Integrative Analysis of Transcriptomics and Metabolomics Provides Insights into Meat Quality Differences in Hu Sheep with Different Carcass Performance Article Swipe
Xinshu Zhang
,
Liming Zhao
,
Huibin Tian
,
Zongwu Ma
,
Qi Zhang
,
Mengru Pu
,
Peiliang Cao
,
Deyin Zhang
,
Yukun Zhang
,
Yuan Zhao
,
Jiangbo Cheng
,
Quanzhong Xu
,
Dan Xu
,
Xiaobin Yang
,
Xiaolong Li
,
Weiwei Wu
,
Chong Li
,
Weimin Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/foods14142477
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/foods14142477
Meat quality is a critical determinant of consumer preference and economic value in the livestock industry. However, the relationship between carcass performance and meat quality remains poorly understood. In our study, we conducted an integrative analysis of transcriptomics and metabolomics to investigate the molecular mechanisms underlying meat quality differences in Hu sheep with high (HHS, n = 10) and low (LHS, n = 10) carcass performance. Phenotypic analysis revealed that the HHS group exhibited superior meat quality traits, including higher intramuscular fat (IMF) content (reflected in elevated marbling scores), along with lower shear force, drip loss, and cooking loss, compared to the LHS group. Transcriptomic analysis identified 376 differentially expressed genes (DEGs) enriched in pathways linked to lipid metabolism, such as the PPAR signaling pathway and long-chain fatty acid metabolic process. Weighted gene co-expression network analysis (WGCNA) revealed important modules and key genes (e.g., ELOVL6, PLIN1, and ARHGEF2) associated with meat quality traits. Metabolomic profiling identified 132 differentially accumulated metabolites (DAMs), with significant enrichment in amino acid metabolism pathways, including D-amino acid metabolism, arginine biosynthesis, and glycine, serine, and threonine metabolism. Integrative analysis of transcriptomic and metabolomic data highlighted six co-enriched pathways, such as the mTOR signaling pathway and amino acid metabolism, underscoring their role in regulating meat quality. These findings provide valuable insights into the genetic and metabolic networks driving meat quality variation and offer potential biomarkers for genetic selection and nutritional strategies to enhance both carcass yield and eating quality in Hu sheep. This research enhances knowledge of the molecular basis of meat quality and supports precision breeding in livestock production.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/foods14142477
- https://www.mdpi.com/2304-8158/14/14/2477/pdf?version=1752639451
- OA Status
- gold
- Cited By
- 1
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412450458
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412450458Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/foods14142477Digital Object Identifier
- Title
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Integrative Analysis of Transcriptomics and Metabolomics Provides Insights into Meat Quality Differences in Hu Sheep with Different Carcass PerformanceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-15Full publication date if available
- Authors
-
Xinshu Zhang, Liming Zhao, Huibin Tian, Zongwu Ma, Qi Zhang, Mengru Pu, Peiliang Cao, Deyin Zhang, Yukun Zhang, Yuan Zhao, Jiangbo Cheng, Quanzhong Xu, Dan Xu, Xiaobin Yang, Xiaolong Li, Weiwei Wu, Chong Li, Weimin WangList of authors in order
- Landing page
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https://doi.org/10.3390/foods14142477Publisher landing page
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https://www.mdpi.com/2304-8158/14/14/2477/pdf?version=1752639451Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2304-8158/14/14/2477/pdf?version=1752639451Direct OA link when available
- Concepts
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Metabolomics, Biology, Transcriptome, Intramuscular fat, Metabolic pathway, Fatty acid metabolism, Lipid metabolism, Phenotype, Genetics, Metabolism, Gene, Biotechnology, Biochemistry, Bioinformatics, Gene expressionTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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48Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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