Category Archives: Genetic medicine

Every year around 460,000 people are admitted to hospital for chronic heart failure just in Germany. More than any other disease. The causes of chronic heart failure include conditions such as high blood pressure, other types of coronary heart disease, heart attacks, or myocarditis. On top of that, some forms of myocardial disease also have a genetic predisposition.

The repercussions of this disease are a significant decline in the quality of life and a higher, premature mortality rate. Treatment of the disease still poses problems. Although there are currently effective drugs available to treat heart failure and especially its underlying conditions, the number of hospitalizations and mortality rates are still very high. However, new research data now offers cause for hope. Two drugs from the group of SGLT 2 inhibitors originally developed for diabetes mellitus have proven to be very effective against heart failure: Dapagliflozin and empagliflozin.

SGLT2 inhibitors block glucose reabsorption back into the blood from the so-called primary urine. This leads to a loss of glucose and thus to a lowering of blood sugar levels. Apart from the loss of glucose via the kidneys, these drugs also cause the body to lose sodium. This consequently leads to metabolic changes that could have a beneficial effect on the heart.

In two large studies conducted in Germany under the direction of the German Cardiology Society (DGK) / Heart and Circulation Research, the effect of the two drugs on cardiac failure has now been examined. These are large multi-center studies with a total of more than 8,000 patients who received double-blind and randomized treatment. Patients with heart failure, with or without diabetes, and with limited ventricular function were included. All of whom continued to receive the optimal standard of care for heart failure.

Both studies revealed that the risk of cardiovascular death and heart failure hospital admissions decreased by approximately 25%. The scientists reported that the effects were comparable in both studies regardless of modern complementary treatments. And in patients with and without diabetes mellitus.

What is impressive is the consistent decline in heart failure complications in diabetics and non-diabetics in these studies, says Prof. Dr. Michael Bhm, press spokesperson of the DGK and scientific director of both studies for Germany. This shows that it is possible to turn a diabetes medication into an effective heart failure medication with proven efficacy in non-diabetics. These study outcomes are really good news for all patients with heart failure. To date, no other drug has shown such convincing results, even more so because it also significantly improves kidney function, emphasizes Prof. Andreas Zeiher, president of the German Society of Cardiology.

Bhm now expects that the SGLT2 inhibitors will most likely be included in the European Guidelines for the Diagnosis and Therapy of Heart Failure, which is due to be published in 2021. This new heart failure guideline is currently being prepared by an international panel of experts.

The results of both studies were published in the New England Journal of Medicine.https://www.nejm.org/doi/10.1056/NEJMc1917241https://www.nejm.org/doi/full/10.1056/NEJMoa2022190

Link:
Diabetes medication effective against chronic heart failure - Innovation Origins

LOS ANGELES, Sept. 23, 2020 (GLOBE NEWSWIRE) -- Dr. Nicole OBrien, a critical care physician and director of the Global Health Certificate Program at Nationwide Children's, has spent the last six years working to understand and address acute neurologic illness and injury in children. Her years of research took her to sub-Saharan Africa, where up to 3% of births, or over 300,000 babies, are born with sickle cell disease each year. NovaSignal, a medical technology company whose mission is to save lives by unlocking the hidden power of blood flow data, has donated eight of its patented Lucid TCD systems to Nationwide Childrens Hospital in support of Dr. OBriens work in Africa.

Sickle cell disease is a genetic condition that affects the shape of red blood cells. One of the major causes of morbidity and mortality of patients with sickle cell is a stroke. Due to an abnormal presence of hemoglobin, the red blood cells become rigid and sickle shaped, making it harder for them to move through blood vessels and get oxygen to the cells and tissues that need it. The cells rigid nature causes clumping along arteries, damaging the walls and exposing tissue to more sickle cells, which further narrows the artery. It is this narrowing that can cause strokes in children and adults. For the many children living with sickle cell disease, routine screening using transcranial Doppler (TCD) ultrasound coupled with regular blood transfusion therapy can significantly decrease their risk of stroke.

Thanks to the generous donation from NovaSignal, Nationwide Childrens work in Africa will benefit from better insight into sickle cell disease and the therapies needed to treat the many children diagnosed with it. The newly released NovaSignal Lucid TCD systems assess blood flow velocity in the main cerebral arteries to monitor for neurological, cardiac, and pulmonary disorders. NovaSignal also produces the first and only fully autonomous cerebral ultrasound that incorporates robotics and artificial intelligence to automatically collect cerebral blood flow data and inform the diagnosis. The use of these systems, especially in early interventions, will provide doctors and medical teams with the critical data needed to treat their young sickle cell patients and avoid stroke.

My doctoral work in Malawi is what inspired me to start NovaSignal and having this opportunity to give back is a very special moment. We will continue to support Dr. OBrien and Dr. Taylor in their important work of improving the lives of children throughout Africa and the rest of the world, said Robert Hamilton, Founder and Chief Scientific Officer, NovaSignal.

Thanks to this donation and ongoing collaboration, well be able to continue our lifesaving work and research battling devastating neurological diseases in African children. This really is a game changer in terms of the scope of work we can accomplish, and we are so grateful, said Dr. OBrien.

We are fortunate to partner with Nationwide Childrens Hospital in support of their critical work, said Diane Bryant, Chairman and CEO, NovaSignal.NovaSignal is committed to developing technology to diagnose life-threatening illnesses. Theres no more fulfilling opportunity than to support the health of the children of Africa sufferingfrom sickle cell and cerebral malaria.

About Nationwide Children's Hospital

Named to the Top 10 Honor Roll onU.S. News & World Reports2020-21 list of Best Childrens Hospitals, Nationwide Childrens Hospital is one of Americas largest not-for-profit freestanding pediatric health care systems providing wellness, preventive, diagnostic, treatment and rehabilitative care for infants, children and adolescents, as well as adult patients with congenital disease. Nationwide Childrens has a staff of more than 13,000 providing state-of-the-art pediatric care during more than 1.6 million patient visits annually. As home to the Department of Pediatrics of The Ohio State University College of Medicine, Nationwide Childrens physicians train the next generation of pediatricians and pediatric specialists. The Abigail Wexner Research Institute at Nationwide Childrens Hospital is one of the Top 10 National Institutes of Health-funded freestanding pediatric research facilities. More information is available atNationwideChildrens.org.

About NovaSignal

Founded in 2013, NovaSignal Corp. is a medical technology company whose mission is to save lives by unlocking the hidden power of blood flow data. The companys FDA-cleared NovaGuide Intelligent Ultrasound combines non-invasive ultrasound, robotics, and artificial intelligence to assess real-time cerebral blood flow. Through the use of cloud computing and data analytics, NovaSignal supports physicians in their clinical decision making to help improve patient outcomes.

About the Nationwide Children's Hospital Foundation

In 1892, a group of citizens held a sale to raise money for the care of sick, impoverished children. That effort led to the opening of Nationwide Childrens Hospital in 1894. For more than a century, we have existed to care for every child, for every reason. The Foundation is the fundraising arm of Nationwide Childrens. Our job with your help is to ensure much needed funds are available for innovative research, emerging trends in pediatric health, and to react to our ever changing world. Philanthropy ensures that we can dream big. Learn more at NationwideChildrens.org/giving.

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NovaSignal Corp. and Nationwide Children's Hospital Partner to Improve the Lives of Children with Sickle Cell Disease - GlobeNewswire

While identifying new genetic targets and developing novel drugs is important for the future of non-small cell lung cancer (NSCLC), more emphasis should be put on improving patient access to existing targeted treatments, according to Dr. Nathan A. Pennell.

In an interview with OncLive, CUREs sister publication, Pennell, an associate professor in thedepartment of medicine and director of the lung cancer medical oncology program at theTaussig Cancer Institute of Cleveland Clinic,spoke about current and emerging treatment options in NSCLC, including immunotherapy combinations and personalized treatments involving T cells.

