Enhancing oxygen utilization and mitigating oxidative stress in Tibetan chickens for adaptation to high-altitude hypoxia

Abstract

Tibetan chicken (TBC) is one of the native poultry species that is well adapted to the high-altitude environment of the Qinghai-Tibet Plateau. To elucidate the genetic mechanisms underlying adaptation, the transcriptomes of five tissues (heart (HE), lung (LU), liver (LI), ovary (OV), and abdominal fat (AB)) were compared between TBCs and Roman chickens (RMCs) inhabiting the plateau for one year. Moreover, weighted gene co-expression network analysis (WGCNA) was applied to detect tissue-associated modules and hub genes. A total of 1105, 239, 400, 483, and 275 differentially expressed genes (DEGs) were identified in the LI, HE, LU, AB, and OV tissues, respectively. Fifteen tissue-specific modules were identified in TBC and thirteen in RMC. Analysis of transcription factor (TF) binding sites revealed nineteen hub TFs in TBC and twenty in RMC across the pool of hub genes in these two breeds. Functional enrichment analyses demonstrated that TBC exhibited robust capacity for oxygen transport, heme binding, oxidative phosphorylation, and antioxidant responses in high-altitude regions. Further investigation of the function of hub TFs indicated the involvement of ATF4, CEBPA, TCF7L1, and GFI1B in improving oxygen transport in TBCs. These hub TFs were associated with angiogenesis or hematopoiesis and likely linked to various regulatory functions and facilitate communication across multiple tissues. In conclusion, TBCs have developed a systemic adaptive mechanism to cope with high altitudes, involving the coordinated transcriptional regulation in multi-tissues to enhance oxygen transport and utilization, along with amelioration of oxidative stress.

Keywords: High-altitude adaptation; Hypoxia; Multi-tissues; Tibetan chicken; Transcriptome.

Fig. 1

Flowchart of the analytical steps from tissue collection to RNA-Seq to network construction.

Fig. 2

Principal component analysis (PCA) plot of samples. (A) PCA plot of all samples. (B-F) PCA plot for five tissues: abdominal fat (B), lung (C), live (D), ovary (E), and heart (F).

Fig. 3

Volcano plots of DEGs. (A) abdominal fat, (B) lung, (C) live, (D) ovary, and (E) heart.

Fig. 4

Heatmap of the correlation between module eigengenes and the five tissues of Tibetan chicken (A) and Roman chicken (B). The x-axis is the five tissues of Tibetan chickens, and the y-axis is the module eigengene (ME). In the heatmap, red represents high adjacency (positive correlation), and blue represents low adjacency (negative correlation). In brackets is the p-value of the correlation test.

Fig. 5

Venn diagram of Differentially expressed (DE) between Tibetan chicken and Roman chicken, Tissue-specific (TS) genes, Transcription factors (TF), Hub genes (HUB). Tibetan chicken (A) and Roman chicken (B).

Fig. 6

Gene co-expression networks constructed by combining results from DEGs and WGCNA with the knowledge of transcription regulators of Tibetan chicken (A) and Roman chicken (B). Each large circle represents a tissue that may include one or more tissue-specific modules. The shape of the nodes in the network indicates the status of differentially expressed genes. The upward triangle or downward triangle indicates upregulation or downregulation within a specific tissue. The small circle represents that the gene is not differentially expressed in that tissue. The rectangular nodes represent hub transcription factors. The colors of nodes represent the corresponding colors of the module to which the gene belongs.

Fig. 7

Sequence logos of part of hub transcription factor binding sites with differentially expressed genes. Y axis indicates the amount of information at each position in the motif. These logos were generated from information obtained from the JASPAR core database. (A) HNF1A binding motif, (B) MYBL1 binding motif, (C) GFI1B binding motif, (D) ESRRB binding motif, (E) CEBPA binding motif, (F) CUX2 binding motif, and (G) HOXB3 binding motif.

Fig. 8

RNA-Seq validation using RT-qPCR. Eight key DEGs were selected to test expression in (A) abdominal fat, (B) liver, and (C) ovary, and eight DEGs with high expression in the liver were selected randomly.