Can chick meconium serve as a source of biomarkers linked to hatchling quality and the age of parent stock?

One possible approach to selecting chicks based on quality involves identifying biomarkers in biological samples. Concurrently, understanding the metabolic profile of chicks from different-aged breeders is essential for developing strategies to mitigate the age-related effects on hatchability. This study investigated whether chick quality and breeder age influence the metabolic profile of layer chick meconium. A total of 143 chicks from laying breeder hens, categorized as young, middle-aged or old, were visually assessed for quality, and meconium samples were collected for metabolomic analysis. Sixteen metabolites were found to be associated with good-quality chicks across all breeder ages, with an overall accuracy of 81.94 %. Using metabolite profiles, the accuracy for predicting young-hen-chick quality was 93.2 %, with high sensitivity (92.30 %) and specificity (93.75 %). Four metabolites were associated with poor-quality chicks with an overall accuracy of 77.53 %. Partial least squares discriminant analysis (PLS-DA) revealed enhanced metabolite separation in good-quality chicks, with five metabolites exhibiting high area under the curve (AUC) values (>90 %) in chicks from young hens compared to chicks from hens of other ages, including metabolites related to energy metabolism, hormonal activity, vitamin D synthesis and peptide constitution. Disregarding maternal age, five metabolites varied between good- and poor-quality chicks, but with a low accuracy of 61.26 % for quality discrimination. Chicks from young, middle-aged, and old hens exhibited 12, 11, and 2 metabolites that varied the expression between good and poor qualities, with accuracies for predicting good-quality chicks of 74.46 %, 70.83 %, and 51.06 %, respectively. Certain metabolites with 70 < AUC < 80 % have emerged as potential biomarkers for distinguishing between good- and poor-quality layer chicks. These include metabolites related to energy and growth metabolism, tryptophan and methionine metabolism, antioxidants and some with no known function in embryos. This work identified potential metabolites that can be investigated to mitigate the effects of hen age on hatchability. Additionally, several metabolites have emerged as potential biomarkers for distinguishing between good- and poor-quality chicks, depending on the breeder's age.

Authors

Institutions

Publication Details

Journal
Poultry Science
Published
2024-12-27
DOI
https://doi.org/10.1016/j.psj.2024.104715
Primary Topic
Animal Nutrition and Physiology
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Can chick meconium serve as a source of biomarkers linked to hatchling quality and the age of parent stock?

Hebréia Oliveira Almeida Souza, Bruno Serpa Vieira, Mário Machado Martins, Ednaldo Carvalho Guimarães et al.
Poultry Science
Animal Nutrition and Physiology
article

Can chick meconium serve as a source of biomarkers linked to hatchling quality and the age of parent stock?

Hebréia Oliveira Almeida Souza, Bruno Serpa Vieira, Mário Machado Martins, Ednaldo Carvalho Guimarães, Simone Sommerfeld, Belchiolina Beatriz Fonseca, Luciana Machado Bastos, Otávio Cintra Lemos Olivieri, A I Aguiar Neves
article en

Abstract

One possible approach to selecting chicks based on quality involves identifying biomarkers in biological samples. Concurrently, understanding the metabolic profile of chicks from different-aged breeders is essential for developing strategies to mitigate the age-related effects on hatchability. This study investigated whether chick quality and breeder age influence the metabolic profile of layer chick meconium. A total of 143 chicks from laying breeder hens, categorized as young, middle-aged or old, were visually assessed for quality, and meconium samples were collected for metabolomic analysis. Sixteen metabolites were found to be associated with good-quality chicks across all breeder ages, with an overall accuracy of 81.94 %. Using metabolite profiles, the accuracy for predicting young-hen-chick quality was 93.2 %, with high sensitivity (92.30 %) and specificity (93.75 %). Four metabolites were associated with poor-quality chicks with an overall accuracy of 77.53 %. Partial least squares discriminant analysis (PLS-DA) revealed enhanced metabolite separation in good-quality chicks, with five metabolites exhibiting high area under the curve (AUC) values (>90 %) in chicks from young hens compared to chicks from hens of other ages, including metabolites related to energy metabolism, hormonal activity, vitamin D synthesis and peptide constitution. Disregarding maternal age, five metabolites varied between good- and poor-quality chicks, but with a low accuracy of 61.26 % for quality discrimination. Chicks from young, middle-aged, and old hens exhibited 12, 11, and 2 metabolites that varied the expression between good and poor qualities, with accuracies for predicting good-quality chicks of 74.46 %, 70.83 %, and 51.06 %, respectively. Certain metabolites with 70 < AUC < 80 % have emerged as potential biomarkers for distinguishing between good- and poor-quality layer chicks. These include metabolites related to energy and growth metabolism, tryptophan and methionine metabolism, antioxidants and some with no known function in embryos. This work identified potential metabolites that can be investigated to mitigate the effects of hen age on hatchability. Additionally, several metabolites have emerged as potential biomarkers for distinguishing between good- and poor-quality chicks, depending on the breeder's age.

Poultry ScienceVol. 104(2)
Universidade Federal de Uberlândia (BR)
Instituto Nacional de Ciência e Tecnologia em Teranóstica e Nanobiotecnologia, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Minas Gerais
Zero hunger
Openalex Percentile: Top 71%
Animal Nutrition and Physiology
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.