Environmental arsenic exposure: From genetic susceptibility to pathogenesis

Environment International 112 (2018) 183–197

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Review article

Environmental arsenic exposure: From genetic susceptibility to pathogenesis 1

T

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Brenda C. Minatel , Adam P. Sage , Christine Anderson, Roland Hubaux, Erin A. Marshall, ⁎ Wan L. Lam, Victor D. Martinez Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada

A R T I C L E I N F O

A B S T R A C T

Keywords: Arsenic Environmental carcinogens Drinking water Genetic susceptibility Epigenetics Cancer

More than 200 million people in 70 countries are exposed to arsenic through drinking water. Chronic exposure to this metalloid has been associated with the onset of many diseases, including cancer. Epidemiological evidence supports its carcinogenic potential, however, detailed molecular mechanisms remain to be elucidated. Despite the global magnitude of this problem, not all individuals face the same risk. Susceptibility to the toxic effects of arsenic is influenced by alterations in genes involved in arsenic metabolism, as well as biological factors, such as age, gender and nutrition. Moreover, chronic arsenic exposure results in several genotoxic and epigenetic alterations tightly associated with the arsenic biotransformation process, resulting in an increased cancer risk. In this review, we: 1) review the roles of inter-individual DNA-level variations influencing the susceptibility to arsenic-induced carcinogenesis; 2) discuss the contribution of arsenic biotransformation to cancer initiation; 3) provide insights into emerging research areas and the challenges in the field; and 4) compile a resource of publicly available arsenic-related DNA-level variations, transcriptome and methylation data. Understanding the molecular mechanisms of arsenic exposure and its subsequent health effects will support efforts to reduce the worldwide health burden and encourage the development of strategies for managing arsenic-related diseases in the era of personalized medicine.

List of abbreviations (IARC) International Agency of Research on Cancer (iAs) Inorganic arsenic (WHO) World Health Organization (arsenate/AsV) Pentavalent arsenic (arsenite/AsIII) Trivalent arsenic (MMA) Monomethylarsonic acid (DMA) Dimethylarsinic acid (ATP) Adenosine triphosphate (NER) Nucleotide excision repair (BER) Base excision repair (OR) Odds ratio (ATO) Arsenic trioxide (APL) Acute promyelocytic leukemia (PML) Promyelocytic protein (RARα) Retinoic acid receptor α (CML) Chronic myelogenous leukemia (PNP) Purine nucleoside phosphorylase (GSH) Glutathione (AS3MT) Arsenic (III)-methyltransferase

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(SAM) s-Adenosylmethionine (ROS) Reactive oxygen species (ATRA) All-trans retinoic acid (lncRNAs) Long non-coding RNAs (GEO) Gene Expression Omnibus (NaAsO2) Sodium arsenite (CIHR) Canadian Institutes for Health Research 1. Background Exposure to arsenic has a major impact on human health across the world. In fact, this naturally occurring metalloid is considered a wellestablished “Class I” human carcinogen by the International Agency of Research on Cancer (IARC) (IARC Working Group on the Evaluation of Carcinogenic Risks to Humans). In the environment, arsenic is most commonly found in its inorganic form (inorganic arsenic, iAs) (Smedley and Kinniburgh, 2002). Despite the ubiquitous distribution of arsenic in soil, air and water, human industrial activities such as mining, combustion of fossil fuels and the use of arsenic-based pesticides potentiate the environmental accumulation of this toxic metalloid (Martinez et al., 2013b; Singh et al., 2015; Vimercati et al., 2016; Vimercati et al.,

Corresponding author at: British Columbia Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC V5Z1L3, Canada. E-mail address: [email protected] (V.D. Martinez). These authors contributed equally to this work.

https://doi.org/10.1016/j.envint.2017.12.017 Received 29 August 2017; Received in revised form 15 November 2017; Accepted 12 December 2017 Available online 22 December 2017 0160-4120/ © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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where arsenic contamination is endemic, such as Taiwan, Bangladesh, Northern Chile and Argentina. In these areas, several studies have demonstrated associations between high arsenic exposure and the onset of adverse health effects, particularly cancer (Ahmed et al., 2017; Liang et al., 2017; Liang et al., 2016; Loewenberg, 2016; Yunus et al., 2011). For example, in Northern Chile the incidence of lung squamous cell carcinoma, a disease which is mainly the result of cigarette smoke, is not decreasing despite the dramatic drop in smoking rates. The rate of lung squamous cell carcinoma in never-smokers is unusually high in this region (Martinez et al., 2012). This is particularly interesting when considering that the molecular signature of lung squamous cell carcinoma is different between smoking-related and arsenic-related tumours, suggesting that the high concentrations of arsenic may be playing a much larger role in the incidence of lung cancer in this region (Martinez et al., 2010). Bangladesh serves as one of the most prevalent cases of high concentrations of arsenic in groundwater, with about 20–45 million individuals exposed to well-above standard levels. It has been estimated that arsenic is the cause of over 19,000 deaths per year in the country (Flanagan et al., 2012). Due to the enormous impact on the health of its population, Bangladesh is one of the most well-studied areas of arsenic exposure, while data from many other countries around the world remains scarce. Among North American countries, the United States has welldocumented case reports of arsenic contamination, with particular high risk areas in the states of Utah, Oregon (West Oregon), Nevada, California, Florida, West Virginia, Kentucky, Maine, Vermont and New Hampshire (Banerjee et al., 2007; Hubaux et al., 2013). In Canada, less attention has been given to this issue, though the accumulation of arsenic in soil derived from mining activities has been noted over time (Bari and Kindzierski, 2016). Particularly, high levels of naturally occurring arsenic in private well water supplies have been documented in a number of provinces, including Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Québec, and Saskatchewan (Martinez et al., 2013b; Saint-Jacques et al., 2014). For example, in Nova Scotia, high levels of arsenic have been found throughout the province and measured concentrations have been shown to exceed the safety guidelines in natural drinking water (Dummer et al., 2015). Additionally, the mobilization of arsenic in soil due to the activities of the Giant Mine in Yellowknife, Northern Canada, represents an enormous risk for water contamination (Martinez et al., 2013b). While these examples shed light on the global magnitude of arsenic contamination, the true impact of arsenic exposure remains veiled by complex molecular mechanisms and data scarcity.

