Comparisons of adipogenesis- and lipid metabolism-related gene expression levels in muscle, adipose tissue and liver from Wagyu-cross and Holstein steers

Liu, Li; Cao, Peili; Zhang, Lupei; Qi, Mei-Yu; Wang, Liang; Li, Zhongqiu; Shao, Guang; Ding, Liyan; Zhao, Xiuhua; Zhao, Xiaochuan; Xu, Shanshan; Zhang, Haifeng; Chai, Jinbao; Yue, Mengmeng; Wang, Genlin; Liu, Di; Sun, Fang

doi:10.1371/journal.pone.0247559

Cited by 15 publications

(13 citation statements)

References 55 publications

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“…Studies have shown that fat deposition in backfat is related to body fat percentage, carcass cross-sectional fat area ratio, intramuscular fat, and flavor and juiciness of beef ( 5 , 6 ). In Japanese Wagyu cattle, IMF content in the longest dorsal muscle was found to be positively correlated with the percentage of subcutaneous fat and maturity in vivo ( 7 , 8 ). Therefore, the identification of key genes related to subcutaneous adipose tissue by a new method is an important strategy for carcass and meat quality genetic improvement.…”

Section: Introductionmentioning

confidence: 99%

Weighted Gene Co-Expression Network Analysis Identifies Key Modules and Central Genes Associated With Bovine Subcutaneous Adipose Tissue

et al. 2022

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BackgroundFat deposition is an important economic trait in livestock and poultry production. However, the relationship between various genes and signal pathways of fat deposition is still unclear to a large extent. The purpose of this study is to analyze the potential molecular targets and related molecular pathways in bovine subcutaneous adipose tissue.ResultsWe downloaded the GSE116775 microarray dataset from Gene Expression Omnibus (GEO). The weighted gene co-expression network (WGCNA) was used to analyze the gene expression profile, and the key gene modules with the highest correlation with subcutaneous adipose tissue were identified, and the functional enrichment of the key modules was analyzed. Then, the “real” Hub gene was screened by in-module analysis and protein–protein interaction network (PPI), and its expression level in tissue samples and adipocytes was verified. The study showed that a total of nine co-expression modules were identified, and the number of genes in these modules ranged from 101 to 1,509. Among them, the blue module is most closely related to subcutaneous adipose tissue, containing 1,387 genes. These genes were significantly enriched in 10 gene ontologies including extracellular matrix organization, biological adhesion, and collagen metabolic process, and were mainly involved in pathways including ECM-receptor interaction, focal adhesion, cAMP signaling pathway, PI3K-AKT signaling pathway, and regulation of lipolysis in adipocytes. In the PPI network and coexpression network, five genes (CAV1, ITGA5, COL5A1, ABL1, and HSPG2) were identified as “real” Hub genes. Analysis of Hub gene expression by dataset revealed that the expression of these Hub genes was significantly higher in subcutaneous adipose tissue than in other tissues. In addition, real-time fluorescence quantitative PCR (qRT-PCR) analysis based on tissue samples and adipocytes also confirmed the above results.ConclusionIn this study, five key genes related to subcutaneous adipose tissue were discovered, which laid a foundation for further study of the molecular regulation mechanism of subcutaneous adipose tissue development and adipose deposition.

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Section: Introductionmentioning

confidence: 99%

Weighted Gene Co-Expression Network Analysis Identifies Key Modules and Central Genes Associated With Bovine Subcutaneous Adipose Tissue

et al. 2022

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“…Since buffalo is abundant in China, it would be desirable to increase its IMF content. It is known that the IMF correlates with maturity ( 43 ) and percentage ( 44 ) of back subcutaneous fat, which therefore may be used as IMF indicator. Therefore, determining the related factors affecting subcutaneous fat deposition can provide theoretical basis for improving meat quality.…”

Section: Discussionmentioning

confidence: 99%

Weighted Gene Co-expression Network Analysis Revealed That CircMARK3 Is a Potential CircRNA Affects Fat Deposition in Buffalo

Xue

Jian-Gao

et al. 2022

Front. Vet. Sci.

