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      Integrated cytokine and metabolite analysis reveals immunometabolic reprogramming in COVID-19 patients with therapeutic implications

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          Abstract

          Cytokine release syndrome (CRS) is a major cause of the multi-organ injury and fatal outcome induced by SARS-CoV-2 infection in severe COVID-19 patients. Metabolism can modulate the immune responses against infectious diseases, yet our understanding remains limited on how host metabolism correlates with inflammatory responses and affects cytokine release in COVID-19 patients. Here we perform both metabolomics and cytokine/chemokine profiling on serum samples from healthy controls, mild and severe COVID-19 patients, and delineate their global metabolic and immune response landscape. Correlation analyses show tight associations between metabolites and proinflammatory cytokines/chemokines, such as IL-6, M-CSF, IL-1α, IL-1β, and imply a potential regulatory crosstalk between arginine, tryptophan, purine metabolism and hyperinflammation. Importantly, we also demonstrate that targeting metabolism markedly modulates the proinflammatory cytokines release by peripheral blood mononuclear cells isolated from SARS-CoV-2-infected rhesus macaques ex vivo, hinting that exploiting metabolic alterations may be a potential strategy for treating fatal CRS in COVID-19.

          Abstract

          Metabolism changes can modulate immune responses in many contexts, and vice versa. Here the authors associate metabolomic, as well as cytokine and chemokine, data from stratified COVID-19 patients to find that arginine, tryptophan and purine metabolic pathways correlate with hyperproliferation, thus hinting at potential therapeutic targets for severe COVID-19 patients.

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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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            Clinical Characteristics of Coronavirus Disease 2019 in China

            Abstract Background Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. Methods We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Results The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. Conclusions During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)
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              COVID-19: consider cytokine storm syndromes and immunosuppression

              As of March 12, 2020, coronavirus disease 2019 (COVID-19) has been confirmed in 125 048 people worldwide, carrying a mortality of approximately 3·7%, 1 compared with a mortality rate of less than 1% from influenza. There is an urgent need for effective treatment. Current focus has been on the development of novel therapeutics, including antivirals and vaccines. Accumulating evidence suggests that a subgroup of patients with severe COVID-19 might have a cytokine storm syndrome. We recommend identification and treatment of hyperinflammation using existing, approved therapies with proven safety profiles to address the immediate need to reduce the rising mortality. Current management of COVID-19 is supportive, and respiratory failure from acute respiratory distress syndrome (ARDS) is the leading cause of mortality. 2 Secondary haemophagocytic lymphohistiocytosis (sHLH) is an under-recognised, hyperinflammatory syndrome characterised by a fulminant and fatal hypercytokinaemia with multiorgan failure. In adults, sHLH is most commonly triggered by viral infections 3 and occurs in 3·7–4·3% of sepsis cases. 4 Cardinal features of sHLH include unremitting fever, cytopenias, and hyperferritinaemia; pulmonary involvement (including ARDS) occurs in approximately 50% of patients. 5 A cytokine profile resembling sHLH is associated with COVID-19 disease severity, characterised by increased interleukin (IL)-2, IL-7, granulocyte-colony stimulating factor, interferon-γ inducible protein 10, monocyte chemoattractant protein 1, macrophage inflammatory protein 1-α, and tumour necrosis factor-α. 6 Predictors of fatality from a recent retrospective, multicentre study of 150 confirmed COVID-19 cases in Wuhan, China, included elevated ferritin (mean 1297·6 ng/ml in non-survivors vs 614·0 ng/ml in survivors; p 39·4°C 49 Organomegaly None 0 Hepatomegaly or splenomegaly 23 Hepatomegaly and splenomegaly 38 Number of cytopenias * One lineage 0 Two lineages 24 Three lineages 34 Triglycerides (mmol/L) 4·0 mmol/L 64 Fibrinogen (g/L) >2·5 g/L 0 ≤2·5 g/L 30 Ferritin ng/ml 6000 ng/ml 50 Serum aspartate aminotransferase <30 IU/L 0 ≥30 IU/L 19 Haemophagocytosis on bone marrow aspirate No 0 Yes 35 Known immunosuppression † No 0 Yes 18 The Hscore 11 generates a probability for the presence of secondary HLH. HScores greater than 169 are 93% sensitive and 86% specific for HLH. Note that bone marrow haemophagocytosis is not mandatory for a diagnosis of HLH. HScores can be calculated using an online HScore calculator. 11 HLH=haemophagocytic lymphohistiocytosis. * Defined as either haemoglobin concentration of 9·2 g/dL or less (≤5·71 mmol/L), a white blood cell count of 5000 white blood cells per mm3 or less, or platelet count of 110 000 platelets per mm3 or less, or all of these criteria combined. † HIV positive or receiving longterm immunosuppressive therapy (ie, glucocorticoids, cyclosporine, azathioprine).
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                Author and article information

                Contributors
                nitang@cqmu.edu.cn
                ahuang@cqmu.edu.cn
                zeping_hu@tsinghua.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                12 March 2021
                12 March 2021
                2021
                : 12
                : 1618
                Affiliations
                [1 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, School of Pharmaceutical Sciences, , Tsinghua University, ; Beijing, 100084 China
                [2 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Tsinghua-Peking Joint Center for Life Sciences, , Tsinghua University, ; Beijing, 100084 China
                [3 ]GRID grid.203458.8, ISNI 0000 0000 8653 0555, Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), , Chongqing Medical University, ; Chongqing, China
                [4 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, NHC Key Laboratory of Biotechnology of Antibiotics, Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, ; Beijing, 100050 China
                [5 ]Agilent Technologies (China), Chaoyang District, Beijing, 100102 China
                [6 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, , Tsinghua University, ; Beijing, 100084 China
                [7 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Beijing Frontier Research Center for Biological Structure, , Tsinghua University, ; Beijing, 100084 China
                Author information
                http://orcid.org/0000-0002-6966-6631
                http://orcid.org/0000-0003-2267-5810
                http://orcid.org/0000-0003-1857-2798
                http://orcid.org/0000-0002-4890-192X
                http://orcid.org/0000-0001-8460-6875
                http://orcid.org/0000-0001-5830-8786
                http://orcid.org/0000-0003-0148-7423
                http://orcid.org/0000-0003-4146-7750
                Article
                21907
                10.1038/s41467-021-21907-9
                7955129
                33712622
                b2058b9d-8540-4778-9e70-49055caef3b0
                © The Author(s) 2021

                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
                : 10 October 2020
                : 19 February 2021
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                © The Author(s) 2021

                Uncategorized
                metabolomics,cytokines,sars-cov-2,prognostic markers
                Uncategorized
                metabolomics, cytokines, sars-cov-2, prognostic markers

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