Biomonitoring of environmental contamination resulting from mining activities on exposed populations

BIOMONITORING OF ENVIRONMENTAL CONTAMINATION RESULTING FROM MINING ACTIVITIES ON EXPOSED POPULATIONS Patrícia Clara dos Santos Coelho Tese de doutoramento em Ciências Biomédicas 2013 Patrícia Clara dos Santos Coelho BIOMONITORING OF ENVIRONMENTAL CONTAMINATION RESULTING FROM MINING ACTIVITIES ON EXPOSED POPULATIONS Tese de Candidatura ao grau de Doutor em Ciências Biomédicas, submetida ao Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto. Orientador – Doutor João Paulo Teixeira Categoria – Investigador Auxiliar Afiliação – Instituto Nacional de Saúde Dr. Ricardo Jorge Co-orientadora – Doutora Denisa Mendonça Categoria – Professora Associada Afiliação – Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto Co-orientadora – Doutora Beatriz Porto Categoria – Professora Auxiliar Afiliação – Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto The experimental work presented in this thesis was performed at the:  Environmental Health Department, Portuguese National Institute of Health – Porto, Portugal;  Toxicology Unit, Department of Psychobiology, University of A Coruña – A Coruña, Spain;  Trace Element Laboratory, Faculty of Health and Medical Sciences, University of Surrey – Guildford, UK;  Department of Chemistry and Geochemistry, Colorado School of Mines – Golden, USA;  Division of Biological Chemistry, Biocenter Innsbruck Medical University – Innsbruck, Austria;  Department of Genetics, Faculty of Medical Sciences, New University of Lisbon – Lisbon, Portugal. Statistical analysis was performed by Dr. Blanca Laffon, Dr. Valentina Daal’Armi and Dr. Roberto Zoffoli under the orientation of Professor Dr. Stefano Bonassi at at IRCCS San Raffaele Pisana, Rome. This work was supported by the Portuguese Foundation for Science and Technology (FCT) under the grant SFRH/BD/47781/2008, under the QREN - POPH - Type 4.1 - Advanced Training, subsidized by the European Social Fund and national funds from the MCTES. . vii CONTENTS ORIGINAL PUBLICATIONS xi LISTS OF FIGURES xiii LISTS OF TABLES xv L IST OF ABBREVIATIONS xvii ABSTRACT xix RESUMO xxi I. REVIEW OF THE LITERATURE 1 1. Introduction 3 2. Mining Activities: Health Impacts 3 3. Panasqueira Mine: Study Area 4 3.1 Site description 4 3.2 Geology and mineralization 6 3.3 Mining activities and potential environmental considerations 8 3.4 Environmental studies: main results 10 4. Metal(loid)s 11 4.1 Human exposure 11 4.2 Toxicokinetics and bioaccumulation 17 4.3 Health effects 18 4.3.1 Genotoxicity, immunotoxicity, and carcinogenicity 18 5. Biomarkers: Human Health 20 5.1 Biomarkers of exposure 22 5.1.1 Biomarkers of internal dose 22 5.1.2 Biomarkers of biologically effective dose 23 5.2 Biomarkers of effect 24 5.2.1 T-cell receptor mutation assay 24 5.2.2 Micronuclei assay 25 5.2.3 Chromosomal aberrations 27 5.2.4 Comet assay 28 5.2.5 Immune markers 30 5.3 Biomarkers of susceptibility 34 5.3.1 Polymorphisms in genes involved in the metabolism (phase I and phase II) 35 5.3.1.1 GSTA2 35 5.3.1.2 GSTM1 36 5.3.1.3 GSTP1 37 viii 5.3.1.4 GSTT1 37 5.3.2 Polymorphisms in genes involved in DNA repair 37 5.3.2.1 Polymorphisms in genes involved in BER pathway 39 5.3.2.1.1 XRCC1 39 5.3.2.1.2 APEX1 39 5.3.2.1.3 MPG 40 5.3.2.1.4 MUTYH 40 5.3.2.1.5 OGG1 40 5.3.2.1.6 PARP1 41 5.3.2.1.7 PARP4 41 5.3.2.2 Polymorphisms in genes involved in NER pathway 41 5.3.2.2.1 ERCC family 41 II. AIM OF THE STUDY 43 III. MATERIAL AND METHODS 47 1. Study Population 49 1.1 Population selection 49 1.2 Study group 50 1.3 Sample collection 53 2. Biomarkers of Exposure 54 2.1 Metal(loid)s in blood, urine, nail and hair samples 54 2.1.1 Instrumentation 54 2.1.2 Reagents and standards 54 2.1.3 Sample preparation 54 2.1.4 Quality control and Quality assurance 55 2.1.5 Determination of creatinine 56 3. Biomarkers of Effect 57 3.1 T-cell receptor mutation assay 57 3.2 Cytokinesis Block MN Assay 57 3.3 Chromosomal Aberrations, aneuploidies and gaps 58 3.4 Comet Assay 58 3.5 Analysis of lymphocytes subsets 59 3.6 Quantification of neopterin, tryptophan, kynurenine, and nitrite 60 4. Biomarkers of Susceptibility 61 4.1 DNA extraction 61 4.2 Genotyping of polymorphisms in genes involved in the metabolism 61 ix 4.2.1 GSTA2 61 4.2.2 GSTM1 and GSTT1 61 4.2.3 GSTP1 62 4.3 Genotyping of polymorphisms in genes involved in DNA repair 62 4.3.1 XRCC1, APEX1, MPG, MUTYH, OGG1, PARP1 and PARP4 62 4.3.2 ERCC1, ERCC4 and ERCC5 63 5. Statistical Analysis 64 IV. RESULTS 67 1. Biomarkers of Exposure 69 1.1 Comparison with reference levels 70 1.2 Effect of exposure after adjustment for confounders 70 1.3 Correlations between matrices 73 1.4 Effect of gender, age and smoking habits 74 2. Biomarkers of Effect 76 2.1 Biomarkers of genotoxicity 76 2.1.1 Effect of exposure, age and smoking habits 76 2.1.2 Effect of gender 78 2.1.3 Synergistic effect of environmental and occupational exposure 79 2.1.4 Effect of metal(loid) concentration 79 2.2 Immune markers 81 2.2.1 Effect of exposure, age and smoking habits 81 2.2.2 Effect of gender 85 2.2.3 Effect of other confounders 85 2.2.4 Effect of metal(loid) concentration 86 2.3 Symptomatology 87 3. Biomarkers of Susceptibility 88 V. DISCUSSION 95 1. Biomarkers of Exposure 96 1.1 Comparison with reference levels 97 1.2 Correlations between matrices 97 1.4 Effect of gender, age and smoking habits 97 2. Biomarkers of Effect 100 2.1 Biomarkers of genotoxicity 100 2.1.1 Effect of metal(loid) concentration 100 2.1.2 Effect age and gender 101 xvi Table 14. Effect of exposure in 4 categories (taking into account the origin village of the occupationally exposed individuals) on TCR-MF, %DNAT and CA-total. Adjustment for age, smoking and genotoxicity parameterspecific actual confounders. 79 Table 15. Effect of the levels of As, Mn, and Pb in toe nails on the genotoxicity parameters. Adjustment for age, smoking and genotoxicity parameter-specific actual confounders. 80 Table 16. Levels of immunotoxicity biomarkers in the study groups. 81 Table 17. Effect of exposure on neopterin, tryptophan, kynurenin and nitrite concentrations stratified by exposure, age, and smoking habit. All models were adjustment for parameter-specific actual confounders. 82 Table 18. Effect of exposure on lympocytes subpopulations. Adjustment for age, smoking and parameter-specific actual confounders. 84 Table 19. Effect of gender and exposure on %CD19+, excluding the occupationally exposed population. 85 Table 20. Effect of the levels of Pb in toe nails on Neo, Kyn and K/T levels . Adjustment for age, smoking and parameter-specific actual confounders. 86 Table 21. Effect of the levels of Mn in blood on %CD8+, CD4+/CD8+ and %CD19+ lymphocytes. Adjustment for age, smoking and immunotoxicity parameter-specific actual confounders. 86 Table 22. Linear regression analysis for symptoms. Adjustment for age, smoking and symptom-specific actual confounders. 87 Table 23. Frequency of genotypes in study populations. 88 Table 24. Influence of biomarkers of susceptibility on genotoxicity parameters (only models showing significant effect are included). Adjustment for age, smoking and genotoxicity parameter-specific actual confounders. 90 xvii LIST OF ABREVIATIONS AMD Acid Mine Drainage AP Apurinic/Apirimidinic ARSC Administração Regional de Saúde do Centro ATSDR Agency for Toxic Substances and Disease Registry BER Base Excision Repair BH4 Tetrahydrobiopterin CA Chromosomal Aberrations CBMN Cytokinesis Block Micronucleus test CD Cluster of Differentiation CI Confidence Interval COFS Cerebro-Oculo-Facio-Skeletal CRM Certified Reference Material CS Cockayne’s Syndrome CYP Cytochrome P450 DDB1 DNA Damage-Binding Protein 1 DSB Double Strand Breaks EDTA Ethylenediamine Tetraacetic Acid ELISA Enzyme-Linked Immunosorbent Assay Endo III Endonuclease III EPA Environmental Protection Agency ERCC Excision Repair Cross- Complementing EU European Union GPA Glycophorin A GSH Glutathione GST Glutathione S-Transferase FISH Fluorescent in situ Hybridization FITC Fluorescein-Isothiocyanate FPG Formamidopyrimidine DNA Glycosylase GTP-CH1 Guanosine- Triphosphatecyclohydrolase-1 HLA Human Leukocyte Antigen HMS Heavy Metal Separation HPLC High Performance Liquid Chromatography HPRT Hypoxanthine-guanine Phosphoribosyltransferase HR Homologous Recombination IAEA International Atomic Energy Agency IARC International Agency for Research on Cancer ICP-MS Inductively Coupled Plasma- Mass Spectrometry ICP-OES Inductively Coupled Plasma- Optical Emission Spectrometry IDO Indoleanine-2,3-dioxygenase IFN Interferon Ig Immunoglobulins IL Interleukin INETI Instituto Nacional de Engenharia, Tecnologia e Inovação LOD Limit of Detection MHC Major Histocompatibility Complex MMR Mismatch Repair MR Mean Ratio NER Nucleotide Excision Repair NHEJ Non-Homologous End Joining NIES National Institute of Environmental Sciences NK Natural Killer MN Micronuclei NO Nitric Oxide xviii NOS Nitric Oxide Synthase NRC National Research Council PBS Phosphate Buffer Solution PCNA Proliferating Cell Nuclear Antigen PCR Polymerase Chain Reaction PE Phycoerytrin PE-Cys Phycoerytrin-Cyanin5 PEEK Polyether Ether Ketone PNAAS Plano Nacional de Acção Ambiental em Saúde QC Quality Control ROS Reactive Oxygen Species SCE Sister Chromatid Exchange SCGE Single-Cell Gel Electrophoresis SD Standard Deviation SDSA Synthesis-Dependent Strand Anneling SNPs Single Nucleotide Polymorphisms SSB Single Strand Breaks Tc T-cytotoxic lymphocytes TCR T-Cell Receptor TCR-Mf TCR Mutation frequency Th T-helper lymphocytes TL Tail Length TM Tail Moment TTD Trichothiodystrophy WHO World Health Organisation XP Xeroderma Pigmentosum %DNAT Percentage of DNA in the tail (tail Intensity) xix ABSTRACT Mining industry is a vital economic sector for many countries but it is also one of the most hazardous activities, both occupationally and environmentally. During these processes several toxic wastes are produced and released into the surrounding environment causing pollution of air, drinking water, rivers and soils. Mining activities cause several health impacts in miners and communities living near the mine site that may persist, even when the mine is abandoned. Major impacts on workers’ health are cancer and respiratory diseases such as asbestosis, silicosis, and pneumoconiosis. The Panasqueira mine (Sn-W) in Central Portugal was selected as the study area due to its past and current activity, and its potential impact on the local ecosystems. There are small villages around the mine site, namely S. Francisco de Assis and Barroca do Zêzere, with local populations strongly dependent on agriculture and farming. Another important factor was Zêzere river, which feeds the Castelo do Bode dam (located 90 Km downstream from the mine), the principal water supply of Lisbon metropolitan area. Any significant spillage into this river can bring serious environmental consequences to regional economy importance. From the results obtained in geochemical sampling campaigns, it was concluded that a significant geochemical dispersion, with anomalous distribution of some metal(loid)s, occurs in the study area. The aim of this project was to evaluate the impact of environmental metal(loid)s contamination in populations living nearby and working in Panasqueira mine, through a multistage approach that integrates information obtained with biomarkers of exposure, effect and susceptibility. The possible health effects caused by this environmental contamination were also discussed. Study group consisted of 41 individuals environmentally exposed, 41 individuals occupationally exposed and 40 individuals without known exposure to metal(loid)s (controls). Biomarkers of exposure included quantification of several metal(loid)s - As, Ca, Cd, Cr, Cu, Fe, Hg, K, Mg, Mn, Mo, Na, Ni, Pb, Se, S, Si e Zn - in blood, urine, nail (finger and toe), and hair samples by inductively coupled plasma-mass spectrometry (ICP-MS) and inductively coupled plasma-optical emission spectrometry (ICP-OES). Several biomarkers of effect were analyzed, such as, cytogenetic techniques – micronucleus (MN) test and chromosomal aberrations (CA); comet assay, T-cell receptor (TCR) mutation assay, alterations in lymphocyte subsets percentages (CD3+, CD4+, CD8+, CD15+56+) and quantification of neopterin, tryptophan, kynurenine and nitrite. Finally several genetic polymorphisms in genes involved in the metabolic pathway of metal(loid)s (GSTA2, GSTM1, GSTP1, and GSTT1) and in genes involved in DNA damage repair (XRCC1, xx APEX1, MPG, MUTYH, OGG1, PARP1, PARP4, ERCC1, ERCC4, and ERCC5) were evaluated as biomarkers of susceptibility. Results obtained in biomarkers of exposure agree with the reported by the environmental studies performed in this area pointing to populations living nearby and working in the mine being exposed to metal(loid)s origination from mining activities. Arsenic was the element with the highest increase in exposed populations. The concentration of other elements such as Cr, Mg, Mn, Ni, Pb, S, Se, and Zn also increased, although at a lesser extent, especially in individuals with environmental exposure and in females. Significant increases in the frequency of all the biomarkers of effect investigated (TCR mutation, CA, MN, and DNA damage measured by the comet assay) were found in both exposed groups, generally higher in those environmentally exposed. The environmentally exposed group also showed significantly lower levels of %CD8+ and higher CD4+/CD8+ ratios, whereas the occupationally exposed individuals showed significant decreases in %CD3+ and %CD4+, and significant increases in %CD16+56+, when compared to controls. Allele frequencies of studied polymorphic genes obtained in this study were similar to the ones described by other authors for Caucasian populations. Significant influences of polymorphisms were observed for GSTM1 deletion and OGG1 rs1052133 on CA frequencies, APEX1 rs1130409 on DNA damage, ERCC1 rs3212986 on DNA damage and CA frequency, and ERCC4 rs1800067 on MN and CA frequencies. Our results show that the metal(loid) contamination in the Panasqueira mine area induced genotoxic damage both in individuals working in the mine or living in the area. The observed effects are closely associated to the internal exposure dose, and are more evident in susceptible genotypes. The contamination is also inducing immunotoxic effects in exposed populations which can lead to a complete deregulation of the immune system increasing populations’ susceptibility to many pathologies. All these findings confirm the need for competent authorities to act as soon as possible in this area and implement strategies aimed to protect exposed populations and the entire ecosystem. xxi RESUMO A exploração mineira produz ao longo dos anos um conjunto assinalável de impactes negativos no ambiente, com consequentes efeitos na saúde das populações residentes na envolvência. Adicionalmente a atividade mineira continua a ser uma das profissões mais perigosas do mundo, sendo os principais efeitos na saúde dos trabalhadores as doenças oncológicas e respiratórias como a silicose e pneumoconiose. A escolha da envolvente da mina da Panasqueira (Sn-W) como objeto de estudo deste projeto reside nos seguintes fatores: (a) trata-se de uma exploração mineira em plena actividade; (b) apresenta escombreiras de volume avultado, bem como barragens de estéreis; (c) a mina coexiste com a presença de pequenas povoações na sua vizinhança; (d) os rejeitados encontram-se nas proximidades do rio Zêzere que alimenta a barragem do Castelo de Bode, a principal fonte abastecedora de água da cidade de Lisboa. Estudos anteriores feitos nesta região, no âmbito de um projeto comunitário, revelaram existir uma significante dispersão de metais/metaloides, com assinaturas geoquímicas anómalas a distâncias consideráveis nos sedimentos ao longo do Rio Zêzere. O objetivo do presente projeto foi avaliar o impacto da contaminação ambiental por metais/metaloides em populações residentes na vizinhança e trabalhadores da mina da Panasqueira, uma abordagem múltipla de forma a integrar a informação obtida na análise de biomarcadores de exposição, efeito e susceptibilidade. Os possíveis efeitos na saúde causados por esta contaminação foram também discutidos. A população em estudo consistiu em 41 indivíduos ambientalmente expostos, 41 indivíduos ocupacionalmente expostos e 40 indivíduos controlo sem histórico de exposição a metais/metaloides. Os biomarcadores de exposição estudados incluíram a quantificação de diversos metais/metaloides – As, Ca, Cd, Cr, Cu, Fe, Hg, K, Mg, Mn, Mo, Na, Ni, Pb, Se, S, Si e Zn - em amostras de sangue, urina, unhas (pés e mãos) e cabelos por espectrometria de massa com plasma indutivamente acoplado (inductively coupled plasma-mass spectrometry - ICP-MS) e espectrometria de emissão óptica com plasma indutivamente acoplado (inductively coupled plasma-mass spectrometry - ICP-OES). Diversos biomarcadores de efeito foram analisados, nomeadamente: técnicas citogenéticas - teste do micronúcleo (MN) e aberrações cromossómicas (AC) -, teste do cometa, teste de mutação do receptor das céluals T (TCR), alterações nas percentagens de subpopulações linfocitárias (CD3+, CD4+, CD8+, CD15+56+) e quantificação de neopterina, triptofano, quinurenina e nitrito. Por último como biomarcadores de susceptibilidade foram estudados polimorfismos de genes relacionados com o metabolismo de metais/metaloides (GSTA2, GSTM1, GSTP1 e GSTT1) e polimorfismos xxii de genes envolvidos no mecanismo de reparação de lesões do DNA (XRCC1, APEX1, MPG, MUTYH, OGG1, PARP1, PARP4, ERCC1, ERCC4 e ERCC5). Os resultados obtidos para os biomarcadores de exposição são concordantes com os relatados nos estudos ambientais efetuados nesta área e que indicam que as populações residentes na vizinhança e os indivíduos que trabalham na mina estão expostos a metais/metaloides com origem nas atividades mineiras. O Arsénio foi o elemento que apresentou maior aumento nas populações expostas quando comparadas com a populações controlo. Também se verificou o aumento de outros elementos tais como Cr, Mg, Mn, Ni, Pb, S, Se, e Zn, apesar de ser a um nível mais baixo, nomeadamente em indivíduos ambientalmente expostos e mais especificamente em indivíduos do sexo feminino. Foram também observados aumentos nas frequências de todos os biomarcadores de efeito estudados (mutações no TCR, AC, MN e dano no DNA detectado através do teste do cometa) em ambos os grupos expostos quando comparados com o grupo controlo, sendo no geral mais elevados nos indivíduos expostos ambientalmente. Relativamente aos biomarcadores de imunotoxicidade foi observada uma diminuição significativa nas percentagens de CD8+ e um aumento significativo na razão CD4+/CD8+ no grupo ambientalmente exposto e uma diminuição significativa nas percentagens de CD3+ e CD4+ e um aumento significativo nas percentagens de CD16+56+ no grupo ocupacionalmente exposto. As frequências alélicas dos genes polimórficos estudados são similares às descritas noutros estudos para as populações caucasianas. Foram detetadas influncias significativas de diversos polimorfismos nos biomarcadores estudados, nomeadamente dos polimorfismos do GSTM1 e do OGG1 rs1052133 nas frequências de AC, do APEX1 rs1130409 no nível de dano no DNA, do ERCC1 rs3212986 no nível de dano no DNA e nas frequências de AC, e finalmente do ERCC4 rs1800067 nas frequências de MN e AC. Os resultados obtidos mostram que a contaminação por metais/metaloides na área da mina da Panasqueira induziu dano genotóxico tanto em indivíduos que residem na vizinhança da mina como nos que nela trabalham. Os efeitos observados estão directamente associados com a dose interna, e são mais evidentes em genótipos mais susceptíveis. A contaminação induziu também efeitos imunotoóxicos nas populações expostas podendo levar a uma desregulação do sistema imunitário aumentando assim a suscetibilidade das populações a diversas patologias. Globalmente estes resultados confirmam a necessidade de atuação imediata das autoridade competentes nesta área e a implementação de estratégias que visem a proteção das populações expostas e de todo o ecossistema. I. REVIEW OF THE LITERATURE REVIEW OF THE LITERATURE 3 1. INTRODUCTION After centuries of economic and social development without concern for contamination of the environment, in recent decades several measures for the adoption of sustainable development models that safeguard the environment have been developed. Studies on the relationship environment/health estimate that every year, the premature death of thousands of citizens can be attributed to environmental factors (OECD, 2008). Human health depends in an essential way on the environment as both a source of resources and a deposit for wastes. Environmental impact assessment is a crucial tool for maintaining and improving environmental quality while carrying out economic development (Health Canada, 2004). Considering that health has a crucial role in the context of sustainable development, the European Union (EU) has prepared some contingency plans. The European Environment and Health action plan 2004-2010, approved on 9th of June 2004, formed the basis of the specific programs that each country should establish. A central objective of this plan was to obtain the information needed to reduce the adverse health effects of environmental pollution. Among the highest priorities was the issue of heavy metals and cancer disease particularly in children. It was also highlighted the role of biological monitoring as a fundamental tool to assess exposure. Portugal only in May 2007 presented a proposal from the National Action Plan for Environment and Health (PNAAS – Plano Nacional de Acção Ambiente em Saúde) which was approved on the 4th of June 2008, in the Resolution of the Council of Ministers 91/2008, and will take place until 2013. This project was intended to be included within the scope of this program. 