Unraveling T Cell Responses for Long Term Protection of SARS-CoV-2 Infection

Front Genet. 2022 May 4;13:871164. doi: 10.3389/fgene.2022.871164. eCollection 2022.


Due to the COVID-19 pandemic, the global need for vaccines to prevent the disease is imperative. To date, several manufacturers have made efforts to develop vaccines against SARS-CoV-2. In spite of the success of developing many useful vaccines so far, it will be helpful for future vaccine designs, targetting long-term disease protection. For this, we need to know more details of the mechanism of T cell responses to SARS-CoV-2. In this study, we first detected pairwise differentially expressed genes among the healthy, mild, and severe COVID-19 groups of patients based on the expression of CD4+ T cells and CD8+ T cells, respectively. The CD4+ T cells dataset contains 6 mild COVID-19 patients, 8 severe COVID-19 patients, and 6 healthy donors, while the CD8+ T cells dataset has 15 mild COVID-19 patients, 22 severe COVID-19 patients, and 4 healthy donors. Furthermore, we utilized the deep learning algorithm to investigate the potential of differentially expressed genes in distinguishing different disease states. Finally, we built co-expression networks among those genes separately. For CD4+ T cells, we identified 6 modules for the healthy network, 4 modules for the mild network, and 1 module for the severe network; for CD8+ T cells, we detected 6 modules for the healthy network, 4 modules for the mild network, and 3 modules for the severe network. We also obtained hub genes for each module and evaluated the differential connectivity of each gene between pairs of networks constructed on different disease states. Summarizing the results, we find that the following genes TNF, CCL4, XCL1, and IFITM1 can be highly identified with SARS-CoV-2. It is interesting to see that IFITM1 has already been known to inhibit multiple infections with other enveloped viruses, including coronavirus. In addition, our networks show some specific patterns of connectivity among genes and some meaningful clusters related to COVID-19. The results might improve the insight of gene expression mechanisms associated with both CD4+ and CD8+ T cells, expand our understanding of COVID-19 and help develop vaccines with long-term protection.

Keywords: COVID-19; RNA-seq; T cell; deep learning; differential expression; functional annotation; gene co-expression network.