But when it comes to the future, Pennell said, identifying targetable genetic alterations in patients and treating them with existing drugs should be a key area of focus.

Studies have shown that probably fewer than half of people with targetable genetic alterations in lung cancer are being identified and never receiving treatment for this, Pennell said, and I think before we move on to the next exciting drug or the next exciting marker, we should spend a little time making sure that every patient is identified and gets access to the treatments that we already have.

Transcription:

We've made such tremendous progress over the last decade. And just it seems like every year, new targets are emerging and new drugs are getting approved. And so, the speed with which we're moving from discovery to actually treating people has been staggering, and I hope that continues.

There continue to be very promising emerging biomarkers including KRAS mutations, again, HER2 mutations. There certainly is lots of room for improving the efficacy of immunotherapy, which can be tremendously life changing and potentially even curative in patients with metastatic disease. But unfortunately, it's only really working in a minority of patients and so lots of room to be improved in that.

I think combinations of immunotherapy and perhaps even more personalized immunotherapy, using T-cells that recognize individual patients tumors, may be the future for this, or personalized tumor vaccines.

But honestly, instead of just focusing on discovering new treatments and new targets, I think we should focus more on applying what we already know. So, we have tremendous treatments for patients with specific subgroups of lung cancer, but studies have shown that probably fewer than half of people with targetable genetic alterations in lung cancer are being identified and never receiving treatment for this. And I think before we move on to the next exciting drug or the next exciting marker, we should spend a little time making sure that every patient is identified and gets access to the treatments that we already have.

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Personalized Medicine, Genetic Testing Could Shape the Future of Non-Small Cell Lung Cancer - Curetoday.com

By UW Medicine | Newsroom

The immune system usually mounts a strong immune response to infection by SARS-CoV-2, the virus that causes COVID-19. That defensive response, however, appears to be weaker in men and people over the age of 60, a study led by researchers at the University of Washington School of Medicine in Seattle has found.

There were some early studies that suggested that there was a fairly weak antiviral response shortly after infection, but we found a very robust immune response in patients at the time of symptom onset, said lead author Nicole Lieberman. But differences in the immune response in older individuals and men may contribute to the greater severity and higher mortality we see in these groups.

Lieberman is a research scientist in the laboratory of Alex Greninger. He is an assistant professor in the department of Laboratory Medicine and Pathology at the University of Washington School of Medicine and head of the project. The results of the study appear in the open-access journal PLOS Biology. Click here for the paper.

In the study, the researchers compared samples swabbed from the noses and throats of 430 people who were infected with SARS-CoV-2 and 54 people who were not. They also worked with colleagues at Columbia University Medical Center in New York City and University of Texas Medical Branch, Galveston, Texas. These groups have developed techniques to infect cells in culture to track changes in the immune response over time.

To assess immune responses the researchers analyzed the RNA in the samples. Because the SARS-CoV-2 stores its genetic instructions in RNA, levels of viral RNA in the samples revealed the amount of virus, or viral load, an indicator of the severity of infection. In human cells. On the other hand, RNA reveals which proteins the cells are producing in response to the infection. Thats because, for the instructions for synthesizing proteins encoded in the DNA of genes to be read by the cells, the code must first be copied, into RNA. As a result, analyzing the RNA transcriptsin a sample can show which genes are being dialed up in response to the infection and which are being dialed down. This sort of analysis can reveal what sort of immune counterattack the cells are mounting against the virus.

The researchers found that the viral load in these patients was high, but also that SARS-CoV-2 triggers a strong antiviral response. This includes up-regulation of genes for a number of antiviral factors that activate the cells defenses against viral invaders. It also includes chemical signals that summon immune cells to fight the infection, such as interferons and chemokines.

The viral load with SARS-CoV-2 infection is one of the highest seen, Greninger said. But the immune response is very strong, and the higher the viral load, the stronger the response.

However, in older individuals over age 60, infection did not activate genes to summon virus-fighting cells called cytotoxic T cells and natural killer cells that are some of the bodys the most effective antiviral weapons.

The older patients activate a weaker immune response like a singer that just cant hit the high notes anymore, Greninger said.

The researchers also found that men mounted a less vigorous response compared to women. The males produced lower levels of transcripts of some anti-viral proteins, and pumped out some proteins that put a damper on the immune response.

In men were seeing an up-regulation of signals that turn off the immune system, Lieberman said. Its speculation, but it appears as though some men may throttle back their immune system too soon before mounting an effective response to infection.

This work was supported by National Institutes of Health (AI146980, AI121349, and NS091263) and the Department of Laboratory Medicine and Pathology at the UW School of Medicine.

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Abstract

Large-scale genomic and transcriptomic initiatives offer unprecedented insight into complex traits, but clinical translation remains limited by variant-level associations without biological context and lack of analytic resources. Our resource, PhenomeXcan, synthesizes 8.87 million variants from genome-wide association study summary statistics on 4091 traits with transcriptomic data from 49 tissues in Genotype-Tissue Expression v8 into a gene-based, queryable platform including 22,515 genes. We developed a novel Bayesian colocalization method, fast enrichment estimation aided colocalization analysis (fastENLOC), to prioritize likely causal gene-trait associations. We successfully replicate associations from the phenome-wide association studies (PheWAS) catalog Online Mendelian Inheritance in Man, and an evidence-based curated gene list. Using PhenomeXcan results, we provide examples of novel and underreported genome-to-phenome associations, complex gene-trait clusters, shared causal genes between common and rare diseases via further integration of PhenomeXcan with ClinVar, and potential therapeutic targets. PhenomeXcan (phenomexcan.org) provides broad, user-friendly access to complex data for translational researchers.

Unprecedented advances in genetic technologies over the past decade have identified over tens of thousands of variants associated with complex traits (1). Translating these variants into actionable targets for precision medicine or drug development, however, remains slow and difficult (2). Existing catalogs largely organize associations between genetic variants and complex traits at the variant level rather than by genes and often are confined to a narrow set of genes or traits (3). This has greatly limited development and application of large-scale assessments that account for spurious associations between variants and traits. As a result, only 10% of genes are under active translational research, with a strong bias toward monogenic traits (4, 5).

Complex diseases are generally polygenic, with many genes contributing to their variation. Concurrently, many genes are pleiotropic, affecting multiple independent traits (6). Phenome-wide association studies (PheWAS) aim to complement genome-wide association studies (GWAS) by studying pleiotropic effects of a genetic variant on a broad range of traits. Many PheWAS databases aggregate individual associations between a genetic variant and a trait, including GeneATLAS [778 traits from the UK Biobank (http://geneatlas.roslin.ed.ac.uk/trait/)] (7), GWAS Atlas [4155 GWAS examined over 2965 traits (https://atlas.ctglab.nl/)] (8), and PhenoScanner [more than 5000 datasets examined over 100 traits (www.phenoscanner.medschl.cam.ac.uk/)] (9). Other PheWAS databases are constructed on the basis of polygenic scores estimated from multiple variants per GWAS locus (10), latent factors underlying groups of variants (11), or variants overlapping between GWAS and PheWAS catalogs (12). By building associations directly from variants (most of which are noncoding), most PheWAS results lack mechanistic insight that can support proposals for translational experiments. Genes are primarily assigned to PheWAS results by genomic proximity to significant variants, which can be misleading (13). Some studies have attempted to improve translation of PheWAS results using gene sets and pathways (14) or networks of PheWAS variants and diseases (15, 16). However, these studies rely on the same variant-trait associations on which PheWAS are built and fall short of prioritizing likely actionable targets.

Integration of genomic, transcriptomic, and other regulatory and functional information offers crucial justification for therapeutic target identification efforts, such as drug development (17). Translational researchers also need access to this integrated information in a comprehensive platform that allows convenient investigation of complex relationships across multiple genes and traits.