2017). Increasing concentrations of arsenic in the environment pose major threats to human health, as exposure through inhalation, ingestion and skin contact can result in a multitude of adverse health effects (Vimercati et al., 2017). A primary route of human exposure to arsenic is through the consumption of contaminated groundwater sources, however, the intake of contaminated food products, such as fish and grains, is also a growing issue (Huang et al., 2015). The greatest impact of arsenic exposure is observed to result from groundwater levels above the World Health Organization (WHO) safety standard of 10 μg/L (Huang et al., 2015; Naujokas et al., 2013). However, recent evidence has uncovered the potential toxicity of arsenic even at levels below this safety guideline (Health Canada, 2017). Several studies indicate that arsenic toxicity is derived from its metabolism and excretion processes (Ebert et al., 2011; Styblo et al., 2002). After the uptake of arsenic into the body, it goes through a biotransformation process as part of its metabolism, wherein pentavalent arsenic (arsenate/AsV, the most prevalent oxidation state of iAs in the environment) is reduced to a trivalent form (arsenite/AsIII), which is subsequently mono-, di-, and tri-methylated (Drobna et al., 2009). These by-products are toxic and can accumulate throughout the body, leading to several genetic and epigenetic disruptions and posing a great threat to many normal biological processes (Bustaffa et al., 2014; Hubaux et al., 2013; Sage et al., 2017). The accumulation of arsenic and its metabolic by-products leads to widespread health effects, ranging from disorders of the cardiovascular and nervous systems, to nephrotoxicity and skin lesions, and particularly cancer (Bhattacharyya et al., 2014; Feseke et al., 2015; Nong et al., 2016; Robles-Osorio et al., 2015; Sage et al., 2017; Tolins et al., 2014; Tsuji et al., 2015). Arsenic is known to be mainly associated with the development of skin, liver, lung, bladder and urinary tract cancers (Sankpal et al., 2012). However, susceptibility to the development of arsenic-associated diseases varies among individuals, and can be influenced by age, gender, and nutrition, as well as alterations in genes involved in arsenic biotransformation (Paul et al., 2015; Schlawicke Engstrom et al., 2009; Yu et al., 2014). The inter-individual genetic susceptibility to arsenic-induced health effects has been related with variations in genes responsible for arsenic biotransformation, such as methyltransferases, and DNA damage repair genes (Paul et al., 2015). Some studies suggest that a higher capacity to methylate arsenic or the decreased activity of DNA repair enzymes are associated with decreased risk of arsenic-associated diseases (Banerjee et al., 2016; Ghosh et al., 2008; Pierce et al., 2013). However, the mechanisms underlying arsenic toxicity and susceptibility are not yet fully understood, particularly due to discrepancies in study design and a lack of animal models that can sufficiently replicate observed human health effects (IARC Working Group on the Evaluation of Carcinogenic Risks to Humans, 2004). Here we review the magnitude of arsenic contamination, as well as the intertwined role of arsenic biotransformation and genetic/epigenetic factors that are associated with susceptibility to arsenic.

3. Health effects derived from arsenic exposure While the effects of acute exposure to high concentrations of arsenic can have directly observable consequences, including vomiting, abdominal pain, and can even be fatal in extreme cases, exposure to levels near the WHO threshold can potentiate chronic health effects (World Health Organization, 2016). In fact, exposure to arsenic is known to be involved in the onset of nephrotoxicity, diabetes, cardiovascular and pulmonary diseases, as well as multiple diseases of the skin (Bhattacharyya et al., 2014; Feseke et al., 2015; Robles-Osorio et al., 2015; Sage et al., 2017; Tolins et al., 2014; Tsuji et al., 2015). Cardiovascular disease is a highly prevalent, particularly in North American populations. Arsenic exposure has been associated with an increased risk of developing peripheral arterial and atherosclerotic cardiovascular diseases (Newman et al., 2016; Nong et al., 2016). Further effects of arsenic on the vascular system can be seen in its involvement in the onset of Blackfoot disease, which severely damages the vasculature in the feet and is mainly seen in the arsenic-endemic area of Southern Taiwan (Tseng, 2005). However, the clearest association can be seen in skin diseases including hyperkeratosis, an abnormal thickening of the skin, which may

2. Worldwide burden of arsenic-exposure Exposure to arsenic occurs largely through the contamination of groundwater sources; however, individuals can also come into contact with this metalloid through food, such as grains including rice, seafood, air sources, and even apple juice (Arslan et al., 2016; Health Canada, 2017; Hubaux et al., 2012; Quarato et al., 2017). It is estimated that over 200 million people are chronically exposed to concentrations at or above the WHO threshold across the world (Naujokas et al., 2013). The global magnitude of the issue of arsenic contamination may be larger than the data suggests, as it is marred by availability and accessibility issues. In fact, high levels of arsenic have been found in groundwater in > 70 countries across 5 continents, including Australia, India, Canada and the United States (Bhattacharjee et al., 2013a; Hubaux et al., 2012; Hubaux et al., 2013). Much of the epidemiological data available are derived from areas 184

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present even after just five years of chronic exposure to arsenic (World Health Organization, 2016). Despite not posing an immediate risk to the life of the individual, skin aberrations as a result of arsenic exposure can act as precursors for the development of skin cancer, one of the most common types of arsenic-associated cancers. Interestingly, the risk of developing these precursor skin lesions is highest around low to moderate doses of arsenic, and can be potentiated by the presence of genetic variants in genes involved in arsenic metabolism (Karagas et al., 2015). The observation that exposure to low doses of arsenic can lead to carcinogenic effects is not limited to skin cancer, but has also been characterized in cancers of the bladder, liver, and interestingly, lung (Martinez et al., 2011a; Tsuji et al., 2014; Wang et al., 2014). As such, it can be suggested that the current guideline of 10 μg/L may not adequately protect individuals from all health effects resulting from chronic exposure to arsenic. In fact, this has been examined in Canada, wherein the guideline for acceptable concentrations of arsenic in packaged apple juice products was lowered to 10 times below the WHO threshold, raising questions regarding other food and beverage products manufactured across the world (Health Canada, 2017). Further characterization of the genetic, and epigenetic mechanisms involved in arsenic-related health effects, particularly in its biotransformation, may help to guide this discussion regarding other food and drinking sources.

exposed to arsenic is predictive of cancer risk and has been used to evaluate the influence of polymorphisms in the susceptibility to arsenic toxicity (Drobna et al., 2009). Several studies have also demonstrated that pre-cancerous skin lesions are associated with a higher ratio of MMA/DMA in urine (Yu et al., 2000). Thus, the MMA/DMA ratio may be used as an indicator of disease risk in arsenic-exposed populations. 4.2. Biological and lifestyle factors influence arsenic-exposure outcome iAs and methylated trivalent arsenic species have also been shown to broadly affect metabolic pathways, as they are able to promote endocrine disruption (Huang et al., 2017). Analysis of a Mexican population identified 132 metabolites that are altered upon arsenic exposure, which are mainly involved in amino acid metabolism, the tricarboxylic acid cycle, as well as pyruvate metabolism (Martin et al., 2015). These pathways are known to be extremely important for adenosine triphosphate (ATP) production, corroborating the observation that arsenic, especially the methylated by-product MMAIII, can lead to mitochondrial dysfunction (Martin et al., 2015; Pace et al., 2016). Interestingly, the metabolic changes appear to be influenced by gender, wherein it has been demonstrated that exposure to iAs alters more metabolites in male as opposed to female mice, which bear an AS3MT gene-knockout (Huang et al., 2017). In fact, other studies also suggest that males and females display different methylation capacities, in which males have higher concentrations of MMA and consequently, a higher risk of developing skin lesions from chronic arsenic exposure (Kile et al., 2011; Lindberg et al., 2007; Lindberg et al., 2008). Lifestyle factors, such as nutrition and smoking status have also been shown to influence susceptibility to arsenic toxicity (Chung et al., 2013; Deb et al., 2013; Pilsner et al., 2009). Studies have associated malnutrition with an increased risk of arsenic-related diseases, in particular, in vivo studies have demonstrated that protein-deficient diets impair arsenic metabolism (Deb et al., 2013; Milton et al., 2010). Lower intake of other nutrients, such as folic acid, methionine, choline, and vitamins B6 and B12 are also associated with an increased risk of skin lesions and cancer (Pilsner et al., 2009). In particular, deficiency in methionine and choline have been shown to reduce S-adenosylmethionine (SAM) levels, leading to impaired methylation and the dysfunctional excretion of arsenic (Gamble et al., 2007; Heck et al., 2009). Likewise, mineral deficient diets were also shown to be associated with increased MMA and decreased DMA in the urine of exposed individuals (Steinmaus et al., 2005). The exact mechanism by which nutrition affects health outcomes in arsenic-exposed populations is not fully understood, particularly due to the wide variation in analytical methods and sample collection used between different studies. However, it is becoming evident that dietary factors may play an important role in arsenic metabolism. Additionally, tobacco smoking has been shown to affect arsenic methylation capability and increase the risk of arsenic-induced cancers, strengthening the hypothesis that arsenic and other chemicals have synergistic effects (Wang et al., 2013). Therefore, a better understanding of the association between these biological and lifestyle factors with arsenic toxicity is extremely important when assessing public measures to mitigate the effects of arsenic exposure.