2. MINING ACTIVITIES: HEALTH IMPACTS Mining is one of the oldest activities in human civilization. Mining industry is a vital economic sector for many countries, but is also one of the most hazardous activities in occupational and environmental context. Nowadays, ecosystems as well as populations in the surroundings of mining areas remain exposed to toxic levels of pollution due to an ineffective requalification of these areas, not only after the ceasing of exploitation, but also during the exploitation process (Coelho et al., 2007). Investment in improving the health of communities and workers affected by mining activities is commendable not only to decrease the current exposure and risk, but also to reveal the need for changes in mining laws and regulations. REVIEW OF THE LITERATURE 10 The Barroca Grande site includes underground mine and portals, a processing plant, mine offices, and employee housing, in addition to the active tailings disposal areas, and the Salgueira water treatment plant (Cavey and Gunning, 2006). A huge tailings pile and two mud dams exist at this site. One of the dams is old and deactivated, although stabilized in geotechnical terms, whilst the other (smaller and disposed over the tailings) is still being fed with steriles (some rich in sulphides) obtained from the ore dressing operations. These tailings and impoundments are exposed to the atmospheric conditions. Surface runoff and water percolation leach the tailings and form AMD. The tailings piles at Barroca Grande are adjacent to the small, but perennially flowing, Casinhas stream, which drains to the Zêzere River. The Salgueira water treatment plant receives surface water from the old tailing pond area, water from the new tailings pond, mine drainage water, and seepage from the base of the tailings. These waters are mainly treated with lime. The precipitated sludge is pumped to the tailings pond while the treated water is pumped into holding tanks for later use in the mill or discharged to the creek channel adjacent to the plant and discharged into the Zêzere River. 3.4 Environmental studies: main results Panasqueira Mine was one of the two Portuguese test sites studied in the scope of the e- EcoRisk project (between 2002 and 2007). This is the biggest study preformed in this area. From the results achieved in the geochemical sampling campaigns, it was concluded that a significant geochemical dispersion, with anomalous patterns, occurs downstream Barroca Grande tailings. These results also identified the anomalous distribution of several metals and metalloids in stream sediments and surface waters collected in local streams, and also in soil samples from nearby villages (Ávila et al., 2008; Grangeia et al., 2011; Salgueiro et al., 2008). Furthermore, when comparing the mean values of Casinhas stream (the small river flowing through S. Francisco de Assis) and Zêzere rivers with the mean values of the background stream sediments, it was apparent there was a moderate to strong enrichment of As (157×; 26×) and Cd (34×; 59×) (Ávila et al. 2008) enrichment when compared with the geochemical background (ratios values: Casinhas/Backgroud; Zêzere River/Background). Recently (since 2010) a group from the Department of Geosciences - University of Aveiro (GeoBioTec) and the National Laboratory of Energy and Geology (LNEG) is collecting and analyzing different types of samples, such as road dusts, soils, vegetables for human consumption, superficial and groundwaters and stream sediments. Their preliminary REVIEW OF THE LITERATURE 11 results agree with the ones from the previous study reporting extremely high concentrations of metals and metalloids in all these matrices (personal communication). 4. METAL(LOID)S In 1981 Mertz defined trace elements as chemical elements found in our body at very low concentrations. Some of them are necessary for growth, development and proper biological function; therefore are essential. Essential elements include Cr, Cu, I, Mo, Se, and Zn. Some of them act as cofactors for various enzymes involved in essential cellular functions. Trace metals/metalloids [=metal(loid)s] are a subclass of trace elements. They are a necessary part of nutrition and physiology; however exposure to high quantities is often toxic (Murray et al., 2009). Some of the elements included in this subclass are As, Cd, Cr, Cu, Fe, Hg, Mn, Pb, Se, and Zn. 4.1 Human exposure Environmental and occupational exposure to metal(loid)s is a reality worldwide, though with different contours, affecting a significant number of individuals. One of the situations where conditions are gathered at highest risk of exposure is the mining context. Water pollution problems by mining activities include AMD which is one of the most important environmental impacts. Highly acidic water solubilises metal(loid)s - Al, As, Cd, Cu, Pb, Ni, and Zn - carrying them into local ground and surface waters. This causes a major problem either through the consumption of fish and other biota that bioaccumulate metal(loid)s, through drinking water that wasn’t treated in such a way to eliminate these elements, and also through dermal contact (Coelho et al., 2011a). Mining processes can also result in the contamination of sediments in local streams. Sediments, coming from increased soil erosion, cause siltation or the smothering of streams beds. This siltation affects fisheries, swimming, domestic water, irrigation, and other uses of streams. Some toxic constituents associated with discharges from mining operations (i.e., Hg and P) may be found at elevated levels in sediments. Sediment contamination provides a long-term source of pollutants through potential redissolution in the water column (Coelho et al., 2011a). This may lead to chronic contamination of water and aquatic organisms REVIEW OF THE LITERATURE 12 Particulate matter is one of the main problems, both occupationally and environmentally. Workers are highly exposed to them due to poor air quality inside the mines. Environmental exposure is also a key issue as it is released when overburden is stripped from the site and stored or returned to the pit. When the soil is removed, vegetation is also removed, exposing the soil to the weather, causing particulates to become airborne through wind erosion and road traffic. Particulate matter can be composed of toxic materials such as metal(loid)s like As, Cd, and Pb. In general, particulates affect human health adversely by contributing to illness related to the respiratory tract, such as emphysema, but they can also be ingested or absorbed through the skin (dermal contact) (Coelho et al., 2011a). These are the main routes of exposure in occupational settings. Gaseous emissions are also important since some of them (e.g. sulfur oxide) affect the downwind environments through acid precipitation or dry deposition. Some metal(loid)s like As, Cd, Hg, and Zn vaporize when heated in pyrometallurgical processes, and if they are not captured and condensed, they affect firstly miners (when not properly equipped) and secondly the surrounding environment. Table 1 presents a summary of the major environmental and human sources of exposure, and consequent effects of some of the most toxic metal(loid)s according to the International Agency for Research on Cancer (IARC) and the Agency for Toxic Substances and Disease Registry (ATSDR) classification. REVIEW OF THE LITERATURE 13 Table 1. Main toxic metal(loid)s found in mining environments, indicating their main sources and effects. Adapted from Coelho et al. (2011a). M ETAL (IARC / ASTDR CLASSIFICATION)* ENVIRONMENTAL SOURCES ENVIRONMENTAL EFFECTS HUMAN EXPOSURE HEALTH EFFECTS Arsenic (Group 1A / 1st) Arsenic can be found naturally on earth in small concentrations. May enter air, water and land through wind-blown dust and water run-off. Large amounts of arsenic end up in the environment and in living organisms due to volcanoes, microorganisms and human activities such as mining and agriculture. It cannot be destroyed once it enters the environment. Large amounts added can spread and cause adverse health effects to humans and animals. High concentrations of arsenic may be found in plants as they absorb arsenic easily. Plant-eating freshwater organisms accumulate arsenic in their bodies and may affect the animals higher up the food chain. Humans can be exposed through food (fish and seafood), water and air. It may also occur through dermal contact with soil or water. Exposure may be higher for people who work with arsenic compounds, drink significant amounts of wine, smoke, live near a mining site, and for those living on farmlands where arseniccontaining pesticides have been applied in the past. Arsenic is one of the most toxic elements. Exposure to it can cause several health effects, namely irritation of the stomach, lungs and intestines, decreased production of red and white cells, skin changes. Significant uptakes of inorganic arsenic can lead to cancer development, especially skin, lung, liver and lymphatic cancers. Higher exposure can cause infertility and miscarriages, skin disturbances, declined resistance to infections, heart disruptions and DNA damage. High exposure to organic arsenic can cause nerve injury and stomachaches. Cadmium (Group 1 / 7th) Cadmium can mainly be found in the earth's crust. Large amounts of cadmium are naturally released in the environment, namely in rivers through weathering of rocks, into air through forest fires and volcanoes. Human activities such as mining also release significant amounts of cadmium in the environment. Soils are the main final destination of the industrial cadmium wastes. Other important source of cadmium in soils Acidified soils enhance cadmium uptake by plants and this causes a potential danger to the animals which feed on them, and to the rest of the food chain. Earthworms and other essential soil organisms are extremely susceptible to cadmium poisoning. High concentrations of cadmium in soils can threaten the whole soil ecosystem. Human uptake of cadmium occurs through food ingestion (liver, mushrooms, shellfish, mussels, cocoa powder and dried seaweed). Exposure to high concentrations of cadmium takes place through tobacco smoke. Cadmium is transported into lungs and then distributed through the whole body. Breathing in cadmium can severely damage the lungs and, in last instance, can cause death. Cadmium is transported to the liver where it is bound to proteins to form complexes that are transported to the kidneys. There, it accumulates and damages filtering operations causing the excretion of essential proteins and sugars from the body. Excretion of bioaccumulated cadmium from the kidney takes a long time. It may cause damages in liver, REVIEW OF THE LITERATURE 14 is the appliance of artificial phosphate fertilizers in farmlands. Waste combustion and burning of fossil fuels are a main source of cadmium in the air. Only small amounts of cadmium are released in water through disposal of wastewater from households and industries. Cadmium may bioaccumulate in several aquatic organisms (mussels, oysters, shrimps, lobsters, fish, etc.). Salt-water organisms are known to be the most resistant. Animals exposed to cadmium commonly present high bloodpressures, liver disease and nerve or brain damage. Other high exposures occur with people who live near hazard waste sites or factories that release cadmium into the air and people who work in refinery industry. diarrhea, stomach pains and severe vomiting; bone fracture, reproductive failure and possibly even infertility, damage to the central nervous and immune system, psychological disorders and it may also cause DNA damage leading to cancer development. Chromium (Group 3 / 78th) Chromium (IV) (Group 1 / 17th) Chromium enters the air, water and soil in the chromium (III) and chromium (VI) forms through natural processes and human activities. The main human activities that increase the concentrations of chromium (III) are steal, leather and textile manufacturing. As for chromium (VI) main activities are chemical, leather and textile manufacturing, and electro painting. These applications mainly increase concentrations of chromium in water. Through coal combustion chromium ends up in air, and through waste disposal chromium ends up in soils. Most of the chromium in air eventually settles in waters or soils. Chromium in soils strongly attaches to soil particles and as a result it moves towards groundwater. In water chromium absorbs on sediment and become immobile. Most plants have adapted their systems to control chromium uptake (to be low enough not to cause any harm). But when the amount of chromium in the soil is extremely high, and the soils are rather acidic, uptake of high concentrations occurs. Plants usually absorb only chromium (III). Chromium is not known to accumulate in fish, but high concentrations of chromium in surface waters can damage their gills. In animals chromium can cause respiratory problems, a lower ability to fight disease, birth defects, infertility and tumor formation. Humans can be exposed to through breathing, eating/drinking, and through dermal contact with chromium or chromium compounds. Exposure to chromium (VI) can occur in workers of steel and textile industries. Other sources of exposure are contaminated well water and tobacco smoke. For most people eating food that contains chromium (III) is the main route of chromium uptake, as it occurs naturally in many vegetables, fruits, meats, yeasts and grains. Various ways of food preparation and storage may alter the chromium contents of food. When food in stores in steel tanks or cans chromium concentrations may rise. The health hazards associated with exposure to chromium are dependent on its oxidation state. Chromium (III) is an essential nutrient for humans but the uptake of too much chromium (III) can cause health effects as well, for instance skin rashes. Chromium (VI) is known to cause various health effects, such as skin rashes, upset stomachs and ulcers, respiratory problems, weakened immune system, kidney and liver damage, alteration of genetic material, and lung cancer. REVIEW OF THE LITERATURE 15 Lead (Group 2A / 2nd) Lead occurs naturally in the environment but most of what is found results from human activities such as mining and agriculture. Lead is a main constituent of several materials like ancient water pipes, lead-acid batteries, television screens and many others. Leaded gasoline was a major source originating lead salts. Other sources are solid waste combustion and industrial processes. The larger particles fall into the ground polluting soils or surface waters. The smaller ones travel long distances and remain in the atmosphere until it rains. Lead can end up in water and soils through corrosion of leaded pipelines and leaded paints. It cannot be broken down, only converted in other forms. Lead accumulates in the body of water and soil organisms which suffer severe health effects from poisoning. Shellfish experience health effects at very small concentrations. Phytoplankton is an essential source of oxygen production and many larger sea-animals feed on it. If their body functions are disturbed global balances are negatively affected. Soil organisms and consequently soil functions are also affected by lead poisoning especially those near highways and farmlands. Lead is a truly dangerous threat as it accumulates not only in individual organisms but also in entire food chains. Lead can enter human body through various routes, such as uptake of contaminated food, water and air. Fruits, vegetables, meats, seafood, soft drinks and wine may contain large amounts of it. Drinking water can become contaminated through corrosion of pipes especially and if is slightly acidic. Public water treatment systems are now required to perform pH- adjustments in drinking water. Cigarette smoke also contains lead but in small amounts. Some of the most relevant health effects in humans are the disruption of the biosynthesis of hemoglobin leading to anemia, high blood pressure, kidney and brain damage, miscarriages and subtle abortions, sperm damage which causes declining fertility in men, disruption of nervous system, reduced learning capacities and behavior disturbance in children. Fetuses can be severely affected by lead once it passes through the placenta causing serious damages to their nervous system and brain. Mercury (Group 3 / 3rd) Methylmercury (2B / 120th) Mercury can be found naturally in the environment but rarely occurs free. It can be found in metal form, as salts or organic compounds. It enters the environment from normal breakdown of minerals in rocks and soils through exposure to wind and water. Human activities highly increase mercury levels in the environment, namely in air through fossil fuel, mining and smelting. The application of agricultural Microorganisms can convert mercury present in surface waters and soils into methyl mercury, which may be quickly absorbed by many organisms. Fishes absorb and accumulate great amounts of this compound which may result in the accumulation in food chains. Some of the effects on animals are kidney and intestines damage, stomach Mercury is not usually found in food but it can enter food chains trough smaller organisms that bioaccumulate it, and which are consumed by man. Mercury can enter the human body through the consumption of plants to which mercury-containing sprays had been applied. It is used in several household products such as High exposure to mercury vapors causes harmful effects, such as brain and kidney damage, lung and eye irritation, skin rashes, vomiting and diarrhea. Other effects of mercury exposure are disruption of nervous system, DNA and chromosomal damage, negative reproductive effects like sperm damage, birth defects and miscarriages. REVIEW OF THE LITERATURE 16 fertilizers and the disposal of industrial wastewater releases mercury into soils and water. It is extremely used in thermometers and barometers (nowadays banned in the EU), recovery of gold from ores and more. Acid surface waters contain much higher mercury concentrations as it is mobilized from the ground. disruption, reproductive failure and DNA alteration. barometers, thermometers and fluorescent light bulbs. If some of those broke, high exposure can happen through breathing while it vaporizes. Nickel (Group 1 / 57th) Nickel occurs in the environment at very low concentrations. It usually occurs in ores which are mined in various countries worldwide. It is applied as a component of steal and other products, including jewelry. Nickel is released into the air by power plants and trash incinerators. It settles to the ground or falls down with rain. Nickel can also end up in surface waters when present in wastewater streams. Most nickel released in the environment becomes immobile as it is absorbed in sediment or soil particles. In acid soils it becomes more mobile and often runs off to groundwater. High concentrations of nickel on sandy soils can severely damage plants and on surface waters it can reduce the growth rates of algae. Microorganisms are also affected but they generally become quickly resistant to nickel. Nickel is an essential element for animals at low concentrations. It is extremely harmful when the maximum tolerable amount is exceeded as it can cause different kinds of cancer, especially in those organisms living near refineries. Humans can be exposed to nickel by breathing contaminated air, drinking contaminated water, or eating contaminated food. Dermal exposure can also occur with contaminated soils and waters. Foodstuffs usually have small amounts of nickel. Chocolate and fats are an exception as they contain higher quantities. Smokers have high uptake of nickel through cigarette smoke. Nickel is an essential element in small amounts but when the uptake is too high it