Altered DNA methylation in liver and adipose tissues derived from individuals with obesity and type 2 diabetes.

BACKGROUND: Obesity is a well-recognized risk factor for insulin resistance and type 2 diabetes (T2D), although the precise mechanisms underlying the relationship remain unknown. In this study we identified alterations of DNA methylation influencing T2D pathogenesis, in subcutaneous and visceral adipose tissues, liver, and blood from individuals with obesity. METHODS: The study included individuals with obesity, with and without T2D. From these patients, we obtained samples of liver tissue (n = 16), visceral and subcutaneous adipose tissues (n = 30), and peripheral blood (n = 38). We analyzed DNA methylation using Illumina Infinium Human Methylation arrays, and gene expression profiles using HumanHT-12 Expression BeadChip Arrays. RESULTS: Analysis of DNA methylation profiles revealed several loci with differential methylation between individuals with and without T2D, in all tissues. Aberrant DNA methylation was mainly found in the liver and visceral adipose tissue. Gene ontology analysis of genes with altered DNA methylation revealed enriched terms related to glucose metabolism, lipid metabolism, cell cycle regulation, and response to wounding. An inverse correlation between altered methylation and gene expression in the four tissues was found in a subset of genes, which were related to insulin resistance, adipogenesis, fat storage, and inflammation. CONCLUSIONS: Our present findings provide additional evidence that aberrant DNA methylation may be a relevant mechanism involved in T2D pathogenesis among individuals with obesity

Reconstruction of ancient microbial genomes from the human gut

Loss of gut microbial diversity in industrial populations is associated with chronic diseases, underscoring the importance of studying our ancestral gut microbiome. However, relatively little is known about the composition of pre-industrial gut microbiomes. Here we performed a large-scale de novo assembly of microbial genomes from palaeofaeces. From eight authenticated human palaeofaeces samples (1,000–2,000 years old) with well-preserved DNA from southwestern USA and Mexico, we reconstructed 498 medium- and high-quality microbial genomes. Among the 181 genomes with the strongest evidence of being ancient and of human gut origin, 39% represent previously undescribed species-level genome bins. Tip dating suggests an approximate diversification timeline for the key human symbiont Methanobrevibacter smithii. In comparison to 789 present-day human gut microbiome samples from eight countries, the palaeofaeces samples are more similar to non-industrialized than industrialized human gut microbiomes. Functional profiling of the palaeofaeces samples reveals a markedly lower abundance of antibiotic-resistance and mucin-degrading genes, as well as enrichment of mobile genetic elements relative to industrial gut microbiomes. This study facilitates the discovery and characterization of previously undescribed gut microorganisms from ancient microbiomes and the investigation of the evolutionary history of the human gut microbiota through genome reconstruction from palaeofaeces

Towards precision medicine: defining and characterizing adipose tissue dysfunction to identify early immunometabolic risk in symptom-free adults from the GEMM family study

Interactions between macrophages and adipocytes are early molecular factors influencing adipose tissue (AT) dysfunction, resulting in high leptin, low adiponectin circulating levels and low-grade metaflammation, leading to insulin resistance (IR) with increased cardiovascular risk. We report the characterization of AT dysfunction through measurements of the adiponectin/leptin ratio (ALR), the adipo-insulin resistance index (Adipo-IRi), fasting/postprandial (F/P) immunometabolic phenotyping and direct F/P differential gene expression in AT biopsies obtained from symptom-free adults from the GEMM family study. AT dysfunction was evaluated through associations of the ALR with F/P insulin-glucose axis, lipid-lipoprotein metabolism, and inflammatory markers. A relevant pattern of negative associations between decreased ALR and markers of systemic low-grade metaflammation, HOMA, and postprandial cardiovascular risk hyperinsulinemic, triglyceride and GLP-1 curves was found. We also analysed their plasma non-coding microRNAs and shotgun lipidomics profiles finding trends that may reflect a pattern of adipose tissue dysfunction in the fed and fasted state. Direct gene differential expression data showed initial patterns of AT molecular signatures of key immunometabolic genes involved in AT expansion, angiogenic remodelling and immune cell migration. These data reinforce the central, early role of AT dysfunction at the molecular and systemic level in the pathogenesis of IR and immunometabolic disorders

Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms

Type 2 diabetes (T2D) affects Latinos at twice the rate seen in populations of European descent. We recently identified a risk haplotype spanning SLC16A11 that explains ∼20% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s). We show that variants on the T2D-associated haplotype have two distinct effects: (1) decreasing SLC16A11 expression in liver and (2) disrupting a key interaction with basigin, thereby reducing cell-surface localization. Both independent mechanisms reduce SLC16A11 function and suggest SLC16A11 is the causal gene at this locus. To gain insight into how SLC16A11 disruption impacts T2D risk, we demonstrate that SLC16A11 is a proton-coupled monocarboxylate transporter and that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism that are associated with increased T2D risk. Our findings suggest that increasing SLC16A11 function could be therapeutically beneficial for T2D. Video Abstract [Figure presented] Keywords: type 2 diabetes (T2D); genetics; disease mechanism; SLC16A11; MCT11; solute carrier (SLC); monocarboxylates; fatty acid metabolism; lipid metabolism; precision medicin

Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

Penetrance of variants in monogenic disease and clinical utility of common polygenic variation has not been well explored on a large-scale. Here, the authors use exome sequencing data from 77,184 individuals to generate penetrance estimates and assess the utility of polygenic variation in risk prediction of monogenic variants

