Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes

Abstract

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

Keywords: AUC, Area under the curve; Bioinformatics; Biomarkers; COVID-19; COVID-19, Coronavirus Disease of 2019; DEG, Differentially expressed gene; Data mining; GEO, Gene Expression Omnibus; GO, Gene Ontology; RNA; RNA-sequencing; ROC, Receiver-operator characteristic; SARS-CoV-2; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2; Virus.

Graphical abstract

Fig. 1

: Flow diagram to visualize the process used to filter relevant public RNA-seq records/studies and reports/samples that were included in the secondary analysis.

Fig. 2

Volcano plot of all differentially expressed genes in severe vs. mild human infection with SARS-CoV-2. Genes that are up or down regulated from blood samples collected from patients having severe symptoms or mild symptoms during infection with SARS-CoV-2. Genes showing statistically significant up-regulation (blue), down-regulated (red), or no significant change (green). X-axis shows the log₂ fold-change values while the y-axis displays false-discovery rate-adjusted p-values to account for multiple hypothesis testing.

Fig. 3

Receiver-operator characteristic (ROC) curve constructed from all expressed genes in severe vs. mild human infection with SARS-CoV-2. Constructing a ROC curve from all RNA-sequencing read quantification values achieved an area-under-the-curve (AUC) value of greater than 96%.

Fig. 4

Principal component analysis (PCA) of all samples based on available severity metadata and collected biomaterial. PCA was applied to all samples based on metadata for disease severity (A) or biomaterial type (B).