Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19
Abstract The increasing body of literature describing the role of host factors in COVID-19 pathogenesis demonstrates the need to combine diverse, multi-omic data to evaluate and substantiate the most robust evidence and inform development of therapies. Here we present a dynamic ranking of host genes...
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2020-12-01
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doaj-023f6bf2b1e744f4911b47679a2be5402020-12-20T12:32:35ZengNature Publishing GroupScientific Reports2045-23222020-12-0110111210.1038/s41598-020-79033-3Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19Nicholas Parkinson0Natasha Rodgers1Max Head Fourman2Bo Wang3Marie Zechner4Maaike C. Swets5Jonathan E. Millar6Andy Law7Clark D. Russell8J. Kenneth Baillie9Sara Clohisey10Roslin Institute, University of EdinburghRoslin Institute, University of EdinburghRoslin Institute, University of EdinburghRoslin Institute, University of EdinburghRoslin Institute, University of EdinburghRoslin Institute, University of EdinburghRoslin Institute, University of EdinburghRoslin Institute, University of EdinburghRoslin Institute, University of EdinburghRoslin Institute, University of EdinburghRoslin Institute, University of EdinburghAbstract The increasing body of literature describing the role of host factors in COVID-19 pathogenesis demonstrates the need to combine diverse, multi-omic data to evaluate and substantiate the most robust evidence and inform development of therapies. Here we present a dynamic ranking of host genes implicated in human betacoronavirus infection (SARS-CoV-2, SARS-CoV, MERS-CoV, seasonal coronaviruses). We conducted an extensive systematic review of experiments identifying potential host factors. Gene lists from diverse sources were integrated using Meta-Analysis by Information Content (MAIC). This previously described algorithm uses data-driven gene list weightings to produce a comprehensive ranked list of implicated host genes. From 32 datasets, the top ranked gene was PPIA, encoding cyclophilin A, a druggable target using cyclosporine. Other highly-ranked genes included proposed prognostic factors (CXCL10, CD4, CD3E) and investigational therapeutic targets (IL1A) for COVID-19. Gene rankings also inform the interpretation of COVID-19 GWAS results, implicating FYCO1 over other nearby genes in a disease-associated locus on chromosome 3. Researchers can search and review the gene rankings and the contribution of different experimental methods to gene rank at https://baillielab.net/maic/covid19 . As new data are published we will regularly update the list of genes as a resource to inform and prioritise future studies.https://doi.org/10.1038/s41598-020-79033-3 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nicholas Parkinson Natasha Rodgers Max Head Fourman Bo Wang Marie Zechner Maaike C. Swets Jonathan E. Millar Andy Law Clark D. Russell J. Kenneth Baillie Sara Clohisey |
spellingShingle |
Nicholas Parkinson Natasha Rodgers Max Head Fourman Bo Wang Marie Zechner Maaike C. Swets Jonathan E. Millar Andy Law Clark D. Russell J. Kenneth Baillie Sara Clohisey Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19 Scientific Reports |
author_facet |
Nicholas Parkinson Natasha Rodgers Max Head Fourman Bo Wang Marie Zechner Maaike C. Swets Jonathan E. Millar Andy Law Clark D. Russell J. Kenneth Baillie Sara Clohisey |
author_sort |
Nicholas Parkinson |
title |
Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19 |
title_short |
Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19 |
title_full |
Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19 |
title_fullStr |
Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19 |
title_full_unstemmed |
Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19 |
title_sort |
dynamic data-driven meta-analysis for prioritisation of host genes implicated in covid-19 |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2020-12-01 |
description |
Abstract The increasing body of literature describing the role of host factors in COVID-19 pathogenesis demonstrates the need to combine diverse, multi-omic data to evaluate and substantiate the most robust evidence and inform development of therapies. Here we present a dynamic ranking of host genes implicated in human betacoronavirus infection (SARS-CoV-2, SARS-CoV, MERS-CoV, seasonal coronaviruses). We conducted an extensive systematic review of experiments identifying potential host factors. Gene lists from diverse sources were integrated using Meta-Analysis by Information Content (MAIC). This previously described algorithm uses data-driven gene list weightings to produce a comprehensive ranked list of implicated host genes. From 32 datasets, the top ranked gene was PPIA, encoding cyclophilin A, a druggable target using cyclosporine. Other highly-ranked genes included proposed prognostic factors (CXCL10, CD4, CD3E) and investigational therapeutic targets (IL1A) for COVID-19. Gene rankings also inform the interpretation of COVID-19 GWAS results, implicating FYCO1 over other nearby genes in a disease-associated locus on chromosome 3. Researchers can search and review the gene rankings and the contribution of different experimental methods to gene rank at https://baillielab.net/maic/covid19 . As new data are published we will regularly update the list of genes as a resource to inform and prioritise future studies. |
url |
https://doi.org/10.1038/s41598-020-79033-3 |
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