基于生物信息学探讨高脂饮食诱导肥胖与脂质代谢基因表达的转录组研究

doi:10.3877/cma.j.issn.2095-9605.2025.02.004

目的

运用生物信息学方法深入探讨由高脂饮食引发小鼠肥胖的生理代谢变化，并通过系统分析相关基因表达的差异，揭示潜在的生物交互机制。

方法

通过GEO基因数据库进行数据检索，最终选取芯片数据集GSE136821作为分析对象。使用Bioconductor中的多个R包（如org.Mm.eg.db、Limma、clusterProfiler）对小鼠肝脏内的基因表达数据进行了差异表达基因分析（DEGs），并对这些基因进行GO功能注释及KEGG通路富集分析。本研究选取前200个DEGs，通过STRING在线数据库构建蛋白质互作网络（PPI），并利用Cytoscape筛选互作网络中的较高度值的Hub基因。

结果

共筛选出1 169个DEGs，高脂组内上调基因755个，下调基因414个。GO富集分析发现DEGs参与脂质代谢、脂肪酸代谢、炎症反应调节等生物过程，并表现出NAD(P)⁺核苷酶活性、脂肪酶活性、脂质转运体活性等多种分子功能。进一步的KEGG通路分析揭示，差异表达基因在多个关键生物学通路中有显著富集，尤其是在PI3K-Akt信号通路、PPAR信号通路以及胆固醇代谢相关通路等。PPI网络分析进一步揭示，PPARγ、Src和Manf为网络中的关键调控节点，提示其在高脂饮食诱导肥胖及脂质代谢异常中发挥重要作用。

结论

高脂饮食显著改变小鼠肝脏中与脂质代谢及炎症调控相关的基因表达，PPARγ、Src和Manf等关键基因通过相互作用构成了复杂的调控网络。

关键词: 生物信息学, 肥胖, 脂质代谢, 差异基因

Objective

This study aimed to investigate the physiological and metabolic changes induced by a high-fat diet in mice using bioinformatics methods, and to reveal the underlying molecular interaction mechanisms through systematic analysis of differential gene expression.

Methods

Gene expression data were retrieved from the GEO database, and the chip dataset GSE136821 was selected for analysis. Differentially expressed genes (DEGs) in mouse liver tissue were identified using several Bioconductor R packages (such as org.Mm.eg.db, Limma, and clusterProfiler). GO functional annotation and KEGG pathway enrichment analyses were performed on these DEGs. The top 200 DEGs were further used to construct a protein–protein interaction (PPI) network via the STRING online database, and high-degree hub genes were subsequently identified using Cytoscape.

Results

A total of 1 169 DEGs were identified, with 755 genes upregulated and 414 genes downregulated in the high-fat diet group. GO enrichment analysis revealed that these DEGs are involved in biological processes such as lipid metabolism, fatty acid metabolism, and regulation of inflammatory responses, and they exhibit various molecular functions including NAD(P)⁺ nucleosidase activity, lipase activity, and lipid transporter activity. KEGG pathway analysis further demonstrated significant enrichment of DEGs in several key biological pathways, notably the PI3K-Akt signaling pathway, PPAR signaling pathway, and cholesterol metabolism-related pathways. PPI network analysis further identified PPARγ, Src, and Manf as critical regulatory nodes, suggesting their important roles in high-fat diet-induced obesity and lipid metabolism dysregulation.

Conclusions

A high-fat diet significantly alters the expression of genes related to lipid metabolism and inflammatory regulation in the mouse liver. Key genes such as PPARγ, Src, and Manf interact to form a complex regulatory network.

Key words: Bioinformatics, Obesity, Lipid metabolism, Differential gene

图1 差异表达基因的火山图注：纵坐标表示log2（倍数变化），横坐标为－log10（P值）。每个点代表一个基因，其中灰色点表示表达无显著差异的基因，红色点表示表达上调的差异基因（755个），蓝色点表示表达下调的差异基因（414个）

图2 差异基因的层次聚类图注：热图中的每一行代表一个基因，不同列代表不同样本；橙色代表上调基因，蓝色代表下调基因

表1 差异表达基因GO富集分析（生物学过程）

序号	GO索引号	GO名称	基因数目
1	GO:0019216	regulation of lipid metabolic process	52
2	GO:0032496	response to lipopolysaccharide	52
3	GO:0045089	positive regulation of innate immune response	33
4	GO:0050727	regulation of inflammatory response	64
5	GO:0019369	arachidonic acid metabolic process	28
6	GO:0006631	fatty acid metabolic process	78
7	GO:0001676	long-chain fatty acid metabolic process	39
8	GO:1903555	regulation of tumor necrosis factor superfamily cytokine production	56
9	GO:0032103	positive regulation of response to external stimulus	92
10	GO:0050900	leukocyte migration	84

