Integrated transcriptomic and metabolomic analysis reveals key regulatory genes and pathways associated with feed conversion efficiency in Tianchang Sanhuang chicken

Improving feed efficiency in Tianchang Sanhuang chickens is essential for reducing production costs and environmental burden. The objective of this study was to integrate transcriptomic and metabolomic analyses to identify key regulatory genes, metabolites, and pathways associated with residual feed intake (RFI) and feed efficiency. In this study, 650 Tianchang Sanhuang laying hens with similar body weights at 36 weeks of age were evaluated for daily feed intake (DFI), RFI, and feed conversion ratio (FCR). The chickens were classified by RFI (mean ± 0.5 SD) into high-RFI (HRFI, n = 165) and low-RFI (LRFI, n = 158) groups. Phenotypes, serum biochemistry, antioxidant indices, and intestinal traits were compared in subsets (n = 8 per group). Duodenal transcriptomes (RNA-seq) and serum metabolomes (LC-MS/MS) were profiled in independent subsets (n = 4 per group). Compared with HRFI, LRFI hens showed lower RFI, FCR, and DFI (P < 0.01), with no differences in expected feed intake (EFI), metabolic body weight (MBW), daily egg mass (DEM), or average daily gain (ADG) (P > 0.05). The LRFI group showed increased breast muscle redness (a)* (P < 0.05), higher leg muscle drip loss (P < 0.01), and significantly lower levels of triglycerides (TG), cholesterol (CHO), low-density lipoprotein cholesterol (LDL-C), and malondialdehyde (MDA) (P < 0.05). The intestinal morphology and molecular analyses revealed enhanced nutrient absorption and intestinal barrier function in the LRFI group. Transcriptomic analysis identified 237 differentially expressed genes (P < 0.05, |log2FC| ≥ 1) enriched in pathways related to digestion, energy metabolism, and appetite regulation. Metabolomic analysis detected 101 differentially expressed metabolites (VIP ≥ 1, |log2FC| ≥ 1), indicating that RFI is closely associated with protein and lipid metabolism. Integrated analysis identified candidate biomarkers for low RFI individuals selection, including genes such as ACSM5, AHSG, CTRB1, PLA2G1B, AMY2A, CPA1, CCKAR and metabolites including taurine, uridine, L-phenylalanine, D-glucose 6-phosphate and 5‑hydroxy-L-tryptophan. Overall, LRFI hens maintain production while achieving lower intake, potentially via reduced inflammation/oxidative stress and enhanced digestion, barrier integrity, appetite, and energy metabolism, offering targets for marker-assisted improvement of feed efficiency in local breeds.

Authors

Institutions

Publication Details

Journal
Poultry Science
Published
2025-09-27
DOI
https://doi.org/10.1016/j.psj.2025.105912
Citations
2
Primary Topic
Animal Nutrition and Physiology
Type
article
Field-Weighted Citation Impact
2.39
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrated transcriptomic and metabolomic analysis reveals key regulatory genes and pathways associated with feed conversion efficiency in Tianchang Sanhuang chicken

Man Ren, Kuixing Yang, Xiaojin Li, Tao Jin et al.
2 citations
Poultry Science
Animal Nutrition and Physiology
2.39
article

Integrated transcriptomic and metabolomic analysis reveals key regulatory genes and pathways associated with feed conversion efficiency in Tianchang Sanhuang chicken

Man Ren, Kuixing Yang, Xiaojin Li, Tao Jin, Shenghe Li, Erhui Jin, Xinyu Liu, Chunfang Zhao, Jiale Li, Mengmeng Jin, Mengmeng Zhuansun
article en
2 citations

Abstract

Improving feed efficiency in Tianchang Sanhuang chickens is essential for reducing production costs and environmental burden. The objective of this study was to integrate transcriptomic and metabolomic analyses to identify key regulatory genes, metabolites, and pathways associated with residual feed intake (RFI) and feed efficiency. In this study, 650 Tianchang Sanhuang laying hens with similar body weights at 36 weeks of age were evaluated for daily feed intake (DFI), RFI, and feed conversion ratio (FCR). The chickens were classified by RFI (mean ± 0.5 SD) into high-RFI (HRFI, n = 165) and low-RFI (LRFI, n = 158) groups. Phenotypes, serum biochemistry, antioxidant indices, and intestinal traits were compared in subsets (n = 8 per group). Duodenal transcriptomes (RNA-seq) and serum metabolomes (LC-MS/MS) were profiled in independent subsets (n = 4 per group). Compared with HRFI, LRFI hens showed lower RFI, FCR, and DFI (P < 0.01), with no differences in expected feed intake (EFI), metabolic body weight (MBW), daily egg mass (DEM), or average daily gain (ADG) (P > 0.05). The LRFI group showed increased breast muscle redness (a)* (P < 0.05), higher leg muscle drip loss (P < 0.01), and significantly lower levels of triglycerides (TG), cholesterol (CHO), low-density lipoprotein cholesterol (LDL-C), and malondialdehyde (MDA) (P < 0.05). The intestinal morphology and molecular analyses revealed enhanced nutrient absorption and intestinal barrier function in the LRFI group. Transcriptomic analysis identified 237 differentially expressed genes (P < 0.05, |log2FC| ≥ 1) enriched in pathways related to digestion, energy metabolism, and appetite regulation. Metabolomic analysis detected 101 differentially expressed metabolites (VIP ≥ 1, |log2FC| ≥ 1), indicating that RFI is closely associated with protein and lipid metabolism. Integrated analysis identified candidate biomarkers for low RFI individuals selection, including genes such as ACSM5, AHSG, CTRB1, PLA2G1B, AMY2A, CPA1, CCKAR and metabolites including taurine, uridine, L-phenylalanine, D-glucose 6-phosphate and 5‑hydroxy-L-tryptophan. Overall, LRFI hens maintain production while achieving lower intake, potentially via reduced inflammation/oxidative stress and enhanced digestion, barrier integrity, appetite, and energy metabolism, offering targets for marker-assisted improvement of feed efficiency in local breeds.

Poultry ScienceVol. 104(12)
Anhui Agricultural University (CN), Anhui University of Science and Technology (CN), Anhui Science and Technology University (CN)
Openalex Percentile: Top 11%
Animal Nutrition and Physiology
2.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.