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BackgroundBuffalo meat is increasingly widely accepted for consumption as it shares several quality attributes with cattle meat (beef). Hence, there is a huge opportunity for growth in the buffalo meat industry. However, buffalo meat has relatively low intramuscular fat (IMF) content, affecting its flavor, tenderness and juiciness. As there is a dearth of information on factors that control fat deposition, this study was undertaken to provide new candidate factor associated with buffalo fat deposition. Circular RNA (circRNA) is a novel class of non-coding RNA with a closed-loop structure, and play an important role in fat deposition.MethodsIn this study, weighted gene co-expression network analysis (WGCNA) was used to construct a circRNA co-expression network and revealed a candidate circRNA that may affect the IMF deposition of buffalo as determined by RT-qPCR, semiquantitative PCR and gain-of-function experiments.ResultsHerein, WGCNA determined that one module (turquoise module) is significantly associated with the growth and development stages of buffalo. Further analysis revealed a total of 191 overlapping circRNAs among differentially expressed (DE) circRNAs and the co-expression module. A candidate circRNA was found, 21:6969877|69753491 (circRNA_ID), with a reported involvement in lipid metabolism. This circRNA is stably expressed and originates from the MARK3 gene, hence the name circMARK3. circMARK3 is highly expressed in adipose tissue and mature adipocytes and is located in the cytoplasm. Gain-of-function experiments demonstrated that circMARK3 promoted adipogenic differentiation of buffalo adipocytes and 3T3-L1 cells by up-regulating the expression levels of adipogenic marker genes PPARG, C/EBPα and FABP4.ConclusionThese results indicate that circMARK3 is a potential factor that promotes fat deposition by regulating adipocyte differentiation and adipogenesis in buffalo.

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“…In the biological process analysis, we identified that these miRNAs were highly enriched with genes in the TGF-β pathway, suggesting that they are plausible candidates. Recently, has been reported that TGF-β through Smad3 transcriptional repression leads to the inhibition of myogenesis differentiation [ 41 , 42 ]. Myogenic differentiation and function may need several cellular signal and growth factors, but also an appropriate environment that may be impair by statins.…”

Section: Discussionmentioning

confidence: 99%

A microRNA Signature for the Diagnosis of Statins Intolerance

Mangas

Pérez-Serra

Bonet

et al. 2022

IJMS

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Atherosclerotic cardiovascular diseases (ASCVD) are the leading cause of morbidity and mortality in Western societies. Statins are the first-choice therapy for dislipidemias and are considered the cornerstone of ASCVD. Statin-associated muscle symptoms are the main reason for dropout of this treatment. There is an urgent need to identify new biomarkers with discriminative precision for diagnosing intolerance to statins (SI) in patients. MicroRNAs (miRNAs) have emerged as evolutionarily conserved molecules that serve as reliable biomarkers and regulators of multiple cellular events in cardiovascular diseases. In the current study, we evaluated plasma miRNAs as potential biomarkers to discriminate between the SI vs. non-statin intolerant (NSI) population. It is a multicenter, prospective, case-control study. A total of 179 differentially expressed circulating miRNAs were screened in two cardiovascular risk patient cohorts (high and very high risk): (i) NSI (n = 10); (ii) SI (n = 10). Ten miRNAs were identified as being overexpressed in plasma and validated in the plasma of NSI (n = 45) and SI (n = 39). Let-7c-5p, let-7d-5p, let-7f-5p, miR-376a-3p and miR-376c-3p were overexpressed in the plasma of SI patients. The receiver operating characteristic curve analysis supported the discriminative potential of the diagnosis. We propose a three-miRNA predictive fingerprint (let-7f, miR-376a-3p and miR-376c-3p) and several clinical variables (non-HDLc and years of dyslipidemia) for SI discrimination; this model achieves sensitivity, specificity and area under the receiver operating characteristic curve (AUC) of 83.67%, 88.57 and 89.10, respectively. In clinical practice, this set of miRNAs combined with clinical variables may discriminate between SI vs. NSI subjects. This multiparametric model may arise as a potential diagnostic biomarker with clinical value.

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Comparisons of adipogenesis- and lipid metabolism-related gene expression levels in muscle, adipose tissue and liver from Wagyu-cross and Holstein steers

Cited by 15 publications

References 55 publications

Weighted Gene Co-Expression Network Analysis Identifies Key Modules and Central Genes Associated With Bovine Subcutaneous Adipose Tissue

Weighted Gene Co-Expression Network Analysis Identifies Key Modules and Central Genes Associated With Bovine Subcutaneous Adipose Tissue

Weighted Gene Co-expression Network Analysis Revealed That CircMARK3 Is a Potential CircRNA Affects Fat Deposition in Buffalo

A microRNA Signature for the Diagnosis of Statins Intolerance

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