Mathematical model insights into arsenic detoxification

Nutrition, one-carbon metabolism and arsenic methylation

Exposure to arsenic (As) is a major public health concern globally. Inorganic As (InAs) undergoes hepatic methylation to form monomethyl (MMAs)- and dimethyl (DMAs)-arsenical species, facilitating urinary As elimination. MMAsIII is considerably more toxic than either InAsIII or DMAsV, and a higher proportion of MMAs in urine has been associated with risk for a wide range of adverse health outcomes. Efficiency of As methylation differs substantially between species, between individuals, and across populations. One-carbon metabolism (OCM) is a biochemical pathway that provides methyl groups for the methylation of As, and is influenced by folate and other micronutrients, such as vitamin B12, choline, betaine and creatine. A growing body of evidence has demonstrated that OCM-related micronutrients play a critical role in As methylation. This review will summarize observational epidemiological studies, interventions, and relevant experimental evidence examining the role that OCM-related micronutrients have on As methylation, toxicity of As, and risk for associated adverse health-related outcomes. There is fairly robust evidence supporting the impact of folate on As methylation, and some evidence from case-control studies indicating that folate nutritional status influences risk for As-induced skin lesions and bladder cancer. However, the potential for folate to be protective for other As-related health outcomes, and the potential beneficial effects of other OCM-related micronutrients on As methylation and risk for health outcomes are less well studied and warrant additional research.

Arsenic toxicokinetic modeling and risk analysis: Progress, needs and applications

Arsenic (As) poses unique challenges in PBTK model development and risk analysis applications. Arsenic metabolism is complex, adequate information to attribute specific metabolites to particular adverse effects in humans is sparse, and measurement of relevant metabolites in biological media can be difficult. Multiple As PBTK models have been published and used or adapted for use in various exposure and risk analysis applications. These applications illustrate the broad utility of PBTK models for exposure and dose-response analysis, particularly for arsenic where multi-pathway, multi-route exposures and multiple toxic effects are of concern. Arsenic PBTK models have been used together with exposure reconstruction and dose-response functions to estimate risk of specific adverse health effects due to drinking water exposure and consumption of specific foodstuffs (e.g. rice, seafood), as well as to derive safe exposure levels and develop consumption advisories. Future refinements to arsenic PBTK models can enhance the confidence in such analyses. Improved estimates for methylation biotransformation parameters based on in vitro to in vivo extrapolation (IVIVE) methods and estimation of interindividual variability in key model parameters for specific toxicologically relevant metabolites are two important areas for consideration.

The association of tryptophan and phenylalanine are associated with arsenic-induced skin lesions in a Chinese population chronically exposed to arsenic via drinking water: a case-control study

OBJECTIVES

We investigated the association of specific serum amino acids (AAs) with the odds of arsenic-induced skin lesions (AISL) and their ability to distinguish patients with AISL from people chronically exposed to arsenic.

DESIGN

Case-control study.

SETTING

Three arsenic-exposed villages in Wuyuan County, Hetao Plain, Inner Mongolia, China were evaluated.

PARTICIPANTS

Among the 450 residents aged 18-79 years, who were chronically exposed to arsenic via drinking water, 56 were diagnosed as having AISL (defined as cases). Another 56 participants without AISL, matched by gender and age (±1 year) from the same population, were examined as controls.

MAIN OUTCOME MEASURES AND METHODS

AA levels were determined by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based metabolomics analysis. Potential confounding variables were identified via a standardised questionnaire and clinical examination. Multivariable conditional logistic regression model and receiver operating characteristic curve analyses were performed to investigate the relationship between specific AAs and AISL.

RESULTS

Tryptophan and phenylalanine levels were negatively associated with AISL (p<0.05). Compared with that in the first quartile, the adjusted OR of AISL in the second, third and fourth quartiles were decreased by 44%, 88% and 79% for tryptophan and 30%, 80% and 80% for phenylalanine, respectively. The combination of these two higher-level AAs showed the lowest OR for AISL (OR=0.08; 95% CI 0.02 to 0.25; p<0.001). Furthermore, both AAs showed a moderate ability to distinguish patients with AISL from the control, with the area under the curve (AUC; 95% CI) as 0.67 (0.57 to 0.77) for tryptophan and 0.70 (0.60 to 0.80) for phenylalanine (p<0.05). The combined pattern with AUC (95% CI) was 0.72 (0.62 to 0.81), showing a sensitivity of 76.79% and specificity of 58.93% (p<0.001).

