Evidence mapPaperPMID 41664043Full record

ArticleBMC veterinary research2026

The role of lncRNA-associated ceRNA networks in yak longissimus dorsi muscle development across feeding systems.

Xiaoming Ma, Xian Guo, Jianbin Liu, Yongfu La, Tong Wang, Pengjia Bao, Min Chu, Xiaoyun Wu, Ping Yan, Chunnian Liang

Abstract read
In one paragraph

Article in BMC veterinary research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Xiaoming MaAnimal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.
Xian GuoAnimal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.
Jianbin LiuAnimal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.
Yongfu LaAnimal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.
Tong WangAnimal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.
Pengjia BaoAnimal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.
Min ChuAnimal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.
Xiaoyun WuAnimal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.
Ping YanAnimal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China. pingyanlz@163.com.
Chunnian LiangAnimal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China. chunnian2006@163.com.

Funding

Science and Technology Projects of Xizang Autonomous Region XZ202502ZY0046-1the Agricultural Science and Technology Innovation Program 25-LZIHPS-01the Central Public-interest Scientific Institution Basal Research Fund 1610322024015the Guidance Fund for Local Science and Technology Development by the Central Committee of the Communist Party of China 25ZYJA008the Lanzhou Youth Science and Technology Talent Innovation Project 2025-QN-030the National Beef Cattle Industry Technology & System CARS-37
6 · The paper itself

Abstract

backgroundYaks (Bos grunniens), native to the high-altitude environments of the Qinghai-Tibet Plateau, are crucial livestock for local herders, providing milk, meat, and other resources. However, traditional grazing practices are becoming increasingly unsustainable due to overgrazing and land degradation. This study investigates the impact of different feeding systems on yak skeletal muscle development and meat quality, focusing on the role of long non-coding RNAs (lncRNAs) in regulating these processes. Using a multi-omics approach, we constructed a comprehensive competing endogenous RNA (ceRNA) network comprising lncRNAs, miRNAs, and mRNAs. Differentially expressed lncRNAs were identified and their regulatory functions assessed.

resultsWe identified a total of 2,009 lncRNA transcripts in yak skeletal muscle. Differential expression analysis between grazing (Group G) and indoor-fed (Group HF) yaks revealed 157 differentially expressed lncRNAs (DElncs) (77 upregulated and 80 downregulated), 51 differentially expressed miRNAs (DEmiRs), and 1,003 differentially expressed mRNAs (DEMs). Functional enrichment analysis demonstrated that these differentially expressed transcripts were significantly enriched in energy metabolism and muscle development pathways, specifically AMPK, Insulin, and FoxO signaling. Based on these datasets, we constructed a muscle-specific ceRNA network. Notably, specific regulatory axes were identified, such as lncRNA TCONS_00010840 competing for miRNAs to regulate the expression of TRIM63, a key gene implicated in muscle atrophy and meat tenderness.

conclusionsOur findings suggest that intensive feeding modulates yak muscle growth and tenderness through IncRNA-mediated pathways involved in metabolic adaptation and extracellular matrix remodeling. The identified TCONS_00010840-miR - 30b - 5p- TRIM63 and TCONS_00013998-ADAMTS2 regulatory nodes represent potential molecular targets for improving yak meat quality. These results provide a theoretical framework for future functional studies into the molecular breeding of yaks.

Indexed as

Animal HusbandryMuscle DevelopmentMuscle, SkeletalRNA, Long NoncodingAnimalsCattleGene Regulatory NetworksMicroRNAsRNA, Competitive EndogenousRNA, MessengerMicroRNAsRNA, Competitive EndogenousRNA, Long NoncodingRNA, MessengerCeRNAFeeding systemsLncRNAsLongissimus dorsiYak

Identifiers

PMID41664043
PMCPMC13001326

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.