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Aquatic Toxicology 105S (2011) 25– 39

Contents lists available at ScienceDirect

Aquatic Toxicology

jou rn al h om epa ge: www.elsev ier .com/ locate /aquatox

rogress and promises in toxicogenomics in aquatic toxicology: Is technicalnnovation driving scientific innovation?

arl Fenta,b,∗, John P. Sumpterc

University of Applied Sciences Northwestern Switzerland, School of Life Sciences, Gründenstrasse 40, CH-4132 Muttenz, SwitzerlandSwiss Federal Institute of Technology (ETHZ), Department of Environmental Sciences, CH-8092 Zürich, SwitzerlandBrunel University, Institute for the Environment, Uxbridge UB8 3PH, United Kingdom

r t i c l e i n f o

eywords:ranscriptomicsene expression analysisishroteomicsetabolomics

ritical review

a b s t r a c t

In the last decade, new technologies have been invented to analyze large amounts of information such asgene transcripts (transcriptomics), proteins (proteomics) and small cellular molecules (metabolomics).Many studies have been performed in the last few years applying these technologies to aquatic toxicology,mainly in fish. In this article, we summarize the current state of knowledge and question whether theapplication of modern technology for descriptive purposes truly represents scientific advancement inaquatic toxicology. We critically discuss the advantages and disadvantages of these technologies andemphasize the importance of these critical aspects. To date, these techniques have been used mainly asa proof of principle, demonstrating effects of model compounds. The potential to use these techniquesto better analyze the mode-of-action of a toxicant or the effects of a compound within organisms hasrarely been met. This is partly due to a lack of baseline data and the fact that the expression of mRNA andprotein profiles is rarely linked to physiology or toxicologically meaningful outcomes. It seems premature

to analyze mixtures or environmental samples until more is known about the expression profiles ofindividual toxicants. Gene transcription, protein, or metabolic data give only a partial view of theseeffects. Thus, we emphasize that data obtained by these technologies must be linked to physiologicalchanges to fully understand their significance. The use of these techniques in aquatic toxicology is stillin its infancy, data cannot yet be applied to environmental risk assessment or regulation until more

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. Introduction

This article presents an overview of the past (over the lastecade) and presents developments in new technologies intro-uced in the field of aquatic toxicology. We critically discuss thetate of the art with a focus on fish, and compare the proposedenefits of these new technologies with the progress made in theeld today. We also explore the limitations of these technologies,s well as provide a perspective on possible future developmentsn this field.

. Toxicogenomics in aquatic toxicology

The advent of new technology often has great potential. Theequencing of the genome of the zebrafish (Danio rerio) and theapanese pufferfish (Takifugu ribripes) has fostered the develop-

∗ Corresponding author at: University of Applied Sciences Northwesternwitzerland, School of Life Sciences, Gründenstrasse 40, CH-4132 Muttenz,witzerland. Tel.: +41 61 467 4571.

E-mail address: karl.fent@fhnw.ch (K. Fent).

166-445X/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.aquatox.2011.06.008

ting the data within their physiological and toxicological contexts.© 2011 Elsevier B.V. All rights reserved.

ment of microarrays for use in biology, development biology, andaquatic toxicology. Currently, genomic information in these fishspecies, as well as in Japanese medaka (Oryzias latipes) and fatheadminnows (Pimephales promelas), finds most frequent application inaquatic toxicology. Forty complete genome sequences are available,409 species are assembled and further 665 species sequenc-ing is in progress (www.ncbi.nlm.nih.gov/genomes/leuks.cgi). Alsoimportant for aquatic toxicology are the entire genome sequencesof zebrafish, the water flea Daphnia pulex (http://wFleaBase.org)(Colbourne et al., 2011), and the algae Chlamydomonas rein-hardtii, Chlorella variabilis NC64A, as well as the frog Xenopustropicalis. The assembly of genomes of 15 fish species is listed,and sequencing is also in progress for a further 22 species(www.ncbi.nlm.nih.gov/genomes/leuks.cgi).

Sequence data have fostered research in many areas includ-ing toxicology and aquatic toxicology. Among other applications,including primer design for quantitative PCR, genomic informa-tion can be used to design and develop microarrays for some

selected genes, thousands of genes, or even almost the entiregenome (e.g. zebrafish). Microarrays are constructed by cDNAsor oligonucleotides and spotted arrays are commercially avail-able for a few fish species. In cases where a large set of genes or

2 ic Toxicology 105S (2011) 25– 39

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Box 1: Applications of toxicogenomics, proteomics andmetabolomics data.

• as a test of principle and proof of concept• to describe and understand the modes of action of a com-

pound• to predict modes of action and effects of a compound based

on the profiles• to discover and describe new biomarkers of effect (Lam et al.,

2008; Gunnarsson et al., 2007)• to perform effect assessments:

◦ environmental chemicals with known modes of action.◦ environmental chemicals with partly or completely

unknown modes of action.◦ compound mixtures.◦ effluents and environmental samples.◦ to distinguish between organisms living in polluted versus

unpolluted environments.• as a diagnostic tool to determine environmental contamina-

tion, and also efficacy of pollution remediation (Roling et al.,2007).

• to identify effects induced by complex mixtures (Garcia-Reyero et al., 2008; Filby et al., 2007)

6 K. Fent, J.P. Sumpter / Aquat

lmost the entire genome is assessed, the term “global” expressionnalysis is used. Microarrays can be used to determine upregu-ation and downregulation of gene transcripts (mRNA) in cells,issues, organs, or even the entire organism in specific physiologicalonditions (e.g. environmental stress such as hypoxia), develop-ent (embryogenesis), or exposure to an environmental chemical,

hemical mixtures, or environmental samples. The term “transcrip-omics” is used when transcriptional changes in exposed organismsre described. The combination and advances in DNA sequencingnd the development of microarray technology made it possibleo analyze transcription of large sets of genes and to obtain annanticipated quantity of data.

In addition, heterologous applications of microarrrays haveeen made, meaning that arrays constructed of oligononucleotidesf known sequences of a fish (e.g. zebrafish) are used for otherpecies of fish. For instance, heterologous applications include thenformation from pufferfish used for rainbow trout (Hogstrandt al., 2002), salmon microarray (Von Schalburg et al., 2008; Riset al., 2004), European flounder (Diab et al., 2008; Cohen et al.,007), carp (Williams et al., 2008) and catfish (Liu et al., 2008). Riset al. (2004) demonstrated heterologous application of the salmonDNA microarray even for distantly related fish. Of course the datatrength is lowered by the use of heterologous arrays, and in generalt is desirable to use species-specific microarrays.

.1. Perspectives and possibilities

.1.1. HypothesesOne of the basic hypotheses behind toxicogenomic is that alter-

tions in gene transcripts represent the primary action betweenhemicals (xenobiotics) and biota; it represents the first actionrior to the onset of physiological and biological changes. Also,nvironmental factors (temperature, pH, anoxic conditions, etc.)ay directly affect transcription. Targeted transcript expression

nalyses of genes using quantitative real-time polymerase chaineactions (qRT-PCR) allow the determination of changes in tran-cript expression of a defined and selected series of genes. Thispproach is straightforward and allows analysis of known or sus-ected expressional changes based on the mode of action of theompound, as for instance the endocrine activity of UV-filtersZucchi et al., 2011a,b). However, a limitation of this approach ishat only a small set of genes can be analyzed, whereas microar-ays offer the opportunity to analyze thousands of genes or almosthe entire genome.

Global approaches using microarrays that measure almost allf the genes transcribed at a specific time and at specific concen-rations are assumed to be superior, as the toxicity of chemicalsnvolves mostly multiple modes of actions and a cascade of genenteractions and pathways rather than the changes in transcriptxpression of only a few genes. Therefore, one of the possibledvantages of toxicogenomics is that microarray technology maynable (in a holistic or global approach) us to analyze all of thexpressional changes in an organism or tissue. Hence, it is thoughthat all relevant expressional changes can be discovered at a givenoint and at a given concentration, like a ‘snapshot’ in time. It isssumed that this will allow identifying the modes of action ofhe compound and associated adverse effects. Additionally, it isypothesized that, based on the mode of action, a specific expres-ional profile will result in a transcriptional fingerprint of theompound. A comparison between the expressional profiles andhe fingerprints of known model compounds will then form the

asis for identifying compounds with a similar mode of action. Itan also be used to identify the compound’s action in mixtures andn environmental samples. Therefore, a compound can be identi-ed by its specific mode of action or affect on a specific organ in an

animal (e.g. brain, liver). This can be used to identify the potentialadverse effects of a compound based on the transcriptional profile.

Ultimately, we hope to identify and describe a pattern ofexpressional changes for a given mode of action. The basis ofthis hypothesis is that microarray-based experiments will reveala mode of action profile as well as identify a link between tran-script expression profiles of genes and the mode of action. Changesin transcript expression are hoped to be toxicant-specific and tobe linked to a mode of action for that chemical. Therefore, differ-ent compounds can be compared to determine if those compoundshave similar modes of action. Furthermore, it is hypothesized thatalteration in transcript expression of genes translates into phys-iological alterations. Transcriptional profiles can than be used toidentify toxic effects.

Similarly to toxicogenomics, new technology focused on theprotein expression pattern (proteomics) and presence of metabo-lites (metabolomics) in conjunction with bioinformatic tools havethe potential to discover the protein and/or metabolite alterationsindicative of the mode of action of chemicals. They could improveour understanding of mechanisms of action of chemicals as wellpredict changes via the comparison of chemicals sharing the samemode of action. Currently, all of these techniques remain in thedomain of research. The application of these methods in environ-mental risk assessment of chemicals and environmental samples iscertainly premature. Data usage is listed in Box 1 .

