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      Methylome Patterns of Cattle Adaptation to Heat Stress

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          Abstract

          Heat stress has a detrimental impact on cattle health, welfare and productivity by affecting gene expression, metabolism and immune response, but little is known on the epigenetic mechanisms mediating the effect of temperature at the cellular and organism level. In this study, we investigated genome-wide DNA methylation in blood samples collected from 5 bulls of the heat stress resilient Nellore breed and 5 bulls of the Angus that are more heat stress susceptible, exposed to the sun and high temperature-high humidity during the summer season of the Brazilian South-East region. The methylomes were analyzed during and after the exposure by Reduced Representation Bisulfite Sequencing, which provided genome-wide single-base resolution methylation profiles. Significant methylation changes between stressful and recovery periods were observed in 819 genes. Among these, 351 were only seen in Angus, 366 were specific to Nellore, and 102 showed significant changes in methylation patterns in both breeds. KEGG and Gene Ontology (GO) enrichment analyses showed that responses were breed-specific. Interestingly, in Nellore significant genes and pathways were mainly involved in stress responses and cellular defense and were under methylated during heat stress, whereas in Angus the response was less focused. These preliminary results suggest that heat challenge induces changes in methylation patterns in specific loci, which should be further scrutinized to assess their role in heat tolerance.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications

              Summary: A combination of bisulfite treatment of DNA and high-throughput sequencing (BS-Seq) can capture a snapshot of a cell's epigenomic state by revealing its genome-wide cytosine methylation at single base resolution. Bismark is a flexible tool for the time-efficient analysis of BS-Seq data which performs both read mapping and methylation calling in a single convenient step. Its output discriminates between cytosines in CpG, CHG and CHH context and enables bench scientists to visualize and interpret their methylation data soon after the sequencing run is completed. Availability and implementation: Bismark is released under the GNU GPLv3+ licence. The source code is freely available from www.bioinformatics.bbsrc.ac.uk/projects/bismark/. Contact: felix.krueger@bbsrc.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                28 May 2021
                2021
                : 12
                : 633132
                Affiliations
                [1] 1Department of Animal Science Food and Nutrition – DIANA, Nutrigenomics and Proteomics Research Centre – PRONUTRIGEN, and Biodiversity and Ancient DNA Research Centre, Università Cattolica del Sacro Cuore , Piacenza, Italy
                [2] 2Istituto di Biologia e Biotecnologia Agraria, Consiglio Nazionale delle Ricerche IBBA CNR , Milan, Italy
                [3] 3School of Veterinary Medicine, Araçatuba, Department of Production and Animal Health, São Paulo State University (unesp) , Araçatuba, Brazil
                [4] 4International Atomic Energy Agency, Collaborating Centre on Animal Genomics and Bioinformatics , Araçatuba, Brazil
                Author notes

                Edited by: Helene Kiefer, INRAE Centre Jouy-en-Josas, France

                Reviewed by: Geoffrey E. Dahl, University of Florida, United States; Pasqualino Loi, University of Teramo, Italy

                *Correspondence: Marcello Del Corvo, marcello.delcorvo@ 123456ibba.cnr.it

                This article was submitted to Livestock Genomics, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2021.633132
                8194315
                34122501
                c4844565-4ca2-46be-ba5f-f9f3d61a3f30
                Copyright © 2021 Del Corvo, Lazzari, Capra, Zavarez, Milanesi, Utsunomiya, Utsunomiya, Stella, de Paula Nogueira, Garcia and Ajmone-Marsan.

                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
                : 24 November 2020
                : 04 May 2021
                Page count
                Figures: 6, Tables: 2, Equations: 1, References: 95, Pages: 13, Words: 0
                Categories
                Genetics
                Original Research

                Genetics
                heat stress,epigenetics,dna methylation,animals welfare,cattle,red blood cell
                Genetics
                heat stress, epigenetics, dna methylation, animals welfare, cattle, red blood cell

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