can cause severe damages to human health. Sensitive individuals may develop dermatitis known as “nickel itch” after exposure to nickel and its compounds. Nickel exposure through breathing can cause pneumonitis as nickel fumes are respiratory irritants. Some of the most important effects are increased chances of developing lung, nose, larynx and prostate cancer, lung embolism, respiratory failure, birth defects, asthma and chronic bronchitis, and heart disorders. * IARC classification according to IARC Monographs, Volumes 1–106 - (http://monographs.iarc.fr/ENG/Classification/ClassificationsGroupOrder.pdf) and ASTDR classification according to the ATSDR 2011 substance priority list - (http://www.atsdr.cdc.gov/SPL/index.html) REVIEW OF THE LITERATURE 17 4.2 Toxicokinetics and bioaccumulation Toxicokinetics involves the conversion of the external dose of a chemical to an internal dose leading to elimination from the body. In other words, it refers to the absorption, distribution, metabolism, and elimination of toxicants. Potential bioaccumulation in tissues and access and effects on the target organ/s depends on these processes. Absorption controls the uptake of metal(loid)s into the organism. The main routes are usually oral and inhalation, being dermal contact possible but at a very limited level (Dorne et al., 2011). They are then distributed to various areas depending on their properties and the different affinities for different cells and biomolecules. Blood and plasma are the main routes, particularly bonded to erythrocytes. A number of metal(loid)s may accumulate to a stablestate level in different body compartments (e.g., tissues, organelles) (McGeer et al., 2004). For the majority of metal(loid)s the metabolic pathways are generally complex and multiples, and not always identified. Some metal(loid)s can undergo limited metabolism, either by conjugation or by eliminating a bound substance (e.g. As and its compounds). There is no degradation of the metal(loid) atom itself, but it may bind to a large variety of molecules in the organism (McGeer et al., 2004). Metal(loids)s can bind to biomolecules that are essential to cellular function (e.g., enzymes), alter their function, and cause toxicity. Elimination occurs at a higher extent through urine (via kidney), and also to a much lower level in the gastrointestinal tract. The time needed for half of the initial amount of the metal(loid) to be excreted from the body (half-life) is highly variable and depends on the element (e.g., 10 to 12 years for Cd and Pb, 4 days for As, and 60 days for Hg) (Dorne et al., 2011). Toxicokinetically, if the net-balance of uptake exceeds elimination for a metal(loid), then bioaccumulation occurs, such as when a metal has a high affinity for tissues that can act as a deposit (Weiss et al., 1996; WHO, 1995). For most metals, long-term accumulation occurs to a great extent in the kidney (e. g., As, Cd, and Hg) and blood (e.g., Pb). It is important to notice that organisms have evolved in the presence of metal(loid)s and in many cases have developed appropriate strategies of their metabolism when concentrations exceed those normally encountered. For instance many metal(loid)s become associated with sulfur-rich proteins, particularly Class B ones (e.g., Ag and Hg) (McGeer et al., 2004). One important factor that needs to be taken into account is the high inter-individual variability in human susceptibility due to genetic polymorphisms in the enzymes involved REVIEW OF THE LITERATURE 18 in the metabolism of metal(loid)s. This is of major importance in the case of As (e.g., polymorphisms of arsenic-methyltransferases and glutathione-S-transferases omega 1 and 2) (Dorne et al., 2011). Another key factor is the interactions among toxic and essential metals. Absorption, distribution, metabolism and elimination should be considered highly correlated for exposed individuals, with susceptibilities resulting in differential effects of multiple metal(loid)s (e.g., Fe inhibits Pb and Cd intestinal uptake due to shared absorption mechanisms; Se may potentially alter both As and methylmercury toxicity) (Sasso et al., 2010). Concentration of metal(loid)s in human tissues and fluids is influenced not only by the environmental/occupational contamination, but also by diet, sex and age, although these factors influence elemental concentration to a lesser degree (Duyff, 2006). The use of cosmetics such as dyes and medical treatments can also influence the metal(loid)s concentration (Kanias, 1985). 4.3 Health effects Most metal(loid)s are very toxic to living organisms and even those considered as essential can be toxic when in excess. They can disturb important biochemical processes, constituting an important threat for the human health. Major health effects include development retardation, endocrine disruption, kidney damage, immunological and neurologic effects, and several types of cancer (Mudgal et al., 2010). Health effects greatly depend on the element/mixture of elements subjects are exposed to. Health effects of the most toxic elements are summarized in Table 1. 4.3.1 Genotoxicity, immunotoxicity, and carcinogenicity Genotoxicity can be referred as the ability to interact with DNA and/or the cellular apparatus that regulates the fidelity of the genome. These alterations can be caused either by direct interaction of metal(loid)s with nuclear DNA, or indirectly through reactive intermediates generated by the interaction of these elements with other cellular components, or both. When the inflicted damage cannot be repaired the cells undergo one of three following fates: necrosis (uncontrolled cell death), apoptosis (controlled cell death), or mutations, resulting from genetic code alterations fixed in the process of DNA duplication and transmission to daughter cells (Bal et al., 2011). REVIEW OF THE LITERATURE 19 Several studies have been published about the genotoxic effects of metal(loid)s, demonstrating that elements like As, Cd, Cr, Fe, Hg, Mn, Ni, and Pb and their compounds are clastogens inducing micronucleus (MN), chromosomal aberrations (CA), sister chromatid exchanges (SCE) and aneugens inducing chromosomal loss (Jadhav et al., 2006). Hartwig et al. (2004) postulated that some metal(loid)s may interfere with the fidelity of DNA repair, thus increasing genotoxic effects. Subsequent studies confirmed that metal(loid)s such as As, Cd, Co, and Ni interfere with both base and nucleotide excision repair (BER and NER) pathways (HERAG5, 2007). There is considerable evidence that reactive oxygen species (ROS)-mediated oxidative damage induced by several metal(loid)s is the main pathway of their genotoxicity, particularly the ones proved to be carcinogenic (Henkler et al., 2010). Metal(loid)-induced oxidative stress has been shown to cause DNA damage through the production of three main species: superoxide anion radical (•O2–), hydrogen peroxide (H2O2) and hydroxyl radical (OH•) (Jadvah et al., 2006). A continuous disturbance of redox homeostasis can be associated with chronic proinflammatory signaling, leading to induction of proto-oncogenes and/or anti-apoptotic factors (Henkler et al., 2010). This causes a persisting overstimulation of the immune system, thus leading to immunotoxicity. Immunotoxicity can be defined as any adverse effect on the structure or function of the immune system, or on other systems as a result of immune system dysfunction (Blank et al., 2000). The susceptibility of the immune system to exposure to metal(loid)s is wellknown. Still reported effects in human populations for the majority of the elements are conflicting. The final outcome greatly depends on the element, its concentration, route of exposure, duration of exposure and biologic availability (Lehmann et al., 2011). Certain metal(loid)s have been demonstrated to cause cancer in a variety of animal species. Based on epidemiologic data some of these metal(loid)s have been classified by IARC as human carcinogens (Group 1), namely As, Be, Cd, Cr, and Ni, and probably human carcinogens (Group 2B) – Co, Pb. Some of these elements have also been ranked in the top positions on a list of hazard substances by ATSDR in 2011 – Table 2. REVIEW OF THE LITERATURE 26 Natarajan, 1988; Fenech, 1993; Lando et al., 1998). This cytogenetic biomarker constitutes a valuable tool for studying environmental and occupational hazards to public health (Bonassi et al., 2005). It has been reported that this test is also predictive of cancer risk in human populations (Bonassi et al., 2007). In the last two decades international efforts, such as the Human Micronucleus project (http://www.humn.org), greatly contributed to improvements in the reliability of the assay, providing guidelines on scoring criteria and analyzing major sources of variability (Kirsch- Volders et al., 2006). Compared with other cytogenetic assays, quantification of micronuclei confers several advantages, including speed and ease of analysis, and no requirement for metaphase cells (El-Zein et al., 2011). MN can be scored in lymphocytes, in erythrocites and also in exfoliated epithelial cells from buccal or nasal mucosa, or urine. The former one is the standard in vitro MN test. The development of the cytokinesis-block micronucleus (CBMN) assay, by addition of the actin polymerisation inhibitor cytochalasin B during the targeted mitosis, allows the identification of cells that have undergone one division as binucleated (Figure 3). This prevents confounding effects caused by differences in cell division kinetics since expression of micronuclei, nucleoplasmic bridges, or nuclear buds is dependent on completion of nuclear division (Fenech, 2000). One of the major advantages of the CBMN assay is that in addition to measuring clastogenic effects easily, the aneugenic effects can also be detected (Martinez et al. 2004). Using fluorescent in situ hybridization (FISH) with probes targeted to the centromere region, one can determine if a whole chromosome or only a fragment was lost. FIGURE 3. Micronuclei observed in binuclear lymphocytes from populations environmentally and occupationally exposed to metal(loid)s – Panasqueira mine area (1000x). a) and b) binucleated cells with one micronuclei; c) binucleated cell with two micronucleus; d) binucleated cell with three micronucleus. REVIEW OF THE LITERATURE 27 5.2.3 Chromosomal aberrations Chromosomal aberrations (CA) are structural and/or numeric changes in the chromosomes. Structural changes are the result of chromosomal breaks (clastogenic effect) and the rearrangement within the chromosome or between chromosomes (Figure 4) (Delft et al., 1998). Numerical changes (i.e. aneuploidy, polyploidy) occur as a result of abnormal chromosomal segregation, either spontaneously or due to an aneugen treatment (Mateuca et al. 2006). Visible structural changes in arrested metaphase-stage cells are usually divided in two types: chromosome-type aberrations (CA-chromosome) and chromatid-type aberrations (CA-chromatid). The former includes changes involving the two chromatids of one or more chromosomes while CA-chromatid includes changes involving only one of the two chromatids of one or more chromosomes (Mateuca and Kirsch-Volders, 2007). In order for these two types of CA to happen one or more DNA double-strand breaks needs to take place. Their mechanism of formation seems to be different concerning the mutagen and also involve different mechanisms of DNA repair (Hagmar et al., 2004). CA- chromosome reflect incomplete or unrepaired double-strand breaks by the nonhomologous end-joining and non-conservative homologous recombination repair mechanisms in G0-G1 phase. CA-chromatid reflect single-strand breaks and base modifications occurring essentially in S-phase. Structural CA in lymphocytes have been used for more than 30 years in occupational and environmental settings as a biomarker of early effects of genotoxic carcinogens (Mateuca and Kirch-Volders, 2007). CA are indicative of potential risk of cancer, since they are associated with several types of human cancer. For example, many aberrations, particularly translocations and inversions are associated with morphological and phenotypic subtypes of leukemias, lymphomas and sarcomas (Heng et al., 2004). CA assay is the most common technique to detect structural changes. Nevertheless this method is labor intensive and requires specifically skilled and experienced staff. More recent techniques using FISH methods allow an accurate and easy detection of structural CA but are significantly more expensive. REVIEW OF THE LITERATURE 28 FIGURE 4. Chromosomal aberrations observed in metaphase lymphocytes from populations environmentally and occupationally exposed to metal(loid)s – Panasqueira mine area (1000x). a) dicentric chromosome with accompanying fragment; b) and c) symmetrical tetraradial figures; d) gap in both chromatids; e) break in both chromatids; f) - h) break in one chromatid; i) gap in one chromatid. 5.2.4 Comet Assay Comet assay (or single-cell gel electrophoresis - SCGE) is a simple, rapid and sensitive technique to assess genotoxic damage in single cells, using a small number of cells, therefore presenting advantages in comparison with other tests for genotoxicity (Tice et al., 2000). Studies with biomonitoring cytogenetic techniques use only proliferating cells and lymphocytes, while the comet assay can be applied to the proliferating cells, nonproliferative tissues and cells that are first to come into contact with mutagenic/carcinogenic substances (e.g. cells from oral and nasal mucosa) (Kassie et al., 2000). REVIEW OF THE LITERATURE 29 The name Comet Assay is due to the fact that, after its completion, cells with damaged DNA present a comet shape, with an intensely bright head and a tail (Figure 5). The length of the tail and the brightness level is related to the number of breaks in the DNA chain. Cells without DNA damage are presented as intact nuclei without a tail. Since 1990 comet assay has been widely used in human biomonitoring studies. The sensitivity in detecting DNA damage in single cells is extremely important since the harmful effects of xenobiotics are specific to each cell and for each tissue (Singh et al., 1988). The comet assay was introduced by Östling and Johanson in 1984. Briefly cells were embedded in agarose, placed on slides, then dipped in lysis solution (containing detergent and high salt concentrations), subjected to electrophoresis under neutral conditions and finally stained with acridine orange. It was developed to detect specifically DNA double-strand breaks. This method was later modified by Singh and coauthors (1988), in which the assay is carried out under alkaline conditions (pH> 13). This led to a more sensitive version of this assay allowing the detection of both DNA double- and single-strand breaks, alkali labile sites, cross-links, and incomplete DNA repair sites. Nowadays this is the most frequently used method among published studies. FIGURE 5 – Images of comets with different degree of damage (lymphocytes from populations environmentally and occupationally exposed to metal(loid)s – Panasqueira mine area). REVIEW OF THE LITERATURE 30 Another procedure was developed by Olive and co-workers, which involves treatment of the slides with a alkaline lysis solution followed by electrophoresis at neutral pH conditions (Olive et al., 1990a) or mild alkaline, pH = 12.3 (Olive et al., 1990b) in order to detect single breaks in DNA. Comets are observed in a fluorescence microscope after staining with suitable dye. The most common are ethidium bromide, propidium iodide, SYBR Gold and SYBR Green. Fluorescence intensity of the tail is proportional to the number of breaks in the DNA and it can be determined by direct observation under a microscope or using a computer program for analysis of images which provide a variety of parameters for each analyzed comet such as tail length, percentage of DNA in the tail (tail intensity), and tail moment (Kassie et al., 2000). In recent years the comet assay has undergone several changes but the basic principles are based on neutral and alkaline versions. Due to its simplicity it can also be used in studies of DNA repair (Collins, 2004). These studies evaluated the ability of any cell to repair different types of damage in the DNA, including double and single breaks, base damage and cross-links. It was also modified by Collins and co-workers to include an incubation step with lesionspecific enzymes. This extra step increases the sensitivity and selectivity of the assay converting damaged bases to DNA breaks. Some of the most commonly used are formamidopyrimidine DNA glycosylase (FPG), hOGG1 and endonuclease III (EndoIII). The comet assay has several clinical applications, and it is widely used to evaluate the genotoxic potential of chemicals and environmental contaminants, and for environmental monitoring purposes (Kumaravel and Jha, 2006). 5.2.5 Immune markers Biomarkers for the assessment of human immunotoxicity include the count of blood components, antibody-mediated immunity (serum concentrations of immunoglobulins), phenotype analysis of lymphocytes flow cytometry, among others (Table 4). REVIEW OF THE LITERATURE 31 TABEL 4 – Biological markers of immunotoxicity in humans. Adapted from Gil and Pla (2001). IMMUNE MARKER EXAMPLES OF ENDPOINTS Full blood count Lymphocyte count Study of antibody-mediated immunity Immunoglobulin concentrations in serum: IgM, IgG, IgA, IgE Phenotypic analysis of lymphocytes by flow cytometry Surface markers: CD3, CD4, CD8, CD20, CD23, etc. Study of cellular immunity Delayed-type hypersensitivity on skin Natural immunity to blood group antigens: anti-A, anti-B Auto antibodies & markers of inflammatory response C-Reactive protein Autoantibodies to nuclei, DNA and mitochondria Measure of non-specific immunity Interleukine analysis: ELISA or RT-PCR Natural killer cell activity: CD56 or CD60 Phagocytosis: chemiluminescence Measurement of complement components Lymphocytes are the primary cells involved in acquired immunity and their number may vary from 20 to 40% of the total cells in human blood. They are highly specialized cells that interact with other cells to initiate an immune response. The specificity of the receptor and functional heterogeneity allows them to respond to virtually any antigen (Descotes, 2004; Tryphonas et al., 2005). T cells comprise approximately 50 to 70% of peripheral blood lymphocytes in humans and express in their surface the TCR, along with the cell differentiation markers CD2 and CD3. This type of cell can be divided into two subpopulations, CD4+ and CD8+, differentiated by their function and surface marker. T helper cells (Th) express on their surface marker CD4 and their main function is to assist B cells, by releasing cytokines, mainly by helping to produce antibodies in response to antigenic attack. This type of lymphocytes, in turn, is divided into two subtypes Th1 and Th2 cells that differ in the profile of cytokines they secrete. The Th1 subtype mainly secreted interleukin (IL) 2 (IL-2) and interferon γ (IFN-γ) and induces cellular responses that increase the microbicidal activity. The Th2 subtype secretes IL-4, IL-5 and IL-10, which stimulate B cells to produce antibodies (Descotes, 2004; Tryphonas et al., 2005). Cytotoxic T lymphocytes (Tc) express on their surface marker CD8 and their function is to capture the target cell through mechanisms of adhesion and release the contents of their granules into it. This induces apoptosis or disruption of the membrane and the death of the target cell (Abbas and Lichtman, 2004). B lymphocytes are precursors of antibody-secreting plasma cells. They originate from hematopoietic stem cells located in the liver in the fetus, then later in the bone marrow, REVIEW OF THE LITERATURE 32 and constitute approximately 10 to 20% of peripheral blood lymphocytes in humans. They can be identified by the presence of surface immunoglobulins - IgM, IgD, IgG, IgA or IgE - and various cell differentiation markers including CD19, CD22, CD23 and CD37. B lymphocytes are activated directly as a result of binding of the immunoglobulin expressed on the cell surface, an antigen or indirectly by interaction with T lymphocytes (Descotes, 2004; Tryphonas et al., 2005). The main cells involved in nonspecific immune responses are natural killer (NK) and various phagocytic cells. NK cells are closely related to T cells, lacking their receptor and expressing CD16 and CD56 markers on their surface (Tryphonas et al., 2005). The main role of NK cells is to directly kill target cells by releasing cytotoxic mediators. NK cells can recognize and kill cells which are both covered with IgG, resulting in an antibody dependent cellular cytotoxicity, as well as cells expressing levels of antigens from the major histocompatibility complex (MHC) lower than normal (Descotes , 2004) The importance