4. Differential susceptibility to arsenic-related health effects The mechanistic complexity underlying the toxic effects of arsenic on the human body is further enhanced by the existence of inter-individual variations in susceptibility (Drobna et al., 2009). As outlined above, different histone methylation patterns have been observed between men and women after arsenic exposure (Chervona et al., 2012). Additionally, a recent in vivo study demonstrated sex-specific metabolomic changes in AS3MT-knockout mice exposed to iAs, raising the possibility of a role for sex hormones in the regulation of arsenite-methyltransferase-catalyzed arsenic metabolism (Huang et al., 2017). Therefore, biological factors such as gender, race, age and nutrition might influence the efficiency of the pathways involved in arsenic metabolism and cytotoxic outcome, conferring inter-individual variations in arsenic susceptibility (Howe et al., 2017; Lindberg et al., 2007; Osmond et al., 2010; Vahter, 2007). Additionally, given the critical biological pathways involved in the metabolism and excretion of arsenic, genetic variations in component genes of these pathways may impact the outcome of arsenic exposure in different individuals (Paul et al., 2015) (Fig. 1). This is particularly relevant where genetic determinants may be used to identify individuals (and populations) that are at a greater risk of developing cancer and other diseases from chronic arsenic exposure. 4.1. Arsenic metabolism as a risk indicator Arsenic has been shown to be involved in the development of several types of cancer (Ng et al., 2003; Sankpal et al., 2012), mainly due to consequences of the biotransformation process and generation of oxidative DNA damage (Hubaux et al., 2013; Kligerman and Tennant, 2007) (Fig. 1). Therefore, polymorphisms in genes involved in these pathways can confer different health outcomes upon arsenic exposure (Breton et al., 2007; Schlawicke Engstrom et al., 2009). Since the methylated forms of arsenic, monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA), are readily excreted in urine, they were initially believed to have less toxic effects than AsV and AsIII. However, studies have since demonstrated that trivalent-methylated arsenicals, especially MMAIII, are highly reactive and genotoxic (Drobna et al., 2009; Vimercati et al., 2009). In fact, it was observed that high urine levels of MMAIII in exposed individuals correlated with an increased risk of developing cancer, while DMAV was inversely correlated with cancer risk (Yu et al., 2000). The MMA/DMA ratio in individuals

4.3. Polymorphisms in the arsenic biotransformation pathway components Since arsenic biotransformation consists of a series of reduction and oxidation reactions, as well as methylation steps (Hubaux et al., 2013), polymorphisms affecting genes encoding important reductases and methyltransferases are hypothesized to be a major cause of inter-individual variations in arsenic susceptibility (Paul et al., 2015) (Table 1). Arsenite-3-methyltransferase (AS3MT) is the key enzyme that catalyzes the transfer of methyl groups from SAM to arsenic species, while purine nucleoside phosphorylase (PNP) and glutathione-S-transferase omega (GSTO; isoforms 1/2) participate in the reduction of arsenic (Antonelli et al., 2014) (Fig. 1). Studies have investigated the 185

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Fig. 1. Molecular pathways and genetic variations affecting arsenic biotransformation. Arsenic biotransformation results in the reduction of AsV by various reducing enzymes (grey rectangles). AsIII is then methylated by As3MT to produce different methylated species (light orange ovals), which are associated with aberrations at the molecular and cellular levels (blue boxes). Genetic polymorphisms (red text) and gene expression alterations (red arrows) potentiate the cytotoxic effects from arsenic exposure (red boxes). Some of these events can exacerbate the genomic and epigenomic disruptions caused by arsenic (purple boxes), culminating in the onset of a variety of disease states. (For interpretation of the references to color in this figure legend, the reader is referred to the online version of this chapter.)

4.4. Polymorphisms in the DNA damage repair pathways

association between polymorphisms in these enzymes and cancer development upon exposure to arsenic, which are summarized in Table 1. The polymorphism rs1191439 in the AS3MT gene was correlated with elevated MMA urine levels in two studies (Antonelli et al., 2014). Additionally, three polymorphisms in codons 20 (His:His), 51 (Gly:Ser), and 57 (Pro:Pro) in the PNP gene were found to be associated with skin lesions (De Chaudhuri et al., 2008). In another example, the GC genotype at rs3740393, located in the AS3MT gene, was associated with diminished urinary fractions of MMA and higher DMA, suggesting increased efficacy of arsenic metabolism and consequent reduced cancer risk (Chung et al., 2009). Therefore, these findings suggest that impaired arsenic metabolism, marked by higher urinary fractions of MMA and less urinary fractions of DMA, increases susceptibility to developing arsenic-induced cancers.

Polymorphisms affecting genes involved with oxidative stress and DNA damage repair pathways have been shown to influence the risk of arsenic-induced cancer (Breton et al., 2007). There are two main molecular mechanisms responsible for repairing DNA strand breaks and base modifications that result from ROS: nucleotide excision repair (NER) and base excision repair (BER) (Melis et al., 2013). NER is the main pathway used by mammals to remove DNA lesions formed by UV light, environmental mutagens, and some cancer chemotherapeutic adducts (Scharer, 2013). NER can occur as global genome NER (anywhere in the genome) or be coupled to transcription (accelerated repair of lesions in the transcribed strand of active genes) (Hanawalt and Spivak, 2008). A polymorphism in codon 751 of the gene Excision Repairs Cross-complementing rodent repair deficiency, Complementation group 2 (ERCC2/XPD), correlates with arsenic-induced hyperkeratosis (Banerjee et al., 2007). This gene encodes an important enzyme in the 186

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Table 1 Polymorphisms in genes associated with arsenic biotransformation alter susceptibility to arsenic-induced cancers. Gene

Polymorphism

Genotype

Increased risk

OR [95% CI]

p-Value

Reference

AS3MT AS3MT

G > A T > C

Skin lesions Skin lesions

3.33 [1.81–6.14] 4.28 [1–18.5]

< 0.0001 0.055

(Das et al., 2016) (Valenzuela et al., 2009)

A > G

Skin lesions

1.86 [1.15–3.00]

0.01

(McCarty et al., 2007)

A > G

Bladder cancer

5.4 [1.5–20.2]

0.03

(Lesseur et al., 2012)

GSTT1 GSTT1 GSTT1 GSTM1 GSTM1 EPHX1

C10orf32 (rs9527) Met287Thr (rs11191439) Iso105Val (rs1695) Ile105Val (rs1695) Null Null Wildtype At least one allele positive Null Tyr113His (rs1051740)

