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      Paternal microbiome perturbations impact offspring fitness

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          Abstract

          The gut microbiota operates at the interface of host–environment interactions to influence human homoeostasis and metabolic networks 14 . Environmental factors that unbalance gut microbial ecosystems can therefore shape physiological and disease-associated responses across somatic tissues 59 . However, the systemic impact of the gut microbiome on the germline—and consequently on the F 1 offspring it gives rise to—is unexplored 10 . Here we show that the gut microbiota act as a key interface between paternal preconception environment and intergenerational health in mice. Perturbations to the gut microbiota of prospective fathers increase the probability of their offspring presenting with low birth weight, severe growth restriction and premature mortality. Transmission of disease risk occurs via the germline and is provoked by pervasive gut microbiome perturbations, including non-absorbable antibiotics or osmotic laxatives, but is rescued by restoring the paternal microbiota before conception. This effect is linked with a dynamic response to induced dysbiosis in the male reproductive system, including impaired leptin signalling, altered testicular metabolite profiles and remapped small RNA payloads in sperm. As a result, dysbiotic fathers trigger an elevated risk of in utero placental insufficiency, revealing a placental origin of mammalian intergenerational effects. Our study defines a regulatory ‘gut–germline axis’ in males, which is sensitive to environmental exposures and programmes offspring fitness through impacting placenta function.

          Abstract

          Disturbances in the gut microbiota of male mice manifest as fitness defects in their offspring by affecting plancenta function, revealing a paternal gut–germline axis.

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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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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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                Author and article information

                Contributors
                jamie.hackett@embl.it
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                1 May 2024
                1 May 2024
                2024
                : 629
                : 8012
                : 652-659
                Affiliations
                [1 ]European Molecular Biology Laboratory (EMBL), Epigenetics & Neurobiology Unit, ( https://ror.org/01yr73893) Rome, Italy
                [2 ]European Molecular Biology Laboratory (EMBL), Structural & Computational Biology Unit, ( https://ror.org/03mstc592) Heidelberg, Germany
                [3 ]GRID grid.14105.31, ISNI 0000000122478951, MRC London Institute for Medical Science (LMS), ; London, UK
                [4 ]Department of Biochemistry, University of Oxford, ( https://ror.org/052gg0110) Oxford, UK
                [5 ]Department of Bioinformatics, Biozentrum, University of Würzburg, ( https://ror.org/00fbnyb24) Würzburg, Germany
                [6 ]Yonsei Frontier Lab (YFL), Yonsei University, ( https://ror.org/01wjejq96) Seoul, South Korea
                Author information
                http://orcid.org/0000-0002-2458-6654
                http://orcid.org/0000-0001-8587-4177
                http://orcid.org/0000-0001-5804-7205
                http://orcid.org/0000-0003-2127-4150
                http://orcid.org/0000-0002-3420-7654
                http://orcid.org/0000-0001-9567-4793
                http://orcid.org/0000-0001-9000-2883
                http://orcid.org/0000-0003-3210-4093
                http://orcid.org/0000-0002-5797-3589
                http://orcid.org/0000-0002-2627-833X
                http://orcid.org/0000-0002-6237-3684
                Article
                7336
                10.1038/s41586-024-07336-w
                11096121
                38693261
                f2a3d737-d3e3-40fc-8f90-e3bece78c575
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 June 2021
                : 20 March 2024
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                © Springer Nature Limited 2024

                Uncategorized
                epigenetic memory,epigenetics,microbial communities,intrauterine growth,reproductive biology

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