of assessing changes in the percentages of different subpopulations is related to the existence of different studies linking exposure to certain substances with immunotoxic properties with these changes and their effect on the immune response (Biró et al., 2002; Boscolo et al., 1999, Oh et al., 2005; Tulinska et al., 2004). Hernandez- Castro et al. (2009) demonstrated that an increased, diminished, or absent function of immune-system cells is clearly associated with autoimmune diseases, deregulation of the immune response, and defective immune response against neoplastic cells or different pathogens. It is imperative to study homogeneous populations, as it is known that the number of lymphocytes vary depending on differing life situations (Moszczy´nski et al. 2001). Each cell of the immune system has the ability to synthesize and release a variety of cytokines which travel to other cells (which can be immune or not) encouraging them to become more or less active. Any change in the function or the number of abnormal cells results in production of cytokines and a loss of control regulator (Tryphonas et al., 2005). In the last decade pro-inflammatory cytokines have been related to cognitive decline and mood disorders. More recently Capuron et al. (2009) showed that increased concentrations of inflammatory markers were associated with reduced quality of life in elderly persons. These elements have the ability to influence the metabolism of neurotransmitters and neuroendocrine functions involved in the development of several behavior symptoms known as sickness behavior (Capuron et al., 2011). The metabolism of some of these neurotransmitters (e.g. serotonin, norepinephine, and dopamine) which REVIEW OF THE LITERATURE 33 are synthesized within the brain from their precursors – tryptophan and tyrosine -, can be affected by immune activation. Briefly, two enzymatic pathways can be activated: indoleamine-2,3-dioxygenase (IDO) pathway and the guanosine-triphosphatecyclohydrolase-1 (GTP-CH1) pathway. When activated, IDO catalyzes the rate-limiting step of tryptophan conversion into kynurenine, being then degraded into several neuroactive metabolites, such as 3- hydroxykynurenine, quinolinic acid, and kynurenic acid (Chen and Guillemin, 2009). The kynurenine/tryptophan (kyn/trp) ratio reflects tryptophan breakdown and is considered to represent one estimate of IDO activity (Widner et al., 1997). The activation of GTP-CH1 is responsible for the production of neopterin and tetrahydrobiopterin (BH4). Neopterin is released from human activated monocytes/macrophages and consequently allows to sensitively monitoring the degree of immune activation (Fuchs et al., 1992, 1993, 1997). BH4 is a cofactor of aromatic amino acid hydroxylases and also nitric oxide synthases (NOS) (Neurauter et al., 2008). The former ones contribute to the biosynthesis of monoamines and NOS in the conversion of arginine to nitric oxide (NO) (Neurauter et al., 2008). The estimation of NO production is usually evaluated by the quantification of the stable NO metabolite – nitrite (NO2-) (Capuron et al., 2011). In humans, an increased formation of neopterin and enhanced degradation of tryptophan have been found in viral infections, malignant disorders and autoimmune diseases (Weiss et al., 1999). The activation of BH4 is believed to be associated with acute inflammatory processes, and the inhibitory role of neopterin with chronic inflammatory processes (Neurauter et al., 2008). Accelerated tryptophan degradation and consequently elevated kyn/trp ratio in serum/plasma has been shown to strongly correlate with neopterin concentrations, and in the vast majority of the studies, degradation of tryptophan was found to be associated with the extent, the activity and the course of the diseases (Jenny et al., 2011). In a context of chronic inflammation, lower levels of nitrite can reflect an imbalance in oxidant/antioxidant mechanisms with reduced antioxidant defenses or impairment in NOS activity (Capuron et al., 2011). There is an increasing amount of information reporting immunotoxic effects of metal(loid)s in animals (Cabassi 2007). As for humans, such investigations are still lacking and most results are inconsistent. REVIEW OF THE LITERATURE 34 5.3 Biomarkers of susceptibility The expression of all the previously described biomarkers (exposure and effect) is significantly influenced by individual factors, acquired (e.g., life styles like smoking habits, and alcohol consumption) and genetic susceptibility categories (e.g., inheritance of variant genes that affect chemical metabolism and DNA repair, etc.) (Au, 2007). It is well known and recognized that even under identical exposure conditions different individuals have different responses. Therefore some individuals are more susceptible/resistant to certain exposures than others. In order to identify these variations investigations with biomarkers of susceptibility have focused on DNA sequence variation in certain genes, such as the ones involved in chemical metabolism and DNA repair, genes related to immune function, and cell cycle control. These studies provide valuable information about the influence of such genes on specific effects of exposure(s) and response to genotoxic agents (Kyrtopoulos, 2006). The study of genetic variability had a clear impact on the project of sequencing the human genome (Bernstein et al., 2012; Sachidanandam et al., 2001). Several types of polymorphisms in the human genome were identified and they include insertions or deletions of one or more bases, duplications, inversions, repeats and genomic rearrangements, but the vast majority (68%) consists of SNPs (Single Nucleotide Polymorphisms). SNPs are single base pair positions in genomic DNA where different sequence alternatives (alleles) exist in normal individuals in some populations, in which the least frequent allele has an abundance of 1% or greater (Brookes, 1999). They can be found in all genomic regions (exons, introns, promoter regions/regulatory and intergenic regions). This variety of locations makes them more susceptible to allow for functional or physiological allelic relevance compared to other types of polymorphisms . In the beginning of 2000, most molecular epidemiological studies, aimed to identify SNPs associated with individual susceptibility to multifactorial disease, and were mainly focused on SNPs located in coding regions or regulatory regions of the genome since these changes are likely to result in phenotypic changes, given the strong possibility of affecting the function or expression of a particular protein (Feuk et al., 2006; Gaspar et al., 2006). Several SNPs have been identified in genes involved in codification of enzymes responsible for chemicals metabolism or cell response to DNA damage. These diverse forms of the same gene can lead to a difference in sensitivity of cell to chemicals effect (Norppa, 1997). Polymorphic genes involved in the metabolism of contaminants in human body may modulate the levels of biomarkers arising from environmental and/or occupational REVIEW OF THE LITERATURE 35 exposure to genotoxic agents (Pavanello and Clonfero, 2000). The identification of genetic polymorphisms which have a key role in the modulating genetic damage can help minimize risks for susceptible subjects (Costa et al., 2008). 5.3.1 Polymorphisms in genes involved in the metabolism (phase I and phase II) Phase I of metabolism involves mainly oxidations, reductions and hydrolysis and works to introduce a polar group into the molecule (contaminant). In what concerns chemical metabolizing enzymes, those of human cytochrome P450 (CYP) are the most studied ones. They present polymorphisms that appear to influence observed damage. CYP enzymes can both detoxify or activate chemicals (Werck-Reichhart and Feyereisen, 2000). Phase II of metabolism consists primarily on conjugation reactions that combine the products of phase I reactions with one of several endogenous molecules to form watersoluble products. Among phase II enzymes, glutathione S-transferases (GST) are the most important group of detoxifying enzymes, being responsible for the glutathione conjugation with reactive species of many chemicals. Based on amino acid similarities, seven classes of cytosolic GSTs are recognized in mammalian species designated Alpha (GSTA), Mu (GSTM), Pi (GSTP), Sigma (GSTS), Theta (GSTT), Omega (GSTO), and Zeta (GSTZ) (Andonova et al., 2010). Polymorphisms of some of these enzymes (GSTT1, GSTM1 e GSTP1) have been associated with an increase of cancer risk (Bolognesi, 2003; Sundberg et al., 1998). In what concerns to alpha class GST (GSTA – GSTA1 e GSTA2) there are few epidemiological studies about the role of these enzymes in cancer susceptibility (Silva et al., 2009). Specific details will only be given for studied polymorphic genes. 5.3.1.1 GSTA2 The alpha class of GSTs is greatly expressed in liver, kidney and adrenal tissue. It is one of the most versatile GST families as it is responsible for GSH conjugation of compounds such as bilirubin, bile acids and penicillin, thyroid and steroid hormones, allowing their solubilisation and storage in the liver (Tetlow and Board, 2004). GSTA1 and GSTA2 are the most expressed alpha GSTs enzymes in the liver. REVIEW OF THE LITERATURE 42 structure specific DNA repair endonuclease that interacts with essential meiotic endonuclease 1 (EME1). The ERCC5 gene (xeroderma pigmentosum, complementation group G - XPG) encodes a protein that is involved in excision repair of UV-induced DNA damage. Mutations in the ERCC1 gene result in cerebro-oculo-facio-skeletal (COFS) syndrome (Suzumura and Arisaka, 2010) and polymorphisms that alter expression of this gene may play a role in carcinogenesis (Goode et al., 2002). Defects in ERCC4 gene are a cause of xeroderma pigmentosum complementation group F (XPF), or xeroderma pigmentosum VI (XP6). Mutations in the ERCC5 gene cause Cockayne syndrome, which is characterized by severe growth defects, mental retardation, and cachexia (Cleaver et al., 1999). Some of the most studied polymorphisms of this family are ones located in codon 504 of 3′-untranslated region of ERCC1 and causes a change of the amino acid lysine (Lys) to Glutamine (Gln); in codon 415 of exon 8 of ERCC4 and causes a change of the amino acid arginine (Arg) to Glutamine (Gln); in codon 1104 of exon 15 and in codon 529 of exon 8 of ERCC5 and causes a change of the amino acid aspartic acid (Asp) to histidine (His) and cysteine (Cys) to serine (Ser), respectively. It is widely known that mining activities are one of the most hazardous both in the occupational and the environmental context. The lack of reliable information on health impacts related to the contamination of the Panasqueira mine area draw attention to the need of a community and workers health study. All the available information in the literature was careful analysed and the project was designed and executed accordingly. II. AIM OF THE STUDY AIM OF THE STUDY 45 Few studies were conducted in our country on the effects of mining activities in human populations and there is little information about these conditions. Environmental studies performed in Panasqueira mine area identified an anomalous concentration of several metal(loid)s in stream sediments, superficial and ground waters from local courses, road dust, soils, and plants for human consumption from nearby villages. In the absence of information of the health impacts of the environmental and occupational exposure to this contamination, we considered of major importance the development of an appropriate population-based study, with the objective/aim of characterizing the state of health of the communities affected, in particular the potential geno- and immunotoxic risk of that environment. This work, as previously mentioned is part of a program of actions defined in in the National Action Plan for Environment and Health (PNAAS – Plano Nacional de Acção Ambiente em Saúde): Action I.7 - survey of human health effects associated with pollutants in soils and sedimentary materials and definition of intervention strategy. There is a further need for a deeper and long-term evaluation of the mining impacts on workers and communities’ health. Appropriate environmental laws with adequate monitoring enforcement need to be adopted to prevent most of the damage caused in mine communities. All these measures will help to better protect the health and safety of people working in, living near and those otherwise impacted by historic, current and proposed mines. The results of this project will have a major impact on the Central region of Portugal. Nevertheless, the results and methodology developed during the project to address this important issue will be useful for populations worldwide, particularly communities living in the vicinity of mines and working in them. Results can be very useful to epidemiologists, physicians and for institutions involved in regulatory affairs, especially for better document awareness raising campaigns. Considering all the points previously mentioned the main objective of this study was to evaluate the effect of the external contamination on selected indexes of internal dose from subjects environmentally and occupationally exposed, relate this to the genotoxic and immunotoxic damage and also evaluate the possible modulating role of genetic polymorphisms involved in metabolism and DNA repair. A multiple approach was used in order to integrate all studied biomarkers: exposure, effect and susceptibility, which will enable a better characterization of the risk. Concentrations of several elements in blood, hair, fingernail and toenail samples, quantified by ICP-MS (As, Cd, Cr, Hg, Mn, Mo, Ni, Pb, and Se), or ICP-OES (Ca, Cu, Fe, K, Mg, Na, S, Si and Zn), were AIM OF THE STUDY 46 used as biomarkers of internal dose. Genetic damage was studied by means of cytogenetic tests (MN and CA), a somatic cell mutation assay (TCR mutation assay) and Comet Assay. Percentages of different lymphocyte subsets and concentrations of neopterin, tryptophan, kynurenine, and nitrite were selected as immunotoxicity biomarkers. For all these exposure and effect biomarkers, the role of potentially confounding factors, such as age, gender and life style factors, was also evaluated. Finally, genetic polymorphisms in genes involved in the metabolism (GSTA2, GSTM1, GSTP1, and GSTT1) and in the DNA repair (XRCC1, APEX1, MPG, MUTYH, OGG1, PARP1, PARP4, ERCC1, ERCC4, and ERCC5) were investigated as biomarkers of susceptibility. III. MATERIAL AND METHODS MATERIAL AND METHODS 49 1. STUDY POPULATION 1.1 Population Selection Study population was chosen according to previous publications with environmental data from the Panasqueira Mine area, particularly the reports from the National Institute of Engineering, Technology and Inovation (INETI - Instituto Nacional de Engenharia, Tecnologia e Inovação) published within the scope of e-Ecorisk programme mention in the review of the literature. Chosen villages were S. Francisco de Assis and Barroca do Zêzere as exposed and Casegas and Unhais-o-Velho as control. In order to establish a protocol of participation and cooperation of health services of the areas covered in this study, we contacted the Department of Public Health and Planning from the Regional Health Administration, Centre region (ARSC, IP – Administração Regional de Saúde do Centro). This protocol was established (see annex I) and meetings with the local authorities of each village were scheduled. Campaigns to sensitize populations to participate in this study were carried out along with the local health services, local authorities and the priests of the villages. Objectives, importance and other relevant information from our study was provided in a leaflet (see annex II). This leaflet also contained the address, telephone number and email to clarify any doubt. These campaigns were fairly successful in some of the villages as many people showed great interest in participating, and we had a high level of participation in the administration of the health questionnaires and the sample collection stage. Study subjects were contacted by local authorities who provide them a sterile bottle for first morning urine collection and two bags for finger and toenails collection. Sample collection and administration of the health questionnaires took place on Sunday mornings. All subjects were fully informed about the procedures and objectives of this study and each of them signed an informed consent prior to the study (see annexe III). Ethical approval for this study was obtained from the institutional Ethical Board of the Portuguese National Institute of Health. MATERIAL AND METHODS 50 1.2 Study group The study population consisted of a total of 122 subjects living in the area of the Panasqueira mine. Forty-one individuals living in villages located in the vicinity of the mine and downstream the Zêzere river (S. Francisco Assis and Barroca do Zêzere – Figure 6) were classified as environmentally exposed (16 males and 25 females), 41 male miners and ex-miners from the Panasqueira mine represented the group of occupationally exposed, and 40 additional subjects without environmental and/or occupational exposure to mining activities, or other known toxic exposure, were the controls. This latter group included individuals living in non contaminated areas upstream the river and on the western side of the mine (Casegas and Unhais-o-Velho – Figure 6). Control individuals worked mainly in administrative offices and were matched with the environmentally exposed group by age, gender, lifestyle, and smoking habits (17 males and 23 females). Only individuals aged over 18 years and living in the same village for at least 5 years before the study were selected (see annex IV). FIGURE 6 – Map showing the location of the 4 study villages: the 2 exposed ones are highlighted with orange squares (S. Francisco de Assis and Barroca do Zêzere), and the 2 controls in green squares (Casegas and Unhais-o-Velho). In the red squares are the main sources of contamination: active tailing (Barroca Grande) and old one (Rio tailing). Rio tailing Barroca do Zêzere Barroca Grande Unhais –o-Velho S. Francisco de Assis Casegas 1 Mi 1 Km MATERIAL AND METHODS 51 Health conditions, medical history, medication, diagnostic tests (X-rays, etc.), and lifestyle factors were assessed by a questionnaire (see annexes V & VI). Subjects also provided information about the presence of specific symptoms related to metal(loid)s exposure and chronic respiratory diseases, such as bronchitis and others; drinking and agricultural water source; agricultural practices, including pesticides usage; diet. The general characteristics of the study groups are summarized in Table 5. Occupationally exposed individuals were also inquired about the years of work and how long ago they stop working. The group was composed of 7 current miners and 34 exminers. From the current miners group 3 were from S. Francisco de Assis (exposed village) and 4 from Unhais-o-Velho (control). As for the ex-miners group 14 were from S. Francisco de Assis and Barroca do Zêzere (exposed villages) and 20 from Unhais-o- Velho (control). Possible differences between groups regarding several variables were assessed. No significant differences in age were observed between the three groups (p=0.063). Smoking habits groups were established as never/ever smokers, since the number of exsmokers was extremely high and the majority of these individuals had been heavy smokers. Gender and smoking habits difference (p<0.001 and p=0.001, respectively) among groups were mostly due to the fact that the occupationally exposed group was composed only of males, the vast majority of whom were smokers. Significant differences were found also for the frequency of individuals reporting their involvement in agricultural practice (p=0.001), while no significant differences were found for the source of drinking water (p=0.714), quantity of fish consumed (p=0.541), and pesticide use in the last year (p=0.408). MATERIAL AND METHODS 58 3.3 Chromosomal aberrations, aneuploidies and gaps Aliquots of 0.5mL of heparinised whole blood were used to establish duplicate lymphocyte cultures the chromosomal aberrations assay as described in Roma-Torres et al. (2006). Cultures were incubated at 37ºC in the dark for 48h. Democolcemid (0.11μg/mL) was added and the culture was continued for a further period of 3h. Cells were harvested by centrifugation, subjected to hypotonic treatment (KCl 0.075mol/L, 37ºC for 10min), fixed twice with freshly prepared cold methanol:acetic acid (3:1), placed on slides and stained with 4%Giemsa in phosphate buffer pH6.8 for 10min. Microscope analyses were performed on a Vickers Instruments light microscope and scored blind by the same reader. One hundred metaphases were analysed for each individual, fifty from each culture duplicate, using a 1000X magnification according to the criteria of Therman et al. (1980). Cells with 46 chromosomes were scored for structural aberrations, according to:  total CA frequency was defined as the number of aberrations, excluding gaps;  chromosome-type aberrations (CA-chromosome) included chromosome-type breaks, ring chromosomes, and dicentrics;  chromatid-type aberrations (CA-chromatid) included chromatid-type breaks. Aneuploidies (An) – cells with 45 and 47 chromosomes – and gaps (single and double) were also scored. 