Scientists, regulators and the public all desire efficient and accurate approaches to assess the toxicologic effects of chemical, physical, and biologic agents on living systems. Yet, no single approach exists to analyze toxicologic responses, a difficult task given the complexity of human and animal physiology and individual variations. Toxicology, the study of adverse effects of chemicals on living organisms, has traditionally been evaluated by the dosing of animals to define well-established cytological, physiological, metabolical, and morphological endpoints aspects (Waters and Fostel 2004, Fokunang et al 2010). However, traditional toxicity testing approaches are time consuming, expensive and unfeasible for tens of thousands of chemicals are used annually in industry. In addition, there is a continuing ethical imperative to reduce the amount of animal testing through the 3R's ("reducing, refining, and replacing") (North and Vulpe 2010). Alternative high-throughput approaches are clearly needed to meet this need.
The advancements of molecular technologies in the past decades have shed lights on the mechanisms of disease development and some key genes, proteins, pathways have been identified ( Yu 2011). Now it is possible to observe potential adverse effects on molecules, subcellular structures, and organelles before they manifest at the organismal level.( Applications of Toxicogenomic Technologies to Predictive Toxicology and Risk Assessment (2007)) This ability has enhanced etiologic understanding of toxicity and made it possible to assess the relevance of molecular changes to toxicity.
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As biologic knowledge progresses with the science of toxicology, "toxicogenomics" has the potential to improve risk assessment and hazard screening. Toxicogenomics is the "application of global gene expression profiling, including DNA microarray technologies and proteomics, to study the relationship between exposure & disease and to understand gene-environment interactions & their impact on human health" (Fig1). National Center for Toxicological Research defined toxicogenomics as the collection, interpretation and storage of information about gene and protein activity in order to identify toxic substances in the environment, and to help treat people at the greatest risk of diseases caused by environmental pollutants or toxicants.
2. Technologies in Toxicogenomics
2.1 Gene Expression Profiling
Gene expression changes (measuring mRNA levels and micro RNA) associated with signal pathway activation can provide compound-specific information on the pharmacological or toxicological effects of a chemical (Hamadeh et al 2002; Zidek et al 2007). A standard method used to study changes in gene expression is the Northern blot and an advantage of this traditional molecular technique is that it definitively shows the expression level of all transcripts (including splice variants) for a particular gene. This method, however, is labor intensive and is practical for examining expression changes for a limited number of genes (Hamadeh et al 2002). Alternate technologies, including DNA microarrays, can measure the expression of tens of thousands of genes in an equivalent amount of time (Hamadeh et al 2001).
Figure Framework for systems toxicology. Indicates the paths from the initial observation (rat in upper left) to an integrated toxicogenomics knowledgebase (blue cylinder), and so to systems toxicology (bottom right). The '-omics' data stream is shown by the clockwise path from rat to knowledgebase; and the 'traditional' toxicology approach is shown in the anti-clockwise path (Source: Waters and Fostel 2004).
DNA microarraysÂ or DNA chipsÂ give a revolutionary platform to compare genome-wide gene expression patterns in dose and time contexts (Hamadeh et al 2002). There are two basic types of microarrays used in gene expression analyses: oligonucleotide-based arrays (Lockhart et al 1996) and cDNA arrays (Schena et al 1995). Microarrays can be custom-made or obtained commercially (e.g., Affymetrix, Agilent Technologies, Incyte Corp., Sigma and Clonetech Laboratories, Inc.) (Heijne et al 2005). Oligonucleotide arrays are made using specific chemical synthesis steps by a series of photolithographic masks, light, or other methods to generate the specific sequence order in the synthesis of the oligonucleotide. The result of these processes is the generation of high-density arrays of short oligonucleotide (~ 20-80 bases) probes that are synthesized in predefined positions. cDNA microarrays differ in that DNA sequences that correspond to unique expressed gene sequences, are usually spotted onto the surface of treated glass slides using high speed robotic printers that allow the user to configure the placement of cDNAs on a glass substrate or chip. Spotted cDNAs can represent either sequenced genes of known function, or collections of partially sequenced cDNA derived from expressed sequence tags (ESTs) corresponding to messenger RNAs of genes of known or unknown function (Hamadeh et al 2002).
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For toxicology studies, there are a number of comparisons that might be considered. For example, one can compare tissue extracted from toxicant treated organism versus that of vehicle exposed animals (Hamadeh et al 2002; Heijne et al 2005). The gene expression levels that are induced or reduced provide a wealth of information on cellular mechanisms that are affected at the gene expression level by the disease or treatment. For spotted cDNA on glass platforms, differential gene expression measurements are achieved by a competitive, simultaneous hybridization using a two-color fluorescence labeling approach Hamadeh (Schena et al 1995).
Multi-color based labels are currently being optimized for adequate utility. Briefly, isolated RNA is converted to fluorescently labeled "targets" by a reverse transcriptase reaction using a modified nucleotide, typically dUTP or dCTP conjugated with a chromophore. The two RNAs being compared are labeled with different fluorescent tags, traditionally either Cy3 or Cy5, so that each RNA has a different energy emission wavelength or color when excited by dual lasers. The fluorescently labeled targets are mixed and hybridized on a microarray chip. The array is scanned at two wavelengths using independent laser excitation of the two fluors, for example, at 632 and 532 nm wavelengths for the red (Cy5) and green (Cy3) labels. The intensity of fluorescence, emitted at each wavelength, bound to each spot (gene) on the array corresponds to the level of expression of the gene in one biological sample relative to the other. The ratio of the intensities of the toxicant-exposed versus control samples is calculated and induction/repression of genes is inferred. Optimal microarray measurements can detect differences as small as 1.2 fold increase or decrease in gene expression (Hamadeh et al 2002).
Although the theoretical applications seem endless, DNA microarrays have certain limitations. These measurements are only semi quantitative due to a number of factors, including cross hybridization and sequence specific binding anomalies. Another limitation is the number of samples that can be processed efficiently at a time. Processing and scanning samples may take several days and generate large amounts of information that can take considerable time to analyze. Automation is being applied to microarray technology, and new equipment such as the automated hybridization stations and auto-loaded scanners will allow higher throughput analysis (Hamadeh et al 2002).
2.1.1 Quantitative Polymerase Chain Reaction (QPCR)
To overcome these limitations, can combine microarrays with quantitative polymerase chain reaction (QPCR) or Taqman and other technologies in development (Tokunaga et al 2000) to monitor the expression of hundreds of genes in a high throughput fashion. This will provide more quantitative output that may be crucial for certain hazard identification processes. In QPCR can offer more quantitative measurements than microarrays do because measurements may be made in "real time" during the time of the amplification and within a linear dynamic range. The PCR reactions may be set up in 96 or 384-well plates to provide a high throughput capability (Hamadeh et al 2002).
