Proteomic research in bivalves

Proteomic research in bivalves

J O U RN A L OF P ROT EO M IC S 7 5 ( 2 0 12 ) 43 4 6 –43 5 9 Available online at www.sciencedirect.com www.elsevier.com/locate/jprot Review Prote...

490KB Sizes 3 Downloads 80 Views

J O U RN A L OF P ROT EO M IC S 7 5 ( 2 0 12 ) 43 4 6 –43 5 9

Available online at www.sciencedirect.com

www.elsevier.com/locate/jprot

Review

Proteomic research in bivalves Towards the identification of molecular markers of aquatic pollution Alexandre Camposa, 1 , Sara Tedescob, 1 , Vitor Vasconcelosa, c , Susana Cristobalb, d,⁎ a

CIIMAR/CIMAR - Centre of Marine and Environmental Research, University of Porto, Rua dos Bragas 289, 4050‐123 Porto, Portugal Department of Clinical and Experimental Medicine, Cell biology, Faculty of Health Science Linköping University, Linköping, Sweden c Department of Biology, Sciences Faculty, University of Porto, Rua do Campo Alegre, 4169‐007 Porto, Portugal d IKERBASQUE, Basque Foundation for Science, Department of Physiology. Basque Country Medical School, Bilbao, Spain b

AR TIC LE I N FO

ABS TR ACT

Article history:

Biomonitoring of aquatic environment and assessment of ecosystem health play essential

Received 9 January 2012

roles in the development of effective strategies for the protection of the environment,

Accepted 20 April 2012

human health and sustainable development. Biomarkers of pollution exposure have been

Available online 2 May 2012

extensively utilized in the last few decades to monitor the health of organisms and hence assess environmental status. However, the use of single biomarkers against biotic or abiotic

Keywords:

stressors may be limited by the lack of sensitivity and specificity. Therefore, more recently,

Bivalves

the search for novel biomarkers has been focused on the application of OMICS

Environmental toxicology

methodologies. Environmental proteomics focuses on the analysis of an organism's

Proteomics

proteome and the detection of changes in the level of individual proteins/peptides in

Biomarkers

response to environmental stressors. Proteomics can provide a more robust approach for the assessment of environmental stress and therefore exposure to pollutants. This review aims to summarize the proteomic research in bivalves, a group of sessile and filter feeding organisms that play an important function as “sentinels” of the aquatic environment. A description of the main proteomic methodologies is provided. The current knowledge in bivalves' toxicology, achieved with proteomics, is reported describing the main biochemical markers identified. A brief discussion regarding future challenges in this area of research emphasizing the development of more descriptive gene/protein databases that could support the OMICs approaches is presented. This article is part of a Special Issue entitled: Farm animal proteomics. © 2012 Elsevier B.V. All rights reserved.



This article is part of a Special Issue entitled: Farm animal proteomics. ⁎ Corresponding author at: Department of Clinical and Experimental Medicine, Cell biology, Faculty of Health Science, Linköping University, Linköping, Sweden. Tel.: +46 10 1030881. E-mail addresses: [email protected] (A. Campos), [email protected] (S. Tedesco), [email protected] (V. Vasconcelos), [email protected] (S. Cristobal). 1 These authors have equally contributed to the article.

1874-3919/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jprot.2012.04.027

4347

J O U RN A L OF P ROT EO M I CS 7 5 ( 2 0 12 ) 43 4 6 –4 35 9

Contents 1. 2. 3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bivalve proteomics, bioindicators in environmental toxicology . 2DE-based proteomics . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Laboratory experiments and chemical pollutants . . . . . 3.2. Technical aspects of 2DE in environmental toxicology . . 3.3. Exposure to other environmental stressors . . . . . . . . 3.4. Post-translational modifications . . . . . . . . . . . . . . 4. Other OMICS in bivalves: applications in environmental studies . 4.1. Genomics . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Metabolomics . . . . . . . . . . . . . . . . . . . . . . . . 5. Future perspectives . . . . . . . . . . . . . . . . . . . . . . . . . Authors' contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1.

Introduction

Biomonitoring has become an important component in programmes currently followed worldwide to assess the water status in the oceans and river basins, providing specific information regarding the dynamics of pollutants (their persistence in the environment), and their effects on species' health. In Europe for instance, biomonitoring programmes have been developed by the member states to implement the Water Framework Directive (EPC, 2000) [1], established by the European Parliament with a general aim to protect and restore clean water across Europe and ensure its long-term, sustainable use. Similar directives have been proposed in non-EU countries such as the USA or from emerging economies such as China, India or Brazil. Biomonitoring concerns, in most cases, the direct quantification of pollutants in the tissues of exposed organisms. This implies the possibility of bioaccumulation. Other parameters which can help to clarify the source of pollution and its toxic potential are the health condition index of the organisms or the expression of specific biomolecules in response to xenobiotics. In this sense, the activity of an array of enzymes involved in oxidative stress defense, energy metabolism and xenobiotic detoxification can be estimated, in addition to the levels of cellular stress derived metabolites (e.g. lipid peroxides) [2,3]. The fluctuation of these enzymatic activities due to biotic and abiotic factors is an additional limitation frequently reported [4]. Nevertheless some limitations to the biomonitoring protocols employed have been recognized, particularly in determining the specific pollutant that affected the ecosystem when analytical data regarding the chemical is absent, and in detecting pollution in an early phase of progression and evaluating long term effects of pollution in wildlife [5]. It is evident that there is a need for a profound knowledge of the response of aquatic organisms to most frequent and persistent water pollutants and a deep characterization of the molecular markers involved. Initiatives dealing with biomonitoring combine the effect of exposure to pollutant mixture with gathering some insight into the pollutant interactions. The pollutant mixtures' interactions, and whether the effects of a combination of pollutants are greater than the sum of the

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

4347 4348 4348 4348 4350 4351 4352 4353 4353 4354 4355 4356 4356 4356

effects of individual pollutants are still relatively un-explored [6]. Biomonitoring may contribute to the identification of more specific, sensitive markers of pollution, and provide an accurate estimation of ecosystem health. The large scale analyses of proteins, genes and other biomolecules, provided by OMICs approaches (proteomics, genomics and metabolomics) can contribute to this search. Environmental proteomics for instance aims to analyze the proteome of organisms and to identify variations in the proteins induced by xenobiotics. This investigation has the potential to identify not only single protein markers, but also to generate protein patterns that react robustly and specifically to particular pollutants [7]. Bivalves have been regarded as preferential species for biomonitoring since they are sessile, filter feeders and thus capable of accumulating high levels of contaminants, providing therefore temporally and spatially integrated levels of contamination [8]. Moreover, different species can be found in most aquatic environments in the world with some having an invasive distribution. Therefore, biomonitoring protocols can be widely used facilitating the comparison and interpretation of data. Historically, mussels have been utilized to evaluate the biological consequences of oil spills in marine environments, such as after the accidents of the Exxon Valdez in Alaska [9] and Prestige in NW Spain [10]. Proteomics has, in the last decade, been applied to bivalves to support the toxicological studies in this group of animals. Data have been gathered from a broad range of hazardous chemicals (e.g. metals, polyaromatic hydrocarbons, diallylphtalate, polybrominated diphenyl ethers) [7,11–13] and also natural toxins, from both laboratorial and field work [14–17]. Current methods of two-dimensional gel electrophoresis of proteins (2DE) and mass spectrometry (MS) have been used most frequently for proteomic analysis, allowing reporting alterations in structural proteins, and key proteins of the oxidative stress defense mechanisms, metabolism of xenobiotics, cell signaling, protein stabilization, energy metabolism and metabolism of lipids, and amino acids [13,18,19]. Other achievements respect the determination of protein/expression signatures with potential to classify conditions where multiple factors (e.g. different chemicals, temperature, salinity) contribute to the physiological state of

4348

J O U RN A L OF P ROT EO M IC S 7 5 ( 2 0 12 ) 43 4 6 –43 5 9

the organism. Some progress in the current state of knowledge may be expected with the utilization of more sensitive techniques involving protein labeling with fluorescent dyes (fluorescence difference gel electrophoresis, DIGE), isotope coded affinity tag (ICAT), isobaric tag for relative and absolute quantitation (iTRAQ) or 16O/18O labeling (Fig. 1). However, the lack of genomic sequences from bivalves is still preventing large-scale utilization of MS-based proteomics in this area. Advanced MS approaches can also be considered in this research topic to better describe the protein posttranslational modifications (PTMs) induced by the action of hazardous chemicals. This review aims to summarize the research undertaken so far in environmental proteomics using bivalves as model organisms, with reference to the main methodologies employed, and by describing the candidate markers retrieved by the investigation. We will discuss the limitations of applying such approaches to organisms with limited genomic information in databases and future challenges in this area of research.

