Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China.
Department of Biochemistry, Medical College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China.
BMC Genomics. 2017 Nov 13;18(1):872. doi: 10.1186/s12864-017-4257-6.
The therapeutic management of obesity is challenging, hence further elucidating the underlying mechanisms of obesity development and identifying new diagnostic biomarkers and therapeutic targets are urgent and necessary. Here, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins.
In the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs) were with a trend of up-regulation in twins with higher BMI when compared to their siblings. Categories of positive regulation of nitric-oxide synthase biosynthetic process, positive regulation of NF-kappa B import into nucleus, and peroxidase activity were significantly enriched within GO database and NF-kappa B signaling pathway within KEGG database. DEGs of NAMPT, TLR9, PTGS2, HBD, and PCSK1N might be associated with obesity. In the WGCNA, among the total 20 distinct co-expression modules identified, coral1 module (68 genes) had the strongest positive correlation with BMI (r = 0.56, P = 0.04) and disease status (r = 0.56, P = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub genes, such as GAL, ASB9, NPPB, TBX2, IL17C, APOE, ABCG4, and APOC2 were also identified. The module eigengene of saddlebrown module (212 genes) was also significantly correlated with BMI (r = 0.56, P = 0.04), and hub genes of KCNN1 and AQP10 were differentially expressed.
We identified significant genes and specific modules potentially related to BMI based on the gene expression profile data of monozygotic twins. The findings may help further elucidate the underlying mechanisms of obesity development and provide novel insights to research potential gene biomarkers and signaling pathways for obesity treatment. Further analysis and validation of the findings reported here are important and necessary when more sample size is acquired.
肥胖症的治疗管理具有挑战性，因此，进一步阐明肥胖发展的潜在机制，并确定新的诊断生物标志物和治疗靶点是迫切和必要的。在这里，我们对 7 对不一致的同卵双胞胎的基因表达谱数据进行了差异基因表达分析和加权基因共表达网络分析（WGCNA），以鉴定与 BMI 相关的显著基因和特定模块。
在差异基因表达分析中，与同卵双胞胎的兄弟姐妹相比，双胞胎中 BMI 较高的双胞胎中 32 个差异表达基因（DEGs）呈上调趋势。GO 数据库中，一氧化氮合酶生物合成过程的正调控、NF-kappa B 入核的正调控和过氧化物酶活性的分类，以及 KEGG 数据库中 NF-kappa B 信号通路的分类显著富集。NAMPT、TLR9、PTGS2、HBD 和 PCSK1N 的 DEGs 可能与肥胖有关。在 WGCNA 中，在所鉴定的 20 个不同的共表达模块中，珊瑚 1 模块（68 个基因）与 BMI（r=0.56，P=0.04）和疾病状态（r=0.56，P=0.04）呈最强的正相关。GO 数据库中，该模块显著富集的分类包括磷脂酶活性的正调控、高密度脂蛋白颗粒清除、乳糜微粒残粒清除、胆固醇逆转运、中密度脂蛋白颗粒、乳糜微粒、低密度脂蛋白颗粒、极低密度脂蛋白颗粒、电压门控钾通道复合物、胆固醇转运蛋白活性和神经肽激素活性。KEGG 数据库中，酒精中毒和细胞粘附分子途径也显著富集。还鉴定了几个枢纽基因，如 GAL、ASB9、NPPB、TBX2、IL17C、APOE、ABCG4 和 APOC2。模块特征基因 saddlebrown 模块（212 个基因）也与 BMI 显著相关（r=0.56，P=0.04），并且 KCNN1 和 AQP10 的枢纽基因差异表达。
我们基于同卵双胞胎的基因表达谱数据，鉴定了与 BMI 相关的显著基因和特定模块。这些发现可能有助于进一步阐明肥胖发展的潜在机制，并为肥胖治疗的潜在基因生物标志物和信号通路提供新的见解。当获得更多的样本量时，对这里报告的发现进行进一步的分析和验证是重要且必要的。
