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      Human Placental Trophoblasts Infected by Listeria monocytogenes Undergo a Pro-Inflammatory Switch Associated With Poor Pregnancy Outcomes

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          Abstract

          The placenta controls the growth of the fetus and ensures its immune protection. Key to these functions, the syncytiotrophoblast (SYN) is a syncytium formed by fusion of underlying mononuclear trophoblasts. The SYN covers the placental surface and is bathed in maternal blood to mediate nutritional and waste exchanges between the mother and fetus. The bacterial pathogen Listeria monocytogenes breaches the trophoblast barrier and infects the placental/fetal unit resulting in poor pregnancy outcomes. In this work, we analyzed the L. monocytogenes intracellular lifecycle in primary human trophoblasts. In accordance with previous studies, we found that the SYN is 20-fold more resistant to infection compared to mononuclear trophoblasts, forming a protective barrier to infection at the maternal interface. We show for the first time that this is due to a significant reduction in L. monocytogenes uptake by the SYN rather than inhibition of the bacterial intracellular division or motility. We here report the first transcriptomic analysis of L. monocytogenes-infected trophoblasts (RNA sequencing). Pathway analysis showed that infection upregulated TLR2, NOD-like, and cytosolic DNA sensing pathways, as well as downstream pro-inflammatory circuitry (NF-κB, AP-1, IRF4, IRF7) leading to the production of mediators known to elicit the recruitment and activation of maternal leukocytes (IL8, IL6, TNFα, MIP-1). Signature genes associated with poor pregnancy outcomes were also upregulated upon infection. Measuring the release of 54 inflammatory mediators confirmed the transcriptomic data and revealed sustained production of tolerogenic factors (IL-27, IL-10, IL-1RA, TSLP) despite infection. Both the SYN and mononuclear trophoblasts produced cytokines, but surprisingly, some cytokines were predominantly produced by the SYN (IL-8, IL-6) or by non-fused trophoblasts (TNFα). Collectively, our data support that trophoblasts act as placental gatekeepers that limit and detect L. monocytogenes infection resulting in a pro-inflammatory response, which may contribute to the poor pregnancy outcomes if the pathogen persists.

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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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            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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              Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

              Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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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 July 2021
                2021
                : 12
                : 709466
                Affiliations
                [1] 1 Department of Microbial Infection and Immunity, The Ohio State University , Columbus, OH, United States
                [2] 2 Department of Microbiology, The Ohio State University , Columbus, OH, United States
                [3] 3 Department of Biomedical Informatics, The Ohio State University , Columbus, OH, United States
                [4] 4 Department of Biomedical Informatics, Center for Biostatistics, The Ohio State University , Columbus, OH, United States
                [5] 5 Pulmonary, Critical Care and Sleep Medicine Division, Department of Internal Medicine, The Ohio State University , Columbus, OH, United States
                [6] 6 Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, The Ohio State University , Columbus, OH, United States
                [7] 7 Infectious Diseases Institute, The Ohio State University , Columbus, OH, United States
                Author notes

                Edited by: Ulrike Kemmerling, University of Chile, Chile

                Reviewed by: Beth Holder, Imperial College London, United Kingdom; Christian Castillo, University of Chile, Chile; Arun K. Bhunia, Purdue University, United States

                *Correspondence: Stephanie Seveau, seveau.1@ 123456osu.edu

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

                Article
                10.3389/fimmu.2021.709466
                8346206
                34367171
                21999905-b1a7-47a5-a43b-5e28e3eb3e6a
                Copyright © 2021 Johnson, Azari, Webb, Zhang, Gavrilin, Marshall, Rood and Seveau

                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
                : 14 May 2021
                : 05 July 2021
                Page count
                Figures: 7, Tables: 4, Equations: 0, References: 119, Pages: 21, Words: 12799
                Funding
                Funded by: National Institute of Allergy and Infectious Diseases 10.13039/100000060
                Award ID: R03AI149371, R21AI105588
                Categories
                Immunology
                Original Research

                Immunology
                trophoblast,fusion,placenta,listeria monocytogenes,infection,inflammation,rnaseq,pregnancy complications

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