2. Bivalve proteomics, bioindicators in environmental toxicology Bivalves have been extensively utilized as bioindicators in environmental toxicology in the past few decades and marine mussels have attracted most of the interest. Most of the scientific research is addressed in using marine mussels rather than freshwater species mainly because 1) they have a strong economic interest associated to human consumption and aquaculture, 2) they are more abundant especially in temperate regions where climate conditions facilitate the sampling, 3) they are more resistant to a wide variety of pollutants and environmental stress, making them ideal sentinel organisms for marine monitoring programs. The National Oceanic and Atmospheric Administration (NOAA) established the National Status and Trends (NS&T) Mussel Watch Project (MWP) in 1986 to assess the thencurrent status and the long-term trends for selected contaminants in US coastal waters [20]. Afterwards, numerous environmental monitoring programs around the world have

undertaken routine sampling and surveillance of bivalve molluscs (Mollusca: Bivalvia). Biomarkers can provide an early signal of significant biological effects in the aquatic environmental monitoring, especially for the sub-organism responses (e.g. molecular, biochemical and physiological) [21]. Several enzymes (e.g. cholinesterase, catalase, glutathione transferase), other proteins (e.g. heat shock proteins, metallothioneins), hormones (e.g. vitellogenin) [22] and organelles (e.g. lysosomes, peroxisomes) [23,24] have been selected as biomarkers for specific chemicals [14], but their use depends on a priori knowledge of toxicity mechanisms and is essentially hypothesis driven [25]. They are biased in favor of relatively few proteins whilst excluding many others which may also be altered by pollutants but cannot be connected to them by any a priori hypothesis [26]. The investigation of the proteome offers, instead, potential to identify new biomarkers and draw conclusions about the molecular mechanisms behind the mode of action of pollutants. In aquatic toxicology, Shephard and Bradley [27] and Shephard et al. [28]] were the first scientists to analyze the proteome in the bivalve Mytilus edulis exposed to copper, polychlorinated biphenyls (PCBs) and salinity stress. Although these studies were performed in laboratory conditions, the observation of protein expression signatures (PESs) from the comparison of two-dimensional gel electrophoresis (2-DE) for each treatment gave a starting point in the use of proteomics in other aquatic species and in field experiments. Subsequently other bivalves such as Mytylus galloprovincialis [29] and Perna viridis [18], clams Ruditapes decussatus [11,12,30], Chamaelea gallina [31] the freshwater mussel Unio pictorumoysters [32], rock oyster Saccostrea glomerata [19] and zebra mussel Dreissena polymorpha [13] were utilized in proteomic studies.

3.

2DE-based proteomics

3.1.

Laboratory experiments and chemical pollutants

For the past 10 years several research groups have been developing proteomics in bivalves, aiming to gather new insights into the toxicity of some dangerous and priority

Proteomics in bivalves

Proteome

2DE

subproteome

Gel-free proteomics

2D-DIGE

Quantitation analysis (imaging softwares)

Spots picking & MS analysis

Protein array

Statistics (e.g. PCA)

Bioinformatics

SELDI-TOF

Traditional

Emerging

Isotope label quantitation

Label-free quantitation

ICAT, iTRAQ,

AUC,

16O/18O

SCI

Unexplored proteomic techniques in bivalves studies

Fig. 1 – Overview of traditional (yellow boxes), emerging (green boxes), and unexplored (blue boxes) proteomic techniques in bivalves. The abbreviation SCI was chosen for Spectral Counting Index.

J O U RN A L OF P ROT EO M I CS 7 5 ( 2 0 12 ) 43 4 6 –4 35 9

hazardous substances listed in the Water Framework Directive [1]. The main achievements of proteomic approaches have been the determination of PESs which represent putative biomarkers of the presence and action of a particular chemical or xenobiotic. Laboratory experiments have also provided a means of investigating in more detail, the molecular mechanisms underlying the toxicity of a xenobiotic and the bioaccumulation and transformation processes. Shepard et al. [28] first reported the alterations in protein expression in M. edulis, when exposed in laboratorial conditions to copper (70 ppb), aroclor 1248 (1 ppb), and to lower levels of salinity, using 2DE and quantitative analysis of protein abundance by gel image analysis. The method provided a clear separation of 500–600 protein spots and discriminated unique PES, considered by the authors as key protein changes for each stressor used. These results emphasized the importance of the method in biomonitoring. Employing 2DE and MS (peptide mass fingerprint and tandem MS, Fig. 1) Rodriguez-Ortega et al. [31] analyzed the alterations in cytosolic proteins of C. gallina when exposed to aroclor and other water contaminants (copper (II), tributyltin and arsenic (III)) in distinct concentrations. The authors were able to detect alterations in the proteome of exposed bivalves, reporting the specificity of the PESs for a particular contaminant. The analysis of 15 proteins by peptide mass fingerprinting (PMF) and liquid chromatography coupled to tandem MS (LC-MS/MS) followed by homology search in SWISS-PROT and TrEMBL databases, led to unambiguous identification of 4 cytoskeletal proteins (tropomyosin, two isoforms of actin and myosin). The exclusive identification of cytoskeletal proteins was suggested by the authors to reflect their relative abundance in the proteome of the organism, their prevalence in databases or their role as major targets of pollutant-related oxidative stress. In fact cytoskeletal proteins are frequently detected in 2DE based approaches, emphasizing that the technique is most appropriate for the separation and analysis of this group of proteins (Table 1). Metal toxicity was investigated in R. decussatus by exposing bivalves to cadmium (Cd) (40 μg/L) over 21 days and then analyzing the variations in the gill and digestive gland proteomes by 2DE. Tissue-specific protein expression profiles to cadmium exposure were reported, which might reflect differences in the metabolism of cadmium accumulation. Proteins identified by LC-MS/MS and homology search further indicated that cadmium toxicity may imply alterations in the cytoskeleton, amino-acid and fatty acid metabolism and regulation of membrane protein traffic [12]. Proteomics has been employed also to characterize the biochemical effects of polychlorinated aromatic hydrocarbons (PAHs) in bivalves. A 2DE approach was employed to investigate the time course response of heat shock protein (HSP70) isoforms in C. gallina, affected by the PAH benzo[a] pyrene (B[a]P). Using specific antibodies against these proteins, the authors reported changes in inducible (HSP70) and in constitutive (HSC70) forms which might reflect a biochemical response of the organisms towards adaptation to stress and to a normal protein synthesis capability, respectively [33]. Increasing the resolution of 2DE separation and complementing the proteomic analysis with high throughput MS/MS, Riva et al. [13] reported variations in distinct functional groups of

4349

proteins in the fresh water bivalve D. polymorpha exposed to the same chemical pollutant, indicating that biochemical pathways of the oxidative stress, signal transduction, cellular structure are altered during xenobiotic exposure, playing a putative role in cellular toxicity. In this work data from male and female animals were interpreted separately allowing observation of a marked gender difference in altered protein levels, emphasizing the need to consider D. polymorpha gender in proteomic-based investigations. Only 37% of the proteins analyzed by MALDI-TOF/TOF were successfully identified, demonstrating the limitations of the reduced gene sequence information to describe the proteome changes. Aiming to understand the role of peroxisomes in the toxicity of xenobiotics, Apraiz et al. [8] conducted a proteomics investigation to the organelle in M. edulis exposed to diallylphtalate, polybrominated diphenylether (PBDE-47) and bisphenol A. Peroxisomes are present in all eukaryotic cells, being involved in the catabolism of fatty acids, D-amino acids, polyamines, and oxidative stress response [34]. It is however recognized that a broad variety of pollutants of industrial, agricultural and urban origin induce peroxisome proliferation which might suggest the involvement of the organelle in processes of defense and xenobiotic metabolism [8]. Enriched peroxisome fractions from groups of control and treated mussels were obtained by differential and density gradient centrifugation. Differential expression of proteins was further investigated using 2D-DIGE, combined with MS/MS analysis. Principal component analysis (PCA, Fig. 1) and hierarchical cluster analysis to the proteomics data were undertaken in order to discriminate pollutant specific PESs. The methodology was shown to be effective for the analysis of peroxisome specific enzymes. Proteomic analysis provided evidence for alterations in the metabolism of amino acids, glycerophospholipids, alcohols, fatty acid catabolism and peroxisomal oxidation (Table 1). Moreover variations in the abundance of cytochrome P450, glutathione transferase, Mn-superoxide dismutase and several catalase isoforms indicated an increased activity in the peroxisomes towards oxidative stress defense and a putative inhibition of phase II reactions of the xenobiotic metabolism. This is probably the first proteomic work reporting specifically alterations in enzymes of xenobiotic metabolism and antioxidant defense in bivalves, emphasizing the relevance of the approach, especially in the fractionation of the proteome, to increase the analytical capacity of specific subsets of proteins. The effects of crude oil, oil spiked with alkylphenols or PAHs in protein expression of bivalves were investigated taking as the object of analysis M. edulis hemolymph. A gelfree proteomics approach was employed based on weak cation-exchange protein chip arrays combined with surfaceenhanced laser desorption/ionization time-of-flight MS (SELDI TOF MS, Fig. 1). Differential mass peaks were reported after mussel exposure to spiked oil or oil respectively, accounting for exposure- or gender-specific responses of certain mass peaks. Multivariate analysis with regression tree based methods allowed selection of protein expression patterns associated with exposure that correctly classified masked samples with 90–95% accuracy [35]. Tissues from the same exposure were analyzed in another laboratory utilizing a gel based-subproteomic approach [6]. Although both laboratory

4350

J O U RN A L OF P ROT EO M IC S 7 5 ( 2 0 12 ) 43 4 6 –43 5 9

Table 1 – List of putative biomarkers of xenobiotic toxicity in bivalves, assessed by proteomics. Protein

Main function

Contaminant

Methodology

Publication

Tropomyosin

Cytoskeleton

Aroclor 1254, Cu (II)

Actin

Cytoskeleton

Tubulin

Cytoskeleton

Rab GDP, Aldehyde dehydrogenase Acyl CoA dehydrogenase HSP70, HSC70 Dual specific phosphatase N-acetyltransferase

Cell maintenance Amino acid metabolism

Aroclor 1254, Cu (II), TBT, As(II), Cd, benzo[a]pyrene Cd, diallylphtalate, PBDE-47, bisphenol A Cd Cd, diallylphtalate, bisphenol A

2DE; PMF and MS/ MS; WB 2DE; PMF and MS/ MS; WB 2DE and MS/MS

Fatty acid ß-oxidation

Cd

2DE and MS/MS

Rodriguez-Ortega et al. [31], Grøsvik et al. [108] Rodriguez-Ortega et al. [31], Grøsvik et al. [108] Chora et al. [8], Apraiz et al. [8] Chora et al. [8] Chora et al. [8], Apraiz et al. [8] Chora et al. [8]