To meet this need, we present PhenomeXcan, a massive integrated resource of gene-trait associations to facilitate and support translational hypotheses. Predicted transcriptome association methods test the mediating role of gene expression variation in complex traits and organize variant-trait associations into gene-trait associations supported by functional information (1820). These methods can describe direction of gene effects on traits, supporting how up- or down-regulation may link to clinical presentations or therapeutic effects. We trained transcriptome-wide gene expression models for 49 tissues using the latest Genotype-Tissue Expression (GTEx; v8) data (21) and tested the predicted effects of 8.87 million variants across 22,515 genes and 4091 traits using an adaptation of the PrediXcan method (18), Summary-MultiXcan (S-MultiXcan), that uses summary statistics and aggregates results across tissues (22). We then prioritized genes with likely causal contributions to traits using colocalization analysis (23). To make computation feasible given the large scale of data in this study, we developed fastENLOC (fast enrichment estimation aided colocalization analysis), a novel Bayesian hierarchical colocalization method. We showed separately that this approach of combining an association and a colocalization method performs better than each method individually at prioritizing causal genes and is comparable to baselines such as the nearest gene while incorporating greater biological context (24). We demonstrate results from integrating this tool with a deeply annotated gene-trait dataset to identify associations; this integration can be performed in any deeply annotated database of genes and traits, including molecular or biological traits rather than disease traits. PhenomeXcan is the first massive gene-based (rather than variant-based) trait association resource. Our approach not only uses state-of-the-art techniques available to biologically prioritize genes with possible contributions to traits but also presents information regarding pleiotropy and polygenicity across all human genes in an accessible way for researchers. Below, we provide several examples that showcase the translational relevance and discovery potential that PhenomeXcan offers.

We built a massive gene-to-phenome association resource that integrates GWAS results with gene expression and regulation data. We ran a version of PrediXcan (18), S-MultiXcan, designed to use summary statistics and aggregate effects across tissues (22) on publicly available GWAS. In total, we tested the predicted effects of 8.87 million variants across 22,515 genes and 4091 traits from publicly available GWAS summary statistics (see Supplementary Materials). Traits incorporate binary, categorical, or continuous data types and range from basic anthropometric measurements to clinical traits and biochemical markers. We inferred association statistics (P values and Z scores) between predicted gene-expression variation and traits using optimal prediction models trained using 49 tissues from GTEx v8 (21, 25). LD (linkage disequilibrium) contamination due to proximity between expression quantitative trait loci (eQTLs) and causal variants can produce noncausal, spurious gene-trait associations (21, 24). We therefore first performed Bayesian fine mapping using the DAP-1/fgwas algorithm in TORUS (26, 27). We then calculated the posterior probability of colocalization between GWAS loci and cis-eQTLs to prioritize possible causal genes via fastENLOC, a newly developed Bayesian hierarchical method that uses precomputed signal clusters constructed from fine mapping of eQTL and GWAS data to speed up colocalization calculations (see Supplementary Materials). The result is a matrix of 4091 traits and 22,515 genes in which each intersection contains a PrediXcan P value aggregated across 49 tissues and refined by a locus regional colocalization probability (locus RCP) (Fig. 1). While a given colocalization threshold may be arbitrary, to minimize false negatives given the conservative nature of colocalization approaches (24), we defined putative causal gene contributors as those genes with locus RCP >0.1.

Blue areas highlight methods that we performed for this project, with fastENLOC being a novel colocalization method developed in the context of PhenomeXcan development. We developed PhenomeXcan by integrating GWAS summary statistics with GTEx v8 using PrediXcan methodology and then performing fine mapping and colocalization to identify the most likely causal genes for a given trait. PhenomeXcan is a massive resource containing PrediXcan P values across 4091 traits and 22,515 genes, aggregated across 49 tissues and refined by locus RCP. SNP, single-nucleotide polymorphism; b, effect size; snpn refers to the nth SNP in the list, after SNP 1, SNP 2, ..., SNP N.

We found 72,994 significant associations (Bonferroni-corrected P value of <5.49 1010) across the entire genome/phenome space, where 22,219 (30.5%) had locus RCP >0.1 (table S1). We constructed a quantile-quantile plot of all associations, which did not show evidence of systematic inflation (fig. S1). These associations represent numerous potential targets for translational studies with biological support.

We evaluated PhenomeXcans performance using three different independent validation approaches. For the first validation, we compared significant results from PhenomeXcan to significant results from the PheWAS catalog, which combines the NHGRI-EBI (National Human Genome Research Institute - European Bioinformatics Institute) GWAS catalog (as of 4/17/2012) and Vanderbilt Universitys electronic health record to establish unique associations between 3144 variants and 1358 traits (https://phewascatalog.org/phewas) (12, 28). These gene-trait pairs, mapped to GWAS loci mostly by proximity, are likely enriched in but do not necessarily represent causal genes. We mapped traits from PhenomeXcan to those in the PheWAS catalog using the Human Phenotype Ontology (HPO) (29). After filtering for genes included in both PhenomeXcan and the PheWAS catalog, we tested 2202 gene-trait associations. At a nominal threshold (P < 0.01), 1005 PhenomeXcan gene-trait associations replicated with matched traits in the PheWAS catalog [area under the curve (AUC) = 0.62; Fig. 2A]. Considering different methods of gene assignments for each GWAS locus (PheWAS: proximity, PhenomeXcan: PrediXcan and Bayesian colocalization), we further evaluated our replication rate using random classifiers in a precision-recall (PR) curve (Fig. 2B) and found significant replicability between PhenomeXcan and PheWAS results (empirical P value of <0.01).

MultiXcan refers to the version of PrediXcan designed to take GWAS summary statistics and aggregate results across tissues (22). (A and B) Receiver operating curve (ROC) and PR curve of PrediXcan significance scores (blue) and fastENLOC (orange) to predict PheWAS catalog gene-trait associations. (C and D) ROC and PR curve of PrediXcan significance scores (blue) and fastENLOC (orange) to predict OMIM catalog gene-trait associations. AP, average precision. The predictive ability of both PrediXcan and fastENLOC demonstrate the statistical validity of PhenomeXcan associations. The maximum fastENLOC colocalization probability across tissues was used for all figures.

For the second validation, we identified a set of high-confidence gene-trait associations using the Online Mendelian Inheritance in Man (OMIM) catalog (30). We previously demonstrated that integrated analysis using PrediXcan (18) and colocalization (23) successfully predicts OMIM genes for matched traits (24). We mapped 107 traits from PhenomeXcan to those in OMIM using the HPO (29) and curated a list of 7809 gene-trait associations with support for causality. We compared gene-trait associations from this standard near GWAS loci (table S2) and found that both PrediXcan and fastENLOC in PhenomeXcan successfully predict OMIM genes (AUC = 0.64; Fig. 2C). The combination of PrediXcan and fastENLOC improves precision in this dataset (fig. S2). The limited precision seen here is expected in the setting of genes, such as those in OMIM, with large effects and rare variants (Fig. 2D). The conservative nature of colocalization analysis can lead to increased false negatives (24), which may contribute to decreased performance of fastENLOC.

For the third validation approach, we applied a medium-throughput approach to examine a disease trait with multiple functionally established gene-trait associations. The Accelerating Medicines Partnership: Type 2 Diabetes (AMP T2D) Knowledge Portal curates a list of genes with causal, strong, moderate, possible, and weak associations to type 2 diabetes based on functional data (table S3) (31). We tested the ability of both PrediXcan and fastENLOC in PhenomeXcan to successfully predict the causal, strong, and moderate genes curated by AMP T2D Knowledge Portal paired with seven UK Biobank traits: type 2 diabetes, type 2 diabetes without complications, type 2 diabetes with ophthalmic complications, type 2 diabetes with peripheral circulatory complications, Self-reported type 2 diabetes, Non-insulin dependent diabetes mellitus, and Unspecified diabetes mellitus. PhenomeXcan successfully predicted the causal gene list for type 2 diabetes (AUC = 0.67; Fig. 3, A and B).

