Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19

Abstract

Standard transcriptomic analyses alone have limited power in capturing the molecular mechanisms driving disease pathophysiology and outcomes. To overcome this, unsupervised network analyses are used to identify clusters of genes that can be associated with distinct molecular mechanisms and outcomes for a disease. In this study, we developed an integrated network analysis framework that integrates transcriptional signatures from multiple model systems with protein-protein interaction data to find gene modules. Through a meta-analysis of different enriched features from these gene modules, we extract communities of highly interconnected features. These clusters of higher-order features, working as a multifeatured machine , enable collective assessment of their contribution for disease or phenotype characterization. We show the utility of this workflow using transcriptomics data from three different models of SARS-CoV-2 infection and identify several pathways and biological processes that could enable in understanding or hypothesizing molecular signatures inducing pathophysiological changes, risks, or sequelae of COVID-19.

Computational workflow for functional characterization of COVID-19 through secondary data analysis

“…Among these, 147 genes are either upregulated (106 genes) or downregulated (41 genes) in all the three studies, representing a potential “core” transcriptomic signature. Figure 2 in ( Ghandikota et al., 2021 ) indicates the transcriptomic concordance among the input models in addition to the different enriched biological processes and pathways in the core signature.…”

“…Among these, 29 modules have at least one gene encoding SARS-CoV-2-host interactant protein. Figure 3C in ( Ghandikota et al., 2021 ) shows example gene modules from the consensus DEG and SARS-CoV-2-host integrated interactome identified using the current protocol. These modules potentially represent the different perturbed states within a disease and are further characterized by computing enrichments of different functional terms, cell types, and phenotypic traits.…”

“…We apply this protocol for functional characterization of COVID-19, but it could be applied to many other diseases. For complete details on the use and execution of this protocol, please refer to Ghandikota et al. (2021) .…”

“…For complete details on the use and execution of this protocol, please refer to Ghandikota et al. (2021) .…”

Human Coronaviruses: Genetics, Virulence Factors and Pathophysiology, Diseases’ Epidemiology

“…The 29 nt region found in SL-CoV civets and early stage human strains is also found in SL-CoV bat [91] . Sequence analysis of SARS-CoV-2 has shown a typical structure to other coronavirus and its genome has been linked to a previously identified coronavirus strain, SARS-CoV that caused the SARS outbreak in 2003 [92] . Structurally, the SARS coronavirus (SARS-CoV) has a well-defined composition comprising 14 binding residues that directly interact with human ACE2.…”