Chaperones Cell signaling

Benzo[a]pyrene Benzo[a]pyrene

1D, 2DE and WB 2DE and MS/MS

Jurgen et al. [13] Riva et al. [13]

Metabolism of arylamines Amino acid metabolism

Benzo[a]pyrene

2DE and MS/MS

Riva et al. [13]

Benzo[a]pyrene

2DE and MS/MS

Riva et al. [13]

Metabolism of alcohols

Benzo[a]pyrene, diallylphtalate, PBDE-47, bisphenol A Benzo[a]pyrene

2DE and MS/MS 2DE and MS/MS

Riva et al. [13], Apraiz et al. [8] Riva et al. [13]

Diallylphtalate, bisphenol A Diallylphtalate, bisphenol A

2DE and MS/MS 2DE and MS/MS

Apraiz et al. [8] Apraiz et al. [8]

2DE and MS/MS

Apraiz et al. [8]

2DE and MS/MS

Apraiz et al. [8]

2DE and MS/MS

Apraiz et al. [8]

2DE and MS/MS

Apraiz et al. [8]

2DE 2DE 2DE 2DE

Apraiz Apraiz Apraiz Apraiz

Aspartate aminotransferase Alcohol dehydrogenase Peroxiredoxin Hydroxyacid oxidase Glutathione transferase Carbonic anhydrase

Cell redox homeostasis— antioxidant Peroxisomal oxidation Xenobiotic metabolism

Hydration of carbon Diallylphtalate, bisphenol A dioxide Mn-superoxide Oxyradical metabolism— Diallylphtalate, bisphenol A dismutase antioxidant Cytochrome C oxidase Respiratory electron Diallylphtalate, bisphenol A subunit II transport chain Catalase Hydrogen peroxide Diallylphtalate, PBDE-47, bisphenol A decomposition ATPase ß-subunit Synthesis of ATP PBDE-47, bisphenol A Peroxin 10 Peroxisomal assembly PBDE-47, bisphenol A Cytochrome P450 Xenobiotic metabolism PBDE-47, bisphenol A Enoyl-CoA hydratase Fatty acid catabolism or PBDE-47, bisphenol A ß-oxidation Phospholipase A2 Metabolism of PBDE-47, bisphenol A glycerophospholipids

exposures, to crude oil, and to oil spiked with alkylphenols or PAHs shared a common PES, the latter showing the stronger variation both in amount and in the level of the differential expression. However, more laboratory experiments should be performed to elucidate the effect of chemical mixtures that is still one of the huge unknowns in toxicology. Nevertheless, without the knowledge of the protein identity, it is not possible to link the key molecules and their functions to provide essential mechanistic information on the underlying toxicity, and as stated above, a strong correlation may be achieved between PES and different environmental factors (e.g. chemical pollutants, salinity, temperature) which makes them powerful tools to classify the ecological status of the environment. More recently Sydney oysters (S. glomerata) haemolymph was utilized to assess the effect of environmentally relevant concentration of different metals (cadmium, copper, lead, zinc) in a 4-day laboratory experiment. Several of the identified proteins were affected only by one type of metal. Furthermore protein synthesis was the biochemical process

2DE and MS/MS 2DE and MS/MS

and and and and

MS/MS MS/MS MS/MS MS/MS

2DE and MS/MS

et al. et al. et al. et al.

[8] [8] [8] [8]

Apraiz et al. [8]

more drastically altered by all four metals [19]. Some potentially novel biomarkers of exposure to metals should be considered such as vasa, a member of the DEAD box helicase family of proteins and involved in RNA metabolism, which most consistently changed in expression over a wide range of metals and doses [19].

3.2.

Technical aspects of 2DE in environmental toxicology

In the area of field experiments, there are still few proteomic studies that have used bivalves to evaluate the effects in aquatic environments with different pollution levels [36–39]. Environmental proteomics, or “ecotoxicoproteomics” is focused mainly on aquatic vertebrates [40] because invertebrates, like bivalves, are not fully covered in the sequence databases. Protein identification therefore is more difficult and possible only by homologies with sequences of other species. Experiments in laboratory conditions make it easier to identify the altered PESs related to the specific stressorexposure compared to the field experiments where multiple

J O U RN A L OF P ROT EO M I CS 7 5 ( 2 0 12 ) 43 4 6 –4 35 9

variables interact with each other. In fact pollutants in aquatic environments may have direct effects on local aquatic biota or they may act in an additive, synergistic, or antagonistic manner in concert with physico-chemical conditions such as salinity, light, pH, temperature, turbidity [41,42]. Moreover, other issues in aquatic environmental monitoring include biological factors; bivalves have seasonal metabolic and enzymatic activity variations related to environmental changes such as temperature, food availability, oxygen levels, physiological factors and gonadal development [43]. Environmental monitoring therefore needs not only seasonal sampling, but also numerous sites to compare different pollution levels. Hence, the sample preparation of biological replicates and relative separation of the whole proteome by 2DE (3–4 technical replicates), followed by quantification and identification analysis is time-consuming and expensive work with difficulties in the interpretation of the large amount of data generated. Bivalves, in comparison to fish, have an additional limitation in proteome studies, related to the size and/or the amount of total proteins present for each tissue. The protein concentration is often relatively low and habitually with a decreasing amount in digestive gland, mantle and gill respectively. In most procedures, and especially for subproteome studies, it is necessary to pool several organisms. This procedure might cause the disappearance of specific proteins that can be visible only in the individual gels [44] and therefore affect the statistical power. By contrast, a high number of biological replicates can decrease global protein spot matching efficiency in 2DE analysis [45]. The constraint of the technical replicates used to consider the error of the experimental technique and the sample scarcity can be largely overcome with DIGE (Fig. 1). This technique has been successfully used only in the study of the peroxisomal proteome in M. edulis under laboratory conditions [6,8]. Up to now, any other DIGE application in bivalves from field experiments were reported, probably due to the high costs of this protein labeling procedure, especially in samples collected during large environmental monitoring campaigns. Another interesting 2DE based proteomic approach in bivalves is the study of the PTMs (e.g. glutathionylation, carbonylation, ubiquitination) through the use of specific antibodies. One of the first articles presented a combination of gel electrophoresis and western blotting for the detection of carbonylation and glutathionylation of proteins in M. edulis [46] sampled from a polluted and reference site in Cork Harbour, Ireland. The technique will be, nevertheless, dependent on the capability of protein recognition by antibodies specific for homologous proteins from other species. Afterwards the study of some PTMs have been used in other bivalve species such as R. decussatus [11,30] or in M. edulis to understand the effects of emerging pollutants such as nanoparticles, under laboratory conditions [47]. Modern proteomic technologies evaluating PTMs in subproteomes are slowly increasing in the environmental research [48] (Fig. 1). 2DE, western blotting, and the sample preparation techniques useful for the purification of peptides and PTMs are available in commercial kits [48–50] and could be potentially useful in routine aquatic monitoring with bivalves.

4351

The studies of subproteomes in environmental research will likely increase in the future because of the implied simplification of the subsequent analysis. As ubiquitous and abundant proteins often dominate the outcome of the proteomic studies surveyed, exploiting sub-proteomes and low-abundant proteins is sometimes desirable [51]. One of the major drawbacks of 2DE-based proteomic is the possibility to resolve only a few thousand proteins in the best case and no more than a few hundred in the format of 2DE maps [52]. In particular, a typical 2DE can visualize only 30– 50% of the entire proteome, depending on the type of tissue [53], excluding low concentration proteins or those that cannot be separated by 2DE due to their physicochemical properties (pI, hydrophobicity, molecular weight) [54]. A possible solution to this problem is visualizing low abundance proteins by adding a further fractionation step in the sample preparation. Amelina et al. [37] showed how combining simple subcellular fractionation with liquid chromatography, coupled with 2DE in the bivalve M. edulis, could be scalable to automation and, potentially, be an affordable technique for large monitoring campaigns. Depending on the studies 2DE can show to be timeconsuming, costly, insensitive to low abundance proteins and unable to separate properly the whole proteome in bivalves, especially for routine sampling in aquatic monitoring [55]. In recent years, efforts have been focused on alternative approaches to 2DE, such as “gel-free” proteomics where hydrophobic proteins and peptides can be analysed. Instead of using 2DE, these approaches use multi-dimensional LC-MS/ MS to separate and identify peptides obtained from the enzymatic digest of an entire protein extract. These shotgun proteomic approaches have the limitation of identifying a protein based on the sequence of a single (or a few) tryptic peptide(s) derived from this protein [53]. Hence, the exclusive use of shotgun proteomics in bivalves, although requiring a much shorter time for the analyses cannot always discriminate between isoforms or important PMTs symptomatic of specific pollutant effects. In these circumstances 2DE may bring some advantages by delivering a map of intact proteins where isoforms or PTMs can be easily visualized by deviations in isoelectric point (pI) (e.g. phosphorylation) or relative mass (Mr) (e.g. glycosylation) whilst this information is lost in gelfree proteomics [56]. For these reasons, the use of 2DE and LCMS will likely be complementary approaches in future proteomic studies in bivalves. 2DE is now routinely used, while “gel-free” proteomic experiments are not and, conversely, are increasing in complexity [57]. Environmental field proteomic studies in bivalves are still quite challenging although opportune integration contribute to a future application in aquatic monitoring.

3.3.

