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      Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19

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      1 , 2 , , 3 , 4 , , 5 , 6 , 7 , 1 , 8 , 1 , 9 , 10 , 11 , 12 , 1 , 13 , 3 , 4 , 8 , 1 , 8 , 8 , 1 , 14 , 15 , 5 , 16 , 8 , 1 , 11 , 12 , 8 , 17 , 18 , 4 , 19 , 1 , 1 , 20 , 20 , 20 , 20 , 5 , 5 , 2 , 1 , 21 , 3 , 4 , 3 , 4 , 3 , 4 , The Yale IMPACT Research Team, 22 , 20 , 10 , 3 , 21 , 4 , 23 , 21 , 11 , 12 , 8 , 9 , 17 , 24 , 4 , 5 , 24 , 3 , 4 , 1 , 1 , 25
      Nature Communications
      Nature Publishing Group UK
      Viral infection, SARS-CoV-2, Systems analysis, Cellular immunity

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          Abstract

          Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100A hi/HLA-DR lo classical monocytes and activated LAG-3 hi T cells are hallmarks of progressive disease and highlights the abnormal MHC-II/LAG-3 interaction on myeloid and T cells, respectively. We also find skewed T cell receptor repertories in expanded effector CD8 + clones, unmutated IGHG + B cell clones, and mutated B cell clones with stable somatic hypermutation frequency over time. In conclusion, our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19.

          Abstract

          SARS-CoV-2 infection can lead to progressive pathology in patients with COVID-19, but information for this disease progression is sparse. Here the authors use multi-omics approach to profile the immune responses of patients, assessing immune repertoire and effects of tocilizumab treatments, to find a dyssynchrony between innate and adaptive immunity in progressive COVID-19.

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          Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

          Summary Background A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0–58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

              Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
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                Author and article information

                Contributors
                ramiu@tlvmc.gov.il
                tomokazu.sumida@yale.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                21 January 2022
                21 January 2022
                2022
                : 13
                : 440
                Affiliations
                [1 ]GRID grid.47100.32, ISNI 0000000419368710, Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, , School of Medicine, Yale University, ; New Haven, CT USA
                [2 ]GRID grid.12136.37, ISNI 0000 0004 1937 0546, Pulmonary Institute, , Tel Aviv Sourasky Medical Center, Tel Aviv University, ; Tel Aviv, Israel
                [3 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Neurology, , School of Medicine, Yale University, ; New Haven, CT USA
                [4 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Immunobiology, , School of Medicine, Yale University, ; New Haven, CT USA
                [5 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Pathology, , Yale School of Medicine, ; New Haven, CT USA
                [6 ]GRID grid.47100.32, ISNI 0000000419368710, Center for Medical Informatics, , Yale School of Medicine, ; New Haven, CT USA
                [7 ]GRID grid.249880.f, ISNI 0000 0004 0374 0039, The Jackson Laboratory for Genomic Medicine, ; Farmington, CT USA
                [8 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Biostatistics, , Yale School of Public Health, Yale University, ; New Haven, CT USA
                [9 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Genetics, , Yale School of Medicine, ; New Haven, CT USA
                [10 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Internal Medicine, , Yale School of Medicine, ; New Haven, CT USA
                [11 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Computer Science, , Yale University, ; New Haven, CT USA
                [12 ]GRID grid.47100.32, ISNI 0000000419368710, Cardiovascular Research Center, Section of Cardiovascular Medicine, , Department of Internal Medicine, Yale School of Medicine, ; New Haven, CT USA
                [13 ]Department of Respiratory Medicine, Hannover Medical School and Biomedical Research in End-stage and Obstructive Lung Disease Hannover, German Lung Research Center (DZL), Hannover, Germany
                [14 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Biomedical Engineering, , Yale University, ; New Haven, CT USA
                [15 ]GRID grid.47100.32, ISNI 0000000419368710, Medical Scientist Training Program, , Yale School of Medicine, ; New Haven, CT USA
                [16 ]GRID grid.47100.32, ISNI 0000000419368710, Yale Center for Genome Analysis/Keck Biotechnology Resource Laboratory, Department of Molecular Biophysics and Biochemistry, , Yale School of Medicine, ; New Haven, CT USA
                [17 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, , School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, ; Shanghai, China
                [18 ]GRID grid.47100.32, ISNI 0000000419368710, Yale Center for Genome Analysis, , Yale School of Medicine, ; New Haven, CT USA
                [19 ]GRID grid.47100.32, ISNI 0000000419368710, School of Medicine, , Yale University, ; New Haven, CT USA
                [20 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Epidemiology of Microbial Diseases, , Yale School of Public Health, ; New Haven, CT USA
                [21 ]GRID grid.47100.32, ISNI 0000000419368710, Section of Infectious Diseases, Department of Internal Medicine, , Yale School of Medicine, Yale University, ; New Haven, CT USA
                [22 ]GRID grid.47100.32, ISNI 0000000419368710, Departments of Anesthesiology & Biomedical Engineering, , Yale University, ; New Haven, CT USA
                [23 ]GRID grid.413575.1, ISNI 0000 0001 2167 1581, Howard Hughes Medical Institute, ; Chevy Chase, MD USA
                [24 ]GRID grid.47100.32, ISNI 0000000419368710, Inter-Departmental Program in Computational Biology and Bioinformatics, , Yale University, ; New Haven, CT USA
                [25 ]West Haven Veterans Affair Medical Center, West Haven, CT USA
                Author information
                http://orcid.org/0000-0003-0965-3326
                http://orcid.org/0000-0002-9806-2642
                http://orcid.org/0000-0001-8348-5717
                http://orcid.org/0000-0001-7505-305X
                http://orcid.org/0000-0002-7714-8076
                http://orcid.org/0000-0003-1441-6122
                http://orcid.org/0000-0003-0411-4307
                http://orcid.org/0000-0002-7251-3687
                http://orcid.org/0000-0003-0841-8156
                http://orcid.org/0000-0001-5558-3758
                http://orcid.org/0000-0003-3040-9029
                http://orcid.org/0000-0002-3301-6143
                http://orcid.org/0000-0003-0027-6480
                http://orcid.org/0000-0001-6015-0279
                http://orcid.org/0000-0003-0335-5897
                http://orcid.org/0000-0001-5828-6425
                http://orcid.org/0000-0001-9023-2339
                http://orcid.org/0000-0002-8661-4454
                http://orcid.org/0000-0001-7230-1409
                http://orcid.org/0000-0002-7824-9856
                http://orcid.org/0000-0003-3911-9925
                http://orcid.org/0000-0003-1195-9607
                http://orcid.org/0000-0003-4957-1544
                http://orcid.org/0000-0003-4664-535X
                http://orcid.org/0000-0001-5917-4601
                http://orcid.org/0000-0002-5258-1797
                Article
                27716
                10.1038/s41467-021-27716-4
                8782894
                35064122
                397190c9-aed6-4f58-936c-a598867c585e
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 July 2020
                : 3 December 2021
                Funding
                Funded by: Department of Internal Medicine at Yale School of Medicine
                Categories
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                © The Author(s) 2022

                Uncategorized
                viral infection,sars-cov-2,systems analysis,cellular immunity
                Uncategorized
                viral infection, sars-cov-2, systems analysis, cellular immunity

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