Crowdsourcing temporal transcriptomic coronavirus host infection data: Resources, guide, and novel insights

Area of Science:

• Virology and immunology
• Computational biology and bioinformatics
• Genomics and transcriptomics

Background:

• The COVID-19 pandemic highlighted the urgent need to understand viral diseases and identify effective treatments.
• Limited early data on SARS-CoV-2 hindered the development of therapies for severe cases.
• Existing transcriptomic data from SARS-CoV-1 infections offer a valuable resource for studying host responses.

Purpose of the Study:

• To derive temporal host response signatures for SARS-CoV-1 infection using existing data.
• To identify key genes, biomarkers, and biological pathways involved in viral infections.
• To explore potential therapeutic compounds for COVID-19 and future coronavirus outbreaks.

Main Methods:

• Coupled existing SARS-CoV-1 transcriptomic data with crowdsourcing statistical approaches.
• Applied unsupervised and supervised machine learning to identify dysregulated genes and biomarkers.
• Analyzed temporal gene expression patterns, cell cycle shifts, and DNA damage response pathways.

Main Results:

• Identified key host response meta-signatures, including sustained CXCL10 and STAT signaling.
• Observed a shift in cell cycle gene expression from G1/G0 to G2/M, correlating with DNA repair gene enrichment.
• Validated SARS-CoV-1 signatures against human SARS-CoV-2 data, showing conserved early immune responses (monocyte-macrophage activation, lymphocyte proliferation).
• Found elevated adrenomedullin in elderly COVID-19 fatalities.
• Identified and partially validated compounds for potential COVID-19 treatment.

Conclusions:

• Leveraging public domain data can yield novel insights into emerging infectious diseases.
• Temporal host response signatures provide a roadmap for understanding viral pathogenesis.
• Identified biomarkers and therapeutic strategies warrant further investigation for pandemic preparedness.