Prabhakar Lavanya, Davis G Dicky John
Department of Bioinformatics, Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research (DU), Chennai, Tamil Nadu - 600116, India.
Bioinformation. 2023 Mar 31;19(3):331-335. doi: 10.6026/97320630019331. eCollection 2023.
Obesity is a global crisis leading to several metabolic disorders. Modernization and technology innovation has been easier for next generation sequencing using open-source online software galaxy, which allows the users to share their data and workflow mapping in an effortless manner. This study is to identify candidate genes for obesity by performing differential expression of genes. RNA-Seq analysis was performed for six different datasets retrieved from GEO database. 258 datasets from obese patients and 55 datasets from lean patients were analysed for differentially expressed genes (DEGs). DEGs analysis showed 1971 upregulated genes and 615 downregulated genes with log2FC count ≥ 2.5 and p-value < 0.05. The Gene enrichment analysis performed using Gene Ontology resource highlighted pathways associated to obesity such as cholesterol metabolism, Fat digestion and absorption and glycerolipid metabolism. Using string database protein-protein interactions network was built and the network clusters were visualized using Cytoscape software. The protein-protein interactions of the upregulated and downregulated genes were mapped to form a network, wherein PNLIP (Pancreatic lipase) and FTO (Fat mass and obesity associated protein) gene clusters were visualized as densely connected clusters in MCODE. PNLIP and FTO with its associated genes were identified as candidate genes for targeting obesity.
肥胖是导致多种代谢紊乱的全球性危机。现代化和技术创新使得使用开源在线软件Galaxy进行下一代测序变得更加容易，该软件允许用户轻松共享他们的数据和工作流程映射。本研究旨在通过进行基因差异表达来鉴定肥胖的候选基因。对从GEO数据库检索到的六个不同数据集进行了RNA-Seq分析。分析了258个肥胖患者数据集和55个瘦患者数据集的差异表达基因（DEG）。DEG分析显示，log2FC计数≥2.5且p值<0.05时，有1971个上调基因和615个下调基因。使用基因本体资源进行的基因富集分析突出了与肥胖相关的途径，如胆固醇代谢、脂肪消化和吸收以及甘油脂质代谢。使用String数据库构建了蛋白质-蛋白质相互作用网络，并使用Cytoscape软件对网络簇进行了可视化。上调和下调基因的蛋白质-蛋白质相互作用被映射以形成一个网络，其中PNLIP（胰脂肪酶）和FTO（脂肪量与肥胖相关蛋白）基因簇在MCODE中显示为紧密连接的簇。PNLIP和FTO及其相关基因被鉴定为针对肥胖的候选基因。
