Kinetics of arsenic methylation by freshly isolated B6C3F1 mouse hepatocytes

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.

Identification of the third binding site of arsenic in human arsenic (III) methyltransferase

Arsenic (III) methyltransferase (AS3MT) catalyzes the process of arsenic methylation. Each arsenite (iAs³⁺) binds to three cysteine residues, methylarsenite (MMA³⁺) binds to two, and dimethylarsenite (DMA³⁺) binds to one. However, only two As-binding sites (Cys156 and Cys206) have been confirmed on human AS3MT (hAS3MT). The third As-binding site is still undefined. Residue Cys72 in Cyanidioschyzon merolae arsenite S-adenosylmethyltransferase (CmArsM) may be the third As-binding site. The corresponding residue in hAS3MT is Cys61. Functions of Cys32, Cys61, and Cys85 in hAS3MT are unclear though Cys32, Cys61, and Cys85 in rat AS3MT have no effect on the enzyme activity. This is why the functions of Cys32, Cys61, and Cys85 in hAS3MT merit investigation. Here, three mutants were designed, C32S, C61S, and C85S. Their catalytic activities and conformations were determined, and the catalytic capacities of C156S and C206S were studied. Unlike C85S, mutants C32S and C61S were completely inactive in the methylation of iAs³⁺ and active in the methylation of MMA³⁺. The catalytic activity of C85S was also less pronounced than that of WT-hAS3MT. All these findings suggest that Cys32 and Cys61 markedly influence the catalytic activity of hAS3MT. Cys32 and Cys61 are necessary to the first step of methylation but not to the second. Cys156 and Cys206 are required for both the first and second steps of methylation. The S^C32 is located far from arsenic in the WT-hAS3MT-SAM-As model. The distances between S^C61 and arsenic in WT-hAS3MT-As and WT-hAS3MT-SAM-As models are 7.5 Å and 4.1 Å, respectively. This indicates that SAM-binding to hAS3MT shortens the distance between S^C61 and arsenic and promotes As-binding to hAS3MT. This is consistent with the fact that SAM is the first substrate to bind to hAS3MT and iAs is the second. Model of WT-hAS3MT-SAM-As and the experimental results indicate that Cys61 is the third As-binding site.

Residues in human arsenic (+3 oxidation state) methyltransferase forming potential hydrogen bond network around S-adenosylmethionine

Residues Tyr59, Gly78, Ser79, Met103, Gln107, Ile136 and Glu137 in human arsenic (+3 oxidation state) methyltransferase (hAS3MT) were deduced to form a potential hydrogen bond network around S-adenosylmethionine (SAM) from the sequence alignment between Cyanidioschyzon merolae arsenite S-adenosylmethyltransferase (CmArsM) and hAS3MT. Herein, seven mutants Y59A, G78A, S79A, M103A, Q107A, I136A and E137A were obtained. Their catalytic activities and conformations were characterized and models were built. Y59A and G78A were completely inactive. Only 7.0%, 10.6% and 13.8% inorganic arsenic (iAs) was transformed to monomethylated arsenicals (MMA) when M103A, Q107A and I136A were used as the enzyme. The V_max (the maximal velocity of the reaction) values of M103A, Q107A, I136A and E137A were decreased to 8%, 22%, 15% and 50% of that of WT-hAS3MT, respectively. The K_M(SAM) (the Michaelis constant for SAM) values of mutants M103A, I136A and E137A were 15.7, 8.9 and 5.1 fold higher than that of WT-hAS3MT, respectively, indicating that their affinities for SAM were weakened. The altered microenvironment of SAM and the reduced capacity of binding arsenic deduced from K_M(As) (the Michaelis constant for iAs) value probably synergetically reduced the catalytic activity of Q107A. The catalytic activity of S79A was higher than that of WT despite of the higher K_M(SAM), suggesting that Ser79 did not impact the catalytic activity of hAS3MT. In short, residues Tyr59 and Gly78 significantly influenced the catalytic activity of hAS3MT as well as Met103, Ile136 and Glu137 because they were closely associated with SAM-binding, while residue Gln107 did not affect SAM-binding regardless of affecting the catalytic activity of hAS3MT. Modeling and our experimental results suggest that the adenine ring of SAM is sandwiched between Ile136 and Met103, the amide group of SAM is hydrogen bonded to Gly78 in hAS3MT and SAM is bonded to Tyr59 with van der Waals, cation-π and hydrogen bonding contacts.

Mathematical model insights into arsenic detoxification

Background

Arsenic in drinking water, a major health hazard to millions of people in South and East Asia and in other parts of the world, is ingested primarily as trivalent inorganic arsenic (iAs), which then undergoes hepatic methylation to methylarsonic acid (MMAs) and a second methylation to dimethylarsinic acid (DMAs). Although MMAs and DMAs are also known to be toxic, DMAs is more easily excreted in the urine and therefore methylation has generally been considered a detoxification pathway. A collaborative modeling project between epidemiologists, biologists, and mathematicians has the purpose of explaining existing data on methylation in human studies in Bangladesh and also testing, by mathematical modeling, effects of nutritional supplements that could increase As methylation.

