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      Identification, Characterization, and Transcriptional Reprogramming of Epithelial Stem Cells and Intestinal Enteroids in Simian Immunodeficiency Virus Infected Rhesus Macaques

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          Abstract

          Epithelial cell injury and impaired epithelial regeneration are considered key features in HIV pathogenesis and contribute to HIV-induced generalized immune activation. Understanding the molecular mechanisms underlying the disrupted epithelial regeneration might provide an alternative approach for the treatment of HIV-mediated enteropathy and immune activation. We have observed a significant increased presence of α defensin5+ (HD5) Paneth cells and proliferating Ki67+ epithelial cells as well as decreased expression of E-cadherin expression in epithelial cells during SIV infection. SIV infection did not significantly influence the frequency of LGR5+ stem cells, but the frequency of HD5+ cells was significantly higher compared to uninfected controls in jejunum. Our global transcriptomics analysis of enteroids provided novel information about highly significant changes in several important pathways like metabolic, TCA cycle, and oxidative phosphorylation, where the majority of the differentially expressed genes were downregulated in enteroids grown from chronically SIV-infected macaques compared to the SIV-uninfected controls. Despite the lack of significant reduction in LGR5+ stem cell population, the dysregulation of several intestinal stem cell niche factors including Notch, mTOR, AMPK and Wnt pathways as well as persistence of inflammatory cytokines and chemokines and loss of epithelial barrier function in enteroids further supports that SIV infection impacts on epithelial cell proliferation and intestinal homeostasis.

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          Most cited references118

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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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            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                23 November 2021
                2021
                : 12
                : 769990
                Affiliations
                [1] 1 Division of Comparative Pathology, Tulane National Primate Research Center , Covington, LA, United States
                [2] 2 Department of Mathematical Sciences, Michigan Technological University , Houghton, MI, United States
                [3] 3 Division of Veterinary Medicine, Tulane National Primate Research Center , Covington, LA, United States
                [4] 4 Division of Microbiology, Tulane National Primate Research Center , Covington, LA, United States
                [5] 5 Department of Biostatistics, Tulane University , New Orleans, LA, United States
                [6] 6 Department of Biological Sciences, California State University, Los Angeles , Los Angeles, CA, United States
                [7] 7 Department of Microbiology and Immunology, Tulane University School of Medicine , New Orleans, LA, United States
                [8] 8 Department of Tropical Medicine, Tulane School of Public Health and Tropical Medicine , New Orleans, LA, United States
                Author notes

                Edited by: Neil A. Mabbott, University of Edinburgh, United Kingdom

                Reviewed by: Sieghart Sopper, Innsbruck Medical University, Austria; Upasana Das Adhikari, Massachusetts General Hospital and Harvard Medical School, United States

                *Correspondence: Bapi Pahar, bpahar@ 123456trudeauinstitute.org Trudeau Institute, Saranac Lake, NY, United States

                †These authors have contributed equally to this work

                This article was submitted to Mucosal Immunity, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2021.769990
                8650114
                f83063d3-0815-4f30-9181-7319f3e9021f
                Copyright © 2021 Boby, Cao, Ransom, Pace, Mabee, Shroyer, Das, Didier, Srivastav, Porter, Sha and Pahar

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 03 September 2021
                : 02 November 2021
                Page count
                Figures: 7, Tables: 2, Equations: 0, References: 118, Pages: 23, Words: 12888
                Funding
                Funded by: National Institutes of Health , doi 10.13039/100000002;
                Funded by: National Institutes of Health , doi 10.13039/100000002;
                Categories
                Immunology
                Original Research

                Immunology
                intestinal stem cell (isc),siv,epithelial cells,lgr5,alpha defensin
                Immunology
                intestinal stem cell (isc), siv, epithelial cells, lgr5, alpha defensin

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