Blood transcriptome responses in patients correlate with severity of COVID-19 disease
Jazyk angličtina Země Švýcarsko Médium electronic-ecollection
Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem
Grantová podpora
U19 AI100625
NIAID NIH HHS - United States
PubMed
36741372
PubMed Central
PMC9896980
DOI
10.3389/fimmu.2022.1043219
Knihovny.cz E-zdroje
- Klíčová slova
- RNA sequencing, SARS-CoV-2, WGCNA, deconvolution, host immune response,
- MeSH
- COVID-19 * genetika MeSH
- lidé MeSH
- neutrofily MeSH
- SARS-CoV-2 MeSH
- stanovení celkové genové exprese MeSH
- transkriptom MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
BACKGROUND: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals display a wide spectrum of disease severity, as defined by the World Health Organization (WHO). One of the main factors underlying this heterogeneity is the host immune response, with severe COVID-19 often associated with a hyperinflammatory state. AIM: Our current study aimed to pinpoint the specific genes and pathways underlying differences in the disease spectrum and outcomes observed, through in-depth analyses of whole blood transcriptomics in a large cohort of COVID-19 participants. RESULTS: All WHO severity levels were well represented and mild and severe disease displaying distinct gene expression profiles. WHO severity levels 1-4 were grouped as mild disease, and signatures from these participants were different from those with WHO severity levels 6-9 classified as severe disease. Severity level 5 (moderate cases) presented a unique transitional gene signature between severity levels 2-4 (mild/moderate) and 6-9 (severe) and hence might represent the turning point for better or worse disease outcome. Gene expression changes are very distinct when comparing mild/moderate or severe cases to healthy controls. In particular, we demonstrated the hallmark down-regulation of adaptive immune response pathways and activation of neutrophil pathways in severe compared to mild/moderate cases, as well as activation of blood coagulation pathways. CONCLUSIONS: Our data revealed discrete gene signatures associated with mild, moderate, and severe COVID-19 identifying valuable candidates for future biomarker discovery.
Centre for Clinical Research in Emergency Medicine Royal Perth Bentley Group Perth WA Australia
Department of Intensive Care Medicine Nepean Hospital Penrith NSW Australia
Department of Microbiology St George Hospital Kogarah NSW Australia
Emergency Department Royal Perth Hospital Perth WA Australia
Institute of Molecular Virology University of Münster Münster Germany
Medical School University of Western Australia Perth WA Australia
School of Chemistry and Molecular Biosciences The University of Queensland Brisbane QLD Australia
Sydney Informatics Hub Core Research Facilities The University of Sydney Sydney NSW Australia
Sydney Institute for Infectious Disease The University of Sydney Sydney NSW Australia
Westmead Hospital Western Sydney Local Health District Westmead NSW Australia
Zobrazit více v PubMed
Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (Covid-19) outbreak in China: Summary of a report of 72 314 cases from the Chinese center for disease control and prevention. Jama (2020) 323(13):1239–42. doi: 10.1001/jama.2020.2648 PubMed DOI
infection WWGotCCaMoC . A minimal common outcome measure set for covid-19 clinical research. Lancet Infect Dis (2020) 20(8):e192–e7. doi: 10.1016/s1473-3099(20)30483-7 PubMed DOI PMC