Methods

We develop a whole body mathematical model of arsenic metabolism including arsenic absorption, storage, methylation, and excretion. The parameters for arsenic methylation in the liver were taken from the biochemical literature. The transport parameters between compartments are largely unknown, so we adjust them so that the model accurately predicts the urine excretion rates of time for the iAs, MMAs, and DMAs in single dose experiments on human subjects.

Results

We test the model by showing that, with no changes in parameters, it predicts accurately the time courses of urinary excretion in mutiple dose experiments conducted on human subjects. Our main purpose is to use the model to study and interpret the data on the effects of folate supplementation on arsenic methylation and excretion in clinical trials in Bangladesh. Folate supplementation of folate-deficient individuals resulted in a 14% decrease in arsenicals in the blood. This is confirmed by the model and the model predicts that arsenicals in the liver will decrease by 19% and arsenicals in other body stores by 26% in these same individuals. In addition, the model predicts that arsenic methyltransferase has been upregulated by a factor of two in this population. Finally, we also show that a modification of the model gives excellent fits to the data on arsenic metabolism in human cultured hepatocytes.

Conclusions

The analysis of the Bangladesh data using the model suggests that folate supplementation may be more effective at reducing whole body arsenic than previously expected. There is almost no data on the upregulation of arsenic methyltransferase in populations chronically exposed to arsenic. Our model predicts upregulation by a factor of two in the Bangladesh population studied. This prediction should be verified since it could have important public health consequences both for treatment strategies and for setting appropriate limits on arsenic in drinking water. Our model has compartments for the binding of arsenicals to proteins inside of cells and we show that these comparments are necessary to obtain good fits to data. Protein-binding of arsenicals should be explored in future biochemical studies.

Utilizing toxicogenomic data to understand chemical mechanism of action in risk assessment

The predominant role of toxicogenomic data in risk assessment, thus far, has been one of augmentation of more traditional in vitro and in vivo toxicology data. This article focuses on the current available examples of instances where toxicogenomic data has been evaluated in human health risk assessment (e.g., acetochlor and arsenicals) which have been limited to the application of toxicogenomic data to inform mechanism of action. This article reviews the regulatory policy backdrop and highlights important efforts to ultimately achieve regulatory acceptance. A number of research efforts on specific chemicals that were designed for risk assessment purposes have employed mechanism or mode of action hypothesis testing and generating strategies. The strides made by large scale efforts to utilize toxicogenomic data in screening, testing, and risk assessment are also discussed. These efforts include both the refinement of methodologies for performing toxicogenomics studies and analysis of the resultant data sets. The current issues limiting the application of toxicogenomics to define mode or mechanism of action in risk assessment are discussed together with interrelated research needs. In summary, as chemical risk assessment moves away from a single mechanism of action approach toward a toxicity pathway-based paradigm, we envision that toxicogenomic data from multiple technologies (e.g., proteomics, metabolomics, transcriptomics, supportive RT-PCR studies) can be used in conjunction with one another to understand the complexities of multiple, and possibly interacting, pathways affected by chemicals which will impact human health risk assessment.

Functional and structural evaluation of cysteine residues in the human arsenic (+3 oxidation state) methyltransferase (hAS3MT)

Arsenic (+3 oxidation state) methyltransferase (AS3MT) catalyzes the methylation of inorganic arsenic (iAs) and plays important role in the detoxication of this metalloid. There are fourteen cysteine residues in the human AS3MT (hAS3MT), among which twelve are absolutely conserved; Cys334 and Cys360 are unique; Cys368 and Cys369 are identified as a CysCys pair. The roles of several conserved cysteine residues in rat AS3MT and hAS3MT have been reported. Herein, the other conserved cysteine residues (Cys72, Cys271, Cys375) and the unique ones (Cys334, Cys360) were systematically replaced by serine using site-directed mutagenesis to study their functions. The mutants were investigated for enzymatic activity, kinetics, thermal stability and secondary structures. Present results indicate that C72S is completely inactive in methylation of iAs and has distinct changes in the secondary structures; Cys72 might form a critical intramolecular disulfide bond with Cys250; Cys271 and Cys375 do not affect the activity and structure of the hAS3MT. However, the mutations of Cys334 and Cys360 can decrease the enzymatic turnovers and change the conformation of the hAS3MT. The kinetic data show that Cys271, Cys334, Cys360 and Cys375 are not involved in the SAM binding. Additionally, all these cysteine residues except Cys375 affect the thermotropic properties of the hAS3MT.

