tetano
Editor, Senior Moderator
Sci Rep
. 2020 Dec 18;10(1):22303.
doi: 10.1038/s41598-020-79033-3.
Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19
Nicholas Parkinson[SUP] 1 [/SUP], Natasha Rodgers[SUP] 1 [/SUP], Max Head Fourman[SUP] 1 [/SUP], Bo Wang[SUP] 1 [/SUP], Marie Zechner[SUP] 1 [/SUP], Maaike C Swets[SUP] 1 2 [/SUP], Jonathan E Millar[SUP] 1 [/SUP], Andy Law[SUP] 1 [/SUP], Clark D Russell[SUP] 1 3 [/SUP], J Kenneth Baillie[SUP] 4 [/SUP], Sara Clohisey[SUP] 5 [/SUP]
Affiliations
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.
. 2020 Dec 18;10(1):22303.
doi: 10.1038/s41598-020-79033-3.
Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19
Nicholas Parkinson[SUP] 1 [/SUP], Natasha Rodgers[SUP] 1 [/SUP], Max Head Fourman[SUP] 1 [/SUP], Bo Wang[SUP] 1 [/SUP], Marie Zechner[SUP] 1 [/SUP], Maaike C Swets[SUP] 1 2 [/SUP], Jonathan E Millar[SUP] 1 [/SUP], Andy Law[SUP] 1 [/SUP], Clark D Russell[SUP] 1 3 [/SUP], J Kenneth Baillie[SUP] 4 [/SUP], Sara Clohisey[SUP] 5 [/SUP]
Affiliations
- PMID: 33339864
- DOI: 10.1038/s41598-020-79033-3
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.