ABSTRACT
We previously reported preliminary characterization of adipose tissue (AT) dysfunction through the adiponectin/leptin ratio (ALR) and fasting/postprandial (F/P) gene expression in subcutaneous (SQ) adipose tissue (AT) biopsies obtained from participants in the GEMM study, a precision medicine research project. Here we present integrative data replication of previous findings from an increased number of GEMM symptom-free (SF) adults (N = 124) to improve characterization of early biomarkers for cardiovascular (CV)/immunometabolic risk in SF adults with AT dysfunction. We achieved this goal by taking advantage of the rich set of GEMM F/P 5 h time course data and three tissue samples collected at the same time and frequency on each adult participant (F/P blood, biopsies of SQAT and skeletal muscle (SKM)). We classified them with the presence/absence of AT dysfunction: low (<1) or high (>1) ALR. We also examined the presence of metabolically healthy (MH)/unhealthy (MUH) individuals through low-grade chronic subclinical inflammation (high sensitivity C-reactive protein (hsCRP)), whole body insulin sensitivity (Matsuda Index) and Metabolic Syndrome criteria in people with/without AT dysfunction. Molecular data directly measured from three tissues in a subset of participants allowed fine-scale multi-OMIC profiling of individual postprandial responses (RNA-seq in SKM and SQAT, miRNA from plasma exosomes and shotgun lipidomics in blood). Dynamic postprandial immunometabolic molecular endophenotypes were obtained to move towards a personalized, patient-defined medicine. This study offers an example of integrative translational research, which applies bench-to-bedside research to clinical medicine. Our F/P study design has the potential to characterize CV/immunometabolic early risk detection in support of precision medicine and discovery in SF individuals.
ABSTRACT
Inorganic arsenic is a well-known carcinogen associated with several types of cancer, but the mechanisms involved in arsenic-induced carcinogenesis are not fully understood. Recent evidence points to epigenetic dysregulation as an important mechanism in this process; however, the effects of epigenetic alterations in gene expression have not been explored in depth. Using microarray data and applying a multivariate clustering analysis in a Gaussian mixture model, we describe the alterations in DNA methylation around the promoter region and the impact on gene expression in HaCaT cells during the transformation process caused by chronic exposure to arsenic. Using this clustering approach, the genes were grouped according to their methylation and expression status in the epigenetic landscape, and the changes that occurred during the cellular transformation were identified adequately. Thus, we present a valuable method for identifying epigenomic dysregulation.