– – – – – T > C

Skin cancer Urothelial carcinoma Skin lesions Skin lesions Urothelial carcinoma Skin cancer

0.05 0.05 0.01 – 0.03 0.04

(Hsu et al., 2015) (Hsu et al., 2011) (McCarty et al., 2007) (Ghosh et al., 2006) (Hsu et al., 2008) (Hsu et al., 2015)

PNP PNP PNP

His20His Gly51Ser Pro57Pro

C > T G > A C > T

Skin lesions Skin lesions Skin lesions

1.74 1.91 1.56 1.73 3.69 3.74 2.99 1.69 1.66 1.67

0.02 0.04 0.04

(De Chaudhuri et al., 2008) De Chaudhuri et al., 2008) (De Chaudhuri et al., 2008)

GSTP1 GSTP1

[1.00–3.02] [1.00–3.65] [1.10–2.19] [1.24–2.22] [1.05–11.20] [1.20–11.66] [1.01–8.83] [1.08–2.66] [1.04–2.64] [1.05–2.66]

Table 2 Polymorphisms in genes associated with DNA damage repair alter susceptibility to arsenic-induced cancers. Gene

Polymorphism

Genotype

Increased risk

OR [95% CI]

p-Value

Reference

APE1 ERCC2 (XPD) ERCC2 (XPD) ERCC2 (XPD) XRCC3 NBS1

Asp148Glu C156A (rs238406) Lys751Gln Lys751Gln T241 M E185Q

– C A A C G

1.93 [1.15–3.19] 2.04 [0.99–4.27] 4.77[2.75–8.23] 2.36 [1.35–4.14] 2.8 [1.1–7.3] 1.44 [1.10–1.88]

– 0.05 < 0.01 0.002 0.01 0.008

(Breton et al., 2007) (Hsu et al., 2015) (Banerjee et al., 2007) (Lin et al., 2010) (Andrew et al., 2009) (Thirumaran et al., 2006)

MPO

Codon 463 (rs2333227) Codon 262 (rs1001179)

G > A

Skin lesions Skin cancer Skin lesions Skin lesions Bladder cancer Skin cancer (Only in males) Skin lesions

2.1 [0.7–6.2]

–

(Ahsan et al., 2003a)

C > T

Skin lesions

1.9 [0.8–4.7]

–

(Ahsan et al., 2003a)

CAT

> > > > >

A C C T C

Gene

Polymorphism

Genotype

Decreased risk

OR [95% CI]

p-Value

Reference

NLRP2 XRCC3 XRCC3 XRCC1

A1052E T241 M T241 M Arg194Trp

C > A C > T C > T –

Skin Skin Skin Skin

0.67 [0.46–0.97] 0.45[0.30–0.67] 0.66 [0.51–0.86] 0.52 [0.17, 1.66]

0.042 < 0.0001 0.002 –

(Bhattacharjee et al., 2013b) (Kundu et al., 2011) (Thirumaran et al., 2006) (Breton et al., 2007)

lesions lesions cancer lesions

observed differential mutational profile.

NER pathway with helicase activities and is also part of the BTF2/TFIIH complex, participating in basal transcription (Ahsan et al., 2003b). However, other studies demonstrate that some polymorphisms promote a protective phenotype, since they associate with decreased cancer risk (Bhattacharjee et al., 2013b; Breton et al., 2007; Chung et al., 2011; Kundu et al., 2011; Thirumaran et al., 2006) (Table 2). Conversely, during BER, the DNA base affected is excised by a glycosylase, such as 8-oxoguanine DNA glycosylase 1 (OGG1). APE1 then fills the excised site, leaving a 3′-hydroxyl and a 5′-deoxyribose 5phosphate, recruiting DNA polymerase beta (POLB), and the appropriate missing nucleotide is subsequently replaced. The residual nick is ligated by a DNA ligase (LIG1) with the assistance of X-ray cross complementing protein 1 (XRCC1) (Ebert et al., 2011; Maynard et al., 2009; Osmond et al., 2010) (Fig. 1). Populations from arsenic-exposed regions bearing a homozygous variant of APE1 (Asp148Glu), displayed an increased odds ratio (OR) for skin lesions (Breton et al., 2007), corroborating the assumption that disruption of the BER pathway exacerbates the carcinogenic effects of arsenic. Table 2 summarizes polymorphisms affecting genes involved in NER and BER pathways, which might influence the risk of developing arsenic-induced cancers. The identification of polymorphisms in genes associated with arsenic metabolism and its mechanism of action may not only predict cancer risk in arsenic-exposed individuals, but also improve the understanding of arsenic-induced carcinogenesis and the

4.5. Genetic predispositions influencing susceptibility to arsenic-based therapy Interestingly, while exposure to arsenic is known to be involved in the onset of a number of human diseases, it is also a potent therapeutic agent. In fact, arsenic trioxide (ATO) is used as a first-line chemotherapeutic to combat tumor growth in patients with acute promyelocytic leukemia (APL). While ATO treatment is effective in APL, patients fully resistant to ATO have also been reported. However, certain genetic and epigenetic variations in APL patients have been linked to both increased sensitivity as well as resistance to the cytotoxic compound (Table 3). The main mechanism of resistance to ATO in APL is the modulation of the PML-RARα fusion protein. One of the major molecular drivers of APL is the translocation between chromosomes 15 and 17, resulting in the fusion of the gene encoding promyelocytic protein (PML) to the gene encoding retinoic acid receptor α (RARα) making the PML-RARα oncoprotein (Testa and Lo-Coco, 2016). Several gene mutations in PML that lead to a single amino acid change have been implicated in resistance and poorer overall survival, particularly: A216T, S214 L, L217F, and S220G (Zhu et al., 2014). These mutations occur in a similar location in the PML gene, a region that is critical for the direct binding of ATO to PML-RARα and subsequent SUMOylation, multimerization, 187

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Table 3 Genetic and epigenetic factors altering susceptibility to arsenic trioxide as a chemotherapeutic. Factors conferring higher sensitivity to arsenic trioxide Gene(s)/genetic factor

Alteration

Cancer/cell line

Reference

Inherent ROS quantity

Higher ROS

Leukemia cell lines Digestive tract tumours

NADPH Oxidase PML/RARalpha Aquaporin-9 Latent membrane protein-1 (EBV-encoded) p53 JNK SUMO-interacting motif (SIM) of nuclear PML isoforms B220/CD45R MRP1/MRP2 ASNA1 Chromosome 17p13 IgVH CD38

Higher Activity Cryptic transcript expression Higher Expression Stable Expression Functional defects Sustained activation Un-mutated SIM Expression at the cell surface Lower expression Lower expression Deletion Wild-type Higher expression

APL cell line APL cell lines APL cell lines and primary blasts Nasopharyngeal carcinoma cells Head and Neck Carcinoma cells Multiple myeloma cell lines Glioblastoma; embryonic kidney; PML deficient cells Leukemic T-cell lines ATO-resistant leukemia cell lines

Yi et al. (2002) (Gao et al., 2002) (Wang et al., 2008) (Shepshelovich et al., 2015) (Iriyama et al., 2013) (Du et al., 2005) (Boyko-Fabian et al., 2014) (Kajiguchi et al., 2006) (Maroui et al., 2012) (Benbijja et al., 2014) (Chen et al., 2016)

B-Cell chronic lymphocytic leukemia

(Merkel et al., 2008)

Co-therapeutic compounds increasing sensitivity to arsenic trioxide Compound/target