Giamarellos-Bourboulis EJ, Netea MG, Rovina N, Akinosoglou K, Antoniadou A, Antonakos N, et al. . Complex immune dysregulation in covid-19 patients with severe respiratory failure. Cell Host Microbe (2020) 27(6):992–1000.e3. doi: 10.1016/j.chom.2020.04.009 PubMed DOI PMC
Chen G, Wu D, Guo W, Cao Y, Huang D, Wang H, et al. . Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Invest (2020) 130(5):2620–9. doi: 10.1172/jci137244 PubMed DOI PMC
Del Valle DM, Kim-Schulze S, Huang HH, Beckmann ND, Nirenberg S, Wang B, et al. . An inflammatory cytokine signature predicts covid-19 severity and survival. Nat Med (2020) 26(10):1636–43. doi: 10.1038/s41591-020-1051-9 PubMed DOI PMC
Hadjadj J, Yatim N, Barnabei L, Corneau A, Boussier J, Smith N, et al. . Impaired type I interferon activity and inflammatory responses in severe covid-19 patients. Sci (New York NY) (2020) 369(6504):718–24. doi: 10.1126/science.abc6027 PubMed DOI PMC
Lei X, Dong X, Ma R, Wang W, Xiao X, Tian Z, et al. . Activation and evasion of type I interferon responses by sars-Cov-2. Nat Commun (2020) 11(1):3810. doi: 10.1038/s41467-020-17665-9 PubMed DOI PMC
Bastard P, Zhang Q, Zhang SY, Jouanguy E, Casanova JL. Type I interferons and sars-Cov-2: From cells to organisms. Curr Opin Immunol (2022) 74:172–82. doi: 10.1016/j.coi.2022.01.003 PubMed DOI PMC
Andreakos E. Stinging type I ifn-mediated immunopathology in covid-19. Nat Immunol (2022) 23(4):478–80. doi: 10.1038/s41590-022-01174-6 PubMed DOI
Palermo E, Di Carlo D, Sgarbanti M, Hiscott J. Type I interferons in covid-19 pathogenesis. Biology (2021) 10(9):829–46. doi: 10.3390/biology10090829 PubMed DOI PMC
Masso-Silva JA, Moshensky A, Lam MTY, Odish MF, Patel A, Xu L, et al. . Increased peripheral blood neutrophil activation phenotypes and neutrophil extracellular trap formation in critically ill coronavirus disease 2019 (Covid-19) patients: A case series and review of the literature. Clin Infect Dis an Off Publ Infect Dis Soc America (2022) 74(3):479–89. doi: 10.1093/cid/ciab437 PubMed DOI PMC
Meizlish ML, Pine AB, Bishai JD, Goshua G, Nadelmann ER, Simonov M, et al. . A neutrophil activation signature predicts critical illness and mortality in covid-19. Blood Adv (2021) 5(5):1164–77. doi: 10.1182/bloodadvances.2020003568 PubMed DOI PMC
Aschenbrenner AC, Mouktaroudi M, Krämer B, Oestreich M, Antonakos N, Nuesch-Germano M, et al. . Disease severity-specific neutrophil signatures in blood transcriptomes stratify covid-19 patients. Genome Med (2021) 13(1):7. doi: 10.1186/s13073-020-00823-5 PubMed DOI PMC
Tan L, Wang Q, Zhang D, Ding J, Huang Q, Tang YQ, et al. . Lymphopenia predicts disease severity of covid-19: A descriptive and predictive study. Signal transduction targeted Ther (2020) 5(1):33. doi: 10.1038/s41392-020-0148-4 PubMed DOI PMC
Huang I, Pranata R. Lymphopenia in severe coronavirus disease-2019 (Covid-19): Systematic review and meta-analysis. J Intensive Care (2020) 8:36. doi: 10.1186/s40560-020-00453-4 PubMed DOI PMC
Ghizlane EA, Manal M, Abderrahim EK, Abdelilah E, Mohammed M, Rajae A, et al. . Lymphopenia in covid-19: A single center retrospective study of 589 cases. Ann Med Surg (2012) (2021) 69:102816. doi: 10.1016/j.amsu.2021.102816 PubMed DOI PMC
Zaboli E, Majidi H, Alizadeh-Navaei R, Hedayatizadeh-Omran A, Asgarian-Omran H, Vahedi Larijani L, et al. . Lymphopenia and lung complications in patients with coronavirus disease-2019 (Covid-19): A retrospective study based on clinical data. J Med Virol (2021) 93(9):5425–31. doi: 10.1002/jmv.27060 PubMed DOI PMC
André S, Picard M, Cezar R, Roux-Dalvai F, Alleaume-Butaux A, Soundaramourty C, et al. . T Cell apoptosis characterizes severe covid-19 disease. Cell Death differentiation (2022) 29(8):1486–99. doi: 10.1038/s41418-022-00936-x PubMed DOI PMC
