Comparative transcriptome analysis reveals key epigenetic targets in SARS-CoV-2 infection

Abstract

COVID-19 is an infection caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2), which has caused a global outbreak. Current research efforts are focused on the understanding of the molecular mechanisms involved in SARS-CoV-2 infection in order to propose drug-based therapeutic options. Transcriptional changes due to epigenetic regulation are key host cell responses to viral infection and have been studied in SARS-CoV and MERS-CoV; however, such changes are not fully described for SARS-CoV-2. In this study, we analyzed multiple transcriptomes obtained from cell lines infected with MERS-CoV, SARS-CoV, and SARS-CoV-2, and from COVID-19 patient-derived samples. Using integrative analyses of gene co-expression networks and de-novo pathway enrichment, we characterize different gene modules and protein pathways enriched with Transcription Factors or Epifactors relevant for SARS-CoV-2 infection. We identified EP300, MOV10, RELA, and TRIM25 as top candidates, and more than 60 additional proteins involved in the epigenetic response during viral infection that has therapeutic potential. Our results show that targeting the epigenetic machinery could be a feasible alternative to treat COVID-19.

Fig. 1. Differential expression analysis of coronavirus-infected cell lines.

a Intersection size of the DEGs common to each viral infection represented as single dots (virus-associated gene sets) and the size of their intersections with the other sets (multiple vertical dots). b Top ten simplified enriched Gene Ontology terms of biological process in the virus-associated gene sets ordered by q-value. c Shared differentially expressed epigenes between virus-associated gene sets; text color corresponds to the gene classification as either TF (red) or epifactor (blue) (upper panel). Log2 fold change of shared differentially expressed epifactors in each cell line are also shown as a heatmap (lower panel); blank color represents non-significant differential expression, text highlight corresponds to the intersections shown in the Venn diagram. d Functional classification of the identified epifactors; text color corresponds to the intersection color of subsection (c). e Characterization of the DNA-binding domain (DBDs) of human transcription factors (TFs) altered by the viral infection of coronaviruses.

Fig. 2. Differential expression analysis of COVID-19 patient samples.

a Number of shared differentially expressed genes between the samples. b Log2 fold change of shared differentially expressed epigenes in patients’ samples. c Top ten simplified Gene Ontology enriched terms belonging to the biological process sub-ontology; ordered by q-value. d Epigenetic processes associated with the shared differentially expressed epigenes between patient samples. Created with BioRender.com.

Fig. 3. Relevant modules for coronavirus infection.

Summary of the analyses used to identify relevant modules for each infection. From left to right, grids show the module-trait correlation, the enrichment of epigenes, the enrichment of DEGs found in cell lines, enrichment of DEGs found in patients’ samples, and information of the module size.

Fig. 4. Protein-protein interactions network containing SARS-CoV-2-DEGs, patient-DEGs, or selected epigenes for modules 4, 6, 8–12.

Nodes and edges represent proteins and the interaction between them, respectively. The node and edge color’s meaning is indicated in the annotation panel.

Fig. 5. Relevant epigenes in SARS-CoV-2 infection with therapeutic potential.

Epigenetic targets are indicated in different processes such as nucleosome occupancy (1), histone modification (2), DNA methylation (3), and also TFs (4). Top gene candidate targets are highlighted in red. Created with BioRender.com.