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      Molecular states during acute COVID-19 reveal distinct etiologies of long-term sequelae

      research-article
      1 , 2 , 3 , 3 , 3 , 3 , 4 , 5 , 6 , 3 , 3 , 7 , 8 , 9 , 3 , 3 , 3 , 4 , 3 , 4 , 10 , 11 , 8 , 9 , 10 , 10 , 4 , 4 , 12 , 13 , 14 , 5 , 15 , 8 , 15 , 16 , 17 , 18 , 19 , 20 , 1 , 3 , 4 , 21 , 22 , 23 , The Mount Sinai COVID-19 Biobank Team, 1 , 24 , 1 , 2 , 25 , 13 , 14 , 3 , 21 , 6 , 26 , 3 , 4 , 10 , 21 , 27 , 8 , 9 , 11 , 28 , 3 , 4 , 27 , 3 , 4 , 10 , 21 , 23 , 27 , 8 , 11 , 1 , 2 , 3 , , 1 , 2 , 3 , 8 , 25 ,
      Nature Medicine
      Nature Publishing Group US
      Gene ontology, Infectious diseases, Transcriptomics, Viral infection, Gene regulation in immune cells

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          Abstract

          Post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are debilitating, clinically heterogeneous and of unknown molecular etiology. A transcriptome-wide investigation was performed in 165 acutely infected hospitalized individuals who were followed clinically into the post-acute period. Distinct gene expression signatures of post-acute sequelae were already present in whole blood during acute infection, with innate and adaptive immune cells implicated in different symptoms. Two clusters of sequelae exhibited divergent plasma-cell-associated gene expression patterns. In one cluster, sequelae associated with higher expression of immunoglobulin-related genes in an anti-spike antibody titer-dependent manner. In the other, sequelae associated independently of these titers with lower expression of immunoglobulin-related genes, indicating lower non-specific antibody production in individuals with these sequelae. This relationship between lower total immunoglobulins and sequelae was validated in an external cohort. Altogether, multiple etiologies of post-acute sequelae were already detectable during SARS-CoV-2 infection, directly linking these sequelae with the acute host response to the virus and providing early insights into their development.

          Abstract

          Transcriptomic analyses of acute phase whole blood from a large cohort of patients with COVID-19 identify molecular determinants of post-infection long-term sequelae.

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          Most cited references73

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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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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                alexander.charney@mssm.edu
                noam.beckmann@mssm.edu
                Journal
                Nat Med
                Nat Med
                Nature Medicine
                Nature Publishing Group US (New York )
                1078-8956
                1546-170X
                8 December 2022
                8 December 2022
                2023
                : 29
                : 1
                : 236-246
                Affiliations
                [1 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [2 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [3 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [4 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [5 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Genetics and Genomic Sciences, , Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [6 ]GRID grid.412004.3, ISNI 0000 0004 0478 9977, Department of Immunology, , University Hospital Zurich, University of Zurich, ; Zurich, Switzerland
                [7 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Susan and Leonard Feinstein Inflammatory Bowel Disease Clinical Center, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [8 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Genetics and Genomic Sciences, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [9 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [10 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [11 ]GRID grid.511393.c, Sema4, a Mount Sinai venture, ; Stamford, CT USA
                [12 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Diagnostic, Molecular and Interventional Radiology, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [13 ]GRID grid.64212.33, ISNI 0000 0004 0463 2320, Institute for Systems Biology, ; Seattle, WA USA
                [14 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Bioengineering, , University of Washington, ; Seattle, WA USA
                [15 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Psychiatry, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [16 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [17 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [18 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [19 ]GRID grid.274295.f, ISNI 0000 0004 0420 1184, Mental Illness Research Education and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, ; Bronx, NY USA
                [20 ]GRID grid.250263.0, ISNI 0000 0001 2189 4777, Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, ; Orangeburg, NY USA
                [21 ]GRID grid.516104.7, ISNI 0000 0004 0408 1530, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [22 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Medicine, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [23 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Medicine, , Division of Hematology and Oncology, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [24 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [25 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Medicine, , Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [26 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Faculty of Medicine, , University of Zurich, ; Zurich, Switzerland
                [27 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Oncological Sciences, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [28 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                Author information
                http://orcid.org/0000-0002-0450-8181
                http://orcid.org/0000-0002-3952-1458
                http://orcid.org/0000-0001-7383-2447
                http://orcid.org/0000-0001-6983-5362
                http://orcid.org/0000-0002-0957-0224
                http://orcid.org/0000-0001-7120-8739
                http://orcid.org/0000-0002-6222-6190
                http://orcid.org/0000-0002-7651-7220
                http://orcid.org/0000-0002-5899-7080
                http://orcid.org/0000-0002-5369-6502
                http://orcid.org/0000-0002-4640-6239
                http://orcid.org/0000-0001-5903-8191
                http://orcid.org/0000-0003-4515-8090
                http://orcid.org/0000-0001-6319-4314
                http://orcid.org/0000-0002-6948-0626
                http://orcid.org/0000-0001-8279-5545
                http://orcid.org/0000-0002-4481-7827
                http://orcid.org/0000-0001-5643-9520
                http://orcid.org/0000-0002-7892-8808
                http://orcid.org/0000-0001-8135-6858
                http://orcid.org/0000-0002-4258-1574
                Article
                2107
                10.1038/s41591-022-02107-4
                9873574
                36482101
                355319a6-9e84-440c-966a-92c68b5db13d
                © 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
                : 5 October 2021
                : 25 October 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation);
                Award ID: NRP 78
                Award ID: 4078P0-198431
                Award ID: 310030-200669
                Award ID: NRP 78
                Award ID: 310030-200669
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000060, U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID);
                Award ID: 3R01AI141953-02S1
                Award ID: 3R01AI141953-02S1
                Award ID: 3R01AI141953-02S2
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000054, U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI);
                Award ID: CA224319
                Award ID: CA224319
                Award ID: DK124165
                Award ID: CA196521
                Award Recipient :
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                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2023

                Medicine
                gene ontology,infectious diseases,transcriptomics,viral infection,gene regulation in immune cells

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