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      Effects of early-life penicillin exposure on the gut microbiome and frontal cortex and amygdala gene expression

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          Summary

          We have established experimental systems to assess the effects of early-life exposures to antibiotics on the intestinal microbiota and gene expression in the brain. This model system is highly relevant to human exposure and may be developed into a preclinical model of neurodevelopmental disorders in which the gut–brain axis is perturbed, leading to organizational effects that permanently alter the structure and function of the brain. Exposing newborn mice to low-dose penicillin led to substantial changes in intestinal microbiota population structure and composition. Transcriptomic alterations implicate pathways perturbed in neurodevelopmental and neuropsychiatric disorders. There also were substantial effects on frontal cortex and amygdala gene expression by bioinformatic interrogation, affecting multiple pathways underlying neurodevelopment. Informatic analyses established linkages between specific intestinal microbial populations and the early-life expression of particular affected genes. These studies provide translational models to explore intestinal microbiome roles in the normal and abnormal maturation of the vulnerable central nervous system.

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          Highlights

          • Low-dose antibiotic exposure perturbs the infant gut mouse microbiome to PND10

          • Frontal cortex and amygdala gene expression were substantially affected

          • Multiple pathways underlying neurodevelopment were affected

          • Specific gut microbial populations were linked with expression of particular genes

          Abstract

          Developmental neuroscience; Microbiome

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          Most cited references75

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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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            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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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
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                15 July 2021
                23 July 2021
                15 July 2021
                : 24
                : 7
                : 102797
                Affiliations
                [1 ]Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY 10016, USA
                [2 ]Department of Medicine, New York University Grossman School of Medicine, New York, NY 10016, USA
                [3 ]Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ 08854, USA
                [4 ]Center for Dementia Research. Nathan Kline Institute, Orangeburg, NY 10962, USA
                [5 ]Departments of Psychiatry, Neuroscience & Physiology, and NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
                Author notes
                []Corresponding author ginsberg@ 123456nki.rfmh.org
                [∗∗ ]Corresponding author martin.blaser@ 123456cabm.rutgers.edu
                [6]

                Lead contact

                Article
                S2589-0042(21)00765-3 102797
                10.1016/j.isci.2021.102797
                8324854
                34355145
                4e96ec75-3198-4e07-9173-10e9c4ff0822
                © 2021 The Author(s)

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

                History
                : 31 January 2021
                : 26 April 2021
                : 24 June 2021
                Categories
                Article

                developmental neuroscience,microbiome
                developmental neuroscience, microbiome

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