Dysregulated transcriptional responses to SARS-CoV-2 in the periphery.
Micah T McClain, Florica J Constantine, Ricardo Henao, Yiling Liu, Ephraim L Tsalik, Thomas W Burke, Julie M Steinbrink, Elizabeth Petzold, Bradly P Nicholson, Robert Rolfe, Bryan D Kraft, Matthew S Kelly, Daniel R Saban, Chen Yu, Xiling Shen, Emily M Ko, Gregory D Sempowski, Thomas N Denny, Geoffrey S Ginsburg, Christopher W Woods
Author Information
Micah T McClain: Durham Veterans Affairs Medical Center, Durham, NC, USA. micah.mcclain@duke.edu. ORCID
Florica J Constantine: Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA.
Ricardo Henao: Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA.
Yiling Liu: Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA.
Ephraim L Tsalik: Durham Veterans Affairs Medical Center, Durham, NC, USA.
Thomas W Burke: Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA.
Julie M Steinbrink: Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA.
Elizabeth Petzold: Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA.
Bradly P Nicholson: Institute for Medical Research, Durham, NC, USA.
Robert Rolfe: Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA.
Bryan D Kraft: Durham Veterans Affairs Medical Center, Durham, NC, USA.
Matthew S Kelly: Duke University Medical Center, Durham, NC, USA.
Daniel R Saban: Department of Opthalmology, Duke University School of Medicine, Durham, NC, USA.
Chen Yu: Department of Opthalmology, Duke University School of Medicine, Durham, NC, USA.
Xiling Shen: Center for Genomics and Computational Biology, Duke University, Durham, NC, USA.
Emily M Ko: Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA.
Gregory D Sempowski: Duke Human Vaccine Institute, Durham, NC, USA.
Thomas N Denny: Duke Human Vaccine Institute, Durham, NC, USA.
Geoffrey S Ginsburg: Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC, USA.
Christopher W Woods: Durham Veterans Affairs Medical Center, Durham, NC, USA.
SARS-CoV-2 infection has been shown to trigger a wide spectrum of immune responses and clinical manifestations in human hosts. Here, we sought to elucidate novel aspects of the host response to SARS-CoV-2 infection through RNA sequencing of peripheral blood samples from 46 subjects with COVID-19 and directly comparing them to subjects with seasonal coronavirus, influenza, bacterial pneumonia, and healthy controls. Early SARS-CoV-2 infection triggers a powerful transcriptomic response in peripheral blood with conserved components that are heavily interferon-driven but also marked by indicators of early B-cell activation and antibody production. Interferon responses during SARS-CoV-2 infection demonstrate unique patterns of dysregulated expression compared to other infectious and healthy states. Heterogeneous activation of coagulation and fibrinolytic pathways are present in early COVID-19, as are IL1 and JAK/STAT signaling pathways, which persist into late disease. Classifiers based on differentially expressed genes accurately distinguished SARS-CoV-2 infection from other acute illnesses (auROC 0.95 [95% CI 0.92-0.98]). The transcriptome in peripheral blood reveals both diverse and conserved components of the immune response in COVID-19 and provides for potential biomarker-based approaches to diagnosis.
References
Tay, M. Z., Poh, C. M., Rénia, L., MacAry, P. A. & Ng, L. F. P. The trinity of COVID-19: immunity, inflammation and intervention. Nat. Rev. Immunol. 20, 363–374 (2020).
[PMID: 32346093]
Ong, E. Z. et al. A dynamic immune response shapes COVID-19 progression. Cell Host Microbe 27, 879–882.e2 (2020).
[PMID: 32359396]
Ouyang, Y. et al. Down-regulated gene expression spectrum and immune responses changed during the disease progression in COVID-19 patients. Clin. Infect. Dis. 71, 2052–2060 (2020).
[PMID: 32307550]
Wen, W. et al. Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing. Cell Discov. 6, 31 (2020).
[PMID: 32377375]
Liao, M. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. 26, 842–844 (2020).
[PMID: 32398875]
Blanco-Melo, D. et al. Imbalanced host response to SARS-CoV-2 drives development of COVID-19. Cell 181, 1036–1045.e1039 (2020).
[PMID: 32416070]
Zhou, Z. et al. Heightened innate immune responses in the respiratory tract of COVID-19 patients. Cell Host Microbe 27, 883–890.e882 (2020).
[PMID: 32407669]
Mick, E. et al. Upper airway gene expression reveals suppressed immune responses to SARS-CoV-2 compared with other respiratory viruses. Nat. Commun. 11, 5854 (2020).
[PMID: 33203890]
Wilk, A. J. et al. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. 26, 1070–1076 (2020).
[PMID: 32514174]
Huang, L. et al. Blood single cell immune profiling reveals the interferon-MAPK pathway mediated adaptive immune response for COVID-19. Preprint at medRxiv https://doi.org/10.1101/2020.03.15.20033472 (2020).
Zhang, J.-Y. et al. Single-cell landscape of immunological responses in patients with COVID-19. Nat. Immunol. 21, 1107–1118 (2020).
[PMID: 32788748]
Arunachalam, P. S. et al. Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans. Science 369, 1210–1220 (2020).
[PMID: 32788292]
Lee, J. S. et al. Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19. Sci. Immunol. 5, eabd1554 (2020).
[PMID: 32651212]
Hadjadj, J. et al. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science 369, 718–724 (2020).