2.1.2. Some complicationsTo complicate the issue, most environmental chemicals have

not only one but a series of molecular effects, or their effects varyin time and with concentration. There are genes responding withina short period of time (hours), others that are only differentiallyregulated after longer terms of exposure, or transiently expressed.Some genes will probably be more sensitive than others, so at lowconcentrations of a chemical, only a reduced suite of genes maybe affected. At higher concentrations, a much larger suite of genesmay well be affected. Very high concentrations of a chemical may

be toxic and lead to other genes being affected as a consequenceof the cellular damage. There does not need to be a correlationbetween the magnitude of expressional changes, responsivenessor the expressional time profile. For chemicals with unknown or

K. Fent, J.P. Sumpter / Aquatic Toxicology 105S (2011) 25– 39 27

Table 1Advantages and disadvantages of microarray studies.a

Advantages Unbiased—transcript expression of every gene can be theoretically determined (both in a tissue or in whole organism).Gives the whole picture on potential effects of a compound rather than merely a small part of it (if whole genome microarrays are used).In theory, transcript expression can be traced to protein synthesis level, and even metabolism (if all three ‘omics’ were to be included in onestudy).Completely unexpected pathways and mechanisms of action can be discovered.Large amounts of data can be obtained allowing new hypotheses to be generated and explored.In theory, an expression profile typical of a specific activity (e.g. estrogenic) can be identified.

Disadvantages Very expensive (for both hardware and consumables and bioinformatics tools).Required skilled and trained researchers, which are often lacking, both in microarray techniques, bioinformatics, and data interpretation.Transcript expression levels of genes (in response to a toxicant) are time-dependent and concentration-dependent. Thus, important timepoints may be missed.Interpretation of results is often highly challenging and difficult.Many genes are not annotated and their functions remain unknown.Currently, the use of diverse techniques is not fully developed or validated. Standardization of data collection and analysis are lacking.Often provides a ‘shotgun’ approach, rather than a hypothesis-driven approach, to research.May not provide any added value to the targeted transcript expression approach using qRT-PCR.Use of heterologous microarrays (application of a microarray of one species to another species) is problematic.Incomplete genome data for many species restrict use of microarrays.Transcript expression data give only a partial view on the effects; they must be linked to physiological changes.Transcriptomics is in its infancy, data cannot yet be applied in environmental risk assessment or regulatory context.

roarray technology to reveal transcript expression patterns of genes, very similar tables,w olomics.

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Box 2: Some current achievements of toxicogenomics.

• Proof of principle in that known genes are altered as expected(e.g. vitellogenin induction).

• Knowledge that transcriptional effects of a chemical aredose-dependent and time-dependent.

• In addition to molecular pathways other instances of alter-ation of genes and pathways were discovered.

• Elucidation of novel modes of action of toxicants.• In addition to expected molecular pathways, alteration of

other genes and additional pathways were discovered.• Knowledge that a single chemical can affect hundreds up to

thousands of genes.• The discovery that alterations of transcript expression of

genes may be more sensitive than physiological endpointsand indicative for physiological changes. They may appearmore problematic than physiological outcomes might revealthem to be.

a Although this table covers the advantages and disadvantages of the use of micith many of the same points, could be constructed for both proteomics and metab

idely dispersed effects, interpretation of data from these newechnologies becomes problematic. Table 1 lists some of the impor-ant benefits and limitations of transcriptomics.

.1.3. Predictive approaches: theypothalamic–pituitary–gonadal axis

In general there are only a few examples where a transcriptxpression profile of a compound with a certain mode of actionas a predictive value for other unknown compounds. It is hopedhat a fingerprint can be found for estrogenic (Kausch et al., 2008),ndrogenic, antiandrogenic and aromatase-inhibiting (Villeneuvet al., 2007) compounds so that they can be used to identify simi-arly acting compounds. Thus far, the data are not very promising,ut rather demonstrate that we have a long way to go to truly fulfillhe potential of the predictive power of expression patterns.

A graphical system model was proposed by Villeneuve et al.2007) to predict effects of endocrine active compounds on the sig-aling along the hypothalamic–pituitary–gonadal (HPG) axis in fishy utilizing existing literature data and by testing the aromatase

nhibitor fadrazole in fathead minnows. Not all predicted changesere observed, perhaps partly because the targeted microarray thatas used focused on a limited and selected gene set. As a kind of

ummary evaluation based on different studies, recently, a conceptf “adverse outcome pathway” has been proposed to forge a mecha-istic link between transcript expression and effects by focusing onve case examples (Ankley et al., 2010). It was proposed that someenes are associated with the initiating events that, via a cascadef biochemical interactions, lead to a predictable adverse outcome.he effects of chemicals acting on the estrogen receptor, ultimatelyausing adverse effects on reproduction, are certainly a good case.owever whether this is enough to predict impacts of chemicalsn organisms or even populations is uncertain.

Yet, another concept has been proposed by the same authors: aranscriptome-based framework of signal transduction pathwaysnd transcriptional regulatory networks on the HPG axis (Wangt al., 2010a,b). In zebrafish, four tissues of both genders were ana-yzed for their transcriptional changes upon various exposures toE2, fadrazole, trenbolone, fipronil, prochloraz, flutamide, vinclo-oline, muscimol, ketoconazole, and trilostane. Analysis revealedhat the pathways and transcription factors showed interactions

mong the HPG axis. Impacts on cellular functions, such as stressesponse, cell cycle, and apoptosis, were common features. Thisynthesis work on several studies of these authors aimed toacilitate hypotheses regarding the mode of action of endocrine dis-

rupters in fish. The authors also tried to identify novel biomarkersfor some of these compounds (Wang et al., 2008).

Yet to date, only a few studies have been performed thatlink changes in transcript expression of genes to physiology orresponses at higher biological levels. Attempts have been madeto explore the links between gene transcript expression responsesand responses at the population level (Soetaert et al., 2007; Connonet al., 2008) in Daphnia magna upon exposure to cadmium. Changesin transcript expression of many genes caused by exposure to cad-mium seem to be linked to somatic growth, development, and(consequently) population growth rate.

Some recent achievements are listed in Box 2 , and Table 1 listssome of the important advantages and disadvantages of microarraystudies. Box 3 lists some important prerequisites, such as baselinedata.

Currently, relatively little is known about the key factors influ-encing gene expression patterns. Put another way, the key baselinedata ideally required for optimal experimental design, appropriateanalysis for results, and correct interpretation of those results, are

currently missing. Microarray technology is presently being used,for example, to investigate the toxicity of a chemical within the nec-essary baselines being established. The difficulties this producescan be readily illustrated by considering the use of plasma vitel-

28 K. Fent, J.P. Sumpter / Aquatic Tox

Box 3: Key baseline data required for optimal experi-mental design and data interpretation.

• Intra-fish variation in expression patterns. The influence ofthe following factors needs to be established:◦ Temporal changes (i.e. how do gene expression responses

change with time?).◦ Changes during ontogeny (e.g. differences between juve-

niles and adults).◦ Changes due to sexual maturation and reproduction.

• Inter-fish variation in expression patterns:◦ Differences between males and females.◦ Differences between species.

• Effects of environmental conditions on expression patterns:◦ Effects of stress.◦ Effects of feeding.◦ Effects of light/dark cycle (including circadian rhythmicity

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ogenin concentrations as a biomarker for estrogenic chemicals;5 years after the variation in plasma vitellogenin concentra-ions in fish, during the reproductive cycle, were first documentedCopeland et al., 1986) concentrations in normal male fish (thecontrol’ value in most ecotoxicological experiments) is still beingebated (Beresford et al., 2011). And if the ‘control’ value is notnown then it can be impossible to know if the concentrationsn experimental fish (in laboratory tests of chemicals, or wild fishxposed to contaminated water) are, or are not, abnormal. The out-ome then varies from unhelpful research to results that cannot beeplicated.

Of course, it is not necessary to have all the baseline data avail-ble before ecotoxicological experiments utilizing microarrays areonducted, as long as the lack of baseline data is accommodatedithin the experimental design. For example, it is not necessary

o know how gene expression patterns vary between males andemales if only one sex is used in an experiment. Nor is it nec-ssary to know how sexual maturation and reproduction effectsene expression patterns if only sexually immature fish are usedn a study. However, the influences of other factors are much

ore difficult to exclude, irrespective of how an experiment isesigned. For example, in response to a challenge by a chemical, theene expression pattern changes dramatically with time: the genesffected after 2 h will be completely different from those effectsisplayed after 12 or 24 h (Moggs, 2005). Similarly, it is likely thathe gene expression changes in the liver induced by, for example,

reproductive toxicant will be radically different to those occur-ing simultaneously in the brain, pituitary gland, and gonad. Hence,ot knowing how gene expression is affected by both internal (e.g.exual maturation) and external (e.g. stress, feeding) factors, andow tissue-specific those gene expression patterns are, can make

t difficult to conduct a meaningful experiment and interpret theata.