The lack of a direct functional correlation between gene transcripts and their corresponding proteins necessitates the use of proteomics as a tool in toxicology. Toxicoproteomics focuses on the proteomic study of toxicity caused by drugs, toxins, environmental stressors, chemicals and any other materials that may cause significant pathological responses (e.g., engineered nanomaterials) (Yu 2011). Proteomics is the systematic analysis of expressed proteins in tissues, by isolation, separation, identification, quantitation and functional characterization of proteins in a cell, tissue, or organism (Hamadeh et al 2002, Summeren et al 2012). Currently, the most commonly used technologies for proteomics (Fig 2) research are gel-based proteomics and shotgun proteomics.
2.2.1 Gel-Based Proteomics
In the gel-based approach, proteins are resolved by electrophoresis and protein features of interest are selected for analysis. This approach is best represented by the use of two-dimensional sodium dodecylsulfate polyacrylamide gel electrophoresis (2D-SDS-PAGE) to separate protein mixtures, followed by selection of spots, and identification of the proteins by digestion to peptides, mass spectrometry (MS) analysis, and database searching (Applications of Toxicogenomic Technologies to Predictive Toxicology and Risk Assessment.) (Summeren et al 2012).
2.2.2 Shotgun Proteomics
Shotgun analyses begin with direct digestion of protein mixtures to complex mixtures of peptides and subsequent analysis by liquid-chromatography-coupled mass spectrometry (LC-MS) (Yates 1998). The resulting collection of peptide tandem mass spectrometry (MS-MS) spectra is searched against databases to identify corresponding peptide sequences and then the collection of sequences is reassembled using computer software to provide an inventory of the proteins in the original sample mixture (Stahl et al 1996; Washburn et al 2002). Application of multidimensional chromatographic separations (for example, ion exchange and then reverse-phase high-performance liquid chromatography) "spreads out" the peptide mixture and greatly increases the number of peptides for which MS-MS spectra are acquired Applications of Toxicogenomic Technologies to Predictive Toxicology and Risk Assessment (2007) (Wolters et al 2001). New hybrid linear ion trap-tandem MS instruments offer more rapid acquisition of MS-MS spectra and more accurate identification of peptide ion mass-to-charge ratio values.
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The most effective quantitative approach for gel-based analyses is DIGE, which involves using amine- or thiol-reactive fluorescent dyes that tag protein samples with different fluorophores for analysis on the same gel. The use of a separate dye and mixed internal standards allows gel-to-gel comparisons of DIGE analyses for larger studies and enables reliable statistical comparisons (Alban et al 2003). Quantitative shotgun proteome analyses have been done with stable isotope tags, which are used to derivatize functional groups on proteins. Stable isotope tagging is usually used in paired experimental designs, in which the relative amounts of a protein or protein form are measured rather than the absolute amount in a sample. The first of these to be introduced were the thiol-reactive isotope-coded affinity tag reagents (Gygi et al 1999), which have been further developed to incorporate solid-phase capture and labelling (Zhou et al 2002). These reagents are available in "heavy" (for example,Â 2H- orÂ 13C-labeled) and "light" (for example,Â 1H- orÂ 12C-labeled) forms. Analysis of paired samples labelled with the light and heavy tags allows relative quantification by comparing the signals for the corresponding light and heavy ions. Other tag chemistries that target peptide N and C termini have been developed and have been widely applied. Quantitative proteomic approaches are applicable not only to comparing amounts of proteins in samples but also to kinetic studies of protein modifications and abundance changes as well as to identification of protein components of multi protein complexes as a function of specific experimental variables (Ranish et al 2003).
2.2.4 New MS Instrumentation and Related Technology for Proteomics
New hybrid tandem MS instruments that couple rapid-scanning linear ion tray analyzers with Fourier transform ion cyclotron resonance (FTICR), high-resolution ion trap, and TOF mass analyzers offer both high mass accuracy measurements of peptide ions and rapid scanning acquisition of MS-MS spectra. This improves the fidelity of identification and the mapping of modifications (Wu et al 2005). New methods for generating peptide sequence data, such as electron transfer dissociation (Syka et al 2004), can improve the mapping of post translational modifications and chemical adducts. An important emerging application of FTICR instrumentation is the tandem MS analysis of intact proteins, which is referred to as "top-down" MS analysis Applications of Toxicogenomic Technologies to Predictive Toxicology and Risk Assessment. (Kelleher 2004). This approach can generate near-comprehensive sequence analysis of individual protein molecular forms, thus enabling sequence-specific annotation of individual modification variants (Coon et al 2005). A limitation of the approach is the requirement of purified proteins and larger amounts of samples than are used in shotgun analyses. This approach has attracted considerable interest as a potential means of biomarker discovery for early detection of diseases, particularly cancers, as well as drug toxicity.
Figure: . Overview of the basic principles of DIGE and LC-MS/MS the two mainly used proteomics techniques (Summeren et al 2012)
MetabolomicsÂ is the analysis of collections of small molecule intermediates and products of diverse biologic processes. Metabolic intermediates reflect the actions of proteins in biochemical pathways and thus represent biologic states in a way analogous to proteomes. Metabolomes comprise a chemically diverse collection of compounds, which range from small peptide, lipid, and nucleic acid precursors and degradation products to chemical intermediates in biosynthesis and catabolism as well as metabolites of exogenous compounds derived from the diet, environmental exposures, and therapeutic interventions. A consequence of the chemical diversity of metabolome components is the difficulty of comprehensive analysis with any single analytical technology. The principal technology platforms for metabolomics are NMR spectroscopy and gas chromatography MS (GC-MS) or LC-MS. NMR has been the dominant technology for metabolomic studies of biofluids ex vivo. Either platform can detect metabolite profile differences to distinguish different toxicities (Hamadeh et al 2002).
Â EpigeneticsÂ refers to the study of reversible heritable changes in gene function that occur without a change in the sequence of nuclear DNA. Epigenetic phenomena include DNA methylation, imprinting, and histone modifications. Of all the different types of epigenetic modifications, DNA methylation is the most easily measured and amenable to the efficient analysis characteristic of toxicogenomic technologies. These changes are major regulators of gene expression. GlobalÂ methylation analysis methods measure the overall level of methyl cytosines in the genome using chromatographic methods or methyl accepting capacity assays.Â Gene-specificÂ methylation analysis methods originally used methylation-sensitive restriction enzymes to digest DNA before it was analyzed by Southern blot analysis or PCR amplification. Sites that were methyllated were identified by their resistance to the enzymes. Recently, methylationsensitive primers or bisulfite conversion of unmethylated cytosine to other bases have been used in methods such as methylation specific PCR and bisulfite genomic sequencing. These methods give a precise map of the pattern of DNA methylation in a particular genomic region or gene and are fast. To identify unknown methylation hot-spots within a larger genomic context, techniques such as Restriction Landmark Genomic Scanning is a method to detect large numbers of methylated cytosines sites in a single experiment using direct end-labeling of the genomic DNA digested with a restriction enzyme and separated by high-resolution two-dimensional electrophoresis and CpG island microarrays (Hayashi et al 2007) have also been developed.
