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.

Objective: We perform a comprehensive heritability study on multiple phenotypes related to metabolic syndrome using Chinese twins to assess the genetic and environmental effects in determining the variation and covariation of the phenotypes in the Chinese population. Methods: The studied sample contains 654 twins collected in the Qingdao municipality. A total of 10 phenotypes covering anthropometric measurements, plasma glucose levels, lipids, blood pressures etc. were examined. Univariate and bivariate structural equation models were fitted for assessing the genetic and environmental contributions. Results: The AE model combining additive genetic (A) and unique environmental (E) factors produced the best fit for all phenotypes except for triglyceride. Modest to high heritability estimates were obtained in univariate analysis ranging from 0.5 for total cholesterol to 0.78 for weight. The bivariate model estimated high genetic correlations between systolic and diastolic blood pressures, between total cholesterol and low density lipoprotein cholesterol, modest genetic correlations between BMI and blood pressures. No significant common environmental correlation was found between any pair of the phenotypes. Conclusions: Our results showed significant genetic contributions to the sub-phenotypes of metabolic syndrome. Although pleiotropic genetic control may exist for some physiologically similar phenotypes, our results do not support a common genetic mechanism among the phenotypes covered in our study.

Depression is currently the leading cause of disability around the world. We conducted an epigenome-wide association study (EWAS) in a sample of 58 depression score-discordant monozygotic twin pairs, aiming to detect specific epigenetic variants potentially related to depression and further integrate with gene expression profile data. Association between the methylation level of each CpG site and depression score was tested by applying a linear mixed effect model. Weighted gene co-expression network analysis (WGCNA) was performed for gene expression data. The association of DNA methylation levels of 66 CpG sites with depression score reached the level of P < 1 × 10⁻⁴. These top CpG sites were located at 34 genes, especially PTPRN2, HES5, GATA2, PRDM7, and KCNIP1. Many ontology enrichments were highlighted, including Notch signaling pathway, Huntington disease, p53 pathway by glucose deprivation, hedgehog signaling pathway, DNA binding, and nucleic acid metabolic process. We detected 19 differentially methylated regions (DMRs), some of which were located at GRIK2, DGKA, and NIPA2. While integrating with gene expression data, HELZ2, PTPRN2, GATA2, and ZNF624 were differentially expressed. In WGCNA, one specific module was positively correlated with depression score (r = 0.62, P = 0.002). Some common genes (including BMP2, PRDM7, KCNIP1, and GRIK2) and enrichment terms (including complement and coagulation cascades pathway, DNA binding, neuron fate specification, glial cell differentiation, and thyroid gland development) were both identified in methylation analysis and WGCNA. Our study identifies specific epigenetic variations which are significantly involved in regions, functional genes, biological function, and pathways that mediate depression disorder.

Although the correlation between cognition and physical function has been well studied in the general population, the genetic and environmental nature of the correlation has been rarely investigated. We conducted a classical twin analysis on cognitive and physical function, including forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), handgrip strength, five-times-sit-to-stand test (FTSST), near visual acuity, and number of teeth lost in 379 complete twin pairs. Bivariate twin models were fitted to estimate the genetic and environmental correlation between physical and cognitive function. Bivariate analysis showed mildly positively genetic correlations between cognition and FEV₁, r_G = 0.23 [95% CI: 0.03, 0.62], as well as FVC, r_G = 0.35 [95% CI: 0.06, 1.00]. We found that FTSST and cognition presented very high common environmental correlation, r_C = -1.00 [95% CI: -1.00, -0.57], and low but significant unique environmental correlation, r_E = -0.11 [95% CI: -0.22, -0.01], all in the negative direction. Meanwhile, near visual acuity and cognition also showed unique environmental correlation, r_E = 0.16 [95% CI: 0.03, 0.27]. We found no significantly genetic correlation for cognition with handgrip strength, FTSST, near visual acuity, and number of teeth lost. Cognitive function was genetically related to pulmonary function. The FTSST and cognition shared almost the same common environmental factors but only part of the unique environmental factors, both with negative correlation. In contrast, near visual acuity and cognition may positively share part of the unique environmental factors.

Serum uric acid (SUA), as the end product of purine metabolism, has proven emerging roles in human disorders. Here based on a sample of 379 middle and old-aged Chinese twin pairs, we aimed to explore the magnitude of genetic impact on SUA variation by performing sex-limitation twin modeling analyses and further detect specific genetic variants related to SUA by conducting a genome-wide association study. Monozygotic (MZ) twin correlation for SUA level (rMZ = 0.56) was larger than for dizygotic (DZ) twin correlation (rDZ = 0.39). The common effects sex-limitation model provided the best fit with additive genetic parameter (A) accounting for 46.3%, common or shared environmental parameter (C) accounting for 26.3% and unique/nonshared environmental parameter (E) accounting for 27.5% for females and 29.9, 33.1, and 37.0% for males, respectively. Although no SUA-related genetic variants reached genome-wide significance level, 25 SNPs were suggestive of association (P < 1 × 10⁻⁵). Most of the SNPs were located in an intronic region and detected to have regulatory effects on gene transcription. The cell-type specific enhancer of skeletal muscle was detected which has been reported to implicate SUA. Two promising genetic regions on chromosome 17 around rs2253277 and chromosome 14 around rs11621523 were found. Gene-based analysis found 167 genes nominally associated with SUA level (P < 0.05), including PTGR2, ENTPD5, well-known SLC2A9, etc. Enrichment analysis identified one pathway of transmembrane transport of small molecules and 20 GO gene sets involving in ion transport, transmembrane transporter activity, hydrolase activity acting on acid anhydrides, etc. In conclusion, SUA shows moderate heritability in women and low heritability in men in the Chinese population and genetic variations are significantly involved in functional genes and regulatory domains that mediate SUA level. Our findings provide clues to further elucidate molecular physiology of SUA homeostasis and identify new diagnostic biomarkers and therapeutic targets for hyperuricemia and gout.

Differential DNA methylation patterns of polycystic ovarian syndrome in whole blood of Chinese women