MultiXcan refers to the version of PrediXcan designed to take GWAS summary statistics and aggregate results across tissues (22). (A and B) ROC and PR curve of PrediXcan significance scores (blue) and fastENLOC (orange) to predict significant associations between a curated gene list from the AMP T2D Knowledge Portal and type 2 diabetes traits. PrediXcan and fastENLOC, particularly PrediXcan, demonstrate predictive ability in the setting of a disease trait with 20 genes with causal, strong, and moderate evidence and present in LD blocks with GWAS signal. The maximum fastENLOC colocalization probability across tissues was used for all figures.

PhenomeXcan provides a resource for hypothesis generation using gene-trait associations, with more than 22,000 potentially causal associations (P < 5.49 1010, locus RCP > 0.1; table S1). As case studies, we discuss associations identified on the basis of trait [Morning/evening person (chronotype)] and gene (TPO).

We reviewed the 15 most significant genes associated with Morning/evening person (chronotype) (a UK Biobank trait) based on PrediXcan P values across the 49 tissues and locus RCP >0.1 (table S4). Three of 15 genes had not been previously reported in any GWAS involving UK Biobank participants related to sleep or chronotype: VIP, RP11-220I1.5, and RASL10B. Notably, a variant associated with VIP (P = 1.812 1017, locus RCP = 0.26) is discussed in a GWAS of 89,283 individuals from the 23andMe cohort who self-report as a morning person (rs9479402 near VIP, 23andMe GWAS P = 3.9 1011) (32). VIP produces vasoactive intestinal peptide, a neurotransmitter in the suprachiasmatic nucleus associated with synchronization of circadian rhythms to light cycles (33). The long noncoding RNA RP11-220I1.5 (P = 6.427 1011, locus RCP = 0.20) and the gene RASL10B (P = 1.098 1010, locus RCP = 0.15) have not been previously reported in any GWAS or functional/clinical studies associated with this trait. RASL10B produces a 23-kDa guanosine triphosphatase protein that demonstrates overexpression in the basal ganglia in GTEx (21), potentially representing a novel association. Besides VIP, three other genes in this set had clinical/functional studies associated with sleep or chronotype in PubMed: RAS4B, CLN5, and FBXL3. RAS4B (P = 1.660 1019, locus RCP = 0.63) was linked to a transcriptional network regulated by LHX1 involved in circadian control (34). CLN5 (P = 5.248 1018, locus RCP = 0.34) mutations are associated with neuronal ceroid lipofuscinosis, which can manifest with sleep-specific dysfunction (35). FBXL3 (P = 1.54 1016, locus RCP = 0.35) assists with turnover of the CRY protein through direct interaction to regulate circadian rhythms (36). Our results were also significant for the overlapping genes PER3 (P = 1.65 1017, locus RCP = 0.08) and VAMP3 (P = 7.317 1018, locus RCP = 0.63). PER3 is one of the Period genes characterized as part of the circadian clock and described in numerous functional studies, animal models, and human polymorphism association studies (37), whereas VAMP3 has little research in chronotype or sleep. VAMP3, in this instance, is likely to be a false positive in the setting of the overlapping gene structure and coregulation.

We also reviewed PhenomeXcans performance in associating chronotype traits with well-established circadian rhythm genes that have been identified through functional approaches. In mammals, the transcription factors CLOCK and BMAL1 influence the expression of the Period genes (PER1 and PER2) and the Cryptochrome genes (CRY1 and CRY2). PER3 stabilizes PER1 and PER2 (38). NPAS2 acts as a paralog to CLOCK. All genes demonstrated nominal significance (P < 0.01) with at least one chronotype trait in PhenomeXcan except CRY2 (strongest association P = 0.11) and CLOCK (strongest association P = 0.08). Except for PER1 (locus RCP = 0.24) and NPAS2 (locus RCP = 0.12), all genes showed locus RCP <0.1.

PhenomeXcan, to our knowledge, is one of the first hypothesis-generating tools to provide unbiased links between a trait and associated genes for the researchers evaluation. In conjunction with rich knowledge obtained from functional studies, PhenomeXcan can be used to generate or support subsequent translational efforts.

We next evaluate PhenomeXcan as a platform to study novel and underreported gene-trait associations. Thyroid peroxidase (TPO) encodes a membrane-bound glycoprotein that plays a crucial role in thyroid gland function (39). The strongest associations in PhenomeXcan support the known role of TPO in thyroid hormone production: Self-reported hypothyroidism or myxedema (P = 1.40 1014, locus RCP = 0.99) and Treatment with levothyroxine (P = 1.54 1010, locus RCP = 0.99). Hypothyroidism has been clinically linked to increased respiratory symptoms. Although the mechanism for this is not well understood (40), our results suggest that these could be explained by common genetic factors; Treatment with salmeterol (a medication used to treat lung disease such as asthma or chronic obstructive pulmonary disease) showed moderate associations with TPO in PhenomeXcan (P = 7.45 105, locus RCP < 0.1). TPO is also contained in the National Institutes of Health (NIH) Biosystems Pathways for the development of pulmonary dendritic cells (41). Time to complete round (drawing as a measure of cognitive function) showed another moderate association in PhenomeXcan (P = 1.19 104, locus RCP < 0.1). Thyroid function has been clinically linked to time to draw a clock as a form of cognitive measurement (42). Other trait associations identified in PhenomeXcan with TPO include Single major depression episode (P = 2.48 104, locus RCP < 0.1) and Treatment with doxazosin (a medication used in the United Kingdom for hypertension) (P = 8.80 104, locus RCP < 0.1), both of which have demonstrated clinical association with thyroid abnormalities (43, 44). When reviewing thyroid dysfunction traits in PhenomeXcan, TPO is among the 35 most significantly associated genes, with the others primarily involved in immune regulation or the hypothalamic-pituitary-thyroid axis. To our knowledge, depression and doxazosin use have not been deeply investigated with TPO previously, highlighting how PhenomeXcan may be useful in expanding gene-trait association studies and functional studies through consideration of independent traits associated with a given gene.

PhenomeXcan allows more complex investigation of associated genes and traits beyond individual queries. As an example, to study genes associated with white blood cell count, we can cluster related genes and traits. Starting from the trait Lymphocyte percentage, the top associated genes include PSMD3, CD69, KLF2, CXCL2, CREB5, CXCL3, ZFP36L2, JAZF1, NCOR1, and TET2. These genes represent pathways associated with chemokine and interleukin signaling as well as peptide ligand binding but are not specific to one particular pathway or genomic location (45). We can assess these genes associations with white blood cell traits (neutrophil count/percentage, lymphocyte count/percentage, eosinophil count/percentage, and monocyte and basophil percentages) and infer some understanding of their causal mechanism. PSMD3, for instance, demonstrates stronger associations with neutrophil and lymphocyte traits (mean P < 1 1030, mean locus RCP = 0.50), whereas ZFP36L2 demonstrates consistent associations across white blood cell, platelets, and red blood cell traits (mean P < 1.54 1024, mean locus RCP = 0.36) (Fig. 4). Disruption of ZFP36L2 results in defective hematopoiesis in mice (46), whereas PSMD3 has been identified in GWAS related to white blood cell count and inflammatory states (47). Clusters of associated genes and traits can support more robust translational hypotheses through similarities in associations and generate more nuanced experimental designs through differences between associations.

Z scores are derived from PrediXcan P values, with the ceiling of association (dark blue) 7. In this heatmap, we demonstrate the associations between the genes PSMD3, CD69, KLF2, CXCL2, CREB5, CXCL3, ZFP36L2, JAZF1, NCOR1, and TET2 and the white blood cell traits neutrophil count and neutrophil percentage, lymphocyte count and lymphocyte percentage, eosinophil count and eosinophil percentage, monocyte percentage and basophil percentage. Platelet count and mean corpuscular volume (for red blood cells) serve as alternate blood traits. ZFP36L2 has consistent associations across platelets and red blood cells relative to other genes. Accordingly, functional studies demonstrate that ZFP36L2 plays a role in hematopoiesis, whereas studies support the other genes involvement in inflammation-related pathways or diseases. These types of clusters can support hypotheses and experimental designs regarding the mechanisms through which genes contribute to traits.

