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.

Multi-omics analysis uncovered systemic lupus erythematosus and COVID-19 crosstalk

“…Notably, the expression of IFN-inducible (IFI) genes, such as IFI27 , IFI44 , IFI44L , IFI6 , IFIT1 , IFIT2 , IFIT3 , and ISG15 , were correlated with disease activity and overexpression of autoimmune antibodies in SLE (Zhao et al 2021 ), and associated with neutrophil-induced immunological imbalance and vascular damage (Mistry et al 2019 ). In COVID-19, they are linked with the antiviral response and demonstrate consistent trends of enhanced inflammatory reactions in patients with severe COVID-19 (Gao et al 2021 ; Wang et al 2022 ; Alberts et al 2022 ; Guo et al 2022 ). The 2’-5’ oligoadenylate synthetase (OAS) gene family, including OAS1, OAS2, and OAS3, are closely related to the IFN-I/II-mediated signaling pathway in SLE (Gao et al 2020 ) and are also tightly linked with the inflammatory response and production of IFN-I/II in COVID-19 (Lee, et al 2023 ; Bruchelt et al 2022 ).…”

Interactomics: Dozens of Viruses, Co-evolving With Humans, Including the Influenza A Virus, may Actively Distort Human Aging

“…Data sources (DEG data set: articles or data set identifier) are numbered and visually delimited by an alternance of light-gray/dark-gray backgrounds, with some sources providing several samples or time points ( supplementary table S10, Supplementary Material online). Samples sources are: (1) Alberts et al (2022) ; (2) Park et al (2022) ; (3) van Solingen et al (2022) ; (4) Chandrashekar et al (2021) ; (5) Chow et al (2021) ; (6) Blanco-Melo et al (2020) ; (7) Deshiere et al (2017) ; (8) Golumbeanu et al (2019) ; (9) van der Heijden et al (2021) ; (10) Bauby et al (2021) ; (11) GSE53993; (12) Coelho et al (2021) ; (13, 14) Devadas et al (2016) ; (15) Bell et al (2022) ; (16) GSE186908; (17) GSE192528; (18) Thompson et al (2020) ; (19, 63–65) Tsalik et al (2021) ; (20, 28, 62) Dissanayake et al (2020) ; . (21) Zhuravlev et al (2020) ; (22) GSE97672; (23, 27) GSE89008; (24) GSE82232; (25) GSE101760; (26) GSE97672; (29) Sheng et al (2020) ; (30) Hong et al (2021) ; (31) Mrozek-Gorska et al (2019) ; (32) Wang et al (2019) ; (33) Yanagi et al (2021) ; (34) GSE84897; (35) Tso et al (2018) ; (36) Journo et al (2018) ; (37) GSE79032; (38) Seelbinder et al (2020) ; (39) Oberstein and Shenk (2017) ; (40–42) Sen et al (2020) ; (43) Zhang et al (2016) ; (44, 49) Jagya et al (2014) ; (45) Yuan et al (2021) ; (46) GSE194179; (47) Zai et al (2022) ; (48) Winer et al (2020) ; (50) Montanari et al (2022) ; (51) Tegtmeyer et al (2021) ; (52) Hojka-Osinska et al (2017) ; (53) Li et al (2020) ; (54) GSE84346; (55) Hu et al (2016) ; (56, 59, 60) Kaczkowski et al (2012) ; (57)…”

Impact of dietary fiber on gut microbiota composition, function and gut-brain-modules in healthy adults – a systematic review protocol

“…The concept of reanalyzing datasets is not new. It has been used to uncover aspects of dysregulated immune responses to COVID-19 infection, as part of the infectious disease mechanism (Alberts et al, 2022). The technique has proved useful in terms of understanding the host response as well as identifying further candidates with diagnostic potential in other infections such as leprosy (Leal-Calvo & Moraes, 2020).…”