Bulk RNA sequencing for analysis of post COVID-19 condition in adolescents and young adults

Abstract

Background: Post COVID-19 condition (PCC) is a complication of SARS-COV-2 infection and can lead to long-term disability.

Methods: The present study was designed to analyse the gene expression patterns of PCC through bulk RNA sequencing of whole blood and to explore the potential molecular mechanisms of PCC. Whole blood was collected from 80 participants enrolled in a prospective cohort study following SARS-CoV-2 infected and non-infected individuals for 6 months after recruitment and was used for bulk RNA sequencing. Identification of differentially expressed genes (DEG), pathway enrichment and immune cell deconvolution was performed to explore potential biological pathways involved in PCC.

Results: We have found 13 differentially expressed genes associated with PCC. Enriched pathways were related to interferon-signalling and anti-viral immune processes.

Conclusion: The PCC transcriptome is characterized by a modest overexpression of interferon-stimulated genes, pointing to a subtle ongoing inflammatory response.

Keywords: Adolescent; Child; Immunology; Long COVID; Post COVID-19 condition; RNA sequencing; Transcriptomics.

Fig. 1

Flow diagram of study design. Participant allocation throughout the study is depicted in Fig. 1: baseline (A), follow-up (B), transcriptome sequencing analysis (C). WHO World Health Organization; PCC Post COVID-19 Condition; PIFS, post-infectious fatigue syndrome

Fig. 2

Dimensionality reduction by principal components analysis based on log2 normalized counts of all genes for all participants. Each dot corresponds to the sample of one participant. The smaller the distance between the dots, the greater the similarity of the gene expression profiles. PC1, principal component 1; PC2, principal component 2; percentage expresses contribution to the overall variability in the data

Fig. 3

Line plot of expression levels of OAS3, an interferon-inducible antiviral effector protein. The colour indicates SARS-COV-2 infection history: pink = prior SARS-CoV-2 infection, green = no SARS-CoV-2 infection. The groups are further divided based on fatigue symptom load: fatigue versus no fatigue. The p-value shows the significance when comparing the expression levels of OAS3 between SARS + subjects with fatigue and no fatigue by Mann Whitney-U test

Fig. 4

Differential gene expression analysis for participants divided into four experimental groups depending on prior SARS-CoV-2 infection and fatigue symptoms. A Volcano plot of differentially expressed genes for participants with prior SARS-CoV-2 infection and fatigue symptoms (SARS + /F +) relatively to fully recovered participants with SARS-CoV-2 infection (SARS + /F-). Genes with significantly increased expression are represented with red dots. The threshold for the adjusted p-value was set to 0.05. The grey dots indicate absolute value of log2 (fold-change) smaller than 0.50. FC, fold change; NS, non-significant. B Heat map of differentially expressed genes in participants with prior SARS-CoV-2 infection and fatigue symptoms (SARS + /F +) relative to participants in the other three groups, with raw expression values being scaled

Fig. 5

Bar chart of enrichment ratios of significantly enriched Gene ontology (GO) biological pathways. The pathways are ranked by increasing adjusted p-value. The enrichment ratio shows the number of observed divided by the number of expected genes in the gene list of the GO category

Fig. 6

Bar chart of enrichment ratios of significantly enriched KEGG pathways. The pathways are ranked by increasing adjusted p-value. The enrichment ratio shows the number of observed divided by the number of expected genes in the gene list of the KEGG category

Fig. 7

Bar chart of enrichment ratios of significantly enriched Reactome pathways. The pathways are ranked by increasing adjusted p-value. The enrichment ratio shows the number of observed divided by the number of expected genes in the gene list of the Reactome category

Fig. 8

Bee swarm plot to visualize the top 5 enriched transcription factors after transcription factor enrichment analysis using UniBind. Each dot represents a transcription factor; N.S. non-significant

Fig. 9

Chord diagram of associations between seven clinical symptoms and 13 DEG expression levels for all samples. Clinical symptoms are represented on the top part of the circle, while DEGs are represented on the bottom part of the circle. The arcs are colour-coded with red for positive effect and blue for negative effect. The width of the arc represents the gene effect on its corresponding clinical symptom. Fatigue score, from the Chalder Fatigue Questionnaire; Post-exertional malaise, from the DePaul Symptom Questionnaire; cognitive symptoms, the sum score across the 3 items memory problems, concentration problems, and decision-making problems; respiratory symptoms, the sum of scores across dyspnea and coughing; symptoms of anxiety, from the Hospital Anxiety and Depression Scale anxiety subscale; symptoms of depression, from the Hospital Anxiety and Depression Scale depression subscale; quality of life, from the Pediatric Quality of Life Inventory