CONCLUSIONS

Specific AAs may be linked to AISL and play important roles in early AISL identification.

TRIAL REGISTRATION NUMBER

NCT02235948.

Nutritional Influences on One-Carbon Metabolism: Effects on Arsenic Methylation and Toxicity

Exposure to inorganic arsenic (InAs) via drinking water and/or food is a considerable worldwide problem. Methylation of InAs generates monomethyl (MMAs^III+V)- and dimethyl (DMAs^III+V)-arsenical species in a process that facilitates urinary As elimination; however, MMAs is considerably more toxic than either InAs or DMAs. Emerging evidence suggests that incomplete methylation of As to DMAs, resulting in increased MMAs, is associated with increased risk for a host of As-related health outcomes. The biochemical pathway that provides methyl groups for As methylation, one-carbon metabolism (OCM), is influenced by folate and other micronutrients, including choline and betaine. Individuals and species differ widely in their ability to methylate As. A growing body of research, including cell-culture, animal-model, and epidemiological studies, has demonstrated the role of OCM-related micronutrients in As methylation. This review examines the evidence that nutritional status and nutritional interventions can influence the metabolism and toxicity of As, with a primary focus on folate.

The Association of Arsenic Metabolism with Cancer, Cardiovascular Disease, and Diabetes: A Systematic Review of the Epidemiological Evidence

BACKGROUND

The available evidence on the role of arsenic metabolism in individual susceptibility to the development of cancer, cardiovascular disease, and diabetes has not been formally and comprehensively reviewed.

OBJECTIVES

Our goal was to systematically investigate the association of arsenic metabolism with cancer, cardiovascular disease, and diabetes-related outcomes in epidemiologic studies. As a secondary objective, we characterized the variation of arsenic metabolism in different populations worldwide.

METHODS

We searched Medline/PubMed and EMBASE from inception to January 2016 and applied predetermined exclusion criteria. Compositional data analysis was used to describe the distribution of arsenic metabolism biomarkers and evaluate the association between arsenic exposure and metabolism.

RESULTS

Twenty-eight studies met the inclusion criteria, 12 on cancer, nine on cardiovascular disease, and seven on diabetes-related outcomes. The median (interquartile range) for mean iAs%, MMA%, and DMA% was 11.2 (7.8-14.9)%, 13.0 (10.4-13.6)%, and 74.9 (69.8-80.0)%, respectively. Findings across studies suggested that higher arsenic exposure levels were associated with higher iAs% and lower DMA% and not associated with MMA%. For cancer, most studies found a pattern of higher MMA% and lower DMA% associated with higher risk of all-site, urothelial, lung, and skin cancers. For cardiovascular disease, higher MMA% was generally associated with higher risk of carotid atherosclerosis and clinical cardiovascular disease but not with hypertension. For diabetes-related outcomes, the pattern of lower MMA% and higher DMA% was associated with higher risk of metabolic syndrome and diabetes.

CONCLUSIONS

Population level of iAs% and DMA%, but not MMA%, were associated with arsenic exposure levels. Overall, study findings suggest that higher MMA% was associated with an increased risk of cancer and cardiovascular disease, while lower MMA% was associated with an increased risk of diabetes and metabolic syndrome. Additional population-based studies and experimental studies are needed to further evaluate and understand the role of arsenic exposure in arsenic metabolism and the role of arsenic metabolism in disease development. https://doi.org/10.1289/EHP577.

