Jiahua Li

Liver rhythm has a significant effect on lipid metabolism and immune function in chickens. However, reports on its underlying mechanisms and key genes are relatively scarce. We collected liver samples at seven time points during one light/dark cycle and investigated the candidate genes and pathways related to hepatic rhythm through transcriptomic sequencing. Trend analysis revealed that the expression of genes in Profile 5 exhibited rhythmic fluctuations, and these genes (e.g., FAM21C, SRSF4, and TLR2A) were enriched in immune function and biological rhythm. The genes (e.g., MSMO1, ELOVL2, and HMGCR) in Profile 2 that were related to lipid metabolism also exhibited a rhythmic trend. A total of 845 differentially expressed genes (e.g., MSMO1 and FAM21C) were detected between light/dark conditions. Lipid metabolism and immune functions showed the most changes between the two conditions. Immune-related processes (e.g., autophagy) were more active in the light phase, while in the dark phase, lipid metabolism (e.g., sterol biosynthesis) was more active. Weighted gene coexpression network analysis revealed that the tan (including C1QA, TLR2A, and others) and cyan (including ELOVL2, IARS1, and others) modules were strongly associated with the hepatic circadian rhythm. Cosinor analysis revealed that 9 lipid-related genes (e.g., MSMO1, HMGCR1, and ELOVL2) and 11 immune-related genes (e.g., FAM21C, TLR4, and TLR2A) exhibited significant rhythmic expression. These findings revealed rhythmic changes in hepatic immune and lipid metabolism, providing important insights into the regulation of disease resistance and lipid deposition in chickens.

Chicken meat represents the most widely consumed source of animal protein globally. The identification of non-coding RNAs (ncRNAs) that affect muscle development provides new selection targets for poultry breeding. In this study, muscle samples from high- and low-breast-weight chickens were collected and sequenced for long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and mRNAs. Using weighted gene co-expression network analysis, we found 95 lncRNAs and 46 circRNAs that were significantly associated with breast muscle traits. Subsequently, 51 candidate lncRNAs and 22 candidate circRNAs were screened through differential expression analysis. Finally, by constructing an ncRNA–mRNA regulatory network and performing pathway enrichment analysis, we identified four lncRNAs (e.g., MSTRG.9172.1) and seven circRNAs (e.g., novel_circ_009419) as key regulatory molecules. Functional analysis revealed that these molecules modulate genes such as CD28, CCND2, TIAM1, and RRM2 through pathways including the actin cytoskeleton, p53 signaling pathway, and other pathways. In conclusion, this study provides clearer insight into the epigenetic regulatory network involved in chicken breast muscle development and offers important molecular markers for chicken genetic selection.

In chickens, meat yield is a crucial trait in breeding programs. Identifying key molecular markers associated with increased muscle yield is essential for breeding strategies. This study applied transcriptome sequencing and machine learning methods to examine gene expression and alternative splicing (AS) events in muscle tissues of commercial broilers and local chickens. On the basis of differentially expressed genes (DEGs) and differentially spliced transcripts (DSTs) significantly related to breast muscle weight percentage (BrP), high-accuracy prediction models were developed by evaluating 10 machine learning models (e.g., eXtreme Gradient Boosting (XGBoost), Generalized Linear Model Network (Glmnet)). Feature importance was assessed using the Shapley Additive exPlanations (SHAP) method. The results revealed that 50 DEGs and 95 DSTs contributed significantly to BrP prediction. The XGBoost model achieved over 90% accuracy when using DEGs, and the Glmnet model reached 95% accuracy when using DSTs. Through Shapley evaluation, genes and AS events (e.g., ENSGALG00010012060, HINTW, and VIPR2-201) were identified as having the highest contributions to BrP prediction. Additionally, the breed effect was effectively mitigated. This study introduces new candidate genes and AS targets for the molecular breeding of poultry breast muscle traits, offering a paradigm shift from traditional gene mining approaches to artificial intelligence-driven predictive methods.