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      Multiomics analysis reveals the presence of a microbiome in the gut of fetal lambs

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          Abstract

          Objective

          Microbial exposure is critical to neonatal and infant development, growth and immunity. However, whether a microbiome is present in the fetal gut prior to birth remains debated. In this study, lambs delivered by aseptic hysterectomy at full term were used as an animal model to investigate the presence of a microbiome in the prenatal gut using a multiomics approach.

          Design

          Lambs were euthanised immediately after aseptic caesarean section and their cecal content and umbilical cord blood samples were aseptically acquired. Cecal content samples were assessed using metagenomic and metatranscriptomic sequencing to characterise any existing microbiome. Both sample types were analysed using metabolomics in order to detect microbial metabolites.

          Results

          We detected a low-diversity and low-biomass microbiome in the prenatal fetal gut, which was mainly composed of bacteria belonging to the phyla Proteobacteria, Actinobacteria and Firmicutes. Escherichia coli was the most abundant species in the prenatal fetal gut. We also detected multiple microbial metabolites including short chain fatty acids, deoxynojirimycin, mitomycin and tobramycin, further indicating the presence of metabolically active microbiota. Additionally, bacteriophage phiX174 and Orf virus, as well as antibiotic resistance genes, were detected in the fetal gut, suggesting that bacteriophage, viruses and bacteria carrying antibiotic resistance genes can be transmitted from the mother to the fetus during the gestation period.

          Conclusions

          This study provides strong evidence that the prenatal gut harbours a microbiome and that microbial colonisation of the fetal gut commences in utero.

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          Most cited references62

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

            Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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              Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

              Massively-parallel cDNA sequencing has opened the way to deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here, we present the Trinity methodology for de novo full-length transcriptome reconstruction, and evaluate it on samples from fission yeast, mouse, and whitefly – an insect whose genome has not yet been sequenced. Trinity fully reconstructs a large fraction of the transcripts present in the data, also reporting alternative splice isoforms and transcripts from recently duplicated genes. In all cases, Trinity performs better than other available de novo transcriptome assembly programs, and its sensitivity is comparable to methods relying on genome alignments. Our approach provides a unified and general solution for transcriptome reconstruction in any sample, especially in the complete absence of a reference genome.
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                Author and article information

                Journal
                Gut
                Gut
                gutjnl
                gut
                Gut
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                0017-5749
                1468-3288
                May 2021
                15 February 2021
                : 70
                : 5
                : 853-864
                Affiliations
                [1 ] departmentFeed Research Institute , Chinese Academy of Agricultural Sciences, National Engineering Research Center of Biological Feed , Beijing, China
                [2 ] departmentState Key Laboratory of Animal Nutrition, Institute of Animal Science , Chinese Academy of Agricultural Sciences , Beijing, China
                [3 ] departmentDepartment of Bacteriology , University of Wisconsin-Madison , Madison, Wisconsin, USA
                [4 ] departmentAgricultural Informaition Institute , Chinese Academy of Agricultural Sciences , Beijing, China
                [5 ] departmentState Key Laboratory of Animal Nutrition, College of Animal Science and Technology , China Agricultural University , Beijing, China
                Author notes
                [Correspondence to ] Professor Qiyu Diao, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China; diaoqiyu@ 123456caas.cn
                Author information
                http://orcid.org/0000-0001-8183-8208
                Article
                gutjnl-2020-320951
                10.1136/gutjnl-2020-320951
                8040156
                33589511
                d3bd53c6-346d-4313-9461-6d6e1c066472
                © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 24 February 2020
                : 08 January 2021
                : 13 January 2021
                Funding
                Funded by: earmarked fund for the China Agriculture Research System;
                Award ID: CARS-38
                Funded by: National Natural Science Foundation of China;
                Award ID: 31702136
                Funded by: Fundamental Research Funds for Central Non-profit Scientific Institution;
                Award ID: 1610382021008, Y2019CG08
                Funded by: National Key Research and Development Program of China;
                Award ID: 2017YFD0500500
                Categories
                Gut Microbiota
                1506
                2312
                Original research
                Custom metadata
                unlocked

                Gastroenterology & Hepatology
                intestinal microbiology
                Gastroenterology & Hepatology
                intestinal microbiology

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