Including Rebinding Reactions in Well-Mixed Models of Distributive Biochemical Reactions

The behavior of biochemical reactions requiring repeated enzymatic substrate modification depends critically on whether the enzymes act processively or distributively. Whereas processive enzymes bind only once to a substrate before carrying out a sequence of modifications, distributive enzymes release the substrate after each modification and thus require repeated bindings. Recent experimental and computational studies have revealed that distributive enzymes can act processively due to rapid rebindings (so-called quasi-processivity). In this study, we derive an analytical estimate of the probability of rapid rebinding and show that well-mixed ordinary differential equation models can use this probability to quantitatively replicate the behavior of spatial models. Importantly, rebinding requires that connections be added to the well-mixed reaction network; merely modifying rate constants is insufficient. We then use these well-mixed models to suggest experiments to 1) detect quasi-processivity and 2) test the theory. Finally, we show that rapid rebindings drastically alter the reaction's Michaelis-Menten rate equations.

The role of ions, heavy metals, fluoride, and agrochemicals: critical evaluation of potential aetiological factors of chronic kidney disease of multifactorial origin (CKDmfo/CKDu) and recommendations for its eradication

The pollution of water and food through human waste and anthropogenic activities, including industrial waste and agricultural runoff, is a mounting problem worldwide. Water pollution from microbes causes identifiable diarrhoeal illnesses. The consumption of water contaminated with heavy metals, fluoride, and other toxins causes insidious illnesses that lead to protracted, non-communicable diseases and death. Chronic kidney disease of unusual/uncertain/unknown aetiology is one such example, began to manifest in the mid-1960s in several dry-zonal agricultural societies in developing economies that are located around the equator. In Sri Lanka, such a disease is affecting the North Central Province, the rice bowl of the country that first appeared in the mid-1990s. Several potential causes have been postulated, including heavy metals, fluoride, cyanobacterial and algae toxins, agrochemicals, and high salinity and ionicity in water, but no specific source or causative factor has been identified for CKD of multifactorial origin (CKDmfo). Three large studies conducted in the recent past failed to find any of the postulated components (heavy metals, cyanobacterial toxins, fluoride, salinity, or agrochemicals) at levels higher than those deemed safe by the World Health Organization and the US Environmental Protection Agency. At the reported low levels in water and with the heterogeneous geographical distribution, it is unrealistic to expect any of these components individually could cause this disease. However, the additive or synergistic effects of a combination of factors and components, even at lower exposure levels, together with malnutrition and harmful behaviours, and/or a yet-unidentified (or not investigated) toxin, can cause this epidemic. Because the cause is unknown, scientists need to work on broader hypotheses, so that key causative elements are not missed. Taken together the plausibility of multiple factors in the genesis of this disease, the appropriate terminology is CKDmfo, a name that also indicates the need for multi-disciplinary research programs to facilitate identifying the cause(s) and the need for multiple approaches to eradicate it. While some potential causes remain to be investigated, existing data point to polluted water as the main source of this disease. This article evaluates pros and cons of each hypothesis and highlights the importance of among others, providing clean water to all affected and surrounding communities. Available data do not support any of the postulated agents, chemicals, heavy metals, fluoride, salinity/ionicity, or individual agrochemical components, such as phosphate or glyphosate, as causative factors for CKDmfo in Sri Lanka. However, as the CKDmfo name implies, a combination of these factors (or an unknown toxin) together with harmful behaviour and chronic dehydration may cause this disease. Irrespective of the cause, prevention is the only way forward for eradication.

Biological and behavioral factors modify urinary arsenic metabolic profiles in a U.S. population

BACKGROUND

Because some adverse health effects associated with chronic arsenic exposure may be mediated by methylated arsenicals, interindividual variation in capacity to convert inorganic arsenic into mono- and di-methylated metabolites may be an important determinant of risk associated with exposure to this metalloid. Hence, identifying biological and behavioral factors that modify an individual's capacity to methylate inorganic arsenic could provide insights into critical dose-response relations underlying adverse health effects.

METHODS

A total of 904 older adults (≥45 years old) in Churchill County, Nevada, who chronically used home tap water supplies containing up to 1850 μg of arsenic per liter provided urine and toenail samples for determination of total and speciated arsenic levels. Effects of biological factors (gender, age, body mass index) and behavioral factors (smoking, recent fish or shellfish consumption) on patterns of arsenicals in urine were evaluated with bivariate analyses and multivariate regression models.

