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pmid:38832038

Identification of potential obese-specific biomarkers and pathways associated with abdominal subcutaneous fat deposition in pig using a comprehensive bioinformatics strategy.

Yongli Yang · Xiaoyi Wang · Mingli Li · Shuyan Wang · Huiyu Wang · Qiang Chen · Shaoxiong Lu

PeerJ · 2024 doi:10.7717/peerj.17486

Paper

complete

Experiment

complete

Data

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Analysis

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Execution

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title
Identification of potential obese-specific biomarkers and pathways associated with abdominal subcutaneous fat deposition in pig using a comprehensive bioinformatics strategy.
doi
10.7717/peerj.17486
pmid
38832038
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[0]
Yongli Yang
[1]
Xiaoyi Wang
[2]
Mingli Li
[3]
Shuyan Wang
[4]
Huiyu Wang
[5]
Qiang Chen
[6]
Shaoxiong Lu
journal
PeerJ
year
2024
abstract
Abdominal subcutaneous fat deposition (ASFD) is not only related to meat quality in the pig industry but also to human health in medicine. It is of great value to elucidate the potential molecular mechanisms of ASFD. The present study aims to identify obese-specific biomarkers and key pathways correlated with ASFD in pigs. The ASF-related mRNA expression dataset GSE136754 was retrieved from the Gene Expression Omnibus (GEO) database and systematically analyzed using a comprehensive bioinformatics method. A total of 565 differentially expressed genes (DEGs) were identified between three obese and three lean pigs, and these DEGs were mainly involved in the p53 signaling pathway, MAPK signaling pathway and fatty acid metabolism. A protein-protein interaction (PPI) network, consisting of 540 nodes and 1,065 edges, was constructed, and the top ten genes with the highest degree scores-ABL1, HDAC1, CDC42, HDAC2, MRPS5, MRPS10, MDM2, JUP, RPL7L1 and UQCRFS1-were identified as hub genes in the whole PPI network. Especially HDAC1, MDM2, MRPS10 and RPL7L1 were identified as potential robust obese-specific biomarkers due to their significant differences in single gene expression levels and high ROC area; this was further verified by quantitative real-time PCR (qRT-PCR) on abdominal subcutaneous fat samples from obese-type (Saba) and lean-type (Large White) pigs. Additionally, a mRNA-miRNA-lncRNA ceRNA network consisting of four potential biomarkers, 15 miRNAs and 51 lncRNAs was established, and two targeted lncRNAs with more connections, XIST and NEAT1, were identified as potentially important regulatory factors. The findings of this study may provide novel insights into the molecular mechanism involved in ASFD.
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To identify key genes involved in ASFD, a DEGA was implemented between ASFTs from the obese and lean pigs by the Limma package in R software (version 4.1.2) (Ritchie et al., 2015).
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To identify key genes involved in ASFD, a DEGA was implemented between ASFTs from the obese and lean pigs by the Limma package in R software (version 4.1.2) (Ritchie et al., 2015).
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A total of 565 differentially expressed genes were identified between abdominal subcutaneous fat tissues from obese and lean pigs.
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The totals of 565 DEGs were identified (P < 0.05) by DEGA between ASFTs from obese and lean pigs, including 412 up- and 153 down-regulated genes in the MAs of ASFT from obese pigs compared with lean pigs (Fig. 2A, Table S3).
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sections[14] "Identification of DEGs" paragraph 2
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The totals of 565 DEGs were identified (P < 0.05) by DEGA between ASFTs from obese and lean pigs, including 412 up- and 153 down-regulated genes in the MAs of ASFT from obese pigs compared with lean pigs (Fig. 2A, Table S3).
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Epigenetic and transcriptomic characterization of pure adipocyte fractions from obese pigs identifies candidate pathways controlling metabolism
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Complete file
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It is of great value to elucidate the potential molecular mechanisms of ASFD. The present study aims to identify obese-specific biomarkers and key pathways correlated with ASFD in pigs. The ASF-related mRNA expression dataset GSE136754 was retrieved from the Gene Expression Omnibus (GEO) database and systematically analyzed using a comprehensive bioinformatics method. A total of 565 differentially expressed genes (DEGs) were identified between three obese and three lean pigs, and these DEGs were mainly involved in the p53 signaling pathway, MAPK signaling pathway and fatty acid metabolism. A protein-protein interaction (PPI) network, consisting of 540 nodes and 1,065 edges, was constructed, and the top ten genes with the highest degree scores-ABL1, HDAC1, CDC42, HDAC2, MRPS5, MRPS10, MDM2, JUP, RPL7L1 and UQCRFS1-were identified as hub genes in the whole PPI network. Especially HDAC1, MDM2, MRPS10 and RPL7L1 were identified as potential robust obese-specific biomarkers due to their significant differences in single gene expression levels and high ROC area; this was further verified by quantitative real-time PCR (qRT-PCR) on abdominal subcutaneous fat samples from obese-type (Saba) and lean-type (Large White) pigs. Additionally, a mRNA-miRNA-lncRNA ceRNA network consisting of four potential biomarkers, 15 miRNAs and 51 lncRNAs was established, and two targeted lncRNAs with more connections, XIST and NEAT1, were identified as potentially important regulatory factors. The findings of this study may provide novel insights into the molecular mechanism involved in ASFD.", "claims": [{"id": "claim-method", "statement": "To identify key genes involved in ASFD, a DEGA was implemented between ASFTs from the obese and lean pigs by the Limma package in R software (version 4.1.2) (Ritchie et al., 2015).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38832038", "locator": "sections[4] \"Identification of differentially expression genes\" paragraph 2", "quote": "To identify key genes involved in ASFD, a DEGA was implemented between ASFTs from the obese and lean pigs by the Limma package in R software (version 4.1.2) (Ritchie et al., 2015)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw measurements are available in the Supplemental Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38832038", "locator": "sections[26] \"Additional Information and Declarations\" paragraph 5", "quote": "The raw measurements are available in the Supplemental Files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 565 differentially expressed genes were identified between abdominal subcutaneous fat tissues from obese and lean pigs.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "The totals of 565 DEGs were identified (P < 0.05) by DEGA between ASFTs from obese and lean pigs, including 412 up- and 153 down-regulated genes in the MAs of ASFT from obese pigs compared with lean pigs (Fig. 2A, Table S3)."}, "evidence": [{"asset": "source:pmid:38832038", "locator": "sections[14] \"Identification of DEGs\" paragraph 2", "quote": "The totals of 565 DEGs were identified (P < 0.05) by DEGA between ASFTs from obese and lean pigs, including 412 up- and 153 down-regulated genes in the MAs of ASFT from obese pigs compared with lean pigs (Fig. 2A, Table S3)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 565 differentially expressed genes were identified between abdominal subcutaneous fat tissues from obese and lean pigs.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38832038"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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