A State-of-the-Science Review of Arsenic's Effects on Glucose Homeostasis in Experimental Models.

BackgroundThe prevalence of type 2 diabetes (T2D) has more than doubled since 1980. Poor nutrition, sedentary lifestyle, and obesity are among the primary risk factors. While an estimated 70% of cases are attributed to excess adiposity, there is an increased interest in understanding the contribution of environmental agents to diabetes causation and severity. Arsenic is one of these environmental chemicals, with multiple epidemiology studies supporting its association with T2D. Despite extensive research, the molecular mechanism by which arsenic exerts its diabetogenic effects remains unclear.ObjectivesWe conducted a literature search focused on arsenite exposure in vivo and in vitro, using relevant end points to elucidate potential mechanisms of oral arsenic exposure and diabetes development.MethodsWe explored experimental results for potential mechanisms and elucidated the distinct effects that occur at high vs. low exposure. We also performed network analyses relying on publicly available data, which supported our key findings.ResultsWhile several mechanisms may be involved, our findings support that arsenite has effects on whole-body glucose homeostasis, insulin-stimulated glucose uptake, glucose-stimulated insulin secretion, hepatic glucose metabolism, and both adipose and pancreatic β-cell dysfunction.DiscussionThis review applies state-of-the-science approaches to identify the current knowledge gaps in our understanding of arsenite on diabetes development. https://doi.org/10.1289/EHP4517

Persistent transcriptional responses show the involvement of feed-forward control in a repeated dose toxicity study

Chemical carcinogenesis, albeit complex, often relies on modulation of transcription through activation or repression of key transcription factors. While analyzing extensive networks may hinder the biological interpretation, one may focus on dynamic network motifs, among which persistent feed-forward loops (FFLs) are known to chronically influence transcriptional programming. Here, to investigate the relevance a FFL-oriented approach in depth, we have focused on aflatoxin B1-induced transcriptomic alterations during distinct states of exposure (daily administration during 5 days followed by a non-exposed period) of human hepatocytes, by exploring known interactions in human transcription. Several TF-coding genes were persistently deregulated after washout of AFB1. Oncogene MYC was identified as the prominent regulator and driver of many FFLs, among which a FFL comprising MYC/HIF1A was the most recurrent. The MYC/HIF1A FFL was also identified and validated in an independent set as the master regulator of metabolic alterations linked to initiation and progression of carcinogenesis, i.e. the Warburg effect, possibly as result of persistent intracellular alterations arising from AFB1 exposure (nuclear and mitochondrial DNA damage, oxidative stress, transcriptional activation by secondary messengers). In summary, our analysis shows the involvement of FFLs as modulators of gene expression suggestive of a carcinogenic potential even after termination of exposure

Formaldehyde and Brain Disorders: A Meta-Analysis and Bioinformatics Approach

While there is significant investigation and investment in brain and neurodegenerative disease research, current understanding of the etiologies of illnesses like Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and brain cancer remains limited. Environmental exposure to the pollutant formaldehyde, an emerging neurotoxin widely used in industry, is suspected to play a critical role in mediating these disorders, although findings are limited and inconsistent. Focusing on highly exposed groups, we performed a meta-analysis of human epidemiological studies of formaldehyde and neurodegenerative disease (N = 19) or brain tumors (N = 12). To assess the biological plausibility of observed associations, we then conducted a bioinformatics analysis using WikiPathways and the Comparative Toxicogenomics Database and identified candidate genes and pathways that may be related to these interactions. We reported the meta-relative risk (meta-RR) of ALS following high exposures to formaldehyde was increased by 78% (meta-RR = 1.78, 95% confidence interval, CI 1.20–2.65). Similarly, the meta-RR for brain cancer was increased by 71% (meta-RR = 1.71; 95% CI 1.07–2.73) among highly exposed individuals. Multiple sensitivity analyses did not reveal sources of heterogeneity or bias. Our bioinformatics analysis revealed that the oxidative stress genes superoxide dismutase (SOD1, SOD2) and the pro-inflammatory marker tumor necrosis factor (TNF) were identified as the top relevant genes, and the folate metabolism, vitamin B(12) metabolism, and the ALS pathways were highly affected by formaldehyde and related to the most brain diseases of interest. Further inquiry revealed the two metabolic pathways are also intimately tied with the formaldehyde cycle. Overall, our bioinformatics analysis supports the link of formaldehyde exposure to ALS or brain tumor reported from our meta-analysis. This new multifactorial approach enabled us to both interrogate the robustness of the epidemiological data and identify genes and pathways that may be involved in these interactions, ultimately lending strong evidence and potential biological plausibility for the association between formaldehyde exposure and brain disease

Deliverable Raport D4.4 Mechanism-ofaction Modelling Tools

This deliverable describes the mechanistic modelling task which was developed within WP 4 of eNanoMapper project. Three main issues were handled in this task: 1) we initiated a ENMs portal with nanosafety-relevant pathways to WikiPathways, an open database for biological pathways, as a basis for pathway analysis; 2) we established workflows for ENMs data analysis, (mainly transcriptomics data), to reveal the significantly and differently affected biological pathways by a variety of exposure scenarios and ENMs and we added this information to the eNanoMapper database. 3) Additionally, we are further describing the implementation of pathway descriptors, an integration methodology to group omics data for QSAR modelling. A case study is presented for descriptors created with information derived from gene ontologies using a proteomics data set