RESULTS

Relative contributions of inorganic, mono-, and di-methylated arsenic to total speciated arsenic in urine were unchanged over the range of concentrations of arsenic in home tap water supplies used by study participants. Gender predicted both absolute and relative amounts of arsenicals in urine. Age predicted levels of inorganic arsenic in urine and body mass index predicted relative levels of mono- and di-methylated arsenic in urine. Smoking predicted both absolute and relative levels of arsenicals in urine. Multivariate regression models were developed for both absolute and relative levels of arsenicals in urine. Concentration of arsenic in home tap water and estimated water consumption were strongly predictive of levels of arsenicals in urine as were smoking, body mass index, and gender. Relative contributions of arsenicals to urinary arsenic were not consistently predicted by concentrations of arsenic in drinking water supplies but were more consistently predicted by gender, body mass index, age, and smoking.

CONCLUSIONS

These findings suggest that analyses of dose-response relations in arsenic-exposed populations should account for biological and behavioral factors that modify levels of inorganic and methylated arsenicals in urine. Evidence of significant effects of these factors on arsenic metabolism may also support mode of action studies in appropriate experimental models.

Supplementation with Folic Acid, but Not Creatine, Increases Plasma Betaine, Decreases Plasma Dimethylglycine, and Prevents a Decrease in Plasma Choline in Arsenic-Exposed Bangladeshi Adults

BACKGROUND

Folic acid (FA) supplementation facilitates urinary excretion of arsenic, a human carcinogen. A better understanding of interactions between one-carbon metabolism intermediates may improve the ability to design nutrition interventions that further facilitate arsenic excretion.

OBJECTIVE

The objective was to determine if FA and/or creatine supplementation increase choline and betaine and decrease dimethylglycine (DMG).

METHODS

We conducted a secondary analysis of the Folic Acid and Creatine Trial, a randomized trial in arsenic-exposed Bangladeshi adults (n = 605, aged 24-55 y, 50.3% male) who received arsenic-removal water filters. We examined treatment effects of FA and/or creatine supplementation on plasma choline, betaine, and DMG concentrations, measured by LC-tandem mass spectrometry at baseline and at week 12. Group comparisons were between 1) 400 and 800 μg FA/d (FA400 and FA800, respectively) compared with placebo, 2) creatine (3 g/d) compared with placebo, and 3) creatine plus FA400 compared with FA400.

RESULTS

Choline decreased in the placebo group (-6.6%; 95% CI: -10.2%, -2.9%) but did not change in the FA groups (FA400: 2.5%; 95% CI: -0.9%, 6.1%; FA800: 1.4%; 95% CI: -2.5%, 5.5%; P < 0.05). Betaine did not change in the placebo group (-3.5%; 95% CI: -9.3%, 2.6%) but increased in the FA groups (FA400: 14.1%; 95% CI: 9.4%, 19.0%; FA800: 13.0%; 95% CI: 7.2%, 19.1%; P < 0.01). The decrease in DMG was greater in the FA groups (FA400: -26.7%; 95% CI: -30.9%, -22.2%; FA800: -27.8%; 95% CI: -31.8%, -23.4%) than in the placebo group (-12.3%; 95% CI: -18.1%, -6.2%; P < 0.01). The percentage change in choline, betaine, and DMG did not differ between creatine treatment arms and their respective reference groups.

CONCLUSION

Supplementation for 12 wk with FA, but not creatine, increases plasma betaine, decreases plasma DMG, and prevents a decrease in plasma choline in arsenic-exposed Bangladeshi adults. This trial was registered at clinicaltrials.gov as NCT01050556.

Mathematical analysis of the regulation of competing methyltransferases

BACKGROUND

Methyltransferase (MT) reactions, in which methyl groups are attached to substrates, are fundamental to many aspects of cell biology and human physiology. The universal methyl donor for these reactions is S-adenosylmethionine (SAM) and this presents the cell with an important regulatory problem. If the flux along one pathway is changed then the SAM concentration will change affecting all the other MT pathways, so it is difficult for the cell to regulate the pathways independently.

METHODS

We created a mathematical model, based on the known biochemistry of the folate and methionine cycles, to study the regulatory mechanisms that enable the cell to overcome this difficulty. Some of the primary mechanisms are long-range allosteric interactions by which substrates in one part of the biochemical network affect the activity of enzymes at distant locations in the network (not distant in the cell). Because of these long-range allosteric interactions, the dynamic behavior of the network is very complicated, and so mathematical modeling is a useful tool for investigating the effects of the regulatory mechanisms and understanding the complicated underlying biochemistry and cell biology.

