Deciphering the Relationship between Obesity and Various Diseases from a Network Perspective

Integrative network analysis of differentially methylated regions to study the impact of gestational weight gain on maternal metabolism and fetal-neonatal growth

Integrative network analysis (INA) is important for identifying gene modules or epigenetically regulated molecular pathways in diseases. This study evaluated the effect of excessive gestational weight gain (EGWG) on INA of differentially methylated regions, maternal metabolism and offspring growth. Brazilian women from "The Araraquara Cohort Study" with adequate pre-pregnancy body mass index were divided into EGWG (n=30) versus adequate gestational weight gain (AGWG, n=45) groups. The methylome analysis was performed on maternal blood using the Illumina MethylationEPIC BeadChip. Fetal-neonatal growth was assessed by ultrasound and anthropometry, respectively. Maternal lipid and glycemic profiles were investigated. Maternal triglycerides-TG (p=0.030) and total cholesterol (p=0.014); fetus occipito-frontal diameter (p=0.005); neonate head circumference-HC (p=0.016) and thoracic perimeter (p=0.020) were greater in the EGWG compared to the AGWG group. Multiple linear regression analysis showed that maternal DNA methylation was associated with maternal TG and fasting insulin, fetal abdominal circumference, and fetal and neonate HC. The DMRs studied were enriched in 142 biological processes, 21 molecular functions,and 17 cellular components with terms directed for the fatty acids metabolism. Three DMGMs were identified:COL3A1, ITGA4 and KLRK1. INA targeted chronic diseases and maternal metabolism contributing to an epigenetic understanding of the involvement of GWG in maternal metabolism and fetal-neonatal growth.

Sedentary behavior, physical activity, sleep duration and obesity risk: Mendelian randomization study

BACKGROUND

Observational studies have suggested associations between sedentary behaviors (SB), physical activity (PA), sleep duration (SD), and obesity, but the causal relationships remain unclear.

METHODS

We used Mendelian randomization (MR) with genetic variation as instrumental variables (IVs) to assess the causality between SB/PA/SD and obesity. Genetic variants associated with SB/PA/SD were obtained from Genome-wide association study (GWAS), and obesity data came from FinnGen. The primary MR analysis used the instrumental variable weighted (IVW) method, with sensitivity tests including Cochran Q, MR-Egger intercepts, and MR-Radial. Expression Quantitative Trait Loci (eQTL) analysis was applied to identify significant genetic associations and biological pathways in obesity-related tissues.

RESULTS

The MR analysis revealed causal relationships between four SB-related lifestyle patterns and obesity. Specifically, increased genetic liability to television watching (IVW MR Odds ratio [OR] = 1.55, [95% CI]:[1.27, 1.90], p = 1.67×10-5), computer use ([OR] = 1.52, [95% CI]:[1.08, 2.13], p = 1.61×10-2), leisure screen time (LST) ([OR] = 1.62, [95% CI] = [1.43, 1.84], p = 6.49×10-14, and driving (MR [OR] = 2.79, [95% CI]:[1.25, 6.21], p = 1.23×10-2) was found to increase the risk of obesity. Our findings indicate that no causal relationships were observed between SB at work, sedentary commuting, PA, SD, and obesity. The eQTL analysis revealed strong associations between specific genes (RPS26, TTC12, CCDC92, NICN1) and SNPs (rs10876864, rs2734849, rs4765541, rs7615206) in both subcutaneous and visceral adipose tissues, which are associated with these SBs. Enrichment analysis further revealed that these genes are involved in crucial biological pathways, including cortisol synthesis, thyroid hormone synthesis, and insulin secretion.

CONCLUSIONS

Our findings support a causal relationship between four specific SBs (LST, television watching, computer use, driving) and obesity. These results provide valuable insights into potential interventions to address obesity effectively, supported by genetic associations in the eQTL and enrichment analysis. Further research and public health initiatives focusing on reducing specific SBs may be warranted.

Inherited Epigenetic Hallmarks of Childhood Obesity Derived from Prenatal Exposure to Obesogens

Childhood obesity has reached epidemic levels in developed countries and is becoming a major cause for concern in the developing world. The causes of childhood obesity are complex and multifactorial, involving the interaction between individual genetics and environmental and developmental factors. Among the environmental factors, there is a growing interest in understanding the possible relationship between the so-called environmental obesogens and the development of obesity in children. Exposure to these obesogens such as phthalates, bisphenol A, or parabens, has been identified as a promoter of obesity through different mechanisms such as the alteration of adipocyte development from mesenchymal progenitors, the interference with hormone receptors, and induced inflammation. However, less attention has been paid to the inheritance of epigenetic modifications due to maternal exposure to these compounds during pregnancy. Thus, the aim of this review is to summarize the current knowledge of epigenetic modifications due to maternal exposure to those obesogens during pregnancy as well as their potential implication on long-term obesity development in the offspring and transgenerational inheritance of epiphenotypes.