Exposure to other environmental stressors

Climate change has been regarded as an important factor for the geographic distribution of species worldwide leading, for instance, to the colonization of new habitats by more adapted invading species. The presence of M. galloprovincialis along the Californian coast, replacing in many areas the native species M. trossulus may reflect an increase in the water temperature in this region. M. galloprovincialis is considered a

4352

J O U RN A L OF P ROT EO M IC S 7 5 ( 2 0 12 ) 43 4 6 –43 5 9

warm climate adapted species, with a natural distribution in the Mediterranean extending northward to the coast of France and the British Isles, whereas M. trossulus is a cold water mussel, present in the Baltic Sea and along the north coast of California and Canada. In order to gather new insights on the molecular mechanisms that account for the differential tolerance to temperature in the two mussel species and that could explain the invasion capability of M. galloprovincialis, the proteomes of both species were investigated in response to acute heat stress [58]. The differences in protein expression were assessed quantitatively using high resolution 2DE and statistical analysis complemented with cluster and PCA analysis to help decipher which proteins contributed most to heat tolerance. The identification of proteins involved in protein folding, proteolysis, energy metabolism, oxidative damage, cytoskeleton and deacetylation revealed common loci of heat stress in both mussels. This study also showed lower sensitivity to high-temperature damage in the warmadapted M. galloprovincialis, providing biochemical evidence for the capacity of colonization in warmer waters [58]. The more cold-adapted M. trossulus for instance showed increasing levels of several (acidic) HSP70 and sHSP isoforms at lower temperatures in comparison with the more warm-adapted M. gallorovincialis underlining the crucial role of chaperones in protecting cells from acute heat stress and indicating interspecific differences in thermal tolerance. The contrasting expression of several proteasome subunits in M. galloprovincialis and M. trossulus, suggests that protein degradation mechanisms have a role in setting thermal limits [58] or indicates that different pools of denatured proteins are produced in the two species upon heat stress. The expression of antioxidant enzymes (thioredoxin, superoxide dismutase) or enzymes associated with oxidative stress tolerance (aldehyde dehydrogenase, transketolase, 6-phospho-glucono lactonase) highlights the robustness of the enzymatic system in M. galloprovincialis to cope with oxidative stress. PCA analysis revealed tubulin as a main marker for the interspecific differences in heat shock responses. The decrease observed for three proteins (cytosolic malate dehydrogenase, aspartate amino transferase and phosphoenolpyruvate carboxykinase) supports the hypothesis that Mytilus gill tissue may decrease the production of NADH but increase the production of NADPH in response to heat stress. This change may, in fact, play an important role in control of oxidative damage in Mytilus. Toxic cyanobacteria proliferation has been regarded as an emerging health hazard, contributing to the degradation of the water quality worldwide. The occurrence of cyanobacteria outbreaks are frequently associated with increased water eutrophication. Whilst the toxic effects of some of the most frequent cyanotoxins in animals and humans are well known, the effects on other aquatic species and communities are relatively less well-characterized. Proteomics has great potential in this research area contributing to the identification of new biotoxins produced by cyanobacteria, and to elucidate the mechanisms of action in target organisms. In an effort to identify new molecular biomarkers of the effects of toxic cyanobacteria, Martins et al. [14] exposed the fresh water clam Corbicula fluminea to live Microcystis aeruginosa cells during 24 hours and compared the 2DE protein profiles of cytosolic proteins from gills and digestive tract samples. Quantitative

variations in 13–16 protein spots were detected in digestive gland tract and gill samples respectively. Some of the proteins were identified by MALDI-TOF/TOF analyses and these perform functions in cytoskeleton assembly, dynamics and carbohydrate metabolism. The authors suggested the inhibition of phosphatase activity by the specific inhibitor microcystin, as a main mechanism responsible for the reported modifications in cytoskeletal proteins. In sequence with this first study a comparative proteomics investigation was undertaken to assess the differential response of two representative bivalve species from marine and fresh water habitats, M. galloprovincialis and C. fluminea, to the toxic cyanobacteria Cylindrospermopsis raciborskii [15]. Alterations in the proteome of gills and digestive tract were assessed employing high resolution 2DE and MALDI-TOF/TOF analysis. The results allowed the first molecular interpretation of physiological stress and toxicity induced by this cyanobacteria species. The estimation of GST and glutathione peroxidase activities supported the conclusion that oxidative stress is a main mediator of protein expression. Lopez et al. [29] analyzed the expression of proteins from the foot of two populations of M. galloprovincialis growing in different ecological environments by 2DE. The quantitative analysis of protein profiles allowed the detection of a high number of PES that the authors assumed to reflect the physiological adaptation of each population to their respective environment. The differential expression of HSP70, identified by PMF and MS/MS analysis, may play a role in mussel adaptation to the most stressful conditions of the intertidal zone [29].

3.4.

Post-translational modifications

Global analysis of protein modification due to oxidative stress is defined as redox proteomics. Some of the most reported oxidative modifications in proteins are the formation of carbonyl groups, which occur by direct amino acid oxidation and the α-amidation pathway or indirectly by forming adducts with lipid peroxidation products or glycation and advanced glycation end-products [59]; formation of disulfide bonds, covalent binding to glutathione or the formation of methionine sulfoxide [60]. Protein PTM by oxidation leads frequently to the loss of protein function, aggregation and degradation by the proteasome and lysosome. Oxidative stress is recognized as one of the main mechanisms of toxicity induced by environmental pollutants. It is therefore likely that this condition increases protein inactivation through PTMs contributing to an imbalanced metabolism and increased cellular stress. In this respect redox proteomics may play an important role in environmental toxicology providing new molecular markers of the presence of pollutants in the environment and helping revealing the mechanisms of action of xenobiotics. Alterations in disulphide bonds (S―S) of proteins were first assessed in M. edulis exposed to hydrogen peroxide (H2O2) to elicit oxidative stress [36]. The authors employed diagonal gel electrophoresis to detect the proteins displaying S―S modifications. Western blot analysis allowed identification of actin among the group of modified proteins. Carbonylation and glutathionylation of proteins was also investigated demonstrating that in addition

J O U RN A L OF P ROT EO M I CS 7 5 ( 2 0 12 ) 43 4 6 –4 35 9

to S―S modifications this cytoskeleton constituent may form glutathione adducts in cells subjected to oxidative stress [36]. Extensive and comparable levels of protein carbonylation, detected in 2DE gels after derivation of the modified proteins with dinitrophenyl (DNP)-hydrazine and using anti-DNP antibodies for their detection, were subsequently reported across a pro-oxidant panel composed of H2O2, CdCl2 and menadione. Results achieved suggest high susceptibility of gill proteome to oxidative PTMs [61]. The redox proteomics approach has been extended to other hazardous compounds such as copper, octane petrol, nonyphenol and gold nanoparticles, demonstrating that the toxicity associated to these compounds may derive from an excessive increase of protein PTMs with subsequent inactivation [12,30,47]. Such biochemical alterations may be reflected for instance in the scope for growth and fitness of exposed bivalves [62]. Higher levels of protein ubiquitination were verified, using 2DE and western blotting with ubiquitin-specific antibodies, in soluble protein fractions from digestive glands and gills of R. decussatus exposed to the environmental contaminants cadmium and nonyphenol [30,63]. The binding of ubiquitin to a protein is a labelling process required for proteasome recognition and subsequent degradation of the protein. High levels of ubiquitination reported due to cadmium and nonyphenol hence supports the hypothesis that these compounds increase the amount of inactive proteins subsequently eliminated via the proteasome. Although significant information was provided using the above methodologies, current methods applied in medical research, involving the isolation/enrichment of oxidative stress modified proteins/ peptides, combined with extensive LC-MS/MS analysis may contribute to the identification of modified proteins and their residues. Such methods will increase our understanding of the role of protein PTMs in the toxicity induced by environmental contaminants [64]. Azaspiracids (AZAs) are a class of algae derived shellfish toxins with a worldwide distribution. The accumulation of these toxins in mussels has been associated with cases of food

4353

poisoning [65]. The hypothesis that the toxicity of AZAs might be dependent on the binding to proteins of target organisms and their bioavailability was investigated using proteomic tools. Proteins from the digestive gland of contaminated mussels were fractionated by size exclusion chromatography or gel electrophoresis and subsequently characterized by electronspray ionization LC-MS/MS. The detection of AZAs in some protein fractions strongly supports this interaction. Furthermore possible oxidative stress induced by AZAs was inferred from the up-regulation of three proteins from the oxidative stress defense [16,17].

4. Other OMICS in bivalves: applications in environmental studies OMICS technologies ranging from genomics, proteomics, metabolomics have been integrating into aquatic environmental risk assessment and environmental monitoring in recent years (Fig. 2) [66].

4.1.

Genomics

Genomics is mainly focused on the genome and it can be divided into genotyping (genome sequence), transcriptomics (gene expression) and epigenomics (epigenetic regulation of genome expression). In aquatic toxicology the integration of genomic and proteomic tools have been mainly used on marine microbes [67] and subsequently on other taxa of model species where the full genome has been sequenced. The choice of the ideal model species for genomics is based on many practical (e.g. genome size, possibility of genetic manipulation, etc.) and scientific criteria (e.g. medical, evolutionary position) [68] and non-model organisms like bivalves, although ecologically relevant in the aquatic environment, have their genome not completely sequenced yet.

OMICs publications in bivalves per year

No. publications

40

30 Genomics in bivalves Proteomics in bivalves Metabolomics in bivalves

20

10

0

91 992 993 994 995 996 997 998 999 000 001 002 003 004 005 006 007 008 009 010 011 2 2 2 2 2 1 2 1 1 1 1 2 1 1 2 2 2 1 2 2

19

Fig. 2 – OMICs publications in bivalves per year (1991–2011). Web of Science Scopus was used for terms “genomic” and “bivalve” in “All fields” search for Genomics publications in bivalves. Books and reviews were not considered in the search criteria. Similarly, the number of publications in Proteomics and Metabolomics in bivalves was determined replacing the term “genomic” with “proteomic” and “metabolomic” respectively as described above.