PhenomeXcan can also be integrated with any gene-trait databases to study pleiotropically linked traits and shared associated genes. We integrated PhenomeXcan with ClinVar, a publicly available archive of rare human diseases and associated genes (including OMIM) and one of the most widely used gene-trait databases in the clinical setting (48). We examined the associations between the 4091 GWAS-derived traits in PhenomeXcan and 5094 ClinVar diseases by (i) calculating PrediXcan Z scores for every gene-trait association in PhenomeXcan and (ii) for each PhenomeXcan/ClinVar trait pair, we computed the average squared PrediXcan Z score considering the genes reported in the ClinVar trait (see Materials and Methods). We then created a matrix of PhenomeXcan traits by ClinVar traits with mean squared Z scores (Fig. 5, A and B), where peaks represent shared genes. We defined significant associations between traits as those with Z score >6; this represents the equivalent of a Bonferroni-adjusted P value of 0.05 based on our map of the distribution of Z scores (fig. S3).

(A) Schematic depicting the development of PhenomeXcan ClinVar. For each PhenomeXcan/ClinVar trait pair, we computed the average squared PrediXcan Z score considering the genes reported in the ClinVar trait. (B) Heatmap visualizing the overall structure of associations in PhenomeXcan ClinVar. Darker blue represents stronger association. Again, complex clusters of intertrait associations can be identified to link common traits and rare diseases. Queries for traits or genes of interest can be submitted through a web application at phenomexcan.org. (C) Heatmap demonstrating an example linked traits in PhenomeXcan (rows) and ClinVar (columns) using the association between Parkinsons disease and red blood cell traits. We see the strongest associations between mean corpuscular volume, mean reticulocyte volume, and mean spherical red cell volume and Parkinson disease 15. In ClinVar, each variant of Parkinsons disease linked to a different gene is listed under a different number, making it expected that associations to other forms of Parkinsons disease are not as strong.

As an example, we found links between the ClinVar trait Parkinson disease 15 and the following traits: mean corpuscular volume, mean reticulocyte volume, and mean spherical red cell volume (Fig. 5C). The gene linked to Parkinson disease 15 in ClinVar is FBXO7. The mean Z score across eight red blood cell traits was 21.14; the mean locus RCP was 0.84 with P values all <1 1030. FBXO7 plays a role in the ubiquitin system; its entry in ClinVar is associated with an autosomal recessive, juvenile-onset form of Parkinsons disease (49). Three GWAS [the HaemGen consortium, eMERGE (Electronic Medical Records and Genomics), and van der Harst et al.] link FBXO7 with red blood cell attributes including mean corpuscular volume and mean cell hemoglobin (5052). At least one mouse model describes defective erythropoiesis and red blood cell changes due to induced mutations in FBXO7 (53). Through PhenomeXcan, we found a pleiotropic relationship between Parkinsons disease and red blood cell traits mediated through FBXO7 that has not been studied in humans. The nearest adjacent genes, SYN3 and BPIFC, are unlikely to be separately affecting red blood cells; they have no published association to red blood cells and demonstrate mean locus RCPs with red blood cell traits in PhenomeXcan of 0.55 and 0, respectively. Validating this finding, one mouse model specifically studies the pleiotropy of FBXO7 on both parkinsonism and red blood cell traits (54). This case study demonstrates how this powerful variation on PhenomeXcan can substantially improve translational hypothesis generation by supporting genetic links between associated rare diseases and common traits across research platforms.

PhenomeXcan offers direct translational applicability, providing genomic evidence to support therapeutic targets and associated side effects. As an example, PCSK9 is a genetically supported, clinically validated target for cardiac prevention through inhibition of its binding to the low-density lipoprotein (LDL) receptor and reduction of blood LDL cholesterol levels (55). We can study the cluster of genes and traits produced by PCSK9 in PhenomeXcan for relevant information about this target. Most of the traits with strongest associations to PCSK9 relate to diagnosis and treatment of elevated cholesterol or atherosclerosis, including familial heart disease. Because inherited PCSK9 variation is associated with increased likelihood of type 2 diabetes, there was concern that PCSK9 therapies could elevate risk to type 2 diabetes. The inhibiting drugs therefore required large substudies from clinical trials to confirm no association with worse diabetes (56, 57). While not at genome-wide significance, PCSK9 has a negative association with type 1 diabetes in PhenomeXcan (P = 8.2 104, locus RCP < 0.1), consistent with the clinical concern that down-regulation of the gene could lead to increased diabetes risk. We recognize that type 1 and type 2 diabetes have different clinical etiologies. For the purpose of drug development, though, assessing PCSK9 in PhenomeXcan produces both its primary target (blood cholesterol levels as related to atherosclerosis) and, through independently identified traits, potential adverse effects via diabetes. The most commonly represented genes associated with the strongest traits for PCSK9 include APOE, LDLR, APOB, PSRC1, CELSR2, SORT1, ABCG8, ABCG5, and HMGCOR. Unsurprisingly, all of these genes have all been implicated in genetic susceptibility to hypercholesterolemia (some, such as SORT1, may be the primary causative gene in their pathway) (58). Examining potential targets in PhenomeXcan could not only help anticipate side effects via independent traits but also identify related gene networks or alternative targets with therapeutic relevance.

Here, we introduce PhenomeXcan, an innovative, powerful resource that makes comprehensive gene-trait associations easily accessible for hypothesis generation. Using PrediXcan allows us to derive gene-based associations with traits in context by integrating GWAS summary statistics with transcriptome-wide predicted expression and regulatory or functional information. We previously demonstrated that integrated analysis using PrediXcan and colocalization improves precision and power for target gene identification (24). To build PhenomeXcan, we also develop a novel, rapid colocalization method, fastENLOC, that could handle data at this scale (4091 traits 22,515 genes 49 tissues) (see Materials and Methods). PhenomeXcan implements the best practices derived from applying GTEx v8 (21, 59) to biologically prioritize genes with possible causal contribution to a given trait.

PhenomeXcans flexible structure and adaptability allow translational researchers to easily explore clinically relevant questions. The resource can be queried by gene or trait and allows identification of novel and underrepresented associations. It offers exploration of polygenicity and pleiotropy dimensions by allowing for queries across multiple genes and traits. It can also be integrated with other gene-trait datasets to explore linked traits and report common associated genes. We offer ClinVar as an example, but any deeply annotated database of genes and traits, including molecular or biological traits, may be integrated in this manner. Other possible translational uses of PhenomeXcan include biomarker exploration, identification of clinically relevant disease modifiers, and polygenic score building (using genes associated with queried traits), as well as novel directions for basic science collaborations and clinical study of linked traits (using traits associated with queried genes).

