Blood transcriptome responses in patients correlate with severity of COVID-19 disease

. 2022 ; 13 () : 1043219. [epub] 20230120

Jazyk angličtina Země Švýcarsko Médium electronic-ecollection

Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid36741372

Grantová podpora
U19 AI100625 NIAID NIH HHS - United States

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 Harry Perkins Institute of Medical Research Royal Perth Hospital Perth WA Australia

Centre for Clinical Research in Emergency Medicine Royal Perth Bentley Group Perth WA Australia

Centre for Immunology and Allergy Research The Westmead Institute for Medical Research Westmead NSW Australia

Centre for Infectious Diseases and Microbiology The Westmead Institute for Medical Research Westmead NSW Australia

Department of Infectious Diseases The University of Melbourne at the Peter Doherty Institute for Infection and Immunity Melbourne VIC Australia

Department of Intensive Care Medicine Nepean Hospital Penrith NSW Australia

Department of Microbiology Immunology and Biochemistry University of Tennessee Health Science Center Memphis TN United States

Department of Microbiology St George Hospital Kogarah NSW Australia

Emergency Department Royal Perth Hospital Perth WA Australia

Faculty of Medicine and Health School of Medical Sciences The University of Sydney Sydney NSW Australia

Faculty of Medicine and Health Sydney Medical School Nepean Nepean Hospital University of Sydney Penrith NSW Australia

Faculty of Medicine and Health Sydney Medical School Westmead Westmead Hospital University of Sydney NSW Westmead Australia

Institute of Molecular Virology University of Münster Münster Germany

Medical ICU 1st Department of Internal Medicine Charles University and Teaching Hospital Pilsen Czechia

Medical School University of Western Australia Perth WA Australia

Research and Education Network Western Sydney Local Health District Westmead Hospital NSW Westmead 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

Victorian Infectious Disease Service The Royal Melbourne Hospital at the Peter Doherty Institute for Infection and Immunity Melbourne VIC Australia

Westmead Hospital Western Sydney Local Health District Westmead NSW Australia

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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

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. 2025 Jul 10 ; 12 (1) : 1175. [epub] 20250710

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