3. Experimental Design and Data Analysis for Toxicogenomics
The greatest challenge of toxicogenomics is no longer data generation but effective collection, management, analysis, and interpretation of data. The transcriptomes, proteomes, and metabolomes are dynamic and their analysis must be linked to the different aspects of cellular responses of the biologic samples under analysis.
3.1 Experimental Design
The types of biologic inferences that can be drawn from toxicogenomic experiments are primarily dependent on experimental design. The design must reflect the question that is being asked, the limitations of the experimental system, and the methods that will be used to analyze the data.
First and foremost is the value of broad sampling of biologic variation (Dobbin and Simon 2005). Experiments have begun to include sampling protocols that provide better estimates of biologic and systematic variation within the data. A second is carefully matched controls and randomization can minimize potential sources of systematic bias and improve the quality of inferences drawn from toxicogenomic datasets. A related question in designing toxicogenomic experiments is whether samples should be pooled to improve population sampling without increasing the number of assays (Dobbin and Simon 2005). Pooling averages variations but may also disguise biologically relevant outliers-for example, individuals sensitive to a particular toxicant. Although individual assays are valuable for gaining a more robust estimate of gene expression in the population under study, pooling can be helpful if experimental conditions limit the number of assays that can be performed. Generally, the greatest power in any experiment is gained when as many biologically independent samples are analyzed as is feasible.
3.2 Types of Experiments
DNA microarray experiments can be categorized into four types: class discovery, class comparison, class prediction, and mechanistic studies.
3.2.1 Class Discovery
Class discovery analysis is generally the first step in any toxicogenomic experiment because it takes an unprejudiced approach to looking for new group classes in the data. For instance, one might consider an experiment in which all nephrotoxic compounds are used individually to treat rats, and gene expression data are collected from the kidneys of these rats after they begin to experience renal failure (Amin et al 2004). Evaluation of the gene expression data may indicate the nephrotoxic compounds can be grouped based on the cell type affected, the mechanism responsible for renal failure, or other common factors. This analysis may also suggest a new subgroup of nephrotoxic compounds that either affects a different tissue type or represents a new toxicity mechanism.
Class discovery analyses rely onÂ unsupervisedÂ data analysis methods and these methods simply group samples together based on similar patterns of expression. Two of the most widely used unsupervised approaches are hierarchical clustering (Wen et al 1998) andÂ k-means clustering (Soukas et al 2000). All these methods will divide data into clusters and critical assessment of these results is essential. In the context of toxicogenomics clustering" used to discover groups of genes that may be involved in cellular responses and imply hypotheses about the modes of action of the compounds. Subsequent experiments can confirm or refute the hypotheses, and identify the cell types and mode of action associated with the response. A goal of this type of research would be to build a database of gene expression profiles of sufficiently high quality to enable a gene expression profile to be used to classify compounds based on their mode of action.
3.2.2 Class Comparison
A common approach to class comparison is to search for a discriminatory gene set among expression profiles generated from studies of toxins representative of known toxicological classes. Statistical significance testing is often used to select the discriminatory gene sets. For example, to select discriminatory genes from a training set of expression profiles from rats exposed to nine toxic metals, an ANOVA F-test was used to find those genes that varied significantly across the nine treatment groups and an OVA (one-versus all) test identified gene expression that varied significantly when each group was compared to the average of the other eight (Tsai et al 2005). The resulting two discriminatory gene sets (the set defined from the F-test and the union of the nine groups returned from the OVA analysis), as well as a third set, consisting of those genes appearing in both original sets, were then evaluated for their ability to classify toxic metals successfully. Another approach is to reduce the dimensionality of the complex data set using dimension-reducing techniques such as principal component analysis (PCA), multidimensional scaling (MDS), or wavelet transformation (Yang et al 2004).
3.2.3 Class Prediction
Class prediction typically relies on supervised learning methods (classifiers) to assign a toxicant to a known group. The methods use a discriminatory gene set (or a data set that has been reduced using a dimension-reducing technique) derived from a training set, to obtain a mathematical model that can predict the class membership of unknowns. One active area of study is the practice of filtering out invariant gene expression signals versus applying classifiers to the full data set. A recent study compares the effects of two different types of data filtering on the performance of four classifiers for distinguishing genotoxic from non-genotoxic compounds (Van Delft et al 2005). There are a variety of classification methods available, including Linear Discriminant Analysis (LDA), nearest-neighbor (NN) methods, NaÄ±Â¨ve Bayesian classifiers, as well as machine learning methods, such as bagging methods, support vector machines (SVM), and artificial neural networks (ANN). Dudoit et al. compared many of these methods for their ability to classify tumors and found that for their test data set, LDA and NN methods yielded the best prediction accuracies (Dudoit et al 2002).
3.2.4 Functional and Network Inference for Mechanistic Analysis
New approaches to predict networks of interacting genes based on gene expression profiles use several modeling techniques, including boolean networks (Savoie et al 2003), probabilistic boolean networks (Â Hashimoto et al 2004 ) and Bayesian networks (Savoie et al 2003). These models treat individual objects, such as genes and proteins, as "nodes" in a graph, with "edges" connecting the nodes representing their interactions. A set of rules for each edge determines the strength of the interaction and whether a particular response will be induced. These approaches have met with some success, but additional work is necessary to convert the models from descriptive to predictive. In metabolic profiling, techniques that monitor metabolic flux and its modeling (Famili et al 2005) also may provide predictive models.
The advent of global toxicogenomic technologies, and the data they provide, offers the possibility of developing quantitative, predictive models of bio logic systems. This approach, dubbed "systems biology," attempts to bring together data from many different domains, such as gene expression data and metabolic flux analysis, and to synthesize them to produce a more complete understanding of the biologic response of a cell, organ, or individual to a particular stimulus and create predictive biomathematical models.
4. Environmental toxicology
Environmental toxicology is aÂ multidisciplinaryÂ field of science concerned with the study of the impacts of pollutants upon the structure and function of ecological system. Environmental toxicology takes and assimilates from a variety of disciplines. Terrestrial and aquatic ecologist, chemist, molecular biologist, geneticist and mathematicians are all important in the evaluation of the impacts of various chemical in the biological system. There are many types of interactions and connections in the environmental toxicology such are research programmes, scientific community, government and regulatory agencies, industry, ecological risk assessment and general public for the management of the ecological system (Landis and Yu 2003; Yu2004).
Research programs are the fundamental part of the environmental toxicology. It includes identification of the toxicity and what causes it. These programs develop testing methods, analytical tools and statistical techniques that allow the acquisition of data from the diverse subjects (Landis and Yu 2003).
