Core developmental modules and microbial biomarkers associated with liver-gut axis maturation in Pekin ducks.
- 1Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, No. 2 yuanmingyuan West Road, Haidian district, Beijing, 100193, China; Chinese Meat Duck Germplasm Innovation Center (Xian County), Xian County, Hebei, 062250, China.
- 2Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, No. 2 yuanmingyuan West Road, Haidian district, Beijing, 100193, China.
- 3Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, No. 2 yuanmingyuan West Road, Haidian district, Beijing, 100193, China; Chinese Meat Duck Germplasm Innovation Center (Xian County), Xian County, Hebei, 062250, China. Electronic address: [email protected].
Abstract
The early post-hatch period is critical for the liver-gut axis maturation in Pekin ducks, yet the high-resolution multi-omics landscape remains unexplored. Using 195 ducks at 7, 14, and 21 days, we integrated phenotypic assessments with liver transcriptomics, targeted metabolomics (bile acids, pyrimidines, taurine), and cecum 16S rRNA sequencing. A DHAV‑3 infection model was employed to challenge the normal developmental trajectory, and machine learning was applied to identify microbial biomarkers. Ducklings transitioned from active hepatic proliferation to metabolic specialization, with reduced lipid droplets by day 21, alongside age-dependent enrichment of lipid transport and receptor tyrosine kinase signaling. Bile acids remodeled from early TCA/HCA elevation to TCDCA/taurine dominance at 21 days, while cecum microbiota shifted from Bacteroidota to Lactobacillales and Candidatus_Arthromitus. Following FDR correction (q < 0.05), multi‑omics integration identified Erysipelatoclostridium (degree = 430), Intestinimonas (degree = 428), and CYP2J2 (degree = 46) as the most highly connected nodes in the gene-genus association network. Virus infection disrupted this trajectory, suppressing iron/IgG and reversing bile acid and microbial patterns. A nested cross‑validation (5‑fold × 10 repeats) prediction model using three microbial genera (Alloprevotella, Anaerobiospirillum, Anaerofustis) achieved excellent discriminative performance (full‑data AUC = 0.956; nested CV mean AUC = 0.846 ± 0.073). Our findings reveal the temporal multi-omics landscape of the early-life liver-gut axis and provide a microbiota‑based framework for monitoring host status, offering novel targets for early nutritional programming and precision management in poultry.
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BibTeX
@article{liang2026core,
title = {{Core developmental modules and microbial biomarkers associated with liver-gut axis maturation in Pekin ducks.}},
author = {Suyun Liang and Meixi Lu and Yi Tang and Shuisheng Hou},
journal = {Poultry science},
year = {2026},
month = oct,
doi = {10.1016/j.psj.2026.107926},
url = {https://europepmc.org/article/MED/42893653},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Core developmental modules and microbial biomarkers associated with liver-gut axis maturation in Pekin ducks.
AU - Suyun Liang
AU - Meixi Lu
AU - Yi Tang
AU - Shuisheng Hou
PY - 2026
DA - 2026/10/08
JO - Poultry science
DO - 10.1016/j.psj.2026.107926
UR - https://europepmc.org/article/MED/42893653
AB - The early post-hatch period is critical for the liver-gut axis maturation in Pekin ducks, yet the high-resolution multi-omics landscape remains unexplored. Using 195 ducks at 7, 14, and 21 days, we integrated phenotypic assessments with liver transcriptomics, targeted metabolomics (bile acids, pyrimidines, taurine), and cecum 16S rRNA sequencing. A DHAV‑3 infection model was employed to challenge the normal developmental trajectory, and machine learning was applied to identify microbial biomarkers. Ducklings transitioned from active hepatic proliferation to metabolic specialization, with reduced lipid droplets by day 21, alongside age-dependent enrichment of lipid transport and receptor tyrosine kinase signaling. Bile acids remodeled from early TCA/HCA elevation to TCDCA/taurine dominance at 21 days, while cecum microbiota shifted from Bacteroidota to Lactobacillales and Candidatus_Arthromitus. Following FDR correction (q < 0.05), multi‑omics integration identified Erysipelatoclostridium (degree = 430), Intestinimonas (degree = 428), and CYP2J2 (degree = 46) as the most highly connected nodes in the gene-genus association network. Virus infection disrupted this trajectory, suppressing iron/IgG and reversing bile acid and microbial patterns. A nested cross‑validation (5‑fold × 10 repeats) prediction model using three microbial genera (Alloprevotella, Anaerobiospirillum, Anaerofustis) achieved excellent discriminative performance (full‑data AUC = 0.956; nested CV mean AUC = 0.846 ± 0.073). Our findings reveal the temporal multi-omics landscape of the early-life liver-gut axis and provide a microbiota‑based framework for monitoring host status, offering novel targets for early nutritional programming and precision management in poultry.
SN - 0032-5791
SN - 1525-3171
ER -