RESULTS

We study the allosteric binding of 5-methyltetrahydrofolate (5 mTHF) to glycine-N-methyltransferase (GNMT) and explain why data in the literature implies that when one molecule binds, GNMT retains half its activity. Using the model, we quantify the effects of different regulatory mechanisms and show how cell processes would be different if the regulatory mechanisms were eliminated. In addition, we use the model to interpret and understand data from studies in the literature. Finally, we explain why a full understanding of how competing MTs are regulated is important for designing intervention strategies to improve human health.

CONCLUSIONS

We give strong computational evidence that once bound GNMT retains half its activity. The long-range allosteric interactions enable the cell to regulate the MT reactions somewhat independently. The low K m values of many MTs also play a role because the reactions then run near saturation and changes in SAM have little effect. Finally, the inhibition of the MTs by the product S-adenosylhomocysteine also stabilizes reaction rates against changes in SAM.

Using mathematical models to understand metabolism, genes, and disease

Mathematical models are a useful tool for investigating a large number of questions in metabolism, genetics, and gene–environment interactions. A model based on the underlying biology and biochemistry is a platform for in silico biological experimentation that can reveal the causal chain of events that connect variation in one quantity to variation in another. We discuss how we construct such models, how we have used them to investigate homeostatic mechanisms, gene–environment interactions, and genotype–phenotype mapping, and how they can be used in precision and personalized medicine.

Interaction of plasma glutathione redox and folate deficiency on arsenic methylation capacity in Bangladeshi adults

Inorganic arsenic(As) is metabolized through a series of methylation reactions catalyzed by arsenic(III)-methyltransferase (AS3MT), resulting in the generation of monomethylarsonic (MMAs) and dimethylarsinic acids (DMAs). AS3MT activity requires the presence of the methyl donor S-adenosylmethionine, a product of folate-dependent one-carbon metabolism, and a reductant. Although glutathione (GSH), the primary endogenous antioxidant, is not required for As methylation, GSH stimulates As methylation rates in vitro. However, the relationship between GSH redox and As methylation capacity in humans is unknown. We wished to test the hypothesis that a more oxidized plasma GSH redox status is associated with decreased As methylation capacity and examine whether these associations are modified by folate nutritional status. Concentrations of plasma GSH and GSSG, plasma folate, total blood As (bAs), total urinary As (uAs), and uAs metabolites were assessed in a cross-sectional study of n=376 Bangladeshi adults who were chronically exposed to As in drinking water. We observed that a decreased plasma GSH/GSSG ratio (reflecting a more oxidized redox state) was significantly associated with increased urinary %MMA, decreased urinary %DMA, and increased total bAs in folate-deficient individuals (plasma folate ≤ 9.0 nmol/L). Concentrations of plasma GSH and GSSG were independently associated with increased and decreased As methylation capacity, respectively. No significant associations were observed in folate-sufficient individuals, and interactions by folate status were statistically significant. Our findings suggest that GSH/GSSG redox regulation might contribute to the large interindividual variation in As methylation capacity observed in human populations.

Mathematical modeling of the effects of glutathione on arsenic methylation

Background

Arsenic is a major environmental toxin that is detoxified in the liver by biochemical mechanisms that are still under study. In the traditional metabolic pathway, arsenic undergoes two methylation reactions, each followed by a reduction, after which it is exported and released in the urine. Recent experiments show that glutathione plays an important role in arsenic detoxification and an alternative biochemical pathway has been proposed in which arsenic is first conjugated by glutathione after which the conjugates are methylated. In addition, in rats arsenic-glutathione conjugates can be exported into the plasma and removed by the liver in the bile.

Methods

We have developed a mathematical model for arsenic biochemistry that includes three mechanisms by which glutathione affects arsenic methylation: glutathione increases the speed of the reduction steps; glutathione affects the activity of arsenic methyltranferase; glutathione sequesters inorganic arsenic and its methylated downstream products. The model is based as much as possible on the known biochemistry of arsenic methylation derived from cellular and experimental studies.