Diao B, Wang C, Tan Y, Chen X, Liu Y, Ning L, et al. . Reduction and functional exhaustion of T cells in patients with coronavirus disease 2019 (Covid-19). Front Immunol (2020) 11:827. doi: 10.3389/fimmu.2020.00827 PubMed DOI PMC
Peñaloza HF, Lee JS, Ray P. Neutrophils and lymphopenia, an unknown axis in severe covid-19 disease. PloS Pathog (2021) 17(9):e1009850. doi: 10.1371/journal.ppat.1009850 PubMed DOI PMC
Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (Redcap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inf (2009) 42(2):377–81. doi: 10.1016/j.jbi.2008.08.010 PubMed DOI PMC
Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, et al. . The redcap consortium: Building an international community of software platform partners. J Biomed Inf (2019) 95:103208. doi: 10.1016/j.jbi.2019.103208 PubMed DOI PMC
Chew T, Sadsad R. Rnaseq-de (Version 1.0) [Computer software]. Sydney Informatics Hub. (2022). Available at: https://github.com/Sydney-Informatics-Hub/RNASeq-DE.
Bushnell B. B. b. bbmap. Berkeley, CA (United States): Lawrence Berkeley National Lab. (LBNL). (2014) Available at: https://www.osti.gov/biblio/1241166.
Andrews S. Fastqc: A quality control tool for high throughput sequence data [Online]. (2010). Babraham Bioinformatics. Available at: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. . The sequence Alignment/Map format and samtools. Bioinformatics (2009) 25(16):2078–9. doi: 10.1093/bioinformatics/btp352 PubMed DOI PMC
Anders S, Pyl PT, Huber W. Htseq–a Python framework to work with high-throughput sequencing data. Bioinformatics (2015) 31(2):166–9. doi: 10.1093/bioinformatics/btu638 PubMed DOI PMC
Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, et al. . Biomart and bioconductor: A powerful link between biological databases and microarray data analysis. Bioinformatics (2005) 21(16):3439–40. doi: 10.1093/bioinformatics/bti525 PubMed DOI
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for rna-seq data with Deseq2. Genome Biol (2014) 15(12):550. doi: 10.1186/s13059-014-0550-8 PubMed DOI PMC
Ritchie ME, Silver J, Oshlack A, Holmes M, Diyagama D, Holloway A, et al. . A comparison of background correction methods for two-colour microarrays. Bioinformatics (2007) 23(20):2700–7. doi: 10.1093/bioinformatics/btm412 PubMed DOI
Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol (2004) 3:1544–6115. doi: 10.2202/1544-6115.1027 PubMed DOI
Blighe K, Rana S, Lewis M. Enhancedvolcano: Publication-ready volcano plots with enhanced colouring and labeling . Available at: https://githubcom/kevinblighe/EnhancedVolcano.
Yu G, Wang LG, Han Y, He QY. Clusterprofiler: An r package for comparing biological themes among gene clusters. Omics J Integr Biol (2012) 16(5):284–7. doi: 10.1089/omi.2011.0118 PubMed DOI PMC
Eklund A. Beeswarm: The beeswarm plot, an alternative to stripchart (2016). Available at: https://CRANR-projectorg/package=beeswarm.
Finotello F, Mayer C, Plattner C, Laschober G, Rieder D, Hackl H, et al. . Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of rna-seq data. Genome Med (2019) 11(1):34. doi: 10.1186/s13073-019-0638-6 PubMed DOI PMC
Becht E, Giraldo NA, Lacroix L, Buttard B, Elarouci N, Petitprez F, et al. . Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol (2016) 17(1):218. doi: 10.1186/s13059-016-1070-5 PubMed DOI PMC
Langfelder P, Horvath S. Wgcna: An r package for weighted correlation network analysis. BMC Bioinf (2008) 9:559. doi: 10.1186/1471-2105-9-559 PubMed DOI PMC
R_Core_Team . R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; (2013).