.2. Applications

Over the last decade, toxicogenomics has found application inquatic toxicology in various ways. In early studies its feasibility haseen demonstrated by using low-density microarrays (Larkin et al.,003), characterizing the response to endocrine disrupters. Addi-ionally, genomic information from the pufferfish has been applied

or studying the response of rainbow trout to zinc (Hogstrand et al.,002). At an early stage, microarrays have also been used to distin-uish contaminated from pristine environmental sites by hepaticene expression profiles in the European flounder Platichtys flexus

icology 105S (2011) 25– 39

(Williams et al., 2003). Since these early studies, toxicogenomicshas been applied in many different organisms, from algae to fish.Of course, data derived from homologous microarrays have morepower than heterologous systems because of a clearer assignmentof gene products.

Gene expression analysis can be used to determine the tran-scription profile of a compound acting via a selected pathway.Subsequently this information, or the compound’s fingerprint, isthought to be used to determine (1) whether compounds withunknown modes of action follow a given mode of action profile,(2) whether compounds in a mixture or in environmental sampleshave a similar mode of action. In the following section differentapproaches are critically discussed.

2.2.1. Compounds with known modes of action (modelcompounds)2.2.1.1. Metals, metallic nanoparticles and organometals. So far,almost all environmental chemicals analyzed by microarrays arechemicals known for their mode of action, both in fish and inver-tebrates. In Daphnia, for instance, the effects of metals have beenanalyzed (Poynton et al., 2007). The toxicity of metals is largelydependent on speciation, and therefore on water chemistry. Thishas also been found by expressional patterns of metals that areinfluenced by chelators present in water. D. magna microarrayswere applied to identify expression profiles in response to copper,cadmium, and zinc exposures. Metallothioneins and ferritin mRNAwere identified (Poynton et al., 2007). The mRNA expression pat-terns supported known mechanisms of metal toxicity. Additionally,in Daphnia, mRNA expression of several genes was assayed aftershort-term metal exposure (Vandenbrouck et al., 2009). Microarrayanalysis of nickel-exposed zooplankton revealed several affectedfunctional gene classes, which were predicted to be involved indifferent metabolic processes, cuticula turnover, transport, and sig-nal transduction. Furthermore, transcription of genes involved inoxygen transport and heme metabolism was downregulated. Aninverse relationship was observed between the mRNA expressionlevels of different cuticula proteins and available energy reserves.In addition to the nickel exposure, exposure to binary mixtures ofnickel and cadmium, or nickel and lead, were analyzed, revealing acomplex interaction.

The transcriptional response of uranium was studied in thebrain of the zebrafish, exposed to 15 and 100 �g/L for 3 and 10 d(Lerebours et al., 2010). A total of 56 transcripts responded to ura-nium exposure, and the anatomical structure of the olfactory bulbwas damaged. Gene response was higher at the lower concentra-tion and the numbers of responding genes common to any twoexposures were much smaller than those unique to each expo-sure. These data showed that the intensity of gene response maynot correlate with concentrations. Instead, different patterns ofmRNA expression of genes occurred for each exposure concentra-tion. Gene responses were categorized into eight functional classes,and the transcriptional responses of genes involved in the olfactorysystem were significantly affected.

The acute toxicity of soluble copper and 80 nm copper nanopar-ticle suspensions were examined in zebrafish (Griffitt et al., 2007).Histological and biochemical analysis revealed that the gill was theprimary target organ for nanocopper. Nanocopper produced differ-ent morphological effects and global transcript expression patternsof genes in the gill compared to soluble copper.

Effects of dietary uptake of methylmercury (MeHg) were studiedin fathead minnows, Pimephales promelas, and the mRNA expres-sion of genes commonly associated with endocrine disruption

were altered (Klaper et al., 2006). Upregulation of vitellogeninmRNA in males and downregulation in female fish was observed.Additional genes identified included those associated with eggfertilization and development, sugar metabolism, apoptosis, and

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lectron transport. MeHg differed in its effect from other metals orompounds known as endocrine-disrupters. The analysis of muscleissue after MeHg exposure results in a different expression profileCambier et al., 2010). Perturbation of protein synthesis, mitochon-rial metabolism, endoplasmic reticulum function, detoxification,nd general stress responses were predicted to be differentiallyegulated in exposed zebrafish. Several other cellular genes had dif-erential mRNA expression, such as those predicted to be involvedn lipid metabolism, calcium homeostasis, iron metabolism, muscleontraction, and cell cycle regulation. A schematic representationf the general cellular response caused by MeHg contaminationemonstrates that many alterations were not common to bothuscle and brain tissues. This clearly shows that expressional pat-

erns differ between tissues.

.2.1.2. Endocrine disrupters. Most environmental chemicals stud-ed so far include organic chemicals acting as endocrineisrupters and metals. Most toxicogenomics studies focusedn chemicals acting on steroid signaling in fish, namelystrogens (17-b-estradiol (E2), 17-a-ethinylestradiol (EE2)), andro-ens (17a-methyldihydrotestosterone, dihydrotestosterone), syn-hetic androgens (trenbolone), antiandrogens, and the aromatasenhibitor fadrazole. Chemicals acting as endocrine disruptersbisphenol A, nonyphenol) have also been studied. Investigated tis-ues include the hypothalamus (Martyniuk et al., 2006; Marlatt etl., 2008), brain (Martyniuk et al., 2007), liver (Moens et al., 2007;enninghoff and Williams, 2008; Hoffmann et al., 2006, 2008), andonads (Santos et al., 2007). Transcriptomic studies have also beenerformed on whole zebrafish (Lam et al., 2008).

Often environmental compounds do not only act via oneut multiple modes of action, which as a consequence canause responses that are phenotypically identical. For exam-le organochlorine pesticides having additional effects besidesndocrine disrupting effects were analyzed in M. salmoides (Garcia-eyero et al., 2006). Thereby, three pathways of endocrineisruption were analyzed (receptor interaction, changes inormone synthesis, changes in hormone metabolism), and tran-criptional alterations occurred in all pathways.

Lam et al. (2008) used whole-body zebrafish transcriptomicso evaluate its potential for predictive and discovery purposes byocusing on two classes of important compounds, Ah-receptor ago-ists such as PAHs and dioxins (represented by benzo[a]pyrene,-methylcholanthrene and 2,3,7,8-tetrachlorodibenzodioxin) andstrogenic compounds (represented by E2, diethylstilbestrol andisphenol A). The data indicate that these two compound classesave different transcriptomic profiles. The authors derived pre-iction models that can differentiate between compounds of theame class and those of different classes in two large independentxperiments. The expression signatures also led to the identifica-ion of biomarkers for potent aryl hydrocarbon receptor (AHR) andstrogen receptor (ER) agonists, respectively, and were validated inultiple targeted tissues.

.2.1.3. Estrogens. Several studies analyzed the effects of E2 insh (Lam et al., 2008; Larkin et al., 2002; Tilton et al., 2006).illiams et al., 2007) evaluated the transcriptional profile in

uropean flounder, Platichthys flesus, to E2. Known biomarkersf estrogen exposure, such as choriogenin L and vitellogenins,howed sustained transcriptional induction. Among 175 identifiedenes, mitochondrial genes, those involved in amino acid synthe-is, ubiquitination and apoptosis were transcriptionally inducedhile those associated with immune function, electron transport,

ell signaling and protein phosphorylation were transcriptionallyepressed.

Additionally, several studies in different fish analyzed tran-cript expression profiles caused by EE2 (Martyniuk et al., 2007;

icology 105S (2011) 25– 39 29

Filby et al., 2007; Wang et al., 2010a,b). Furthermore, the expres-sion profiles of male zebrafish exposed to different estrogenswere compared (Kausch et al., 2008); the expression profiles inresponse to exposure to bisphenol A did not resemble those fromEE2 or genistein. Only well known vitellogenin and zona radiataprotein transcripts were regulated in common among the threecompounds, of which bisphenol A caused the fewest expressionalchanges. This example demonstrates that mainly already knowntoxicological effects of estrogens were identified, thus being con-firmative rather than complementary to previous knowledge.

2.2.1.4. Androgens and antiandrogens. Exposure of rainbow troutto trenbolone resulted in altered mRNA expression of 64 genes,far fewer than the number observed in rainbow trout followingexposure to EE2 (Hook et al., 2006). Further, a greater proportionof genes were transcriptionally upregulated following exposure totrenbolone, whereas more genes were transcriptionally downreg-ulated following exposure to EE2.

The androgenic compound trenbolone and the antiandrogenflutamide have been extensively studied in fish by microarraystudies. Garcia-Reyero et al. (2009) studied these compounds infathead minnow ovaries as single chemicals and as a mixture. Flu-tamide altered about twice the number of genes as trenbolone,most of which were not associated with the action via the andro-gen receptor. The antagonistically acting compounds resulted in 37genes that were assigned to human homologs which were recip-rocally regulated by trenbolone and flutamide. Out of these, theauthors suggest that some could be used as potential biomark-ers for androgenic or antiandrogenic activity. The microarraystudy revealed that many different genes and pathways werealtered, revealing a complex effect pattern that cannot be easilyresolved.

Moens et al. (2006) examined the transcriptional effects ofendocrine disrupters with different modes of action, where flu-tamide did not group closely with vinclozolin, another modelantiandrogen, but instead appeared to be more closely linked tothe effects of T3 (triiodothyronine), T4 (thyroxine), and dibutylph-thalate. Once again, this study demonstrates a complex pattern ofaffected pathways.

