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...

Full description

Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Nature Publishing Group 2020-12-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-020-79033-3
id doaj-023f6bf2b1e744f4911b47679a2be540
record_format Article
spelling 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
work_keys_str_mv AT nicholasparkinson dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19
AT natasharodgers dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19
AT maxheadfourman dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19
AT bowang dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19
AT mariezechner dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19
AT maaikecswets dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19
AT jonathanemillar dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19
AT andylaw dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19
AT clarkdrussell dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19
AT jkennethbaillie dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19
AT saraclohisey dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19
_version_ 1724376444327428096