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      Endothelial SOCS3 maintains homeostasis and promotes survival in endotoxemic mice

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          Abstract

          SOCS3 is the main inhibitor of the JAK/STAT3 pathway. This pathway is activated by interleukin 6 (IL-6), a major mediator of the cytokine storm during shock. To determine its role in the vascular response to shock, we challenged mice lacking SOCS3 in the adult endothelium (SOCS3 iEKO) with a nonlethal dose of lipopolysaccharide (LPS). SOCS3 iEKO mice died 16–24 hours postinjection after severe kidney failure. Loss of SOCS3 led to an LPS-induced type I IFN–like program and high expression of prothrombotic and proadhesive genes. Consistently, we observed intraluminal leukocyte adhesion and neutrophil extracellular trap–osis (NETosis), as well as retinal venular leukoembolization. Notably, heterozygous mice displayed an intermediate phenotype, suggesting a gene dose effect. In vitro studies were performed to study the role of SOCS3 protein levels in the regulation of the inflammatory response. In human umbilical vein endothelial cells, pulse-chase experiments showed that SOCS3 protein had a half-life less than 20 minutes. Inhibition of SOCS3 ubiquitination and proteasomal degradation led to protein accumulation and a stronger inhibition of IL-6 signaling and barrier function loss. Together, our data demonstrate that the regulation of SOCS3 protein levels is critical to inhibit IL-6–mediated endotheliopathy during shock and provide a promising therapeutic avenue to prevent multiorgan dysfunction through stabilization of endothelial SOCS3.

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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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            STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

            Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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              Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

              We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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                Author and article information

                Contributors
                Journal
                JCI Insight
                JCI Insight
                JCI Insight
                JCI Insight
                American Society for Clinical Investigation
                2379-3708
                22 July 2021
                22 July 2021
                22 July 2021
                : 6
                : 14
                : e147280
                Affiliations
                [1 ]Department of Molecular and Cellular Physiology,
                [2 ]Division of Pulmonary and Critical Care Medicine, and
                [3 ]Department of Ophthalmology, Albany Medical Center, Albany, New York, USA.
                Author notes
                Address correspondence to: Alejandro P. Adam, Department of Molecular and Cellular Physiology, Albany Medical College, 43 New Scotland Avenue, Albany, New York 12208, USA. Phone: 518.262.9776; Email: adama1@ 123456amc.edu .
                Author information
                http://orcid.org/0000-0003-4714-2260
                http://orcid.org/0000-0001-6285-7235
                Article
                147280
                10.1172/jci.insight.147280
                8410050
                34138760
                c3f1ff60-729b-4b81-915c-a8d25c459f42
                © 2021 Martino et al.

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 December 2020
                : 16 June 2021
                Funding
                Funded by: National Institutes of Health, https://doi.org/10.13039/100000002;
                Award ID: R01GM124133
                Funded by: American Heart Association, https://doi.org/10.13039/100000968;
                Award ID: 18TPA34170561
                Funded by: National Institutes of Health, https://doi.org/10.13039/100000002;
                Award ID: R01HL141127,R01HL153019,K01-HL130704
                Categories
                Research Article

                inflammation,vascular biology,cytokines,endothelial cells,signal transduction

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