Genomic of metabolic diseases

Tema del mesLas enfermedades metabólicas (EMet) son un problema de salud pública y México es uno de los países con prevalencia más elevada. En los últimos años se han identificado varios polimorfismos asociados al riesgo para desarrollar EMet. El conocimiento de la base genética de la enfermedad está evolucionando gracias a los avances tecnológicos y a la capacidad de reunir grandes colecciones de pacientes. Idealmente, en un futuro se incluirán en el algoritmo pronóstico del “riesgo de EMet” marcadores genéticos que incidan en la estratificación del paciente con EMet, predicción de la gravedad de las manifestaciones clínicas y definición de la terapia para cada uno de los pacientes. Actualmente se están identificando polimorfismos que son muy frecuentes en población de origen amerindio, como la variante R230C del gen ABCA1 y un haplotipo en el gen SLC16A11, fuertemente asociados con bajos niveles de colesterol HDL, obesidad y diabetes mellitus tipo 2. La medicina personalizada seguirá mejorando a medida que la lista de marcadores y la identificación de interacciones gen-gen aumenten. Por todo lo anterior, es evidente la imperiosa necesidad de entender cómo estos polimorfismos cambian la biología molecular y celular del metabolismo y predisponen al desarrollo de las diferentes EMet.Metabolic diseases are a major public health and Mexico is one of the countries with the highest prevalence. Recently, have been identified several polymorphisms associated with risk for developing metabolic disorders. Knowledge of the genetic basis of the disease is improving due to technological advances and the ability to raise large collections of patients. It is possible that soon a "metabolic disorders risk" prognostic algorithm will include genetic markers for patient stratification, prediction of disease manifestations and definition of targeted therapy. Currently are being identified polymorphisms that are much more common in individuals with Native American ancestry than in other populations, such as R230C variant in the ABCA1 gene and a haplotype in the SLC16A11 gene, strongly associated with low levels of HDL cholesterol, obesity, and type 2 diabetes. Personalized medicine will continue to improve as the list of number of markers and identification of gene-gene and gene-environment interactions increase. It is clear the urgent need to understand how these polymorphisms change the molecular and cell biology of the metabolism pathways that predispose to the development of the different metabolic diseases

Total Antioxidant Capacity in Obese and Non-Obese Subjects and Its Association with Anthropo-Metabolic Markers: Systematic Review and Meta-Analysis

The total antioxidant capacity (TAC) has been related to the development of and complications associated with chronic diseases, but its importance during obesity is not entirely clear. We conducted a systematic review and meta-analysis to clarify whether there are differences or similarities in the TAC between subjects with obesity (SO) and subjects with normal weight (NW). Following the recommendations of PRISMA and Cochrane, we performed a systematic search in the PubMed, Scopus, Web of Science, Cochrane, and PROSPERO databases, identifying 1607 studies. Among these, 22 studies were included in the final analysis, comprising 3937 subjects (1665 SO and 2272 NW) in whom serum TAC was measured, and from these 19,201 subjects, the correlation of serum TAC with anthropo-metabolic parameters was also estimated. The Newcastle–Ottawa method was used for the evaluation of the risk of bias. Using a random-effect model (REM), TAC was reduced in SO independently of age (SMD, −0.86; 95% CI −1.38 to −0.34; p = 0.0012), whereas malondialdehyde (SMD, 1.50; 95% CI 0.60 to 2.41), oxidative stress index (SMD, 1.0; 95% CI 0.16 to 1.84), and total oxidant status (SMD, 0.80; 0.22 to 1.37) were increased. There were seven significant pooled correlations of TAC with anthropometric and metabolic parameters: weight (r = −0.17), hip circumference (r= −0.11), visceral adipose index (r = 0.29), triglycerides (r = 0.25), aspartate aminotransferase (r = 0.41), alanine aminotransferase (r = 0.38), and uric acid (r = 0.53). Our results confirm a decrease in TAC and an increase in markers of oxidative stress in SO and underpin the importance of these serum biomarkers in obesity

Correction: Whole genome variation in 27 Mexican indigenous populations, demographic and biomedical insights.

[This corrects the article DOI: 10.1371/journal.pone.0249773.]

Additional file 1 of Altered DNA methylation in liver and adipose tissues derived from individuals with obesity and type 2 diabetes

Figure S1. Clustering of methylation data from tissue samples from individuals with obesity. Figure S2. Comparison of methylation averages among tissue types. Figure S3. Comparison of DMCs between different tissues. Figure S4. Differential gene expression. Table S1. List of DMCs in WB in the comparison between the DO and NDO groups. Table S2. List of DMCs in SAT in the comparison between the DO and NDO groups. Table S3. List of DMCs in VAT in the comparison between the DO and NDO groups. Table S4. List of DMCs in LT in the comparison between the DO and NDO groups. Table S5. Gene ontology enrichment analysis using the genes with DMCs in SAT. Table S6. Gene ontology enrichment analysis using the genes with DMCs in VAT. Table S7. Gene ontology enrichment analysis using the genes with DMCs in LT. Table S8. Differential gene expression in WB in the comparison between DO and NDO groups. Table S9. Differential gene expression in SAT in the comparison between DO and NDO groups. Table S10. Differential gene expression in VAT in the comparison between DO and NDO groups. Table S11. Differential gene expression in LT in the comparison between DO and NDO groups. Table S12. List of genes with correlation between alteration of DNA methylation and differential gene expression in WB. Table S13. List of genes with correlation between alteration of DNA methylation and differential gene expression in SAT. Table S14. List of genes with correlation between alteration of DNA methylation and differential gene expression in VAT. Table S15. List of genes with correlation between alteration of DNA methylation and differential gene expression in LT. Table S16. Gene ontology enrichment analysis using the genes with correlation between alteration of DNA methylation and differential gene expression. (PDF 3440 kb