Identification of PBMC-based molecular signature associational with COVID-19 disease severity

Abstract

The longevity of COVID-19 as a global pandemic, and the devastating effects it has had on certain subsets of individuals thus far has highlighted the importance of identifying blood-based biomarkers associated with disease severity. We employed computational and transcriptome analyses of publicly available datasets from PBMCs from 126 patients with COVID-19 admitted to ICU (n = 50), COVID-19 not admitted to ICU (n = 50), non-COVID-19 admitted to ICU (n = 16) and non-COVID-19 not admitted to ICU (n = 10), and utilized the Gencode V33 assembly to analyze protein coding mRNA and long noncoding RNA (lncRNA) transcriptomes in the context of disease severity. Our data identified several aberrantly expressed mRNA and lncRNA based biomarkers associated with SARS-CoV-2 severity, which in turn significantly affected canonical, upstream, and disease functions in each group of patients. Immune, interferon, and antiviral responses were severely suppressed in COVID-19 patients admitted to ICU versus those who were not admitted to ICU. Our data suggests a possible therapeutic approach for severe COVID-19 through administration of interferon therapy. Delving further into these biomarkers, roles and their implications on the onset and disease severity of COVID-19 could play a crucial role in patient stratification and identifying varied therapeutic options with diverse clinical implications.

Keywords: Biomarker; COVID-19; PBMCs; Severity; Transcriptome.

Figure 1

Identification of mRNA-based biomarkers associated with SARS-CoV-2 severity. (a) Heatmap image depicting putative markers associated with each of the indicated pathological condition (COVID-19 ICU, COVID-19 no ICU, Non-COVID-19 ICU, Non-COVID-19 non-ICU) employing the marker discovery algorithm. Enriched gene ontology (GO) associations are indicated on the y axis with enrichment p values indicated. Venn diagram depicting the overlap in upregulated (b) or downregulated (c) mRNAs in CIC vs NCIC, CIC vs CNIC, CNIC vs NCNIC, and NCIC vs NCNIC). CIC: COVID-19 ICU, CNIC: COVID-19 non ICU, NCIC: Non-COVID-19 ICU, and NCNIC: Non-COVID-19 non ICU).

Figure 2

Significantly affected canonical, upstream, and disease and bio function classification based on differentially expressed genes in the indicted comparison groups. Enrichment heat map of canonical pathways (a), upstream regulator (b) and disease and bio function classifications (c) based on differentially expressed genes in COVID-19 ICU, COVID-19 no ICU, Non-COVID-19 ICU, Non-COVID-19 non-ICU employing the IPA algorithm. Activation Z score is depicted according to the color scale (2.0 ≤ Z score ≤ −2.0). Red indicated activation, while blue indicated suppression. Squares with a filled circle denote canonical with an absolute activation Z score <2.0.

Figure 3

Downstream effector analysis of differentially expressed genes in COVID-19 ICU vs COVID-19 non ICU. (a) Tree map (hierarchical heat map) depicting affected functional categories based on differentially expressed genes where the major boxes represent a category of diseases and functions. Each individual colored rectangle is a particular biological function or disease and the color range indicates its predicted activation state—increasing (orange) or decreasing (blue). Darker colors indicate higher absolute Z-scores. The size of the rectangles is correlated with increasing overlap significance. Illustration of suppressed immune cell trafficking (b) and activated infectious disease (c) functional categories in COVID-19 ICU vs COVID-19 non ICU.

Figure 4

Expression of selected markers according to COVID-19 disease severity. Expression of selected panel of markers enriched in the COVID-19 ICU (a) or COVID-19 non ICU (b) group. Data are presented as violin plots with the Anova p value indicated on each plot.

Figure 5

Identification of lncRNA-based biomarkers associated with SARS-CoV-2 severity. (a) Heatmap image depicting putative lncRNA-based markers associated with each of the indicated pathological condition (COVID-19 ICU, COVID-19 non ICU, Non-COVID-19 ICU, Non-COVID-19 non-ICU) employing the marker discovery algorithm. (b) Principal component analysis (PCA) for the lncRNA transcriptome of each pathological condition. Expression of selected lncRNA genes enriched in COVID-19 ICU (c) or COVID-19 non ICU (d).