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Biomarkers of oxidative stress
The organisms response mechanisms to chemical pollutants are important sources of free radicals in biological species, whose abnormal levels in the cell can lead to an oxidative stress. Free radicals are often non-specific regarding its biochemical targets, and can damage all components of the cell, including proteins (protein dysfunction through oxidation), lipids (membrane dysfunction through lipid peroxidation), and DNA (oxidative damage can lead to mutations) (Figure I-3). Proteins, being the major component of most biological systems and having high rate constants for reactions, are a major target for oxidants (Davies, 2005). Xenobiotics can increase oxidative damage through many different processes, either by interfering with the normal antioxidant mechanisms, or by directly participating in reactions leading to oxidative stress (such as reactions from phase I and II detoxification system) (Di Giulio, 1995; Lushchak, 2011; Newman, 2015). Cellular defense against these radicals is provided by the production of antioxidants (ex. vitamin E, vitamin C, glutathione) or antioxidant enzymes (ex. superoxide dismutase SOD, catalase CAT, glutathione peroxidase GPX) that reduce the amount of circulating free radicals.
The application of biomarkers of oxidative stress increased considerably after verifying the importance of free radical damage in the mechanisms of toxicity of chemical pollutants. Molecular biomarkers are often used to test an oxidative stress in sentinel organisms (Enis Yonar et al., 2011; Nunes et al., 2006; Radwan et al., 2010; Sroda and Cossu-Leguille, 2011), mainly by analyzing the activities of enzymatic
Vitellogenin is the precursor protein of the egg yolk proteins, and is expressed in females of almost all oviparous animal species. Due to the action of endocrine disrupting compounds (EDCs), male fishes can express the Vtg gene in a dose dependent manner in contaminated conditions. Vtg induction is therefore a widely used biomarker of exposure to environmental estrogen mimetics, especially in fish species (Garcia-Reyero et al., 2004; Sumpter and Jobling, 1995; Tyler et al., 1996). This induction can be measured both at the protein (Carnevali et al., 2003; Lomax et al., 1998) and mRNA level (Garcia-Reyero et al., 2004).
Cross-species transferability: illustration with endocrine disruption biomarkers
These specific and non-specific effects of pollutants account for several limitations in biomarker development, especially for their transferability across different taxa. The best example is the one of EDCs, which have a well-known mode of action, but in a restricted phylogenetic group (e.g. synthetic estrogens in vertebrates, juvenoid insecticides in insects, …). EDCs are exogenous substances that interfere with hormone-regulated physiological processes and provoke adverse health effects in exposed organisms and/or in its progeny (Birnbaum, 2013). EDCs will typically interfere with hormone signaling, acting as agonist or antagonist, or with hormone synthesis, through anti-hormonal effects for example (LeBlanc, 2007; Rodriguez et al., 2007) (Figure I-4). These compounds constitute a worldwide concern for potential human health implications (Cravedi et al., 2007), especially due to the multiple developmental and reproductive disorders observed in wildlife (Crisp et al., 1998; Ford et al., 2004; Höss and Weltje, 2007; Jobling and Tyler, 2003; Kloas et al., 2009; Oehlmann et al., 2007; Oetken et al., 2004; Rodríguez et al., 2007; Soin and Smagghe, 2007). One of the particularities of these compounds is that they can exert toxic effects at much lower concentrations that other types of toxicants. Due to the large amount of discharges in aquatic systems (from pulp and paper mills, agricultural practices, pharmaceutical use etc.), these compounds are prevalent in these environments. Therefore, some of the best-documented examples of the effects of these compounds in wildlife are for aquatic vertebrates, especially fishes (Jobling and Tyler, 2003; Tyler et al., 1998). However, regardless of the species, the reproductive processes are always connected to the coordination of the hormonal system: in fish through estradiol and testosterone; in arthropods mainly through ecdysone, juvenile hormone, and peptide hormones. Therefore, interactions between EDCs and hormone-related molecules of living organisms will lead to consequences such as, for example, male feminization events (Ford et al., 2004; Sumpter and Jobling, 1995; Yuan et al., 2013), reproductive impairments (Jobling et al., 1996), and intersex incidence (Depiereux et al., 2014; Ford et al., 2004).
Multibiomarker approaches as an indicator of the health of organisms
Seeking an exhaustive assessment, multibiomarker approaches try to associate a wide range of biological responses to reveal potential stress due to different classes of contaminants. The interpretation of these responses in an integrated manner has been proposed through several indices such as the Integrated Biomarker Response (IBR) (Beliaeff and Burgeot, 2002), the expert system Health Status Index (HIS) (Dagnino et al., 2007), the aquatic ecosystem health index (Yeom and Adams, 2007) and the Biomarker Response Index (Hagger et al., 2008). These approaches were designed to simplify the interpretation of individual biomarkers and create an index capable of detecting and monitoring the biological effects of pollutants in organisms. Marigomez et al. 2013 (Marigómez et al., 2013) compared five different integrative indices, and showed that despite having different sensitivities, all of them provide coherent information for ecosystem health assessment.
