CoV-2 and COVID-19.

The immediate end goal was met when COVID-19 vaccines and treatments became available. But the impact of that research is far from over, according to Deepta Bhattacharya, PhD, keynote speaker at the inaugural University of Arizona Health Sciences Tomorrow is Here Lecture Series. He believes the lessons learned during the COVID-19 pandemic have the potential to change the future of science.

The pandemic has shown us that the tools are out there to make infectious disease far less burdensome, not only in the U.S., but globally, said Dr. Bhattacharya, professor of immunobiology in the UArizona College of Medicine Tucson and BIO5 Institute member. We've shown what our technology can do and what our responses can be, and I don't see any reason to accept the status quo anymore.

One of the pandemics biggest lessons, Dr. Bhattacharya said, is that the basics matter.

When people say the COVID-19 vaccines were developed in record time, they really weren't, Dr. Bhattacharya said. They were built on the backs of decades of research that allowed us to move quickly.

Three decades before an unknown virus surfaced in Wuhan, China, scientists were undertaking a massive endeavor known as the Human Genome Project. The intent was to sequence and map all of the genes 3 billion in total that make up the human genome.

In the beginning, the available technology was unreliable and slow, preventing researchers from sequencing more than a few hundred genes at a time. As technology improved, sequencing rates increased dramatically, and in April 2003, the Human Genome Project succeeded in reading the complete genetic blueprint of a human being.

We've shown what our technology can do and what our responses can be, and I don't see any reason to accept the status quo anymore.Deepta Bhattacharya, PhD

The Human Genome Project was criticized by people who asked, What are we really learning from this? What diseases have been cured by understanding and knowing the human genome sequence? Dr. Bhattacharya said. But it's important not to just focus on immediately translatable outcomes. Think about all of the outcomes that came as a result of that project, some of which undoubtedly were the sequencing technologies.

The same sequencing technologies that unraveled the mysteries of the human genome could be applied to viruses. Fast forward to January 2020, and within weeks of being confronted by an unknown pathogen, scientists sequenced and identified the novel coronavirus they dubbed SARS-CoV-2.

Some of the technologies people criticized for not necessarily having an immediate translational impact, now very obviously did, Dr. Bhattacharya said.

The Human Genome Project started in 1990, but the research that laid the foundation for the COVID-19 vaccines has an even longer history. As early as the mid-1970s, immunologists were studying common coronaviruses that affected other species, including mouse hepatitis virus.

It was, in some ways, thankless work. The researchers were asked, why are you studying this? This is a mouse coronavirus why do you care what disease it causes? Dr. Bhattacharya said. What the pandemic has shown us is that those studies taught us an awful lot in terms of preparedness. From these studies, it turned out that the immune response needed to be aimed at a particular protein that the virus makes called spike.

Identifying the viruss Achilles heel wasnt enough, though. Researchers needed to find a way to engineer the spike protein to create an immune response against the virus. That work happened at the National Institutes of Healths Vaccine Research Center. There, scientists were studying respiratory syncytial virus, which causes severe respiratory infections in children, and another common coronavirus that causes cold-like symptoms.

Once engineered, the spike protein needed to be safely delivered to the cells nucleus without killing the cell. Again, the answer came from research that was decades in the making in this case, messenger RNA (mRNA) research at the University of Pennsylvania.

All of that early work that sort of circuitous path science sometimes takes led us to figure out the perfect solution to generate vaccines and immune responses to emerging pathogens, said Dr. Bhattacharya.

On the scientific front, one of the biggest applications from the pandemic can be found in the immunology that led to the development of the highly effective COVID-19 vaccines.

I think structure-based vaccinology is the wave of the future, said Dr. Bhattacharya, whose primary research focuses on a family of viruses known as flaviviruses, which cause diseases including dengue, Zika, Japanese encephalitis, yellow fever and West Nile. The pandemic really showed the power of that particular approach to actually control the immune system and what it's aimed at. Some of the technologies that came from COVID-19 can absolutely be applied to the flaviviruses, as well.

Dr. Bhattacharya, who hopes to develop an effective vaccine for flaviviruses, says none of the flaviviruses have come close to causing the worldwide destruction perpetuated by SARS-CoV-2, though scientists were surprised by the spread of the Zika virus, which reached epidemic status in Brazil in 2016. Still, no one knows which virus could be the source of the next pandemic.

We don't really know what's going to come next, so that means studying families of not just viruses, but also bacteria and fungi, and building up that broad knowledge base and technology that allows us to move quickly, he said. Prevention and preparedness are worth many tons of cure for infectious diseases.

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A New Era: Creating Defenses Against Disease After COVID-19 - The University of Arizona Health Sciences |