3.4 Comet assay Peripheral blood mononuclear leukocytes were isolated as described in the previous section (TCR mutation assay protocol). Cells were suspended in freezing medium (50% foetal serum, 40% RPMI 1640, 10% DMSO) to obtain 107cells/mL, and stored at -80ºC until time of analysis. On the day of the analysis cells were quickly thawed at 37ºC and cell viability was accessed by trypan blue exclusion technique being in all cases higher than 85%. The alkaline version of the comet assay was performed as described by Costa et al. (2008) with minor modifications. Briefly, cells collected by centrifugation (9000rpm for 3min), and suspended in 100μL of 0.6% low-melting-point agarose in PBS (pH 7.4), and dropped (5μL drops) onto a frosted slide precoated with a layer of 1% normal melting point agarose (4 drops per individual, 12 drops per slide). Slides were placed in the fridge for 10 min and allowed to solidify. Slides were then immersed in freshly prepared lysing MATERIAL AND METHODS 59 solution (2.5M NaCl, 100mM Na2EDTA, 10mM TrisBase, 0.25M NaOH, pH 10 supplemented with 1% triton X-100 30min before use) for 1h at 4ºC, in the dark (fridge). After lysis, slides were placed on a horizontal electrophoresis tank in an ice bath. The tank was filled with freshly made alkaline electrophoresis solution (1mM Na2EDTA, 300mM NaOH, pH 13) to cover the slides, and they were left for 20min in the dark to allow DNA unwinding and alkali-labile site expression. Electrophoresis was carried out for 20min at 30V and 300mA (1 V/cm). The slides were then washed for 10min with PBS 7.4 followed by 10min wash with ice-cold bidistilled water. Slides were then dehydrated by placing them for 15 min in 70% and 96% ethanol solutions. Prior to analysis, gels were stained with 120μL of SYBR Green solution (1μL of SYBR green 10000X + 10mL of TE, aliquoted per 1mL and stored at -20ºC). After staining the slides were washed twice with ice-cold bidistilled water and let to dry for 30min to 1h. Before scoring a drop of water and a cover slip were placed on the top of each slide. A ‘blind’ scorer examined 25 randomly selected cells from each gel (100 cells/donor) using a magnification of 400X. Image analysis was performed with Comet Assay IV software (Perceptive Instruments). Comet tail length (TL), percentage of DNA in the tail (tail intensity - %TDNA) and tail moment (TM) were the DNA damage parameters evaluated. For the statistical analysis only %TDNA was analysed according to what has been recommended by Kumaravel et al. (2009). 3.5 Analysis of lymphocytes subsets The percentages of different lymphocyte subsets, namely T lymphocytes (CD3+ lymphocytes), T helper (Th) lymphocytes (CD4+ lymphocytes), T cytotoxic (Tc) lymphocytes (CD8+ lymphocytes), B lymphocytes (CD19+ lymphocytes), and natural killer (NK) cells (CD16+ and CD56+ lymphocytes) were evaluated by flow cytometry as described in García-Lestón et al. (2011). Three-color direct immunofluorescence surface marker analysis was performed to determine the percentages of the following lymphocyte subpopulations: T lymphocytes, T helper (Th) lymphocytes, T cytotoxic (Tc) lymphocytes, B lymphocytes and natural killer (NK) cells. Whole blood collected in EDTA containers (100µl) was incubated for 15min in the dark with the following antibodies (Becton Dickinson), according to manufacturer’s instructions: FITC-labeled antiCD3 (for T lymphocytes), PE-labeled antiCD4 (for Th lymphocytes), phycoerytrin-cyanin 5 (PE-Cy5)-labeled antiCD8 (for Tc lymphocytes), PE- Cy5-labeled antiCD19 (for B lymphocytes), and PE-labeled CD16 and CD56 (for NK cells). The erythrocytes were removed by lysis through the addition of FACS Lysing MATERIAL AND METHODS 60 solution (Becton Dickinson). After washing with PBS (phosphate buffer solution), cells were fixed with CellFix (Becton Dickinson), and analysed within 24h on a FACScalibur flow cytometer using Cell Quest Pro software (Becton Dickinson). After gating the lymphocytes based on forward/side scatter plots, fluorescence data of FL1 (FITC), FL2 (PE) and FL3 (PE-Cy5) were obtained. At least 104 events in the lymphocytes window were acquired. 3.6 Quantification of neopterin, tryptophan, kynurenine, and nitrite Neopterin concentration was determined by a commercially available enzyme-linked immunosorbent assay (ELISA) kit (BRAHMS, Hennigsdorf, Germany), according to the manufacturer’s instructions. The limit of detection (LOD) was 2 nmol/L neopterin. A high-performance liquid chromatography (HPLC) methodology with 3-nitro-L-tyrosine as internal standard was used as previously described (Widner et al., 1997) to measure tryptophan and kynurenine concentrations. The kynurenine to tryptophan ratio (Kyn/Trp) was calculated to estimate the extent of tryptophan breakdown and expressed in micromoles kynurenine per millimole tryptophan. In order to estimate nitrite acid (NO) production, the stable NO metabolite nitrite (NO2-) was determined by the Griess reaction assay (Promega, Madison, Wisconsin) (Griess, 1879). MATERIAL AND METHODS 61 4. BIOMARKERS OF SUSCEPTIBILITY 4.1 DNA extraction Genomic DNA was obtained from 200 μL of heparinised whole blood samples using a commercially available kit according to the manufacturer’s instructions (QIAamp DNA extraction kit - Qiagen, Hilden, Germany). Each DNA sample was stored at −20 ºC until analysis. 4.2 Genotyping of polymorphisms in gene involved in the metabolism 4.2.1 GSTA The GSTA2 Ser112Thr (rs2180314) polymorphisms was genotyped by real-time PCR (AB7300) using TaqMan SNP Genotyping Assays from Applied Biosystems (ABI Assays reference: C_22275149_30, respectively) according to the manufacturer's instructions. To carry out the allelic discrimination the DNA samples were quantified by PicoGreen dsDNA Quantification Reagent (Molecular Probes, Eugene, OR, USA) according to the manufacturer's recommendations 4.2.2 GSTM1 and GSTT1 GSTM1 and GSTT1 genotyping for gene deletions were carried out by a multiplex PCR as described by Lin et al. (1998) with minor modifications described in Teixeira et al. (2004). DNA samples were amplified with the primers: 5´-GAA CTC CCT GAA AAG CTA AAG C- 3´and 5´-GTT GGG CTC AAA TAT ACG GTG G-3´ for GSTM1, which produced a 219bp product and 5´-TCA CCG GAT CAT GGC CAG CA-3´ and 5´-TTC CTT ACT GGT CCT CAC ATC TC-3´ for GSTT1, which produced a 459bp product. Amplification of albumin gene with the primers 5´-GCC CTC TGC TAA CAA GTC CTA C-3´ and 5´-GCC CTA AAA AGA AAA TCC CCA ATC-3´ was used as internal control, and produced a 350bp product. PCR was performed in a final volume of 50μL, consisting of DNA (0.1μg), dNTP (0.2mM each) (Perkin-Elmer), MgCl2 (2.5mM), each primer (1.0, 0.3 and 0.2μM for GSTM1, GSTT1 and albumin, respectively), AmplitaqGold polymerase (1.25U) (Perkin-Elmer), reaction buffer and 2% DMSO. Amplification was performed with an initial denaturation at 95ºC for 12min, followed by 35 cycles of amplification with 94ºC for 1min, 62ºC for 1min and 72ºC for 1min, and a final extension step at 72ºC for 10min. A GeneAmp 9600 MATERIAL AND METHODS 62 thermal cycler (Perkin-Elmer) was used. The amplification products were visualised in an ethidium bromide stained 1.5% agarose gel. 4.2.3 GSTP1 The GSTP1 Ile105Val (rs1695) polymorphism was determined by PCR and RFLP according to the method of Harries et al. (1997),with minor modifications described in Teixeira et al. 2004. DNA samples were amplified with the primers: 5´-ACC CCA GGG CTC TAT GGG AA-3´ and 5´-TGA GGG CAC AAG AAG CCC CT-3´ (Perkin-Elmer). The PCR amplification was carried out with 50ng DNA in 10mM Tris–HCI, pH 8.3, 50mM KCl, 1.5mM MgCl2, 0.3mM dNTP (Perkin-Elmer), 50ng of each primer and 1.25U of Taq polymerase (AmplitaqGold; Perkin-Elmer) in a total volume of 50μL. Amplification was performed with an initial denaturation at 95ºC for 7min, followed by 35 cycles at 94ºC for 30s, 62ºC for 30s, and 72ºC for 30s, and a final extension at 72ºC for 10min. The amplification product (20μL) was digested with five units of BsmAI (New England Biolabs) in 50mM NaCl, 10mM Tris–HCl, 10mM, and 1mM dithiothreitol (DTT) at 55ºC incubation for 16h. The resulting fragments lengths were then separated on a 2% agarose gel and stained with ethidium bromide (0.5mg/mL). When the BsmAI restriction site was present, the fragment of 176bp was digested into two fragments of 91 and 85bp. Homozygous wild type individuals (Ile/Ile) lacked the 91 and 85bp fragment, and heterozygous (Ile/Val) had three bands; homozygous individuals (Val/Val) lacked the large parent band and had the two smaller bands. 4.3. Genotyping of polymorphisms in genes involved in DNA repair 4.3.1 XRCC1, APEX1, MPG, MUTYH, OGG1, PARP1 and PARP4 The XRCC1 Arg194Trp (rs1799782) and Arg399Gln (rs25487), APEX1 Asp148Glu (rs1130409), MPG Lys17Gln (rs3176383), MUTYH Gln335His (rs3219489), OGG1 Ser326Cys (rs1052133), PARP1 Val762Ala (rs1136410), and PARP4 Gly1280Arg (rs13428) and Pro1328Thr (rs1050112), gene polymorphisms were determined by Real- Time PCR using TaqMan® SNP Genotyping Assays from Applied Biosystems (ABI Assays reference: C_11463404_10, C_622564_10, C_8921503_10, C_32323403_10, C_27504565_10, C_3095552_1, C_1515368_1, C_8700143_10, and C_8700142_10, respectively) following Conde et al. (2009), Gomes et al. (2010) and Silva et al. (2010). In order to carry out the allelic discrimination for these polymorphisms the DNA samples MATERIAL AND METHODS 63 were quantified using the Quant-iT™ Picogreen® dsDNA Assay Kit (Invitrogen) according to the manufacturer's recommendations. The Real-Time PCR amplification was performed in 10μl reactions containing 10ng of genomic DNA, 1X SNP Genotyping Assay Mix (containing two primer/probe pairs in each reaction and two fluorescent dye detectors - FAM® and VIC®) and 1X TaqMan Universal PCR Master Mix containing the AmpliTaqGold® DNA polymerase, dNTPs and optimized buffer components. The amplification conditions consisted of an initial AmpliTaq Gold® activation at 95°C during 10min, followed by 40 or more amplification cycles consisting of denaturation at 92°C for 15s and annealing/extension at 60°C for 1min. Approximately 10-15% of the genotype determinations were carried out twice in independent experiments with 100% of concordance between experiments. 4.3.2 ERCC1, ERCC4 and ERCC5 ERCC1 Lys504Gln (rs3212986), ERCC4 Arg415Gln (rs1800067), and ERCC5 Asp1104His (rs17655) and Cys529Ser (rs2227869) polymorphisms were determined using the TaqMan SNP genotyping assay (Applied Biosystems, codes C_2532948_10, C_3285104_10, C_1891743_10 and C_15956775_10, respectively), following Costa et al. (2008). The PCR amplification was performed in 10μL reactions containing 10ng of genomic DNA, 1X SNP Genotyping Assay Mix, and 1X TaqMan Universal PCR Master Mix containing optimised buffer components and Rox reference dye. The amplification conditions consisted of an initial AmpliTaq GoldR activation at 95ºC during 10min, followed by 40 or more amplification cycles consisting of denaturation at 92ºC for 15s and annealing/extension at 60ºC for 1min. Amplification was performed in the 7300 Real-Time PCR System (Applied Biosystems) and sequences were detected by the SDS-Sequence Detection Software (version 1.3.1). All the genotype determinations were carried out twice in independent experiments and all the inconclusive samples were reanalyzed. MATERIAL AND METHODS 64 5. STATISTICAL ANALYSIS A general description of the study population was performed through univariate analysis. The distribution within the three study groups of gender, age and lifestyle factors potentially influencing the levels of studied biomarkers (i.e., smoking habits, water and fish consumption, agricultural activity and use of pesticides) was evaluated with the Chisquare test for categorical variables and the analysis of variance (ANOVA) for continuous variables. The effect of exposure on the concentration of metals was preliminarily tested with the ANOVA of log-transformed data. A multiple regression analysis was performed to estimate the effect of the exposure, adjusted for actual confounders. All models included age and smoking habits. Three multiple regression models were applied for the multivariate analysis, depending on the characteristics and statistical distribution of variables. Details are given in Table 6. Associations between two variables were tested by Spearman correlation. Table 6. Multiple regression models applied according to the characteristics and statistical distribution of variables. Log-linear Poisson Negative-binominal Fe-B, Mg-B, S-FN, S-TN, S-H, Se-B, Si-B As-H, Cd-B, Cd-U, Cd-FN, Cd- TN, Cd-H, Cr-U, Cr-H, Hg-U, Hg-FN, Hg-TN, Hg-H, Mn-H, Ni- H, Pb-FN, Pb-TN, Se-FN, Se- TN, Se-H As-B, As-U, As-FN, As-TN, Cr- FN, Cr-TN, Cu-B, Cu-H, Fe-H, Mg-FN, Mg-TN, Mg-H, Mn-B, Mn- U, Mn-FN, Mn-TN, Mo-B, Ni-U, Ni-FN, Ni-TN, Pb-B, Pb-U, Pb-H, Se-U, Zn-B, Zn-FN, Zn-TN, Zn-H The effect of exposure on the level of genotoxicity biomarkers was preliminarily tested through ANOVA. To achieve a better approximation to the normal distribution, a logtransformation of the data was applied to TCR-Mf and %DNAT. No transformation was needed for MN frequency. The Kruskal-Wallis test was performed for CA-total, CA- chromosome, CA-chromatid, and aneuplodies. Best fitting multiple regression models were used to estimate the effect of the exposure. Linear regression was applied on the log-transformed TCR and %DNAT; negative binomial regression on non transformed data was carried out with MN and CA-total; lastly, Poisson regression on non transformed data was fitted for CA-chromosome, CA-chromatid, and aneuploidies. All models included age, smoking habit (as previously mentioned smokers were classified as ever/never smokers given the high number of heavy smokers that declared to be ex-smokers), and actual confounders. A possible role as effect modifiers of candidate biomarkers of susceptibility, MATERIAL AND METHODS 65 on the genotoxic damage induced by exposure, was also tested. When the number of subjects with homozygous mutations was small, these were merged with the group of subjects with heterozygous mutations. Thus, a dominant model was hypothesized in this case; an additive model was tested in all other cases. Mean ratio (MR) was used as the point estimate of effect accompanied by its 95% confidence interval (CI). To take into account the village of origin and to evaluate the accumulation of exposure from the work and the environment, the occupationally exposed population was divided into: i) subjects occupationally and environmentally exposed (working in the mine and living in villages near the mine); ii) subjects only occupationally exposed, (working in the mine and living in villages upstream the river). Linear regression on log-TCR-Mf and log-%DNAT, and negative binomial regression on CA-total were fitted to estimate the effect of exposure according to these new groups. Adjustment for age, smoking habit and parameter-specific actual confounders was applied. An ancillary analysis was carried out to quantitatively assess the association between metal concentration and genotoxicity. The study subjects were divided into three groups according to the tertile distribution of each metal. The resulting three-level factors (one factor for each metal) were, in turn, fitted in a regression model on the genotoxicity biomarkers. For each biomarker, the best fitting regression method was chosen. All models included age, smoking habits, and model-specific confounders. The effect of exposure on the level of immunotoxicity was preliminarily tested with the ANOVA. To achieve a better approximation to the normal distribution, a log-transformation of the data was applied to neopterin, tryptophan, kynurenine and nitrite levels, kynurenine/tryptophan ratio and percentages of the different lymphocyte subsets. A multiple linear regression analysis was performed to estimate the effect of the exposure on the log-transformed data. Adjustment for age and smoking habits was applied. For all biomarkers, actual confounders were identified and estimations adjusted accordingly. A sub-analysis on the control and environmentally exposed population was performed to evaluate the role of gender as a confounder and/or effect modifier. An ancillary analysis was carried out to quantitatively assess the effect of metal concentration on biomarkers of immunotoxicity. The study subjects were divided into three groups according to the tertile distribution of each metal. The resulting three-level factors (one factor for each metal) were, in turn, fitted in a linear regression, to the log-transformed value of immunotoxicity biomarkers. All models included age, smoking habits, and model-specific confounders. A logistic regression model was applied to identify the relationship between selected symptoms and environmental and/or occupational exposure. Adjustment for age, smoking MATERIAL AND METHODS 66 habits and model-specific confounders was applied. Associations between two variables were tested by Spearman correlation. The critical limit for significance was set at P<0.05. The statistical software used for the analyses were StataCorp. 2011, Stata Statistical Software: Release 12, College Station, TX: StataCorp LP, and SPSS Inc. Released 2004, SPSS for Windows, Version 13, Chicago, SPSS Inc. IV. RESULTS RESULTS 74 1.4 Effect of gender, age and smoking habits The effects of gender, age and smoking habits on the levels of metal(loid)s in the different matrices were also evaluated and the results are presented in Table 10. TABLE 10. Effect of gender, age and smoking on the levels of metals in the different biological samples (only significant effect on at least one parameter are shown). Gender (effect in males) Age (effect with regard to 25-50 years) Smoking (effect in smokers) 51-60 years 61-70 years ≥71 years As-U (µg/g creat ) ↓ ↑ ↑↑ As-TN (µg/g) ↓ Cr-U (µg/g creat) ↑ Cr-FN (µg/g) Cr-TN (µg/g) ↓ Cu-B (µg/L) ↓ ↓ Cu-H (µg/g) ↑↑ Fe-H (µg/g) ↑↑ ↑ ↑↑ Hg-H (µg/g) ↑ ↓ K-B (µg/L) ↑ Mg-B (µg/L) ↑ ↑ Mg-TN (µg/g) ↓↓ ↑ Mg-H (µg/g) ↓↓ ↓ Mn-TN (µg/g) ↓ ↑ ↑↑ ↑↑ ↑ Pb-B (µg/L) ↑↑ ↑ ↑ S-H (µg/g) ↑ Se-U (µg/g creat) ↓↓ ↑ Zn-B (µg/L) ↑↑ Zn-FN (µg/g) ↓↓ ↓ Zn-TN (µg/g) ↓ Zn-H (µg/g) ↓↓ ↓ Arrows up indicate increase, arrows down indicate decrease; One arrow: P<0.05; Two arrows: P<0.01; Concerning gender, the majority of the elements showed significantly higher values in males in blood and hair samples (except for Mg in hair, higher in females), and significantly higher values in females in urine, fingernails and toenail. Regarding age, the effects depended mainly on the matrix. All elements showed a significant increase in older groups in blood and urine samples, except Cu in blood, and a significant decrease with age in fingernails, toenails and hair samples, except for Mn in toenails and Fe in hair. RESULTS 75 As regards smoking, significantly higher levels were generally observed in smokers in the different matrices, except for Hg in hair which was significantly lower. These results are also shown schematically in Figure 7. FIGURE 7. Schematic representation of the effect of gender, age and smoking on the concentration of metal(loid)s in the different matrices. Variations in the relative concentration of metal(loid)s are represented horizontally. RESULTS 76 2. BIOMARKERS OF EFFECT 2.1 Biomarkers of Genotoxicity Univariate comparisons of genotoxicity biomarkers by study group are reported in Table 11. TABLE 11. Levels of genotoxicity biomarkers in the study groups. Controls Environmentally Exposed Occupationally Exposed N mean ± SD N mean ± SD N mean ± SD P-value* MN (‰) 40 6.45 ± 4.47 41 8.46 ± 5.27 41 4.98 ± 3.06 0.002 TCR-Mf (10 -4 ) 39 3.80 ± 2.11 34 4.92 ± 3.86 38 5.80 ± 3.93 0.018 %DNAT 40 12.40 ± 3.04 41 24.58 ± 7.75 41 18.73 ± 7.60 < 0.001 CA-total 40 2.65 ± 2.11 41 5.56 ± 2.92 41 3.24 ± 2.45 < 0.001 CA-cromosome_type 40 0.55 ± 1.04 41 1.22 ± 1.39 41 0.71 ± 1.10 0.018 CA-chromatide_type 40 2.10 ± 1.52 41 4.34 ± 2.56 41 2.54 ± 1.90 < 0.001 Aneuploidies (45) 40 1.65 ± 1.46 41 3.32 ± 1.77 41 2.66 ± 1.51 < 0.001 Aneuploidies (47) 40 0.13 ± 0.33 41 0.20 ± 0.46 41 0.32 ± 0.57 0.221 Gaps-single 40 0.83 ± 1.03 41 2.15 ± 1.86 41 1.39 ± 1.51 0.001 Gaps-double 40 0.00 ± 0.00 41 0.37 ± 0.54 41 0.17 ± 0.44 < 0.001 *ANOVA test Significant differences were found for all biomarkers but for the frequency of aneuploidies, with higher values in the exposed groups, particularly in the environmentally exposed. 