Biomarkers linking PCB exposure and obesity

Abstract

Recently the prevalence of obesity has increased dramatically across much of the world. Obesity, as a complex, multifactorial disease, and its health consequences probably result from the interplay of environmental, genetic, and behavioral factors. Several lines of evidence support the theory that obesity is programmed during early development and that environmental exposures can play a key role. We therefore hypothesize that the current epidemic might associated with the influence of chemical exposures upon genetically controlled developmental pathways, leading to metabolic disorders. Some environmental chemicals, such as PCBs and pesticide residues, are widespread in food, drinking water, soil, and they exert multiple effects including estrogenic on cellular processes; some have been shown to affect the development of obesity, insulin resistance, type 2 diabetes, and metabolic syndrome. To bring these lines of evidence together and address an important health problem, this narrative review has been primarily designed to address PCBs exposures that have linked with human disease, obesity in particular, and to assess the effects of PCBs on gene expression in a highlyexposed population. The results strongly suggest that further research into the specific mechanisms of PCBs-associated diseases is warranted.

Fig. 1

The work flow illustrates the concepts outlining the evolution of our hypothesis and the main steps towards validation of metabolic disease risk in assessing the future disease risks in the vulnerable exposed population. This includes the global gene expression of the PCBs exposed population connecting the reported end points of particular phenotypes (metabolic disorders like obesity and type 2 diabetes) comparing the same disease in formation with public and shared database. The longitudinal studies are important to see the gene expression level over time and that not due to the ontogeny. Strong statistical data interpretation for exposure predisposition combined with Meta data analysis of the candidate genes is also recommended.

Fig. 2

Differentially expressed genes in the important signaling pathway and their connectivity while emphasized into metabolic disease and disorders in the 45 month PCBs exposed population in Slovakia between genes expressed (with ≥1.5 fold change, t-test, p <0.05) in a cellular level with some downstream effects, e.g.; Gene expression, Differentiation, Cell survival, etc. Geometric figures in red denote up-regulated genes and those are green indicate down-regulation. Genes in the top networks were allowed to grow our pathway with the direct/indirect relationship from the IPA knowledge base with the stringent filter, experimentally observed, those who were only from human study. Canonical pathways (functions/signaling; CP) viz., Insulin Receptor signaling, Type 1 and Type 2 Diabetes Mellitus Signaling, Maturity-Onset Diabetes of Young (MODY) signaling that are highly represented are shown within the box. Genes in uncolored notes were not identified as differentially expressed in our experiment and were integrated into computational generated networks based on evidence stored in the IPA knowledge base.

Fig. 3

Quantitative Real-time PCR (qRT-PCR) validation of the selected genes (ARNT and RRAD) by Taqman Low Density Array (TLDA) in ABI platform (7900HT Fast Real-Time PCR System) after analyzed by SDS RQ Manager Version 1.2.1. The relative quantification of the genes showing up/down-regulation among the subjects in a short population (n=71) validation. The other panels (in inset) with the respective genes represent the relative quantification of the genes with the same gene transcript that has been used in in vitro studies (n=6). The relative quantification is calculated in contrast to calibrator samples, i.e.; no-exposure in in vitro studies and the subjects with no/background PCBs exposures in the population studies.

Fig. 4

Our envision on the future application of PCBs biomarkers, if validated through large-scale population, is represented here. It will use non-invasive gene expression tools to study the early pathogenesis of the disease, before the clinical symptoms arise. There is an even greater need to change our focus from treating diseases after they are detected to prevention. The immediate applications of our findings might include using microarrays to generate unique gene expression profiles (fingerprints or signatures) that would also provide markers through high-throughput assays that are more sensitive and predictive. This will not only identify the disease probability at an early stage, even as early as at the time of birth, but also able to monitor the disease stages in progression over time. This will help us to develop diagnosis assay products for multiple chronic diseases, where few or no products are available on transcriptional gene expression profiling towards practical interference, which we can expect the greatest impact, in terms of timely interventions that improve health, in coming years.