We note some caveats. Diseases with variability not related to changes in gene expression (e.g., epigenetic regulation or traits with important environmental contributions) are not expected to be captured well by this method. With just expression levels, this resource is a starting point, and additional molecular traits, such as microRNA levels, protein levels, and alternative splicing structures, are a priority for us to incorporate as data become available in sufficiently large sample sizes. Our model also better captures common overall genetic contributors rather than genes identified from rare variants. We do note that our validation standards tend to favor larger-effect genes with monogenic etiology, while the PhenomeXcan association method itself is less biased. Regulatory pleiotropy is widespread across the genome (21). In our chronotype example, VAMP3 and PER3 demonstrate regulatory pleiotropy. VAMP3, from our findings associated with chronotype, is likely to be a false positive because of coregulation of both genes by causal variants. With that degree of proximity, large-scale tools are not able to well distinguish causal genes, exemplifying the need for additional functional data to determine the causality of the gene (21). We discuss this finding to acknowledge how PhenomeXcan encounters this phenomenon and show the benefit of performing these associations across all human genes. We offer colocalization as a possible means of prioritizing causal variants, but significance of association, colocalization, and coregulatory sites must be taken into account in our results. Work from large-scale statistical genetics tools, such as PhenomeXcan, and Mendelian genetics and functional studies must then be combined to best understand the breadth of genetic contributors to complex traits. We have favored a locus RCP threshold of 0.1 to limit false negatives related to colocalization. Poor RCP (locus RCP ~ 0) may reflect a lack of sufficient evidence with available data, particularly for understudied genes, rather than true lack of causality. We therefore reported traits in this paper that had a locus RCP <0.1 but had functional support for potential association. Similarly, the genome-wide threshold of significance is conservative, and we discuss associations with functional support even with less significant P values. GWAS summary statistics used in this project were for participants and patients of European ancestry. Improving the applicability of this type of work to global populations remains of paramount importance throughout genetic medicine, and we will continue to integrate more GWAS summary statistics from broader consortia.

Resources that translate biologically relevant genomic and transcriptomic information into gene-trait associations are already critical for hypothesis generation and clinically relevant research (60). We offer PhenomeXcan, an integrated mapping for the function of every human gene, as a publicly available resource to advance the investigation of complex human diseases by improving the accessibility of relevant links between the entire genome and the phenome.

S-MultiXcan is a method in the PrediXcan family (18) that associates genes and traits by testing the mediating role of gene expression variation in complex traits but (i) requires only GWAS summary statistics and (ii) uses multivariate regression to combine expression information across tissues (22). First, linear prediction models of genotype in the vicinity of the gene to expression are trained in reference transcriptome datasets such as the GTEx project (21). Second, predicted expression based on actual genetic variation is correlated to the trait of interest to produce a gene-level association result for each tissue. In S-MultiXcan, the predicted expression is a multivariate regression of expression across multiple tissues. To avoid collinearity issues and numerical instability, the model decomposes the predicted expression matrix into principal components and keeps only the eigenvectors of non-negligible variance. We considered a principal components analysis regularization threshold of 30 to be a conservative choice. This approach improves detection of associations relative to use of one tissue type alone and offers a reduced false-negative rate relative to a Bonferroni correction. We used optimal prediction models based on the number and proportion of colocalized gene-level associations (24). These models select features based on fine mapping (25) and weights using eQTL effect sizes smoothed across tissues using mashr (59). The result of this approach is a genome-wide gene-trait association list for a given trait and GWAS summary statistic set.

Bayesian fine mapping was performed using TORUS (27). We estimated probabilities of colocalization between GWAS and cis-eQTL signals using Bayesian RCP, as described in the ENLOC (enrichment estimation aided colocalization analysis) methodology (23). For this particular study, given the large scale of the data, we developed a novel implementation, entitled fastENLOC. fastENLOC was applied for all trait-tissue pairs, and the maximum colocalization probability across all tissues was used, thus obtaining a single RCP value for each gene-trait pair. This aggregation of RCP values across tissues allowed us to combine results from fastENLOC and S-MultiXcan.

We evaluated the accuracy of gene-trait associations in PhenomeXcan by using two different gene-trait association datasets (PheWAS catalog and OMIM) as well as genes linked with functional evidence with type 2 diabetes (T2D) according to the AMP T2D. We then derived the receiver operating characteristic curve (ROC) and PR curves for PrediXcan and fastENLOC independently and a combination of both.

We mapped traits from PhenomeXcan to those in either PheWAS catalog (28) or OMIM (30) by using the HPO (29) and the GWAS catalog as intermediates. For traits in the PheWAS catalog, we tested 2202 gene-trait associations that could be mapped in both PhenomeXcan and the PheWAS catalog, from a total 19,119 gene-traits associations consisting of all genes present in an LD block with GWAS signal. For the OMIM traits, we developed a standard (table S2) of 7809 high-confidence gene-trait associations that could be used to measure the performance of PhenomeXcan, of which 125 presented in the LD block of GWAS signal so those were included in the analysis. This standard, as described in our recent work (24), was obtained from a curated set of trait-gene pairs from the OMIM database by mapping traits in PhenomeXcan to those in OMIM. Briefly, traits in PhenomeXcan were mapped to the closest phecode using the GWAS catalogtophecode map proposed in (28). As disease description in OMIM has been mapped to the HPO (29), we created a map from phecodes to terms in HPO, which allowed us to link our GWAS traits to OMIM disease description by using phecodes and HPO terms as intermediate steps. For each gene-trait pair considered causal in this standard, we determined whether PhenomeXcan identified that association as significant on the basis of the resulting P value. The OMIM-based standard is publicly available through R package (https://github.com/hakyimlab/silver-standard-performance).

For T2D, we obtained a list of predicted effector transcripts identified by AMP T2D and used 76 genes categorized as causal, strong, or moderate as our gold standard for evaluation (table S3). As we did for OMIM and PheWAS catalogs, 20 of these causal genes could be mapped in PhenomeXcan, from a total of 5036 genes present in an LD block with GWAS signal. We used seven traits highly related to T2D: International Classification of Diseases 10 codes E11 and E14, Self-reported type 2 diabetes (data-field 20002 in UK Biobank with code 1223), and four phenotypes manually curated by the FinnGen Consortium (type 2 diabetes without complications, type 2 diabetes with ophthalmic complications, type 2 diabetes, and type 2 diabetes with peripheral circulatory complications); then, we took the maximum Z score obtained (for MultiXcan) and the maximum RCP (for fastENLOC) across the seven T2D traits for each gene evaluated. The results are shown in Fig. 3 and fig. S2. Notice that multiple testing is not an issue, since for the performance curves, we are not using a significance threshold, but all levels are assessed in terms of the false-positive and true-positive rates.

PhenomeXcan results for case studies were included on the basis of their P values and locus RCP. We defined putative causal gene contributors as those genes with P values less than 5.49 1010 and locus RCP >0.1. Given these conservative measures, however, we did discuss associations that were less significant or had a lower locus RCP with functional evidence. We used the NHGRI-EBI GWAS catalog (21 October 2019) to identify GWAS results both using the UK Biobank (given the predominance of this dataset in PhenomeXcan) and other datasets. We performed systematic literature searches on PubMed using the gene name alone, with the specific trait category and trait name to identify functional studies relevant to a trait of interest.

We examined links between 4091 PhenomeXcan traits and 5094 ClinVar traits and associated genes. ClinVar traits were excluded if they did not have known associated genes in PhenomeXcan. To compare a PhenomeXcan trait t and a ClinVar trait d, we calculated the mean squared Z scoreavg(t,d2)=1ki=1kZt,i2where k is the number of genes reported in ClinVar for trait d and Z is the Z score of gene i obtained with S-MultiXcan for trait t. We then created a matrix of PhenomeXcan traits by ClinVar traits with mean squared Z scores. We defined significant associations between traits as those with Z score >6; this represents the equivalent of a Bonferroni-adjusted P value of 0.05 based on our map of the distribution of Z scores (fig. S3).

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PhenomeXcan: Mapping the genome to the phenome through the transcriptome - Science Advances

CAMBRIDGE, Mass., Sept. 09, 2020 (GLOBE NEWSWIRE) -- Sarepta Therapeutics, Inc. (NASDAQ:SRPT), the leader in precision genetic medicine for rare diseases, today announced that it has completed a Type C written response only meeting with the Office of Tissues and Advanced Therapies (OTAT), part of the Center for Biologics Evaluation and Research (CBER) at the U.S. Food and Drug Administration (FDA), to obtain OTATs concurrence on the commencement of its next clinical trial for SRP-9001 using commercial process material. SRP-9001 (AAVrh74.MHCK7.micro-dystrophin) is Sareptas investigational gene transfer therapy for the treatment of Duchenne muscular dystrophy.

