Evidence map›Paper›PMID 41826822›Full record

ArticleBMC genomics2026

Integrative transcriptomics and machine learning reveal key regulatory genes for meat quality traits in pigs.

Shuya Ma, Xiezong Hu, Jianwei Yang, Kunpeng Shi, Xiaodong Zhang, Yueyun Ding, Xudong Wu, Mengting Zhu, Zongjun Yin, Xianrui Zheng

Abstract read
In one paragraph

Article in BMC genomics, 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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0citing papers 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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.

Shuya Ma *College of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, P. R. China.
Xiezong Hu *College of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, P. R. China.
Jianwei YangCollege of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, P. R. China.
Kunpeng ShiCollege of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, P. R. China.
Xiaodong ZhangCollege of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, P. R. China.
Yueyun DingCollege of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, P. R. China.
Xudong WuInstitute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei city, 230001, P. R. China.
Mengting ZhuKey Laboratory of Jianghuai Agricultural Product Fine Processing and Resource Utilization of Ministry of Agriculture and Rural Affairs, School of Food and Nutrition, Anhui Agricultural University, Hefei, 230036, P. R. China.
Zongjun Yin *College of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, P. R. China. yinzongjun@ahau.edu.cn.
Xianrui Zheng *College of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, P. R. China. zxr07sk1@163.com.

Funding

Accurate Identifcation Project of Livestock and Poultry Germplasm Resources 202114Cooperative Innovation Project of Anhui Provincial Universities GXXT-2023-059Major special science and technology project of Anhui Province 202103a06020013
6 · The paper itself

Abstract

backgroundMeat quality traits are typically regulated by multiple genes, each contributing a small effect. In this study, to pinpoint candidate genes involved in meat quality traits, we performed transcriptome profiles of porcine longissimus dorsi (LD) muscle and applied machine learning (ML) models to analyze RNA-seq data. We also carried out Gene Set Enrichment Analysis (ssGSEA), Weighted gene co-expression network analysis (WGCNA) and functional validation of putative target genes to better support the biological relevance of our findings.

resultsIn this study, LD muscle samples were collected from 142 Huoshou Black (HSH) pigs and 191 Anqing Six-end-white (AQLB) pigs. Based on results of the estimated breeding values (EBV) analysis, of meat quality traits, we selected 101 HSH pigs and 99 AQLB pigs for transcriptomic analysis. Using an integrative analytical framework that combined ssGSEA and WGCNA, we identified 197 candidate genes 197 candidate genes. These genes were significantly associated with various metabolic pathways, including fatty-acid elongation and metabolism, amino-acid catabolism, protein turnover, and biosynthetic processes. To further refine the identification of key regulatory genes, we systematically evaluated ten ML models, ultimately selecting XGBoost, Random Forest, and Lasso Regression for subsequent analysis. This approach pinpointed CYSLTR1 and LPCAT2 as the key regulatory genes. To investigate the functional roles of CYSLTR1 and LPCAT2 in intramuscular fat (IMF) deposition, we established a porcine intramuscular adipocyte model via siRNA-mediated knockdown of either CYSLTR1 or LPCAT2. RNA-Seq analysis identified 339 differentially expressed genes (DEGs) in the siLPCAT2 group and 2,376 DEGs in the siCYSLTR1 group relative to the control. Heatmap analysis indicated that genes involved in triacylglycerol (TAG) biosynthesis were upregulated in the siCYSLTR1 group, but downregulated in the siLPCAT2 group. KEGG pathway enrichment analysis further demonstrated that LPCAT2-associated DEGs were predominantly enriched in the MAPK signaling pathway, mTOR signaling pathway, biosynthesis of unsaturated fatty acids, and glycerolipid metabolism—pathways closely linked to cell proliferation, nutrient sensing, and lipid remodeling. In contrast, CYSLTR1-associated DEGs were significantly enriched in lipid metabolism, atherosclerosis, and adipocytokine signaling pathways—processes directly implicated in adipocyte differentiation, lipid storage, and inflammatory crosstalk within adipose tissue. Collectively, these findings elucidate distinct yet complementary regulatory roles for CYSLTR1 and LPCAT2 in intramuscular adipogenesis and provide mechanistic support for targeting these genes to modulate IMF content and improve pork quality traits.

conclusionsIn conclusion, CYSLTR1 and LPCAT2 were identified as pivotal regulatory genes governing IMF deposition. Functional enrichment and pathway analyses revealed that both genes exert their effects on IMF accumulation through the coordinated regulation of lipid metabolism–associated pathways—including fatty acid synthesis, triglyceride assembly, and phospholipid remodeling. These findings offer mechanistically grounded evidence and actionable biological insights for improving pork quality traits, particularly marbling and tenderness.

Indexed as

Gene Expression ProfilingMachine LearningMeatTranscriptomeAnimalsGene Expression RegulationGene Regulatory NetworksMuscle, SkeletalSwineMachine learningMeat quality traitsPigsTranscriptome

Identifiers

PMID41826822
PMCPMC13101129

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.