Evidence mapPaperPMID 36833290Full record

ArticleGenes2023

Comparative Proteomic Analysis of Glycolytic and Oxidative Muscle in Pigs.

Xiaofan Tan, Yu He, Yuqiao He, Zhiwei Yan, Jing Chen, Ruixue Zhao, Xin Sui, Lei Zhang, Xuehai Du, David M Irwin and 2 more

Open access · goldAbstract read
In one paragraph

Article in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.2field-weighted citation impact, top 13% of its field
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

11 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
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  5. WGCNA-based analysis ofFrontiers in veterinary science · 2025
    Article
  6. Review
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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

12 authors at 2 institutions in 2 countries.

Xiaofan TanCollege of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang 110866, China.ORCID 0000-0002-1379-9005
Yu HeCollege of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang 110866, China.
Yuqiao HeCollege of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang 110866, China.
Zhiwei YanCollege of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang 110866, China.
Jing ChenCollege of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang 110866, China.
Ruixue ZhaoCollege of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang 110866, China.
Xin SuiCollege of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang 110866, China.
Lei ZhangCollege of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang 110866, China.
Xuehai DuLiaoning Provincial Animal Husbandry Development Center, Liaoning Province Agricultural Development Service Center, Shenyang 110032, China.
David M IrwinDepartment of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada.
Shuyi ZhangCollege of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang 110866, China.
Bojiang LiCollege of Animal Science and Veterinary Medicine, Shenyang Agricultural University, Shenyang 110866, China.ORCID 0000-0002-7265-1756
Shenyang Agricultural University · CNUniversity of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The quality of meat is highly correlated with muscle fiber type. However, the mechanisms via which proteins regulate muscle fiber types in pigs are not entirely understood. In the current study, we have performed proteomic profiling of fast/glycolytic biceps femoris (BF) and slow/oxidative soleus (SOL) muscles and identified several candidate differential proteins among these. We performed proteomic analyses based on tandem mass tags (TMTs) and identified a total of 26,228 peptides corresponding to 2667 proteins among the BF and SOL muscle samples. Among these, we found 204 differentially expressed proteins (DEPs) between BF and SOL muscle, with 56 up-regulated and 148 down-regulated DEPs in SOL muscle samples. KEGG and GO enrichment analyses of the DEPs revealed that the DEPs are involved in some GO terms (e.g., actin cytoskeleton, myosin complex, and cytoskeletal parts) and signaling pathways (PI3K-Akt and NF-kappa B signaling pathways) that influence muscle fiber type. A regulatory network of protein-protein interaction (PPI) between these DEPs that regulates muscle fiber types was constructed, which demonstrates how three down-regulated DEPs, including PFKM, GAPDH, and PKM, interact with other proteins to potentially control the glycolytic process. This study offers a new understanding of the molecular mechanisms in glycolytic and oxidative muscles as well as a novel approach for enhancing meat quality by transforming the type of muscle fibers in pigs.

Indexed as

Phosphatidylinositol 3-KinasesProteomicsAnimalsMuscle Fibers, SkeletalMuscle, SkeletalOxidative StressSwinePhosphatidylinositol 3-Kinasesdifferentially expressed proteinsglycolytic musclemeat qualityoxidative musclepig

Identifiers

PMID36833290
PMCPMC9957308
OpenAlexW4318484942

What Socratic holds

Textmetadata
LicenceCC BY
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.