2.1.1 Effect of exposure, age and smoking habits The genotoxic effect of exposure, age, and smoking habits, adjusted for the presence of confounding, was evaluated with multivariate modeling (Table 12). RESULTS 77 TABLE 12. Effect of exposure, age and smoking habits on the biomarkers of genotoxicity. Adjustment for age, smoking and genotoxicity parameter-specific actual confounders. TCR-Mf %DNAT MN CA-total CA-chromosome CA-chromatide MR [95% CI] MR [95% CI] MR [95% CI] MR [95% CI] MR [95% CI] MR [95% CI] Exposure Controls (N=40) 1.00 1.00 1.00 1.00 1.00 1.00 Env. Exposed (N=41) 0.99 [0.75;1.32] 1.78** [1.54;2.06] 1.21 [0.89;1.64] 2.15** [1.57;2.93] 2.68** [1.52;4.72] 2.05** [1.53;2.73] Occup.Exposed (N=41) 1.38* [1.04;1.82] 1.45** [1.25;1.68] 0.75 [0.54;1.02] 1.12 [0.81;1.56] 1.23 [0.67;2.26] 1.24 [0.91;1.69] Age (years) 25-50 (N=23) 1.00 1.00 1.00 1.00 1.00 1.00 51-60 (N=35) 1.30 [0.93;1.82] 0.96 [0.80;1.14] 1.35 [0.92;1.99] 1.20 [0.82;1.77] 1.73 [0.90;3.31] 1.09 [0.75;1.58] 61-70 (N=38) 1.78** [1.28;2.48] 1.13 [0.95;1.34] 1.17 [0.80;1.68] 1.20 [0.84;1.72] 1.04 [0.57;1.91] 1.24 [0.87;1.76] >71 (N=26) 1.28 [0.89;1.83] 1.30** [1.09;1.56] 1.14 [0.77;1.68] 1.27 [0.87;1.87] 0.94 [0.47;1.88] 1.30 [0.92;1.84] Smoking habits Never smokers (N=73) 1.00 1.00 1.00 1.00 1.00 1.00 Ever smokers (N=49) 0.88 [0.69;1.12] 1.09 [0.96;1.23] 0.81 [0.61;1.07] 1.13 [0.87;1.48] 1.02 [0.64;1.62] 1.21 [0.94;1.56] MR – Mean Ratio *P<0.05, **P<0.01 RESULTS 78 Significant increases observed in exposed groups when compared to controls confirmed the result of univariate analysis, with the only exception of MN which did not show any significant differences. The environmentally exposed group showed significantly higher %DNAT, CA-total, CA-chromosome and CA-chromatid when compared to the control group. The occupationally exposed group showed significantly higher TCR-Mf and %DNAT. Significant effect of age was observed for TCR-Mf in the 61-70 years age-class and also for %DNAT in individuals older than 71 years (>71). No significant effect of smoking habits was observed on any biomarker. Higher mean ratios (MR) were generally observed in individuals living in the polluted villages than in those who worked in the mine. 2.1.2 Effect of gender The effect of gender was evaluated only in the group of exposed residents and in controls, since miners were only males. Not surprisingly, only MN frequency was influenced by this factor, with females showing significantly higher frequencies than males in controls and in the environmentally exposed group (Table 13). TABLE 13. Effect of gender and exposure on MN, excluding the occupationally exposed population. Controls Environmentally Exposed MR [95% CI] MR [95% CI] Males 1.00 0.93 [0.59;1.47] (n=17) (n=16) Females 1.78** [1.78;2.69] 1.51 [0.89;2.56] (n=23) (n=25) MR – Mean Ratio *P<0.01, significant difference with regard to control males. The effect of exposure in this biomarker was revealed by the increased MR in females environmentally exposed as compared to non exposed females (MR: 1.41; 95% CI: 1.00- 1.97). No significant differences between males were observed. RESULTS 79 2.1.3 Synergistic effect of environmental and occupational exposure The presence of synergy between occupational and environmental exposure was evaluated comparing miners living in polluted villages vs. those living in villages upstream the mine. No biomarker showed higher frequency in the group of subjects exposed to metal(oid)s from both sources (Table 14). TABLE 14. Effect of exposure in 4 categories (taking into account the origin village of the occupationally exposed individuals) on TCR-MF, %DNAT and CA-total. Adjustment for age, smoking and genotoxicity parameter-specific actual confounders. TCR-Mf %DNAT CA-total MR [95% CI] MR [95% CI] MR [95% CI] Controls (N=40) 1.00 1.00 1.00 EE (N=41) 0.99 [0.74;1.31] 1.80** [1.57;2.07] 2.24** [1.66;3.02] OEO (N=24) 1.49* [1.08;2.05] 1.21* [1.04;1.42] 0.82 [0.56;1.20] O+EE (N=17) 1.20 [0.82;1.77] 1.87** [1.57;2.22] 1.62* [1.11;2.38] EE: environmentally exposed; OEO: occupationally exposed only; O+EE: occupationally + environmentally exposed. MR – Mean Ratio *P<0.05, **P<0.01 2.1.4. Effect of metal(loid) concentration To evaluate the effect of metal(loid)s concentration on the selected biomarkers we used measure of individual exposure for our subjects (biomarkers of exposure). Subjects were divided according to the tertile distribution of each metal(loid) level, in all biological matrices analysed (blood, urine, hair, fingernails, and toenails). Most specific results were obtained for As, Mn and Pb in toenails (TN) (Table 15). RESULTS 80 TABLE 15. Effect of the levels of As, Mn, and Pb in toenails on the genotoxicity parameters. Adjustment for age, smoking and genotoxicity parameter-specific actual confounders. TCR-Mf %DNAT MN CA-total CA-chromosome CA-chromatide MR [95% CI] MR [95% CI] MR [95% CI] MR [95% CI] MR [95% CI] MR [95% CI] As-TN 1st tertile 1.00 1.00 1.00 1.00 1.00 1.00 2nd tertile 0.86 [0.75;1.32] 1.03 [0.88;1.21] 1.13 [0.84;1.53] 1.16 [0.83;1.62] 2.07 [0.90;4.78] 1.06 [0.75;1.49] 3rd tertile 0.96 [1.04;1.82] 1.42** [1.22;1.66] 1.67** [1.28;2.20] 1.96** [1.44;2.68] 4.57** [2.10;9.95] 1.57** [1.14;2.17] Mn-TN 1st tertile 1.00 1.00 1.00 1.00 1.00 1.00 2nd tertile 1.08 [0.81;1.45] 1.01 [0.84;1.21] 1.23 [0.95;1.71] 1.32 [0.94;1.86] 1.71 [0.82;3.56] 1.09 [0.78;1.54] 3rd tertile 1.02 [0.76;1.38] 1.18 [0.99;1.40] 1.41** [1.07;1.87] 1.72* [1.22;2.41] 2.18** [1.05;4.51] 1.31 [0.95;1.82] Pb-TN 1st tertile 1.00 1.00 1.00 1.00 1.00 1.00 2nd tertile 1.05 [0.93;1.82] 1.11 [0.93;1.34] 1.25 [0.92;1.71] 1.54** [1.10;2.16] 1.93 [0.89;4.18] 1.49* [1.05;2.12] 3rd tertile 1.36 [0.99;1.89] 1.11 [0.92;1.33] 1.16 [0.86;1.56] 1.43* [2.00;1.03] 2.58** [1.26;5.30] 1.24 [0.87;1.75] MR – Mean Ratio *P<0.05, **P<0.01 RESULTS 81 Levels of As in the 3rd tertile were significantly associated with the level of %DNAT, MN, CA-total, CA-chromosome, and CA-chromatid. Mn levels in the 3rd tertile determined a significant increase of MN, CA-total and CA-chromosome. Higher levels of Pb were generally associated with higher frequencies of CA-total, CA-chromosome and CA- chomatid. 2.2 Immune markers Results concerning the level of immunotoxicity biomarkers in the study groups are presented in Table 16. TABLE 16. Levels of immunotoxicity biomarkers in the study groups. Controls Environmentally Exposed Occupationally Exposed N mean ± SD N mean ± SD N mean ± SD P-value* Neopterin (nmol/L) 24 4.35 ± 0.83 22 4.77 ± 0.78 32 4.90 ± 1.63 0.255 Tryptophan (µmol/L) 24 51.24 ± 7.73 22 51.95 ± 7.63 32 52.76 ± 8.13 0.773 Kynurenine (µmol/L) 24 1.80 ± 0.38 22 2.04 ± 0.6 32 1.93 ± 0.49 0.277 Kyn/Trp (µmol/mmol) 24 35.42 ± 6.14 22 39.26 ± 10.04 32 37.13 ± 10.13 0.361 Nitrite (µmol) 24 23.44 ± 20.50 22 16.54 ± 12.26 32 32.87 ± 32.16 0.112 %CD3+ 40 75.46 ± 8.85 35 70.6 ± 9.99 38 68.88 ± 12.93 0.022 %CD4+ 40 47.18 ± 6.92 35 46.59 ± 8.82 38 40.07 ± 9.84 0.001 %CD8 + 40 25.98 ± 9.52 34 20.93 ± 6.27 38 26.62 ± 11.17 0.022 CD4 + /CD8 + 40 2.01 ± 0.65 34 2.40 ± 0.84 38 1.87 ± 1.11 0.037 %CD19 + 40 8.77 ± 6.91 35 7.68 ± 3.27 38 7.15 ± 3.55 0.339 %CD16+56+ 40 13.65 ± 6.75 35 17.19 ± 7.72 38 20.19 ± 11.90 0.008 *ANOVA test Significant differences were observed in the univariate analysis for %CD3+, %CD4+, %CD8+, %CD16+56+, and CD4+/CD8+ ratio. No significant differences were obtained for %CD19+ and the levels of neopterin, tryptophan, kynurenine, Kyn/Trp and nitrite among the three groups. 2.2.1 Effect of exposure, age and smoking habits When multivariate modelling was applied, levels of neopterin, kynurenine and Kyn/Trp were found to be influenced by age (Table 17), with significantly higher MR in older individuals, when compared to the youngest group (25-50 years). RESULTS 82 TABLE 17. Effect of exposure on neopterin, tryptophan, kynurenin and nitrite concentrations stratified by exposure, age, and smoking habits. All models were adjustment for parameter-specific actual confounders. Neopterin Tryptophan Kynurenine Kyn/Trp Nitrite MR [95% CI] MR [95% CI] MR [95% CI] MR [95% CI] MR [95% CI] Exposure Controls (N=40) 1.00 1.00 1.00 1.00 1.00 Env. Exposed (N=41) 1.02 [0.90;1.17] 1.03 [0.93;1.14] 1.11 [0.95;1.28] 1.07 [0.94;1.23] 0.73 [0.43;1.20] Occup. Exposed (N=41) 1.03 [0.91;1.17] 1.03 [0.94;1.13] 0.96 [0.84;1.11] 0.93 [0.82;1.07] 0.95 [0.58;1.56] Age (years) 25-50 (N=23) 1.00 1.00 1.00 1.00 1.00 51-60 (N=35) 1.11 [0.95;1.30] 1.02 [0.90;1.15] 1.40** [1.17;1.69] 1.38** [1.16;1.63] 1.75 [0.93;3.31] 61-70 (N=38) 1.21* [1.03;1.42] 0.98 [0.87;1.11] 1.28* [1.06;1.55] 1.31** [1.10;1.56] 1.62 [0.85;3.08] >71 (N=26) 1.44** [1.21;1.72] 0.97 [0.85;1.11] 1.59** [1.30;1.95] 1.63** [1.36;1.97] 1.33 [0.67;2.66] Smoking habits Never smokers (N=73) 1.00 1.00 1.00 1.00 1.00 Ever smokers (N=49) 1.11 [0.99;1.23] 0.98 [0.90;1.06] 1.00 [0.88;1.13] 1.02 [0.91;1.14] 1.19 [0.78;1.82] MR – Mean Ratio *P<0.05, **P<0.01 RESULTS 83 No significant effect of exposure or smoking habits was observed on any of these markers, but significant correlations were obtained for neopterin with kynurenine (r=0.569, P<0.01) and Kyn/Trp (r=0.616, P<0.01). Significant effects of exposure and age were observed on the percentage of the different lymphocyte subsets (Table 18). RESULTS 90 The influence of the polymorphisms of genes encoding for metabolic and DNA repair enzymes on the level of genotoxicity markers was evaluated, and statistically significant results are gathered in Table 24. Table 24. Influence of biomarkers of susceptibility on genotoxicity parameters (only models showing significant effect are included). Adjustment for age, smoking and genotoxicity parameterspecific actual confounders. Control Environmentally Exposed Occupationally Exposed N Mean Ratio [95%CI] N Mean Ratio [95%CI] N Mean Ratio [95%CI] GSTM1 deletion CA-total Positive 16 1 14 2.31* [1.44,3.70] 14 1.78* [1.11,2.86] Null 24 1.11 [0.71,1.75] 27 0.90 [0.50,1.60] 27 0.46* [0.25,0.85] CA-chromosome Positive 16 1 14 7.17** [2.38,21.62] 14 3.88* [1.26,11.6 Null 24 3.02* [1.01,9.04] 27 0.25* [0.07,0.87] 27 0.16* [0.04,0.61] APEX1 rs1130409 %DNAT TT 11 1 12 1.91** [1.48,2.46] 12 1.22 [0.94,1.57] TG 14 1.02 [0.80,1.29] 15 0.83 [0.60,1.16] 15 1.40* [1.01,1.95] GG 15 0.90 [0.71,1.13] 14 1.01 [0.72,1.41] 14 1.13 [0.81,1.58] OGG1 rs1052133 CA-Total CC 23 1 28 2.58** [1.74,3.84] 24 1.79** [1.17,2.74] CG+GG 17 1.81* [1.15,2.86] 13 0.70 [0.40,1.24] 17 0.34** [0.18,0.65] CA-chromatid CC 23 1 28 2.38** [1.64,3.47] 24 1.84** [1.22,2.76] CG+GG 17 1.65* [1.04,2.61] 13 0.75 [0.43,1.31] 17 0.33** [0.17,0.61] ERCC1 rs3212986 %DNA GG 23 1 21 1.48** [1.24,1.77] 16 1.19 [0.99,1.43] GT+TT 17 0.93 [0.78,1.13] 20 1.47** [1.13,1.89] 25 1.44** [1.11,1.87] CA-chromosome GG 23 1 21 5.29** [2.22,12.62] 16 1.08 [0.36,3.23] GT+TT 17 2.62* [1.02,6.73] 20 0.27* [0.09,0.81] 25 0.91 [0.24,3.42] ERCC4 rs1800067 MN GG 29 1 30 1.42 [1.00,2.01] 30 0.76 [0.53,1.09] GA+AA 11 1.13 [0.73,1.75] 11 0.53 [0.28,1.00] 11 0.96 [0.50,1.85] CA-chromatid GG 29 1 30 2.51** [1.78,3.54] 30 1.29 [0.90,1.84] GA+AA 11 1.02 [0.61,1.72] 11 0.49* [0.25,0.98] 11 0.94 [0.48,1.87] *P<0.05, **P<0.01 RESULTS 91 The wildtype homozygous or positive (in the case of GSTM1) genotypes were always the reference category. CA-total and CA-chromosome mean ratios increased in the GSTM1 positive exposed individuals; the increases were more pronounced in the environmentally exposed group. Besides, a significantly higher mean of the CA-chromosome mean ratios was observed in the null control group. Unexpectedly, GSTM1 null exposed individuals showed significant decreases in the CA-total and CA-chromosome mean ratios. A significant increase in the mean %DNAT ratio was observed in the homozygous wild type subjects for APEX1 polymorphism from the environmentally exposed group, and in TG heterozygous individuals from the occupationally exposed group. CA-total and CA-chromatid mean ratios were significantly increased in the OGG1 homozygous wild type exposed groups and in the control individuals carrying the G variant allele. Also, the same parameters were significantly reduced in occupationally exposed G allele carriers. The ERCC1 genotypes influenced the %DNAT mean ratio in the environmentally exposed group (GG and GT+TT), and in the occupationally exposed individuals carrying the T variant allele. Similarly to what has been observed for GSTM1, ERCC1 genotypes showed significant increases of CA-chromosome mean ratio in environmentally exposed homozygous wild type individuals and an opposite trend in controls, where T allele carriers show higher frequencies. Finally, CA-chromatid mean ratio significantly increased in environmentally exposed wild type homozygotes for ERCC4 polymorphism, while environmentally exposed individuals with the A variant allele showed lower frequencies for CA-chromatid (P<0.01) and MN (P=0.051). RESULTS 92 V. DISCUSSION DISCUSSION 95 Biological monitoring provides an integrated estimate of exposure by all routes of absorption into the body and evaluates the overall exposure as the sum of different sources of contamination. It gives information on long-term exposure in some cases and helps to assess exposure of an individual within the working environment and the individual factors influencing the pharmacokinetics of the xenobiotic (Maroni, 1983). Nevertheless, when routes of exposure are integrated or combined, environmental monitoring can be helpful either to clarify which route is more significant or to identify the compounds that have to be taken into account in the biological monitoring practice (Maroni, 2000). Risk assessment should include the measurement of actual impacts on biological endpoints from trace element contamination in soil, surface water, groundwater, air and sediments. By providing risk assessment data, scientific studies may have a strong influence on regulatory policies and on establishing disease prevention strategies. The results obtained in the environmental geochemical campaigns performed near Panasqueira mine reported a high degree of contamination by several metal(loid)s (Ávila et al., 2008; Grangeia et al., 2011; Salgueiro et al., 2008). Moreover, local health statistics report an elevated number of health complains, with a significant number of individuals with cardiac, respiratory and urinary diseases, and a high rate of deaths by cancer (personal communication). It was then considered of paramount importance to conduct a study to evaluate the role of environmental contamination in populations living and working nearby, with the following main tasks: • quantifying the level of several elements - As, Ca, Cd, Cu, Cr, Fe, Hg, K, Mg, Mn, Mo, Na, Ni, Pb, S, Se, Si and Zn - in different biological matrices - blood, urine, finger and toe nails and hair (biomarkers of exposure); • evaluating the genotoxic and immunotoxic damage caused by this contamination (biomarkers of effect); • analysing the possible influence of a set of genetic polymorphisms (biomarkers of susceptibility). DISCUSSION 96 1. BIOMARKERS OF EXPOSURE Blood and urine are still the specimens of choice to be analyzed in biomonitoring studies. The levels of most elements have been extensively studied in these matrices and numerous validated biomarkers of exposure are available. Concerning nails and hair samples, in recent years a number of studies using these matrices have been carried out bringing new and improved information that can help to quantify elements in these matrices and validate this approach to measure exposure in the near future. In biomonitoring of environmental and occupational exposure to toxic elements, nails are generally preferred to hair, as they do not become so easily contaminated (Button et al., 2009). Further, toenails may be a more reliable sample than fingernails, since the latter come into contact with the atmosphere, metallic objects and other substances containing trace elements such as dyes. In contrast, toenails most of the time are hidden in shoes and therefore they have lower contact with trace elements. Up to now few studies have been conducted comparing the levels of diverse elements in all five different matrices (He, 2011). The information provided by each of them is rather different as blood and urine generally reflect recent exposures (days/few weeks), and hair and nails, particularly toenails, reflect exposures occurring in the last weeks/months. This distinction may be not straightforward for some elements, such as Cd and Pb which accumulate in the human body for years (half life of 10 – 12 years) (Dorne et al., 2011). The combination of results from all the matrices will allow better characterization of the exposure. Taking into account all this information, our results point to different types of exposure (past/recent) for different elements in the exposed groups (Table 8). The group exposed to environmental toxins only apparently experienced a pronounced and continuous (past and recent) exposure to As, a moderate but continuous exposure to Mg, Mn and Zn, a recent exposure to Mo and past exposure to Cr, Ni and S. Since Mo was only analysed in blood samples little can be said about the timeframe of exposure to this compound. The second group (occupationally exposed) experienced a continuous exposure to Zn, recent exposure to Se, and long standing exposure to As, Mn and Pb. DISCUSSION 97 1.1 Comparison with reference ranges For some of the elements studied the concentrations reported here for blood and urine were above the published reference ranges (Table 7). These reference ranges were obtained from samples collected from Italian and Swedish healthy volunteers without a detailed description of the environmental or occupational exposure therefore are only indicative and may change significantly from population to population. Numerous factors such as site of residence, gender, age, diet, lifestyle or geochemical environment need to be taken into account when establishing the reference ranges for a population (Rodushkin et al., 2000), and any comparison must be interpreted carefully. Regarding the Portuguese population, there are few studies that report metal(loid)s levels in exposed populations, and they show wide changes in levels within the same population in different geographic areas (Coelho et al., 2012). Therefore, in this study we compared the element concentration obtained for the exposed populations with those of the controls, as they were matched for age, gender (only the environmentally exposed group), diet, lifestyle, geochemical environment, residence. 1.2 Correlation between matrices If exposure occurs continuously significant and positive correlations between the different sample types are expected. As and Hg were the only metals that showed correlations between matrices compatible with the presence of recent and past exposure (Table 9). As expected, significant and positive correlations between fingernails and toenails were obtained for most of these elements; significant correlations between finger/toenails and hair, and between blood and urine, were archived for some of them. It is important to highlight the fact that for some of the elements not all the matrices were analysed. 