Category Archives: Human Genetics

image:Dr. J. Andrew Pospisilik, Chair of the Department of Epigenetics, Van Andel Institute view more

Credit: Courtesy of Van Andel Institute

GRAND RAPIDS, Mich. (September 12, 2022) A team led by Van Andel Institute scientists has identified two distinct types of obesity with physiological and molecular differences that may have lifelong consequences for health, disease and response to medication.

The findings, published today in the journal Nature Metabolism, offer a more nuanced understanding of obesity than current definitions and may one day inform more precise ways to diagnose and treat obesity and associated metabolic disorders.

The study also reveals new details about the role of epigenetics and chance in health and provides insights into the link between insulin and obesity.

Nearly two billion people worldwide are considered overweight and there are more than 600 million people with obesity, yet we have no framework for stratifying individuals according to their more precise disease etiologies, said J. Andrew Pospisilik, Ph.D., chair of Van Andel Institutes Department of Epigenetics and corresponding author of the study. Using a purely data-driven approach, we see for the first time that there are at least two different metabolic subtypes of obesity, each with their own physiological and molecular features that influence health. Translating these findings into a clinically usable test could help doctors provide more precise care for patients.

Currently, obesity is diagnosed using body mass index (BMI), an index correlated to body fat that is generated by comparing weight in relation to height. It is an imperfect measure, Pospisilik says, because it doesnt account for underlying biological differences and can misrepresent an individuals health status.

Using a combination of laboratory studies in mouse models and deep analysis of data from TwinsUK, a pioneering research resource and study cohort developed in the United Kingdom, Pospisilik and his collaborators discovered four metabolic subtypes that influence individual body types: two prone to leanness and two prone to obesity.

One obesity subtype is characterized by greater fat mass while the other was characterized by both greater fat mass and lean muscle mass. Somewhat surprisingly, the team found that the second obesity type also was associated with increased inflammation, which can elevate the risk of certain cancers and other diseases. Both subtypes were observed across multiple study cohorts, including in children. These insights are an important step toward understanding how these different types impact disease risk and treatment response.

After the subtypes were identified in the human data, the team verified the results in mouse models. This approach allowed the scientists to compare individual mice that are genetically identical, raised in the same environment and fed the same amounts of food. The study revealed that the inflammatory subtype appears to result from epigenetic changes triggered by pure chance. They also found that there seems to be no middle ground the genetically identical sibling mice either grew to a larger size or remained smaller, with no gradient between them. A similar pattern was seen in data from more than 150 human twin pairs, each of whom were virtually the same genetically.

Our findings in the lab almost carbon copied the human twin data. We again saw two distinct subtypes of obesity, one of which appeared to be epigenetically triggerable, and was marked by higher lean mass and higher fat, high inflammatory signals, high insulin levels, and a strong epigenetic signature, Pospisilik said.

Depending on the calculation and traits in question, only 30%50% of human trait outcomes can be linked to genetics or environmental influences. That means as much as half of who we are is governed by something else. This phenomenon is called unexplained phenotypic variation (UPV) and it offers both a challenge and untapped potential to scientists like Pospisilik and his collaborators.

The study indicates that the roots of UPV likely lie in epigenetics, the processes that govern when and to what extent the instructions in DNA are used. Epigenetic mechanisms are the reason that individuals with the same genetic instruction manual, such as twins, may grow to have different traits, such as eye color and hair color. Epigenetics also offer tantalizing targets for precision treatment.

This unexplained variation is difficult to study but the payoff of a deeper understanding is immense, Pospisilik said. Epigenetics can act like a light switch that flips genes on or off, which can promote health or, when things go wrong, disease. Accounting for UPV doesnt exist in precision medicine right now, but it looks like it could be half the puzzle. Todays findings underscore the power of recognizing these subtle differences between people to guide more precise ways to treat disease.

Pospisilik is hopeful that the teams findings will inform development of future precision medicine strategies and lead to a version of their method that may be used in doctors offices to better understand individual patients health and inform care.

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Chih-Hsiang Yang, Ph.D., and Luca Fagnocchi, Ph.D., of VAI are co-first authors of the study. Other authors include Stefanos Apostle, M.S., Vanessa Wegert, M.Sc., Ilaria Panzeri, Ph.D., Darrell P. Chandler, Ph.D., Di Lu, Ph.D., Tao Yang, Ph.D., Elizabeth Gibbons, Ph.D., Rita Guerreiro, Ph.D., and Jos Brs, Ph.D. of VAI; Erez Dror, Ph.D., Steffen Heyne, Ph.D., Till Wrpel of Max Planck Institute of Immunobiology and Epigenetics; Salvador Casani-Galdn, Ph.D. of BioBam Bioinformatics; Kathrin Landgraf, Ph.D., of University of Leipzig; Martin Thomasen, Louise G. Grunnet, Ph.D., and Allan A. Vaag, M.D., Ph.D., D.MSc., of Rigshospitalet; Linn Gillberg, Ph.D., of University of Copenhagen; Elin Grundberg, Ph.D., of Childrens Mercy Research Institute; Ana Conesa, Ph.D., of the Spanish National Research Council and University of Florida; Antje Krner, M.D., of University of Leipzig and Helmholtz Institute for Metabolic, Obesity and Vascular Research; and PERMUTE. The authors thank the MPI-IE Facilities, and Van Andel Institutes Bioinformatics and Biostatistics Core, Genomics Core, Optical Imaging Core, Pathology and Biorepository Core, and Vivarium Core. Access to twin data was generously provided by UKTwins, without whom this study would not have been possible.

Research reported in this publication was supported by Van Andel Institute; Max Planck Gesellschaft; the European Unions Horizon 2020 Research and Innovation Program under Marie Skodowska-Curie grant agreement no. 675610; the Novo Nordisk Foundation and the European Foundation for the Study of Diabetes; the Danish Council for Independent Research; the National Human Genome Research Institute of the National Institutes of Health under award no. R21HG011964 (Pospisilik); and the NIH Common Fund, through the Office of the NIH Director (OD), and the National Human Genome Research Institute of the National Institutes of Health under award no. R01HG012444 (Pospisilik and Nadeau). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other granting organizations. Approximately 5% ($50,000) of funding for this study is from federal sources; approximately 95% ($950,000) is from non-U.S. governmental sources.

