Article (Scientific journals)
Integrating identification and targeted proteomics to discover the potential indicators of postmortem lamb meat quality.
Huang, Caiyan; Blecker, Christophe; Chen, Li et al.
2023In Meat Science, 199, p. 109126
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Keywords :
Glycolysis; Meat quality; Muscle contraction; Oxidative phosphorylation; Protein indicators; Proteomics; Sheep; Animals; Muscles/metabolism; Muscle Contraction; Meat/analysis; Muscle, Skeletal/chemistry; Proteomics/methods; Red Meat/analysis; Bioinformatics analysis; Lamb meats; Muscle contractions; Potential indicators; Protein indicator; Targeted proteomics; Meat; Muscle, Skeletal; Muscles; Red Meat; Food Science
Abstract :
[en] The aim of this study was to identify the potential indicators of lamb meat quality by TMT and PRM-based proteomics combined with bioinformatic analysis. Lamb muscles were divided into three different meat quality groups (high, middle and low) according to tenderness (shear force, MFI value), colour (a* value, R630/580), and water-holding capacity (cooking loss, drip loss) at 24 h postmortem. The results showed that the abundance of phosphoglycerate kinase 1 (PGK1), β-enolase (ENO3), myosin-binding protein C (MYBPC1) and myosin regulatory light chain 2 (MYLPF) was significantly different in the three groups and could be used as potential indicators to characterize meat quality. Moreover, the postmortem processes of glycolysis, oxidative phosphorylation, and muscle contraction remarkably changed in different groups, and were the key biological pathways influencing meat quality. Overall, this study depicted the proteomic landscape of meat that furthers our understanding of the molecular mechanism of meat quality and provides a reference for developing non-destructive detection technology for meat quality.
Disciplines :
Food science
Author, co-author :
Huang, Caiyan ;  Université de Liège - ULiège > TERRA Research Centre
Blecker, Christophe ;  Université de Liège - ULiège > TERRA Research Centre > Technologie Alimentaire (TA)
Chen, Li;  Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences, Key Laboratory of Agro-Products Quality & Safety Harvest, Storage, Transportation, Management and Control, Ministry of Agriculture and Rural Affairs, Beijing 100193, China
Xiang, Can;  Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences, Key Laboratory of Agro-Products Quality & Safety Harvest, Storage, Transportation, Management and Control, Ministry of Agriculture and Rural Affairs, Beijing 100193, China
Zheng, Xiaochun;  Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences, Key Laboratory of Agro-Products Quality & Safety Harvest, Storage, Transportation, Management and Control, Ministry of Agriculture and Rural Affairs, Beijing 100193, China
Wang, Zhenyu;  Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences, Key Laboratory of Agro-Products Quality & Safety Harvest, Storage, Transportation, Management and Control, Ministry of Agriculture and Rural Affairs, Beijing 100193, China. Electronic address: food2006wzy@163.com
Zhang, Dequan;  Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences, Key Laboratory of Agro-Products Quality & Safety Harvest, Storage, Transportation, Management and Control, Ministry of Agriculture and Rural Affairs, Beijing 100193, China. Electronic address: dequan_zhang0118@126.com
Language :
English
Title :
Integrating identification and targeted proteomics to discover the potential indicators of postmortem lamb meat quality.
Publication date :
May 2023
Journal title :
Meat Science
ISSN :
0309-1740
eISSN :
1873-4138
Publisher :
Elsevier Ltd, England
Volume :
199
Pages :
109126
Peer reviewed :
Peer Reviewed verified by ORBi
Funding text :
This study was funded by the Key R&D projects of the Ningxia Hui Autonomous Region, China (NO 2021BBF02037), and Agricultural Science and Technology Innovation Program, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2022-IFST). We thank Mr. Chongxing Mo for his support of ASReml-R data analysis.This study was funded by the Key R&D projects of the Ningxia Hui Autonomous Region , China (NO 2021BBF02037 ), and Agricultural Science and Technology Innovation Program , Institute of Food Science and Technology , Chinese Academy of Agricultural Sciences ( CAAS-ASTIP-2022-IFST ). We thank Mr. Chongxing Mo for his support of ASReml-R data analysis.
Available on ORBi :
since 27 December 2023

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