Scientific community is the intellectual and industrial force behind the conduct of research and publication of papers in peer reviewed journals, books and other publication that report the information generated by the research programs (Landis and Yu 2003)
4.1 Frame work for environmental toxicology
Environmental toxicology can be simplified to the understanding of only three functions. First is the fate and distribution of the xenobiotic material in the biosphere after release in to the environment. Second the interaction of the material with the site of action. Third are impacts of these molecular interactions upon the function of an ecosystem. All these functions depends on the chemical and physical properties, bioaccumulation, biotransformation, biodegradation and site of action of xenobiotic materials; biochemical monitoring, physiological and behavioural effects, population parameters, community parameters and ecosystem effects caused by xenobiotic material(Landis and Yu 2003; Yu2004).
4.2 Toxicity testing
Toxicity is the property or properties of a material that produces a harmful effect upon a biological system. A toxicant is the material that produces this biological effect. The toxicant may be anthropogenic origin or produced by biological system. Anthropogenically derived compounds can produced in millions of pounds per year.
Materials introduced into the environment come from two basic types of sources. Point discharges derived from sewage discharges, waste streams from industrial sources, hazardous waste disposal and accidental spills. Point discharges are easy to characterise as to the types of materials released, rate of release and total amounts. Non point discharge are those materials released from agricultural runoff, contaminated soils and aquatic sediments, atmospheric deposition and urban runoff from sources as parking lots and residential release. In most situations, discharges from non point sources are complex mixture, amount of toxicants are difficult to characterize and rates and the timing of discharges are difficult to predict as rain (Landis and Yu 2003).
4.3 Routes of Exposure and Modes of Action
A pollutant may get into an animal through a series of pathways. The routes may include exposure, uptake, transport, storage, metabolism and excretion. Exposure occurs through dermal or eye contact, inhalation or ingestion leads to uptake of the pollutants. Once absorbed a rapid transportation of the substance throughout the body takes place via lymphatic or blood circulation and distributed to various body tissues (liver, lung, bone, fat tissue etc) including those storage depots and sites of metabolism. Then in the pollutants undergoes phase I and Phas II metabolism. The important features of these is alters the solubility and detoxifies the xenobiotics and its subsequent excretion through kidneys, lungs or intestinal tracts. The toxic action of the pollutants involves compounds with intrinsic toxicity or activated metabolites. Environmental pollutants cause an adverse effect on living organisms through disruption of cell structure (SO2, O3, NO2), direct chemical combination with a cell constituent (CO- binds with haemoglobin), influence on enzyme (Mercury, Cadmium, Lead- binds with SH group enzyme molecule; Fluoride- inactivates cofactor involved in enzyme active site; Beryllium- competes with cofactor for enzyme active site; Sodium fluoroacetate- inhibit enzyme activity), and initiation of a secondary action (cause release of certain substances which are injurious to cells) (Landis and Yu 2003; Yu2004).
4.4 Factors modifying the activity of toxins
Characteristics such as whether a pollutant is solid, liquid or gas; whether it soluble in water or lipid and whether it is organic or inorganic, ionized or nonionized etc can affects the toxicity of the pollutants. For instances since membrane are more permeable to a nonionized than ionized substance, a nonionized substance will generally have more toxicity than ionized substance. Concentration and exposure time are another determinant of toxic effects. Continuous exposure is more injurious than intermittent exposure. Environmental factors such as light, temperature, PH and humidity also influences the toxicity of pollutants. Various biological factors are also affects the toxicity like genetic factors, age, sex and nutritional status. Apart that interaction between different pollutions likes synergism, additive, potentiation and antagonism (Landis and Yu 2003; Yu2004).
5. Use of transcriptomics in Toxicogenomics for understanding mechanisms of toxicity and adverse
drug reactions (ADR)
The rapid accumulation of sequence information from the genome projects and the fast growth of microarray technology, the biological information that could be retrieved from microarray experiments increased exponentially. When transcriptional data link to phenotype, toxicogenomics became very useful in predicting toxicity and understanding the underlying mechanisms (Cui and Paules 2010). A large number of microarray studies were carried out in order to develop transcription `fingerprints' to classify or predict chemical agents with different toxic mechanisms, with the assumption that drug toxicity is accompanied by transcriptional changes in gene expression that are causally linked to or downstream of the toxicity (Bulera et al 2001; Waring 2001).
5.1 Transcription profile of Saccharomyces cerevisiae for methyl methanesulfonate toxicity
One of the pioneer toxicogenomic studies examined the transcription profile of Saccharomyces cerevisiae upon exposure to the alkylating agent methyl methanesulfonate using Affymetrix (CA, USA) GeneChipÂ® oligonucleotide arrays representing the whole yeast genome (Jelinsky and Samson 1999). It was found that the transcription of more than 300 genes were induced by the exposure, far more than the number of genes known to be induced by a DNA-damaging agent at the time, and the expression of 76 genes were decreased. In addition, the changes in the gene-expression profile also provided evidence for the initiation of a process to eliminate and replace alkylated proteins in the cell. This work demonstrated that a global gene-expression profile is useful in the discovery of novel genes and pathways involved in chemical toxicity (Cui and Paules 2010).
5.2 Lead Toxicity
Gene expression profiling was used to identify a potential mode of action of lead-induced neurotoxicity characterized by a loss of integrity of the blood- brain barrier. Previous studies identified protein kinase C activation as an intracellular target of lead neurotoxicity (Hossain et al 2000). However, the link between protein kinase C activation and the downstream effects leading to a compromised blood-brain barrier remained to be elucidated. Exposure of human foetal astrocytes culture to lead causes 3-fold increase in expression of vascular endothelial growth factor which forms the blood-brain barrier, consequently mediating increased blood vessel formation at the blood-brain barrier and compromising its integrity. DNA gene expression arrays also demonstrated the up-regulation of the annexin-V gene in astrocyte cultures, the protein product of which is a calcium-dependent phospholipid-binding protein involved in intracellular signalling. Lead acetate affects binding of annexin-V to phospholipid-based liposomes, implicates annexin-V as a direct cellular target of lead toxicity (Bouton et al 2001). Furthermore, gene expression profiling revealed that lead exposure induced the expression of a variety of stress response genes, amino acid biosynthesis genes, and transfer RNA synthetase genes in astrocytes correlated with other known mechanisms of action of lead on these cellular processes (Bouton et al 2001).
5.3 Arsenic toxicity
Gene expression arrays of liver biopsies obtained from chronically exposed humans displaying classical signs of arsenic toxicity compared with healthy individuals. People chronically exposed to arsenic had increased gene expression for numerous cell cycle regulators and transcription factors involved in oncogenesis and cell differentiation. In addition, increased expressions of DNA-damage repair genes (e.g., ERCC2, ERCC5, topoisomerase II, replication factor C) also observed. These results revealed that exposure to arsenic induces DNA damage, micronuclei formation, and DNA strand breaks. This is concordant with the idea that dysregulation of cell cycle components due to arsenic exposure is an important element in arsenic-induced neoplasia formation (Lu et al 2001).