Mechanism of induction

Cancer/cell line

Reference

Suberoylanilide hydroxamic acid Docosahexaenoic acid Anthraquinones Thiostrepton Rapamycin Tannic Acid Curcumin Sulforaphane Oridonin Sulindac, sulindac sulfide/sulfone Diethyloxadicarbocyanine (DODC) Emodin Ascorbic acid Nutlin-3 Cathepsin L (CatL) Special AT-rich binding protein 1 (STAB1) Heme oxygenase-1 (HO-1) Heat Shock Factor 1 (HSF1) Xeroderma pigmentosum group c (XPC) TWIST1 S100 Calcium-binding protein family (S100A8) Annexin A1 (ANXA1)

Co-administration Co-administration Co-administration Co-administration Co-administration Co-administration Co-administration Co-administration Co-administration Co-administration Co-administration Co-administration Co-administration Co-administration shRNA-mediated knockdown shRNA-mediated knockdown shRNA-mediated knockdown shRNA-mediated knockdown siRNA-mediated knockdown siRNA-mediated knockdown siRNA-mediated knockdown siRNA-mediated knockdown

(Chien et al., 2011) (Lindskog et al., 2006) (Yang et al., 2004) (Bowling et al., 2008) (Dembitz et al., 2015) (Chen et al., 2009a) (Zeng et al., 2016) (Doudican et al., 2012) (Chen et al., 2012) (Stepnik et al., 2011) (Zhang et al., 2003) (Yi et al., 2004) (Grad et al., 2001) (Zheng et al., 2014) (Primon et al., 2013) (Zhang et al., 2014) (Zhong et al., 2014) (Yih et al., 2012) (S.Y. Liu et al., 2010) (Seo et al., 2014) (Yang et al., 2012) (Zhang et al., 2015a)

VEGF Bcl-2 PARP1 Galectin-3 (Gal-3) AKT

Depletion via VEGF antisense sequence Bcl-2 antisense oligonucleotide inhibition PARP1 inhibitor Gal-3 inhibitor (MCP) PI3K/AKT inhibitor (VIII, LY294002, BpV)

Non-small-cell lung cancer cells Neuroblastoma APL cell lines Melanoma cell lines Non-APL AML cells AML cell lines Leukemia stem-like cells Multiple myeloma cells Hepatocellular carcinoma cells Leukemia cell lines HEK 293 cells HeLa cells Multiple myeloma cells Helapocellular carcinoma cells Glioblastoma spheroids Osteosarcoma cells Osteosarcoma cells HeLa and HEK 293 T cells Glioma cell line Non-small-cell lung cancer cells APL and CML cell lines Esophageal squamous cell carcinoma cells Pancreatic carcinoma cells Chronic myelogenous leukemia cells Leukemia cell lines Hepatocellular carcinoma cells Renal cell carcinoma cells HeLa-S3 cells

MEK1 miR-21 Aquaporin-9 (AQP9) Autocrine Human Growth Hormone Thioredoxin-1 (Trx-1)

MEK1 inhibitor (PD184352) Anti-miR-21 Oligo Transfection-mediated expression Transfection-mediated expression Trx-1 inhibitor (PX-12) Cyclosporine A; inactivation via active site mutation Transfection-mediated expression Transfection-mediated expression

hsa-miR-203 miR-539

AML patient samples Chronic myelogenous leukemia cells Lung cancer cell lines Breast cancer cell lines Acute myeloid leukemia cells Liver carcinoma cells

(Luo et al., 2013) (Zhang and Shen, 2003) (Luo et al., 2015) (Xu et al., 2013) (Yih et al., 2013) (Tabellini et al., 2005) (Lunghi et al., 2006) (Li et al., 2010) (Miao et al., 2009) (Zekri et al., 2013) (Tan et al., 2014) (Tian et al., 2008)

Chronic myelogenous leukemia cells Hepatocellular carcinoma cells

(He et al., 2013) (Zhu et al., 2016)

Alterations conferring resistance to arsenic trioxide Gene(s)/genetic factor

Alteration

Cancer/cell line

Reference

Peroxiredoxin 1/2/6 Catalase Gamma-glutamyltransferase Promyelocytic leukemia protein NRF2 pathway genes

High High High High High

ATO-resistant leukemia cell lines ATO-resistant leukemia cell lines Melanoma cell lines Hepatocellular carcinoma cells NCI-60 cell line panel AML cell line

(Song et al., 2014) (Song et al., 2014) (Giommarelli et al., 2009) (Zhang et al., 2015b) (Q. Liu et al., 2010) (Peng et al., 2016) (continued on next page)

expression expression expression expression expression

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Table 3 (continued) Alterations conferring resistance to arsenic trioxide Gene(s)/genetic factor

Alteration

Cancer/cell line

Reference

PI3K pathway genes NPM1

High expression Mutation (NPMc +; W288C) – aberrant localization High expression Low expression Elevated levels High expression High Expression Down regulation Down regulation Functional inactivation Functional inactivation High expression

Leukemia cell lines Leukemia cell lines

(Roszak et al., 2013) (Huang et al., 2013)

ATO-resistant multiple myeloma cell line

(Matulis et al., 2012)

ATO-sensitive and resistant bladder urothelial carcinoma cells

(Hour et al., 2004)

CGL-4 tumourigenic cell line

(Wu et al., 2008)

Leukemia cell lines

(Jang et al., 2012)

Bfl-1 Noxa Glutathione Superoxide disumtase hMSH2 NF-kappaB AP-1 BubR1 Mad2 Carbonyl reductase (CBR1)

and methylation reactions, leads to the generation of mono-, di- and trimethylated forms of inorganic arsenic (Fig. 1) (Kligerman and Tennant, 2007). Glutathione (GSH) and methyltransferases, especially AS3MT, play key roles in arsenic methylation. Essentially, GSH reduces pentavalent arsenicals, facilitating further methylation steps (Niedzwiecki et al., 2014). The enzyme arsenite methyltransferase, encoded by the AS3MT gene, catalyzes the methylation reaction, transferring a methyl group from SAM to trivalent arsenic species (Ajees et al., 2012; Martinez et al., 2011b). As the methylation step facilitates arsenic excretion, it was first recognized as a detoxification process, however the fact that the methylated species are actually more toxic might define it as a bioactivation process as well (Niedzwiecki et al., 2014). In fact, early in vitro experiments showed that trivalent methylated arsenic species are powerful cytotoxins, which display enzyme inhibition abilities and are able to promote cell proliferation and the production of growth-promoting cytokines (Vega et al., 2001). Despite the fact that the mechanisms of arsenic toxicity are not yet fully understood, it is thought that the production of methylated arsenic species leads to several epigenetic and genetic alterations that culminate in genomic instability, DNA damage, chromosomal aberrations and deregulated gene expression (Hubaux et al., 2013).