Gene transcript expression profiles of the androgen 11-ketotestosterone (11-KT), the antiandrogen flutamide, and theantiandrogenic fungicide vinclozolin were studied in medaka(Oryzias latipes) larvae (Leon et al., 2008). Flutamide, vinclozolin,and 11-KT caused significant differential transcript expression ofat least 87, 82 and 578 genes, respectively. Two sets of respon-sive genes are associated to vertebrate sex differentiation andgrowth, and 50 genes were useful in discriminating between thecompounds. The discriminating capacity was confirmed by the sim-ilarity of the antiandrogenic expression profiles of vinclozoline andflutamide, which were distinct from the androgenic profile of 11-K. Therefore, this study contradicts the findings of Moens et al.(2006), where flutamide and vinclozolin apparently caused differ-ent expression profiles in fathead minnows.

Hepatic gene expression profiling was performed in femalezebrafish exposed to 17a methyl-dihydrotestosterone (MDT) for24 and 168 h at three concentrations (Hoffmann et al., 2006). Expo-sure to MDT resulted in an increase in plasma E2 while plasmalevels of testosterone were reduced. A rather small number ofgene transcripts, involved in a variety of biological processes, werealtered. Genes involved in retinoic acid metabolism, steroidogene-sis and steroid metabolism, hormone transport, and regulation ofcell growth and proliferation were affected. In the liver, 171 genes

were affected in a concentration-dependent manner. Genes iden-tified in this study provide information on the potential mode ofaction of strong androgens in female fish, but the mode of actionsof MDT could not be identified.

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.2.1.5. Organophosphates. Organophosphates such as dithiocar-amates (DTC) are widely used in pesticides and residues are found

n the environment. DTC are found to be teratogenic to verte-rates, but the mechanisms are not well known. Tetraethylthiuramthiram), tetramethylthiuram (disulfiram), and sodium metammetam) lead to craniofacial abnormalities in developing zebrafishmbryos (Van Boxtel et al., 2010). In order to better understandhe molecular mechanisms underlying DTC teratogenesis, embry-nic zebrafish (PAC2) cells were exposed to thiram and disulfiramnd changes in transcript expression were analyzed by microar-ays. The transcript expression of 166 genes that were specificor exposure to DTC was reported, and a network of genes wasdentified which were related to connective tissue developmentnd function. Additionally, eight transcriptionally downregulatedenes related to transforming factor b-1 (TGF-b1) signaling weredentified, including an essential transcription factor for zebrafishraniofacial development, SRY-box—containing gene 9a (sox9a).he authors also showed that sox9a transcript expression is alteredn the ceratobranchial arches of DTC-exposed zebrafish. Thereforehe microarray data suggest that this is an important event in theevelopment of DTC-induced craniofacial abnormalities. By the usef microarrays, this study demonstrates its usefulness in elucidat-ng the molecular basis for teratogenicity of such compounds, andn gaining a better understanding of DTC-induced teratogenesis inertebrates.

.2.1.6. Pharmaceuticals. Effects of different concentrations ofianserin on zebrafish (Danio rerio) transcript expression using a

rain-specific microarray have been studied (Van der Ven et al.,006). Similarly, the impact of exposure on egg production, fertil-

zation, and hatching has also been assessed. After 2 d of exposureicroarray analysis showed a clear effect of mianserin on impor-

ant neuroendocrine-related genes (e.g., aromatase and estrogeneceptor), suggesting that antidepressants can modulate neuroen-ocrine processes. Adverse effects on egg viability were seen after4 d of exposure at the highest concentration.

We investigated the effects of the benzodiazepine diazepamValiumTM), a known sedative in mammals, by whole genome

icroarrrays in the brain of zebrafish (Oggier et al., 2010). Tran-criptomics was used to identify molecular effects of diazepam ando discover its neurotoxic mode of action combined with neuro-ehavioral studies and thus it was possible to relate expressionalhanges with physiology. Male zebrafish and zebrafish embryosere exposed for 14 d or up to 3 d after hatching, respectively,

o determined water concentrations of 235 ng/L and 291 �g/L.mong the 51 and 103 altered transcripts at both concentrations,espectively, the transcript expression of genes involved in theircadian rhythm in adult zebrafish and eleuthero-embryos weref particular significance, as revealed both by microarrays anduantitative PCR (Fig. 1a). The microanalysis showed a strikingffect on the circadian rhythm not previously described. There-ore, toxicogenomics led to the identification of a new mode ofction of this environmental toxicant. The swimming behavior ofleuthero-embryos was significantly altered at 273 �g/L (Fig. 1b).he study leads to the conclusion that diazepam-induced alter-tions of genes involved in circadian rhythm are paralleled byhe effects in neurobehavior at high, but not at low, diazepamoncentrations. Environmental concentrations, however, lead toxpressional changes.

To identify molecular effects of the antineoplastic agent, proteininase C inhibitor 412 (PKC412) (midostaurin), we applied genexpression profiling to zebrafish using whole-genome microarrays

Oggier et al., 2011). Behavioral, developmental, and physiologicalffects were investigated in order to identify correlations betweenltered transcript expression profiles with effects on developmentnd physiology. Zebrafish embryos were exposed for 6 d postfertil-

icology 105S (2011) 25– 39

ization to nominal levels of 2 and 40 �g/L PKC412. Among the 259and 511 altered transcripts at both concentrations, respectively, thetranscript expressions of genes involved in the circadian rhythmwere further investigated (Fig. 2a). No alteration in swimmingbehavior was observed. The pathways affected by PKC412 wereangiogenesis, apoptosis, DNA damage response, and response tooxidative stress. Angiogenesis was analyzed in double-transgeniczebrafish embryos but no major defects occurred. Apoptosisoccurred in olfactory placodes of embryos exposed to 40 �g/L, andDNA damage was induced at both PKC412 concentrations (Fig. 2b).However, there were no significant effects on reactive oxygenspecies formation. This study demonstrates that PKC412-inducedalterations of gene transcripts are partly paralleled in physiologicaleffects at high, but not at low, PKC412 concentrations. Addition-ally, this study demonstrates the complex expressional patternof chemicals on the transcriptome and the importance of linkingexpressional profiles with physiological effects.

The aromatase inhibitor fadrozole was investigated in sev-eral studies. Villeneuve et al. (2009) reported on global transcriptexpression profiles after exposure to 25 or 100 �g/L in the brainand ovaries of zebrafish after 24, 48 and 96 h. During the study,hundreds of gene transcripts were altered. Based on the trans-criptional changes, it was hypothesized that fadrozole inducesneurodegenerative stress in the brain. In the ovary, the affectedbiological processes produced transcript expression changes dif-ferent from those of the brain. They were functionally linked tocell–cell adhesion, extracellular matrix, vasculogenesis, and devel-opment. It was also hypothesized that changes in gene expressionin the ovary indicate decreased oocyte maturation and ovulationbecause of impaired vitellogenesis. These hypotheses, derived fromthe microarray results, provide a basis to test for other similarly act-ing compounds. Again this study shows the complex transcriptionprofile of a chemical as well as its transcriptional responses, reveal-ing significant differences at different times and concentrations.

2.2.1.7. Additional compounds. Tilton et al. (2008) analyzed perflu-oroactanoic acid (PFOA) in rainbow trout for its tumor promotingactivity. Extremely high concentrations of PFOA, and another tumorpromoter, dehydroepiandrosterone, resulted in estrogenic genesignatures with strong correlation to E2, whereas clofibrate hadno regulated genes in common with E2. The data suggest thatthe tumor-promoting activities of PFOA in trout are due to novelmechanisms involving estrogenic signaling and are independent ofperoxisome proliferation.

Gene expression profiles of cyanobacterial toxins for the earlylife stages of zebrafish were analyzed, both as extracts and puremicrocystin-LR (Rogers et al., 2011). Changes in global gene expres-sion were also evaluated. The number of differentially transcribedgenes increased with MC-LR concentration and included genesrelated to known mechanisms of action for MC-LR. Transcriptionalupregulation of vitellogenin was observed in Microcystis-exposedlarvae but not in larvae exposed to MC-LR, suggesting that Micro-cystis may be a source of environmental estrogens. A summary ofinsights obtained with model compounds is given in Box 4 .

2.2.2. Compounds with unknown or partly unknown modes ofaction

Model compounds having a defined toxicological profiledemonstrate a complex expression profile. The expressional pro-file may eventually be used to predict the toxicological propertiesof a given compound. Hence, it is believed that a specific expres-sional pattern can be applied to differentiate between compounds

of the same toxicological class and those of different classes (Lamet al., 2008). However, the identification of toxicological pathwaysof compounds with unknown toxicological features is even morechallenging. Similar to the way in which the net ecotoxicological

K. Fent, J.P. Sumpter / Aquatic Toxicology 105S (2011) 25– 39 31

Fig. 1. Comparison of the level of transcript expression of selected genes of the circadian rhythm (a) in zebrafish brain determined by microarray, qRT-PCR (zebrafish brainand eleuthero-embryos, respectively), and locomotor activity (b) of zebrafish eleuthero-embryos exposed to 273 ng/L and 273 �g/L diazepam. Many genes are altered dose-dependently. Alterations found by microarrays (white bars) are confirmed by qPCR in both adult brain (grey bars) and eleuthero-embryos (black bars). As a consequence oftranscriptional alterations of genes of the circadian rhythm, a measurable physiological outcome, neurobehavior, is changed by diazepam. The locomotor activity increased(which seems paradoxically, but this is also seen in humans (children) and animals after diazepam treatment). (a) Values at both diazepam concentrations derived frommicroarrays (14 d exposure, n = 4 replicates, 15 male fish pooled, white bars), qRT-PCR in brain of adults (n = 5, 15 male fish pooled, grey bars, and in zebrafish eleuthero-embryos (n = 6, 15 pooled, black bars) are expressed as average fold change (log2) with standard error compared to controls. Locomotor activity (percent of total time spentin locomotion during embryos 2 h) of eleuthero-embryos exposed for up to 3 d after hatching. ‘*’, statistically significant difference to control (p < 0.05).Adapted from Oggier et al. (2010) and Oggier et al. (2011).