A study conducted by Kozul et al 2009 confirmed several significant alterations in gene expression by transcriptome profiling in the mouse lung after chronic low-dose (10 and 100 ppb) of arsenic. The expression profiles of the animals revealed numerous changes in the mRNA levels of genes involved with cell adhesion and migration, various cellular channels and receptors, differentiation and proliferation, and most remarkably, the immune response genes such as interleukin 1Î², interleukin 1 receptor, a number of toll-like receptors, and several cytokines and cytokine receptors were significantly altered in the lungs of arsenic exposed mice, which was the dominant group of affected genes in the analysis (Kozul et al 2009).
Global gene expression profile in humans after benzene exposure reveled altered gene expression forÂ CXCL16, ZNF331, JUN andÂ PF4, as potential biomarkers of early response to benzene exposure. An another study, using 2 different microarray platforms (Affymetrix & Illumina), confirmed altered expression of these 4 genes and revealed impacts on apoptosis and lipid metabolism in 8 individuals exposed to benzene. Many of the altered genes were involved in apoptosis, and, immune and inflammatory responses (McHale et al 2010).
A study in welded workshopers identified 35 genes from eight significant functional pathways that had altered expression levels after metal-fume exposure. The most interesting finding was the identification of several genes involved in every aspect of the inflammatory response, including proinflammatory mediators, cytokine receptors, downstream signal transduction genes, and cytotoxic granulysin. Five genes (IL8,Â IL1A,Â CXCR4,Â RALBP1, andÂ SCYE1) have been implicated in chemotaxis of the early inflammatory response, especiallyÂ IL8, which is a critical mediator for neutrophil-dependent acute inflammation.Â IL8Â has a wide range of actions on different cell types, including neutrophils, lymphocytes, monocytes, endothelial cells, and fibroblasts.Â IL8Â is produced from various cell types in response to a wide variety of stimuli, including proinflammatory cytokines, microbes and their products, and environmental changes such as hypoxia, reperfusion and hyperoxia. In our study,Â IL8Â and other cytokines and receptor genes were transcriptionally down-regulated in whole-blood total RNA in response to metal particulate exposure (Wang et al 2004).
Gene expression pattern in rat models treated with compounds known to cause hepatotoxicity such as carbon tetrachloride (CCL4), chloroform, 1-naphthylisothiocyanate(ANIT), tetracycline(TE), erythromycin (EE) and acetaminophen(AAP) revealed that five genes found to be deregulated by all six hepatotoxicants namely ATP-binding cassette subfamily B (MDR/TAP) member 11, flavin containing monooxygenase (Fmo1), monoamine oxidase (Maob), liver glycogen phosphorylase (Pygl), and thioredoxin reductase (Txnrd1). The carbohydrate metabolism enzyme, Pygl, and the xenobiotic metabolism Phase 1 enzymes, Fmo1, and Maob, were significantly down regulated by 2-3, 2-10, or 1.6-3 folds. Abcb11, which is involved in transporter activity, was significantly down regulated by all hepatotoxicants except for ANIT which showed significant diametrical regulation of 1.8-fold. The oxidative stress enzyme, Txnrd1, was significantly up regulated by hepatotoxic compounds except for EE. The xenobiotic metabolism enzyme Fmo1 increases solubility and thereby ensures rapid excretion of xenobiotics. It's down regulation may contribute to hepatotoxicity due to lack of elimination and emphasizes the importance of xenobiotic metabolism. The down regulation of Maob, this catalyzes the oxidative deamination of biogenic and xenobiotic amines. The carbohydrate metabolism enzyme Pygl, was down regulated by all hepatotoxicants indicating a decrease in glycogenolysis. Txndr1 is involved in protecting cells from oxidative stress. Its up regulation indicates the generation of reactive oxygen species, potentially contributes to DNA and protein damage. Abcb11 plays a major role in the hepatobiliary excretion of bile salts. Down regulation by hepatotoxicants could result in severe hepatic effects, including intrahepatic cholestasis and hepatic steatosis. The changes in gene expression allowed us to an accurate prediction of hepatotoxicity (Zidek et al 2007).
In an another study revealed both subtoxic and toxic doses of APAP resulted in downregulation of genes involved in energy-consuming biochemical pathways including gluconeogenesis (glucose-6-phosphatase), fatty acid synthesis (fatty acid synthase; sterol-C4- methyl oxidase-like), cholesterol synthesis (3-hydroxy-3-methylglutaryl-coenzyme A synthase 1) and porphyrin synthesis (aminolevulinic acid synthase 1), and upregulation of genes involved in energy-producing biochemical pathways, such as glycolysis/ gluconeogenesis (6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 1) and mitochondrial Ï‰-hydroxylation (rat Cyp4a locus, encoding cytochrome p450 [IVA3] mRNA), as well as upregulation of stress-response-related genes, such as metallothionein and phospholipase C Î³1, which have been implicated in the cellular defense system against oxidative stress. All of these changes in gene expression were APAP dose dependent with increased magnitude and number of genes changed in the same pathway and accompanied by mitochondrial damage and ATP depletion, providing mechanistic cues of APAP-induced mitochondrial injury and oxidative stress on the molecular level. This study also suggested the importance of using subtoxic dose treatment in mechanistic toxicogenomic studies (Cui and Paules 2010).
5.7 Drug Induced Phospholipidosis
Gene expression analyses in order to understand the pathogenesis of drug-induced phospholipidosis using human hepatoma HepG2 cells was performed by Sawada et al 2004. The results established four important pathways that were altered and which contribute to the development of phospholipidosis. Altered gene expression was consistent with lysosomal phospholipase inhibition with increased expression of phospholipid degradation genes such as N-acylsphingosine amidohydrolase 1 and sphingomyelin phosphodiesterase. There was also a role for reduced lysosomal enzyme transport demonstrated by decreased expression of genes such as adaptor-related protein complex 1 sigma 1 which transports lysosomal enzymes between golgi network and the lysosyme. The results also indicated a role for increased phospholipid and cholesterol biosynthesis (stearoyl-CoA desaturase andHMGCoA synthase, respectively). Both of these are triggers for phospholipidosis. Thus, phospholipidosis results from the combination of events involving both increased synthesis and decreased degradation of phospholipids and an in vitro screening test they identified a set of 12 marker genes for predicting phospholipidosis (Cunningham and McKeeman 2005).