4354

J O U RN A L OF P ROT EO M IC S 7 5 ( 2 0 12 ) 43 4 6 –43 5 9

Despite the lack of genome sequence, the knowledge of single or small subsets of genes (microsatellites) in bivalves showed their utility in monitoring genetic variation of farmed stocks, population, parentage assignment and taxonomy studies [69–72]. In ecotoxicology epigenomic applications are very limited although epigenetic changes can help to elucidate the effects of environmental stress caused by metal, persistent organic pollutants or endocrine disrupting chemicals. Epigenetic mechanisms induce changes in gene activity without altering the DNA sequence and these changes can be transferred to multiple generations, even if these are no longer exposed to the external factors that originally induced the epigenetic change [73]. The best-studied epigenetic mechanism is DNA methylation, which occurs through the transfer of a methyl group to position 5 of cytosines. This mechanism is generally associated with transcriptional repression by interfering with transcription factor binding proteins. The identification of the gene affected by these epigenetic modifications and linking them to alterations at the transcriptional or proteomic level are needed to understand the nature of the specific pollutant or compound which causes DNA methylation changes. However, extremely few epigenetic studies in bivalves have been performed to date. The first epigenetic study underlined that bivalves were first categorized as having “echinoderm-type” DNA methylation patterns based on the experimental evidence in M. edulis and later in the clam Donax truculus [74,75]. Only Gavery and Roberts 2010 showed, for the first time, how DNA methylation has regulatory functions in the Pacific oyster C. gigas, especially in gene families involved in stress and environmental response [76]. Instead, a transcriptomic approach made an initial effort in aquatic toxicology examining the gene expression by gene micro- and macroarrays [77]. However, the high cost of microarrays imposed severe restrictions on the number of doses, replicates and time-points that can be assessed after chemical administration to biological systems. Later, this weakness was overcome not only by an improvement in the array methodology itself but also in reduced costs and commercial availability for a wider range of species [66]. Transcriptomic studies using non-model species like bivalves developed rapidly once both small- and large-scale expressed sequence tags (ESTs) became more available. ESTs are costeffective and provide a rapid strategy in the identification of genes in bivalves and can be used for expression profiling, evolutionary and taxonomy studies. An early EST project in blue mussels revealed an expression profile and sequence data of known and unknown genes [78] used subsequently for designing a low-density DNA oligoarray for stress response detection [25,79]. Briefly, a microarray (chip or slide) approach is based on complementary base pairing. Identification and quantification of a mixture of mRNA present in a specific sample can be easily measured by using specific gene sequences or ESTs coated on a solid surface. Thus, the use of microarrays in bivalves allows highthroughput detection of transcriptomic changes providing a molecular fingerprint in aquatic environmental toxicology.

A recent interesting transcriptomic-based study in bivalves highlighted how environmental factors (i.e. temperature, salinity, pH, etc.) and their combination can affect gene expression in the eastern oyster C. virginica [80]. The authors employed gene expression data used in their previous work [81] and 160 additional arrays whilst artificial neural networks were used to analyze the complex transcriptomic data. Intriguingly, many of the genes typically investigated for environmental stress (e.g. cytochrome P-450, superoxide dismutase, metallothioneins), were conspicuously absent in the list of their experimental data. These findings showed how environmental conditions such as pH and temperature had stronger impact on gene expression compared to contaminants like metals and organic pollutants underlying the importance of not relying exclusively on laboratory-based studies in aquatic environmental toxicology [82]. Microarrays represent undoubtedly a high-throughput and versatile tool in transcriptomic studies and a similar technology has been adapted in proteomic studies of bivalves by using the combination of protein chip arrays with SELDI-TOF MS. Hemolymph proteins of M. edulis [35] and C. virginica [83] can be first purified and separated by chromatographic chip surfaces, and the protein selectively absorbed by these protein chips can be then analyzed by SELDI-TOF. This MS analyses provides m/z and protein peaks and the different expression between the samples can be “signatures” useful to aquatic environmental studies. In fact, this is another approach enabling to overcome the lack of genome information of bivalves. The main caveat of genomic techniques is that although they provide important information about changes at the mRNA level, these might not correlate with the protein content. Any mRNA produced can be degraded rapidly, translated inefficiently or edited by attachment of lipid, carbohydrate or phosphate residues [84]. The proteome, in contrast to the genome, is more complex and dynamic, changing in response to time, cell type and environmental stressors, subjected to translational and PTM regulation (as described above). For instance, combining transcriptomic tools with functional genomics was utilized to study the interactive effects of a pesticide and heavy metal mixture in marine bivalves [85] or the integration of proteomics and transcriptomics to analyze the effects of a neonicotinoid insecticide mixture in the marine mussel M. galloprovicialis [86].

4.2.

Metabolomics

There is a growing interest in the use of metabolomics in bivalves. This refers to the study of the endogenous low molecular weight metabolites within a cell, tissue or biofluid [87]. Unlike genes, transcripts and proteins, metabolites are not encoded in the genome and they are chemically diverse, composed by carbohydrates, amino acids, lipids, nucleotides, and more [88]. To our knowledge 10 publications have been reached so far regarding metabolomic applications in bivalves (Fig. 2), highlighting the potentialities in environmental studies. The reproducibility of the metabolomic data has been already evaluated between seven laboratories from United Kingdom, United States, Canada and Australia participating in an inter-laboratory comparison of 10 different

J O U RN A L OF P ROT EO M I CS 7 5 ( 2 0 12 ) 43 4 6 –4 35 9

datasets using one dimensional 1H NMR (nuclear magnetic resonance), the most often applied technique at present [89]. One of the advantages of using a metabolomic approach is that the metabolome encloses a wide range of chemical structures and it is highly variable and time-dependent [90]. Metabolomics has the potential to have a holistic view through unbiased and simultaneous measurement of thousands of metabolites, enabling understanding the biological effects due to environmental (natural and anthropogenic) changes, sex, gender, life-cycle conditions and biotic-biotic interactions such as infection and herbivory [91]. Another advantage of metabolomic applications in bivalves is its flexibility with which it can be applied in any organism irrespective of the knowledge of the species genome [87]. Metabolomic findings underlined for the first time the necessity of having an appropriate strategy for environmental, field samples. Hines and colleagues investigated this issue in the marine mussel M. galloprovincialis, comparing the stress-induced phenotype in animals sampled directly from the coastline, living in a variable environment and those stabilized in the laboratory for 60 hours prior to analysis [92]. Intriguingly, the laboratory adaptation increased the variability in adductor muscle tissue, thereby masking the metabolomic changes associated with hypoxia. The authors found it was crucial to have the previous knowledge of species and phenotype (e.g. gender, age, sex) for a correct interpretation of metabolomic data. In a later publication, Hines and colleagues incorporated a novel reverse transcriptase polymerase chain reaction (RT-PCR) and NMR-based metabolomics, respectively, in order to identify a robust methodology for sex and gender determination of ripe and spent mussels (M. edulis and M. galloprovincialis) [93]. This information is quite often unknown in field environmental studies but their knowledge is required to detect more subtle inter-individual metabolic changes that arise from an environmental stressor [91]. Subsequently, metabolomic studies in mussels were mainly addressed to discriminate and understand the mode of action of pesticides like atrazine and lindane [94], pentachlorophenol [95] and metals like copper and cadmium [95,96]. An initial study was conducted in the American oyster Crassostrea virginica aiming in the detection of tissue-specific metabolomic variations by NMR spectroscopy [97]. This provided first knowledge in the design of metabolomic studies in toxicology. Subsequent studies were undertaken in gills of Manila clam R. philippinarum exposed to mercury [98] and to benzo[a]pyrene [99]. Use of NMR for environmental metabolomic studies in bivalves offers obvious advantages compared to the environmental proteomics due to the simplicity of the sample preparation methods, unbiased assessment and highthroughput analyses [100]. However it is limited only to small molecules like amino acids, leaving behind the identification and/or quantification of other biomolecules, such as proteins, with important roles in biologic pathways, associated with specific environmental stressors. In addition, many metabolites cannot be resolved into chemical structures but the incorporation of other analytical methods or MS availability (e.g. Fourier transform ion cyclotron resonance MS) can greatly expand the possibility

4355

in future research. Although there is no evidence to in situ metabolomic studies in bivalves as in proteomics, this type of approach is potentially extremely powerful because it is able to localize metabolomes into the cell and tissue easily by image analysis. Appropriate genetically encoded sensors can be placed precisely in specific cellular organelles to identify metabolites and visualised by fluorescence resonance energy transfer (FRET). For tissue analyses, MS approaches can be used [88]. The development of imaging technologies to identify and quantify in situ endogenous or exogenous molecules such as proteins, lipids, drugs, and their metabolites is expected to be a potential tool in OMICS applications. Mass spectrometry imaging (MSI) can perform analyses of multiple molecules in complex samples without labeling, which offers advantages over the preexisting methods for labelfree and simultaneous detection of protein, peptides and metabolites [101]. There are currently few laboratories that have the opportunity to use these sophisticated and expensive instruments and their use is limited primarily to medical research. Overall the recent OMICS techniques are revolutionizing the classic approach based on the exclusive use of traditional biomarkers in aquatic environmental studies. Nevertheless, even as these high-throughput approaches rapidly become more available it is also clear that any single OMICS approach may be not sufficient to characterize the complexity of biological systems [102]. This is particularly true in the case of non-model organisms like bivalves where the integration of multiple OMICS might be the key to understand how environmental stressors may have pleiotropic effects in vivo and perturb multiple cell pathways [78]. Future environmental studies in bivalves should have a more multidisciplinary approach; collaboration between genomics, proteomics and metabolomics research centers can offer big advantages in terms of time and in achieving that amount of data crucial for a holistic and accurate view of the mechanisms behind the biological effects caused by environmental stress. On the other hand, the large amount of data generated can be extremely difficult to interpret. Bioinformatic and modeling approaches can help to extrapolate the data in a reproducible and unbiased methodology within and between laboratories, enabling processing of OMICS data heterogeneity by discarding artifacts, multivariate statistics and avoiding possible arbitrary interpretations. The appropriate use of such sophisticated software linked to updated OMICS databases might help to understand better the modes of action of chemicals, the mechanisms behind them and predicting possible biological effects in particular environmental scenarios.

