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      Serotonin reduction in post-acute sequelae of viral infection

      research-article
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          SUMMARY

          Post-acute sequelae of COVID-19 (PASC, “Long COVID”) pose a significant global health challenge. The pathophysiology is unknown, and no effective treatments have been found to date. Several hypotheses have been formulated to explain the etiology of PASC, including viral persistence, chronic inflammation, hypercoagulability, and autonomic dysfunction. Here, we propose a mechanism that links all four hypotheses in a single pathway and provides actionable insights for therapeutic interventions. We find that PASC are associated with serotonin reduction. Viral infection and type I interferon-driven inflammation reduce serotonin through three mechanisms: diminished intestinal absorption of the serotonin precursor tryptophan; platelet hyperactivation and thrombocytopenia, which impacts serotonin storage; and enhanced MAO-mediated serotonin turnover. Peripheral serotonin reduction, in turn, impedes the activity of the vagus nerve and thereby impairs hippocampal responses and memory. These findings provide a possible explanation for neurocognitive symptoms associated with viral persistence in Long COVID, which may extend to other post-viral syndromes.

          In brief

          Post-viral syndromes are associated with serotonin reduction, which may contribute to the neurological and cognitive symptoms seen in individuals with Long COVID.

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          Most cited references102

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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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            SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor

            Summary The recent emergence of the novel, pathogenic SARS-coronavirus 2 (SARS-CoV-2) in China and its rapid national and international spread pose a global health emergency. Cell entry of coronaviruses depends on binding of the viral spike (S) proteins to cellular receptors and on S protein priming by host cell proteases. Unravelling which cellular factors are used by SARS-CoV-2 for entry might provide insights into viral transmission and reveal therapeutic targets. Here, we demonstrate that SARS-CoV-2 uses the SARS-CoV receptor ACE2 for entry and the serine protease TMPRSS2 for S protein priming. A TMPRSS2 inhibitor approved for clinical use blocked entry and might constitute a treatment option. Finally, we show that the sera from convalescent SARS patients cross-neutralized SARS-2-S-driven entry. Our results reveal important commonalities between SARS-CoV-2 and SARS-CoV infection and identify a potential target for antiviral intervention.
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              Comprehensive Integration of Single-Cell Data

              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Journal
                0413066
                2830
                Cell
                Cell
                Cell
                0092-8674
                1097-4172
                5 June 2024
                26 October 2023
                16 October 2023
                06 July 2024
                : 186
                : 22
                : 4851-4867.e20
                Affiliations
                [1 ]Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
                [2 ]Institute for Immunology and Immune Health, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
                [3 ]Institute for Obesity, Diabetes and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
                [4 ]Institute for Molecular Bio Science, Goethe University Frankfurt, Frankfurt am Main, Germany
                [5 ]Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
                [6 ]Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
                [7 ]Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA, USA
                [8 ]Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
                [9 ]Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA
                [10 ]Parker Institute for Cancer Immunotherapy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
                [11 ]Division of Protective Immunity, Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104, USA
                [12 ]Metabolomics Core, Penn Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA, USA
                [13 ]Department of Physical Medicine and Rehabilitation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
                [14 ]The Wistar Institute, Philadelphia, PA, USA
                [15 ]Department of Microbiology and Immunology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
                [16 ]Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA
                [17 ]Rush Center for Integrated Microbiome and Chronobiology Research, Chicago, IL, USA
                [18 ]Division of Experimental Medicine, University of California, San Francisco, San Francisco, CA, USA
                [19 ]Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
                [20 ]Division of Pulmonary and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
                [21 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                A.C.W. conceived the study, designed and performed the experiments, interpreted the results, and wrote the manuscript. A.S.D., I.C.U., T.O.X., L.D., J.P., Z.E., L.T.I., Jihee Kim, S.L.P., S.W., A.D.M., R.D.P., Junwon Kim, N.B., S.P., K.T., S.M., J.-C.B., S.F.N., M.F.M., B.M.M., M.J.L., B.M., O.D.-M., C.P.C., and H.R. performed experiments. L.L., P.L., and H.C.D. performed computational and statistical analyses. M.T., A.S.H., G.B.-Z., L.B.G., A.E.B., A.R.G., C.K., K.M., L.A.L., M.F., U.O., M.A.-M., A.L.L., A.K., T.J.H., S.G.D., M.J.P., and N.J.M. acquired clinical samples and data. J.H.-M., B.S., K.A.J., K.E.W., and E.J.W. provided essential tools and insights. B.A.A., S.C., C.A.T., and M.L. conceived the study, designed the experiments, interpreted the results, and wrote the manuscript.

                Article
                NIHMS1937987
                10.1016/j.cell.2023.09.013
                11227373
                37848036
                a4c15801-c72b-4ed6-ae86-1ab15c623bce

                This is an open access article under the CC BY-NC license ( http://creativecommons.org/licenses/by-nc/4.0/).

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                Cell biology
                Cell biology

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