The most commonly used multibiomarker index is the IBR, initially developed by Beliaeff and Burgeot (Beliaeff and Burgeot, 2002). This method uses computation of star plots to allow a visual integration of a set of early warning responses (biochemical biomarkers). Since most of the validated individual biomarkers concern marine environment, the use of biomarker index mainly focused in marine fish and mussel species. The biomarkers used for integration were the enzymatic activities from glutathione-S-transferase (GST), AChE, CAT, EROD, and genotoxicity from DNA adducts. When using integrative approaches, biomarkers are carefully selected a priori so that each one can provide different information associated with their specificity. This choice is done depending on the specific objectives of the study and the characteristics of the targeted sites. The IBR normally correlates well with the contamination data obtained from the sites, and provides a simple interpretation of biological effects of pollution in biomonitoring. Improvements to this approach were proposed by several authors, associated with new calculations (Devin et al., 2014), or addition of other types of biomarkers into the index (Fossi Tankoua et al., 2013; Richardson et al., 2011; Yeom and Adams, 2007).
Despite the several proposals and appeals to the use of these multibiomarker indices in ecotoxicology, such methods have been rarely applied since the first proposal of the IBR in 2002. This can be related to some limitations connected to the use of a single value to discriminate pollution-related changes from natural variations or from biological factors like inter-individual variability (Roland et al., 2016). However, several in situ applications demonstrated the usefulness and coherence of these approaches for either determining organism health status and/or discriminating between contaminated sites, especially using mussel species (Table I-1). Multibiomarker indices also present some constraints at the methodological level.
Protein identification by mass spectrometry
Mass spectrometry is a versatile and crucial tool in the field of proteomics. Its high sensitivity allows for the analysis of proteins at the fentomolar level with relatively small mass errors (less than 10 ppm) (Jensen, 2006; Vidova and Spacil, 2017). A mass spectrometer determines the mass/charge (m/z) ratios of gas-phase ions. Firstly, molecules are ionized in the ion source, originating gas-phase ions with different masses and charges (sometimes multiple charges). Secondly, gas ions are separated according to their m/z in the mass analyzer, and thirdly, the number of ions at each m/z is detected by the ion detector (Han et al., 2008) (Figure I-9). These three components can be combined in several fashions in order to create different platforms with different capabilities. Mass spectrometry applications in proteomic analyses originated after the development of soft ionization techniques such as Electrospray ionization (ESI) (Loo et al., 1989), and Matrix-assisted laser desorption/ionization (MALDI) (Tanaka et al., 1988), that allowed a less destructive ionization of peptides and proteins. However, the mass analyzer is the key component for the analysis, since it allows ion separation based on their m/z. Iontraps, Orbitraps, and quadrupoles, separate ions based on their stability, the ICR (Ion Cyclotron Resonance) based on their cyclotronic resonance, and time-of-flight (TOF) analyzers based on their time of flight. Nowadays, hybrid analyzers are the most commonly used, as they allow tandem mass spectrometry analyses, i.e., the analysis of previously MS separated ions. The targeted ions (precursor ions) are isolated and fragmented, and the product ions are detected in a second stage of mass spectrometry (Khalsa-Moyers and McDonald, 2006).
The efficiency of a MS proteomic analysis is highly dependent on the separation technology employed for protein/peptide separation in complex mixtures. For a long time, the most common approach was protein separation by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). In 2D-PAGE, proteins are resolved according to their isoelectric point and molecular weight, being posteriorly stained and digested in the gel. This allows a good separation of abundant proteins based on two distinct properties, and to visually identify the protein spots after gel staining. Protein spots of interest can then be isolated and analyzed by mass spectrometry in order to identify the protein through peptide mass fingerprinting. By using reference gels with annotated protein spots, it is possible to identify the protein without performing the MS analysis, just by superposing the gels. However, gel-based approaches have some limitations linked to their low-sensitivity, detection linearity, and gel reproducibility (Monteoliva and Albar, 2004). Liquid chromatography (LC) separation emerged as an excellent alternative to gel-based separation, because it allows separation at the peptide level (instead of the whole protein), and based on other properties depending on the column used, such as charge or hydrophobicity (America and Cordewener, 2008). This increases substantially the sensitivity and selectivity of the separation (Chen and Pramanik, 2009).
LC-MS is the method of choice for the analysis of complex protein samples, and opened the door for toxicologists addressing their biological problems in a more broad manner, through large-scale protein analysis, rather than being limited by the conventional focus on the role of single genes or proteins (Wetmore and Merrick, 2004). The “shotgun” proteomics approach, illustrated in Figure I-10, is the most commonly used LC-MS based technique, a global protein analysis where the aim is to identify the maximum number of proteins in a sample. In shotgun proteomics, proteins are enzymatically digested prior to the MS analysis with specific peptidases (e.g., trypsin, pepsin, proteinase K, endopeptidase Asp-N), followed by separation and sequencing of peptides by LC-MS/MS. Protein digestion can be performed in gel or in solution. Hundreds of proteins are identified through the comparison of the masses of MS/MS spectra obtained for the proteolytic peptides in the sample, with those predicted in silico from proteins present in public databases available in NCBI.