5.

Future perspectives

In the last decade, the application of proteomics in environmental toxicology has emerged to assess the dynamic changes in the proteome of organisms exposed to environmental stressors. Nowadays, environmental pollution and climate change are among the environmental stressors that require more thorough understanding to support continuous and sustainable growth.

4356

J O U RN A L OF P ROT EO M IC S 7 5 ( 2 0 12 ) 43 4 6 –43 5 9

The scientific community has become increasingly concerned about the potential adverse health effects to humans and wildlife resulting from environmental exposure to anthropogenic, persistent, industrial, emerging pollutants. The first step to prevent the deleterious effect of these active substances is to improve future toxicological screening methods and risk assessment strategies. The OMICS technologies have provided researchers with new tools for developing biomarkers, specifically indicators that reflect both chemical exposure and the subsequent biological effect. It has been described in this review how in applying proteomics, we can obtain complex information of alterations in biochemical pathways in tissues from sentinel organisms exposed to aquatic pollution both in laboratories or in field experiments. Moreover, the PES profiles or “fingerprint” can then be used as a tool for classifying chemical exposures [103] and predicting mode of action [13]. Nevertheless, proteomic research in bivalves is still far from the application of the state of art MS that could provide more complex answers and aid in the development of future monitoring strategies. The main impediment in the application of MS-based proteomics is the lack of sequencing genomes in bivalves. At the level of mtDNA sequencing improvement has been observed from the sequence of 13.9 kb of the mitochondrial genome of M. edulis determined in 1992 which contained 37 genes [104] to the recent mitochondrial phylogenomics studies of Bivalva; more than 15 complete mtDNA genome sequences are available from Mytiloida and Venerioda [105]. However, complete genome sequencing of Bivalva genomes has not yet been released. Shotgun proteomic strategies based on identifying proteins from complex mixtures of digested proteins cannot be applied until a database can be populated with our sentinel organism genomes. Therefore, the attention should be focused on how to apply the state of art of MS-based proteomics such as a multiple reaction monitoring (MRM) or selected ion monitoring (SIM) to improve environmental assessment and biomonitoring. Those techniques, SIM and MRM modes have been applied for the determination of the toxin profile of M. galloprovincialis collected from an area with remarkable concentration of Alexandrium ostenfeldii cells in seawater. This study described a very complex multi-toxin profile from both polar and lipophilic mussel extracts which revealed that the presence of toxin in shellfish has become more complex than in the past [106]. The lessons learnt from biomedical studies suggested that the application of this technology to selectively identify biomarkers would be reproducible and could enable a rapid, sensitive, and quantitative analysis of large biomonitoring programmes. However, we still need to determine whether this shortcut could overcome the limitation of the lack of full sequenced genomes. Environmental proteomics is not any longer in its infancy. The application to biomonitoring, marine pollution assessment of the effects of environmental stressors utilizing bivalves as sentinel organisms could be considered as one of the pioneer areas of research in environmental proteomics. However, we have still a long way to go to reach the achievement of proteomics in biomedicine, inter-laboratory validation of PES or expression pattern, developing targeting proteomics methodologies for the verification or validation of selected target candidates, systematic exploration of PTMs, join efforts to tackle pollutant mixtures in aquatic environment and possible synergic effects are some of the future goals. Finally, the next generation sequencing technologies has already overcome the initial

scepticism from the classical Sanger-based sequencing. Numerous applications and enormous scientific achievements have been reported. Among them metagenomics has already stuck out as a strong application to visualize the microbial world and with potentiality to answer environmental questions [107]. The question that arises is whether there could be found a platform that combines next generation sequencing and proteomic data in next generation biomonitoring. Proteomics research in bivalves has been historically one of strongest research areas in marine pollution biomonitoring. PES from different fish species are the main alternative however, it should not be forgotten that the sessile nature of bivalves could provide a better correlation between stressors analyzed and the topographic information. The proteomic research in bivalves supported by high throughput technologies could still be on the front line if the integration of genomics and proteomics data could improve biomonitoring programmes and define the mechanisms underlying pollutant toxicity.

Authors' contributions AC and ST have equally contributed to this manuscript and therefore share the first authority. All authors read and approved the final manuscript.

Acknowledgements This review was supported by grants from the Swedish Research Council-Natural Science and Medicine (VR-M and VR-NT) (SC), Carl Trygger Foundation (SC), VINNOVA-Vinnmer program (SC), Magnus Bergvalls Foundation (SC), Oscar Lilli Lamms Minne Foundation (SC), Längmanska kulturfonden (SC), Lars Hiertas Minne Foundation (SC), IKERBASQUE, Basque Foundation for Science (SC), CBR-SSF (SC), Ångpanneförening Research Foundation (SC). Alexandre Campos contract work is supported by the Ciência 2007 program of the Ministério da Educação e Ciência (MEC, Lisbon, Portugal).

REFERENCES

[1] European Parliament Council. Directive 2000/06/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Off J Eur Commun 2000;L327:1–72. [2] Lima I, Moreira SM, Osten JR-V, Soares AMVM, Guilhermino LC. Biochemical responses of the marine mussel (Mytilus galloprovincialis) to petrochemical environmental contamination along the North-western coast of Portugal. Chemosphere 2007;66:1230–42. [3] Sarkar A, Ray D, Shrivastava A, Sarker S. Molecular biomarkers: their significance and application in marine pollution monitoring. Ecotoxicology 2006;15:333–40. [4] Hagger JA, Lowe D, Dissanayake A, Jones MB, Galloway TS. The influence of seasonality on biomarker responses in Mytilus edulis. Ecotoxicology 2010;19:953–62. [5] Dietz R, Born EW, Riget F, Aubail A, Sonne C, Drimmie R, et al. Temporal trends and future predictions of mercury

J O U RN A L OF P ROT EO M I CS 7 5 ( 2 0 12 ) 43 4 6 –4 35 9

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

concentrations in Northwest Greenland polar bear (Ursus maritimus) hair. Environ Sci Technol 2011;45:1458–65. Mi J, Apraiz I, Cristobal S. Peroxisomal proteomic approach for protein profiling in blue mussels (Mytilus edulis) exposed to crude oil. Biomarkers 2007;12:47–60. Knigge T, Monsinjon T, Andersen OK. Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry approach to biomarker discovery in blue mussels (Mytilus edulis) exposed to polyaromatic hydrocarbons and heavy metals under field conditions. Proteomics 2004;4:2722–7. Apraiz I, Mi J, Cristobal S. Identification of proteomic signatures of exposure to marine pollutants in mussels (Mytilus edulis). Mol Cell Proteomics 2006;5:1274–85. Thomas RE, Brodersen C, Carls MG, Babcock M, Rice SD. Lack of physiological responses to hydrocarbon accumulation by Mytilus trossulus after 3–4 years chronic exposure to spilled Exxon Valdez crude oil in Prince William Sound. Comp Biochem Physiol C Pharmacol Toxicol Endocrinol 1999;122: 153–63. Apraiz I, Cajaraville MP, Cristobal S. Peroxisomal proteomics: biomonitoring in mussels after the Prestige's oil spill. Mar Pollut Bull 2009;58:1815–26. Dowling V, Hoarau PC, Romeo M, O'Halloran J, van Pelt F, O'Brien N, et al. Protein carbonylation and heat shock response in Ruditapes decussatus following p, p’-ichlorodiphenyldichloroethylene (DDE) exposure: a proteomic approach reveals that DDE causes oxidative stress. Aquat Toxicol 2006;77:11–8. Chora S, Starita-Geribaldi M, Guigonis J-M, Samson M, Roméo M, Bebianno MJ. Effect of cadmium in the clam (Ruditapes decussatus) assessed by proteomic analysis. Aquat Toxicol 2009;94:300–8. Riva C, Binelli A, Rusconi F, Colombo G, Pedriali A, Zippel R, et al. A proteomic study using zebra mussels (D. polymorpha) exposed to benzo(α)pyrene: the role of gender and exposure concentrations. Aquat Toxicol 2011;104:14–22. Martins JC, Leao PN, Vasconcelos V. Differential protein expression in Corbicula fluminea upon exposure to a Microcystis aeruginosa toxic strain. Toxicon 2009;53:409–16. Puerto M, Campos A, Prieto A, Camean A, de Almeida AM, Coelho AV, et al. Differential protein expression in two bivalve species; Mytilus galloprovincialis and Corbicula fluminea; exposed to Cylindrospermopsis raciborskii cells. Aquat Toxicol 2011;101: 109–16. Nzoughet KJ, Hamilton JT, Floyd SD, Douglas A, Nelson J, Devine L, et al. Azaspiracid: first evidence of protein binding in shellfish. Toxicon 2008;51:1255–63. Nzoughet JK, Hamilton JT, Botting CH, Douglas A, Devine L, Nelson J, et al. Proteomics identification of azaspiracid toxin biomarkers in blue mussels, (Mytilus edulis). Mol Cell Proteomics 2009;8:1811–22. Leung PTY, Wang Y, Mak SST, Ng WC, Leung KMY. Differential proteomic responses in hepatopancreas and adductor muscles of the green-lipped mussel (Perna viridis) to stresses induced by cadmium and hydrogen peroxide. Aquat Toxicol 2011;105:49–61. Thompson EL, Taylor DA, Nair SV, Birch G, Haynes PA, Raftos DA. A proteomic analysis of the effects of metal contamination on Sydney Rock Oyster (Saccostrea glomerata) haemolymph. Aquat Toxicol 2011;103:241–9. Apeti DA, Lauenstein GG, Christensen JD, Kimbrough K, Johnson WE, Kennedy M, et al. A historical assessment of coastal contamination in Birch Harbor, Maine based on the analysis of mussels collected in the 1940s and the Mussel Watch Program. Mar Pollut Bull 2010;60:732–42. Fang JKH, Au DWT, Wu RSS, Zheng GJ, Chan AKY, Lam PKS, et al. Concentrations of polycyclic aromatic hydrocarbons and polychlorinated biphenyls in green-lipped mussel (Perna

