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. 2022 Dec;11(1):227-238.
doi: 10.1080/21623945.2022.2063471.

Analysis of the different characteristics between omental preadipocytes and differentiated white adipocytes using bioinformatics methods

Xinyu Yang  1   2 , Lu Li  1   2 , Canming Xu  1   2 , Meichen Pi  1   2 , Changhua Wang  1   2 , Yemin Zhang  1   2   3
Affiliations

Analysis of the different characteristics between omental preadipocytes and differentiated white adipocytes using bioinformatics methods

Xinyu Yang et al. Adipocyte. 2022 Dec.

Abstract

Obesity is emerging as an epidemiological issue, being associated with the onset and progress of various metabolism-related disorders. Obesity is characterized by the white adipose expansion, which encounters white adipocyte hypertrophy and hyperplasia. White adipocyte hyperplasia is defined as adipogenesis with the increase in the number of the white adipocytes from the preadipocytes. Adipogenesis contributes to distributing excess triglycerides among the smaller newly formed adipocytes, reducing the number of hypertrophic adipocytes and secreting anti-inflammatory factor. Therefore, adipogenesis is emerging as a new therapeutic target for the treatment of obesity. In the present study, for a better understanding of the contribution of the alteration of the omental differentiated white adipocytes to the systemic metabolic disorders, we downloaded the mRNA expression profiles from GEO database GSE1657, 328 differentially expressed genes (DEGs) were screened between the undifferentiated preadipocytes (UNDIF) and omental differentiated white adipocytes (DIF). The contributions of the upregulated and downregulated DEGs to the system were performed via the Gene Ontology (GO) analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Protein-Protein Interaction (PPI) network, respectively. The potential contribution of the whole altered genes in the differentiated white adipocytes was explored with the performance of Gene Set Enrichment Analysis (GSEA), especially on the GO analysis, KEGG analysis, hallmark analysis, oncogenic analysis and related miRNA analysis. The output of the current study will shed light on the new targets for the treatment of obesity and obesity-related disorders.

Keywords: Differentiated white adipocyte; GSAE; bioinformatics; different characteristic; preadipocytes.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1.
Figure 1.
Heatmap of 328 DEGs screened by limma package in R software. Red areas represent upregulated genes and green areas represent downregulated genes in the DIF and UNDIF group. DEG: differentially expressed gene; DIF: differentiated white adipocyte; UNDIF: undifferentiated preadipocytes.
Figure 2.
Figure 2.
Volcano plot analysis identifies DEGs. Red dots represent 185 upregulated genes and green dots represent 143 downregulated genes from the DIF and UNDIF group.
Figure 3.
Figure 3.
The PPI network of DGEs and the most significant modules of DEGs. (a) The PPI network was analysed by String software. There were 325 nodes and 655 edged in the PPI network. Red represents upregulated genes; green represents downregulated genes. (b) The most significant module identified by MCODE (score = 27.863) Dark blue represents the top 10 genes with the most neighbours and expanded nodes. DEG: differentially expressed gene; PPI: protein–protein interaction.
Figure 4.
Figure 4.
GO enrichment result of upregulated and downregulated DEGs respectively. Abscissa represents -LOG (pValue), and ordinate represents BP, CC, and MF terms. GO: Gene Ontology. BP: biological processes, CC: cellular components, MF molecular functions (MF).
Figure 5.
Figure 5.
KEGG enrichment result of upregulated and downregulated DEGs respectively. DEGs. Abscissa represents -Log (pValue), and ordinate represents KEGG terms. KEGG: Kyoto Encyclopedia of Genes and Genomes.
Figure 6.
Figure 6.
Gene sets enrichment in GSEA analysis of all altered genes on GOBP, KEGG. a: BP of GO analysis of all altered genes in GSEA. b: KEGG analysis of all altered genes in GSEA.
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Grants and funding

This work was supported by the National Natural Science Foundation of China [Grant NO 81870550, NO 82170843].

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