Mature neutrophils and a NF-κB-to-IFN transition determine the unifying disease recovery dynamics in COVID-19

Silvin, 2020, Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19, Cell, 182, 1401, 10.1016/j.cell.2020.08.002 Schulte-Schrepping, 2020, Severe COVID-19 is marked by a dysregulated myeloid cell compartment, Cell, 182, 1419, 10.1016/j.cell.2020.08.001 Aschenbrenner, 2021, Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients, Genome. Med., 13, 7, 10.1186/s13073-020-00823-5 Stephenson, 2021, Single-cell multi-omics analysis of the immune response in COVID-19, Nat. Med., 27, 904, 10.1038/s41591-021-01329-2 Su, 2020, Multi-Omics resolves a sharp disease-state shift between mild and moderate COVID-19, Cell, 183, 1479, 10.1016/j.cell.2020.10.037 Georg, 2022, Complement activation induces excessive T cell cytotoxicity in severe COVID-19, Cell, 185, 493, 10.1016/j.cell.2021.12.040 Wen, 2020, Author Correction: immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing, Cell. Discov., 6, 41, 10.1038/s41421-020-00187-5 Zheng, 2020, Longitudinal transcriptome analyses show robust T cell immunity during recovery from COVID-19, Signal Transduct. Target. Ther., 5, 294, 10.1038/s41392-020-00457-4 Bernardes, 2020, Longitudinal multi-omics analyses identify responses of megakaryocytes, erythroid cells, and plasmablasts as hallmarks of severe COVID-19, Immunity, 53, 1296, 10.1016/j.immuni.2020.11.017 Frishberg, 2021, Multiple trajectory alignment reconstructs disease dynamics for discovery and clinical benefit, BioRxiv Warnat-Herresthal, 2021, Swarm Learning for decentralized and confidential clinical machine learning, Nature, 594, 265, 10.1038/s41586-021-03583-3 Bergamaschi, 2021, Longitudinal analysis reveals that delayed bystander CD8+ T cell activation and early immune pathology distinguish severe COVID-19 from mild disease, Immunity, 54, 1257, 10.1016/j.immuni.2021.05.010 Zanella, 2021, Time course of risk factors associated with mortality of 1260 critically ill patients with COVID-19 admitted to 24 Italian intensive care units, Intensive. Care. Med., 47, 995 Kooistra, 2021, Body mass index and mortality in coronavirus disease 2019 and other diseases: a cohort study in 35,506 ICU patients, Crit. Care. Med., 50, e1, 10.1097/CCM.0000000000005216 Ali, 2020, Elevated level of C-reactive protein may be an early marker to predict risk for severity of COVID-19, J. Med. Virol., 92, 2409, 10.1002/jmv.26097 Liu, 2021, The chronic kidney disease and acute kidney injury involvement in COVID-19 pandemic: a systematic review and meta-analysis, PLoS One., 16, e0244779, 10.1371/journal.pone.0244779 Sabaka, 2021, Role of interleukin 6 as a predictive factor for a severe course of Covid-19: retrospective data analysis of patients from a long-term care facility during Covid-19 outbreak, BMC. Infect. Dis., 21, 308, 10.1186/s12879-021-05945-8 Meizlish, 2021, A neutrophil activation signature predicts critical illness and mortality in COVID-19, Blood. Adv., 5, 1164, 10.1182/bloodadvances.2020003568 Zheng, 2021, Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity across viruses, Immunity, 54, 753, 10.1016/j.immuni.2021.03.002 Andres-Terre, 2015, Integrated, multi-cohort analysis identifies conserved transcriptional signatures across multiple respiratory viruses, Immunity, 43, 1199, 10.1016/j.immuni.2015.11.003 Karami, 2021, Weighted gene co-expression network analysis combined with machine learning validation to identify key modules and hub genes associated with SARS-CoV-2 infection, J. Clin. Med., 10, 3567, 10.3390/jcm10163567 Martonik, 2021, The role of Th17 response in COVID-19, Cells, 10, 1550, 10.3390/cells10061550 Pacha, 2020, COVID-19: a case for inhibiting IL-17?, Nat. Rev. Immunol., 20, 345, 10.1038/s41577-020-0328-z Hennighausen, 2020, Activation of the SARS-CoV-2 receptor Ace2 through JAK/STAT-Dependent enhancers during pregnancy, Cell. Rep., 32, 108199, 10.1016/j.celrep.2020.108199 Saeed, 2021, Coronavirus disease 2019 and cardiovascular complications: focused clinical review, J. Hypertens., 39, 1282, 10.1097/HJH.0000000000002819 Lee, 2021, Lymphopenia as a biological predictor of outcomes in COVID-19 patients: a