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      Comparative Transcriptome Analysis of Gayal ( Bos frontalis), Yak ( Bos grunniens), and Cattle ( Bos taurus) Reveal the High-Altitude Adaptation

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          Abstract

          Gayal and yak are well adapted to the local high-altitude environments, yet the transcriptional regulation difference of the plateau environment among them remains obscure. Herein, cross-tissue and cross-species comparative transcriptome analysis were performed for the six hypoxia-sensitive tissues from gayal, yak, and cattle. Gene expression profiles for all single-copy orthologous genes showed tissue-specific expression patterns. By differential expression analysis, we identified 3020 and 1995 differentially expressed genes (DEGs) in at least one tissue of gayal vs . cattle and yak vs . cattle, respectively. Notably, we found that the adaptability of the gayal to the alpine canyon environment is highly similar to the yak living in the Qinghai-Tibet Plateau, such as promoting red blood cell development, angiogenesis, reducing blood coagulation, immune system activation, and energy metabolism shifts from fatty acid β-oxidation to glycolysis. By further analyzing the common and unique DEGs in the six tissues, we also found that numerous expression regulatory genes related to these functions are unique in the gayal and yak, which may play important roles in adapting to the corresponding high-altitude environment. Combined with WGCNA analysis, we found UQCRC1, COX5A are the shared differentially expression hub genes related to the energy supply of myocardial contraction in the heart-related modules of gayal and yak, and CAPS is a shared differentially hub gene among the hub genes of the lung-related module, which is related to pulmonary artery smooth muscle contraction. Additionally, EDN3 is the unique differentially expression hub gene related to the tracheal epithelium and pulmonary vasoconstriction in the lung of gayal. CHRM2 is a unique differentially expression hub gene that was identified in the heart of yak, which has an important role in the autonomous regulation of the heart. These results provide a basis for further understanding the complex transcriptome expression pattern and the regulatory mechanism of high-altitude domestication of gayal and yak.

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          Most cited references113

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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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            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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              WGCNA: an R package for weighted correlation network analysis

              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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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
                11 January 2022
                2021
                : 12
                : 778788
                Affiliations
                Laboratory of Molecular Biology and Bovine Breeding , Institute of Animal Science , Chinese Academy of Agricultural Sciences , Beijing, China
                Author notes

                Edited by: Suxu Tan, Michigan State University, United States

                Reviewed by: Ikhide G. Imumorin, Georgia Institute of Technology, United States

                Loan To Nguyen, University of Queensland, Australia

                *Correspondence: Junya Li, lijunya@ 123456caas.cn ; Xue Gao, gaoxue76@ 123456126.com

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

                Article
                778788
                10.3389/fgene.2021.778788
                8789257
                35087567
                5c82560d-70e1-454f-bb10-c28fe771d787
                Copyright © 2022 Ma, Zhang, Wang, Chen, Cai, Zhu, Xu, Gao, Zhang, Li and Gao.

                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
                : 17 September 2021
                : 06 December 2021
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Funded by: Agricultural Science and Technology Innovation Program , doi 10.13039/501100012421;
                Categories
                Genetics
                Original Research

                Genetics
                gayal,yak,differentially expressed genes,co-expression,high-altitude adaptation,hypoxia
                Genetics
                gayal, yak, differentially expressed genes, co-expression, high-altitude adaptation, hypoxia

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