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ABOUT VAN ANDEL INSTITUTEVan Andel Institute (VAI) is committed to improving the health and enhancing the lives of current and future generations through cutting-edge biomedical research and innovative educational offerings. Established in Grand Rapids, Michigan, in 1996 by the Van Andel family, VAI is now home to nearly 500 scientists, educators and support staff, who work with a growing number of national and international collaborators to foster discovery. The Institutes scientists study the origins of cancer, Parkinsons and other diseases and translate their findings into breakthrough prevention and treatment strategies. Our educators develop inquiry-based approaches for K-12 education to help students and teachers prepare the next generation of problem-solvers, while our Graduate School offers a rigorous, research-intensive Ph.D. program in molecular and cellular biology. Learn more at vai.org.

Nature Metabolism

Independent phenotypic plasticity axes define distinct obesity sub-types

12-Sep-2022

More:
Scientists redefine obesity with discovery of two major subtypes - EurekAlert

Ethics statement

All experimental procedures were approved by the Animal Ethics Committee of KU Leuven (P004/2020), in accordance with European Community Council Directive 86/609/EEC, the ARRIVE guidelines and the ILAR Guide to the Care and Use of Experimental Animals. Researchers obtained informed consent for publication from all identifiable persons to display and reuse videos.

The study was carried out on 794 female and 746 castrated male Pitrain x PIC Camborough pigs (Vlaamse Pitrain Fokkerij, Belgium; offspring from 73 different sires and 204 dams), which had a mean age of 83.4 (2.2) days and a mean weight of 30.6 (5.1) kg at the start of the experiment. Observations were made during the fattening period which could span up to 120days and ended when pigs reached a body weight of approximately 115kg. Per sire, a median of 26 crossbred piglets (full-sibs and half-sibs from the same Pitrain sire) were allocated in equal numbers to two identical pens in mixed-sex groups. The pig building (experimental farm, located in Belgium) consisted of seventeen identical compartments with eight semi-slatted pens (2.5m4.0m) per compartment and on average thirteen pigs per pen (0.77m2 per pig). Food and water were provided ad-libitum in each pen throughout, from one trough and one nipple drinker.

Pigs were weighed individually over their fattening period every two weeks from January to July 2021. Pen-by-pen, all individuals were driven to the stables central hallway, after which pigs were weighed sequentially. Weighing was carried out between 08:00 a.m. and 16:00 p.m. and was video-recorded. All piglets were weighed for the first at thirteen days after arrival at the fattening farm. For practical limitations, only one out of two pens per sire was hereafter selected for subsequent follow-up. All 1556 pigs were weighed up to eight times, resulting in a total of 7428 records.

Additionally, each pig was scored manually during weighing on the following physical abnormalities: ear swellings or hematomas (0=none, 1=one ear, 2=both ears); the presence and size of umbilical hernia (0=not present, 1=present); ear biting wounds (0=none, 1=one ear, 2=both ears) and tail biting wounds (0=none, 1=small scratches, 3=bloody and/or infected tail; Additional File 1). All recordings were collected by the same trained professional. Lean meat percentage was recorded individually at the slaughterhouse of the Belgian Pork Group in Meer (Belgium) using AutoFom III (Frontmatec, Smoerum A/S, Denmark)31. Feed intake was measured at the pen level.

The walk-through pig weighing setup consisted of a ground scale weighing platform, a radio frequency identification (RFID) reader, a video camera and a computer (Fig.1). The ground scale platform (3.4m1.8m) had an accuracy of0.5kg (T.E.L.L. EAG80, Vreden, Germany) and was situated in the central hallway of the pig building. A wooden aisle helped pigs to walk individually and forward over the balance (2.5m0.6m; Fig.1a; Additional File 2Video S1). Body weights were registered electronically and coupled to the pigs ID using an RFID-reader and custom-made software. The camera (Dahua IPC-HDW4831EMP-ASE, Dahua Technology Co., Ltd, Hangzhou, China) was mounted 2.5m above floor at the center of the weighing scale. Pigs were recorded from an overhead camera perspective with a frame rate of 15 frames per second and a resolution of 38402160. An example of our data collection and a video recording is provided in Fig.1b.

Experimental setup (created with BioRender.com). (a) Schematic top view diagram of the experimental setup used in this study in the center hallway of the pig building. The blue area indicates the ground scale platform with a wooden aisle (in red). The red dashed lines indicate gates to regulate individual pig passage. (b) Schematic side view diagram of the experimental setup.

DeepLabCut 2.2b.827 was installed in an Anaconda environment with Python 3.7.7.30 on a custom-built computer running a Windows 10 64-bit operating system with Intel Core i5-vPro CPU processor (2.60GHz) and 8GB RAM memory. Training, evaluation and analysis of the neural network was performed using DeepLabCut in the Google Colaboratory (COLAB) (https://colab.research.google.com/).

To detect body parts on a pig that is walking through the experimental setup, a neural network was trained using DeepLabCut 2.2b27 as described in Nath et al.32. A minimalistic eight body part configuration (Fig.2a; Table 1) was necessary to estimate hip width, shoulder width and body length. Operational definitions can be found in Table 1. Head body parts (Nose, Ear left, and Ear right) were also labeled, but not included in our final structural model as these body parts were frequently occluded in consecutive frames.

(a) Schematic overview of the eight body positions annotated for pose configuration in DeepLabCut27 (created with BioRender.com). 1=Spine1; 2=Shoulder left; 3=shoulder right; 4=Center; 5=Spine2; 6=Hip left; 7=Hip right; 8=Tail base. (b) Example of a labeled pig during weighing using the DeepLabCut software.

Seven videos of approximately one hour recorded on two different days were selected to include variable pig sizes (20120kg) and each video contained multiple pig weighings. From these seven videos, several frames were extracted for annotation using k-means clustering in DeepLabCut. We first annotated 457 frames (~1 frame per pig) which were split into a training dataset (95%; 434 frames) and a test dataset (5%; 23 frames). The network was trained in Google Colaboratory using the ResNet-50 architecture with a batch size of 2. We trained our algorithm until the loss function reached an optimum, which indicated a minimal loss with a minimum number of iterations in this study. Next, we compared mean pixel errors of several models within this optimal region. Models with lowest mean pixel errors were visually checked for body part tracking performance on entire videos. Hereafter, the model that performed optimal was tested for flexibility using unseen single pig videos with pigs of variable size (20 vs 120kg) weighed on different days. As model performance was suboptimal at first, poorly tracked outlier frames were extracted using the DeepLabCut jump algorithm32. This algorithm identifies frames in which one or more body parts jumped more than a criterion value (in pixels) from the last frame32. These outlier frames were refined manually and hereafter added to the training dataset for re-training. In total, 150 outlier frames were extracted from six novel videos containing one single pig to improve tracking performance (25 frames per pig). The final training dataset consisted of 577 (95%) frames and a test dataset of 30 frames (5%). The network was then trained again using the same features as the first training. Additional File 3Video S2 shows an example of a pig with body part tracking.

