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Heightened Innate Immune Responses in the Respiratory Tract of COVID-19 Patients
Open accessAbstract
Summary The outbreaks of 2019 novel coronavirus disease (COVID-19) caused by SARS-CoV-2 infection have posed a severe threat to global public health. It is unclear how the human immune system responds to this infection. Here, we used metatranscriptomic sequencing to profile immune signatures in the bronchoalveolar lavage fluid of eight COVID-19 cases. The expression of proinflammatory genes, especially chemokines, was markedly elevated in COVID-19 cases compared to community-acquired pneumonia patients and healthy controls, suggesting that SARS-CoV-2 infection causes hypercytokinemia. Compared to SARS-CoV, which is thought to induce inadequate interferon (IFN) responses, SARS-CoV-2 robustly triggered expression of numerous IFN-stimulated genes (ISGs). These ISGs exhibit immunopathogenic potential, with overrepresentation of genes involved in inflammation. The transcriptome data was also used to estimate immune cell populations, revealing increases in activated dendritic cells and neutrophils. Collectively, these host responses to SARS-CoV-2 infection could further our understanding of disease pathogenesis and point toward antiviral strategies.
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Self-citationCytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Self-citationClinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Self-citationclusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259
fastp: an ultra-fast all-in-one FASTQ preprocessor
Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty560 · Cited by 29,721
A novel coronavirus from patients with pneumonia in China, 2019
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244
Self-citationfeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415
Self-citationCausal analysis approaches in Ingenuity Pathway Analysis
Krämer, A., Green, J., Pollard, J., & Tugendreich, S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt703 · Cited by 5,724
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Longitudinal analyses reveal immunological misfiring in severe COVID-19
Lucas, C., Wong, P., Klein, J., Castro, T. B. R., Silva, J., Sundaram, M., Ellingson, M. K., Mao, T., Oh, J. E., Israelow, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2588-y · Cited by 2,074
Long COVID or post-COVID-19 syndrome: putative pathophysiology, risk factors, and treatments
Yong, S. J. (2021) · Infectious Diseases · doi:10.1080/23744235.2021.1924397 · Cited by 1,339
Activation and evasion of type I interferon responses by SARS-CoV-2
Lei, X., Dong, X., Ma, R., Wang, W., Xiao, X., Tian, Z., Wang, C., Wang, Y., Li, L., Ren, L., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17665-9 · Cited by 980
Self-citationWinkler, E. S., Bailey, A. L., Kafai, N. M., Nair, S., McCune, B. T., Yu, J., Fox, J. M., Chen, R. E., Earnest, J. T., Keeler, S. P., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0778-2 · Cited by 901
Immune response, inflammation, and the clinical spectrum of COVID-19
García, L. F. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01441 · Cited by 739
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Abou-Ismail, M. Y., Diamond, A., Kapoor, S., Arafah, Y., & Nayak, L. (2020) · Thrombosis Research · doi:10.1016/j.thromres.2020.06.029 · Cited by 694
The cGAS–STING pathway drives type I IFN immunopathology in COVID-19
Domizio, J. D., Gulen, M. F., Saidoune, F., Thacker, V. V., Yatim, A., Sharma, K., Nass, T., Guenova, E., Schaller, M., Conrad, C., et al. (2022) · Nature · doi:10.1038/s41586-022-04421-w · Cited by 538
Immunopathology of Hyperinflammation in COVID-19
Gustine, J. N., & Jones, D. (2021) · The American Journal of Pathology · doi:10.1016/j.ajpath.2020.08.009 · Cited by 516
Syncytia formation by SARS‐CoV‐2‐infected cells
Buchrieser, J., Dufloo, J., Hubert, M., Monel, B., Planas, D., Rajah, M. M., Planchais, C., Porrot, F., Guivel‐Benhassine, F., Van der Werf, S., et al. (2020) · The EMBO Journal · doi:10.15252/embj.2020106267 · Cited by 462
Interplay between SARS-CoV-2 and the type I interferon response
Sa Ribero, M., Jouvenet, N., Dreux, M., & Nisole, S. (2020) · PLOS Pathogens · doi:10.1371/journal.ppat.1008737 · Cited by 443
Mouse model of SARS-CoV-2 reveals inflammatory role of type I interferon signaling