and degradation of the oncoprotein (Zhu et al., 2014). This region in the PML gene is important for ATO-induced differentiation and apoptosis of APL cells, which illustrates how these mutations may confer resistance to ATO-treatment in APL (Bai and Zheng, 2016; Tomita et al., 2013; Zhu et al., 2014). Additionally, ATO is less effective in patients with APL lacking the characteristic t(15:17) translocation. However, some of these patients still present with PML-RARα expression, through cryptic transcript expression, which can be treated with ATO but may be missed with current diagnostic techniques such as Fluorescence in situ Hybridization (Shepshelovich et al., 2015). Thus, accurate characterization and analysis of the molecular landscape of this disease is critical for the design of effective therapeutic strategies. As the epigenetic effects of arsenic biotransformation are critical to carcinogenesis, such effects may also be exploited to enhance therapy. Mutations in epigenetic modifier genes have been shown to coexist with the PML-RARα fusion protein in APL, and mutations in genes encoding methyltransferases, particularly DNMT3A, are associated with poor prognosis in acute leukemias (Shen et al., 2011). Yan et al. reported three separate missense mutations at Arg882 of DNMT3A in 20% of acute monocytic leukemia cases that led to a pronounced reduction in the DNA methylation capability of this enzyme (Yan et al., 2011). Furthermore, this type of mutation has been associated with poor outcome in both disease-free and overall survival despite treatment (Shen et al., 2015), suggesting that alterations in epigenetic modifiers may hamper ATO-based treatment of acute leukemias. While the current understanding of genetic and epigenetic mechanisms that modulate ATO-based treatment is mainly the result of studies in acute leukemias, recent work has sought to describe ATO action in other cancers, including solid tumours such as hepatocellular carcinomas, liver and gastric cancers, as well as small cell lung cancers (Chen et al., 2009b; Zheng et al., 2013; Zheng et al., 2014).

6. Genotoxic effects induced by arsenic Although arsenic is not described as a direct mutagen at biological doses, it has been proposed that inorganic arsenic acts as a co-mutagen with other chemicals and UV light in mammalian systems (Shen et al., 2013). In skin models of carcinogenesis, arsenic combined with UV led to increased oxidative stress, evidenced as higher levels of 7,8-dihydro8-oxoguanine (8-oxoguanine), culminating in extensive DNA damage (Uddin et al., 2005). Interestingly, whole genome sequencing of lung tumours has shown that the mutational profiles associated with arsenic exposure are markedly different from lung tumours promoted by other chemicals, such as tobacco smoke (Martinez et al., 2013a). Although most of the evidence supporting this is epidemiological, it has been proposed that arsenic and tobacco smoke can synergistically increase oxidative stress and DNA oxidation in the lung (Hays et al., 2006). Either in combination with other chemicals or alone, arsenic exposure has been associated with increased oxidative stress, apoptosis and reduced activity of DNA damage repair pathways.

5. Arsenic metabolism as a mechanism of toxicity As stated above, many of the health effects derived from arsenic exposure have been linked to its biotransformation. In the first step of arsenic metabolism, the PNP enzyme catalyzes the reduction of AsV to AsIII. AsV can compete for phosphate anion transporters and replace phosphate in some biochemical reactions, resulting, for example, in the depletion of ATP from cellular systems (Shen et al., 2013). However, it has been postulated that AsIII shows a higher toxicity, mainly due to a higher affinity for mono- and dithiol groups. Thiol groups (-SH) play a pivotal role in the establishment of disulfide bonds in proteins. Thus, arsenic in a trivalent state has the ability to interfere with the correct conformation and function of proteins, as well as its recruitment of, and interaction with, other complex components (Shen et al., 2013). The human body uses biomethylation as the main mechanism of arsenic detoxification. This process, which involves a series of reduction

6.1. Arsenic-induced oxidative DNA damage The generation of reactive oxygen species (ROS) and the occurrence of oxidative DNA damage has been accepted as a consequence of arsenic exposure and proposed as one of the main mechanisms of arsenic carcinogenicity (Hubaux et al., 2013). In fact, oxidative DNA damage has been described as a frequent event in cancer initiation and 189

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cells (Alimoghaddam, 2014). Treatment of APL cells with ATO in combination with ATRA has been shown to induce differentiation through the downregulation of PRTN3 expression, a gene known to be associated with differentiation arrest, to a much greater extent than ATRA alone (Sumi et al., 2016). However as described above, a number of genetic factors have been associated with the development of resistance to both ATRA and ATO; particularly mutations resulting in a single amino acid change in PML and RARα that are linked with inhibition of ATO-mediated degradation of the oncoprotein and subsequent proliferation pathways (Tomita et al., 2013). These mutations highlight the complex genetic mechanisms involved in the response to arsenic exposure, and how simple inter-individual differences may confer sensitivity to the genotoxic effects of this compound. Thus, the determination of defined genetic and epigenetic susceptibility markers is not only critical for the remediation and understanding of arsenicinduced disease and but also in defining patients that are suitable for arsenic-based therapies.

progression, as it has been observed to be responsible for the generation of gene mutations and genomic instability (Roos et al., 2016). Trivalent arsenic compounds, especially MMAIII, affect the mitochondrial electron transport chain, leading to the liberation of electrons and formation of various ROS, which are known to promote DNA adducts, doublestrand breaks, mutations and deletions (Hubaux et al., 2013; Kligerman et al., 2010; Naranmandura et al., 2011). The main species of ROS generated through arsenic biotransformation, are superoxide anion radicals (O2•−), hydrogen peroxide (H2O2), and hydroxyl radicals (OH•) (Shi et al., 2004). Since DNA bases are sensitive to oxidation, exposure to ROS leads to the production of modified bases, particularly 8-oxoguanine (Sekiguchi and Tsuzuki, 2002). The main consequence of 8-oxoguanine formation is that it can bypass DNA replication, leading to a base mispairing and the generation of transversion mutations. Thus, levels of 8-oxoguanine may be used as a biomarker of oxidative stress (David et al., 2007). 6.2. Impairment of DNA damage repair pathways and induction of apoptosis

7. Large-scale epigenomic changes derived from arsenic biotransformation

The association between cancer development and defects in DNA damage repair pathways has long been described as a frequent event upon exposure to environmental mutagens (Helleday et al., 2014). As mentioned above, arsenic is known to disrupt DNA damage repair by affecting the expression and activity of genes involved with NER and BER repair pathways, such as PARP-1, XPC, APE1, OGG-1, XRCC and ERCC (Nollen et al., 2009; Osmond et al., 2010; Walter et al., 2007) (Fig. 1). Studies have demonstrated that defects in these repair pathways can generate specific mutational patterns, for example, it was shown that disruptions in OGG1 are associated with G ∙ C → T∙A transversions (Smart et al., 2006). This mutational damage generated upon arsenic exposure can lead to the disruption of protein-coding and noncoding RNA genes involved in other key cellular processes, such as apoptosis (Banerjee et al., 2008). The generation of ROS also reduces the mitochondrial membrane potential, leading to cytochrome c release and consequent activation of caspases, which ultimately alters the normal ratio between anti-apoptotic and pro-apoptotic proteins, activating cell death (Kligerman and Tennant, 2007; Tait and Green, 2010). A comparative toxicogenomic study revealed that arsenic compounds mostly affect genes involved with cell proliferation, cell cycle and apoptosis. Additionally, this study also highlights the fact that different arsenic compounds can affect the same pathways; however, there are specific responses against each compound (Davis et al., 2008). Interestingly, one of the main mechanisms of action of ATO in the treatment of APL is the induction of apoptosis. The activation of caspases upon exposure to ATO is believed to rely on the upstream upregulation of members of the MAPK family, JNK1/2 and ERK1/2, along with upregulation of p53 (Eguchi et al., 2011). Therefore, in order to fully understand the mechanism of action of arsenic, it is extremely important to take into consideration the specific differences between distinct types of arsenic compounds, variations in dose-response and even the experimental model in use.

