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ORIGINAL RESEARCH article

Front. Vet. Sci.
Sec. Livestock Genomics
Volume 11 - 2024 | doi: 10.3389/fvets.2024.1486258
This article is part of the Research Topic Epigenetics and multi-omics studies of important traits in livestock View all articles

Screening of functional genes for hypoxia adaptation in Tibetan pigs by combined genome resequencing and transcriptome analysis

Provisionally accepted
Bin Ni Bin Ni 1Lin Tang Lin Tang 2*Li Zhu Li Zhu 2*Xinpeng Li Xinpeng Li 2Kang Zhang Kang Zhang 1*Hongyu Nie Hongyu Nie 1*Zeyu Ye Zeyu Ye 1*Yiwen Wang Yiwen Wang 1*Lijun Zhu Lijun Zhu 1*Xiaoyan Kong Xiaoyan Kong 2*Xiao Gou Xiao Gou 1*
  • 1 Foshan University, Foshan, Guangdong Province, China
  • 2 College of Animal Science and Technology, Yunnan Agricultural University, Kunming, China

The final, formatted version of the article will be published soon.

    The high-altitude, low-oxygen environment of the Qinghai-Tibet Plateau poses significant challenges for the introduction of superior livestock breeds. However, local plateau species have adapted to thrive and reproduce under these harsh conditions. Understanding the molecular mechanisms behind plateau animals' adaptation to low-oxygen environments is essential for breeding livestock suited to high-altitude regions. Tibetan pigs, which have undergone long-term natural selection and artificial breeding, have developed the ability to survive and reproduce in hypoxic environments. In this study, we conducted whole-genome resequencing of 30 Tibetan pigs from high-altitude regions and 30 Diannan small-ear pigs from low-altitude areas, to identify candidate genes that support Tibetan pigs' adaptation to hypoxic conditions through selection signal analysis. Additionally, we performed transcriptome sequencing on five tissues (heart, liver, spleen, lung, and bone marrow) from both Tibetan pigs and Diannan small-ear pigs to identify genes with significant differential expression between the two breeds. We then integrated the genomic and transcriptomic data by examining the expression of candidate genes identified in selection signal analysis across different tissues. The selection signal analysis identified 10 genes-HES4, ANGPT1, HIF3A, SPHK2, PCK2, RCN3, HIGD2A, DNM2, IRF9, and SRF-that were under positive selection in the Tibetan pig population and are associated with hypoxia adaptation. When combined with transcriptome data, we found that five of these genes-HIF3A, RCN3, HIGD2A, PCK2, and IRF9-exhibited differential expression. Through an integrated approach of selection signal and transcriptome analysis, we identified five key functional genes that contribute to the adaptation of Tibetan pigs to hypoxic environments. These findings offer new insights into the adaptability of plateau animals.

    Keywords: Tibetan pig, Resequencing, Transcriptome, Hypoxia adaption, functional gene

    Received: 25 Aug 2024; Accepted: 08 Oct 2024.

    Copyright: © 2024 Ni, Tang, Zhu, Li, Zhang, Nie, Ye, Wang, Zhu, Kong and Gou. 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) or licensor 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.

    * Correspondence:
    Lin Tang, College of Animal Science and Technology, Yunnan Agricultural University, Kunming, China
    Li Zhu, College of Animal Science and Technology, Yunnan Agricultural University, Kunming, China
    Kang Zhang, Foshan University, Foshan, 528000, Guangdong Province, China
    Hongyu Nie, Foshan University, Foshan, 528000, Guangdong Province, China
    Zeyu Ye, Foshan University, Foshan, 528000, Guangdong Province, China
    Yiwen Wang, Foshan University, Foshan, 528000, Guangdong Province, China
    Lijun Zhu, Foshan University, Foshan, 528000, Guangdong Province, China
    Xiaoyan Kong, College of Animal Science and Technology, Yunnan Agricultural University, Kunming, China
    Xiao Gou, Foshan University, Foshan, 528000, Guangdong Province, China

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