[22]

[23]

[24]

[25]

[26]

[27]

[28]

[29]

[30]

[31]

[32]

[33]

[34] [35]

[36] [37]

[38]

4357

viridis) from Victoria Harbour, Hong Kong and possible human health risk. Mar Pollut Bull 2009;58:615–20. Andrew M, O'Connor W, Dunstan R, MacFarlane G. Exposure to 17α-ethynylestradiol causes dose and temporally dependent changes in intersex, females and vitellogenin production in the Sydney rock oyster. Ecotoxicology 2010;19: 1440–51. Raftopoulou EK, Dimitriadis VK. Assessment of the health status of mussels (Mytilus galloprovincialis) along Thermaikos Gulf (Northern Greece): an integrative biomarker approach using ecosystem health indices. Ecotoxicol Environ Saf 2010;73:1580–7. Ortiz-Zarragoitia M, Trant JM, Cajaraville MP. Effects of dibutylphthalate and ethynylestradiol on liver peroxisomes, reproduction, and development of zebrafish (Danio rerio). Environ Toxicol Chem 2006;25:2394–404. Dondero F, Piacentini L, Marsano F, Rebelo M, Vergani L, Venier P, et al. Gene transcription profiling in pollutant exposed mussels (Mytilus spp.) using a new low-density oligonucleotide microarray. Gene 2006;376:24–36. Vioque-Fernández A, Alves de Almeida E, López-Barea J. Assessment of Doñana National Park contamination in Procambarus clarkii: integration of conventional biomarkers and proteomic approaches. Sci Total Environ 2009;407:1784–97. Shepard JL, Bradley BP. Protein expression signatures and lysosomal stability in Mytilus edulis exposed to graded copper concentrations. Mar Environ Res 2000;50:457–63. Shepard JL, Olsson B, Tedengren M, Bradley BP. Protein expression signatures identified in Mytilus edulis exposed to PCBs, copper and salinity stress. Mar Environ Res 2000;50:337–40. López JL, Mosquera E, Fuentes J, Marina A, Vázquez J, Alvarez G. Two-dimensional gel electrophoresis of Mytilus galloprovincialis: differences in protein expression between intertidal and cultured mussels. Mar Ecol Prog Ser 2001;224:149–56. Chora S, McDonagh B, Sheehan D, Starita-Geribaldi M, Roméo M, Bebianno MJ. Ubiquitination and carbonylation as markers of oxidative-stress in Ruditapes decussatus. Mar Environ Res 2008;66:95–7. Rodriguez-Ortega MJ, Grosvik BE, Rodriguez-Ariza A, Goksoyr A, Lopez-Barea J. Changes in protein expression profiles in bivalve molluscs (Chamaelea gallina) exposed to four model environmental pollutants. Proteomics 2003;3: 1535–43. Marie B, Zanella-Cléon I, Le R, Nathalie, Becchi M, Luquet G, et al. Proteomic analysis of the acid-soluble nacre matrix of the bivalve Unio pictorum: detection of novel carbonic anhydrase and putative protease inhibitor proteins. ChemBioChem 2010;11:2138–47. Jurgen F, Valerio M, Roberto R, Paolo SG, Marta M. 2-DE proteomic analysis of HSP70 in mollusc Chamelea gallina. Fish Shellfish Immunol 2011;30:739–43. Gabaldon T. Peroxisome diversity and evolution. Philos Trans R Soc Lond B Biol Sci 2010;365:765–73. Björnstad A, Larsen BK, Skadsheim A, Jones MB, Andersen OK. The potential of ecotoxicoproteomics in environmental monitoring: biomarker profiling in mussel plasma using ProteinChip array technology. J Toxicol Environ Health 2006;69: 77–96. McDonagh B, Tyther R, Sheehan D. Redox proteomics in the mussel, Mytilus edulis. Mar Environ Res 2006;62:S101–4. Amelina H, Apraiz I, Sun W, Cristobal S. Proteomics-based method for the assessment of marine pollution using liquid chromatography coupled with two-dimensional electrophoresis. J Proteome Res 2007;6:2094–104. Monsinjon T, Andersen O, Leboulenger F, Knigge T. Data processing and classification analysis of proteomic changes: a case study of oil pollution in the mussel, Mytilus edulis. Proteome Sci 2006;4:17.

4358

J O U RN A L OF P ROT EO M IC S 7 5 ( 2 0 12 ) 43 4 6 –43 5 9

[39] Romero-Ruiz A, Carrascal M, Alhama J, Gómez-Ariza JL, Abian J, López-Barea J. Utility of proteomics to assess pollutant response of clams from the Doñana bank of Guadalquivir Estuary (SW Spain). Proteomics 2006;6:S245–55. [40] Lemos MFL, Soares AMVM, Correia AC, Esteves AC. Proteins in ecotoxicology—how, why and why not? Proteomics 2010;10:873–87. [41] Cairns J, Heath AG, Parker BC. The effects of temperature upon the toxicity of chemicals to aquatic organisms. Hydrobiologia 1975;47:135–71. [42] Thurberg FPGE. Pollutant effects upon cod, haddock, pollock, and flounder of the inshore fisheries of Massachusetts and Cape Cod Bays. In: Buchsbaum RPJ, Robinson WE, editors. The decline of fisheries resources in New England: evaluating the impact of overfishing, contamination, and habitat degradation Cambridge (MA): MIT Sea Grant College Program; 2005. p. 43–66. [43] Widdows J. Combined effects of body size, food concentration and season on the physiology of Mytilus edulis. J Mar Biol Assoc U K 1978;58:109–24. [44] Diz AP, Truebano M, Skibinski DOF. The consequences of sample pooling in proteomics: an empirical study. Electrophoresis 2009;30:2967–75. [45] Voss T, Haberl P. Observations on the reproducibility and matching efficiency of two-dimensional electrophoresis gels: consequences for comprehensive data analysis. Electrophoresis 2000;21:3345–50. [46] McDonagh B, Tyther R, Sheehan D. Carbonylation and glutathionylation of proteins in the blue mussel (Mytilus edulis) detected by proteomic analysis and Western blotting: actin as a target for oxidative stress. Aquat Toxicol 2005;73: 315–26. [47] Tedesco S, Doyle H, Redmond G, Sheehan D. Gold nanoparticles and oxidative stress in Mytilus edulis. Mar Environ Res 2008;66: 131–3. [48] Nesatyy VJ, Suter MJF. Proteomics for the analysis of environmental stress responses in organisms. Environ Sci Technol 2008;41:6891–900. [49] Regnier FE, Julka S. Primary amine coding as a path to comparative proteomics. Proteomics 2006;6:3968–79. [50] Cañas B, Piñeiro C, Calvo E, López-Ferrer D, Gallardo JM. Trends in sample preparation for classical and second generation proteomics. J Chromatogr A 2007;1153:235–58. [51] Monsinjon T, Knigge T. Proteomic applications in ecotoxicology. Proteomics 2007;7:2997–3009. [52] Mi J, Orbea A, Syme N, Ahmed M, Cajaraville MP, Cristóbal S. Peroxisomal proteomics, a new tool for risk assessment of peroxisome proliferating pollutants in the marine environment. Proteomics 2005;5:3954–65. [53] Baggerman G, Vierstraete E, De Loof A, Schoofs L. Gel-based versus gel-free proteomics: a review. Comb Chem High Throughput Screen 2005;8:669–77. [54] Gygi SP, Corthals GL, Zhang Y, Rochon Y, Aebersold R. Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc Natl Acad Sci 2000;97: 9390–5. [55] Rabilloud T. Two-dimensional gel electrophoresis in proteomics: old, old fashioned, but it still climbs up the mountains. Proteomics 2002;2:3–10. [56] Görg A, Weiss W, Dunn MJ. Current two-dimensional electrophoresis technology for proteomics. Proteomics 2004;4:3665–85. [57] Lambert J-P, Ethier M, Smith JC, Figeys D. Proteomics: from gel based to gel free. Anal Chem 2005;77:3771–88. [58] Tomanek L, Zuzow MJ. The proteomic response of the mussel congeners Mytilus galloprovincialis and M. trossulus to acute heat stress: implications for thermal tolerance limits and metabolic costs of thermal stress. J Exp Biol 2010;213: 3559–74.

