Free-range ecological farming is considered an ideal approach for improving meat quality in local chicken breeds, yet the molecular mechanisms underlying quality enhancement remain unclear. In this study, Hetian chickens were divided into a free-range ecological farming group (ZCJ) and a conventional caged control group (CK). We employed 16S rRNA sequencing, untargeted metabolomics, and targeted lipidomics to analyze cecal contents and serum samples. Additionally, a random forest machine learning model was applied to construct and interpret the "microbiota-metabolite-lipid" multi-omics regulatory network. The results revealed that the ZCJ group significantly enhanced the alpha diversity of gut microbiota in Hetian chickens, enriched fiber-degrading bacteria such as Eubacterium and Ruminococcus, while reducing the relative abundance of Proteobacteria, a phylum that harbors both commensal and opportunistic taxa. Furthermore, free-range ecological farming significantly upregulated beneficial long-chain polyunsaturated fatty acids and secondary bile acids in the cecum, while downregulating fat deposition-associated triglycerides and pro-inflammatory ceramides in serum, Serum untargeted metabolomics further showed upregulation of CE(18:3), CE(18:2), and the ether phospholipid PC(O-18:2/18:1). In targeted serum lipidomics, LPC(O-20:0/0:0) was the only significantly upregulated lipid among 81 differential features, whereas most differential triglycerides were downregulated. Parallel slaughter and meat-quality measurements (n = 10 per group) showed that ZCJ chickens had higher redness, intramuscular fat, and inosine monophosphate contents, together with lower drip and cooking losses. A nested-cross-validated random forest model identified Lachnospiraceae_NK4A136 as a hub taxon associated with lipid-metabolic remodeling (cross-validation accuracy 93.3%; independent-validation accuracy 87.5%; AUC = 0.906). Collectively, free-range ecological farming was associated with coordinated shifts in the gut microbiota and host metabolic networks, accompanied by measurable changes in several meat-quality traits.
This study evaluated the inclusion of sire-by-herd (S-H) interactions and refined unknown parent groups (UPGs) in genetic evaluations of Iranian Holstein dairy cattle for 305-day milk yield (MY) and first calving interval (CI). UPGs were refined using an optimized Q-matrix implementation in the nadiv R library and the diagnostic framework of Legarra and Aguilar (2025). S-H interaction was fitted as a random effect to account for macro-genotype-by-environment interaction. The number of UPGs was reduced from 40 to 29 for MY and from 38 to 27 for CI. After refinement, the maximum standard error of an individual UPG solution was 0.41 genetic standard deviations (SD) for MY and 0.79 genetic SD for CI; the maximum standard error of adjacent-group contrasts was 0.054 genetic SD for MY and 0.47 genetic SD for CI. The strongest negative correlation between adjacent UPG estimation errors was reduced from −0.94 before refinement to −0.15 after refinement for MY (and from −0.92 to −0.13 for CI). Likelihood-ratio tests supported the inclusion of the S-H variance component for both traits, although the component accounted for only about 1.1% of phenotypic variance in MY and 0.4% in CI. Heritability estimates changed little across models. Rank correlations for elite animals were 0.88–0.89 for MY and 0.99 for CI when models with and without S-H were compared while holding UPG status constant. Thus, S-H inclusion improved statistical model fit, but the evidence for a practical effect on selection was modest for MY and limited for CI. UPG inclusion mainly altered the estimated level of bias in the within-sample LR comparison. These results support evaluating S-H and UPG effects jointly in populations with heterogeneous herd environments and incomplete pedigrees, while distinguishing improved model fit from predictive performance.
Fetal growth restriction in pregnant heifers due to nutritional disturbances highlights the need to study muscle fiber development genes for optimizing prenatal nutrition and improving offspring growth. This study analyzed the effects of protein supplementation in pregnant Nellore beef heifers on the skeletal muscle development and growth performance of their offspring. A total of 180 heifers were divided into two groups: one received only a mineral supplement (CON), while the other received additional protein (SU) during mid-to-late gestation. After calving, all cow-calf pairs were managed together until weaning, and muscle biopsies were taken from the calves to examine muscle fiber characteristics and gene expression. Calves from SU heifers had significantly higher birth weights (P < 0.001), but their weaning weight (P = 0.267) and pre-weaning growth were similar to those from CON heifers. However, SU calves showed improved weight gain (P = 0.037) during the backgrounding phase, leading to a 14.6 kg advantage over CON calves. Muscle analysis revealed SU calves had larger and more numerous fibers (P < 0.0001), confirming that maternal supplementation enhanced both hyperplastic and hypertrophic mechanisms. Furthermore, a treatment × calf sex interaction (P ≤ 0.050) was observed for Number of the muscle fibers, Mean fiber diameter, Fiber volume, Fiber perimeter and Volume/area ratio. Gene expression analysis indicated differences in growth-related molecular pathways between groups. In conclusion, maternal protein supplementation positively influences muscle development and translates into superior post-weaning growth performance in Nellore calves. These findings provide a strategy for improving beef cattle production efficiency in tropical environments.