After posture extractions of body parts using DeepLabCut, body dimension parameters were estimated. The raw dataset contained body part positions and tracking probabilities of 5,102,260 frames. Individual pig IDs were first coupled with video recordings based on time of measurement from the weight dataset. The following steps and analyses were performed in R33. Frames with a mean tracking probability<0.1 over all eight body parts were removed (2,792,252 frames left). This large reduction in number of frames (50% removed) was mainly caused by video frames without any pigs, for example in between weighing of different pens or in between weighings of pigs.

Next, for every weighing event, start and end points were determined to estimate body dimensions and activity traits. For a specific weighing event, a subset was first created containing all frames between the previous and next weighing event. The time of entrance and departure of the pig on the weighing scale was estimated using the x-position (in pixels) of the tail base, as the movement of pigs was predominantly along the x-axis (from right to left; Fig.2b). The frame of entrance was defined as the first frame of a subset where the rolling median (per 10 frames) of the tail base x-position exceeded 1100 pixels (Fig.3). Likewise, the first frame after a pigs weighing event with a rolling median tail base x-position<250 pixels was used to determine time of departure. If these criteria were not met, the first frame and/or the frame at which the weight record took place were used for the time of entrance/departure.

Determination of time window for a weight recording. (a) First, a subset is created as all tail base x-positions between time of recording of the next (orange) and previous (red) weight recording. The start time of the time window is determined as the first value before the own weight recording (green) above the threshold of 1100 pixels (dashed purple line; pig entering weighing scale). The end time of the time window is determined as the first value after the own weight recording (green) below the threshold of 250 pixels (dashed purple line; pig leaving weighing scale). (b) The extracted time window on which body part dimensions will be estimated and trajectory analysis will be performed.

Hip width, shoulder width and body length of a pig were estimated by using the median value of the distance between certain body parts over all frames for a specific weight recording (Table 1, Fig.2). These body dimensions in pixels, were transformed to metrics as 1cm was calculated to be equivalent to 29.1 pixels. The conversion ratio from pixels to centimeters was based on the distance between tiles of the weighing scale, which was known to be exactly 50cm. Total surface area was estimated using the mean value of the area calculated with the st_area function in R from the R-package sf34 using all outer body part locations. Standard deviations of the body part positions were also calculated for all frames between entrance and departure after quality control (as described above), to assess the stability of estimates.

Trajectory analysis was performed using the R-package trajr35 for left and right shoulder, left and right hip and the tail base. For each body part, pixel coordinates were extracted, trajectories were rescaled from pixels to cm and a smoothed trajectory was created using the TrajSmoothSG function. From these smoothed trajectories, the following activity-related features were derived: mean and standard deviation of speed and acceleration (TrajDerivatives), a straightness index (TrajStraightness) and sinuosity (TrajSinuosity2).

The straightness index and sinuosity are related to the concept of tortuosity and associated with an animals orientation and searching behavior35,36. The straightness index is calculated as the Euclidean distance between the start and the endpoint divided by the total length of the movement36. The straightness index is an indication of how close the animals path was to a straight line connecting the start and final point and varies from 0 to 1. Thus it quantifies path efficiency whereas the closer to 1, the higher the efficiency. In our experiment, this path efficiency will be highest when a pigs walks in a straight line during weighing (straightness index=1). Any deviations from this straight linedue to an increased activity of the pig during weighingwill lower the straightness index towards zero. Sinuosity tries to estimate the tortuosity of a random research path by combining step length and the mean cosine of an animals turning angles35,36,37. The sinuosity of a trajectory varies between 0 (random movement) and 1 (directed movement).

In this study we hypothesize that mean speed, straightness index and sinuosity are related to pigs activity during weighing. In an extreme case, a pig will walk in a straight line towards the RFID reader, stand motionless until weight is recorded and continues its walk in a straight line after the gate is opened. This would result in a low mean speed (m/s), a sinuosity >0 and a straightness index of 1. We hypothesize that more active pigs will present more lateral movements, increasing the mean speed and lowering the straightness index and sinuosity. So generally, more calm pigs during weighing will display a lower mean speed, although they might have run with a high speed towards the RFID reader.

The estimations of body dimensions using video recordings analyzed with DeepLabCut were validated by an independent set of 60 pigs after the initial experiment. These pigs came from five pens of different ages (92166days) and were measured manually for tail-neck length and hip width using a simple measuring tape. Pig surface area was estimated for the manual recordings as the multiplication of tail-neck length and hip width. The manual estimates for tail-neck length, hip width and pig surface area were then compared to the estimates from the video analysis by calculating Pearson correlations and root mean squared error (RMSE).

Automated activity traits were validated by comparing these values with manual activity scores given by five trained observers. Video footage of 1748 pig weighings were manually scored for pig activity by at least two observers per pig on a scale from 1 (calm) to 5 (very active). This ordinal activity scale was constructed based on DEath et al. and Holl et al.17,24. The average activity score per pig was then compared with automated activity scores by calculating Pearson correlations.

After estimation of body dimension and activity traits, additional quality control was performed. First, estimates of hip and shoulder width, tail-neck length and pig surface area were set to missing for records with frame by frame standard deviation estimates higher than the mean+3 standard deviations for all records. The thresholds were 10.2cm for hip distance (132 records), 11.8cm for shoulder distance (135 records), 20.6cm for tail-neck length (121 records) and 0.058 m2 for pig surface area (96 records). If the standard deviation of the estimated hip widths over frames within one weighing event of a pig was>8.9cm, the record was set to missing.

Second, for every individual with at least four records (941 pigs, 6807 records), outliers were determined using a second order polynomial regression on the variable of interest in function of age in days. Based on the distribution of the difference between observed and predicted phenotypes for all animals, a threshold for exclusion (record set to missing) was set as three times the standard deviation of the differences. The thresholds were 2.1cm for hip distance (61 records), 2.2cm for shoulder distance (58 records), 6.4cm for tail-neck length (75 records), 0.021 m2 for pig surface area (85 records) and 3.7kg for weight (86 records).