Israelow, B., Song, E., Mao, T., Lu, P., Meir, A., Liu, F., Alfajaro, M. M., Wei, J., Dong, H., Homer, R. J., et al. (2020) · Journal of Experimental Medicine · doi:10.1084/jem.20201241 · Cited by 415
Dysregulation of type I interferon responses in COVID-19
Acharya, D., Liu, G., & Gack, M. U. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-0346-x · Cited by 454
The type I interferon response in COVID-19: implications for treatment
Lee, J. S., & Shin, E.-C. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-00429-3 · Cited by 421
Zheng, Y., Zhuang, M.-W., Han, L., Zhang, J., Nan, M.-L., Zhan, P., Kang, D., Liu, X., Gao, C., & Wang, P.-H. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00438-7 · Cited by 302
Pathogenesis of COVID-19-induced ARDS: implications for an ageing population
Torres Acosta, M. A., & Singer, B. D. (2020) · European Respiratory Journal · doi:10.1183/13993003.02049-2020 · Cited by 247
Temporal and spatial heterogeneity of host response to SARS-CoV-2 pulmonary infection
Desai, N., Neyaz, A., Szabolcs, A., Shih, A. R., Chen, J. H., Thapar, V., Nieman, L. T., Solovyov, A., Mehta, A., Lieb, D. J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-20139-7 · Cited by 245
Vascular Disease and Thrombosis in SARS-CoV-2-Infected Rhesus Macaques
Aid, M., Busman-Sahay, K., Vidal, S. J., Maliga, Z., Bondoc, S., Starke, C., Terry, M., Jacobson, C. A., Wrijil, L., Ducat, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.005 · Cited by 215
Cheemarla, N. R., Watkins, T. A., Mihaylova, V. T., Wang, B., Zhao, D., Wang, G., Landry, M. L., & Foxman, E. F. (2021) · Journal of Experimental Medicine · doi:10.1084/jem.20210583 · Cited by 213
Bai, H., Si, L., Jiang, A., Belgur, C., Zhai, Y., Plebani, R., Oh, C. Y., Rodas, M., Patil, A., Nurani, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29562-4 · Cited by 171
Asymptomatic and symptomatic SARS-CoV-2 infections elicit polyfunctional antibodies
Dufloo, J., Grzelak, L., Staropoli, I., Madec, Y., Tondeur, L., Anna, F., Pelleau, S., Wiedemann, A., Planchais, C., Buchrieser, J., et al. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100275 · Cited by 89
Multiscale PHATE identifies multimodal signatures of COVID-19
Kuchroo, M., Huang, J., Wong, P., Grenier, J.-C., Shung, D., Tong, A., Lucas, C., Klein, J., Burkhardt, D. B., Gigante, S., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01186-x · Cited by 72
Molecular mechanisms and epidemiology of COVID-19 from an allergist’s perspective
Hosoki, K., Chakraborty, A., & Sur, S. (2020) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2020.05.033 · Cited by 63
Cross-validation of SARS-CoV-2 responses in kidney organoids and clinical populations
Helms, L., Marchiano, S., Stanaway, I. B., Hsiang, T.-Y., Juliar, B. A., Saini, S., Zhao, Y. T., Khanna, A., Menon, R., Alakwaa, F., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.154882 · Cited by 47
Ahmed-Hassan, H., Sisson, B., Shukla, R. K., Wijewantha, Y., Funderburg, N. T., Li, Z., Hayes, D., Demberg, T., & Liyanage, N. P. M. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01979 · Cited by 47
Baricitinib attenuates the proinflammatory phase of COVID-19 driven by lung-infiltrating monocytes
Dobosh, B., Zandi, K., Giraldo, D. M., Goh, S. L., Musall, K., Aldeco, M., LeCher, J., Giacalone, V. D., Yang, J., Eddins, D. J., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110945 · Cited by 19
Self-citationCOVID-19 and earlier pandemics, sepsis, and vaccines: A historical perspective
Cavaillon, J.-M., & Osuchowski, M. F. (2021) · Journal of Intensive Medicine · doi:10.1016/j.jointm.2021.04.003 · Cited by 17
anuk, D. (2023) · Turkish Journal of Oncology · doi:10.5505/tjo.2023.3934
“[ 8–11] The COVID-19 virus is very stressful, as it threat-ens human life due to its rapid spread and increasing deaths.”
Background
Metadata
- Authors
- Zhou, Z., Ren, L., Zhang, L., Zhong, J., Xiao, Y., Jia, Z., Guo, L., Yang, J., Wang, C., Jiang, S., Yang, D., Zhang, G., Li, H., Chen, F., Xu, Y., Chen, M., Gao, Z., Yang, J., Dong, J., Liu, B., Zhang, X., Wang, W., He, K., Jin, Q., Li, M., & Wang, J.
- Venue
- Cell Host & Microbe
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 27
- Issue
- 6
- Pages
- 883-890.e2
- ISSN
- 1931-3128 (ISSN-L)
- OpenAlex
- W3020655115
- Cited by
- 938
- References
- 16
- FWCI
- 23.84
- Type
- journal article
- Language
- en
- Open access
- bronze
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