Gene Co-Expression Network Analysis Unraveling Transcriptional Regulation of High-Altitude Adaptation of Tibetan Pig

Abstract

Tibetan pigs have survived at high altitude for millennia and they have a suite of adaptive features to tolerate the hypoxic environment. However, the molecular mechanisms underlying the regulation of hypoxia-adaptive phenotypes have not been completely elucidated. In this study, we analyzed differentially expressed genes (DEGs), biological pathways and constructed co-expression regulation networks using whole-transcriptome microarrays from lung tissues of Tibetan and Duroc pigs both at high and low altitude. A total of 3,066 DEGs were identified and this list was over-represented for the ontology terms including metabolic process, catalytic activity, and KEGG pathway including metabolic pathway and PI3K-Akt signaling pathway. The regulatory (RIF) and phenotypic (PIF) impact factor analysis identified several known and several potentially novel regulators of hypoxia adaption, including: IKBKG, KLF6 and RBPJ (RIF1), SF3B1, EFEMP1, HOXB6 and ATF6 (RIF2). These findings provide new details of the regulatory architecture of hypoxia-adaptive genes and also insight into which genes may undergo epigenetic modification for further study in the high-altitude adaptation.

Comparative transcriptomic and proteomic analyses provide insights into the key genes involved in high-altitude adaptation in the Tibetan pig

“…Indigenous animals of the Tibetan Plateau exhibit heritable adaptations to this extreme environment due to natural selection; therefore, the Tibetan pig is an ideal animal model for research on the molecular ecology and pathology related to hypoxia 3–5 .…”

Co-expression network analysis predicts a key role of microRNAs in the adaptation of the porcine skeletal muscle to nutrient supply

“…The majority of nutrigenomic studies in domestic animals have investigated the effects of dietary factors on the mean expression of messenger RNAs (mRNAs) [1], whereas the potential consequences of nutrition on the expression profiles of microRNAs (miRNAs) and long intergenic non-coding RNAs (lincRNAs) have not been explored in depth. Although changes in the expression of porcine genes in response to dietary and genetic factors have been reported in previous studies [2][3][4][5][6], the regulatory co-expression networks underlying such changes have not been fully elucidated yet [3,7,8]. Moreover, gene expression variance (GEV), also referred as gene dispersion, has been often overlooked, being considered just as experimental noise without any biological significance [9].…”

Enhanced identification of significant regulators of gene expression

“…It has been used extensively for analysing data on livestock animals, like cattle and pigs. In such studies the RIF scores have been used to identify key regulators for feed efficiency [6], puberty [7,8], and intramuscular fat content [9] in cattle, or growth and metabolism [10], highaltitude adaptation [11], and muscle characteristics [12] in pigs, to give a few examples. However, the RIF approach has also been used to analyse human data, in cases as diverse as colorectal cancer [13], effects of melphalan treatment [14], and biomarker candidates for total sleep deprivation [15].…”