1.3 Effect gender, age and smoking habits From the results in Table 7 it can be seen that the concentrations of As, Cr, Mn, and Pb, vary significantly among the three groups, with significant differences in three different media. These results were influenced by confounding variables such as gender, age, smoking habits, and factors directly associated to exposure, such as agriculture practice, fish consumption and source of water for consumption. When adjusting for all these variables some of the elements were no longer significantly different between groups (Table 7 vs. Table 8). DISCUSSION 98 Modifying factors, such as gender, age, socioeconomic status, and lifestyle factors in exposure and susceptibility to metal(loid)s have been reported in the literature to play a role in modulating exposure to metal(loid)s. However, most of these parameters are often overlooked and additional studies need to be performed in order to fill gaps. Identification of these gaps provides information on susceptibility factors which is critical to design guidelines for preventive measures (Berglund et al., 2011). Regarding the effect of gender on the metals levels in the different matrices, our results show that females have higher concentrations of As, Cr, Mg, Mn and Se in urine, fingernails and toenails, while males have higher levels of Fe, Hg, Mg, Pb, S and Zn in blood and hair samples (Table 10). The only exception was Mg in hair which was presented with significantly higher concentrations in females. Interestingly, this difference was reported before (Chojnacka et al., 2006; Nowak and Kozlowski, 1998; Quereshi, 1982; Takeuchi et al., 1982). The higher levels of Pb in blood in males is also known and reported in several studies. Milman et al. (1994) suggested that the difference in Pb blood levels between females and males could be explained by the higher content of haemoglobin in men. Gender differences in the exposure to toxic metals are well documented. In a review paper by Vahter et al. (2007), several references to studies describing significant differences in internal levels of elements between males and females were described and commented. Berglund et al. (2011) reported that females seemed to be more at risk for toxic metal exposure than males, and this founding was confirmed in our study, where females had significantly higher levels of several genotoxic elements, i.e. As, Cr, and Mn. Differences between genders can be due to different patterns of exposure, with one of the genders being more exposed to certain metal(loid)s, although the presence of different toxicokinetic mechanisms between both genders should be taken into account. Concerning the effect of age, we found older individuals having higher concentrations in blood and urine (As, Cu, Mg, and Zn), and younger individuals having higher concentrations in nails and hair (As, Cr, Fe, Mg, Mn, and Se) (Table 10). These differences, besides different patterns of exposure, may be due to different toxicokinetic rates, possibly because of the lower proportion of water in the organism, lower absorption and excretion rates, and possible nutritional deficiencies, and comorbidities in older individuals. Finally, the analysis of the effect of smoking showed as smokers have significantly higher concentration of several elements, except for Hg in hair that was higher in non-smokers DISCUSSION 99 (Table 10). Since tobacco smoke contains Hg, this result was not expected; nevertheless it was also obtained in other studies, specifically in the one published in 2006 by Chojnacka et al. DISCUSSION 106 3. BIOMARKERS OF SUSCEPTIBILITY 3.1 Polymorphisms in genes involved in the metabolism Polymorphic genes involved in the metabolism of xenobiotics have been studied in recent decades in order to understand the possible modulator effect of these genetic determinants in genetic damage. Despite the vast number of studies where associations between polymorphic enzymes and individual cancer susceptibility have been established, few studies to evaluate the effect of genetic polymorphisms on genetic damage caused by both environmental and occupational exposure to metal(loid)s have been performed. In the present study we found a significant influence of GSTM1 on the effect of exposure to genotoxic agents (Table 24). This polymorphic gene has been extensively studied, as individuals presenting the null genotype have a decreased ability to detoxify carcinogens, thus having a higher sensitivity to genetic damage and an increased cancer risk (Rossi et al. 2009). Accordingly, our results showed among the control group a higher CA- chromosome mean ratio in the GSTM1 null individuals. On the other hand, the effect of this genotype on the presence of exposure was opposite, i.e. lower CA-total and CA- chromosome mean ratios. Two of the major studies which evaluated the influence of GSTM1 deletion on CA frequencies were performed by Rossi et al. (2009) and Skjelbred et al. (2010), and they did not find any significant effects. 3.2 Polymorphisms in genes involved in DNA repair DNA repair mechanisms are vital responses to multiple types of DNA damage, specifically those from exposure to environmental and endogenous carcinogens (McWilliams et al., 2008). Genetic variations in DNA repair genes may modulate DNA repair capacity and, therefore influence risk for development of cancer and other mutation related diseases (Kiyohara and Yoshimasu, 2007). As regards the DNA repair genotypes analyzed, significant influences were found for APEX1 rs1130409, OGG1 rs1052133, ERCC1 rs3212986, and ERCC4 rs1800067 (Table 24). The APEX1 gene encodes the major apurinic/apirimidinic (AP) endonuclease and has a major role in the BER of DNA damage (Shen et al., 2005). A significant increase in the DNA damage mean ratio was found in both exposed groups in individuals carrying the T allele. This result is in agreement with evidence supporting an association between the G DISCUSSION 107 allele and lower levels of DNA damage with a consequent decreased risk of a number of human cancers (Yu et al., 2012). The human OGG1 gene encodes a protein responsible for excision of 8-oxoGua, the main base lesion caused by oxidative damage to DNA. If the lesion is not repaired, it can pair with adenine leading to a GC to TA transversion. Significant influence of exposure on CA- total and CA-chromatid was observed in all exposed individuals wild type homozygous, as well as in the controls carriers of the variant G allele. The vast majority of studies report a lower ability of the G allele to repair DNA damage and consequently this genotype should prevent mutagenic events (Bravard et al., 2009; Yamane et al., 2004). Our results showed also combined effect of exposure and genotype in occupationally exposed people carrying the variant G allele, although these findings go in the opposite direction i.e. decreases in the mean ratios. With regard to this result, it has been suggested that the effect of this polymorphism on DNA repair capacity may differ with the type and extent of exposure and can be influenced by the interaction with other genetic polymorphisms (Mateuca et al. , 2008). The excision repair cross-complementing (ERCC) gene family reduces damage to DNA via NER. The ERCC1 gene encodes a protein that, along with ERCC4, functions in a complex involved in the 5' incision made during NER process (van Vuuren et al., 1995). Our results for this polymorphism showed significant combined effect with exposure in both exposed groups concerning %DNAT (increases in the mean ratio), and only in the environmentally exposed group for CA-chromosome (decrease in the mean ratio). These contrasting results might be in part explained by the different genotoxic endpoint detected by these assays. The number of reports analysing the effect of this polymorphism is scarce. Costa et al. (2008) did not find a significant influence in any of the genotoxicity biomarkers studied, whereas Zienolddiny et al. (2006) found T allele to be less frequent in non-small cell lung cancer cases with higher PAH-DNA adduct levels, and speculated that individuals presenting the T allele may have suboptimal DNA repair capacity. In agreement with this report the result of this study showed an increase in the CA- chromosome mean ratio in the control individuals carrying the T allele. Finally, the influence of ERCC4 polymorphism on the frequency of MN and CA-chromatid seems to be due to the protective effect of the A allele (lower frequencies) in the environmental exposed group. Also for this polymorphism few reports have been published. No effect was observed by Costa et al. (2008) on MN test, SCE or comet assay results. In addition, Park et al. (2002) reported no significant relationship between ERCC4 DISCUSSION 108 and lung cancer. Nevertheless, significant association between the A allele and breast cancer risk was reported by Smith et al. (2003), particularly when in combination with the variants alleles of XRCC1 (rs1799782 and rs25487) and XRCC3 (rs861539). V. CONCLUSIONS CONCLUSIONS 111 Overall our results are in agreement with those obtained in the previous environmental studies performed in Panasqueira Mine area, showing that populations living nearby and working in the mine are exposed to several metal(loid)s originated by mining activities. The most significant exposure was to As, particularly in individuals exposed through the environment. Exposure to other elements such as Cr, Mn, Ni, Pb, Se and Zn also occurs but not at the same extent (amount of element/time of exposure). Our results indicate that the environmentally exposed group is more affected, specifically females, as they presented significantly higher values of the most toxic elements, i.e., As, Cr, Mn and Ni. Our results also show major effects of host factors and smoking habits on the levels of metal(loid)s in the different biological samples analysed, strongly influencing the results obtained. Studies with a higher number of individuals, analysing all the elements in all the matrices need to be performed in order to better characterize the factors influencing the exposure, the toxicokinetic processes in the population groups, and the feasibility of the different biological samples for exposure assessment. Results also point to increased genotoxic damage and immunotoxicity experienced by populations environmentally and occupationally exposed to metal(loid)s contamination derived from the Panasqueira mine activities. To strengthen the absence of causal association between mine activities and this damage, the level of most biomarkers of exposure was quantitatively associated with the intensity of genotoxic and immunotoxic damage. The extent of genetic damage associated to exposure to metal(loid)s was modulated by some of the selected genetic polymorphisms of enzymes involved in the metabolism of metal(loid)s and DNA repair processes. Still these data are difficult to interpret and definitive conclusions on their influence cannot be reached. In conclusion, the presence of genotoxic damage and immunotoxicity in exposed populations, their consistency with individual data of exposure, and the identification of sub-groups of susceptible populations pose a public threat that may result in an increased risk of developing cancer and other diseases. Competent authorities are urged to intervene in this area and implementing preventive policies aimed to help protecting exposed populations. Not only human populations are at risk but the entire ecosystem. Results from this study are of paramount importance not only for these particular populations but to others exposed to similar conditions. A greater understanding of the relationship between exposure, individual susceptibility and biological effect, enables the development of preventive measures, leading to a reduction in cancer risks for humans CONCLUSIONS 112 exposed to similar conditions. It is crucial to conduct regular monitoring and surveillance activities of the environment, miners and community’s health. The combination of environmental and human biomonitoring studies may synergistically increase the knowledge about the bioaccessibility and bioavailability of toxic elements, which is essential to assess the potential risks to human health. VII. REFERENCES REFERENCES 115 Abbas, A.K., and Lichtman, A.H. 2004. Células y tejidos del sistema inmunitario. In Inmunología celular y molecular. 5ª edición. Elsevier. Madrid. pp. 16-39. Abdullah, M., Chai, P.S., Chong, M.Y., Tohit, E.R., Ramasamy, R., Pei, C.P., and Vidyadaran, S. 2012. Gender effect on in vitro lymphocyte subset levels of healthy individuals. Cell Immunol. 272: 214-219. Akiyama, M., Kyoizumi, S., Hirai, Y., Kusunoki, Y., Iwamoto, K. S., and Nakamura, N. 1995. Mutation frequency in human blood cells increases with age. Mutat. Res. 338: 141-149. Albertini, R.J., Anderson, D., Douglas, G.R., Hagmar, L., Hemminki, K., Merlo, F., Natarajan, A.T., Norppa, H., Shuker, D.E., Tice, R., Waters, M.D., and Aitio, A. 2000. IPCS guidelines for the monitoring of genotoxic effects of carcinogens in humans. International Programme on Chemical Safety. Mutat Res. 463(2):111-172. Albertini, R., Bird, M., Doerrer, N., Needham, L., Robinson, S., Sheldon, L., and Zenick, H. 2006. The use of biomonitoring data in exposure and human health risk assessment. Environ. Health Perspect. 114: 1755-1762. Alimonti, A., Bocca, B., Mannella, E., Petrucci, F., Zennaro, F., Cotichini, R., D'Ippolito, C., Agresti, A., Caimi, S., and Forte, G. 2005. Assessment of reference values for selected elements in a healthy urban population. Ann. Ist. Super Sanita 41(2): 181-187. Amorim L. 2003. Os biomarcadores e a sua aplicação na avaliação da exposição aos agentes químicos ambientais. Ver. Bras. Epidemiol. 6(1): 158-170. Andonova IE, Justenhoven C, Winter S, Hamann U, Baisch C, Rabstein S, Spickenheuer A, Harth V, Pesch B, Brüning T, Ko YD, Ganev V, and Brauch H. 2010. No evidence for glutathione S-transferases GSTA2, GSTM2, GSTO1, GSTO2, and GSTZ1 in breast cancer risk. Breast Cancer Res. Treat. 121(2):497-502. Antonini, J.M., Zeidler-Erdely, P.C., Young, S.H., Roberts, J.R., and Erdely, A. 2012. Systemic immune cell response in rats after pulmonary exposure to manganesecontaining particles collected from welding aerosols. J. Immunotoxicol. 9: 184-192. Ataian, Y., and Krebs, J.E. 2006. Five repair pathways in one context: chromatin modification during DNA repair. Biochem. Cell Biol. 84: 490-504. Ávila, P., Silva, E., Salgueiro, A., and Farinha, J. 2008. Geochemistry and mineralogy of mill tailings impoundments from the Panasqueira mine (Portugal): Implications for the surrounding environment. Mine Water and the Environ. 27(4): 210-224. Au, W.W. 2007. Usefulness of biomarkers in population studies: From exposure to susceptibility and to prediction of cancer. Int. J. Hyg. Environ. Health 10: 239-246. Bal, W., Protas, A.M., and Kasprzak, K.S. 2011. Genotoxicity of metal ions: chemical insights. Met. Ions Life Sci. 8: 319-373. Balmain, A., Gray, J., and Ponder, B. A. 2003. The genetics and genomics of cancer. Nat. Genet. 33: 238-44. Barale, R., Chelotti, L., Davini, T., Del Ry, S., Andreassi, M.G., Ballardin, M., Bulleri, M., He, J., Baldacci, S., Di Pede, F., Gemignani, F., and Landi, S. 1998. Sister chromatid REFERENCES 122 Griess, P. 1879. Bemerkungen zu der Abhandlung der HH. Weselky und Benedikt “Ueber einige Azoverbindungen.” Chemistry 12: 426–428. Guikema, J.E., Linehan, E.K., Tsuchimoto, D., Nakabeppu, Y., Strauss, P.R., Stavnezer, J., and Schrader, C.E. 2007. APE1- and APE2-dependent DNA breaks in immunoglobulin class switch recombination. J. Exp. Med. 204: 3017-3026. Hagmar, L., Stromberg, U., Bonassi, S., Hansteen, I.-L., Knudsen, L.E., Lindholm, C., and Norppa, H. 2004. Impact of types of lymphocyte chromosomal aberrations on human cancer risk: results from Nordic and Italian cohorts. Cancer Res. 64: 2258–2263. Harries, L.W., Stubbins, M.J., Forman, D., Howard, G.C., and Wolf, C.R. 1997. Identification of genetic polymorphisms at the glutathione S-transferase Pi locus and association with susceptibility to bladder, testicular and prostate cancer. Carcinogenesis 18: 641-644. Harris, M.J., Coggan, M., Langton, L., Wilson, S.R., and Board, P.G. 1998. Polymorphism of the Pi class glutathione S-transferase in normal populations and cancer patients. Pharmacogenetics 8(1): 27-31. Hartwig, A. 1994. Role of DNA Repair Inhibition in Lead- and Cadmium-Induced Genotoxicity: A Review. Environ. Health Perspect. 102: 45-50. He, K. 2011. Trace elements in nails as biomarkers in clinical research. Europ. J. Clin. Inv. 4: 98-102. Health Canada 2004. Canadian Handbook on Health Impact Assessment (4 vols.) A Report of the Federal/Provincial/Territorial Committee on Environmental and Occupational Health. Ottawa: Health Canada. Heng, H.H., Stevens, J.B., Liu, G., Bremer, S.W., and Ye, C.J. 2004. Imaging genome abnormalities in cancer research. Cell Chromosome 3(1): 1. Henkler, F., Brinkmann, J., and Luch, A. 2010. The role of oxidative stress in carcinogenesis induced by metals and xenobiotics. Cancers 2: 376–396. HERAG05 (Health Risk Assessment Guidance for Metals fact sheet 05). 2007. Eurometaux-ICMM-Eurofer Fact sheets related to Effects Assessment – Mutagenicity. Available at: http://www.ebrc.de/industrial-chemicals-reach/projects-and- references/downloads/HERAG_FS_05_August_07.pdf [accessed 16 of September 2012]. Hernandez-Castro, B., Doniz-Padilla, L. M., Salgado-Bustamante, M., Rocha, D., Ortiz- Perez, M. D., Jimenez-Capdeville, M. E., Portales-Pérez, D. P., Quintanar-Stephano, A., and González-Amaro, R. 2009. Effect of arsenic on regulatory T cells. J. Clin. Immunol. 29: 461–69. Hinwood, A., Sim, M., de Klerk, N., Drummer, O., Gerostamoulos, J., and Bastone, E. 2002. Are 24-hour urine samples and creatinine adjustment required for analysis of inorganic arsenic in urine in urine in population studies? Environ. Res. Sec. A 88: 219– 224. REFERENCES 123 Hoeijmakers, J.H. 1993. Nucleotide excision repair: II. From yeast to mammals. Trends Genet. 9: 211-217. Huang, R.S., Chen, P., Wisel, S., Duan, S., Zhang, W., Cook, E.H., Das, S., Cox, N.J., and Dolan, M.E. 2009. Population-specific GSTM1 copy number variation. Hum. Mol. Genet.18: 366-372. Jadhav, S.H., Sarkar, S.N., and Tripathit, H.C. 2006. Cytogenetic effects of a mixture of selected metals following subchronic exposure through drinking water in male rats. Indian J. Exp. Biol. 44: 997–1005. Jenny, M., Klieber, M., Zaknun, D., Schroecksnadel, S., Kurz, K., Ledochowski, M., Schennach, H., and Fuchs, D. 2011. In vitro testing for anti-inflammatory properties of compounds employing peripheral blood mononuclear cells freshly isolated from healthy donors. Inflamm. Res. 60: 127-135. Jentsch-Ullrich, K., Koenigsmann, M., Mohren, M., and Franke, A. 2005. Lymphocyte subsets' reference ranges in an ageand gender-balanced population of 100 healthy adults - A monocentric German study. Clin. Immunol. 116: 192-197. Julivert, F., Fontboté, J., Ribeiro, A., and Conde, L. 1974. Mapa Tectonico de la Península Ibérica y Baleares, escala 1:1,000,000. Inst. Geol. Minero España, Madrid, pp. 113. Kanias, G. 1985. Determination of trace elements in eyeshadow face powder and rouge make-up cosmetics by neutron activation analysis. J. Radioanal. Nucl. Chem. 89(2): 487–496. Kassie, F., Parzefall, W., and Knasmuller, S. 2000. Single cell gel electrophoresis assay: a new technique for human biomonitoring studies. Mutat. Res. 463:13-31. Kauppinen, T.M., Chan, W.Y., Suh, S.W., Wiggins, A.K., Huang, E.J., and Swanson, R.A. 2006. Direct phosphorylation and regulation of poly(ADP-ribose) polymerase-1 by extracellular signal-regulated kinases 1/2. Proc. Natl. Acad. Sci. USA 103:7136–7141. Kelly, W.C., and Rye, R.O. 1979. Geologic, fluid inclusion and stable isotope studies of the tin-tungsten deposits of Panasqueira, Portugal. Econ. Geol. 74: 1721-1822. Kirsch-Volders, M., Mateuca, R.A., Roelants, M., Tremp, A., Zeiger, E., Bonassi, S., Holland, N., Chang, W.P., Aka, P.V., DeBoeck, M., Godderis, L., Haufroid, V., Ishikawa, H., Laffon, B., Marcos, R., Migliore, L., Norppa, H., Teixeira, J.P., Zijno, A., and Fenech, M. 2006. The effects of GSTM1 and GSTT1 polymorphisms on micronucleus frequencies in human lymphocytes in vivo. Cancer Epidemiol. Biomarkers Prev. 15: 1038–1042. Kitteringham, N.R., Palmer, L., Owen, A., Lian, L.Y., Jenkins, R., Dowdall, S., Gilmore, I., Park, B.K., and Goldring, C.E. 2007. Detection and biochemical characterisation of a novel polymorphism in the human GSTP1 gene. Biochim. Biophys. Acta 1770(8):1240- 1247. Kiyohara, C., and Yoshimasu, K. 2007. Genetic polymorphisms in the nucleotide excision repair pathway and lung cancer risk: a meta-analysis. Int. J. Med. Sci. 4: 59–71. Knight, J.A. 2000. Review: Free radicals, antioxidants and immune system. Ann. Clin. Lab. Sci. 30:145–58. REFERENCES 124 Kronenberg, M., Siu, G., Hood, L. E., and Shastri, N. 1986. The molecular genetics of the T-cell antigen receptor and T-cell antigen recognition. Annu. Rev. Immunol. 4: 529- 591. Kuangfei, L., Yaling, X., Xuefeng, L., Zouli, W., Bukkens, S., Tommaseo, M., and Paoletti, M. 1999. Metallic elements in hair as a biomarker of human exposure to environmental pollution: A preliminary investigation in Hubei Province. Crit. Rev. Plant Sci. 8: 417–428. Kubota, M., Lin, Y.W., Hamahata, K., Sawada, M., Koishi, S., Hirota, H., and Wakazono, Y. 2000. Cancer chemotherapy and somatic cell mutation. Mutat. Res. 470: 93-102. Kumaravel, T.S., and Jha, A.N. 2006. Reliable Comet assay measurements for detecting DNA damage induced by ionising radiation and chemicals. Mutat Res. 605(1-2): 7-16. Kumaravel, T.S., Vilhar, B., Faux, S.P., and Jha, A.N. 2009. Comet Assay measurements: a perspective. Cell Biol. and Toxicol. 25: 53-64. Kyrtopoulos, S.A. 2006. Biomarkers in environmental carcinogenesis research: striving for a new momentum. Toxicol. Lett. 162(1): 3-15. Laantri, N., Jalbout, M., Khyatti, M., Ayoub, W.B., Dahmoul, S., Ayad, M., Bedadra, W., Abdoun, M., Mesli, S., Kandil, M., Hamdi-Cherif, M., Boualga, K., Bouaouina, N., Chouchane, L., Benider, A., Ben-Ayed, F., Goldgar, D., and Corbex, M. 2011. XRCC1 and hOGG1 genes and risk of nasopharyngeal carcinoma in North African countries. Mol. Carcinog. 