Results

We show that the model predicts and helps explain recent experimental data on the effects of glutathione on arsenic methylation. We explain why the experimental data imply that monomethyl arsonic acid inhibits the second methylation step. The model predicts time course data from recent experimental studies. We explain why increasing glutathione when it is low increases arsenic methylation and that at very high concentrations increasing glutathione decreases methylation. We explain why the possible temporal variation of the glutathione concentration affects the interpretation of experimental studies that last hours.

Conclusions

The mathematical model aids in the interpretation of data from recent experimental studies and shows that the Challenger pathway of arsenic methylation, supplemented by the glutathione effects described above, is sufficient to understand and predict recent experimental data. More experimental studies are needed to explicate the detailed mechanisms of action of glutathione on arsenic methylation. Recent experimental work on the effects of glutathione on arsenic methylation and our modeling study suggest that supplements that increase hepatic glutathione production should be considered as strategies to reduce adverse health effects in affected populations.

Folate and cobalamin modify associations between S-adenosylmethionine and methylated arsenic metabolites in arsenic-exposed Bangladeshi adults

Chronic exposure to inorganic arsenic (InAs) through drinking water is a major problem worldwide. InAs undergoes hepatic methylation to form mono- and dimethyl arsenical species (MMA and DMA, respectively), facilitating arsenic elimination. Both reactions are catalyzed by arsenic (+3 oxidation state) methyltransferase (AS3MT) using S-adenosylmethionine (SAM) as the methyl donor, yielding the methylated product and S-adenosylhomocysteine (SAH), a potent product-inhibitor of AS3MT. SAM biosynthesis depends on folate- and cobalamin-dependent one-carbon metabolism. With the use of samples from 353 participants in the Folate and Oxidative Stress Study, our objective was to test the hypotheses that blood SAM and SAH concentrations are associated with arsenic methylation and that these associations differ by folate and cobalamin nutritional status. Blood SAM and SAH were measured by HPLC. Arsenic metabolites in blood and urine were measured by HPLC coupled to dynamic reaction cell inductively coupled plasma MS. In linear regression analyses, SAH was not associated with any of the arsenic metabolites. However, log(SAM) was negatively associated with log(% urinary InAs) (β: -0.11; 95% CI: -0.19, -0.02; P = 0.01), and folate and cobalamin nutritional status significantly modified associations between SAM and percentage of blood MMA (%bMMA) and percentage of blood DMA (%bDMA) (P = 0.02 and P = 0.01, respectively). In folate- and cobalamin-deficient individuals, log(SAM) was positively associated with %bMMA (β: 6.96; 95% CI: 1.86, 12.05; P < 0.01) and negatively associated with %bDMA (β: -6.19; 95% CI: -12.71, 0.32; P = 0.06). These findings suggest that when exposure to InAs is high, and methyl groups are limiting, SAM is used primarily for MMA synthesis rather than for DMA synthesis, contributing additional evidence that nutritional status may explain some of the interindividual differences in arsenic metabolism and, consequently, susceptibility to arsenic toxicity.

A pilot study: the importance of inter-individual differences in inorganic arsenic metabolism for birth weight outcome

Inorganic arsenic (iAs) exposure is detrimental to birth outcome. We lack information regarding the potential for iAs metabolism to affect fetal growth. Our pilot study evaluated postpartum Romanian women with known birth weight outcome for differences in iAs metabolism. Subjects were chronically exposed to low-to-moderate drinking water iAs. We analyzed well water, arsenic metabolites in urine, and toenail arsenic. Urine iAs and metabolites, toenail iAs, and secondary methylation efficiency increased as an effect of exposure (p<0.001). Urine iAs and metabolites showed a significant interaction effect between exposure and birth weight. Moderately exposed women with low compared to normal birth weight outcome had greater metabolite excretion (p<0.03); 67% with low compared to 10% with normal birth weight outcome presented urine iAs >9 μg/L (p=0.019). Metabolic partitioning of iAs toward excretion may impair fetal growth. Prospective studies on iAs excretion before and during pregnancy may provide a biomarker for poor fetal growth risk.