表1 差异表达基因GO富集分析（生物学过程）

序号	GO索引号	GO名称	基因数目
1	GO:0019216	regulation of lipid metabolic process	52
2	GO:0032496	response to lipopolysaccharide	52
3	GO:0045089	positive regulation of innate immune response	33
4	GO:0050727	regulation of inflammatory response	64
5	GO:0019369	arachidonic acid metabolic process	28
6	GO:0006631	fatty acid metabolic process	78
7	GO:0001676	long-chain fatty acid metabolic process	39
8	GO:1903555	regulation of tumor necrosis factor superfamily cytokine production	56
9	GO:0032103	positive regulation of response to external stimulus	92
10	GO:0050900	leukocyte migration	84

表2 差异表达基因GO富集分析（分子功能）

序号	GO索引号	GO名称	基因数目
1	GO:0016298	lipase activity	15
2	GO:0005324	long-chain fatty acid transporter activity	5
3	GO:0005504	fatty acid binding	13
4	GO:0015245	fatty acid transmembrane transporter activity	6
5	GO:0050135	NAD(P)⁺ nucleosidase activity	7
6	GO:0034185	apolipoprotein binding	9
7	GO:0120319	long-chain fatty acid omega-1 hydroxylase activity	9
8	GO:0005319	lipid transporter activity	31
9	GO:0051861	glycolipid binding	13
10	GO:0016712	oxidoreductase activity	30

表2 差异表达基因GO富集分析（分子功能）

序号	GO索引号	GO名称	基因数目
1	GO:0016298	lipase activity	15
2	GO:0005324	long-chain fatty acid transporter activity	5
3	GO:0005504	fatty acid binding	13
4	GO:0015245	fatty acid transmembrane transporter activity	6
5	GO:0050135	NAD(P)⁺ nucleosidase activity	7
6	GO:0034185	apolipoprotein binding	9
7	GO:0120319	long-chain fatty acid omega-1 hydroxylase activity	9
8	GO:0005319	lipid transporter activity	31
9	GO:0051861	glycolipid binding	13
10	GO:0016712	oxidoreductase activity	30

表3 差异表达基因KEGG富集分析

序号	Pathway索引号	Pathway名称	基因数目
1	mmu05418	atherosclerosis	22
2	mmu04151	PI3K-Akt signaling pathway	45
3	mmu05417	Lipid and atherosclerosis	33
4	mmu04979	Cholesterol metabolism	12
5	mmu04064	NF-kappa B signaling pathway	20
6	mmu03320	PPAR signaling pathway	18
7	mmu04933	AGE-RAGE signaling pathway in diabetic complications	22
8	mmu04750	Inflammatory mediator regulation of TRP channels	26
9	mmu04060	Cytokine-cytokine receptor interaction	48
10	mmu00590	Arachidonic acid metabolism	24

表3 差异表达基因KEGG富集分析

序号	Pathway索引号	Pathway名称	基因数目
1	mmu05418	atherosclerosis	22
2	mmu04151	PI3K-Akt signaling pathway	45
3	mmu05417	Lipid and atherosclerosis	33
4	mmu04979	Cholesterol metabolism	12
5	mmu04064	NF-kappa B signaling pathway	20
6	mmu03320	PPAR signaling pathway	18
7	mmu04933	AGE-RAGE signaling pathway in diabetic complications	22
8	mmu04750	Inflammatory mediator regulation of TRP channels	26
9	mmu04060	Cytokine-cytokine receptor interaction	48
10	mmu00590	Arachidonic acid metabolism	24

图3 蛋白质互相作用网络图注：A为PPI网络图，显示重要的节点有PPARγ、Src等；B为PPI网络图导入Cytoscap3.9.1软件得到，其中颜色越深代表度值越高。PPARγ为过氧化物酶体、Src为蛋白酪氨酸激酶、Manf为神经营养因子。C为用Cytohubba插件中最大集团中心性（MCC）分析方法筛选top10 Hub基因，颜色越深代表基因Rank值越高，Manf、Src排序第一；PPARγ排序第三；Tuba4a排序第四；Anxa5、Endod1排序第五；Cidec、Apoa4、Apoc2、Vimp排序第七

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The Translational Genomics Study on the Gene Expression of Lipid Metabolism Induced by High-Fat Diet in Obesity Based on Bioinformatics

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The Translational Genomics Study on the Gene Expression of Lipid Metabolism Induced by High-Fat Diet in Obesity Based on Bioinformatics