Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19

Area of Science:

• Virology
• Genetics
• Immunology

Background:

• Host factors are crucial in COVID-19 pathogenesis.
• Multi-omic data integration is needed for robust evidence and therapy development.
• A dynamic ranking of host genes implicated in betacoronavirus infections is presented.

Purpose of the Study:

• To create a ranked list of host genes involved in human betacoronavirus infections (SARS-CoV-2, SARS-CoV, MERS-CoV, seasonal coronaviruses).
• To inform the development of novel therapeutic strategies for COVID-19 and related diseases.
• To provide a dynamic, updated resource for researchers investigating host-pathogen interactions.

Main Methods:

• Systematic review of experiments identifying potential host factors.
• Integration of gene lists from diverse sources using Meta-Analysis by Information Content (MAIC).
• Data-driven gene list weightings to generate a comprehensive ranked list.

Main Results:

• A ranked list of host genes implicated in betacoronavirus infections was generated from 32 datasets.
• PPIA (cyclophilin A) was the top-ranked gene, identified as a druggable target.
• Other highly-ranked genes include CXCL10, CD4, CD3E, and IL1A, with implications for prognosis and therapy.

Conclusions:

• The study provides a ranked list of host genes crucial for betacoronavirus infections, including COVID-19.
• PPIA, a druggable target, is highlighted as a key factor.
• The findings support the interpretation of genetic association studies and prioritize future research and therapeutic development.