Although studies have revealed the significant impact of dietary fiber on growth performance and nutrient digestibility, the specific characteristics of the intestinal microbiota and gene regulation in pigs capable of digesting high-fiber diets remained unclear. To investigate the traits associated with roughage tolerance in the Chinese indigenous pig breed, we conducted comparative analysis of growth performance, apparent fiber digestibility, intestinal microbiota, SCFA concentrations and intestinal transcriptome in Tunchang pigs, feeding them diets with different wheat bran levels. The results indicated that the growth performance of Tunchang pigs was not significantly impacted, and the apparent total tract digestibility of crude fiber was significantly improved with increasing dietary fiber content. High-fiber diets altered the diversity of intestinal microbiota, and increased the relative abundance of Prevotella, CF231, as well as the concentrations of isobutyrate, valerate and isovalerate. The LDA analysis identified potential microbial biomarkers that could be associated with roughage tolerance, such as Prevotella stercorea, and Eubacterium biforme. In addition, appropriate high-fiber diets containing 4.34% crude fiber upregulated the mRNA expressions of PYY, AQP8, and SLC5A8, while downregulating the mRNA expressions of CKM and CNN1.This indicated that appropriate high-fiber diets may inhibit intestine motility and increase the absorption of water and SCFAs.

Transposable elements (TEs) are pivotal drivers of eukaryotic genome evolution and phenotypic diversity. However, their functional contributions to complex traits remain largely obscured by expression quantification challenges arising from high sequence homology and multi-mapping ambiguities. Here, we present LATTE , an efficient computational framework for defining and quantifying TE expression at locus-specific resolution by leveraging an innovative multi-indicator Expectation-Maximization (EM) algorithm. Extensive benchmarking against simulated datasets demonstrated that LATTE significantly outperformed existing state-of-the-art tools, achieving an accuracy of 0.998 at the subfamily level and 0.839 at the locus-specific level. Applying LATTE to 813 RNA-seq datasets across humans, cattle, and chickens, we quantified expression profiles of 2,703 TEs, followed by TE-expression quantitative trait loci (TE-eQTL) mapping. The colocalization rates between TE-eQTL and host gene-eQTL was low, revealing a distinct regulatory landscape of TE expression. This decoupled correlation between TEs and host genes are likely mediated by the differential expression of alternative transcripts. Through integrated TE-eQTL and genome-wide association studies on 3,746 complex traits across three species, we demonstrated that TEs constitute 204 (8.7%) additional associations with complex traits beyond gene-eQTL. More specifically, the Sjögren’s syndrome-associated variant rs10954213 acts as a TE-eQTL that shifts the splicing landscape of IRF5 , upregulating TE-containing transcripts while simultaneously suppressing canonical ones. Collectively, LATTE provides an efficient framework for studying TE expression across species, and our findings highlight the key role of TEs in understanding the genetic architecture of complex phenotypes.

Structural variations (SVs) represent a significant source of genomic diversity, with demonstrated roles in livestock gene expression and traits. However, a comprehensive understanding of the SV landscape across large sample sets and its impact on gene regulation in cattle remains incomplete. This study aimed to construct high-fidelity pangenome graphs by integrating both assembly-based and whole-genome sequencing (WGS) derived SV catalogs. We evaluated the efficacy of pangenome graphs for SV genotyping and identified 80,328 high-quality SVs from a cohort of 2929 samples. We systematically characterized these SVs, including their linkage disequilibrium with single nucleotide polymorphisms (SNPs), functional annotations, formation mechanisms, and genomic distributions. Furthermore, we generated paired WGS (24.4 ×) and blood RNA-seq data in 170 Simmental cattle. Utilizing our pangenome graphs, we identified 637 SV-expression quantitative trait loci (SV-eQTL), which accounted for 10.81% of expression heritability of target genes, with 38.09% of the effects linked to promoter/enhancer regions. Forty-six of these SV-eQTL were replicated using CattleGTEx results through SV imputation using a joint SNP-SV reference panel. Notably, insertions in the GHSR gene were significantly associated with its expression levels, likely linked to Bos indicus cattle adaptation to heat tolerance. Our findings provide novel insights into the SV landscape and its contribution to gene regulation, underscoring its importance in cattle genetics and genomics.