tensorGSEA: Detecting Differential Pathways in Type 2 Diabetes via Tensor-Based Data Reconstruction

Detecting significant signaling pathways in disease progression highlights the dysfunctions and pathogenic mechanisms of complex disease development. Since tensor decomposition has been proven effective for multi-dimensional data representation and reconstruction, differences between original and tensor-processed data are expected to extract crucial information and differential indication. This paper provides a tensor-based gene set enrichment analysis, called tensorGSEA, based on a data reconstruction method to identify relevant significant pathways during disease development. As a proof-of-concept study, we identify the differential pathways of diabetes in rats. Specifically, we first arrange gene expression profiles of each documented pathway as tensors with three dimensions: genes, samples, and periods. Then we compress tensors into core tensors with lower ranks. The pathways with lower reconstruction rates are obtained after reconstructing gene expression profiles in another state via these cores. Thus, differences underlying pathways are extracted by cross-state data reconstruction between controls and diseases. The experiments reveal several critical pathways with diabetes-specific functions which otherwise cannot be identified by alternative methods. Our proposed tensorGSEA is efficient in evaluating pathways by achieving their empirical statistical significance, respectively. The classification experiments demonstrate that the selected pathways can be implemented as biomarkers to identify the diabetic state. The code of tensorGSEA is available at https://github.com/zhxr37/tensorGSEA .

Altered glucocorticoid metabolism in girls with central obesity

BACKGROUND

Dysregulation of glucocorticoid metabolism is known to be a causative factor of obesity. However, only a few studies have evaluated the enzymatic activities involved in glucocorticoid metabolism in the pediatric population.

OBJECTIVE

To examine whether circulating glucocorticoid metabolites and their ratios reflecting the activities of metabolic enzyme are associated with obesity and body composition in girls.

METHODS

A total of 227 girls aged 7-13 years (131 control, 45 overweight, 51 obese) were enrolled in this study. Serum concentrations of glucocorticoids (11-deoxycortisol, cortisol, tetrahydrocortisol [THF], allo-THF, allo-dihydrocortisol [allo-DHF], and cortisone) were evaluated by gas chromatography-mass spectrometry. Enzyme activities corresponding to the ratios of cortisol and cortisone to their respective precursors and metabolites were also assessed.

RESULTS

Serum levels of allo-THF were significantly higher in obese girls compared with those in overweight and control girls (P = 0.018); however, concentrations of other cortisol metabolites were not significantly different between the groups studied. According to the severity of obesity, increasing trends in the metabolic ratios reflecting the activity of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) [(cortisol + allo-DHF + allo-THF + THF)/cortisone], relative 5α/5β-reductase [allo-THF/THF] activity, and 3α-HSD [allo-THF/allo-DHF] activity, were noted (P-for-trend <0.05). Body fat percentage and waist-to-height ratio positively correlated with the activities of 11β-HSD1 and 3α-HSD (P < 0.05). Following covariate control, girls with central obesity demonstrated significantly higher metabolic ratios reflecting 11β-HSD1, relative 5α/5β-reductase, and 3α-HSD activities (P < 0.05).

CONCLUSIONS

We found an altered glucocorticoid metabolism suggesting increased production of cortisol by 11β-HSD1 and increased metabolic clearance of cortisol catalyzed by 3α-HSD in girls with central obesity.

Cross talk of chromosome instability, CpG island methylator phenotype and mismatch repair in colorectal cancer

Colorectal cancer is a severe cancer associated with a high prevalence and fatality rate. There are three major mechanisms for colorectal cancer: (1) Chromosome instability (CIN), (2) CpG island methylator phenotype (CIMP) and (3) mismatch repair (MMR), of which CIN is the most common type. However, these subtypes are not exclusive and overlap. To investigate their biological mechanisms and cross talk, the gene expression profiles of 585 colorectal cancer patients with CIN, CIMP and MMR status records were collected. By comparing the CIN+ and CIN-samples, CIMP+ and CIMP-samples, MMR+ and MMR-samples with minimal redundancy maximal relevance (mRMR) and incremental feature selection (IFS) methods, the CIN, CIMP and MMR associated genes were selected. Unfortunately, there was little direct overlap among them. To investigate their indirect interactions, downstream genes of CIN, CIMP and MMR were identified using the random walk with restart (RWR) method and a greater overlap of downstream genes was indicated. The common downstream genes were involved in biosynthetic and metabolic pathways. These findings were consistent with the clinical observation of wide range metabolite aberrations in colorectal cancer. To conclude, the present study gave a gene level explanation of CIN, CIMP and MMR, but also showed the network level cross talk of CIN, CIMP and MMR. The common genes of CIN, CIMP and MMR may be useful for cross-subtype general colorectal cancer drug development.