Among other items, OTAT has requested that Sarepta utilize an additional potency assay for release of SRP-9001 commercial process material prior to dosing in a clinical study. Sarepta has several existing assays and data that it believes could be employed in response to OTATs request. However, additional dialogue with the Agency is required to determine the acceptability of the potency assay approach.

We look forward to working with OTAT to potentially satisfy their requests and to obtain clarity on the timing of the commencement of our commercial supply study. We will provide further updates as we are able, said Doug Ingram, president and chief executive officer, Sarepta Therapeutics. Every day, thousands of children degenerate from the irreversible damage caused by Duchenne muscular dystrophy. It is for that reason that we will work relentlessly with the Division to satisfy any requests of OTAT and continue the advancement of a potentially transformative therapy for these patients.

About SRP-9001 (AAVrh74.MHCK7.micro-dystrophin)SRP-9001 is an investigational gene transfer therapy intended to deliver the micro-dystrophin-encoding gene to muscle tissue for the targeted production of the micro-dystrophin protein. Sarepta is responsible for global development and manufacturing for SRP-9001 and plans to commercialize SRP-9001 in the United States. In December 2019, the Company announced a licensing agreement granting Roche the exclusive right to launch and commercialize SRP-9001 outside the United States. Sarepta has exclusive rights to the micro-dystrophin gene therapy program initially developed at the Abigail Wexner Research Institute at Nationwide Childrens Hospital.

AboutSarepta TherapeuticsAt Sarepta, we are leading a revolution in precision genetic medicine and every day is an opportunity to change the lives of people living with rare disease. The Company has built an impressive position in Duchenne muscular dystrophy (DMD) and in gene therapies for limb-girdle muscular dystrophies (LGMDs), mucopolysaccharidosis type IIIA, Charcot-Marie-Tooth (CMT), and other CNS-related disorders, with more than 40 programs in various stages of development. The Companys programs and research focus span several therapeutic modalities, including RNA, gene therapy and gene editing. For more information, please visitwww.sarepta.com or follow us on Twitter, LinkedIn, Instagram and Facebook.

Sarepta Forward-Looking Statements

This press release contains "forward-looking statements." Any statements contained in this press release that are not statements of historical fact may be deemed to be forward-looking statements. Words such as "believes," "anticipates," "plans," "expects," "will," "intends," "potential," "possible" and similar expressions are intended to identify forward-looking statements. These forward-looking statements include statements regarding Sareptas belief that its existing assays and data could be employed in response to OTATs request; the acceptability of Sareptas potency assay approach by the FDA; our plan to work with OTAT to potentially satisfy their requests and to obtain clarity on the timing of the commencement of our commercial supply study; and the potential of SRP-9001 to be a transformative therapy for DMD patients.

These forward-looking statements involve risks and uncertainties, many of which are beyond Sareptas control. Known risk factors include, among others: delays in the commencement of Sareptas next clinical study for SRP-9001 could delay, prevent or limit our ability to gain regulatory approval for SRP-9001; any inability to complete successfully clinical development could result in additional costs to Sarepta or impair Sareptas ability to generate revenues from product sales, regulatory and commercialization milestones and royalties; SRP-9001 may not result in a viable treatment suitable for commercialization due to a variety of reasons, including the results of future research may not be consistent with past positive results or may fail to meet regulatory approval requirements for the safety and efficacy of product candidates; Sarepta may not be able to execute on its business plans and goals, including meeting its expected or planned regulatory milestones and timelines, clinical development plans, and bringing its product candidates to market, due to a variety of reasons, many of which may be outside of Sareptas control, including possible limitations of company financial and other resources, manufacturing limitations that may not be anticipated or resolved for in a timely manner, regulatory, court or agency decisions, such as decisions by the United States Patent and Trademark Office with respect to patents that cover Sareptas product candidates and the COVID-19 pandemic; and those risks identified under the heading Risk Factors in Sareptas most recent Annual Report on Form 10-K for the year ended December 31, 2019, and most recent Quarterly Report on Form 10-Q filed with the Securities and Exchange Commission (SEC) as well as other SEC filings made by Sarepta which you are encouraged to review.

Any of the foregoing risks could materially and adversely affect Sareptas business, results of operations and the trading price of Sareptas common stock. For a detailed description of risks and uncertainties Sarepta faces, you are encouraged to review the SEC filings made by Sarepta. We caution investors not to place considerable reliance on the forward-looking statements contained in this press release. Sarepta does not undertake any obligation to publicly update its forward-looking statements based on events or circumstances after the date hereof.

Internet Posting of Information

We routinely post information that may be important to investors in the 'For Investors' section of our website atwww.sarepta.com. We encourage investors and potential investors to consult our website regularly for important information about us.

Source: Sarepta Therapeutics, Inc.

Sarepta Therapeutics, Inc.

Investors:Ian Estepan, 617-274-4052iestepan@sarepta.com

Media:Tracy Sorrentino, 617-301-8566tsorrentino@sarepta.com

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Sarepta Therapeutics Provides Program Update for SRP-9001, its Investigational Gene Therapy for the Treatment of Duchenne Muscular Dystrophy -...

A member of the Black Doctors COVID-19 Consortium, formed to help address health disparities in the African American community, tests a patient. Racial disparities in COVID-19 cases are better explained by structural racism than by genetic differences.

Recent protests across the United States and the world have called attention to anti-Black racism in policing, employment, housing, and education. Science and medicine also have long histories of racism (1, 2). This unfortunate yet persistent aspect of science and medicine includes the use of obsolete concepts of race to measure human biological difference and the false belief, by some, that differences in disease outcomes stem primarily from pathophysiological differences between racial groups (3, 4).

We are particularly concerned that explanations for the disproportionate rates of coronavirus disease 2019 (COVID-19) in Black, Latino, Indigenous, and other communities of color will mistakenly point to innate racial differences instead of long-standing institutionalized racism and other underlying social, structural, and environmental determinants. Although genetic risk factors may contribute to severity of COVID-19 (5, 6), race is a poor proxy to understand the population distribution of such risk factors (7). Compelling evidence shows that racism, not race, is the most relevant risk factor (8, 9). We are hopeful that scientists will not turn to racial sciencea reflection of long-standing beliefs about superiority and inferiority that have no place in scientific and clinical practice (1, 10)to explain COVID-19 disparities and justify policy responses to it. However, racial categories have been misused in the past.

In 2016, we called for the elimination of the use of race as a means to classify biological diversity in both laboratory and clinical research. Since that time, little has changed (11). The National Institutes of Health (NIH) made progress by releasing a request for applications in support of research leading to the creation of best practices for the study of race and other population identifiers (12). However, R01 awards could take years to address these issues, and NIH still offers no guidance about the use of racial and ethnic identifiers in research beyond recruitment. There is an urgent need for NIH to provide scientists with information about what utility racial data have beyond fostering diversity in research, how such information should or should not be used in data analysis, and what identifiers of human populations might be better suited for use in biomedical research.

To begin to address the misuse of racial measures in scientific and clinical practice, we urge the director of NIH to lead education efforts directed at both scientists and the public about the nature of human genetic diversity and the ongoing need and obligation to confront racism in science. In these troubled times, a clear statement regarding use and misuse of population identifiers in the pursuit of characterizing human difference could help alleviate ongoing and widespread confusion on such matters.

NIH should then support the National Academy of Sciences to bring together a diverse group of scientists and scholars to develop a consensus statement on best practices in genetic, clinical, and social scientific studies for characterizing human genetic diversity, including guidance for using racial categories to study racism's impact on human health. Guidelines for federally funded science should also include best practices for the integration of biological, social, structural, and environmental health determinants into the study of human health and disease.

NIH should continue and expand its work to hire more career scientists and clinicians from underrepresented minority groups. It should also substantially increase the extramural funding that supports scientists from underrepresented groups at every level of training and throughout career development. We have the tools to remedy this challenge. The time to act is now.

