International Journal of Molecular Zoology, 2025, Vol.15, No.1, 10-19 http://animalscipublisher.com/index.php/ijmz 13 3.3 Regulatory networks and non-coding RNAs Karimi et al. (2021) found that some long non-coding RNAs (lncRNAs) showed significant expression differences between high-efficiency and low-efficiency chickens in liver tissues. These lncRNAs may regulate fat and carbohydrate metabolism, as well as genes related to growth and energy balance. Circular RNAs (circRNAs) exhibit specific tissue expression patterns in the hypothalamus and liver. This expression is often not a simple linear change. They can also co-express with mRNA or interact with miRNA and RNA-binding proteins. Affect the feeding behavior and RFI of chickens (Yuan et al., 2024). Karimi et al. (2021) and Yuan et al. (2024) both believe that these non-coding RNAs may potentially become biomarkers for predicting feed efficiency in the future, bringing new ideas to the genetic regulation research of this complex trait of feed efficiency. 4 Metabolomic Insights into Feed Efficiency 4.1 Targeted and untargeted metabolomics platforms The research conducted by Beauclercq et al. (2018) and Metzler-Zebeli et al. (2019) found that nuclear magnetic resonance (^1H NMR) technology has been used in the sample analysis of serum, ileum and cecum to help identify some metabolite markers that can predict digestive efficiency and feed efficiency (such as AMEn and RFI). Targeted analysis mainly focuses on specific amino acid and lipid metabolites, which can precisely quantify known metabolites. Non-targeted analysis can reveal broader metabolic changes such as biogenic amines and phospholipids under different feeding conditions, which is helpful for discovering new metabolic markers. 4.2 Identified metabolic signatures Metzler-Zebeli et al. (2019) found that in chickens with low feed efficiency (high RFI), the levels of amino acids such as isoleucine, lysine, valine, histidine and ornithine in the serum were relatively high; Some biogenic amines, such as carnosine, putrescine, spermidine, and specific diacylphospholipids, are positively correlated with feed intake and weight gain. Proline in serum, fumaric acid in ileum and glucose in cecum have a significant relationship with AMEn (apparent metabolic energy) and may become biomarkers of digestive efficiency (Beauclercq et al., 2018). The levels of uric acid and cholesterol can also respectively reflect the nutritional status and RFI level of chickens (Metzler-Zebeli et al., 2019). 4.3 Gut microbiota-host metabolite interactions Metabolomic studies have found that the interaction between gut microbiota and the metabolism of chickens themselves can affect feed efficiency. Metabolites from microbial fermentation (such as butyric acid derivatives) were included in the AMEn prediction model, indicating that the activity of microorganisms is very important in nutrient utilization. Beauclercq et al. (2018) also found that the composition of metabolites in the ileum and cecum is greatly influenced by the microbiota, and these metabolites can explain most of the differences in digestive efficiency. 5 Integration of Transcriptomics and Metabolomics 5.1 Multi-omics data integration strategies Common methods, such as weighted gene co-expression network analysis (WGCNA) in co-expression network analysis, can link traits such as gene expression, metabolite abundance and feed efficiency. In the same year, 2024, Ye et al. and Yuan et al. established multiple co-expression modules in their research, linking certain specific transcripts (such as lncRNA and circRNA) and metabolic pathways related to feed efficiency in the liver, muscle, and intestine. Functional enrichment analysis and protein-protein interaction (PPI) network analysis were also used to explain differentially expressed genes (DEGs) and their possible metabolic functions (Yang et al., 2020; Karimi et al., 2021; Xiao et al., 2021). 5.2 Commonly enriched biological themes Some genes in fat metabolism, carbohydrate metabolism and energy balance pathways are often associated with differences in feed efficiency (Karimi et al., 2021; Xiao et al., 2021; Wang et al., 2022). Chickens with low RFI (high feed efficiency) usually show enhanced mitochondrial activity and more active oxidative phosphorylation pathways, indicating that their ATP synthesis is more efficient and their control ability over reactive oxygen
RkJQdWJsaXNoZXIy MjQ4ODYzNA==