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      Cyclooxgenase-2 is induced by SARS-CoV-2 infection but does not affect viral entry or replication

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          Abstract

          Identifying drugs that regulate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and its symptoms has been a pressing area of investigation during the coronavirus disease 2019 (COVID-19) pandemic. Nonsteroidal anti-inflammatory drugs (NSAIDs), which are frequently used for the relief of pain and inflammation, could modulate both SARS-CoV-2 infection and the host response to the virus. NSAIDs inhibit the enzymes cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2), which mediate the production of prostaglandins (PGs). PGE 2, one of the most abundant PGs, has diverse biological roles in homeostasis and inflammatory responses. Previous studies have shown that NSAID treatment or inhibition of PGE 2 receptor signaling leads to upregulation of angiotensin-converting enzyme 2 (ACE2), the cell entry receptor for SARS-CoV-2, thus raising concerns that NSAIDs could increase susceptibility to infection. COX/PGE 2 signaling has also been shown to regulate the replication of many viruses, but it is not yet known whether it plays a role in SARS-CoV-2 replication. The purpose of this study was to dissect the effect of NSAIDs on COVID-19 in terms of SARS-CoV-2 entry and replication. We found that SARS-CoV-2 infection induced COX-2 upregulation in diverse human cell culture and mouse systems. However, suppression of COX-2/PGE 2 signaling by two commonly used NSAIDs, ibuprofen and meloxicam, had no effect on ACE2 expression, viral entry, or viral replication. Our findings suggest that COX-2 signaling driven by SARS-CoV-2 may instead play a role in regulating the lung inflammation and injury observed in COVID-19 patients.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China

              In December 2019, novel coronavirus (2019-nCoV)-infected pneumonia (NCIP) occurred in Wuhan, China. The number of cases has increased rapidly but information on the clinical characteristics of affected patients is limited.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                25 September 2020
                : 2020.09.24.312769
                Affiliations
                [a ]Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, USA
                [b ]Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
                [c ]Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA
                Author notes

                Author contributions

                Jennifer S. Chen: Conceptualization, Formal analysis, Investigation, Validation, Visualization, Writing – original draft, Mia Madel Alfajaro: Methodology, Investigation, Jin Wei: Methodology, Investigation, Ryan D. Chow: Formal analysis, Visualization, Renata B. Filler: Investigation , Stephanie C. Eisenbarth: Supervision , Craig B. Wilen: Conceptualization, Formal analysis, Funding acquisition, Resources, Supervision, Writing - original draft. All authors reviewed and edited the manuscript.

                [# ]Address correspondence to Craig B. Wilen, craig.wilen@ 123456yale.edu
                Article
                10.1101/2020.09.24.312769
                7523115
                32995789
                d43f3c69-524c-47e0-89fe-3ea617b03f56

                It is made available under a CC-BY-NC-ND 4.0 International license.

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