50: 732-737. Laffon, B., Teixeira, J.P., Silva, S., Roma-Torres, J., Pérez-Cadahía, B., Méndez, J., Pásaro, E., and Mayan, O. 2006. Assessment of occupational genotoxic risk in the production of rubber tyres. Ann. Occup. Hyg. 50: 583–592. Landi, S. 2000. Mammalian class theta GST and difefrential susceptibility to carcinogens: a review. Mutat. Res. 463: 247-283. Lando, C., Hagmar, L., and Bonassi, S. 1998. Biomarkers of cytogenetic damage in human and risk of cancer. The European Study Group on Cytogenetic Biomarkers and Health (ESCH). Med. Lav. 89: 124–131. Lehmann, I., Sack, U., and Lehmann, J. 2011. Metal ions affecting the immune system. In Metal ions in toxicology: effects, interactions, interdependencies edited by Sigel, A., Sigel, H. and Sigel, R.K.O. Cambridge, UK: The Royal Society of Chemistry. pp. 157- 185. Lin, D.-X., Tang, Y.-M., Peng, Q., Lu, S.-X., Ambrosone, C.B., and Kadlubar, F.F. 1998. Susceptibility to Esophageal Cancer and Genetic Polymorphisms in Glutathione S Transferases T1 , P1 , and M1 and Cytochrome P450 2E1. Cancer Epidemiol. Biomarkers Prev. 7: 1013-1018. Liu, Q., Cao, J., Liu, Y., Lü, Y.M., Qin, B., Jiang, B., Jiang, L.P., Fu, B.H., Zhao, F.L., Jiang, E.H., Su, X., and Fan, F.Y. 2010. Follow-up study by chromosome aberration analysis REFERENCES 125 and micronucleus assays in victims accidentally exposed to 60Co radiation. Health Phys. 98(6):885-8. Lotze, F. 1945. Observation respect a la division de los variscides de la Meseta Ibérica. Publ. Estrag. Geol. Espanã 5:149-166. Luebke, R.W., Chen, D.H., Dietert, R., Yang, Y., King, M., and Luster, M.I. 2006. The comparative immunotoxicity of five selected compounds following developmental or adult exposure. J. Toxicol. Environ. Health B 9: 1–26. Malanga, M., and Althaus, F.R. 2005. The role of poly(ADP-ribose) in the DNA damage signaling network. Biochem Cell Biol. 83(3): 354-364. Maroni, M. 1983. Recent acquisitions in biological monitoring of human exposure to chloroorganic and organophosphate pesticides. Proceedings of the International Conference on Environmental Hazards of Agrochemicals in Developing Countries, 1, pp. 345–371. Maroni, M., Colosio, C., Ferioli, A., and Fait, A. 2000. Biological Monitoring of Pesticide Exposure: a review. Introduction. Toxicology 143: 1-118. Martínez, V., Creus, A., Venegas, W., Arroyo, A., Beck, J.P., Gebel, T.W., Surrallés, J., and Marcos, R. 2004. Evaluation of micronucleus induction in a Chilean population environmentally exposed to arsenic. Mutat. Res. 564(1): 65-74. Mateuca, R., and Kirsch-Volders, M. 2007. Chromosomal damage. In State of validation of biomarkers of carcinogen exposure and early effects and their applicability to molecular epidemiology edited by Farmer P., Kyrtopoulos S. and Emeny J. Poland: Nofer Institute of Occupational Medicine. Mateuca, R., Lombaert, N., Aka, P.V., Decordier, I., and Kirsch-Volders, M. 2006. Chromosomal changes: induction, detection methods and applicability in human biomonitoring. Biochimie. 88(11): 1515-1531. Mateuca, R.A., Roelants, M., Iarmarcovai, G., Aka, P.V., Godderis, L., Tremp, A., Bonassi, S., Fenech, M., Bergé-Lefranc, J.L., and Kirsch-Volders, M. 2008. hOGG1(326), XRCC1(399) and XRCC3(241) polymorphisms influence micronucleus frequencies in human lymphocytes in vivo. Mutagenesis 23(1): 35-41. McGeer, J., Gerry H., Roman L., Nicholas F., Sappington K., and Drexler J. 2004. Issue paper on the bioavailability and bioaccumulation of metals Submitted to: US. Environ. Protect. Agency. Lexington, MA, USA. McWilliams, R.R., Bamlet, W.R., Cunningham, J.M., Goode, E.L., Andrade, M., Boardman, L.A., and Petersen, G.M. 2008. Polymorphisms in DNA repair genes, smoking, and pancreatic adenocarcinoma risk. Cancer Res. 68: 4928–4935. Mehra, R., and Juneja, M. 2005. Fingernails as biological indices of metal exposure. J. Biosci. 30(2): 253-257. Mertz, W. 1981. The essential trace elements. Science 213: 1332-1338. Milman, N., Mathiassen, B., Hansen, J., and Bohm, J. 1994. Blood levels of lead, cadmium and mercury in a Greenlandic Inuit hunter population from Thule district. J. Trace Elem. Electrol. 11: 3-8. REFERENCES 126 Mo, Z., Gao, Y., Cao, Y., Gao, F., and Jian, L. 2009. An updating meta-analysis of the GSTM1, GSTT1, and GSTP1 polymorphisms and prostate cancer: a HuGE review. Prostate 69: 662-688. Møller, P. 2006. Assessment of reference values for DNA damage detected by the comet assay in human blood cell DNA. Mutat. Res. 612: 84-104. Morton, J., Carolan, V. A. and Gardiner, P.H.E. 2002. Removal of exogenously bound elements from human hair by various washing procedures and determination by inductively coupled plasma mass spectrometry. Anal. Chim. Acta. 455 (1): 23-34. Moszczy´nski., P, Zabi´nski, Z, Moszczy´nski, P. Jr., Rutowski, J., Słowi´nski, S., and Tabarowski, Z. 2001. Immunological findings in cigarette smokers. Toxicol. Lett. 118: 121–27. Mudgal V, Madaan N, Mudgal A, Singh R, and Mishra S. 2010. Toxic Metals on Human Health. The Open Nutraceuticals Journal 3: 94-99. Murray, R., Bende,r D., Botham, K., Kennelly, P., Rodwell, V., Weil, P., and Mayes, P. 2009 Micronutrients: Vitamins & Minerals. In Harper's Illustrated Biochemistry, Chapter 44 (28th ed.). New York: McGraw-Hill. Nakata, A., Araki, S., Park, S.-H., Park, J-T., Kim, D.-S., Park, H.-C., and Yokoyama, K. 2006. Decreases in CD8+T, naive (CD4+CD45RA+)T, and B (CD19+) lymphocytes by exposure to manganese fume. Ind. Health. 44: 592-597. National Research Council (NRC) Report. 2006. Human Biomonitoring for Environmental Chemicals. The National Academy Press, Washington, DC. pp. 1–213. Needham, L. L., Calafat, A. M., and Barr, D. B. 2007. Uses and issues of biomonitoring. Int. J. Hyg. Environ. Health. 210: 229-238. Neurauter, G., Schrocksnadel, K., Scholl-Burgi, S., Sperner-Unterweger, B., Schubert, C., Ledochowski, M., and Fuchs, D. 2008. Chronic immune stimulation correlates with reduced phenylalanine turnover. Curr. Drug Metab. 9: 622– 627. Noronha, F., Doria, A., Dubessy, J., and Charoy, B. 1992. Characterization and timing of the different types of fluids present in the barren and ore veins of the W-Sn deposit of Panasqueira, Central Portugal. Mineral. Deposita 27: 72-79. Norppa, H. 1997. Cytogenetic markers of susceptibility: influence of polymorphic carcinogen-metabolizing enzymes. Environ. Health Perspect. 105(4): 829-385. Norppa, H. 2004. Cytogenetic biomarkers and genetic polymorphisms. Toxicol. Lett. 149: 309-334. Nowak, B. and Kozlowski, H. 1998. Heavy metals in human hair and teeth: the correlation with metal concentration in the environment. Biol. Trace Elem. Res. 62: 213:228. OECD. 2008. Health and the environment. Policy Briefs are prepared by the Public Affairs Division, Public Affairs and Communications Directorate. They are published under the responsibility of the Secretary-General. Available from: http://www.oecd.org/dataoecd/38/18/40396531.pdf [accessed 16 September 2012]. REFERENCES 127 Oh, E., Lee, E., Im, H., Kang, H.-S., Jung, W.-W., Won, N.H., Kim, E.-M., and Sul, D. 2005. Evaluation of immuno- and reproductive toxicities and association between immunotoxicological and genotoxicological parameters in waste incineration workers. Toxicology 210: 65-80. Olive, P.L., Banath, J.P., and Durand, R.E. 1990a. Detection of etoposide resistance by measuring DNA damage in individual Chinese hamster cells. J. Natl. Cancer Inst. 82: 779-783. Olive, P.L., Banath, J.P., and Durand, R.E. 1990b. Heterogeneity in radiation-induced DNA damage and repair in tumor and normal cells measured using the “comet” assay. Radiat. Res. 122: 86-94. Orloff, K., Mistry, K., and Metcalf, S. 2009. Biomonitoring for environmental exposures to arsenic. J. Toxicol. Environ. Health B 12: 509–524. Park, J.Y., Park, S.H., Choi, J.E., Lee, S.Y., Jeon, H.S., Cha, S.I., Kim, C.H., Park, J.H., Kam, S., Park, R.W., Kim, I.S., and Jung, T.H. 2002. Polymorphisms of the DNA repair gene xeroderma pigmentosum group A and risk of primary lung cancer. Cancer Epidemiol. Biomarkers Prev. 11(10 Pt 1): 993-997. Parl, F.F. 2005. Glutathione S-transferase genotypes and cancer risk. Cancer Lett. 221: 123-129. Pavanello, S., and Clonfero, E. 2000. Biological indicators of genotoxic risk and metabolic polymorphisms. Mutat. Res. 463: 285–308. Pedersen, E.R., Midttun, Ø., Ueland, P.M., Schartum-Hansen, H., Seifert, R., Igland, J., Nordrehaug, J.E., Ebbing, M., Svingen, G., Bleie, Ø., Berge, R., and Nygård, O. 2011. Systemic markers of interferon-γ-mediated immune activation and long-term prognosis in patients with stable coronary artery disease. Arterioscler Thromb. Vasc. Biol. 31: 698-704. Pemble, S., Schroeder, K.R., Spencer, S.R., Meyer, D.J., Hallier, E., Bolt, H.M., Ketterer, B., and Taylor, J.B. 1994. Human glutathione S-transferase Theta (GSTT1): cDNA cloning and the characterization of a genetic polymorphism. Biochem. J. 300: 271-276. Peplow, D. and Edmonds, R. 2005. The effects of mine waste contamination at multiple levels of biological organization. Ecol. Eng. 24: 101-119. Quereshi, I. 1982. Trace elements concentration in head hair of the inhabitants of the Rawalpindi-Islamabad area. J. Radioanal. Chem. 68: 209-214. Reis, A.C. 1971. As Minas da Panasqueira. Bol. de Minas 8(1): 3-34. Roberts, M.R., Shields, P.G., Ambrosone, C.B., Nie, J., Marian, C., Krishnan, S.S., Goerlitz, D.S., Modali, R., Seddon, M., Lehman, T., Amend, K.L., Trevisan, M., Edge, S.B., and Freudenheim, J.L. 2011 Single-nucleotide polymorphisms in DNA repair genes and association with breast cancer risk in the web study. Carcinogenesis 32: 1223-1230. REFERENCES 128 Rodrigues Abrantes, J., and Gonzalez, V. 1973 O tratamento mecânico do minério nas Minas da Panasqueira. Bol. de Minas 19(4): 239-247. Rodushkin, I. and Axelsson, M. 2000. Application of double focusing sector field ICP-MS multielemental characterization of human hair and nails. Part II. A study of the inhabitants of northen Sweden. T. Sci. Tot. Environm. 262: 21-36. Roma-Torres, J., Teixeira, J.P., Silva, S., Laffon, B., Cunha, L.M., Méndez, J., and Mayan, O. 2006. Evaluation of genotoxicity in a group of workers from a petroleum refinery aromatics plant. Mutat. Res. 604(1-2):19-27. Rossi, A.M., Hansteen, I.L., Skjelbred, C.F., Ballardin, M., Maggini, V., Murgia, E., Tomei, A., Viarengo, P., Knudsen, L.E., Barale, R., Norppa, H., and Bonassi, S. 2009. Association between frequency of chromosomal aberrations and cancer risk is not influenced by genetic polymorphisms in GSTM1 and GSTT1. Environ. Health. Perspect. 117(2): 203-208. Sachidanandam, R., Weissman, D., Schmidt, S.C., Kakol, J.M., Stein, L.D., Marth, G., Sherry, S., Mullikin, J.C., Mortimore, B.J., Willey, D.L., Hunt, S.E., Cole, C.G., Coggill, P.C., Rice, C.M., Ning, Z., Rogers, J., Bentley, D.R., Kwok, P.Y., Mardis, E.R., Yeh, R.T., Schultz, B., Cook, L., Davenport, R., Dante, M., Fulton, L., Hillier, L., Waterston, R.H., McPherson, J.D., Gilman, B., Schaffner, S., Van Etten, W.J., Reich, D., Higgins, J., Daly, M.J., Blumenstiel, B., Baldwin, J., Stange-Thomann, N., Zody, M.C., Linton, L., Lander, E.S., and Altshuler, D. 2001. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409: 928-933. Salgueiro, A., Ávila, P., Pereira, H., and Silva, E. 2008. Assessment of the Impact of Pollution by Arsenic in the Vicinity of Panasqueira Mine (Portugal). Proceedings of the geoENV 2008 - 7th International Conference on Geostatistics for Environmental Applications, September 8-10, Southampton, UK. Salnikow, K., and Zhitkovich, A. 2008. Genetic and epigenetic mechanisms in metal carcinogenesis and cocarcinogenesis: nickel, arsenic, and chromium. Chem. Res. Toxicol. 21(1): 28-44. Sansoni, P., Cossarizza, A., Brianti, V., Fagnoni, F., Snelli, G., Monti, D., Marcato, A., Passeri, G., Ortolani, C., Forti, E., Fagiolo, U., Passeri, M., and Franceschi, C. 1993. Lymphocyte subsets and natural killer cell activity in healthy old people and centenarians. Blood 82: 2767–2773. Sasso, A.F., Isukapalli, S.S., and Georgopoulos, P.G. 2010. A generalized physiologicallybased toxicokinetic modeling system for chemical mixtures containing metals. Theor. Biol. Med. Model. 7:17. Schindowski, K., Frohlich, L., Maurer, K., Muller, W.E., and Eckert, A. 2002. Age related impairment of human T lymphocytes’ activation: specific differences between CD4(+) andCD8(+) subsets. Mech. Ageing Dev. 123: 375–390. Schrauzer, G., Shrestha, K., and Flores-Arce, M. 1992. Lithium in scalp hair of adults, students, and violent criminals. Biol. Trace Elem. Res. 34: 161–176. Schroecksnadel, K., Wirleitner, B., Winkler, C., and Fuchs, D. 2006. Monitoring tryptophan metabolism in chronic immune activation. Clin. Chim. Acta 364: 82-90. REFERENCES 129 Schulte, P.A., and Perera, F.P. 1993. Validation. In Molecular Epidemiology. San Diego, CA: Academic Press. pp. 81–104. Shahabuddin, S., Al-Ayedi, I., Gad El-Rab, M.O., and Qureshi, M. I. 1998. Age-related changes in blood lymphocyte subsets of Saudi Arabian healthy children. Clin. Diagn. Lab. Immunol. 5: 632–635. Shen, M., Berndt, S.I., Rothman, N., Mumford, J.L., He, X., Yeager, M., Welch, R., Chanock, S., Keohavong, P., Donahue, M., Zheng, T., Caporaso, N., and Lan, Q. 2005. Polymorphisms in the DNA base excision repair genes APEX1 and XRCC1 and lung cancer risk in Xuan Wei, China. Anticancer Res. 25(1B): 537-542. Shinmura, K., Yamaguchi, S., Saitoh, T., Takeuchi-Sasaki, M., Kim, S.R., Nohmi, T., and Yokota, J. 2000. Adenine excisional repair function of MYH protein on the adenine: 8- hydroxyguanine base pair in double-stranded DNA. Nucleic Acids Res. 28: 4912–4918. Singh, N., McCoy, T., Tice, R., and Schneider, L. 1988. A simple technique for quantitation of low levels of DNA damage in individual cells. Exp. Cell Res. 175: 184- 191. Silva, S.N., Azevedo, A.P., Teixeira, V., Pina, J.E., Rueff, J., and Gaspar, J.F. 2009. The role of GSTA2 polymorphisms and haplotypes in breast cancer susceptibility: a casecontrol study in the Portuguese population. Oncol. Rep. 22(3): 593-598. Silva, S.N., Tomar, M., Paulo, C., Gomes, B.C., Azevedo, A.P., Teixeira, V., Pina, J.E., Rueff, J., and Gaspar, J.F. 2010. Breast cancer risk and common single nucleotide polymorphisms in homologous recombination DNA repair pathway genes XRCC2, XRCC3, NBS1 and RAD51. Cancer Epidemiol. 34(1): 85-92. Skjelbred, C.F., Svendsen, M., Haugan, V., Eek, A.K., Clausen, K.O., Kure, E.H., Tuimala, J.T., Svendsen, M.V., Norppa, H., and Hansteen, I.L. 2011. Influence of GSTM1, GSTT1, GSTP1, NAT1, NAT2, EPHX1, MTR and MTHFR polymorphism on chromosomal aberration frequencies in human lymphocytes. Carcinogenesis 32(3): 399-405. Slotnick, M., Meliker, J., Avruskin, G., Ghosh, D., and Nriagu, J. 2007. Toenails as a biomarker of inorganic arsenic intake from drinking water and food. J. Toxicol. Environ. Health A 70: 148–158. Smith, M, 2006. Panasqueira the tungsten giant at 100+. Operation Focus. Int. Mining 2: 10-14. Smith, T.R., Levine, E.A., Perrier, N.D., Miller, M.S., Freimanis, R.I., and Lohman, K. 2003. Case LD, Xu J, Mohrenweiser HW, Hu JJ. 2003. DNA-repair genetic polymorphisms and breast cancer risk. Cancer Epidemiol. Biomarkers Prev. 12(11 Pt 1): 1200-1204. Spencer, M.E., Jain, A., Matteini, A., Beamer, B.A., Wang, N.Y., Leng, S.X., Punjabi, N.M., Walston, J.D., and Fedarko, N.S. 2010. Serum levels of the immune activation marker neopterin change with age and gender and are modified by race, BMI, and percentage of body fat. J. Gerontol. A Biol. Sci. Med. Sci. 65: 858–865. Stephens, C. and Ahern, M. 2002. Worker and Community Health Impacts Related to Mining Operations Internationally. A Rapid Review of the Literature. Paper prepared for MMSD. London: IIED and WBCSD. REFERENCES 130 Still, I.H., Vince, P., and Cowell, J.K. 1999. Identification of a novel gene (ADPRTL1) encoding a potential Poly(ADP-ribosyl)transferase protein. Genomics 62(3): 533-536. Strange, R.C., Jones, P.W., and Fryer, A.A. 2000. Glutathione S-transferase: genetics and role in toxicology. Toxicol. Lett. 112-113: 357-363. Sucher, R., Schroecksnadel, K., Weiss, G., Margreiter, R., Fuchs, D., and Brandacher, G. 2010. Neopterin, a prognostic marker in human malignancies. Cancer Lett. 287: 13-22. Sundberg, K., Johansson, A., Stenberg, G., Widersten, M., Seidel, A., Mannervik, B. and Jernstrom, B. 1998. Differences in the catalytic efficiences of allelic variants of glutathione transferase P1-1 towards carcinogenic diol epoxides of polycyclic aromatic hydrocarbons. Carcinogenesis 19: 433–436. Surrallés, J., Falck, G., and Norppa, H. 1996. In vivo cytogenetic damage revealed by FISH analysis of micronuclei in uncultured human T lymphocytes. Cytogenet Cell Genet. 75: 151–154. Suzumura, H., and Arisaka, O. 2010. Cerebro-oculo-facio-skeletal syndrome. Adv. Exp. Med. Biol. 685: 210-214. Takeuchi, T., Hayashi, T., and Takada, J. 1982. Variation of elemental concentration in hair of the Japanese in term of age, sex and hair treatment. J. Radioanal. Chem. 70: 29-55. Taooka, Y., Takeichi, N., Noso, Y., Kawano, N, Apsalikov, K., and Hoshi, M. 2006. Increased T-cell receptor mutation frequency in radiation-exposed residents living near the Semipalatinsk nuclear test site. J. Radiat. Res. 47: 179–181. Taylor, A., Branch, S., Day, M., Patriarca, M., and White, M. 2011. Atomic spectrometry update: clinical and biological materials, foods and beverages. J. Anal. At. Spectrom. 26: 653-692. Taylor, M.W., and Feng, G.S. 1991. Relationship between interferon-gamma, indoleamine 2,3-dioxygenase, and tryptophan catabolism. FASEB J. 5: 2516–2522. Tell, G., Quadrifoglio, F., Tiribelli, C., and Kelley, M.R. 2009. The many functions of APE1/Ref- 1: not only a DNA repair enzyme. Antioxid. Redox Sign. 11: 601-620. Tetlow, N., and Board, P.G. 2004. Functional polymorphism of human glutathione transferase A2. Pharmacogenetics 14(2): 111-116. Teixeira, J.P., Gaspar, J., Martinho, G., Silva, S., Rodrigues, S., Mayan, O., Martin, E., Farmer, P.B., and Rueff, J. 2004. Aromatic DNA adduct levels in coke oven workers: correlation with polymorphisms in genes GSTP1, GSTM1, GSTT1 and CYP1A1. Mutat. Res. 517(1-2): 147-155. Therman E. 1980. Human Chromosomes: Structure, Behavior, Effects. New York: Springer-Verlag. pp 235. Thygesen, L.C., Hvidtfeldt, U.A., Mikkelsen, S., and Bronnum-Hansen, H. 2011. Qunatification of the healthy worker effect: a nationwide cohort among electricians in Denmark. BMC Public Health 11: 571. REFERENCES 131 Tice, R., Agurell, E., Anderson, D., Burlinson, B., Hartmann, A., Kobayashi, H., Miyamae, Y., Rojas, E., Ryu, J.-C., and Sasaki, F. 2000. Single cell gel/comet assay: guidelines for in vitro and in vivo genetic toxicology testing. Environ. Mol. Mutagen. 35: 206-221. Tulinska, J., Jahnova, E., Dusinska, M., Kuricova, M., Liskova, A., Ilavska, S., Horvathova, M., Wsolova, L., Kyrtopoulos, S., Collins, A., Harrington, V., and Fuortes, L. 2004. Immunomodulatory effects of mineral fibres in occupationally exposed workers. Mutat. Res. 553: 111-124. Tryphonas, H., Fournier, M., Blakley, B., Smits, J., and Brousseau, P. 2005. Structural and functional complexity of the immune system and its relationship to immunotoxicology. In Investigative Immunotoxicology edited by Blakley, B.R. and Kouassi, E. Taylor and Francis, Boca Raton. pp. 3-10. Vahter, M., Akesson, A., Liden, C., Ceccatelli, S., and Berglund, M. 2007. Gender differences in the disposition and toxicity of metals. Environ. Res. 104: 85–95. van Vuuren, A.J., Appeldoorn, E., Odijk, H., Humbert, S., Moncollin, V., Eker, A.P., Jaspers, N.G., Egly, J.M., and Hoeijmakers, J.H. 1995. Partial characterization of the DNA repair protein complex, containing the ERCC1, ERCC4, ERCC11 and XPF correcting activities. Mutat. Res. 337(1): 25-39. Weiss, G., Murr, C., Zoller, H., Haun, M., Winder, B., Ludescher, C., and Fuchs, D., 1999. Modulation of neopterin formation and tryptophan degradation by Th1- and Th2-derived cytokines in human monocytic cells. Clin. Exp. Immunol. 116: 435-440. Werck-Reichhart. D, and Feyereisen, R. 2000. Cytochromes P450: a success story. Genome Biol. 1(6): REVIEWS3003. Wick, G., Romen, M., Amberger, A., Metzler, B., Mayr, M., Falkensammer, G., and Xu, Q. 1997. Atherosclerosis, autoimmunity, and vascular-associated lymphoid tissue. FASEB J. 11: 1199–1207. Widner, B., Werner, E. R., Schennach, H., Wachter, H., and Fuchs, D. 1997. Simultaneous measurement of serum tryptophan and kynurenine by HPLC. Clin. Chem. 43: 2424–2426. Wilson, D.M., Kim, D., Berquist, B.R., and Sigurdson, A.J. 2011. Variation in base excision repair capacity. Mutat. Res. 711: 100-112. World Health Organization (WHO). 1995. IPCS Environmental Health Criteria 165 Inorganic Lead. World Health Organization (WHO). 2001. Biomarkers in risk assessment: validity and validation. Geneva. EHC 222. Xanthoudakis, S., Miao, G.G., and Curran, T. 1994. The redox and DNA-repair activities of Ref-1 are encoded by nonoverlapping domains. Proc. Natl. Acad. Sci. USA 91: 23- 27. Yamane, A., Kohno, T., Ito, K., Sunaga, N., Aoki, K., Yoshimura, K., Murakami, H., Nojima, Y., and Yokota, J. 2004. Differential ability of polymorphic OGG1 proteins to suppress mutagenesis induced by 8-hydroxyguanine in human cell in vivo. Carcinogenesis 25(9):1689-1694.