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NIH must confront the use of race in science - Science Magazine

The immune system usually mounts a strong immune response to infection by SARS-CoV-2, the virus that causes COVID-19. That defensive response, however,appears to be weaker in men and people over the age of 60, a study led by researchers at the University of Washington School of Medicine in Seattle has found.

There were some early studies that suggested that there was a fairly weak antiviral response shortly after infection, but we found a very robust immune response in patients at the time of symptom onset, said lead author Nicole Lieberman.But differences in the immune response in older individuals and men may contribute to the greater severity and higher mortality we see in these groups.

Lieberman is a research scientist in the laboratory of Alex Greninger. He isan assistant professor in the department of Laboratory Medicine and Pathology at the University of Washington School of Medicine and head ofthe project. The results of the study appear in the open-access journal PLOS Biology. Here is the paper.

In the study, the researchers compared samples swabbed from the noses and throats of 430 people who were infected with SARS-CoV-2 and 54 people who were not. They also worked with colleagues at Columbia University Medical Center in New York Cityand University of Texas Medical Branch, Galveston, Texas. These groups havedeveloped techniques to infect cells in cultureto track changes in the immune response over time.

To assess immune responses the researchers analyzed the RNA in the samples. Because the SARS-CoV-2 stores its genetic instructionsin RNA, levels of viral RNA in the samples revealed the amount of virus, or viral load, an indicator of the severity of infection. In human cells. On the other hand, RNA reveals which proteins the cells are producing in response to the infection. Thats because,for the instructions for synthesizing proteins encoded in the DNA of genes tobe read by the cells, the code must first be copied, into RNA. As a result, analyzing the RNA transcriptsin a sample can showwhichgenes are being dialed upin response to the infection and which are being dialed down. This sort of analysis canrevealwhat sort of immune counterattack the cells are mounting against the virus.

The researchers found that the viral load in these patients was high, butalso that SARS-CoV-2 triggers a strong antiviral response. This includes up-regulation of genes for a number of antiviral factors that activate the cells defenses against viral invaders. It also includeschemical signals that summon immune cells to fight the infection, such as interferons and chemokines.

The viral load with SARS-CoV-2 infection is one of the highest seen, Greninger said. But the immune response is very strong, and the higher the viral load,the stronger the response.

However, in older individualsover age 60, infection did not activate genes to summon virus-fighting cells called cytotoxic T cells and natural killer cells that are some of the bodys the most effective antiviral weapons.

The older patients activate a weaker immune response like a singer that just cant hit the high notes anymore, Greninger said.

The researchers also found that men mounted a less vigorous response compared to women. The malesproducedlower levels of transcripts of some anti-viral proteins,and pumped outsome proteins that put a damper onthe immune response.

In men were seeing an up-regulation of signals that turn off the immune system, Lieberman said. Its speculation, but it appears as though some men may throttleback their immune system too soon before mounting an effective response to infection.

This work was supported by National Institutes of Health (AI146980, AI121349, and NS091263) and the Department of Laboratory Medicine and Pathology at the UWSchool of Medicine.

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Why is COVID-19 more severe in men and elders? - UW Medicine Newsroom

23andme has long been known as a consumer genetic testing unicorn. But CEO Anne Wojcicki describes it differently, as the peoples research company.

The California-based unicorn is now focused on a partnership with pharma giant GlaxoSmithKline to discover new drugs using data culled from millions of 23andMe customers, and Wojcicki said Wednesday at the STAT Health Tech Summit that she hoped the companys customers would feel proud if a drug developed with their data reaches the market.

Wojcicki, answering a question from moderator Matthew Herper of STAT, didnt outline any specific steps that the company would take to ensure that its customers could benefit from medications developed with their data. Nor did she detail how 23andMe would work with GSK on access issues.

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Any such decisions are years away, a 23andMe spokesperson later noted, which should give the company time to figure out a way to ensure its customers benefit. The first medicine being developed by the partners, an anti-cancer antibody, is only now starting clinical trials.

If you look at our mission, its about people having access to, understanding, and benefiting from the human genome, Wojcicki said. I think that they can do that with information as well with medications.

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I think theres a lot to do with the marketing and messaging and actually how [a drug] is sold that we will be able to address when were lucky enough to actually have that kind of program, she said.

The experimental cancer drug that entered clinical trials in July was actually one of the early programs under Richard Scheller, Wojcicki noted. Scheller, 23andMes former chief scientific officer who was considered an instrumental figure in the companys push into drug development, left in July 2019.

In 2018, the company signed an exclusive four-year deal with GSK, with the option to extend their work an extra year. The companies are working on about 30 drug programs together.

At the time the deal was announced, 23andMe faced some backlash for providing information distilled from its consumer clients to pharmaceutical companies. Both companies have argued that they are transparent about how consumers data is used; the press release announcing the deal noted that 23andMe customers would have to opt-in before their de-identified data was shared with GSK.

I think what you see is that for people who are really sick, they want to make a difference. They want to make a difference in their life or they want to make a difference in the lives of their children, Wojcicki said on Wednesday. People participate all the time in studies at Stanford or with Pfizer or with other groups. People want to see their information used for good.

As part of the collaboration, GSK invested $300 million in the genetic testing company; the companies agreed to evenly split the cost of the work done as part of the collaboration, which GSK hoped would improve the probability of R&D success, according to its 2018 annual report.

That focus on genetically validated targets can double the probability of success, said GSKs chief scientific officer Hal Barron, who joined Wojcicki on the summit panel. For the same investment, we could get twice as many molecules out.

GSKs $300 million investment was enough to buy 14.5% of 23andMe, which is still privately held; Wojcicki noted Wednesday that after 14 years, it is not a profitable company.

23andMes core business is selling genetic testing packages to people who are interested in learning more about their ancestry, their predisposition to certain genetic diseases, or their ability to metabolize certain drugs.

In 2017, the company received clearance from the Food and Drug Administration to market its tests as a way to detect a select group of hereditary conditions: Parkinsons disease, a type of Gaucher disease, several hereditary blood disorders, and alpha-1 antitrypsin deficiency. Another FDA clearance followed in 2018 for pharmacogenetic tests, which look for genetic markers of drug metabolism.

Wojcicki said the company would be rolling out a number of [pharmacogenetics] reports to our customers in the near future.

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23andMe's aim: Getting customers drugs developed with their data - STAT

Impact RTO Holdings is the largest franchisee in the Rent a Center system serving locations spread across the Southeastern US within the states of Florida, Georgia, North Carolina, South Carolina, Tennessee, Alabama, Arkansas, and Mississippi. Impact RTO Holdings was looking for the next generation, proven, science-based digital wellness solution that can be delivered remotely to protect and improve the health of their diverse workforce and their families.

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About Digbi HealthDigbi Health is the leader in Precision Digital Care. Digbi Cares is the next-generation, prescription-grade digital therapeutic platform that uses artificial intelligence (AI) to synthesize and individuals' genetics, gut microbiome, lifestyle medicine, socioeconomic and behavioral risk patterns to deliver nutrition, fitness, sleep and stress management personalized program proven to reduce weight, reverse weight-related inflammatory gut, musculoskeletal, cardiovascular and insulin-related illnesses. Follow us onLinkedIn,Twitter, Instagram, andFacebook.

About Impact RTO HoldingsImpact RTO is part of the retail division of Impact Properties. Impact Properties is a full-service investment, operating and development company originally founded in 1981. Impact has built an extensive track record with over 38 years of experience in the hospitality and franchise industry. Impact has expanded across the Southeast region into many nationally recognized, segment leading franchise brands within the hotel, restaurant, and retail segments. Impact currently owns and operates 87 units with an additional 16 units under development. It is this experience and diversification that has allowed Impact to create a unique culture of inclusion and success that invites innovation and growth in today's rapidly changing economy.

SOURCE Digbi Health

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Impact RTO Holdings partners with Digbi Health to provide the leading gut microbiome and genetics-based digital care program to their Rent a Center...