Chen CH, Lin SW, Shen CF, Hsieh KS, Cheng CM. Biomarkers during covid-19: Mechanisms of change and implications for patient outcomes. Diagnostics (Basel Switzerland) (2022) 12(2):509–24. doi: 10.3390/diagnostics12020509 PubMed DOI PMC
Su M, Chen Y, Qi S, Shi D, Feng L, Sun D. A mini-review on cell cycle regulation of coronavirus infection. Front veterinary Sci (2020) 7:586826. doi: 10.3389/fvets.2020.586826 PubMed DOI PMC
Wong NA, Saier MH, Jr. The sars-coronavirus infection cycle: A survey of viral membrane proteins, their functional interactions and pathogenesis. Int J Mol Sci (2021) 22(3):1308–71. doi: 10.3390/ijms22031308 PubMed DOI PMC
Bagga S, Bouchard MJ. Cell cycle regulation during viral infection. Methods Mol Biol (Clifton NJ) (2014) 1170:165–227. doi: 10.1007/978-1-4939-0888-2_10 PubMed DOI PMC
Yuan X, Yao Z, Wu J, Zhou Y, Shan Y, Dong B, et al. . G1 phase cell cycle arrest induced by sars-cov 3a protein Via the cyclin D3/Prb pathway. Am J Respir Cell Mol Biol (2007) 37(1):9–19. doi: 10.1165/rcmb.2005-0345RC PubMed DOI
Gregory PD, Wagner K, Hörz W. Histone acetylation and chromatin remodeling. Exp Cell Res (2001) 265(2):195–202. doi: 10.1006/excr.2001.5187 PubMed DOI
Chlamydas S, Papavassiliou AG, Piperi C. Epigenetic mechanisms regulating covid-19 infection. Epigenetics (2021) 16(3):263–70. doi: 10.1080/15592294.2020.1796896 PubMed DOI PMC
Ozturkler Z, Kalkan R. A new perspective of covid-19 infection: An epigenetics point of view. Global Med Genet (2022) 9(1):4–6. doi: 10.1055/s-0041-1736565 PubMed DOI PMC
Pinto BGG, Oliveira AER, Singh Y, Jimenez L, Gonçalves ANA, Ogava RLT, et al. . Ace2 expression is increased in the lungs of patients with comorbidities associated with severe covid-19. J Infect Dis (2020) 222(4):556–63. doi: 10.1093/infdis/jiaa332 PubMed DOI PMC
Takeda K, Akira S. Toll-like receptors in innate immunity. Int Immunol (2005) 17(1):1–14. doi: 10.1093/intimm/dxh186 PubMed DOI
Aboudounya MM, Heads RJ. Covid-19 and toll-like receptor 4 (Tlr4): Sars-Cov-2 may bind and activate Tlr4 to increase Ace2 expression, facilitating entry and causing hyperinflammation. Mediators Inflammation (2021) 2021:8874339. doi: 10.1155/2021/8874339 PubMed DOI PMC
Manik M, Singh RK. Role of toll-like receptors in modulation of cytokine storm signaling in sars-Cov-2-Induced covid-19. J Med Virol (2022) 94(3):869–77. doi: 10.1002/jmv.27405 PubMed DOI PMC
Consortium CM-oBAC . A blood atlas of covid-19 defines hallmarks of disease severity and specificity. Cell (2022) 185(5):916–38.e58. doi: 10.1016/j.cell.2022.01.012 PubMed DOI PMC
Edgar R, Domrachev M, Lash AE. Gene expression omnibus: Ncbi gene expression and hybridization array data repository. Nucleic Acids Res (2002) 30(1):207–10. doi: 10.1093/nar/30.1.207 PubMed DOI PMC