5. 8 Nephrotoxicants
Nephrotoxicity is one of the common adverse effects seen in many therapeutic drugs. The vast majority of pharmacological compounds and their metabolites are excreted via the urine, some of which may either exert direct toxicity by actively interacting with the complex structure of the kidney or have indirect effects by disturbing the electrolyte balance or renal blood flow. Some of the nephrotoxic drugs, such as cisplatin and gentamicin have been used as model nephrotoxicants by many researchers for mechanistic research and discovery of bio markers of nephro toxicity. Both cisplatin and gentamicin are tubule toxicants, causing cell death in the proximal tubules. Temporal transcriptional changes in a rat kidney that were associated with administration of three different nephrotoxicants, cisplatin, gentamicin and puromycin, were examined in a study (Kramer et al 2004). The analysis of gene-expression profiles not only revealed sample separation based on dose, time and degree of renal injuries but also reflected gene expression alterations that correlated with biological processes relevant to nephron segment specific toxicity. For instance, a group of genes was found to be strongly down regulated in samples that exhibited proximal tubular necrosis. Many of these genes, which are known to be involved in sugar metabolism (such as glucose-6-phosphatase), xenobiotic metabolism (such as glutathione S-transferase, L-hydroxyacid oxidase and peroxisomes), or peptide/amino acid metabolism (such as aminopeptidases), are functionally localized to the proximal tubules. This suggested that the down regulation of these genes may be related to the proximal tubule-specific toxicity. It was also found that after high-dose cisplatin and gentamicin treatment a grouping of genes appeared to be upregulated in a dose- and time dependent fashion. These genes include kallikrein, hemeoxygenase-1, clusterin, osteopontin and KIM-1, which have been implicated in the mechanisms of renal toxicity, and some of these genes, such as KIM-1 and osteopontin, have become among the most promising biomarkers of nephrotoxicity today. They concluded that cisplatin and gentamicin-induced renal dysfunction may be better explained by reduction of these transporters in the proximal tubules rather than perturbation of metabolic pathways inside the kidney cells (Kramer et al 2004; Thukral et al 2005).
5.9 Testicular toxicity
1,3-dinitrobenzene (DNB) induced testicular toxicity in rats were conducted by Matsuyama et al 2011 and they found the genes associated with oxidative stress-response (i.e., Hmox1 and Pon2), cellular protection against oxidative stress (i.e., Gstp1, Akr7a3 and Akr1b8), apoptosis (i.e., Gadd45g, Ddit4 and Nos3), to cell adhesion [(Cdh2, catenin alpha (Ctnna1), vinculin (Vcl), zyxin (Zyx), integrin beta 1 (Itgb1), Testin, laminin gamma 3 (Lamc3), poliovirus receptor-related 2 (Pvrl2, also known as nectin 2) and gelsolin (Gsn))], development or cell differentiation were up-regulated 24Â hrs after the DNB treatment. On the other hand locomotory behavior or cellular process genes were down-regulated. In this study, oxidative stress-related genes were up-regulated supporting that DNB induces oxidative stress in the rat testis. Up regulation of DNA damage-responsive genes such as Gadd45g and Ddit4 which will lead to apoptosis. Furthermore, Nos3 was up-regulated may be associated with DNB-induced cellular apoptosis in the testis. These data implies that oxidative stress would be one of the early events caused by DNB, followed by cell death including apoptosis. The gene ontology analysis revealed characteristic up-regulations of cell adhesion-related genes such as Cdh2 and Testin genes at 24Â h after DNB treatment. Cdh2, also known as N-cadherin, is reported to localize at basal inter-Sertoli junctions, Sertoli-spermatocyte junctions and Sertoli-elongate spermatid junctions. Thus, the up-regulation of Cdh2 gene may indicate loss of Sertoli-germ cells adhesion. On the other hand, Testin is a secretory protein from Sertoli cells and localized on the Sertoli cell side of the ectoplasmic specialization surrounding developing spermatids. Expression of Testin relates to the disruption of Sertoli-germ cells junctions, therefore, induction of the Testin expression appears to be an indicator for monitoring the loss of integrity of inter-testicular cell junctions. Disruption of Sertoli-germ cells contacts cause germ cell sloughing from seminiferous epithelium. Thus, the up-regulation of testicular cell adhesion-related genes such as Cdh2 and Testin could be potential biomarkers to evaluate such a cellular response elicited in the DNB-type testicular toxicity (Matsuyama et al 2011).
6. Toxicoproteomics in toxicology and Environmental pollutions
Generation of a large amount of toxicoproteomic data that might lead to biomarkers and a better mechanistic understanding would potentially reduce the attrition rate and costs in drug development by detecting or predicting toxicity at the earliest stage. As pointed out earlier, toxicoproteomic studies are not limited to drug development. In fact, a number of toxicoproteomic analyses have been performed for environmental toxicants including air pollutants, pesticides, chemicals in water systems, and so on. Early detection of toxicity induced by drug administration and other toxicants using qualified biomarkers would have a great potential to prevent disease development.
Proteomics investigation towards acetaminophen toxicity in mice was performed byÂ Fountoulakis et al. (2000). The liver samples of this study contained 35 modified proteins after acetaminophen treatment. Some of these proteins were known targets for covalent modification ofÂ N-acetyl-p-benzoquinoneimine, which is the most toxic metabolite of acetaminophen (Fountoulakis et al 2000). This demonstrates that proteomics can identify protein targets for toxic compoundsÂ in vivo. Koen et al. (2007) used radioactively labeled compounds to detect targets for the reactive metabolites of bromobenzene.Â Mice were treated withÂ 14C-bromobezene and bromobenzene-target proteins were visualized after measuring the radioactivity of the spots in the gel. In total 33 unique protein targets for bromobenzene were detected including glutathione S-transferases, protein disulfide isomerases and liver fatty acid-binding protein (Koen et al 2007).
Proteomics can be used to provide insight in toxicity mechanisms. Thioacetamide-induced hepatotoxicity in rat liver and showed a down-regulation of enzymes from the metabolic pathways of fatty acid beta-oxidation, branched chain amino acids and methionine breakdown. Furthermore, an up-regulation of proteins related to oxidative stress and lipid peroxidation was detected. The hydrazine treatment induced altered expression of proteins related to lipid metabolism, calcium homeostasis, thyroid hormone pathways and stress response in the rats (Kleno et al 2004). The liver samples from rats treated 14Â days with troglitazone were analyzed with DIGE showed 104 deregulated protein spots, from which 55 spots were identified by Maldi TOF/TOF. These proteins belong mostly to the pathways of fatty acid metabolism, PPARÎ±/RXR activation, oxidative stress and cholesterol biosynthesis (Kleno et al 2004).
To generate marker panels for carcinogenicity and genotoxicity proteome analysis was used in anÂ in vivoÂ 28-day repeated dose study of 63 chemical compounds. The proteome of rat livers was analyzed by DIGE and the carcinogenic characteristic proteins were classified. In this study, 79.3Â % of the genotoxic compounds and 76.5% of the non-genotoxic compounds correctly classified (Yamanaka et al 2007). In a comparable gene expression signature for predicting non-genotoxic hepatocarcinogens was conducted by Fielden et al. (2007)Â and they found an accuracy of 63-69%, whereas the gene expression signature accuracy for predicting non-genotoxic carcinogens was between 55% and 64% (Nie et al 2006). Although these accuracies still involve a high number of false negative results, proteomics techniques show promising results in the detection of toxicity signatures.