32 K. Fent, J.P. Sumpter / Aquatic Toxicology 105S (2011) 25– 39

Fig. 2. Effects of protein kinase inhibitor PKC412 on transcript expression (a) in zebrafish eleuthero-embryos determined by microarray, and DNA damage determined byComet assay (b). Embryos (n = 4 replicates, 80 eleuthero-embryos pooled) were analyzed after exposure to 1.3 �g/L and 21 �g/L PKC412, respectively, in the microarrayexperiment (a), and to 1.6 �g/L and 31 �g/L PKC412, respectively, in the Comet assay (b). At both concentrations, PKC412 led to a transcriptional downregulation of xpc, theproduct of which is important for DNA damage recognition. The Comet assay was performed to evaluate whether the alteration of xpc transcription has a correlate at thephysiological level. PKC412 (and the positive control hydrogen peroxide) induced a doseas average fold change (log2) with standard error compared to controls, and Comet assa(p < 0.05).Adapted from Oggier et al. (2011).

Box 4: General insights gained by transcriptomics.

• Transcriptional profiles of known model compounds in fishreveal that many more genes and pathways are altered thanpreviously thought.

• Transcriptomics aids in the identification of new toxicologicaleffects as well as molecular, cellular, and biochemical path-ways. It contributes to our understanding of the moleculareffects of a compound.

• However, studies often support established knowledge, e.g.vitellogenin induction for estrogenic compounds.

• Expressional profiles and patterns are often so complex thatno clear pathways or modes of action can be identified.

• It is often difficult to delineate the specific molecular effectsof a given compound from more general cellular stressresponses. Therefore, numerous cellular and biochemicalpathways reveal alterations, often based on cellular stress,apoptosis and repair mechanisms.

• In general the studies have shown the complex nature of achemical’s effect on transcript expression of genes at a spe-cific point in time (usually short exposure time) and at fewconcentrations (usually two).

• Different time points and concentrations often result (thoughnot always) in different expressional profiles which com-plicates our understanding of the specific effects of acompound. Profiles differ between tissues, making identifi-cation of a general mode of action of a compound difficult.This is also true for the delineation of the specific effects ofa compound from general cellular damage and adaptive orrepair mechanisms.

• Only a few studies have tried to link the transcriptionalchanges with physiological effects, or generally, toxicologi-cal effects at a higher level of biological organisation or evenat population level.

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ffect of environmental samples is evaluated, toxicogenomics mayeveal the entire set of expressional changes that can be interpretedy pathway analysis. This information can be subsequently used toropose the chemical’s mode of action and associated physiological

ffects. Hence, toxicogenomics can be used as a predictive tool foroxicological effects.

The UV-filter 2-ethyl-hexyl-4-trimethoxycinnamate (EHMC) isbundant in wastewater-contaminated surface waters, bathing

-dependent increase in DNA damage (tail moment). Microarray data are expressedy values are presented as the mean ±SEM. ‘*’, significantly different from controls

waters, and is also accumulates in biota (Fent et al., 2010).EHMC was found to interact with the hormonal system of fish(Christen et al., 2011). Subsequently, we performed trancriptomicsin zebrafish to suggest molecular effects and to identify additionalpathways affected (Zucchi et al., 2011a). EHMC lead to the alterationof as many as 1100 transcripts in whole-body analyses after 14 d ofexposure. The affected genes belonged to many different pathways(lipid biosynthesis and metabolism, vitamin A metabolic process,DNA damage and apoptosis, regulation of cell growth etc.) as wellas hormonal pathways. The analysis showed tissue-specific geneprofiles, revealing that EHMC produces a complex pattern of tran-scriptional changes. This study also points to a more general insightgained by transcriptomics: a compound may result in alteration ofa surprisingly large number genes.

The proof of concept, i.e. that unknown mechanisms of actionsof a compound can be unravelled, is still yet to be provided by tran-scriptome studies. More compounds need to be analyzed. Only thencan we judge whether or not a toxicological characterization ofcompounds with unknown toxicological activity can be predictedin aquatic toxicology based on the transcription (or proteome) pro-file.

2.2.3. Classification of compounds and new biomarkersBy analyzing model compounds we also hope to discover a

unique expression profile specific for compounds acting by a spe-cific mode of action, or an expression profile of a specific tissue(e.g. brain, liver). Additionally, by using microarrays, we hope todiscover new biomarkers, or expressional profiles, that are typicalfor target tissues or modes of action. For instance, a compari-son of the hepatic tumor promoters indole-3-carbinol, E2, andb-naphtoflavone in rainbow trout showed that the expression pro-file of indole-3 carbinol resembled that of E2. However, a generalfeature of tumor-promoting compounds cannot (of course, as theyact via different mechanisms) be deduced (Tilton et al., 2006). Thus,a classification for such acting compounds cannot be made.

Lam et al. (2008) tried to distinguish different toxicants suchas PAHs and estrogenic compounds based on transcriptional pro-files in whole zebrafish microarrays in two large experiments,applying algorithms that cluster specific toxicant profiles. The aim

was to derive prediction models that can differentiate betweencompounds of the same class and those of different classes. Net-works were generated from the available human homologs mappedfrom the discriminatory gene sets. They were able to distinguish

K. Fent, J.P. Sumpter / Aquatic Tox

Box 5: Fingerprints and biomarkers.

• So far, there seems to have been more hope and promisethan real scientific achievement in detecting:(a) unique expression profiles for different classes of chem-

icals (e.g. unique fingerprints).(b) novel biomarkers of effects specific to a chemical with a

known mode of action or activity.• Only a few examples are documented of compounds

acting via known mechanisms of action, such as thearyl hydrocarbon or estrogen receptors (e.g. Lam et al.,2008), or through established pathways, such as thehypothalamic–pituitary–gonadal axis (e.g., Ankley et al.,2010), which would aid in the identification of molecularbiomarkers.

• More research is needed to identify and characterize novelbiomarkers and expression profiles for different classes ofchemicals.

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etween the aryl hydrocarbon receptor and estrogen receptor ago-ists. Compounds with no affinity to these receptors were also

dentified. Some of the genes selected as biomarkers are, however,ell known, including vitellogenin and Cyp1A, respectively. Othersere not clearly related to known toxicological functions or have a

unction that is unknown, thus rendering their value open. A sum-ary of the state of the art in compound profiling and biomarker

efinition based on transcriptomics is given in Box 5 .

.2.4. Compound mixturesMainly endocrine disrupters and metals were studied for their

ffects when occurring in mixtures. Mixtures of differently actingompounds were studied in an attempt to identify the specific fin-erprint of a compound acting by a specific, and known mode ofction, within a complex mixture. In one example, trout (O. mykiss)as exposed to six different compounds with specific modes of

ction, including endocrine disrupters such as EE2 (estrogen), tren-olone (androgen), the flame retardant BDE-47 (putative thyroidormone activity), benzo(a)pyrene (Cyp1a inducer and cancero-en), diquat (oxidative stress) and hexavalent chromium (oxidativetress, cancerogen) (Hook et al., 2006). The transcript expressionrofiles of the mixture often contained many transcripts that coulde related to the mode of action of a given compound. However,

t is not possible to univocally identify the net effect of this mix-ure or the components of that mixture causing the key expressionhanges based on expression profiles.

A mixture may evoke different expressional profiles, not beingerely the sum of the mixture components. Vandenbrouck et al.

2009) studied binary mixtures of metals in Daphnia. The datahowed additionally affected pathways after mixture treatment,ndicating interactive molecular responses which are not merelyhe additive sum of the individual metals. These findings indicatehe complex nature of mixture toxicity on the expressional level.

It seems that it is too early to assess effects of mixtures based onranscript expression profiles, unless the expression profiles of thendividual compounds are clearly defined. And this is truly wherehe challenge lies. Therefore, more should be learned first on thendividual profiles of single compounds.

.2.5. Effluents and environmental samplesIt is even more challenging to analyze contaminated environ-

ental samples by microarrays and to identify the compoundsesponsible for adverse effects by transcript expression analysesf genes. By performing microarray studies on environmental sam-les it is hoped that from the gene expression profile resulting from

icology 105S (2011) 25– 39 33

exposure to chemical mixtures or environmental samples, specificelements can be deduced, which can be traced to the individualchemicals responsible.

For example, a cross-species microarray has been used in tur-bot to detect the presence of a particular class of chemicals in anenvironmental effluent (Baker et al., 2009). Transcript expressionprofiles of genes of unexposed and exposed fish were also com-pared (Fisher and Oleksiak, 2007). The data indicate that up to 17%of genes in the brain relating to metabolism displayed adaptivechanges in the polluted site. In European flounder, transcriptomicsand bioinformatics were used to predict the site of origin of fishfrom the environment (polluted or non-polluted), based on stress-responsive genes (Falciani et al., 2008). Altered expression mayshow the potential to identify patterns of genes associated withdifferent types of pollution, although many environmental factorsmay affect the expressional pattern.

