Dataset Information

A blood RNA transcriptome signature for COVID-19.

ABSTRACT:

Background

COVID-19 is a respiratory viral infection with unique features including a more chronic course and systemic disease manifestations including multiple organ involvement; and there are differences in disease severity between ethnic groups. The immunological basis for disease has not been fully characterised. Analysis of whole-blood RNA expression may provide valuable information on disease pathogenesis.

Methods

We studied 45 patients with confirmed COVID-19 infection within 10 days from onset of illness and a control group of 19 asymptomatic healthy volunteers with no known exposure to COVID-19 in the previous 14 days. Relevant demographic and clinical information was collected and a blood sample was drawn from all participants for whole-blood RNA sequencing. We evaluated differentially-expressed genes in COVID-19 patients (log2 fold change ≥ 1 versus healthy controls; false-discovery rate < 0.05) and associated protein pathways and compared these to published whole-blood signatures for respiratory syncytial virus (RSV) and influenza. We developed a disease score reflecting the overall magnitude of expression of internally-validated genes and assessed the relationship between the disease score and clinical disease parameters.

Results

We found 135 differentially-expressed genes in the patients with COVID-19 (median age 35 years; 82% male; 36% Chinese, 53% South Asian ethnicity). Of the 117 induced genes, 14 were found in datasets from RSV and 40 from influenza; 95 genes were unique to COVID-19. Protein pathways were mostly generic responses to viral infections, including apoptosis by P53-associated pathway, but also included some unique pathways such as viral carcinogenesis. There were no major qualitative differences in pathways between ethnic groups. The composite gene-expression score was correlated with the time from onset of symptoms and nasal swab qPCR CT values (both p < 0.01) but was not related to participant age, gender, ethnicity or the presence or absence of chest X-ray abnormalities (all p > 0.05).

Conclusions

The whole-blood transcriptome of COVID-19 has overall similarity with other respiratory infections but there are some unique pathways that merit further exploration to determine clinical relevance. The approach to a disease score may be of value, but needs further validation in a population with a greater range of disease severity.

SUBMITTER: Kwan PKW

PROVIDER: S-EPMC8193593 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body’s ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients. Methods COVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the “Deseq2” package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the “limma” package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification. Results In this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients. Conclusion In this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.