The liver samples of mice treated with phenobarbital and 3-methylcholanthrene and microsomal fractions were subjected to 2-DE. Despite the fractionation, the cytosolic fraction revealed more information than the subcellular microsome fraction. The microsomal changes after treatment with phenobarbital and 3-methylcholanthrene were quite similar. While the cytosolic response after phenobarbital and 3-methylcholanthrene treatment could be clearly distinguished from each other (Zgoda et al 2006). A high amount of CYP450 enzymes was expected in the microsomal fraction, however only two CYP450 enzymes were detected. This can be explained that CYP450 enzymes are membrane-associated proteins which are difficult to analyze by 2-DE. For example, microsomes isolated from carbon tetrachloride (CCl4)-treated rat liver were used to analyze the changes in protein expression and reveled altered expression of 17 CYP450 proteins. Among them, the expression of 2C11, 3A2, and 2E1 was down-regulated, while that of 2C6, 2B2, and 2B1 was up-regulated (Jia et al 2007).
Study of the mitochondrial liver proteome of rats with ethanol-induced hepatotoxicity was conducted by (Venkatraman et al 2004). In total 43 differentially expressed proteins were identified, comprising enzymes of the Î²-oxidation cycle, nuclear encoded subunits of the oxidative phosphorylation system, mitochondrial chaperones and enzymes of amino acid metabolism. Furthermore a decrease of several polypeptides of the respiratory complexes in the ethanol treated rats was observed.
Since most proteins in blood are secreted by the liver, plasma/serum is a potential source for hepatotoxicity biomarkers, represented by aberrantly secreted proteins or proteins leaked from the liver due to injury. Recently, protein expression in Z24-treated and untreated rat livers was studied in parallel with the plasma proteome from the animals (Wang et al 2010). Z24 is a synthetic anti-angiogenic compound that inhibits growth and metastasis of tumors. However, it was shown that Z24 induces hepatotoxicity in rodents. Twenty-four hours after the final administration, blood samples and whole livers were collected. From these samples differentially expressed proteins were analyzed. Twenty two (22) non-redundant liver proteins and 11 plasma proteins were found differentially expressed. These proteins are involved in several important metabolic pathways, including carbohydrate, lipid, amino acid, and energy metabolism, biotransformation, and apoptosis. Most of the identified hepatic proteins are located in mitochondria, where Z24 also increased the ROS production and decreased the NAD (P) H levels. Therefore, it was concluded that Z24 inhibits the aerobic carbohydrate oxidation, fatty acid Î²-oxidation, and oxidative phosphorylation pathways resulting in mitochondrial dysfunction and apoptosis-mediated cell death. In addition, potential biomarkers for Z24-induced hepatotoxicity, namely fetub protein and argininosuccinate synthase were detected in the plasma (Wang et al 2010).
Recently, the proteins secreted by HepG2/C3A cells in response to ethanol exposure were investigated (Lewis et al 2010). All differentially expressed proteins are related with knownÂ in vivoÂ effects of ethanol exposure. These effects vary from apoptosis and inflammation to cell leakage from disturbed cells, indicating the identification of potential toxicity markers (Lewis et al 2010). The value of the secretome of HepG2 is confirmed by a study ofÂ Choi et al. (2010)Â where potential biomarkers for the genotoxic compound di (2-ethylhexyl) phthalate were found in the secretome of exposed HepG2 cells. This revealed 35 differential proteins belonging to several functional groups. Based on the differentially expressed proteins, di (2-ethylhexyl) phthalate was found to affect the formation of cell structure, apoptosis, and tumor progression (Choi et al 2010).
Â Farkas et al. (2005)Â analyzed the secretome of primary rat hepatocytes exposed to aflatoxin B1 showed a decreased expression of Î±2-macroglobulin and Î±1-antitrypsin. Patients with Î±1-antitrypsin deficiency have an increased risk for liver carcinoma and cirrhosis; these symptoms are also seen with aflatoxin B1 intoxication. Therefore, it is likely that the decreased expression of these protease inhibitors in the medium is linked to an impairment of the liver. In another study the medium of human immortalized hepatocytes with an over-expression of the CYP3A4, the most common type of CYP450 enzyme in human liver, was studied after exposure to hepatotoxic (troglitazone, ciglitazone, farglitazar and ritonavir) and non-hepatotoxic compounds (DMSO, indinavir, rosiglitazone and tesaglitazar) (Gao et al., 2004). This analysis revealed two proteins, referred to as BMS-PTX-265 and BMS-PTX-837 that were significantly increased in the secretome of the cells treated with each of the hepatotoxic compounds. The response of these two proteins to an expanded set of 20 compounds was further analyzed. For all 20 drugs, elevations of BMS-PTX-265 correlated exactly with the known safety profile; whereas changes in BMS-PTX-837 correctly predicted the safety profile from 19 of the 20 drugs (Gao et al 2004). This study exemplifies that proteomics and in particularly secretome analysis can reveal biomarkers for hepatotoxicity screening.
Cyclosporine A is a calcineurin inhibitor that has been a mainstay for immunosuppressive therapy following solid-organ transplantation. Cyclosporine A blocks immune responses by inhibiting the calcineurin-dependent dephosphorylation of the nuclear factor of activated T cells (NFAT). However, it causes a dose-dependent nephrotoxicity. Proteomic study of kidney homogenates identified a decrease in the 28-kDa kidney protein as calbindin-D using protein micro sequencing. These toxicoproteomic studies published a decade ago represented an important advance in understanding a part of cyclosporine-induced pathophysiology in kidney. More recently, the contribution of calbindin-D28k has been clarified by the generation of genetically modified mice. Cyclosporine A-induced hypercalciuria represents two pathophysiological processes: a down-regulation of calbindin-D28k with subsequent impaired renal calcium reabsorption, and a cyclosporine A-induced high turnover bone disease. In addition, there is evidence that one biochemical mechanism underlying cyclosporine A and other calcineurin inhibitors may be a drug-induced mitochondrial dysfunction (Merrick and Witzmann 2009).
The effects of cyclosporine A on gene up-regulation were advanced by a 2D gel proteomic analysis of newly synthesized methionine-labeled proteins in murine T cells activated in the absence or presence of cyclosporine A. Remarkably, these investigations found more than 100 proteins not present in resting or activated T cells that could be induced by cyclosporine A exposure. It is important to emphasize that the discovery nature of this proteomics study was capitalized upon with the identification of the corresponding genes under the same treatment conditions using a transcript enrichment technique called "representational difference analyses". Among the up-regulated transcripts, a new gene was found named CSTAD, for "cyclosporine A-conditional, T cell activation-dependent" gene. CSTAD encodes two proteins of 104 and 141 amino acids that are localized in mitochondria. CSTAD up-regulation is observed in mice after cyclosporine A treatment, suggesting that up-regulation of CSTAD and perhaps many other genes are implicated in cyclosporine A toxicity (Merrick and Witzmann 2009).