Along with the described genotoxic effects, arsenic has also been shown to promote several epigenetic changes, which mainly affect patterns of DNA methylation, histone post-translational modifications, largely disturbing cellular processes and homeostasis (Bailey et al., 2013). Current research is focusing on the epigenetic arsenic-mediated deregulation of non-coding RNAs – including miRNA, lncRNA, and piRNA – genes with major regulatory functions, which have been shown to be upregulated in response to iAs treatment (Sturchio et al., 2014) (Luo et al., 2016; Tani et al., 2014; Wen et al., 2016). 7.1. Global changes in methylation patterns A major consequence of the biotransformation process is the depletion of the cellular availability of methyl groups (Reichard and Puga, 2010). Since SAM is the main donor of methyl groups during the methylation of both arsenic and DNA, the high demand imposed on this enzyme during the biotransformation process can lead to global DNA hypomethylation (Hubaux et al., 2013; Ren et al., 2011) (Fig. 1). Changes in DNA methylation, particularly global DNA hypomethylation, have been described as important features of malignant transformation by facilitating chromosomal abnormalities, which culminate in overall genomic instability (Reichard and Puga, 2010). Studies have demonstrated that widespread DNA hypomethylation induced by arsenic is also associated with promoter activation and involved in carcinogenesis by increasing the expression of oncogenes, such as c-MYC, ER-α and Cyclin D1 (Hubaux et al., 2013; Miao et al., 2015; Ren et al., 2011). Conversely, specific promoter hypermethylation is also observed after arsenic exposure, affecting CpG islands close to transcriptional start sites of tumour suppressor genes such as TP53, P16, and P21, inhibiting their transcription (Ren et al., 2011). Therefore, alterations in methylation status are of great importance in arsenic-induced carcinogenesis.

6.3. Genotoxic effects of arsenic based therapies 7.2. Changes in histone post-translational modifications As previously discussed, arsenic is readily used as a chemotherapeutic agent in the treatment of APL. The PML-RARα fusion protein driving APL can be targeted for the sensitization of cells to inducedmaturation by all-trans retinoic acid (ATRA), another effective compound used for treating APL (Lo-Coco and Hasan, 2014). ATO has been shown to induce the degradation of PML-RARα, and to have a synergistic effect with ATRA, thus increasing the therapeutic potential of these compounds (Lo-Coco and Hasan, 2014; Mi et al., 2015). Thus, the effects of arsenic exposure on cellular machinery are being exploited in the treatment of genetically volatile malignanicies. ATO can also induce both differentiation and apoptosis of leukemic

Histone post-translational modifications are characterized as fundamental epigenetic features induced by arsenic exposure as they actively influence gene transcription (Howe and Gamble, 2016). For example, methylation of histone H3 at lysine 4 (H3K4me) is associated with gene activation, but methylation at lysine 27 (H3K27me) is associated with gene repression (Ramirez et al., 2008). In vitro studies have demonstrated that arsenic exposure increased global levels of H3K4me2/3 and diminished levels of H3K27me3 (Zhou et al., 2008), however, in vivo studies have emphasized that gender may influence the results obtained (Howe and Gamble, 2016; Tyler et al., 2015). This 190

Platform

Agilent-014850 Whole Human Genome Microarray 4 × 44 K Affymetrix Human Gene 1.0 ST Array Affymetrix Human HG-Focus Target Array Affymetrix Human Genome U95 Version 2 Array Illumina HumanMethylation450 BeadChip Illumina HiSeq 2500 Affymetrix Human Gene 1.0 ST Array Affymetrix Human Transcriptome Array 2.0 Affymetrix Human Genome U133 Plus 2.0 Array Agilent-026652 Whole Human Genome Microarray Illumina HumanMethylation450 BeadChip NimbleGen Human DNA Methylation 2.1 M Deluxe Promoter Array Affymetrix Human Genome U133 Plus 2.0 Array Affymetrix Human Gene 2.0 ST Array Agilent-031181 Unrestricted_Human_miRNA_V16.0_Microarray Affymetrix Human Genome U133 Plus 2.0 Array Agilent-014850 Whole Human Genome Microarray 4x44K Agilent Human custom chip-on-chip 2 × 105 k tiling microarray Agilent-014707 Human Promoter ChIP-on-Chip Set 244 K Agilent-014706 Human Promoter ChIP-on-Chip Set 244 K Phalanx Human OneArray Affymetrix Human Gene 1.0 ST Array HIS/SIBS Human 15 K cDNA array Affymetrix Human Promoter 1.0R Array Affymetrix Human Gene 1.0 ST Array Affymetrix Human Genome U133 Plus 2.0 Array Affymetrix Human HG-Focus Target Array Affymetrix Human Genome U133 Plus 2.0 Array Ambion Human_Mouse_Rat mirVANA miRNA Bioarray

GEO Accession #

GDS5498 GDS4915 GDS2780 (GSE6907) GDS2603 (GSE7101) GSE71678 GSE63935 GSE57711 GSE60760 GSE57051 GSE67420 GSE62924 GSE44173 GSE44169 GSE48354 GSE48353 GSE46909 GSE33520 GSE38929 GSE38928 GSE38926 GSE37192 GSE36684 GSE24946 GSE26073 GSE20320 GSE14519 GSE8865 GSE7967 GSE6020

Table 4 Available data from arsenic-based studies on the Gene Expression Omnibus. Human patient sample/human cell line BEAS-2B lung epithelial TK6 lymphoblastoid Liver carcinoma HepG2 TR9–7 fibroblast Placenta samples Neural, endothelial, mesenchymal, immune Peripheral blood mononuclear HeLa BJ diploid foreskin Leukemia HL-60 newborn cord blood leukocyte A549 epithelial lung carcinoma A549 epithelial lung carcinoma Newborn cord blood Newborn cord blood Jurkat T BEAS-2B lung epithelial Arsenite-transformed RWPE-1 Arsenite-transformed UROtsa and RWPE-1 Arsenite-transformed UROtsa and RWPE-1 HaCaT keratinocyte BEAS-2B lung epithelial Leukemia K562 Blood samples TK6 lymphoblastoid Myeloma (U266, MM.1 s, KMS11, 8226/S) HepG2 human hepatoma Newborn cord blood TK6

Exposure dose; Time 2.5 μM; 6 m 0.1 μM; 24 h 20 μM; 6 h 5 μM; 3 h > 0.0148 ng/g placenta 5 μM; 2–7 d 50–1000 μg/L 1 μM; 36 d 2 μM; 24 h 0.5 mM; 24 h 0.456–236 μg/L 0.08–2 μM; 1–8w 0.08–2 μM; 1–8 w < 236 μg/L < 236 μg/L 3 μM; 6 h 2.5 μM; 6 m 5 μM; < 33w 5 μM; < 33w 5 μM; < 33 w 0.5 μg/mL; < 22 passages 2.0 μM; 8 w 1.5 μM; < 48 h < 1100 μg/L 0.1 μM; 24 h 2–4 μM; < 48 h 6 μM; < 48 h ≥ 10 μg/L 2 μM; 6 d

Arsenic species As2O3 NaAsO2 As2O3 NaAsO2 Ground H2O NaAsO2 Ground H2O NaAsO2 NaAsO2 As2O3 Ground H2O NaAsO2 NaAsO2 Ground H2O Ground H2O As2O3 As2O3 NaAsO2 NaAsO2 NaAsO2 As2O3 NaAsO2 As2O3 Ground H2O NaAsO2 As2O3 As2O3 Ground H2O NaAsO2