The final dataset after data cleaning included 7428 records from 1556 finishing pigs descending from 73 Pitrain sires and 204 crossbred dams. Pedigree comprised 4089 animals, where the median pedigree depth of Pitrain sires was 15 generations (min 10; max 17) and 3 (min 0; max 6) for crossbred dams.

We estimated genetic parameters (heritability and genetic correlations) using the blupf90 suite of programs38. Genetic variances and heritabilities were estimated with average information REML, implemented in airemlf90 and invoked with the R-package breedR39 with the options EM-REML 20, "use_yams" and se_covar_function. Genetic parameters were first estimated on the full dataset and thereafter on subsets per pigs weight recording (1 to 8). The first weight recording, for example, corresponds with a dataset of 1176 pigs between 78 and 89days of age (Table 2). We estimated h2 as the proportion of additive genetic variance divided by total variance, whereas the common environmental effect (c2) was estimated as the proportion of variance explained by random environmental effects (c), divided by total variance.

Genetic correlations (rg) between traits were estimated using bivariate animal models (airemlf90). Genetic correlations were first calculated between all possible trait combinations using the full dataset. Hereafter, the genetic correlations within traits for all pairwise weighing events were estimated (so two recordings of the same trait were treated as two different traits). By doing this, we can evaluate if a trait genetically changes over time.

The estimated animal models were of the form:

where y is the vector with phenotypes for the studied trait(s); b is the vector containing the fixed effects (sex, 2 levels; parity of dam, 4 levels) and covariates (age); a is the vector of additive genetic effects (4089 levels); c is the vector of random environmental effects (65 levels); e is the vector of residual effects; X, Z and W are incidence matrices for respectively fixed effects, random animal effects and random permanent environmental effects. The random environmental effect c is a combination of date of entrance at the fattening farm and weighing date. Every two weeks, a new batch of pigs arrived at fattening farm. Parity of dams consisted of four classes (1, 23, 45, 6+).

More here:
Estimating genetics of body dimensions and activity levels in pigs using automated pose estimation | Scientific Reports - Nature.com

THOUSAND OAKS, Calif., Sept. 12, 2022 /PRNewswire/ -- Amgen AMGN will present at Bank of America Merrill Lynch's 2022 Global Healthcare Conference at 4:55 a.m. ET on Thursday, Sept. 15, 2022. Peter H. Griffith, executive vice president and chief financial officer at Amgen, will present at the conference. The webcast will be broadcast over the internet simultaneously and will be available to members of the news media, investors and the general public.

The webcast, as with other selected presentations regarding developments in Amgen's business given by management at certain investor and medical conferences, can be found on Amgen's website, http://www.amgen.com, under Investors. Information regarding presentation times, webcast availability and webcast links are noted on Amgen's Investor Relations Events Calendar. The webcast will be archived and available for replay for at least 90 days after the event.

About AmgenAmgen is committed to unlocking the potential of biology for patients suffering from serious illnesses by discovering, developing, manufacturing and delivering innovative human therapeutics. This approach begins by using tools like advanced human genetics to unravel the complexities of disease and understand the fundamentals of human biology.

Amgen focuses on areas of high unmet medical need and leverages its expertise to strive for solutions that improve health outcomes and dramatically improve people's lives. A biotechnology pioneer since 1980, Amgen has grown to beone ofthe world'sleadingindependent biotechnology companies, has reached millions of patients around the world and is developing a pipeline of medicines with breakaway potential.

Amgen is one of the 30 companies that comprise the Dow Jones Industrial Average and is also part of the Nasdaq-100 index. In 2021, Amgen was named one of the 25 World's Best Workplaces by Fortune and Great Place to Work and one of the 100 most sustainable companies in the world by Barron's.

For more information, visitwww.amgen.comand follow us onwww.twitter.com/amgen.

CONTACT: Amgen, Thousand OaksMegan Fox, 805-447-1423 (media)Jessica Akopyan, 805-447-0974 (media)Arvind Sood, 805-447-1060 (investors)

SOURCE Amgen

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Amgen (NASDAQ:AMGN) AMGEN ANNOUNCES WEBCAST OF 2022 BANK OF AMERICA MERRILL LYNCH GLOBAL HEALTHCARE CON - Benzinga

BY GARETH WILLMER

Game theory mathematics is used to predict outcomes in conflict situations. Now it is being adapted through big data to resolve highly contentious issues between people and the environment.

Game theory is a mathematical concept that aims to predict outcomes and solutions to an issue in which parties with conflicting, overlapping or mixed interests interact.

In theory, the game will bring everyone towards an optimal solution or equilibrium. It promises a scientific approach to understanding how people make decisions and reach compromises in real-world situations.

Game theory originated in the 1940s in the field of economics. The Oscar-winning movieA Beautiful Mind (2001)is about the life of mathematician John Nash (played by Russell Crowe), who was awarded the1994 Nobel Prize in Economic Sciencesfor his work in this area.

Although the concept has been around for many decades, the difference now is the ability to build it into computer-based algorithms, games and apps to apply it more broadly, said Professor Nils Bunnefeld, a social and environmental scientist at the University of Stirling, UK. This is particularly true in the age of big data.

Game theory as a theoretical idea has long been around to show solutions to conflict problems, he said. We really see the potential to move this to a computer to make the most of the data that can be collected, but also reach many more people.

Conservation conflicts

Prof Bunnefeld led the EU-backedConFooBioproject, which applied game theory to scenarios where people were in conflict over resources and the environment. His team wanted to develop a model for predicting solutions to conflicts between food security and biodiversity.

The starting point was that when we have two or more parties at loggerheads, what should we do, for example, with land or natural resources? Should we produce more food? Or should we protect a certain area for biodiversity? he said.

The team focused on seven case studies, ranging from conflicts involving farmers and conservation of geese in Scotland to ones about elephants and crop raiding in Gabon.

ConFooBio conducted more than 300 game workshops with over 900 people in numerous locations including Gabon, Kenya, Madagascar, Tanzania and Scotland.

Ecological challenges

Prof Bunnefeld realised it became necessary to step back from pure game theory and instead build more complex games to incorporate ecological challenges the world currently faces, like climate change. It also became necessary to adopt a more people-based approach than initially planned, to better target the games.

Participants included people directly involved in these conflicts, and in many cases that were very unhappy, said Prof Bunnefeld.

