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      The Expression Regulatory Network in the Lung Tissue of Tibetan Pigs Provides Insight Into Hypoxia-Sensitive Pathways in High-Altitude Hypoxia

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          Abstract

          To adapt to a low-oxygen environment, Tibetan pigs have developed a series of unique characteristics and can transport oxygen more effectively; however, the regulation of the associated processes in high-altitude animals remains elusive. We performed mRNA-seq and miRNA-seq, and we constructed coexpression regulatory networks of the lung tissues of Tibetan and Landrace pigs. HBB, AGT, COL1A2, and EPHX1 were identified as major regulators of hypoxia-induced genes that regulate blood pressure and circulation, and they were enriched in pathways related to signal transduction and angiogenesis, such as HIF-1, PI3 K- Akt, mTOR, and AMPK. HBB may promote the combination of hemoglobin and oxygen as well as angiogenesis for high-altitude adaptation in Tibetan pigs. The expression of MMP2 showed a similar tendency of alveolar septum thickness among the four groups. These results indicated that MMP2 activity may lead to widening of the alveolar wall and septum, alveolar structure damage, and collapse of alveolar space with remarkable fibrosis. These findings provide a perspective on hypoxia-adaptive genes in the lungs in addition to insights into potential candidate genes in Tibetan pigs for further research in the field of high-altitude adaptation.

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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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            Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

            The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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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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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                07 October 2021
                2021
                : 12
                : 691592
                Affiliations
                [1] 1College of Animal Science and Technology, Gansu Agricultural University , Lanzhou, China
                [2] 2Research on Quality Standard of Animal Husbandry, Xinjiang Academy of Animal Sciences , Xinjiang, China
                [3] 3State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences , Harbin, China
                [4] 4College of Grassland Science, Gansu Agricultural University , Lanzhou, China
                Author notes

                Edited by: Aline Silva Mello Cesar, University of São Paulo, Brazil

                Reviewed by: Bárbara Silva-Vignato, University of São Paulo, Brazil; Wellison J. S. Diniz, Auburn University, United States

                *Correspondence: Shengguo Zhao zhaoshengguo0628@ 123456hotmail.com

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

                Article
                10.3389/fgene.2021.691592
                8529057
                34691141
                82973acd-4a95-4052-92eb-e91c651b4751
                Copyright © 2021 Yang, Yuan, Yang, Li, Gao, Jiao, Cai and Zhao.

                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
                : 06 April 2021
                : 31 August 2021
                Page count
                Figures: 8, Tables: 2, Equations: 0, References: 72, Pages: 14, Words: 8325
                Categories
                Genetics
                Original Research

                Genetics
                hypoxia,tibetan pigs,pi3k-akt pathway,mirna-mrna network,lung tissue
                Genetics
                hypoxia, tibetan pigs, pi3k-akt pathway, mirna-mrna network, lung tissue

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