The nephrotoxic effects of gentamicin on protein expression were studied in rat kidney. Results revealed the identities of more than 20 proteins involved the citric acid cycle, gluconeogenesis, fatty acid synthesis, and transport or cellular stress responses. Impairment of energy production and mitochondrial dysfunction were involved in gentamicin-induced nephrotoxicity (Charlwood et al 2002). Proteomic mapping of rat urine proteins studied by 2D-MS resolved 350 protein spots from which 111 protein components were identified including transporters, transport regulators, chaperones, enzymes, signaling proteins, cytoskeletal proteins, pheromone-binding proteins, receptors, and novel gene products (Thongboonkerd et al 1999).
Following puromycin treatment, a gradual increase in higher mass proteins was observed on 2D gels, particularly albumin, at 32 h after dosing. By 120 h, albumin, transthyretin and vitamin D-binding protein (Gc) were identified as major urinary proteins from puromycin-induced kidney damage. After 672 h, the urinary protein profile in 2D gels had largely returned to normal. Many of these plasma-derived proteins appearing in the urine over 0-672 h following puromycin were consistent with loss of glomerular integrity and major leakage of plasma protein in urine (Merrick and Witzmann 2009).
George et al 2011 conducted quantitative proteomics in mouse skin exposed to cypermethrin, a synthetic pyrethroid insecticide, is performed using 2-DE coupled with MALDI-TOF/TOF and LC-MS/MS analysis. They identified 27 that were statistically significant (pÂ <Â 0.05) and differentially expressed in response to cypermethrin exposure. Among them 6 up regulated proteins (carbonic anhydrase 3, Hsp-27, S100A6, galectin-7, S100A9, S100A11) and 1 down-regulated protein (SOD 1) play important roles in many cellular functions, including oxidative stress response, proliferation, binding of calcium ions and apoptosis. Disturbance of these processes plays an important role in carcinogenesis. Thus supports that these proteins were associated with induction of cell proliferation and might be responsible for the neoplastic transformation of mouse skin preneoplastic lesions by cypermethrin (George et al 2011).
Mancozeb (C4H6MnN2S4)Ã-(Zn)y, a broad spectrum fungicide and a well known member of ethylene(bis)dithiocarbamate (EBDC) family, is widely used in agriculture, professional turf management and horticulture. Thus recently, using 2-DE and MS in combination with other techniques we studied the molecular mechanism that participates in mancozeb-induced carcinogenesis. The level of 2 proteins (S100A6 and S100A9) was significantly up regulated in the mancozeb exposed mouse skin and later found to be higher in mancozeb-exposed human keratinocytes HaCaT cells. These proteins are known markers of keratinocyte differentiation and proliferation, and suggested their possible role in mancozeb-induced neoplastic alterations in mammalian skin system (Tyagi et al 2011).
Santos et al 2009Â used Saccharomyces cerevisiaeÂ model to analyze the impact of mancozeb on the proteome and to get insights into the molecular mechanisms of mancozeb toxicity. The main functional groups of proteins whose abundance was affected by mancozeb exposure include proteins that participated in the antioxidant response, carbohydrate and energy metabolism, protein chaperone activity, protein synthesis and protein degradation through the proteasome.
Glyphosate, an organophosphate herbicide has been extensively used by agricultural workers for controlling weeds. Considering the uses of glyphosate throughout the world, genotoxic/carcinogenic risk associated with its use. Results of the animal carcinogenicity bioassay showed that topical application of glyphosate was capable of promoting 7, 12-dimethylbenz[a]anthracene-initiated mouse skin cells. Further proteomic analysis using 2-DE and MS, identified that SOD 1, calcyclin (S100A6) and calgranulin-B (S100A9) are associated with its tumor promoting potential and may be useful as biomarkers for tumor promotion (George and Shukla 2011).
Organochlorines are a class of chemical pesticides that act as xenoestrogens to disrupt normal endocrine function. A proteomic-based study was carried out to evaluate the molecular pathway involved behind methoxychlor and toxaphene-induced endocrine disruption by utilizing the breast cancer cell lines, MCF7 (ER+) and MDA-MB-231 (ER-) (Hale et al 2010). The elevated expression of a small subset of mitochondrial proteins was found in response to long-term exposure of these organochlorines. Fang et al 2010 investigated global alterations in the proteome of livers taken from both male and female rare minnow (Gobiocypris rarus) following pentachlorophenol (an organochlorine compound shown to lead to hepatoxicity and incidence of liver tumors in human) exposure of 0.5, 5, 50Â Î¼g/L. The proteins were characterized using MALDI-TOF-MS analysis. Based on the proteins identified, the observation showed that these proteins were involved in transport, metabolism, response to oxidative stress and other biological processes.
Changes in multiple types of proteins were identiï¬ed from the proteome maps of mouse skin exposed to various chromium complexes. These included cytokeratin (CK) - 10, CK-14, CK-17, CK-16, K27, and K25. Patterns of cytokeratin proteins during cell transformation and tumor development have become an important tool in clinical diagnoses. In response to severe skin injury, K16 and K17, markers of hyperproliferation, appeared to be highly expressed after exposure to various chromium compounds. Overexpression of K17 may deï¬ne skin cells treated with chromium nitrate to be most at risk of undergoing malignant transformation. In addition to structural proteins, ATP synthase, which is involved in energy metabolism, was detected at various levels among different treatments. There was a notable increase in HSP60 after in vivo exposure to chromium compounds. HSPs can be upregulated at sites of inï¬‚ammation, and immune reactivity to HSP60 is suggested to play a regulatory role in various chronic inï¬‚ammatory diseases (Pan et al 2009).
TCDD (2,3,7,8-Tetrachlorodibenzo-pdioxin) is a ubiquitously distributed and is recognized to be the most potent toxic compound among the polychlorinated dibenzo-p-dioxins (PCDD). Identification of the proteins profiles from female Sprague-Dawley rats received various concentrations TCDD orally. The upregulated proteins identified were selenium binding protein 2, glutathione S-transferase mu type 3, Lrpap 1 protein, NADPH, and peptidylprolyl isomerase D. Prohibitin and N-ethylmaleimidesensitive factor proteins were expressed in lower amounts compared with controls. Selenium-binding protein 2 (SBP2), also known as 56-kD acetaminophen- binding protein has specific binding properties for selenium and APAP (N-acetylp-aminophenol). SBP2 is concomitantly induced by dioxin binding to AhR and by TCDD-induced oxidative stress. These finding may yield clues to the mechanisms underlying TCDD-mediated oxidative stress on reproductive and endocrine functions (Chen et al 2009).
6.8 Maneb + Paraquat pesticides
Down-regulation in the expression of complexi