Integration and Reanalysis of Four RNA-Seq Datasets Including BALF, Nasopharyngeal Swabs, Lung Biopsy, and Mouse Models Reveals Common Immune Features of COVID-19

Abstract

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has spread over the world causing a pandemic which is still ongoing since its emergence in late 2019. A great amount of effort has been devoted to understanding the pathogenesis of COVID-19 with the hope of developing better therapeutic strategies. Transcriptome analysis using technologies such as RNA sequencing became a commonly used approach in study of host immune responses to SARS-CoV-2. Although substantial amount of information can be gathered from transcriptome analysis, different analysis tools used in these studies may lead to conclusions that differ dramatically from each other. Here, we re-analyzed four RNA-sequencing datasets of COVID-19 samples including human bronchoalveolar lavage fluid, nasopharyngeal swabs, lung biopsy and hACE2 transgenic mice using the same standardized method. The results showed that common features of COVID-19 include upregulation of chemokines including CCL2, CXCL1, and CXCL10, inflammatory cytokine IL-1β and alarmin S100A8/S100A9, which are associated with dysregulated innate immunity marked by abundant neutrophil and mast cell accumulation. Downregulation of chemokine receptor genes that are associated with impaired adaptive immunity such as lymphopenia is another common feather of COVID-19 observed. In addition, a few interferon-stimulated genes but no type I IFN genes were identified to be enriched in COVID-19 samples compared to their respective control in these datasets. These features are in line with results from single-cell RNA sequencing studies in the field. Therefore, our re-analysis of the RNA-seq datasets revealed common features of dysregulated immune responses to SARS-CoV-2 and shed light to the pathogenesis of COVID-19.

Keywords: COVID-19; Cytokines; Host-virus interactions; Immune response; Innate immunity; RNA-seq datasets.

Figure 1. Differentially expressed genes in re-analyzed four RNA-seq datasets of COVID-19 studies. (A) Regulated genes for Zhou et al. dataset on BALF of 8 COVID-19 patients and 20 healthy controls (BALF dataset). (B) Regulated genes for Lieberman et al. dataset on nasopharyngeal swabs of 430 COVID-19 cases and 54 healthy controls (NP dataset). (C) Regulated genes for Blanco-Melo et al. dataset on one lung biopsy of a COVID-19 patient and two lung biopsies of healthy controls (LUNG dataset). (D) Regulated genes for Israelow et al. dataset on a mouse model for SARS-CoV-2 of two virus-infected and two mock-infected mice (TG-ACE2 dataset).

Figure 2. Enrichment analysis of KEGG pathways on regulated genes after SARS-CoV-2 infection. (A) Enrichment analysis on up-regulated genes for three human datasets including BAFL (A), NP (B) and LUNG (C). (B) Enrichment analysis on down-regulated genes for the same three human datasets. (C) Enrichment analysis for up-regulated genes on murine KEGG pathways of the TG-ACE2 dataset. (D) Enrichment analysis on down-regulated genes in the TG-ACE2 dataset.

Figure 3. Significantly regulated cytokines and related genes after SARS-CoV-2 infection. (A) Heatmap showing log₂ fold-changes of genes in seven groups. Column SARS2 shows the fold-changes calculated using DESeq2 using all samples and columns C1 until C8 show fold-changes for individual patients. (B) Regulated cytokines for the NP dataset on nasopharyngeal swabs of 430 COVID-19 cases and 54 healthy controls. (C) Regulated cytokines for the LUNG dataset on one lung biopsy of a COVID-19 patient and two lung biopsies of healthy controls. (D) Regulated cytokines for the TG-ACE2 dataset on a mouse model for SARS-CoV-2 on two virus-infected and two mock-infected mice. (E) Increased expression of S100A8 and S100A9 in the BALF and LUNG datasets in COVID-19 samples compared to their respective controls.

Figure 4. Significantly regulated interferon-stimulated genes after SARS-CoV-2 infection. (A) Heatmap showing log₂ fold-changes of ISGs in our global DESeq2 analysis (column SARS2) and for the individual patients C1 until C8. (B) Regulated ISGs for the NP dataset on nasopharyngeal swabs of 430 COVID-19 cases and 54 healthy controls. (C) Regulated ISGs for the LUNG dataset on one lung biopsy of a COVID-19 patient and two lung biopsies of healthy controls. (D) Regulated ISGs for the TG-ACE2 dataset on a mouse model for SARS-CoV-2 on two virus infected and two mock infected mice.

Figure 5. Immune cell composition analysis in healthy and COVID-19 patients. Proportion of nine major immune cell types calculated by CIBERSORT using the BALF, NP and LUNG datasets.

ns, not significant; ^*p<0.05, ^**p<0.01, ^***p<0.001, ^****p<0.0001.