In another study, wastewater effluents were analyzed bymicroarrays in carp (Moens et al., 2007). The data revealed thatthe main predicted molecular pathways affected were associatedwith the energy balance of the fish and digestive enzymes, but notwith endocrine disruption or the immune system response. Thetotal activity could not be assigned to the effects of specific chemi-cals. In another study, hepatic transcriptomics and protein analysisdata showed that exposure of rainbow trout, for 14 d, to wastewa-ter effluents affected transcript expression of stress-related genes,hormone receptor genes, and genes related to immune function(Ings et al., 2011). In addition to altered transcript expression ofhepatic genes, elevated plasma cortisol, glucose, and vitellogeninlevels were noted. In fathead minnows, transcript expression datawere compared with expression profiles of target genes in testis andliver of model compounds, EE2 and 11-ketotestosterone, as well aswith vitellogenin induction and competitive nest holding behav-ior (Garcia-Reyero et al., 2011). Significant changes in transcriptexpression were observed in both liver and testis, which corre-lated to vitellogenin induction and reduced competitive behavior.Microarray results showed that the transcript expression signaturefrom effluent-exposed fish were in some part similar to estrogenand androgen signatures, which contrasted with the data by Moenset al. (2007). However, overall, they were different, revealing thecomplexity of compounds present in sewage and their differentmodes of action. Overall, not much progress seems visible based onthese studies when compared to former, similar, studies performedusing more classical techniques.

A simulated diesel oil spill was evaluated by microarrays in con-junction with measurements of survival and growth (Mos et al.,2008). The study showed changes in genes including cyp, glutha-tione S transfereases, both known biomarkers of compounds actingvia AhR, as well as other genes. Additionally, new series’ of genesregulated by diesel were found, such as endocrine disruptive effectsin the form of transcriptionally upregulated estrogen-dependentgenes, and transcriptionally downregulated testosterone-relatedand thyroid hormone-dependent genes, as well as genes indicat-ing immune system alterations. Gene transcription changes werefound to be more sensitive than effects on growth and survival. Box6 summarizes current state of play on the use of toxicogenomics toimprove our understanding of how complex mixtures of chemicalsaffect fish.

2.3. Limitations and questions

On the basis of current knowledge and development the fol-lowing question arises: “Is technical innovation also resulting in

scientific innovation?” The current influence that DNA microar-rays are having in medicine and biology, and also toxicology, isremarkable, but is this also true for ecotoxicology? The use of tran-scriptomics in the field of ecotoxicology seems to have raised just

34 K. Fent, J.P. Sumpter / Aquatic Tox

Box 6: Mixtures and environmental samples.

• Characterization of the effects of defined mixtures of chem-icals, or of ill-defined complex environmental matrices,remains extremely challenging, and the new technologieshave not yet provided much additional useful information.

• The new technologies mainly demonstrate that exposedorganisms differ from non-exposed ones; they have not pro-vided data that can be easily interpreted and they essentiallyconfirm what was previously known.

• It appears premature to tackle these issues using sophis-ticated and expensive technologies (transcriptomics, pro-teomics, metabolomics).

• Major insights and advances can be gained using thesetechnologies only after a reasonable number of importantselected environmental chemicals have been investigated.

• If unique fingerprints for different classes of chemicals canbe established, these may eventually allow the responsesto complex, environmentally relevant mixtures to be inter-preted.

• There is a long way to go before mixtures and environmentalsamples can be assessed through meaningful studies usingthese technologies that will provide added value to currentwhole-effluent testing procedures.

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dian rhythms, stress response, and DNA repair (Weger et al.,2011).It is often assumed that gene transcriptional profiles may

s many questions as it has answered. Among the many reasonsor this is the complexity of data, its interpretation, and the lack ofnowledge of the mechanisms of action of chemicals. It should beoted that gene expression profiling using microarrays so far rep-esents a ‘snapshot’ of the gene transcriptional responses occurringt a given point in time, based on between one and a maximum ofhree concentrations, and within a particular tissue.

While sequence information of genes for many aquatic speciesas become more accessible, their function still remains ahallenge. Functional annotation identifies genes with known func-ional characteristics, such as molecular function, pathways, oriological processes. The basic assumption of annotation is thatimilar genes have an identified function in other organisms. Forxample, the genes in zebrafish are similar to those in humans.owever, even zebrafish has a high percentage of genes withnknown functions. The more distant the species is from mam-als, the more difficult the functional annotation of the genes that

re altered upon chemical exposure. Therefore, less useful infor-ation will be obtained with more distant species such as algae orussels, unless more is known on their gene functions.There are many key open questions associated with these

ew technologies assessing a large number of mRNA (transcrip-omics), proteins (proteomics), or small molecules (metabolomics).ut of these, most progress has been made in transcriptomics,ver the last decade, whereas, to date, small progress has beenade on proteomics and metabolomics. The kinetics of chemically-

nduced transcript and protein expression can be complex andose-dependent. Core questions are still not sufficiently answereduch as “What do these alterations really mean in terms of adverseffects?” Often, alterations are identified in a largely descrip-ive manner and are not coupled with relevant hypotheses. Howo transcript (transcriptomics) and protein (proteomics) expres-ion, as well as metabolite (metabolomics) concentrations, relateo physiological and ecological outcomes? A link between the

olecular (transcriptomics) and biochemical responses to adverselterations in physiology, growth, development, reproduction, and

urvival is necessary for better understanding of adverse effects ofhemicals.

icology 105S (2011) 25– 39

It seems that there is too little hypothesis-driven research, andoften little thought has gone into experimental design. In ouropinion, some of the basic questions that need to be addressedhave not yet been, sufficiently. This conclusion strongly sug-gests that technical innovation is driving the use of microarraysas well as other similarly demanding techniques (proteomics,metabolomics), rather than the technology being used only whereappropriate to answer specific scientific questions that are difficult,or impossible, to answer through the use of other techniques. Thefollowing list some of the critical problems surrounding this issue:

(a) Technical challenges and variability of dataExperiments in aquatic toxicology using transcriptomics have

so far been diverse in many aspects, encompassing differentmicroarrays (both hetero- and homologuous), organisms (fish,invertebrates, algae), exposure routes, single chemicals, mix-tures and environmental samples. They represent case studiesthat do not follow a standardized protocol. Some validation andstandardization of organisms, study design, data collection andinterpretation will be necessary in the future, as variability isan ongoing concern with these technologies.

There are also some rather technical issues to be solved. Cur-rently, different significance levels are defined, some authorsconsider expressional changes by 1.5 times as sufficient,although more often a factor of 2 is considered as a relevant sig-nificance level. Inter-laboratory microarray comparisons havedemonstrated that the use of non-stringent gene filteringresults in non reproducible gene lists with little correlationto real-time PCR validation (Chen et al., 2007). Many stud-ies lack appropriate bioinformatics and statistical support.Additional sources of variability, and possible technical arte-facts, concerning the experimental design, such as inadequatesample numbers and chemical concentrations, methods of pro-cessing, and analysis (normalization, statistics) are common.Inter-individual variability between the responses of exposedorganisms is not sufficiently known, and very rarely, individualsare analyzed (normally pooled samples are analyzed).

b) Interpretation of expressional changesEven with zebrafish many transcripts cannot be ascribed to a

known proteins, but are assumed as having the same function asin humans. The annotation of genes is even more difficult in het-erologous microarrays, where most genes have to be ascribedto genes of a related species. In general, annotation of genesin non-mammalian organisms remains a challenge. Functionalannotation relies on genes with known functional characteris-tics. However, this is lacking for most genes in fish or loweranimals or plants. Therefore, annotation is mainly based onsimilarity to known genes in mammals. The more distant theorganism is to mammals, the fewer genes can be annotated.

In addition, the extent of the variability in transcriptexpression data between individual organisms and repeatexperiments has not been explored, and inter-laboratory anal-yses have not been performed, to our knowledge. Moreover, thevariability of expressional changes between individual organ-isms is poorly characterized. We do not think that extensiveand expensive inter-calibration exercises are necessary, butwe think that a minimal knowledge about the accuracy ofdata and inter-individual variability should be gained to aidin understanding the data. Even the time of sampling andthe light regime may influence the expressional response. Forinstance, light induces gene transcription in zebrafish, as shownby the number of 117 light-regulated genes involved in circa-

be similar within classes of contaminants (i.e. metals, endocrinedisrupters, PAHs etc.). Yet this has not been clearly demon-

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K. Fent, J.P. Sumpter / Aquat

strated, although attempts have been made to pinpoint thechemicals action on the HRG axis, for instance (Wang et al.,2010a,b; Ankley et al., 2010). But it is conceivable that similargene expression profiles may eventually diverge to produce dif-ferent toxicological effects. The protein kinase inhibitor PK412affects a multitude of distinct biochemical pathways (Oggieret al., 2011). This leads to a diverse expressional pattern ofmany different pathways. In this and other cases, microarraydata become exceedingly complex to analyze. In many casesemploying whole genome microarrays, a relatively large num-ber of genes are altered, and a multitude of transcript expressionpatterns are altered in response to chemical exposure (Oggieret al., 2011, Zucchi et al., 2011a). These expression patterns mayin part be associated with cellular damage/repair processes. Incontrast, expression profiles induced by different contaminantsmay converge to yield similar toxicological effects, for exam-ple reduction in fertility and reproduction. Therefore, we thinkthat it is very demanding to use toxicogenomics by clusteringof expression responses to predict mechanisms of toxicity.