Through the games, we got high engagement from communities, even from those where conflict is high and people can be reluctant to engage in research. We showed that people are able to solve conflicts when they trust each other and have a say, and when they get adequate payments for conservation efforts.

The team developed a modelling framework to predict wildlife management outcomes amid conflict. Freely available, it has been downloaded thousands of times from theConFooBio website.

Conservation game

The researchers also created an accessible game about conservation calledCrops vs Creatures, in which players decide between a range of options from shooting creatures to allocating habitat for conservation.

Prof Bunnefeld hopes these types of game become more available on a mainstream basis via app stores such as one on conflicts in the realm of biodiversity and energy justice in a separate initiative he works on called the Beacon Project.If you tell people you have an exciting game or you have a complex model, which one are they going to engage with? I think the answer is pretty easy, he said.

In the ConFooBio project, weve been able to show that our new models and algorithms can adapt to new situations and respond to environmental and social changes, added Prof Bunnefeld. Our models are useful for suggesting ways of managing conflicts between stakeholders with competing objectives.

Social media dynamics

Another project,Odycceus, harnessed elements of game theory to investigate what social media can tell us about social dynamics and potentially assist in the early detection of emerging social conflicts.

They analysed the language, content and opinions of social media discussions using data tools.

Such tools are required to analyse the vast amount of information in public discourse, explained Eckehard Olbrich, coordinator of the Odycceus project, and a physicist at the Max Planck Institute for Mathematics in the Sciences in Leipzig, Germany.

His work is partially motivated by trying to understand the reasons behind the polarisation of views and the growth of populist movements like far-right organisation Pegida, which was founded in his hometown of Dresden in 2014.

The team created a variety of tools accessible to researchers via an open platform known asPenelope. These included the likes of theTwitter Explorer, which enables researchers to visualise connections between Twitter users and trending topics to help understand how societal debates evolve.

Others included two participatory apps known as the Opinion Observatory and the Opinion Facilitator, which enable people to monitor the dynamics of conflict situations, such as by helping interlink news articles containing related concepts.

Patterns of polarisation

These tools have already allowed us to get a better insight into patterns of polarisation and understanding different world views, said Olbrich.

He said, for example, that his team managed to develop a model about the effect of social feedback on polarisation thatincorporated game-theoretic ideas.

The findings suggested that the formation of polarised groups online was less about the traditional concept of social media bubbles and echo chambers than the way people build their identity by gaining approval from their peers.

He added that connecting the dots between game theory and polarisation could have real-life applications for things like how best to regulate social media.

In a game-theoretic formulation, you start with the incentives of the players, and they select their actions to maximise their expected utility, he said. This allows predictions to be made of how people would change their behaviour if you, for instance, regulate social media.

Olbrich added that he hopes such modelling can furnish a better understanding of democracy and debates in the public sphere, as well as indicating to people better ways to participate in public debates. Then we would have better ways to deal with the conflicts we have and that we have to solve, he said.

But there are also significant challenges in using game theory for real-world situations, explained Olbrich.

Varying outlooks

For example, incorporating cultural differences into game theory has proved difficult because such differences may mean two people have hugely varying ways of looking at a problem.

The problem with game theory is that its looking for solutions to the way a problem can be solved, added Prof Bunnefeld.

Having looked at conflicts over the last few years, to me it is clear that we cant solve conflicts, we can only manage them. Building in factors like climate change and local context is also complex.

But game theory is a useful way to explore models, games and apps for dealing with conflicts, he said. Game theory is, from its very simple basics to quite complex situations, a good entry point, said Prof Bunnefeld.

It gives us a framework that you can work through and also captures peoples imagination.

Research in this article was funded via the EUsEuropean Research Council and originally publishedin Horizon, the EU Research and Innovation Magazine.

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Measurement of lipid flux to advance translational research: evolution of classic methods to the future of precision health | Experimental &...

What would it be if we had to pick one piece of information most important for the genetic diagnosis of rare disease patients?

The information required for genetic diagnosis is very diverse, such as the correlation between genes and diseases, the function of genes, important location information that determines gene function, and the frequency of finding genetic mutations.

However, if there is only one important piece of information, it would be the pathogenic genetic mutation information that determined a patient's diagnosis in the past.

Suppose a genome decoding of a patient waiting for diagnosis shows the same genetic mutation as the previously confirmed patient. In that case, this becomes a strong basis for diagnosis as it means that the patient waiting for diagnosis has the same disease as the previously diagnosed patient.

The increase in pathogenic mutation information means that we can secure the basis for interpreting more genetic mutations, which, in turn, means that more rare disease patients can receive an accurate diagnosis.

With the recent innovation of genome decoding technology, we can decode genome information of many patients, which helped us rapidly accumulate pathogenic mutation information.

However, it cannot be said that there is enough information on pathogenic genetic mutations to be able to diagnose rare diseases at a sufficient level.

Then how much more mutation information do we require?

The human genome is composed of three billion DNA. If we count the number of single-nucleotide variants (SNVs) where DNA at each location is converted into a different type of DNA, the number reaches nine billion.

Considering all the various types of genetic mutations, such as DNA disappearing, overlapping, and multiple DNA changes simultaneously, the number of mutations in the human genome is virtually infinite.

However, only about 1.54 million mutations are registered in ClinVar, the largest database of genetic mutations worldwide.

When compared with the nine billion SNV types, we can infer that the pathogenic information of genetic mutations currently known to mankind is insufficient.

To overcome this situation, the global clinical genetics community has established a public database with ClinVar. It collects information on the pathogenicity of genetic mutations of patients obtained through genetic testing.

If we share the genetic mutation information of the patient we have diagnosed, we can diagnose rare patients with the same mutation worldwide.

If we share the diagnosis of patients with rare diseases, everyone benefits together, and the diagnosis rate of patients with rare diseases increases.

Hospitals and major global genetic diagnosis companies are actively sharing this genetic mutation information.

In terms of numbers, companies share the most genetic mutation information. The top 8 companies, including Invitae, a U.S. company, have shared 1 million cases, GeneDx 310,000 cases, and Color Health with 70,000 cases, accounting for 72 percent of ClinVars genetic mutations database.

In Korea, 23 institutions shared 3,533 mutation data to ClinVar, with 3billion accounting for 87 percent of all reported mutations from Korea with 3,074 cases.

There are many reasons why diagnostic companies actively share pathogenic genetic mutation information, which is an asset to the company.