[59] Madian AG, Regnier FE. Profiling carbonylated proteins in human plasma. J Proteome Res 2010;9:1330–43. [60] Shacter E. Quantification and significance of protein oxidation in biological samples. Drug Metab Rev 2000;32: 307–26. [61] McDonagh B, Sheehan D. Redox proteomics in the blue mussel Mytilus edulis: carbonylation is not a pre-requisite for ubiquitination in acute free radical-mediated oxidative stress. Aquat Toxicol 2006;79:325–33. [62] Prevodnik A, Gardestrom J, Lilja K, Elfwing T, McDonagh B, Petrovic N, et al. Oxidative stress in response to xenobiotics in the blue mussel (Mytilus edulis L.): evidence for variation along a natural salinity gradient of the Baltic Sea. Aquat Toxicol 2007;82:63–71. [63] Chora S, McDonagh B, Sheehan D, Starita-Geribaldi M, Romeo M, Bebianno MJ. Ubiquitination and carbonylation of proteins in the clam Ruditapes decussatus, exposed to nonylphenol using redox proteomics. Chemosphere 2010;81:1212–7. [64] Madian AG, Regnier FE. Proteomic identification of carbonylated proteins and their oxidation sites. J Proteome Res 2010;9: 3766–80. [65] James KJ, Furey A, Lehane M, Ramstad H, Aune T, Hovgaard P, et al. First evidence of an extensive northern European distribution of azaspiracid poisoning (AZP) toxins in shellfish. Toxicon 2002;40:909–15. [66] Van Aggelen G, Ankley GT, Baldwin WS, Bearden DW, Benson WH, Chipman JK, et al. Integrating omic technologies into aquatic ecological risk assessment and environmental monitoring: hurdles, achievements, and future outlook. Environ Health Perspect 2010;118. [67] Thomas T, Egan S, Burg D, Ng C, Ting L, Cavicchioli R. Integration of genomics and proteomics into marine microbial ecology. Mar Ecol Prog Ser 2007;332:291–9. [68] Feder ME, Mitchell-Olds T. Evolutionary and ecological functional genomics. Nat Rev Genet 2003;4:649–55. [69] Geist J, Kuehn R. Genetic diversity and differentiation of central European freshwater pearl mussel (Margaritifera margaritifera L.) populations: implications for conservation and management. Mol Ecol 2005;14:425–39. [70] Zhan A, Hu J, Hu X, Zhou Z, Hui M, Wang S, et al. Fine-scale population genetic structure of zhikong scallop (Chlamys farreri): do local marine currents drive geographical differentiation? Mar Biotechnol 2009;11:223–35. [71] Chiesa S, Nonnis Marzano F, Minervini G, De Lucrezia D, Baccarani G, Bordignon G, et al. The invasive Manila clam Ruditapes philippinarum (Adams and Reeve, 1850) in Northern Adriatic Sea: population genetics assessed by an integrated molecular approach. Fish Res 2011;110:259–67. [72] Li Q, Wang Q, Qi M, Ge J, Cong R. Development, characterization, and inheritance of 113 novel EST-SSR markers in the Pacific oyster (Crassostrea gigas). Genes Genom 2011;33:313–6. [73] Vandegehuchte M, Janssen C. Epigenetics and its implications for ecotoxicology. Ecotoxicology 2011;20:607–24. [74] Bird AP, Taggart MH. Variable patterns of total DNA and rDNA methylation in animals. Nucleic Acids Res 1980;8: 1485–97. [75] Petrović V, Pérez-García C, Pasantes JJ, Atović E, Prats E, Plohl M. A GC-rich satellite DNA and karyology of the bivalve mollusk Donax trunculus: a dominance of GC-rich heterochromatin. Cytogenet Genome Res 2009;124:63–71. [76] Gavery MR, Roberts SB. DNA methylation patterns provide insight into epigenetic regulation in the Pacific oyster (Crassostrea gigas). BMC Genomics 2010;11. [77] Martyniuk CJ, Griffitt RJ, Denslow ND. Omics in aquatic toxicology: not just another microarray. Environ Toxicol Chem 2011;30:263–4. [78] Venier P, Pallavicini A, De Nardi B, Lanfranchi G. Towards a catalogue of genes transcribed in multiple tissues of Mytilus galloprovincialis. Gene 2003;314:29–40.

J O U RN A L OF P ROT EO M I CS 7 5 ( 2 0 12 ) 43 4 6 –4 35 9

[79] Dupont S, Wilson K, Obst M, Sköld H, Nakano H, Thorndyke MC. Marine ecological genomics: when genomics meets marine ecology. Mar Ecol Prog Ser 2007;332:257–73. [80] Chapman RW, Mancia A, Beal M, Veloso A, Rathburn C, Blair A, et al. The transcriptomic responses of the eastern oyster, (Crassostrea virginica), to environmental conditions. Mol Ecol 2011;20:1431–49. [81] Chapman RW, Mancia A, Beal M, Veloso A, Rathburn C, Blair A, et al. A transcriptomic analysis of land-use impacts on the oyster, Crassostrea virginica, in the South Atlantic bight. Mol Ecol 2009;18:2415–25. [82] Vandersteen W. Detecting gene expression profiles associated with environmental stressors within an ecological context. Mol Ecol 2011;20:1322–3. [83] Li Y, Siddiqui G, Wikfors GH. Non-lethal determination of sex and reproductive condition of Eastern oysters (Crassostrea virginica) Gmelin using protein profiles of hemolymph by Proteinchip® and SELDI-TOF-MS technology. Aquaculture 2010;309:258–64. [84] Garcia-Reyero N, Perkins EJ. Systems biology: leading the revolution in ecotoxicology. Environ Toxicol Chem 2011;30: 265–73. [85] Dondero F, Banni M, Negri A, Boatti L, Dagnino A, Viarengo A. Interactions of a pesticide/heavy metal mixture in marine bivalves: a transcriptomic assessment. BMC Genomics 2011;12:195. [86] Dondero F, Negri A, Boatti L, Marsano F, Mignone F, Viarengo A. Transcriptomic and proteomic effects of a neonicotinoid insecticide mixture in the marine mussel (Mytilus galloprovincialis, Lam.). Sci Total Environ 2010;408: 3775–86. [87] Viant MR. Metabolomics of aquatic organisms: the new ‘omics’ on the block. Mar Ecol Prog Ser 2007;332:301–6. [88] Baker M. Metabolomics: from small molecules to big ideas. Nat Methods 2011;8:117–21. [89] Viant MR, Bearden DW, Bundy JG, Burton IW, Collette TW, Ekman DR, et al. International NMR-based environmental metabolomics intercomparison exercise. Environ Sci Technol 2009;43:219–25. [90] Vlaanderen J, Moore LE, Smith MT, Lan Q, Zhang L, Skibola CF, et al. Application of OMICS technologies in occupational and environmental health research; current status and projections. Occup Environ Med 2010;67:136–43. [91] Bundy J, Davey M, Viant M. Environmental metabolomics: a critical review and future perspectives. Metabolomics 2009;5:3–21. [92] Hines A, Oladiran GS, Bignell JP, Stentiford GD, Viant MR. Direct sampling of organisms from the field and knowledge of their phenotype: key recommendations for environmental metabolomics. Environ Sci Technol 2007;41:3375–81. [93] Hines A, Yeung WH, Craft J, Brown M, Kennedy J, Bignell J, et al. Comparison of histological, genetic, metabolomics, and lipid-based methods for sex determination in marine mussels. Anal Biochem 2007;369:175–86.

4359

[94] Tuffnail W, Mills GA, Cary P, Greenwood R. An environmental 1H NMR metabolomic study of the exposure of the marine mussel Mytilus edulis to atrazine, lindane, hypoxia and starvation. Metabolomics 2009;5:33–43. [95] Hines A, Staff FJ, Widdows J, Compton RM, Falciani F, Viant MR. Discovery of metabolic signatures for predicting whole organism toxicology. Toxicol Sci 2010;115:369–78. [96] Wu H, Wang WX. NMR-based metabolomic studies on the toxicological effects of cadmium and copper on green mussels Perna viridis. Aquat Toxicol (Amsterdam, Netherlands) 2010;100:339–45. [97] Tikunov AP, Johnson CB, Lee H, Stoskopf MK, Macdonald JM. Metabolomic investigations of American oysters using1H-NMR spectroscopy. Mar Drugs 2010;8:2578–96. [98] Liu X, Zhang L, You L, Yu J, Zhao J, Li L, et al. Differential toxicological effects induced by mercury in gills from three pedigrees of Manila clam (Ruditapes philippinarum) by NMR-based metabolomics. Ecotoxicology 2011;20:177–86. [99] Zhang L, Liu X, You L, Zhou D, Wang Q, Li F, et al. Benzo(a) pyrene-induced metabolic responses in Manila clam (Ruditapes philippinarum) by proton nuclear magnetic resonance (1H NMR) based metabolomics. Environ Toxicol Pharmacol 2011;32: 218–25. [100] Robertson DG. Muscling mussels: metabolomic evaluation of toxicity. Toxicol Sci 2010;115:305–6. [101] Greer T, Sturm R, Li L. Mass spectrometry imaging for drugs and metabolites. J Proteomics 2011;74:2617–31. [102] Zhang W, Li F, Nie L. Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies. Microbiology 2010;156:287–301. [103] Apraiz I, Leoni G, Lindenstrand D, Persson J-O, Cristobal S. Proteomic analysis of mussels exposed to fresh and weathered prestige's oil. J Proteomics Bioinform 2009;2:255–61. [104] Hoffmann RJ, Boore JL, Brown WM. A novel mitochondrial genome organization for the blue mussel, (Mytilus edulis). Genetics 1992;131:397–412. [105] Doucet-Beaupre H, Breton S, Chapman EG, Blier PU, Bogan AE, Stewart DT, et al. Mitochondrial phylogenomics of the Bivalvia (Mollusca): searching for the origin and mitogenomic correlates of doubly uniparental inheritance of mtDNA. BMC Evol Biol 2010;10:50. [106] Ciminiello P, Dell'Aversano C, Fattorusso E, Forino M, Tartaglione L, Boschetti L, et al. Complex toxin profile of Mytilus galloprovincialis from the Adriatic sea revealed by LC-MS. Toxicon 2010;55:280–8. [107] Albu M, Nikbakht H, Hajibabaei M, Hickey DA. The DNA barcode linker. Mol Ecol Resour 2011;11:84–8. [108] Grøsvik BE, Jonsson H, Rodríguez-Ortega MJ, Roepstorff P, Goksøyr A. CYP1A-immunopositive proteins in bivalves identified as cytoskeletal and major vault proteins. Aquat Toxicol 2006;79:334–40.