(Stueckle et al., 2012) (Benton et al., 2011) (Kawata et al., 2007) (McNeely et al., 2006) (Green et al., 2016) (Schwartz et al., 2015) (Munoz et al., 2015) (Riedmann et al., 2015) (Qiu et al., 2015) (Sumi et al., 2016) (Rojas et al., 2015) (van Breda et al., 2015) (van Breda et al., 2015) (Rager et al., 2014) (Rager et al., 2014) (Shao et al., 2013) (Stueckle et al., 2012) (Severson et al., 2013) (Severson et al., 2013) (Severson et al., 2013) (Udensi et al., 2011) (Clancy et al., 2012) (Xia et al., 2013) (Smeester et al., 2011) (Benton et al., 2011) (Matulis et al., 2009) (Kawata et al., 2009) (Fry et al., 2007) (Marsit et al., 2006)

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9. Conclusions

gender influence is also observed among arsenic-exposed populations, since inverse correlations between tri-methylation of H3K27 and arsenic were found in exposed Bangladeshi men and women (Chervona et al., 2012). These findings highlight the need for further studies of arsenic molecular mechanisms of action at a population level that examine genetic and demographic characteristics of individual cohorts.

While it is known that high doses of arsenic are extremely toxic, exposure to levels at or even below current guidelines may be a contributing factor to many debilitating diseases, including cancer. The extent of the genotoxic effects induced by its biotransformation remains only broadly understood. Metabolism of arsenic generates by-products that contribute to the generation of ROS, induce DNA damage, and promote chromosomal instability. The biotransformation of arsenic places a high demand on cellular epigenetic machinery, leading to aberrant DNA methylation patterns, gene expression changes and the deregulation of the regulatory non-coding regions of the genome. Further, genetic variation in arsenic biotransformation pathway components is associated with differential susceptibility to arsenic-induced carcinogenesis. However, a more comprehensive understanding of the molecular mechanisms of arsenic-induced disease and carcinogenesis is required to aid in the identification of populations that are particularly at risk, even at low doses of exposure. As arsenic is ubiquitously distributed throughout the environment, human industrial activities may serve to greatly increase the exposure of populations in many areas of the world. Moreover, economic and practical issues such as the cost and efficacy of current remediation techniques make limiting these effects particularly difficult. Further, the magnitude of the problem is veiled by insufficient epidemiological and molecular data, which is both hard to generate, and difficult to access. Increasing available data and building a comprehensive understanding of both the molecular mechanisms of arsenic-based genotoxicity and the extent of exposure will aid in the remediation and management of arsenic-related health risks. Collectively, current evidence points to an association between chronic arsenic exposure and a multitude of adverse effects in normal biological processes, leading to the onset of many diseases. Therefore, current guidelines may need to be re-examined to ensure that safe exposure levels are maintained globally, which in line with coordinated research initiatives from multiple disciplines may direct strategies to alleviate the risk of arsenic-induced health effects.

8. Challenges and future perspectives in arsenic susceptibility research Research into arsenic toxicity and subsequent health effects is hampered by the limited availability of patient material, which has greatly hindered the comprehensive understanding of its widespread molecular effects. The scarcity of arsenic-based genetic and epigenetic data from human patient samples in the Gene Expression Omnibus (GEO) repository serves as a striking example (Table 4). Furthermore, it is challenging to identify cases where the onset of cancer can be directly linked to consumption of arsenic as they are commonly overshadowed by other known carcinogens such as smoking, collectively making clinically relevant samples very scarce. Factors such as age, sex and ethnicity can also influence the data recorded. For example, two populations of differing ethnicities from the same area of Western China showed divergent concentrations of total arsenic and MMA in urine samples (Fu et al., 2014). These observed differences may also be the result of epigenetic characteristics between populations, such as the sex-specific modification of global DNA methylation patterns, an observation that is closely associated with arsenic metabolism (Niedzwiecki et al., 2015). Studies examining the role of arsenic in cancer and cancer therapy are restricted to cell and animal models, geography-specific epidemiological studies, or computational approaches (Table 4). These restrictions limit information at the population level and from a clinical perspective, but also misguide the understanding of the metabolomic mechanism involved in arsenic-related diseases. For example, rats may provide a misleading model for arsenic research, since rat hemoglobin has a stronger biding affinity to arsenic compounds, resulting in the greater accumulation of arsenic in blood, which may misrepresent arsenic exposure in humans (Lu et al., 2004). Additionally, many current studies use ATO or sodium arsenite (NaAsO2) as the source of arsenic, with ATO more commonly used in therapeutic studies and NaAsO2 used in studies designed to evaluate carcinogenic effects, although not exclusively. However, recent analyses suggest discordant mechanisms and effects of these compounds, wherein ATO was shown to induce more DNA damage, chromosomal instability and apoptosis in lung adenocarcinoma cells (Jiang et al., 2013). Heightening these challenges is the strong variation in doses used between studies, and the lack of a universal definition for what constitutes a biologically effective dosage of arsenic (Chen et al., 2001). As such, the determination of the effects of arsenic exposure is convoluted by experimental methodologies, and its subsequent extension to human cases is exceedingly difficult. Future perspectives on the study of arsenic-related health effects involve the translation of current findings into tools that can be beneficial for at-risk population, both at the epidemiological or clinical level. For example, it would be highly relevant to focus research into the early molecular events characterizing malignant transformation associated with arsenic exposure, and how this process might differ between human tissues. This knowledge could be further translated into the development of exposure/monitoring biomarkers, which will help to identify populations at risk and/or individuals at higher risk within these populations. Finally, due to the i) great extent of individuals potentially at risk, ii) interindividual variations in responses, and iii) heterogeneous geographical, ethnical, socio-economical and other factors in affected countries; a great fraction of the effort would need to be dedicated to the development of strategies for implementation of these scientific advances.

Acknowledgements The authors would like to acknowledge Heather Saprunoff and Kevin Ng for their expert critique of the manuscript Funding This work was supported by grants from the Canadian Institutes for Health Research (CIHR FRN-143345). APS and EAM are also supported by the Frederick Banting and Charles Best Scholarship from CIHR. BCM, APS, CA, EAM and VDM are supported by scholarships from the University of British Columbia. Authors' contributions BCM and APS performed literature search, drafted the manuscript and designed figures and tables. CA, RH, and EAM assisted in writing, contributed in the production of figures and tables, critically revised the manuscript. WLL and VDM participated in the design, writing and analysis of the manuscript. References Ahmed, S., Akhtar, E., Roy, A., von Ehrenstein, O.S., Vahter, M., Wagatsuma, Y., Raqib, R., 2017. Arsenic exposure alters lung function and airway inflammation in children: a cohort study in rural Bangladesh. Environ. Int. 101, 108–116. Ahsan, H., Chen, Y., Kibriya, M.G., Islam, M.N., Slavkovich, V.N., Graziano, J.H., Santella, R.M., 2003a. Susceptibility to arsenic-induced hyperkeratosis and oxidative stress genes myeloperoxidase and catalase. Cancer Lett. 201, 57–65. Ahsan, H., Chen, Y., Wang, Q., Slavkovich, V., Graziano, J.H., Santella, R.M., 2003b. DNA

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