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      Comprehensive RNA-Seq Profiling Reveals Temporal and Tissue-Specific Changes in Gene Expression in Sprague–Dawley Rats as Response to Heat Stress Challenges

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          Abstract

          Understanding heat stress physiology and identifying reliable biomarkers are paramount for developing effective management and mitigation strategies. However, little is known about the molecular mechanisms underlying thermal tolerance in animals. In an experimental model of Sprague–Dawley rats subjected to temperatures of 22 ± 1°C (control group; CT) and 42°C for 30 min (H30), 60 min (H60), and 120 min (H120), RNA-sequencing (RNA-Seq) assays were performed for blood (CT and H120), liver (CT, H30, H60, and H120), and adrenal glands (CT, H30, H60, and H120). A total of 53, 1,310, and 1,501 differentially expressed genes (DEGs) were significantly identified in the blood ( P < 0.05 and |fold change (FC)| >2), liver ( P < 0.01, false discovery rate (FDR)–adjusted P = 0.05 and |FC| >2) and adrenal glands ( P < 0.01, FDR-adjusted P = 0.05 and |FC| >2), respectively. Of these, four DEGs, namely Junb, P4ha1, Chordc1, and RT1-Bb, were shared among the three tissues in CT vs. H120 comparison. Functional enrichment analyses of the DEGs identified in the blood (CT vs. H120) revealed 12 biological processes (BPs) and 25 metabolic pathways significantly enriched (FDR = 0.05). In the liver, 133 BPs and three metabolic pathways were significantly detected by comparing CT vs. H30, H60, and H120. Furthermore, 237 BPs were significantly (FDR = 0.05) enriched in the adrenal glands, and no shared metabolic pathways were detected among the different heat-stressed groups of rats. Five and four expression patterns ( P < 0.05) were uncovered by 73 and 91 shared DEGs in the liver and adrenal glands, respectively, over the different comparisons. Among these, 69 and 73 genes, respectively, were proposed as candidates for regulating heat stress response in rats. Finally, together with genome-wide association study (GWAS) results in cattle and phenome-wide association studies (PheWAS) analysis in humans, five genes ( Slco1b2, Clu, Arntl, Fads1, and Npas2) were considered as being associated with heat stress response across mammal species. The datasets and findings of this study will contribute to a better understanding of heat stress response in mammals and to the development of effective approaches to mitigate heat stress response in livestock through breeding.

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          Most cited references103

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              BEDTools: a flexible suite of utilities for comparing genomic features

              Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu 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
                09 April 2021
                2021
                : 12
                : 651979
                Affiliations
                [1] 1Key Laboratory of Animal Genetics, Breeding and Reproduction, MARA, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University , Beijing, China
                [2] 2Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph , Guelph, ON, Canada
                [3] 3Department of Animal Sciences, Purdue University , West Lafayette, IN, United States
                Author notes

                Edited by: Naoyuki Kataoka, The University of Tokyo, Japan

                Reviewed by: Makoto Kashima, Aoyama Gakuin University, Japan; Argyris Papantonis, University Medical Center Göttingen, Germany

                *Correspondence: Yachun Wang wangyachun@ 123456cau.edu.cn

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

                Article
                10.3389/fgene.2021.651979
                8063118
                63f9576e-a02c-4d92-9078-b80371615d86
                Copyright © 2021 Dou, Cánovas, Brito, Yu, Schenkel and Wang.

                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
                : 11 January 2021
                : 02 March 2021
                Page count
                Figures: 5, Tables: 4, Equations: 0, References: 103, Pages: 17, Words: 13037
                Categories
                Genetics
                Original Research

                Genetics
                heat stress response,rna-sequencing,thermal tolerance,rats' transcriptome,mammals
                Genetics
                heat stress response, rna-sequencing, thermal tolerance, rats' transcriptome, mammals

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