| Application: | 44288, A meta-investigation of combinatorial mutation signatures in broad disease categories |
| Title: | Identification and Analysis of Shared Risk Factors in Sepsis and High Mortality Risk COVID-19 Patients |
| Size: | 929487 B |
| Archived: | 19 Mar 2021 |
| Stability: | Complete |
| Personal: | No individual-level data |
Return 3251
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Notes
BACKGROUNDCoronavirus disease 2019 (COVID-19) is a novel coronavirus strain disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease is highly transmissible and severe disease including viral sepsis has been reported in up to 16% of hospitalized cases. The admission characteristics associated with increased odds of hospital mortality among confirmed cases of COVID-19 include severe hypoxia, low platelet count, elevated bilirubin, hypoalbuminemia and reduced glomerular filtration rate. These symptoms correlate highly with severe sepsis cases. The diseases also share similar co-morbidity risks including dementia, type 2 diabetes mellitus, coronary heart disease, hypertension and chronic renal failure. Sepsis has been observed in up to 59% of hospitalized COVID-19 patients. It is highly desirable to identify risk factors and novel therapy/drug repurposing avenues for late-stage severe COVID-19 patients. This would enable better protection of at-risk populations and clinical stratification of COVID-19 patients according to their risk for developing life threatening disease.
METHODS
As there is currently insufficient data available for confirmed COVID-19 patients correlating their genomic profile, disease severity and outcome, co-morbidities and treatments as well as epidemiological risk factors (such as ethnicity, blood group, smoking, BMI etc.), a direct study of the impact of host genomics on disease severity and outcomes is not yet possible. We therefore ran a study on the UK Biobank sepsis cohort as a surrogate to identify sepsis associated signatures and genes, and correlated these with COVID-19 patients. Sepsis is itself a life-threatening inflammatory health condition with a mortality rate of approximately 20%. Like the initial studies for COVID-19 patients, standard genome wide association studies (GWAS) have previously failed to identify more than a handful of genetic variants that predispose individuals to developing sepsis.
RESULTS
We used a combinatorial association approach to analyze a sepsis population derived from UK Biobank. We identified 70 sepsis risk-associated genes, which provide insights into the disease mechanisms underlying sepsis pathogenesis. Many of these targets can be grouped by common mechanisms of action such as endothelial cell dysfunction, PI3K/mTOR pathway signaling, immune response regulation, aberrant GABA and neurogenic signaling.
CONCLUSION
This study has identified 70 sepsis related genes, many of them for the first time, that can reasonably be considered to be potentially relevant to severe COVID-19 patients. We have further identified 59 drug repurposing candidates for 13 of these targets that can be used for the development of novel therapeutic strategies to increase the survival rate of patients who develop sepsis and potentially severe COVID-19
Application 44288
A meta-investigation of combinatorial mutation signatures in broad disease categories
The proposed study aims to address three research questions: * Are there genetic defect patterns common to broad categories of disease such as all cancers, all psychiatric disorders, or all musculoskeletal disorders? * Conversely, are there genetic patterns that help protect people against broad categories of disease, such as cancer or cardiovascular disease? * Are there genotypic variant signatures allowing stratification of patients that could inform the risk of developing a disorder and likelihood of drug therapy response? The proposed research would improve our understanding of the genomic basis of disease formation (or disease prevention). We hope to identify individual genetic defects or combinatorial defect clusters that are commonly associated with broad categories of disease, that is, found in significantly higher numbers of patients compared to healthy controls. Similarly, we hope to identify protective signatures that are found in many more healthy controls compared to afflicted individuals. In addition, we hope to develop new improved ways of identifying patients at risk of developing a disease or its complications, and enable patients to be treated with a drug therapy regimen that is tailored to their individual needs. Successful results would help future researchers identify means to increase human longevity and wellness by manipulating genetic mechanisms involved in broad categories of disease. Through follow-on studies, researchers may identify new drugs that work across broad categories of disease. Such drugs with broad applicability may cost less to develop, test, and bring to market, thus helping everyone afflicted with those diseases. Tailoring therapies to patients' individual needs may significantly reduce the burden on the healthcare system through reduced side effects due to drug interaction or lack of therapy response, leading to hospital admissions. The project duration is 24 months.
| Lead investigator: | Dr Sayoni Das |
| Lead institution: | PrecisionLife Ltd |
2 related Returns
| Return ID | App ID | Description | Archive Date |
|---|---|---|---|
| 3252 | 44288 | Analysis of Genetic Host Response Risk Factors in Severe COVID-19 Patients | 19 Mar 2021 |
| 3134 | 44288 | Systematic drug repurposing to enable precision medicine: A case study in breast cancer | 18 Feb 2021 |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 3252 | Identification and Analysis of Shared Risk Factors in Sepsis and High Mortality Risk COVID-19 Patients | Das et al | 2014 | medRxiv (2020) |
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