nationwide cohort study, Cancers, 13, 471, 10.3390/cancers13030471 Liu, 2020, Lymphopenia predicted illness severity and recovery in patients with COVID-19: a single-center, retrospective study, PLoS. One, 15, e0241659, 10.1371/journal.pone.0241659 Garrafa, 2021, Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score, Elife, 10, e70640, 10.7554/eLife.70640 Chen, 2020, Longitudinal hematologic and immunologic variations associated with the progression of COVID-19 patients in China, J. Allergy Clin. Immunol., 146, 89, 10.1016/j.jaci.2020.05.003 Frishberg, 2019, Cell composition analysis of bulk genomics using single-cell data, Nat. Methods, 16, 327, 10.1038/s41592-019-0355-5 Reusch, 2021, Neutrophils in COVID-19, Front. Immunol., 12, 652470, 10.3389/fimmu.2021.652470 Hazeldine, 2021, Neutrophils and COVID-19: active participants and rational therapeutic targets, Front. Immunol., 12, 680134, 10.3389/fimmu.2021.680134 Shen-Orr, 2013, Computational deconvolution: extracting cell type-specific information from heterogeneous samples, Curr. Opin. Immunol., 25, 571, 10.1016/j.coi.2013.09.015 Anft, 2020, COVID-19-Induced ARDS is associated with decreased frequency of activated memory/effector T cells expressing CD11a++, Mol. Ther., 28, 2691, 10.1016/j.ymthe.2020.10.001 Xi, 2019, GSDMD is required for effector CD8+ T cell responses to lung cancer cells, Int. Immunopharmacol., 74, 105713, 10.1016/j.intimp.2019.105713 Cruikshank, 2008, lnterleukin-16: the ins and outs of regulating T-cell activation, Crit. Rev. Immunol., 28, 467, 10.1615/CritRevImmunol.v28.i6.10 Lechuga, 2021, SARS-CoV-2 proteins bind to hemoglobin and its metabolites, Int. J. Mol. Sci., 22, 9035, 10.3390/ijms22169035 Choi, 2017, THEMIS enhances TCR signaling and enables positive selection by selective inhibition of the phosphatase SHP-1, Nat. Immunol., 18, 433, 10.1038/ni.3692 Zhang, 2013, Loss of β-arrestin 2 exacerbates experimental autoimmune encephalomyelitis with reduced number of Foxp3+ CD4+ regulatory T cells, Immunology, 140, 430, 10.1111/imm.12152 Lu, 2018, Human Semaphorin-4A drives Th2 responses by binding to receptor ILT-4, Nat. Commun., 9, 742, 10.1038/s41467-018-03128-9 Nelms, 1999, The IL-4 receptor: signaling mechanisms and biologic functions, Annu. Rev. Immunol., 17, 701, 10.1146/annurev.immunol.17.1.701 Huang, 2021, The predicting roles of carcinoembryonic antigen and its underlying mechanism in the progression of coronavirus disease 2019, Crit. Care., 25, 234, 10.1186/s13054-021-03661-y Krämer, 2021, Early IFN-α signatures and persistent dysfunction are distinguishing features of NK cells in severe COVID-19, Immunity, 54, 2650, 10.1016/j.immuni.2021.09.002 Hadjadj, 2020, Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients, Science, 369, 718, 10.1126/science.abc6027 Ruetsch, 2021, Functional exhaustion of type I and II interferons production in severe COVID-19 patients, Front. Med., 7, 603961, 10.3389/fmed.2020.603961 Geense, 2017, MONITOR-IC study, a mixed methods prospective multicentre controlled cohort study assessing 5-year outcomes of ICU survivors and related healthcare costs: a study protocol, BMJ. Open., 7, e018006, 10.1136/bmjopen-2017-018006 Rockwood, 2005, A global clinical measure of fitness and frailty in elderly people, CMAJ (Can. Med. Assoc. J.), 173, 489, 10.1503/cmaj.050051 Geense, 2020, Changes in frailty among ICU survivors and associated factors: results of a one-year prospective cohort study using the Dutch Clinical Frailty Scale, J. Crit. Care., 55, 184, 10.1016/j.jcrc.2019.10.016 Muscedere, 2017, The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis, Intensive Care Med., 43, 1105, 10.1007/s00134-017-4867-0 Ware, 1992, The MOS 36-ltem short-form health survey (SF-36), Med. Care., 30, 473, 10.1097/00005650-199206000-00002 Aguirre-Gamboa, 2016, Differential effects of environmental and genetic factors on T and B cell immune traits, Cell. Rep., 17, 2474, 10.1016/j.celrep.2016.10.053 Velten, 2022, Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO, Nat. Methods, 19, 179, 10.1038/s41592-021-01343-9 Hao, 2021, Integrated analysis of multimodal single-cell data, Cell, 184, 3573, 10.1016/j.cell.2021.04.048