However, after recognizing the limitation that a single institution cannot secure the necessary and sufficient number of genetic mutation information for the diagnosis of genetic diseases, companies are voluntarily sharing data to fulfill their mission and responsibility as a diagnosis company if it means they can accurately diagnose even a single patient.

Governments worldwide invest huge amounts of money to secure patient genetic mutation information through large-scale genome research projects and disclose them for public use.

Korea has currently secured the data of 15,000 patients with rare diseases through a national bio big data pilot project. In addition, the government is planning to disclose pathogenic genetic mutation information so it can be used for public purposes.

The genetic mutation information accumulated on a large scale by the government, business, and academia is not only used as evidence data for direct patient diagnosis. Still, it is also used as learning and test data for artificial intelligence models for pathogenicity prediction.

In addition, it can be utilized in interpreting genetic mutations for which there is no basis and as a resource to improve rare genetic disease diagnosis technology in various forms.

When everyone makes some sacrifices and helps each other, the entire ecosystem can prosper to a greater extent.

The cooperation of global governments, companies, and academia to disclose genetic variation information was possible because they prioritized the public good of diagnosing and treating patients with rare genetic diseases.

Companies that disclose genetic mutations that have sacrificed their potential profit-making data for the public good are rewarded with improved market credibility and growth.

In the future, I look forward to the active data sharing between the government, academia, and companies for diagnosing and treating rare genetic diseases and using this virtuous cycle to establish a growing ecosystem.

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[3billion's Rare Disease Story] United we stand strong: Genetic diagnosis that evolves through information sharing - KBR

The identified variant p.(Gln77Ter) is new and absent from the Genome Aggregation Database. It was evidenced that pathogenic variants in BICD2 are extremely rare in the population, predicted to be damaging by most tools, and occur in specific hotspots within key BICD2 functional domains [8]. Furthermore, WES did not identify any variant(s) in any of the OMIM genes with an acknowledged disease association (including VPS13B gene). Although BICD2 is essential for the proper development of the cerebral cortex [5] but there have been no other clinical reports of individuals with loss of-function variants in BICD2 showing lissencephaly and cerebellar hypoplasia. However, lissencephaly and cerebellar hypoplasia are consistent with that observed after BICD2 knockdown in mice showing defects in laminar organization of the cerebral cortex, hippocampus and cerebellar cortex, indicative of radial neuronal migration defects. Cell-specific inactivation of BICD2 in astrocytes and neuronal precursors revealed that radial cerebellar granule cell migration is non-cell-autonomous and intrinsic to cerebellar Bergmann glia cells [9, 10]. Therefore, we considered BICD2 to be a convincing candidate gene in the context of lissencephaly and cerebellar hypoplasia. The absence of homozygous loss of function BICD2 variants in the healthy family members supports the clinical relevance of BICD2.

Recently, biallelic variant c.731T>C p.(Leu244Pro) in BICD2 was described in a girl with abnormal gyral pattern in fronto-temporo-parietal regions [6] (Table1). The girl displayed additionally moderate intellectual disability and Cohen-like features [6]. In comparison, our patient showed congenital microcephaly, profound delay, seizures, lissencephaly and cerebellar hypoplasia. Unlike the patient with Cohen-like features, our patient showed spasticity and developed contracture deformities and did not show neutropenia. Interestingly, a heterozygous missense variant c.2080C>T, p.(Arg694Cys) was reported in two unrelated patients with mild perisylvian polymicrogyria, and mild cerebellar vermis hypoplasia [4]. Moreover, a BICD2 nonsense variation p.(Lys775Ter) was identified in a boy with lissencephaly and subcortical band heterotopia [5]. These heterozygous variants are located within the highly conserved CC3 domain of BICD2 (Table1). Nevertheless, the heterozygous missense variants within the CC1 domain were not associated with abnormalities of cortical development but even showed a milder course of SMALED2A and a higher frequency of foot deformities [8]. Indeed, a larger cohort is required to draw conclusions regarding genotype-phenotype correlations.

Lissencephaly and cerebellar hypoplasia noticed in our patient appeared similar to those with LIS1 variants. This is not surprising as LIS1 interacts with the dynein/dynactin complex and BICD2 to recruit cellular structures [11]. In the mean time, these brain MRI features may overlap with RELN-mutated patients phenotype. However, the cortical migration defect was more severe in our patient than in RELN-mutated patients. In addition, our patient had mild cerebellar hypoplasia unlike RELN-mutated patients who had profoundly hypoplastic and dysplasic cerebellum with no identifiable folia [12].

Our study provides valuable findings into human developmental brain malformations disorders associated with definitive loss-of function variants in BICD2.

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A homozygous loss-of-function variant in BICD2 is associated with lissencephaly and cerebellar hypoplasia | Journal of Human Genetics - Nature.com

CAMBRIDGE, Mass.--(BUSINESS WIRE)--Alnylam Pharmaceuticals, Inc. (Nasdaq: ALNY), the leading RNAi therapeutics company, announced today that the Company and collaborators have identified mutations in the INHBE gene associated with protection against abdominal obesity and metabolic syndrome a condition impacting more than 20 percent of adults worldwide. The discovery leveraged sequencing data from more than 360,000 individuals in UK Biobank, and was published in the 13th issue of Nature Communications. The published data show that rare mutations in the liver-expressed INHBE gene are associated with lower waist-to-hip ratio adjusted for body mass index (WHRadjBMI), a surrogate for abdominal fat that is causally linked to type 2 diabetes and coronary heart disease. Findings support the potential of INHBE to be evaluated as a novel therapeutic target for the treatment of cardiometabolic disease. The Company plans to pursue a development candidate for INHBE and its gene product, Activin E, leveraging its liver IKARIA platform.

We are thrilled that our investment in genetic databases like UK Biobank is proving to be fruitful in identifying novel targets in highly prevalent diseases with continued unmet need, said Paul Nioi, Ph.D., Vice President, Discovery and Translational Research, and the Leader of Alnylams Human Genetics Group. There is a well-established causal link between increased waist-to-hip ratio and a persons risk of cardiometabolic conditions. By exploring the genetic determinants of waist-to-hip ratio in this study, important insights into the mechanisms that contribute to body fat distribution were uncovered helping identify potential therapeutic targets for abdominal obesity, like INHBE. The results of this exome-wide analysis suggest that targeting INHBE is predicted to have broad beneficial effects on all facets of metabolic syndrome with potential reductions in the risk of type 2 diabetes and coronary heart disease. We are currently testing this hypothesis, with the goal of pursuing a development candidate targeting INHBE in the near future.

