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      Viable bacterial colonization is highly limited in the human intestine in utero

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          Abstract

          Mucosal immunity develops in the human fetal intestine by 11–14 weeks gestation, yet whether viable microbes exist in utero and interact with the intestinal immune system is unknown. Bacterial-like morphology was identified in pockets of human fetal meconium at mid-gestation by scanning electron microscopy (n=4) and a sparse bacterial signal was detected by 16S rRNA sequencing (n=40 of 50) compared to environmental controls (n=87). Eighteen taxa were enriched in fetal meconium with Micrococcaceae (n=9) and Lactobacillus (n=6) the most abundant. Fetal intestines dominated by Micrococcaceae exhibited distinct patterns of T cell composition and epithelial transcription. Fetal Micrococcus luteus , isolated only in the presence of monocytes, grew on placental hormones, remained viable within antigen presenting cells, limited inflammation ex vivo, and possessed genomic features linked with survival in the fetus. Thus, viable bacteria are highly limited in the fetal intestine at mid-gestation, though strains with immunomodulatory capacity are detected in subsets of specimens.

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          Most cited references68

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          Is Open Access

          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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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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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

                Journal
                9502015
                8791
                Nat Med
                Nat Med
                Nature medicine
                1078-8956
                1546-170X
                13 March 2020
                24 February 2020
                April 2020
                10 May 2021
                : 26
                : 4
                : 599-607
                Affiliations
                [1 ]Division of Gastroenterology, Department of Medicine, University of California, San Francisco, San Francisco, California, USA.
                [2 ]Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, California, USA.
                [3 ]Division of Neonatology, Department of Pediatrics, University of California, San Francisco, San Francisco, California, USA.
                [4 ]Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, California, USA.
                [5 ]College of Science and Engineering, San Francisco State University, San Francisco, California, USA.
                [6 ]Chan Zuckerberg Biohub, San Francisco, California, USA.
                [7 ]Department of Microbiology and Immunology, University of California, San Francisco, San Francisco California, USA.
                [8 ]Duke University School of Medicine, Raleigh, NC, USA.
                Author notes
                [†]

                Present address: Genentech, South San Francisco, CA.

                Author Contributions

                E.R. designed the study, performed research, analyzed the data, and wrote the manuscript; J.H. contributed to study design, data analysis, and manuscript development; E.M.F. assisted in bacterial identification, performed growth curve analysis, and analyzed the data; C.H. contributed to study design and performed electron microscopy; V.F.M. performed immune cell isolations; E.D.C. assisted in sequencing methods development; K.E.F. contributed to data analysis and manuscript development; T.D.B. contributed to study design; S.V.L. designed the study, contributed to data analysis, and wrote the manuscript. All authors discussed the results and edited the manuscript.

                [* ]Correspondence should be addressed to susan.lynch@ 123456ucsf.edu
                Article
                NIHMS1549228
                10.1038/s41591-020-0761-3
                8110246
                32094926
                aa26f25c-100e-41dd-8a77-84df49a6615b

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                Medicine
                Medicine

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