New insights into the mechanism of arsenite methylation with the recombinant human arsenic (+3) methyltransferase (hAS3MT)

The catalytic mechanism of the recombinant human arsenic (+3) methyltransferase (hAS3MT) was studied using kinetics, initial velocity and spectroscopy. The production and the distribution of methylated arsenicals changed with various concentrations of arsenite/S-adenosyl-L-methionine (SAM)/thiols, enzyme contents, and incubation times. These results suggest a sequential methylation of arsenite to monomethylated arsenicals (MMA) and dimethylated arsenicals (DMA). In addition, competition exists between the two reactions. hAS3MT showed the greatest activity at pH 8.5 with glutathione (GSH) as the reductant. This might indicate that a balance between the deprotonation and protonation of sulfhydryl groups is required. Initial velocity studies illuminate an ordered sequence for the binding of SAM and arsenite to the hAS3MT; while GSH should probably be placed either as the first reactant or as a reactant combining with the enzyme only after products have been released. The interactions between substrate/cofactors and the hAS3MT were first monitored by UV-visible and circular dichroism spectroscopy. It revealed that arsenite and SAM combined with the hAS3MT before reaction started; whereas, no interactions between GSH and the hAS3MT were detected. Integrating the results from kinetics, initial velocity and spectroscopy studies, an ordered mechanism are originally attained, with the SAM as the first reactant that adds to the hAS3MT and arsenite as the second one. Arsenite is successively methylated reductively, rather than a stepwise oxidative methylation. GSH should combine with the hAS3MT after the methylation to reduce the disulfide bond formed during the catalytic cycle in the hAS3MT to resume the active form of the enzyme.

Structure-function roles of four cysteine residues in the human arsenic (+3 oxidation state) methyltransferase (hAS3MT) by site-directed mutagenesis

Cysteine (Cys) residues are often crucial to the function and structure of proteins. Cys157 and Cys207 in recombinant mouse arsenic (+3 oxidation state) methyltransferase (AS3MT) are shown to be related to enzyme activity and considered to be the catalytic sites. The roles of some conserved Cys residues in the N-terminal region of the rat AS3MT also have been examined. However, little is known about the roles of the Cys residues in the middle region. The metabolism of inorganic arsenic in human is different from rat and mouse in some aspects though the AS3MT has a high degree of similarity in these species. In order to determine whether the Cys156 and Cys206 (corresponding to the catalytic sites, Cys157 and Cys207 in the mouse AS3MT) in the hAS3MT act as the catalytic sites and to study the roles of the Cys residues (Cys226 and Cys250) near the catalytic center in the middle region, we designed and prepared four mutants (C156S, C206S, C226S, and C250S) in which one Cys residue replaced by serine by PCR-based site-directed mutagenesis. The native form and cysteine/serine mutants were assayed for enzyme activity, free thiols, and the secondary structures by circular dichroism and Fourier transform infrared. Our data show that, besides C156S and C206S, C250S is another potential important site. C226S seems to have the same action as the wild-type hAS3MT with the consistent K(M) and V(max) values. Meanwhile, selenium can also inhibit the methylation of inorganic arsenic by C226S. All the mutants except C226S are calculated to have dramatic changes in the secondary structures. Cys250 might form an intramolecular disulfide bond with another Cys residue. These findings demonstrate that Cys residues at positions 156, 206, and 250 play important roles in the enzymatic function and structure of the hAS3MT.

Research toward the development of a biologically based dose response assessment for inorganic arsenic carcinogenicity: a progress report

Cancer risk assessments for inorganic arsenic have been based on human epidemiological data, assuming a linear dose response below the range of observation of tumors. Part of the reason for the continued use of the linear approach in arsenic risk assessments is the lack of an adequate biologically based dose response (BBDR) model that could provide a quantitative basis for an alternative nonlinear approach. This paper describes elements of an ongoing collaborative research effort between the CIIT Centers for Health Research, the U.S. Environmental Protection Agency, ENVIRON International, and EPRI to develop BBDR modeling approaches that could be used to inform a nonlinear cancer dose response assessment for inorganic arsenic. These efforts are focused on: (1) the refinement of physiologically based pharmacokinetic (PBPK) models of the kinetics of inorganic arsenic and its metabolites in the mouse and human; (2) the investigation of mathematical solutions for multi-stage cancer models involving multiple pathways of cell transformation; (3) the review and evaluation of the literature on the dose response for the genomic effects of arsenic; and (4) the collection of data on the dose response for genomic changes in the urinary bladder (a human target tissue for arsenic carcinogenesis) associated with in vivo drinking water exposures in the mouse as well as in vitro exposures of both mouse and human cells. An approach is proposed for conducting a biologically based margin of exposure risk assessment for inorganic arsenic using the in vitro dose response for the expression of genes associated with the obligatory precursor events for arsenic tumorigenesis.