We are delighted to see that the uniquely detailed data within UK Biobank - generously donated by our half a million participants - is accelerating research into important health conditions. Thanks to the collaboration with leading life sciences companies in the UK Biobank Exome Sequencing Consortium, the UK Biobank resource is helping to rapidly identify new therapeutic targets for abdominal obesity, said Professor Naomi Allen, UK Biobank Chief Scientist.

Using whole exome-sequencing data from UK Biobank, Alnylam and collaborators mined for gene variants associated with lower WHRadjBMI in more than 360,000 individuals of European ancestry, revealing loss of function in INHBE as a novel genetic factor contributing to a healthier fat distribution. Rare predicted loss of function (pLOF) variants in INHBE, were carried by one in 587 individuals and were associated with lower abdominal fat. In vitro characterization of the most common INHBE pLOF variant in the study, indicated an approximately 90% reduction in secreted activin E levels. Further analysis of INHBE pLOF carriers revealed a favorable metabolic profile, including decreased triglycerides, increased high-density lipoprotein cholesterol, and decreased fasting glucose. There were no associations suggesting adverse effects of INHBE pLOF, and carriers of these variants did not show evidence of excess mortality. The study also detected associations with lower WHRadjBMI for variants in ACVR1C, encoding an activin receptor, further highlighting the involvement of activins in regulating fat distribution.

About UK Biobank

UK Biobank is a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants. The database, which is regularly augmented with additional data, is globally accessible to approved researchers and scientists undertaking vital research into the most common and life-threatening diseases. UK Biobanks research resource is a major contributor to the advancement of modern medicine and treatment and has enabled several scientific discoveries that improve human health.

The UK Biobank Exome Sequencing Consortium (UKB-ESC)

In 2018, Alnylam and partners Regeneron, AbbVie, AstraZeneca, Biogen, and Pfizer announced an agreement with UK Biobank to form the UK Biobank Exome Sequencing Consortium (UKB-ESC), a pre-competitive consortium that aims to sequence the whole exomes of 500,000 volunteer participants in the biomedical resource. The goal of the consortium, which represents the largest ever effort to use genome sequencing to map the DNA of a group of people, is to uncover insights that allow researchers to pinpoint new drug targets at the core of human disease in order to develop effective treatments for patients. To date, the UKB-ESC has made whole-exome sequencing data from 450,000 participants available to the global health community for research purposes and will continue to make all sequenced data available at no cost under the terms of the UKB-ESC charter and the founding principles of UK Biobank.

About Cardiometabolic Disease

Cardiometabolic diseases are the number one cause of death in the world; these include but are not limited to cardiovascular disease, obesity, diabetes mellitus, and non-alcoholic fatty liver disease. An estimated 47 million people in the U.S. alone are living with some form of cardiometabolic disease. Despite the availability of many well-established treatments for cardiometabolic diseases, the substantial mortality associated with this group of diseases underscores the high unmet medical need for new therapeutic options, including those directed to novel disease-modifying targets, and with potential to address poor medication adherence.

About IKARIA Platform

Alnylams IKARIA platform takes advantage of more than two decades of experience in developing RNAi therapeutics. IKARIA enables an extended duration of activity in preclinical studies, with potential for annual dosing in humans, and has design features which provide exquisite specificity, further widening the potential therapeutic index, with enhanced target reduction levels.

About RNAi

RNAi (RNA interference) is a natural cellular process of gene silencing that represents one of the most promising and rapidly advancing frontiers in biology and drug development today. Its discovery has been heralded as "a major scientific breakthrough that happens once every decade or so," and was recognized with the award of the 2006 Nobel Prize for Physiology or Medicine. By harnessing the natural biological process of RNAi occurring in our cells, a new class of medicines, known as RNAi therapeutics, is now a reality. Small interfering RNA (siRNA), the molecules that mediate RNAi and comprise Alnylam's RNAi therapeutic platform, function upstream of todays medicines by potently silencing messenger RNA (mRNA) the genetic precursors that encode for disease-causing or disease pathway proteins, thus preventing them from being made. This is a revolutionary approach with the potential to transform the care of patients with genetic and other diseases.

About Alnylam Pharmaceuticals

Alnylam (Nasdaq: ALNY) has led the translation of RNA interference (RNAi) into a whole new class of innovative medicines with the potential to transform the lives of people afflicted with rare and prevalent diseases with unmet need. Based on Nobel Prize-winning science, RNAi therapeutics represent a powerful, clinically validated approach yielding transformative medicines. Since its founding 20 years ago, Alnylam has led the RNAi Revolution and continues to deliver on a bold vision to turn scientific possibility into reality. Alnylams commercial RNAi therapeutic products are ONPATTRO (patisiran), GIVLAARI (givosiran), OXLUMO (lumasiran), AMVUTTRA (vutrisiran), and Leqvio (inclisiran) being developed and commercialized by Alnylams partner, Novartis. Alnylam has a deep pipeline of investigational medicines, including six product candidates that are in late-stage development. Alnylam is executing on its Alnylam P5x25 strategy to deliver transformative medicines in both rare and common diseases benefiting patients around the world through sustainable innovation and exceptional financial performance, resulting in a leading biotech profile. Alnylam is headquartered in Cambridge, MA. For more information about our people, science and pipeline, please visit http://www.alnylam.com and engage with us on Twitter at @Alnylam, on LinkedIn, or on Instagram.

