# Transcriptomic Profiling of Hypoxia-Adaptive Responses in Tibetan Goat Fibroblasts

## TL;DR

This study identifies gene-environment interactions in Tibetan goats that help them adapt to high-altitude hypoxia, offering insights for breeding and biomedical research.

## Contribution

The study reveals transcriptional plasticity as a key mechanism in hypoxia adaptation through novel integration of RNA-seq, WGCNA, and machine learning.

## Key findings

- Interaction-driven DEGs outnumber breed-specific and oxygen-responsive DEGs, highlighting transcriptional plasticity.
- Environmental Stress Genes are enriched in oxidative stress defense and metabolic adaptation pathways.
- Genetic Adaptation Genes are enriched in vascular remodeling and transcriptional regulation pathways.

## Abstract

The Tibetan goat (Capra hircus) has evolved genetic adaptations to high-altitude hypoxia. This study integrated RNA sequencing (RNA-seq), weighted gene co-expression network analysis (WGCNA), and machine learning to explore gene–environment interactions (G × E) in hypoxia adaptation. Fibroblasts derived from the Tibetan goat and lowland goat were cultured under hypoxic (1% O2) and normoxic (21% O2) conditions, revealing transcriptional plasticity as a key mechanism. Our findings provide novel insights into high-altitude livestock breeding and hypoxia-related biomedical research.

The Tibetan goat (Capra hircus) exhibits remarkable adaptations to high-altitude hypoxia, yet the molecular mechanisms remain unclear. This study integrates RNA-seq, WGCNA, and machine learning to explore gene-environment interactions (G × E) in hypoxia adaptation. Fibroblasts from the Tibetan goat and Yunling goat were cultured under hypoxic (1% O2) and normoxic (21% O2) conditions, respectively. This identified 68 breed-specific (G), 100 oxygen-responsive (E), and 620 interaction-driven (I) Differentially Expressed Genes (DEGs). The notably higher number of interaction-driven DEGs compared to other effects highlights transcriptional plasticity. We defined two gene sets: Environmental Stress Genes (n = 632, E ∪ I) and Genetic Adaptation Genes (n = 659, G ∪ I). The former were significantly enriched in pathways related to oxidative stress defense and metabolic adaptation, while the latter showed prominent enrichment in pathways associated with vascular remodeling and transcriptional regulation. CTNNB1 emerged as a key regulatory factor in both gene sets, interacting with CASP3 and MMP2 to form the core of the protein–protein interaction (PPI) network. Machine learning identified MAP3K5, TGFBR2, RSPO1 and ITGB5 as critical genes. WGCNA identified key modules in hypoxia adaptation, where FOXO3, HEXIM1, and PPARD promote the stabilization of HIF-1α and metabolic adaptation through the HIF-1 signaling pathway and glycolysis. These findings underscore the pivotal role of gene–environment interactions in hypoxic adaptation, offering novel perspectives for both livestock breeding programs and biomedical research initiatives.