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      Discovery of SARS-CoV-2 Antivirals through Large-scale Drug Repositioning

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          Summary

          The emergence of the novel SARS coronavirus 2 (SARS-CoV-2) in 2019 has triggered an ongoing global pandemic of severe pneumonia-like disease designated as coronavirus disease 2019 (COVID-19) 1 . The development of a vaccine is likely to require at least 12-18 months, and the typical timeline for approval of a novel antiviral therapeutic can exceed 10 years. Thus, repurposing of known drugs could significantly accelerate the deployment of novel therapies for COVID-19. Towards this end, we profiled a library of known drugs encompassing approximately 12,000 clinical-stage or FDA-approved small molecules. We report the identification of 100 molecules that inhibit viral replication, including 21 known drugs that exhibit dose response relationships. Of these, thirteen were found to harbor effective concentrations likely commensurate with achievable therapeutic doses in patients, including the PIKfyve kinase inhibitor apilimod 24 , and the cysteine protease inhibitors MDL-28170, Z LVG CHN2, VBY-825, and ONO 5334. Notably, MDL-28170, ONO 5334, and apilimod were found to antagonize viral replication in human iPSC-derived pneumocyte-like cells, and the PIKfyve inhibitor also demonstrated antiviral efficacy in a primary human lung explant model. Since most of the molecules identified in this study have already advanced into the clinic, the known pharmacological and human safety profiles of these compounds will enable accelerated preclinical and clinical evaluation of these drugs for the treatment of COVID-19.

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          Most cited references42

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          Is Open Access

          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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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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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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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                24 July 2020
                24 July 2020
                October 2020
                24 January 2021
                : 586
                : 7827
                : 113-119
                Affiliations
                [1 ]Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA, 92037, USA
                [2 ]State Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
                [3 ]Center for Integrative Bioinformatics Vienna, Max Perutz Laboratories, University of Vienna and Medical University of Vienna, Austria
                [4 ]Calibr at Scripps Research, La Jolla, CA, 92037, USA
                [5 ]Department of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
                [6 ]Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD 20892, USA
                [7 ]Biological Sciences Graduate Program, University of Maryland, College Park, MD 20742, USA
                [8 ]Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
                [9 ]Global Health and Emerging pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
                [10 ]Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
                [11 ]Huffington Foundation Center for Cell-based Research in Parkinson’s Disease, Department for Cell, Regenerative and Developmental Biology, Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [12 ]Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
                [13 ]Texas Biomedical Research Institute, San Antonio, TX, USA
                [14 ]Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
                [15 ]Department of Biochemistry, Purdue University, West Lafayette, IN, USA.
                [16 ]Inception Therapeutics, 6175 Nancy Ridge Dr, San Diego, 92121
                [17 ]Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA.
                [18 ]Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
                [19 ]Department of Medicine - Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
                [20 ]The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
                Author notes

                Author contributions

                LR, SY, XY, PT, T-TN, JF-WC, PDDJ, MVH, JC, VK-MP, AR, YP, CN, AC, RR, MS, MD, MEC, EKL designed and/or performed experiments. LR, SY, XY, NM, SB-M, LP, KMH, MWC, KC, MS, MEC, EKL, ADM, CB, AIS, RJG, PT analyzed data. LR, SY, XY, LM-S, LM, MD, TPZ, LM-S, W-CL, KMW, RA, JRJ, K-YS generated critical reagents. ER, RS, PGS, ADM, AGS, AKC, K-YY, SKC oversaw the conception and design of the experiments. LR, XY, NM, LM-S, KMH and SKC wrote the manuscript.

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                Article
                NIHMS1613359
                10.1038/s41586-020-2577-1
                7603405
                32707573
                e1e5cf63-1194-43e8-b58e-1557a60a08af

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