Horns are an important breeding trait for sheep. However, no widely recognized viewpoint on the regulatory genes and mechanisms of horns is available, and the genetic basis of the four-horn phenotype (FHP) is unclear. This work conducted a genome-wide association study with 100 sheep genomes from multiple breeds to investigate the genetic basis of the FHP. The results revealed three significant associations (corrected as p < 1.64 × 10⁻⁸) of the InDels (CHR2: g.133,742,709delA, g.133,743,215insC, and g.133,743,940delT) for FHP in the intergenic sequence (IGS) between the MTX2 and the LOC105609047 of CHR2. Moreover, 14 significant associations (corrected as p < 1.42 × 10⁻⁹) of SNPs with the FHP phenotype were identified in CHR2 and CHR16, including five (e.g., CHR16: g.40,351,378G > A and g.40,352,577G > A) located in the intron of the ADAMTS12 gene, eight (e.g., CHR2: g.133,727,513C > T and g.133,732,145T > G) in the IGS between MTX2 and LOC105609047, and only one (CHR2: g.133,930,761A > G) in the IGS between HOXD1 and MTX2. Obvious divergence was also observed in genotype patterns between the FHP and others (two horns and hornless) in the HOXD1 and ADAMTS12 gene regions. An extremely significant linkage also occurred between Loci I and Loci II within 100 individuals (LD = −156.02186, p < 0.00001). In summary, our study indicated that the genomic sequences from CHR2 and CHR16 contributed to the FHP in sheep, specifically the key candidate genes HOXD1 and ADAMTS12. These results improved our understanding of the Mendelian genetic basis of the FHP in sheep.

CaMK4, a calcium/calmodulin-dependent protein kinase, is an important mediator of cellular signal transduction, yet its role in the regulation of skeletal muscle satellite cells (MuSCs) in goats has remained unclear. In this study, CaMK4 overexpression and knockdown models were established, and integrated transcriptomic and proteomic analyses were performed to systematically elucidate its regulatory network. CaMK4 overexpression altered key pathways associated with cell proliferation and muscle development, including cAMP, PI3K-Akt, and actin cytoskeleton regulation, while proteomic data highlighted calcium signaling and JAK-STAT pathways. Conversely, CaMK4 knockdown enhanced MuSC proliferation by upregulating cell cycle-related genes and proteins. Integrated analyses further identified that Galectin-9 (LGALS9), Collagen triple helix repeat containing-1 (CTHRC1), Hyaluronan Synthase 1 (HAS1), and L-Threonine Dehydrogenase (TDH) may serve as potential key nodes regulating cell cycle, apoptosis, and metabolic control. This suggests a regulatory role for CaMK4. Collectively, these findings provide a mechanistic framework for understanding CaMK4 function in ruminant muscle development and may offer insights for improving goat muscle growth, meat quality traits, and production efficiency.

The artificial intelligence (AI)‐driven diet‐intestinal microbiota‐host health integration paradigm has emerged as a novel framework for advancing host health research. Intestinal microbiota serves as a key mediator linking dietary signals to host homeostasis, while AI enables efficient integration of multi‐omics data to construct microbial metabolic models, identify interaction patterns across biological scales, and support prediction‐informed modulation of diet‐microbiota interactions. This paradigm synergizes dietary intervention design, AI technology, intestinal microbiota modulation, and host health enhancement, offering innovative solutions for chronic disease management, animal health breeding, and food safety monitoring. In this review, we summarize the core mechanisms of the four‐dimensional interaction and the application value of AI‐driven informed optimization. This review also discusses the major challenges currently facing the field, including model interpretability, multi‐source data heterogeneity, and cross‐species translation, and further highlights that future research should focus on establishing interpretable and iterative AI‐driven closed‐loop systems. This integrated approach holds immense potential to revolutionize host wellness research and promote the development of nutritional health and related industries. Unlike existing diet‐microbiota‐host frameworks and AI‐assisted precision nutrition approaches that mainly focus on association analysis or outcome prediction, our proposed paradigm positions AI as an active coordination layer linking dietary inputs, microbial responses, and host outcomes. By integrating mechanism‐informed modeling with iterative feedback, this framework enables dynamic optimization of diet‐microbiota interactions and supports more precise host health regulation.