(c) Basic toxicological conceptsConcentration-response data

As the concentration of a chemical increases, does theresponse also increase? Does the fold-increase (or decrease)in expression of a gene change in the expected mannerwith the dose/concentration? Are the same genes alteredat different concentrations, or are more genes affectedas the concentration increases? Current data show thatsome expressional changes are consistent between differentdoses, but there are also distinct differences in the tran-scription profiles at different doses. Patterns and the reasonsfor these differences have not yet been fully addressed. Forinstance, uranium showed different expressional patternsin the brain of zebrafish at two different concentrations(Lerebours et al., 2010).Time-course data

How does the gene expression profile change with time?Mostly, only one or two time points are studied. How-ever, gene expression undergoes distinct temporal phases inresponse to toxicants, but do phases of expression correlatewith toxicological effect responses to contaminants? Tem-poral patterns in gene expression may provide an importantrepresentation of the regulation that occurs in response totoxic effects in time. This question needs to be addressedmore deeply. The data may lead to a categorisation of differ-ent toxicity phases, in which cells/tissue first respond to theinitial exposure, followed by counter-active responses thatensure continued cellular and physiological function, andsurvival of the organism, during and after toxicant exposure.Data from different tissues and life stages

Mostly, studies on transcript expression profiles in fishare performed with embryos or the livers or the brainsfrom adults. But if the compound is a reproductive toxicant(an estrogen, androgen, or antiandogen, for example) doesliver analysis make sense? Reproductive toxicants seemto act primarily on the HPG axis. Not many studies havelooked at expression profiles in different tissues from thesame fish, to see how similar or different they are. Tar-geted expression approaches reveal different tissue-specificexpressional changes, as, for example, in the case of UV-filters (Zucchi et al., 2011a,b). The alternative, to analyzewhole fish samples, also has its disadvantage; the overallexpressional profile as a sum of all changes in all tissues does

not allow the identification of the target tissues. Only whenthe target tissue is known can this dilemma be solved. Inthe case of MeHg, both brain (Klaper et al., 2006) and mus-cle tissue (Cambier et al., 2010) were analyzed, revealing

icology 105S (2011) 25– 39 35

different expressional profiles, and few genes and predictedpathways were common between the tissues. Certainly, thenervous system is the target of MeHg, thus rendering analy-sis of brain tissue more meaningful. In case of the UV-filtersbenzopheone-4 and EHMC, we observed distinct expressionprofiles in different organs (Zucchi et al., 2011a,b).

Also, not many studies have compared transcript expres-sion changes of genes in different life stages such as embryosand adult tissues. In case of the pharmaceutical diazepam,we found a good correlation between different life stages(Oggier et al., 2010), but more research has to be devoted tocompare tissues and life stages.

We think much more of the baseline data are requiredin order to be able to meaningfully interpret the resultsof a microarray exposure experiment. An example is thecomprehensive list of developmentally regulated zebrafishgenes and their transcript expression profiles duringembryogenesis provided by Mathavan et al. (2005) includ-ing novel information on the temporal expression of severalthousand previously uncharacterized genes.

d) Costs, training, bioinformatics capabilitiesToxicogenomics is a demanding technology that is costly

and requires great skill. In addition, individual responses (vari-ability), data processing, and complexity of data interpretationremain obstacles.

(e) Ecotoxicological relevanceToxicogenomics has shown its value in identifying new

modes of toxic action of contaminants that were previouslyunexpected. However, the link between transcript expressionchanges and effects at higher biological levels has rarely beenaddressed. In particular, integrative assessments of the changesin gene transcription and physiology encoding for adversehealth effects are scarce. To date, transcriptomics has rarelybeen assessed in concert with physiological, histological andphenotypic measures of chemical exposure. Thus, often tran-criptomic data remain descriptive, though they can provideinsights into new pathways affected by the compound. Whetheror not these changes translate into physiological adverse effectsremains in many cases unknown. Forthcoming studies musttherefore try to establish solid links between expressionalchanges and responses measured at the cell or tissue level andadverse outcomes traditionally measured at the individual orpopulation level.

3. Proteomics and metabolomics

We mentioned that expression signatures often lack any relationto the responses of the organism, such as physiological changes,growth, fertility or reproduction, thus limiting their value. Focus-ing on the transcription of genes is only an intermediate stepin the process of converting genetic information into proteinsand physiological function. Ideally, the information on gene tran-scription should be complemented by information on the protein,metabolic, and physiological levels. Similar to toxicogenomics,alterations in protein profiles (proteomics) or in the metabolitesprofile (metabolomics) have developed as novel techniques todetermine effects of chemical exposure. They also lend them-selves to generating an enormous amount of information. To date,advances in aquatic toxicology have mainly been achieved in tox-icogenomics, whereas proteomics and metabolomics are rarelyapplied.

Changes in the mRNA level do not necessarily translate to theprotein or physiological level. Proteomics determines the occur-rence of a series of proteins in a cell, organ or in body fluids,whereas metabolomics determines the occurrence of generally

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mall molecules in body fluids or tissues. Both techniques are alsohallenging and need sophisticated analytical chemical equipment,or proteomics 2D electrophoresis coupled with MALDI-TOF MS and

S/MS, and for metabolomics, nuclear magnetic resonance spec-roscopy. So far, only a very few chemicals have been analyzed byroteomics. This is probably due to the complexity, and lack of func-ional protein annotation and costs. Metabolomics seems to have

ore applications. However, similar to trancriptomics, proteomicnd metabolomic techniques are very expensive and demanding.he challenges and questions associated with these techniques areimilar to those with transcriptomics.

.1. Proteomics

The assessment of a series of tens to hundreds or even thou-ands of proteins in tissues or body fluids, combined with functionalnnotation and pathway analysis, offers information about cellularunctions pathways and processes involved. One important aspectn toxicogenomics is to determine how transcript expression is cor-elated to proteins. Currently, it is poorly understood how well anncrease in mRNA correlates with increased protein abundance. Thealf-lives of mRNA and protein often differ; therefore, protein pro-les are not necessarily identical to the mRNA expression profiles.tudies analyzing both profiles in combination are very rare.

To date there are only a few quantitative proteomic-based stud-es in fish, and less in invertebrates such as Daphnia (Fröhlicht al., 2009). In rainbow trout liver, 15 proteins were alteredfter wastewater exposure (Albertsson et al., 2007). Also pro-eomic response to hypoxia has been studied in medaka (Oehlerst al., 2007). Proteomics has been used to evaluate the effects ofycrocystin-LR (Wang et al., 2010a,b), perfluorooctane sulfonate

Shi et al., 2009), flame retardants (De Wit et al., 2008; Kling andörlin, 2009), trenbolone (Martyniuk et al., 2009), cadmium (Zhut al., 2006; Ling et al., 2009), and wastewater (Albertsson et al.,007). Usually, only one or very few concentrations were analyzed,nd only at one point in time. The studies also show that manyltered proteins could not be identified, and only a small set ofroteins were actually identified and interpreted.

Exposure of zebrafish to microcystin-LR caused damage toiver cellular ultrastructure, and the abundance of 22 proteins

as remarkably altered in response to toxin exposure (Wangt al., 2010a,b). Among them 19 proteins could be identified andnnotated to cytoskeleton assembly, macromolecule metabolism,xidative stress, and signal transduction. This study shows that pro-ein profiles correlate with ultrastructural changes, and indicatesdditional effects (oxidative stress) to those previously known.

Responses of MeHg in the brain of Atlantic cod (Gadus morhua)evealed 71 protein spots, of which 40 were identified, to be signif-cantly altered following injection of the metal into the body (Bergt al., 2010). Many of the proteins were associated with knownargets and mechanisms of MeHg, while several of them have notreviously been associated with MeHg exposure.

Kling and Förlin (2009) analyzed the proteome response ofebrafish liver cells after 24- and 72-h exposure to hexabromo-yclododecane (HBCD), TBBPA, as well as a mixture of both. Aotal of 7 altered proteins in response to HBCD, two in response toBBPA, and 9 in response to the mixture, were identified. Zebrafishmbryos exposed to 0.5 mg/L perfluoroocane sulfonate (PFOS) at aingle point in time (192 hpf) expressed 69 altered proteins, 38 ofhich were analyzed and 18 proteins identified. These proteins can

e annotated to different functional classes such as detoxification,nergy metabolism, lipid transport/steroid metabolic process, cell

tructure, signal transduction, and apoptosis. However, the otherltered proteins remain unknown and their functional roles remainet to be identified, thus revealing the limitations to the insight thatan be gained from proteomic analysis.

icology 105S (2011) 25– 39

3.2. Metabolomics

As with transcriptomics and proteomics, many fundamentalrequirements must be met so that metabolomics can be appliedin toxicological assessment of environmental chemicals. Similarto the other techniques, metabolomics also represents a snapshotin time, in this case of the low molecular weight metabolites thatcan change upon exposure to environmental chemicals. Whethermetabolomics may ultimately serve as a diagnostic tool or a methodfor a better understanding of a chemical’s adverse effects remainsunknown. It will be a long time before we can use metabolomicsfor analysis of biota in contaminated environments or in any sortof risk assessment.

An inter-comparison exercise with seven laboratories resultedin comparable results in liver extracts of European flounder, andthe same set of metabolites differentiated fish from clean sites fromthose from contaminated sites (Viant et al., 2009). Urinary metabo-lite profiles were assessed in fathead minnows after exposure tomodel compounds, which in the case of cyproterone acetate wasnot very different from that of controls, but differed after coexpo-sure to the androgen trenbolone (Collette et al., 2010). A targetedmetabolite profiling performed in roach (Rutilus rutilus) exposed toEE2 in bile and plasma, as well as in liver and gonads, showed thatsex steroid and glucocorticoid pathways were altered at 10 ng/L(Flores-Valverde et al., 2010).

