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Arsenic is a carcinogen, and long-term exposure to it may result in the development of multiorgan disease. Understanding the underlying intricate molecular network of toxicity and carcinogenicity is crucial for identifying a small set of differentially expressed biomarker genes to predict the risk of the exposed population. In this paper, a multiple kernel learning (MKL) embedded multi-objective swarm intelligence technique has been proposed to identify the candidate biomarker genes from the transcriptomic profile of arsenicosis samples. To achieve the optimal classification accuracy along with the minimum number of genes, a multi-objective random spatial local best particle swarm optimization (MO-RSplbestPSO) has been utilized. The proposed MO-RSplbestPSO also guides the multiple kernel learning mechanism which provides data specific classification. The proposed computational framework has been applied to the developed whole genome DNA microarray prepared using blood samples collected from a specific arsenic exposed area of the Indian state of West Bengal. A set of twelve biomarker genes, with four novel genes, are successfully identified for the classification of exposure to arsenic and its subcategories, which can be used as future prognostic biomarkers for screening of arsenic exposed populations. Also, the biological significance of each gene is detailed to delineate the complex molecular networking and mode of toxicity.

PURPOSE Graves' disease (GD) is an autoimmune disorder affecting primarily the thyroid gland. The most common extrathyroidal manifestation of GD is known as Graves' orbitopathy (GO). Bone marrow-derived fibrocytes represent a subset of monocytes in peripheral blood mononuclear cells (PBMCs), infiltrate the orbital tissues, and contribute to the pathogenesis of GO. Hence objectives of the study included whether the concentration of fibrocytes in peripheral blood was higher in GO, whether TSHR m RNA expression and TSHR surface expression in peripheral blood were higher in GO in comparison to Graves' Disease (GD) and Control subjects. METHODS The percentage of circulating fibrocytes (FC) along with TSHR on its cell surface (CD 34+, CD 45+, CXCR4+, Collagen 1+, TSHR+) were assessed by flow cytometry of 50 patients with GD and GO and 15 healthy donors (Control). TSHR mRNA expression was measured by q RT PCR. RESULT The concentration of circulating fibrocytes was significantly higher in GO compared to GD and control [GO 17% vs GD 3% vs control 0.7% (p

Chronic arsenic toxicity has become a global concern due to its adverse pathophysiological outcome and carcinogenic potential. It is already established that arsenic induced reactive oxygen species alters mitochondrial functionality. Major regulatory genes for mitochondrial biogenesis, i.e., PGC1α, Tfam, NRF1and NRF2 are located in the nucleus. As a result, mitochondria-nucleus crosstalk is crucial for proper mitochondrial function. This previous hypothesis led us to investigateinvolvement of epigenetic alteration behindenhanced mitochondrial biogenesis in chronic arsenic exposure. An extensive case-control study was conducted with 390 study participants (unexposed, exposed without skin lesion, exposed with skin lesion and exposed skin tumour) from highly arsenic exposed areas ofWest Bengal, India. Methylation specific PCRrevealed significant promoter hypomethylation oftwo key biogenesis regulatory genes, PGC1αandTfam in arsenic exposed individuals and also in skin tumour tissues. Linear regression analysis indicated significant negative correlation between urinary arsenic concentration and promoter methylation status. Increased expression of biogenesis regulatory genes wasobtained by quantitative real-time PCR analysis. Moreover, altered mitochondrial fusion-fission regulatory gene expression was also observed in skin tumour tissues. miR663, having tumour suppressor gene like function was known to be epigenetically regulated through mitochondrial retrograde signal. Promoter hypermethylation with significantly decreased expression of miR663 was found in skin cancer tissues compared to non-cancerous control tissue. In conclusion, results indicated crucial role of epigenetic alteration in arsenic induced mitochondrial biogenesis and arsenical skin carcinogenesis for the first time. However, further mechanistic studies are necessary for detailed understanding of mitochondria-nucleus crosstalk in arsenic perturbation.

Groundwater arsenic contamination has become a serious global concern due to its adverse effects on human health. Arsenic-induced reactive oxygen species trigger oxidative stress inside mitochondria, which initiate a cascade of events including altered mitochondrial (mt) membrane potential, uncoupling of electron transport chain, and mtDNA damage. A case-control study was conducted to examine the association between arsenic exposure and differences in mtDNA methylation and to assess the downstream consequences. We recruited 221 arsenic-exposed individuals, including 106 individuals with skin lesions (WSL) and 115 subjects without any skin lesions (WOSL) from the Murshidabad district, West Bengal, India. The unexposed group included 101 individuals from the arsenic unexposed area in East Midnapore. We analyzed the status of mtDNA methylation in D-loop region and ND6 gene by methylation-specific PCR. Gene expression was studied by quantitative real-time PCR. Significant hypomethylation in both D-loop and ND6 was observed with a consequent increase in their target gene expression and higher mtDNA copy number in arsenic-exposed populations compared to controls. Further mechanistic insights regarding mitochondrial epigenetic alteration in arsenic exposure will be of critical importance for the prevention of adverse health effects.

