Crowdsourcing Temporal Transcriptomic Coronavirus Host Infection Data: resources, guide, and novel insights
Cov Infection James Flynn, Mehdi M Ahmadi, Chase T Mcfarland, Michael D Kubal, Mark A Taylor, Zhang Cheng, Enrique C Torchia, Michael G Edwards
doi:10.1093/biomethods/bpad033/7420215
The emergence of SARS-CoV-2 reawakened the need to rapidly understand the molecular etiologies, pandemic potential, and prospective treatments of infectious agents. The lack of existing data on SARS-CoV-2 hampered early attempts to treat severe forms of COVID-19 during the pandemic. This study coupled existing transcriptomic data from SARS-CoV-1 lung infection animal studies with crowdsourcing statistical approaches to derive temporal meta-signatures of host responses during early viral accumulation and subsequent clearance stages. Unsupervised and supervised machine learning approaches identified top dysregulated genes and potential biomarkers (e.g., CXCL10, BEX2, and ADM). Temporal meta-signatures revealed distinct gene expression programs with biological implications to a series of host responses underlying sustained Cxcl10 expression and Stat signaling. Cell cycle switched from G1/G0 phase genes, early in infection, to a G2/M gene signature during late infection that correlated with the enrichment of DNA Damage Response and Repair genes. The SARS-CoV-1 meta-signatures were shown to closely emulate human SARS-CoV-2 host responses from emerging RNAseq, single cell and proteomics data with early monocyte-macrophage activation followed by lymphocyte proliferation. The circulatory hormone adrenomedullin was observed as maximally elevated in elderly patients that died from COVID-19. Stage-specific correlations to compounds with potential to treat COVID-19 and future coronavirus infections were in part validated by a subset of twenty-four that are in clinical trials to treat COVID-19. This study represents a roadmap to leverage existing data in the public domain to derive novel molecular and biological insights and potential treatments to emerging human pathogens.
Competing Interests JF, MK and ZC work for Illumina, the commercial developer of BSCE. ME, ET, MT, CM, MA report no competing interests.
Author contributions JF, ME and ET were responsible for the conception and design of the studies, performed bioinformatics analyses, biological interpretation and manuscript drafting. MK, ZC, MT, CM and MA performed bioinformatics analyses and created the interactive portal. All authors had access to and assisted in data interpretation. All authors helped to critically revise the intellectual content of the manuscript and approved the final submission.
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'agents. The lack of existing data on SARS-CoV-2 hampered early attempts to treat severe forms '
'of COVID-19 during the pandemic. This study coupled existing transcriptomic data from '
'SARS-CoV-1 lung infection animal studies with crowdsourcing statistical approaches to derive '
'temporal meta-signatures of host responses during early viral accumulation and subsequent '
'clearance stages. Unsupervised and supervised machine learning approaches identified top '
'dysregulated genes and potential biomarkers (e.g., CXCL10, BEX2, and ADM). Temporal '
'meta-signatures revealed distinct gene expression programs with biological implications to a '
'series of host responses underlying sustained Cxcl10 expression and Stat signaling. Cell '
'cycle switched from G1/G0 phase genes, early in infection, to a G2/M gene signature during '
'late infection that correlated with the enrichment of DNA Damage Response and Repair genes. '
'The SARS-CoV-1 meta-signatures were shown to closely emulate human SARS-CoV-2 host responses '
'from emerging RNAseq, single cell and proteomics data with early monocyte-macrophage '
'activation followed by lymphocyte proliferation. The circulatory hormone adrenomedullin was '
'observed as maximally elevated in elderly patients that died from COVID-19. Stage-specific '
'correlations to compounds with potential to treat COVID-19 and future coronavirus infections '
'were in part validated by a subset of twenty-four that are in clinical trials to treat '
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'ISSN': ['2396-8923'],
'subject': [ 'General Agricultural and Biological Sciences',
'General Biochemistry, Genetics and Molecular Biology'],
'published': {'date-parts': [[2023, 11, 14]]}}