Metabolic changes were assessed in earthworms after expo-sure to 3-fluoro-4-nitropheol (Bundy et al., 2001) or pyrene (Joneset al., 2008). Both site- and contaminant-specific effects on themetabolic profiles could be discerned from earthworms from sevensites with different levels of metal contamination (Bundy et al.,2007). Metabolic signatures have also been assessed in chemicallyexposed marine mussels (Mytilus edulis) to two copper and pen-tachlorophenol concentrations for 7 d, and subsequently, musselswere collected in the field from rural and industrialized sites andtheir metabolome compared to that of laboratory-exposed mussels(Hines et al., 2010). Both compounds induced metabolic and physi-ological changes, and the metabolic signatures predicted a reducedfitness of mussels from contaminated sites. Box 7 summarizes thecurrent state of these technologies.

4. Combination of toxicogenomics, proteomics andmetabolomics

More research is needed to relate transcriptional changes witheffects on the physiology of the organism or with generally assessedparameters of toxicity. Often, changes in transcripts, proteins ormetabolites are purely descriptive, without links to known phys-iological effects of toxicants. Additionally, only a very few studieshave been performed trying to combine toxicogenomics with pro-teomics and/or metabolomics. Aquatic toxicology should movetowards the integration of genomic data (and possibly proteomic,and metabolomic data) to better understand the underlying phys-iology and toxicology rather than being purely descriptive (e.g.which genes are affected at what concentrations). Bioinformat-ics approaches, such as a pathway analysis, will be important inproviding functional insight into genomics and physiological out-comes.

Studies integrating both transcriptome and proteome analysesare rare. De Wit et al. (2008) analyzed the transcriptional changesin the liver of adult zebrafish exposed to two concentrations oftetrabromobisphenol-A (TBBPA), as well as one concentration of

TBBP, for protein alterations. The data indicate that TBBPA inter-feres with the thyroid and vitamin A homeostasis, affects the energybalance, the onset of oxidative stress, and causes a general stressresponse. Additionally, numerous differentially expressed tran-

K. Fent, J.P. Sumpter / Aquatic Tox

Box 7: State of the art of proteomics and metabolomics.

• It is necessary for a sequenced genome of a species of inter-est to be available for insightful proteomic analysis, but thisholds true for only a few species.

• Great challenges have to be overcome, such as the annota-tion of proteins to elucidate their function.

• Proteomic studies have focused mostly on a single point intime, at one or two exposure concentrations. These onlyallow a restricted insight into the toxicological effects ormodes of action.

• The potential of proteomics to be used to discover newmechanisms of physiological and toxic action as well as thediscovery of biomarkers of exposure and effects has not yetbeen met.

• Moreover, the protein profiles have rarely been linked to tran-scriptomics or to phenotypic or physiological effects, andtheir combination has not yet resulted in the discovery ofsignificant insights.

• Interpretation and combination of proteomic data with phys-iology and ecotoxicologically relevant measures is stilllacking.

• Similar to proteomics, metabolomics seems to be in itsinfancy and currently demonstrates the proof of principle,rather than advancing knowledge of the modes of action ofchemicals and/or on their adverse effects.

• Proteomic and metabolomic studies often confirm thatexposed animals have a different protein and metabolic pat-tern, respectively, than unexposed ones. However, they areso far not very helpful in further elucidating effects of envi-ronmental pollutants.

• Whether a chemical’s mode of action can be predicted by theproteomic or metabolic profile, is still open.

• Currently, it seems that these technically demanding meth-ods and expensive technology has not resulted in much novelscientific insight; instead, their application has mainly con-firmed what was already known.

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cripts could be associated with cellular defence mechanisms andellular metabolism. The study also shows that many of the alteredepatic proteins could not be identified. To a small extent, thebserved responses at the protein level were confirmative of theRNA changes, but a correlation between transcriptional and pro-

ein profiles was demonstrated for only a few proteins (e.g. stresseaction, gluconeogenesis). Thus, both responses seem to demon-trate rather their unique profiles, rather than being correlated andonfirmative; they seem rather complementary. The potential of aombined genomics and proteomics approach to elucidate molec-lar effects has yet to be demonstrated.

In stickleback (Gasterosteus aculeatus) a known cancerogenicAH, 1,2:5,6-dibenzanthracene, was studied for its effects on theranscription of genes in the liver and on the metabolite profile athree concentrations after 4 d of exposure (Williams et al., 2009).nduction of expression of known genes (cyp) and genes relatedo bile acid biosynthesis, steroid metabolism and endocrine func-ion were found, while metabolomics revealed notable changesn taurine, malonate, glutamate and alanine. This study showedome relationships between transcriptional changes and metabolicesponses. Also copper (Cu) was studied in stickleback usingranscriptomics and metabolomics in the liver after exposure to.2–128 �g/L for 4 d (Santos et al., 2010). Cu also resulted in

ncreased DNA damage in blood cells. Transcriptional effects were

n line with previously documented effects (i.e. induction of oxida-ive stress). Metabolomics data supported known mitochondrialffects (i.e. effect on oxidative phosphorylation). The effects notednclude those that were expected on the basis of previous knowl-

icology 105S (2011) 25– 39 37

edge. However, also some additional targets (i.e. downregulationof cholestrol biosynthesis) were noted. Some of the changes inmetabolites could be related to transcriptional changes, but thepotentially new insights from a combination of transcription andmetabolites profiles were not obvious.

5. Conclusions and outlook: where to go?

The presence of chemicals that cause adverse effects on wildlifeand/or humans is of great concern. The identification of chemicalsof most concern (out of the very large number present) is important.We want to know what effects they cause, either individually or inmixtures, and what degree of environmental risk is posed by thoseeffects. We are convinced that the new technologies, in particu-lar the microarray technology, can help to address, and hopefullyanswer, some of these questions. As environmental scientists ouraim is to protect the environment from the threats it faces. Thereby,these novel technologies could, in the future, play a very helpful rolein environmental risk assessments of toxicants, but we think thatthey need to be used more wisely than they currently are.

Toxicogenomics has important merits in elucidating the modesof action of chemicals and reflecting their molecular effects. Thistechnology allows a broader view of possible mechanisms by whichcompounds act, and even gives the entire picture of its effects.However, these merits can only be met when baseline data areknown, and a proper experimental design is used. Toxicologicalconcepts, including proper dose–response curves, choice of targettissues (preferably multiple tissues), and time-courses, should beinvestigated.

Currently, toxicogenomics, and certainly proteomics andmetabolomics, are in a research state. Data of these technologiesare not yet fully understood, and therefore cannot be meaningfullyutilized for ecological risk assessment of environmental toxicantsunless these technologies are combined and associated with toxi-cological and physiological measurements.

Gene expression profiling, using microarrays, and also pro-teomics and metabolomics, are new tools in aquatic toxicology.They are complex and sophisticated tools, and very expensive. Sofar, the vast majority of published studies provide descriptions onlyof how genes, or proteins, or metabolites are altered by stresses.Some studies attempt to link those changes with physiological pro-cesses. However, research should be driven by scientific questionsand hypotheses, not the availability of (and fascination for) a newtechnique. The question should dictate the study, and what experi-mental techniques are used in that study, whereas presently muchof the research appears to originate rather from the fascinationfor a new and sophisticated tool. We should not forget that everytechnique has advantages as well as its limitations.

It seems that we are moving too fast in this field and want toanswer ‘big’ questions before we have answered the basic ones. Thefascination for a sophisticated and demanding tool seems to hinderthe posing of critical questions that aid in the advancement of sci-ence. Better baseline data are needed in order that effects (changesto the baseline) can be identified and interpreted correctly. There-fore, studies should be conducted to define baseline activities (geneexpression, protein or metabolic profiles). Without such data a hugeamount of time and resources will be wasted.

It is too premature to analyze mixture effects or environmentalsamples by means of transcriptomics, proteomics or metabolomics.First, profiles of single compounds, their reproducibility, their valueand potential for making predictions (such as utility as biomak-

ers) have to be demonstrated before more complex matrices canbe investigated for meaningful insights.

In forthcoming studies more emphasis should be placed onunderstanding the expressional, protein, or metabolite profiles

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ith respect to the toxicant’s action, instead of on mere descrip-ion of how many genes, proteins or metabolites are altered andhat processes may be affected.

We are convinced that transcriptomics, proteomics, andetabolomics must be related to physiology and toxicological

ffects at the tissue or organism level. This is the direction wehould go in the future. Without linking the many changes mea-ured to physiological and toxicological outcomes (e.g. functionalnalyses), they remain descriptive and do not advance our knowl-dge of potential effects of compounds. So far, in most studies itemains unclear whether or not measured changes in transcriptxpression, protein, or metabolic profiles should be considereddverse. Whether or not these changes translate to physiologicalhanges, and whether those, in turn, lead to reduced (ecological)tness (in particular, development, reproduction, survival) is theore point. Studies relating such changes to physiology are neededn the future to make beneficial use of the new technologies anddvance ecotoxicology.

A final point: we wrote this article from an ecotoxicological per-pective, as we are interested in the effects of chemicals on wildlifeo help preventing hazards. But we should be aware that theseew technologies, especially transcriptomics, demonstrated theirxtreme usefulness in medicine and biology, and to some extent inoxicology. Any environmental change will, presumably, alter genexpression patterns, as biota respond to that change and adapt.or example, transcriptomics could be a useful tool in investigat-ng how aquatic organisms will respond to environmental changes,ncluding climate change. This will show what physiological mech-nisms are involved in adaptation to these environmental changes.

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