Arsenic exposure and its adverse health outcome, including the association with cancer risk are well established from several studies across the globe. The present study aims to analyze the epigenetic regulation of key mismatch repair (MMR) genes in the arsenic-exposed population.A case-control study was conducted involving two hundred twenty four (N=224) arsenic exposed [with skin lesion (WSL=110) and without skin lesion (WOSL=114)] and one hundred and two (N=102) unexposed individuals. The methylation status of key MMR genes i.e. MLH1, MSH2, and PMS2 were analyzed using methylation-specific PCR (MSP). The gene expression was studied by qRTPCR. The expression of H3K36me3, which was earlier reported to be an important regulator of MMR pathway, was assessed using ELISA.Arsenic-exposed individuals showed significant promoter hypermethylation (p0.0001) of MLH1 and MSH2 compared to those unexposed with consequent down-regulation in their gene expression [MLH1 (p=0.001) and MSH2 (p0.05)]. However, no significant association was found in expression and methylation of PMS2 with arsenic exposure. We found significant down-regulation of H3K36me3 in the arsenic-exposed group, most significantly in the WSL group (p0.0001). The expression of SETD2, the methyltransferase of an H3K36me3 moiety was found to be unaltered in arsenic exposure, suggesting the involvement of other regulatory factors yet to be identified.In summary, the epigenetic repression of DNA damage repair genes due to promoter hypermethylation of MLH1 and MSH2 and inefficient recruitment of MMR complex at the site of DNA damage owing to the reduced level of H3K36me3 impairs the mismatch repair pathway that might render the arsenic-exposed individuals more susceptible towards DNA damage and associated cancer risk.

Methylation of arsenic compounds in the human body occurs following a series of biochemical reactions in the presence of methyl donor S-adenosylmethionine (SAM) and catalyzed by arsenite methyltransferase (AS3MT). However, the extent and pattern of methylation differs among the arsenic exposed individuals leading to differential susceptibility. The mechanism for such inter-individual difference is enigmatic. In the present case-control study we recruited exposed individuals with and without arsenic induced skin lesion (WSL and WOSL), and an unexposed cohort, each having 120 individuals. Using ELISA, we observed a reduction in SAM levels (p < 0.05) in WSL compared to WOSL. Linear regression analysis revealed a negative correlation between urinary arsenic concentration and SAM concentration between the study groups. qRT-PCR revealed a significant down-regulation (p < 0.01) of key regulatory genes like MTHFR, MTR, MAT2A and MAT2B of SAM biogenesis pathway in WSL cohort. Methylation-specific PCR revealed significant promoter hypermethylation of AS3MT (WSL vs. WOSL: p < 0.01) which resulted in its subsequent transcriptional repression (WSL vs. WOSL: p < 0.001). Linear regression analysis also showed a negative correlation between SAM concentration and percentage of promoter methylation. Taken together, these results indicate that reduction in SAM biogenesis along with a higher utilization of SAM results in a decreased availability of methyl donor. These along with epigenetic down-regulation of AS3MT may be responsible for higher susceptibility in arsenic exposed individuals.

Long term exposure to arsenic may lead to the development of multi-organ ailments with several non-dermatological, non-malignant outcomes and hence considered as a paradoxical human carcinogen, requires major attention with comprehensive clinical data analysis. An experimental investigation has been pursued to analyze microarray data set and classify biologically arsenic affected genes endorsed by different machine learning approaches. Microarray data consists of a huge number of biologically insignificant genes. Thus it leads to an expensive computation and restrains the classification accuracy. A thorough and critical analysis has been performed to determine a small set of nonredundant genes to classify the arsenic disease. The analysis outcomes could be further put into action to interpret the therapeutic measures to be taken care of the disease. An experimental investigation has been carried out to deal with this population-based learning problem with the objective of classifying a the small set of gene expressions of the disease for particular demography. The analysis has been accomplished with the implementation of a hybrid metaheuristic optimization based machine learning methods for classification of arsenic disease into various classes.