Mass-spectrometry-based protein quantitation
In large-scale proteome analysis, and especially when performing comparative proteomics between different experimental conditions, one must determine the abundance of the proteins present in the sample. MS protein quantification is a valid alternative to the classic ELISA immunoassays, with the advantage that it is not antibody-dependent. From a biomarker development point of view, the development of a MS quantification method is easier, quicker, and cheaper than ELISA. Moreover, the high-throughput capabilities of a mass spectrometer allow analyzing much more proteins simultaneously. Quantitative proteomics by MS can be divided in two major approaches: labelled and label-free approaches. Stable isotope labelling is the most used approach and consists in spiking the sample with a labelled version of the peptide or protein of interest, i.e. a “mass-tagged” variant of the peptide/protein. Labelled peptides/proteins are obtained through the introduction of one or more non-radioactive isotopes (2H, 13C, 15N, 18O), the mass tags, that can be inserted chemically after expression, metabolically during in vivo expression, by synthesizing the isotopically-labeled references or enzymatically by inserting the labels during protein digestion (Table I-2) (DeSouza and Siu, 2013).
Table of contents :
CHAPTER I STATE OF THE ART
1. PROTEIN BIOMARKERS IN ECOTOXICOLOGY AND MULTI-BIOMARKER APPROACHES
1.1. Biomarkers: definition, classification, examples and limitations
1.1.1. Examples of protein biomarkers used in animal Ecotoxicology
1.1.2. Limitations and perspectives
1.2. Multibiomarker approaches as an indicator of the health of organisms
2. OMICS TOOLS AND NEW STRATEGIES FOR MOLECULAR BIOMARKER DEFINITION
2.2.1. Protein identification by mass spectrometry
2.2.2. Proteogenomics for protein discovery in non-model species
2.2.3. Mass-spectrometry-based protein quantitation
2.3. Proteomics applications in Aquatic Ecotoxicology
2.4. Proposal of a proteomics biomarker pipeline for environmental applications ]
3. GAMMARIDS AS SENTINEL SPECIES FOR ENVIRONMENTAL MONITORING OF FRESHWATERS
3.1. Classification, morphology and ecology of Gammarus fossarum
3.2. Crustacean endocrine systems
3.3. Gammarus in Ecotoxicology and currently used biomarkers
3.4. Current status in “omics” approaches using Gammarus
4. OBJECTIVES OF THE THESIS
Development and application of a multiplexed protein biomarker measurement by mass spectrometry
Endocrine disruption biomarkers
CHAPTER II MATERIALS AND METHODS
1. BIOTESTS IN GAMMARUS FOSSARUM
1.1. Sampling and maintenance of organisms
1.2. Biological procedures
1.2.1. Reproductive toxicity test
1.2.2. Gonad dissection
1.2.3. Sampling of organisms at different physiological conditions
1.3. Protocols of contamination exposures
1.3.1. Laboratory exposures to model contaminants
1.3.2. In situ exposures
2. PROTEOMIC ANALYSES
2.1. Targeted proteomic analysis by Selected Reaction Monitoring
2.1.1. Candidate selection based on proteogenomics-derived protein sequences
2.1.2. Analytical validation of the methodology
2.1.3. Protein extraction and digestion
2.1.4. LC-MS/MS analysis
2.1.5. Data exploitation and absolute quantification
2.2. Shotgun proteomics of male gonads
2.2.1. Protein extraction and digestion
2.2.2. LC-MS/MS analysis
2.2.3. Protein identification and relative quantification by spectral counting
3. GENETIC ANALYSES
3.1. Sequence alignments and phylogenetic analyses
3.2. Protocols for gene expression
3.2.1. Total RNA extraction and cDNA synthesis
3.2.2. PCR amplification, cloning and sequencing
3.2.3. Quantitative real-time PCR
3.2.4. Data exploitation and relative quantification of gene expression
CHAPTER III DEVELOPMENT AND APPLICATION OF A MULTIPLEXED PROTEIN BIOMARKER MEASUREMENT BY MASS SPECTROMETRY IN GAMMARUS FOSSARUM
CHAPTER IV DEVELOPMENT OF SPECIFIC BIOMARKERS FOR ENDOCRINE DISRUPTION ASSESSMENT IN GAMMARUS FOSSARUM
PUBLICATION Nº 4
NOTE 1: IDENTIFICATION OF CANDIDATE GENES VIA LITERATURE SEARCH
NOTE 2: REPROTOXICITY ASSESSMENT OF MODEL MOLECULES IN FEMALE GAMMARIDS
CHAPTER V SYNTHESIS AND GENERAL DISCUSSION
1. DEVELOPMENT OF A NEW MS-BASED MULTIBIOMARKER ABSOLUTE QUANTIFICATION IN GAMMARUS
2. ENDOCRINE DISRUPTION BIOMARKERS
2.1. Comparative shotgun proteomics for ED biomarker discovery
2.2. Candidate gene approach
CONCLUSIONS & PERSPECTIVES