Yan Hu | Search PubMed

PubMed2026

Effects of domestication on the body morphology and genetic diversity of the yellowfin seabream (Acanthopagrus latus).

The yellowfin seabream (Acanthopagrus latus) is a significant economic fish along the southeast coast of China. Recently, the drastic decline in the wild populations, exacerbated by overfishing and climate change, has heightened our reliance on aquaculture. However, the current lack of research on its domestication hinders effective conservation of wild populations and balanced management alongside the aquaculture industry. Studies on body characteristics have shown that wild yellowfin seabream possess a higher body, while cultured ones exhibit a wider body. Whole-genome SNP analysis revealed moderate genetic differentiation between cultured and wild populations. Further analyses of linkage disequilibrium, heterozygosity, and genetic diversity revealed that the degree of SNP linkage was lower in the wild population compared to the cultured population. In contrast, heterozygosity and nucleotide polymorphisms were significantly higher in the wild population (P < 0.001 and P < 0.05, respectively). Additionally, over 300 candidate genes were identified in each cultured population through genomic selection signature analysis, with 67 key genes shared among all three, which were linked to growth and development (ghrb, ghsra, and cfl1), immune response (aire, cd36, and igbp1), and salinity adaptation (abcc3, clic4, and kcnk15). Enrichment analysis indicated that the key candidate genes were significantly enriched in pathways related to protein kinase activity, ion binding and growth hormone synthesis, secretion and action (FDR < 0.05). The findings provide valuable insights into the variation in body size of yellowfin seabream under domestication selection and offer an important theoretical basis for the genetic improvement of yellowfin seabream.

Animals↗

PubMed2025

Refining sequence-to-activity models by increasing model resolution.

Decoding the cis-regulatory syntax that controls gene expression is essential for improving our understanding of cell differentiation and disease. To identify regulatory motifs and their regulatory syntax, deep learning based sequence-to-activity (S2A) models learn transcription factor binding motifs and their combinations from DNA sequence by modeling measured chromatin accessibility. Previously, we developed AI-TAC, a S2A model that predicts chromatin accessibility across various immune cell types in multi-task fashion, effectively decoding the regulatory syntax underlying immune cell differentiation. While ATAC-seq is commonly used to measure regional accessibility, it also provides high-resolution profiles, the distribution of Tn5 insertion sites, that offer additional insights into the precise location and strength of TF binding sites. Here we demonstrate that modeling ATAC-seq profiles alongside accessibility consistently improves predictions of differential chromatin accessibility across cell types. Moreover, we also find that multi-task learning across related immune cell types consistently outperforms single-task models. To understand what additional information bpAITAC learns from ATAC-seq profiles, we systematically compare sequence attributions from models trained with and without ATAC-seq profiles. We identify novel motifs with strong effect sizes that emerge only when profile data is included. Our findings suggest that modeling ATAC-seq at base-pair resolution enables the model to learn a more nuanced and sensitive representation of the cis-regulatory syntax driving immune cell-specific chromatin landscapes.

ATAC-seq↗