Network methods for inferring effects of complex genetic variation : development and application in SARS-COV-2 entry
Abstract/Contents
- Abstract
- Many human diseases depend on complex genetic architectures, whereby genotypes or expression changes at multiple loci contribute cooperatively to the observed phenotype. High-throughput targeted mutagenesis techniques enable systematic genotype-phenotype mapping that encompasses near-complete first-order mutational landscapes at the level of both amino acids and genes. However, identifying the central patterns of variation that underlie a phenotype remains challenging due to the sparsity of experimental data relative to the combinatorial variant space. To address this limitation, we develop a hierarchical framework for interpreting large-scale screening data through biological network topologies at the level of amino acids, genes, and drugs. We apply this approach to variant screening datasets for critical SARS-CoV-2 entry proteins to identify key patterns of genetic variation that contribute to viral entry. Specifically, we first develop a framework for inferring epistatic relationships from deep mutational scanning data and use it to identify combinatorial Spike mutations conferring high activity. We next develop an improved pathway analysis algorithm that enables more accurate interpretation of functional genomics datasets and apply it to a collection of SARS-CoV-2 entry-factor screens. Finally, we perform mechanistic prioritization of drug repurposing opportunities in COVID-19 based on functional genomics datasets. We perform both experimental and large-scale, retrospective clinical validation of top candidate drugs, identifying a protective effect for spironolactone against severe COVID-19 consistent with an androgen-dependent mechanism. Our work provides broadly applicable bioinformatic tools that enable identification of conserved SARS-CoV-2 entry pathways, culminating in supporting evidence for a protective role for spironolactone in severe COVID-19.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Cousins, Henry Corbett |
|---|---|
| Degree supervisor | Altman, Russ |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Chen, Jonathan H |
| Thesis advisor | Cong, Le |
| Degree committee member | Chen, Jonathan H |
| Degree committee member | Cong, Le |
| Associated with | Stanford University, School of Medicine |
| Associated with | Stanford University, School of Medicine, Department of Biomedical Data Science |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Henry C. Cousins. |
|---|---|
| Note | Submitted to the Department of Biomedical Data Science |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/zf832jh0369 |
Access conditions
- Copyright
- © 2023 by Henry Corbett Cousins
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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