Alnylam Forward Looking Statements

Various statements in this release concerning Alnylam's future expectations, plans and prospects, including, without limitation, Alnylams views with respect to pursuing INHBE as a therapeutic target for cardiometabolic disease and its goal to identify a development candidate targeting INHBE in the near future, Alnylams aspiration to become a leading biotech company, and the planned achievement of its Alnylam P5x25 strategy, constitute forward-looking statements for the purposes of the safe harbor provisions under The Private Securities Litigation Reform Act of 1995. Actual results and future plans may differ materially from those indicated by these forward-looking statements as a result of various important risks, uncertainties and other factors, including, without limitation: the direct or indirect impact of the COVID-19 global pandemic or any future pandemic on Alnylams business, results of operations and financial condition and the effectiveness or timeliness of Alnylams efforts to mitigate the impact of the pandemic; the potential impact of the recent leadership transition on Alnylams ability to attract and retain talent and to successfully execute on its Alnylam P5x25 strategy; Alnylam's ability to discover and develop novel drug candidates, including a development candidate targeting INHBE, and delivery approaches, and successfully demonstrate the efficacy and safety of its product candidates; the pre-clinical and clinical results for its product candidates; actions or advice of regulatory agencies and Alnylams ability to obtain and maintain regulatory approval for its product candidates, as well as favorable pricing and reimbursement; successfully launching, marketing and selling its approved products globally; delays, interruptions or failures in the manufacture and supply of its product candidates or its marketed products; obtaining, maintaining and protecting intellectual property; Alnylams ability to successfully expand the indication for OXLUMO, ONPATTRO and AMVUTTRA in the future; Alnylam's ability to manage its growth and operating expenses through disciplined investment in operations and its ability to achieve a self-sustainable financial profile in the future without the need for future equity financing; Alnylams ability to maintain strategic business collaborations; Alnylam's dependence on third parties for the development and commercialization of certain products, including Novartis, Sanofi, Regeneron and Vir; the outcome of litigation; the potential impact of current and the risk of future government investigations; and unexpected expenditures; as well as those risks more fully discussed in the Risk Factors filed with Alnylam's most recent Quarterly Report on Form 10-Q filed with the Securities and Exchange Commission (SEC) and in its other SEC filings. In addition, any forward-looking statements represent Alnylam's views only as of today and should not be relied upon as representing its views as of any subsequent date. Alnylam explicitly disclaims any obligation, except to the extent required by law, to update any forward-looking statements.

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Alnylam Uncovers Genetic Mutations in INHBE That Protect Against Abdominal Obesity - Business Wire

James Noonan

James Noonan, who has made critical and novel contributions to the fields of human evolutionary genetics and neurodevelopment, was recently appointed the Albert E. Kent Professor of Genetics and Professor of Neuroscience, effective immediately.

Noonan received his B.S. in biology and English literature from the State University of New York at Binghamton in 1997, and his Ph.D. in genetics from Stanford University School of Medicine in 2004. He completed a postdoctoral fellowship in the Genomics Division at Lawrence Berkeley National Laboratory from 2004 to 2007. In 2007, he was recruited to Yale as assistant professor and was promoted to associate professor in 2013, and professor in 2021. He has a secondary appointment in Yales Department of Neuroscience.

Noonans research program is focused on deciphering the role of gene regulatory changes in the evolution of uniquely human traits. This work addresses a central hypothesis in human evolution, proposed more than 40 years ago: that changes in the level, timing, and location of gene expression account for biological differences between humans and other primates. Noonan has discovered thousands of human-specific genetic changes that alter gene expression and regulation, and by pioneering novel genetic models, his lab has begun to reveal how human-specific regulatory changes alter developmental traits. His work has provided key insights into the genetic origins of human biological uniqueness and has driven the rise of a new field: human evolutionary developmental biology.

Noonans seminal research discovered two classes of gene regulatory elements implicated in human evolution. The first are Human Accelerated Regions (HARs), which encode transcriptional enhances which are highly conserved across species and show many human-specific sequence changes (Science 2006, Science 2008). Using humanized mouse models, he has shown that HARs alter developmental gene expression and drive the evolution of novel phenotypes. As an example, he recently showed that one HAR altered expression of a transcription factor that has a role in limb development, possibly contributing to changes in skeletal patterning in human limb evolution (Nature Communications, 2022). These findings provide mechanistic insight into how HARs modified gene expression in human evolution. Using massively parallel assays, he has also comprehensively characterized the effect of thousands of human-specific sequence changes in HARs on their activity during neurodevelopment (Proceedings of the National Academy of Sciences, 2021)

He also identified thousands of human-specific changes in enhancer activity by direct analysis of developing human and nonhuman tissues. These loci, termed Human Gain Enhancers (HGEs), have gained activity in the developing human limb (Cell, 2013) and cerebral cortex (Science, 2015). These studies identified the biological pathways in limb and cortical development likely altered by human-specific regulatory changes, providing the basis for understanding their effects using genetic and experimental models.

Noonan has also contributed substantially to the educational programs of Yale School of Medicine, revolutionizing its graduate training landscape and empowering experimental genetics research across many labs at Yale. He designed the first course in genomics in the medical school more than 12 years ago, serving hundreds of students and faculty with the skills required to excel at the frontier of modern biomedical science. His training efforts have helped to set the standards of genomic research at Yale and ensured that the university remains a world leader in genomics.

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Noonan appointed Kent Professor of Genetics and Professor of Neuroscience - Yale News

The biotech sector can offer unique opportunities and strong returns to investors willing to stomach the volatility heightened by the current economic environment.

Shares of Verve Therapeutics (VERV) traded up 22% last Tuesday after the companys recent announcement that it dosed its first human patient with an investigational in vivo base-editing medicine, VERVE-101, as a potential treatment for heterozygous familial hypercholesterolemia, according to recent commentary from ARK Invest.

The treatment from the Cambridge, Massachusetts-headquartered company could offer an alternative for hypercholesterolemia patients who have difficulty managing the side effects of statins and other therapeutic options. Founded by world-renowned experts in cardiovascular medicine, human genetics, and gene editing, Verve Therapeutics develops transformative once-and-done therapies for coronary heart disease, according to ARK.

Shares of Verve Therapeutics are up over 83% over a one-month period, according to YCharts. Over a five-day period, shares are down over 7%, but they are rebounding and up nearly 1% in mid-day trading on Tuesday.

Investors can get exposure to Verve Therapeutics with the ARK Genomic Revolution ETF (ARKG A-). ARKG is an actively managed equity strategy that aims to provide exposure to DNA sequencing technology, gene editing, CRISPR, therapeutics, agricultural biology, and molecular diagnostics.

Companies within ARKG are focused on and are expected to substantially benefit from extending and enhancing the quality of human and other life by incorporating technological and scientific developments and advancements in genomics into their businesses, according to the firm.

The funds top holdings as of July 26 include Exact Sciences Corp. (EXAS, 7.42%), Teladoc Health Inc. (TDOC, 5.56%), Ionis Pharmaceuticals Inc. (IONS, 5.30%), CRISPR Therapeutics AG (CRSP, 4.78%), and Signify Health Inc. Class A (SGFY, 4.70%), according to the funds website.

ARKG typically holds between 40 and 60 securities and charges an expense ratio of 75 basis points.

For more news, information, and strategy, visit our Disruptive Technology Channel.

Excerpt from:
Verve Therapeutics Shares Up 84%; ARKG Offers Exposure - ETFdb.com