[PMID: 32661059]
Kuri-Cervantes, L. et al. Comprehensive mapping of immune perturbations associated with severe COVID-19. Sci. Immunol. 5, eabd7114 (2020).
[PMID: 32669287]
Nakaya, H. I. et al. Systems biology of vaccination for seasonal influenza in humans. Nat. Immunol. 12, 786–795 (2011).
[PMID: 21743478]
Herberg, J. A. et al. Diagnostic test accuracy of a 2-transcript host RNA signature for discriminating bacterial vs viral infection in febrile children. JAMA 316, 835–845 (2016).
[PMID: 27552617]
Sweeney, T. E., Wong, H. R. & Khatri, P. Robust classification of bacterial and viral infections via integrated host gene expression diagnostics. Sci. Transl. Med. 8, 346ra391 (2016).
[DOI: 10.1126/scitranslmed.aaf7165]
Tsalik, E. L. et al. Host gene expression classifiers diagnose acute respiratory illness etiology. Sci. Transl. Med. 8, 322ra311 (2016).
[DOI: 10.1126/scitranslmed.aad6873]
McClain, M. T. et al. A genomic signature of influenza infection shows potential for presymptomatic detection, guiding early therapy, and monitoring clinical responses. Open Forum Infect. Dis. 3, ofw007 (2016).
[PMID: 26933666]
Zaas, A. K. et al. A host-based RT-PCR gene expression signature to identify acute respiratory viral infection. Sci. Transl. Med. 5, 203ra126 (2013).
[PMID: 24048524]
Woods, C. W. et al. A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2. PLoS One 8, e52198 (2013).
[PMID: 23326326]
Zaas, A. K. et al. Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans. Cell Host Microbe 6, 207–217 (2009).
[PMID: 19664979]
Mejias, A. et al. Whole blood gene expression profiles to assess pathogenesis and disease severity in infants with respiratory syncytial virus infection. PLoS Med. 10, e1001549 (2013).
[PMID: 24265599]
Spiegel, M. et al. Inhibition of beta interferon induction by severe acute respiratory syndrome coronavirus suggests a two-step model for activation of interferon regulatory factor 3. J. Virol. 79, 2079–2086 (2005).
[PMID: 15681410]
Magro, C. et al. Complement associated microvascular injury and thrombosis in the pathogenesis of severe COVID-19 infection: a report of five cases. Transl. Res.: J. Lab. Clin. Med. 220, 1–13 (2020).
[DOI: 10.1016/j.trsl.2020.04.007]
Poissy, J. et al. Pulmonary embolism in COVID-19 patients: awareness of an increased prevalence. Circulation 142, 184–186 (2020).
Lewis, D. A. et al. Whole blood gene expression profiles distinguish clinical phenotypes of venous thromboembolism. Thrombosis Res. 135, 659–665 (2015).
[DOI: 10.1016/j.thromres.2015.02.003]
Kalil, A. C. et al. Baricitinib plus Remdesivir for hospitalized adults with Covid-19. N. Engl. J. Med. https://doi.org/10.1056/NEJMoa2031994 (2020).
Stone, J. H. et al. Efficacy of Tocilizumab in patients hospitalized with Covid-19. N. Engl. J. Med. 383, 2333–2344 (2020).
[PMID: 33085857]
Takahashi, T., Luzum, J. A., Nicol, M. R. & Jacobson, P. A. Pharmacogenomics of COVID-19 therapies. npj Genom. Med. 5, 35 (2020).
[PMID: 32864162]
Zhao, R. et al. Early detection of SARS-CoV-2 antibodies in COVID-19 patients as a serologic marker of infection. Clin. Infect. Dis. 71, 2066–2072 (2020).
Zhao, J. et al. Antibody responses to SARS-CoV-2 in patients with novel coronavirus disease 2019. Clin. Infect. Dis. 71, 2027–2034 (2020).
[PMID: 32221519]
Chen, W. et al. SARS-CoV-2 neutralizing antibody levels are correlated with severity of COVID-19 pneumonia. Biomed. Pharmacother. 130, 110629–110629 (2020).
[PMID: 33406577]
Liu, S. T. H. et al. Convalescent plasma treatment of severe COVID-19: a propensity score–matched control study. Nat. Med. 26, 1708–1713 (2020).
[PMID: 32934372]
McClain, M. T. et al. A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study. Lancet Infect. Dis. S1473-3099, 30486–2 (2020).
Xiong, Y. et al. Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients. Emerg. Microbes Infect. 9, 761–770 (2020).
[PMID: 32228226]
Yu, K. et al. Dysregulated adaptive immune response contributes to severe COVID-19. Cell Res. 30, 814–816 (2020).
[PMID: 32759967]
He, X. et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat. Med. 26, 672–675 (2020).
[PMID: 32296168]
Gupta, R. K. et al. Concise whole blood transcriptional signatures for incipient tuberculosis: a systematic review and patient-level pooled meta-analysis. Lancet Respiratory Med. 8, 395–406 (2020).
[DOI: 10.1016/S2213-2600(19)30282-6]
Mayhew, M. B. et al. A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections. Nat. Commun. 11, 1177 (2020).
[PMID: 32132525]
McCall, M. N., Bolstad, B. M. & Irizarry, R. A. Frozen robust multiarray analysis (fRMA). Biostatistics 11, 242–253 (2010).
[PMID: 20097884]
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
[PMID: 23104886]
Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
[PMID: 20196867]
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
[PMID: 25605792]
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
[PMID: 20808728]
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
[PMID: 25822800]