Functional genomics in aquatic toxicology—Do not forget the function

Aquatic Toxicology 105S (2011) 16–24

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Functional genomics in aquatic toxicology—Do not forget the function Mikko Nikinmaa ∗ , Kalle T. Rytkönen Department of Biology, University of Turku, Finland

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Article history: Received 19 April 2011 Accepted 28 May 2011 Keywords: mRNA Genome Transcription Reference gene Epigenetics HIF Temperature Hypoxia ARNT Aryl hydrocarbon receptor

a b s t r a c t Toxicological responses of an organism are disturbances of function. This as a starting point we review and discuss issues that we consider important in applying functional genomics to aquatic toxicology. Functional genomics includes all the steps in gene expression pathway. Thus, ultimately the goal is to relate genome information to protein activity. In ecotoxicogenomics the toxicological responses must further be combined with responses to natural environmental changes. We focus on fish, but also consider commonly used invertebrates, mainly Daphnia. We first go through the toxicologically important features of genomes of aquatic animals, and then review the reference gene approach to quantify transcript amount. Thereafter we emphasize the need to relate the mRNA and protein levels, and protein activity of individual genes. Finally we discuss how functional genomic investigations may be important in resolving current environmental problems and give our views of valuable future research topics. © 2011 Elsevier B.V. All rights reserved.

1. Introduction – from gene sequences to toxicological responses The major limitation of most genomics studies is that functional information is seldom combined with the genomic information (Furlong, 2011). Toxicological responses are necessarily functional disturbances. Functional disturbances must occur even in the case that the substance is genotoxic. In that case heritable alterations in the genome affect organismic functions. During the past 20 years genomic methods have become available also for aquatic toxicology. The term ecotoxicogenomics has been coined for ecotoxicological research focussing on gene information. Aquatic Toxicology has tried to keep abreast with the genomic studies in the field, e.g., a special issue on genomics was published in 2010. Because toxicological responses are normally functional ones, it becomes very important that one is able to relate genomic responses to functional responses. This is not always done. We have analyzed 104 articles (from Marine Genomics, Aquatic Toxicology, Comparative Biochemistry and Physiology, Journal of Experimental Biology and Physiological Genomics) from the period 2006 to 2011 to see, if articles which use genomic data to infer functional responses in aquatic animals also give information of the protein level and activity. Only 25% of the studies include both mRNA and

protein information, only two articles have tried to relate mRNA expression to protein expression, and none have associated mRNA expression to changes of function measured simultaneously. Functional genomics and ecotoxicogenomics include all the steps of transferring information in DNA to functional gene product and assessing the effects that are occurring, i.e. complete gene expression. Gene expression can be controlled at transcription, RNA processing (RNA transcript to mRNA), RNA transport and localization (nucleus vs. cytosol), mRNA stability, translation and protein stability or structure, affecting its activity (Alberts et al., 2008). All the steps of gene expression pathway can be regulated both by environmental contaminants and conditions. In ecotoxicogenomics one often uses microarray and quantitative PCR methodology. Both methods evaluate one step of the gene expression pathway, namely transcription. In addition, sequence information, gene ontologies, RNA interference, epigenetics, etc. are commonly used phrases in ecotoxicogenomic studies. In the following we give our views of important components of gene expression pathways in aquatic toxicological studies, what should be taken into account to combine genomics and function in toxicological aquatic studies, and give opinions of what we see as valuable topics to study in future years. 2. Genomes of aquatic animals: some features affecting toxicological responses

∗ Corresponding author at: Department of Biology, University of Turku, FI-20014 Turku, Finland. Tel.: +358 23335731; fax: +358 23336598. E-mail addresses: miknik@utu.fi, mikko.nikinmaa@utu.fi (M. Nikinmaa). 0166-445X/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.aquatox.2011.05.019

There has been at least one genome-wide gene duplication event more in ray-finned fish than in tetrapods (Christoffels et al., 2004).

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Consequently, teleost genomes contain more material for neofunctionalization of genes than, e.g., mammalian genomes. As a result of gene duplication, the old function can be retained while the “new” gene can be assigned a new function. This possibility has probably been utilized, as exemplified by the facts that some fish antifreeze protein genes are related to pancreatic trypsinogen genes (Cheng and Detrich, 2007) and that one form of the androgen receptor duplicates has accumulated a large number of substitutions in Percomorphs (Douard et al., 2008). It appears that evolutionary rates in teleost fish are more rapid than in tetrapods. This conclusion is supported by several independent findings. First, the cichlid diversity of some big African lakes has been generated quite rapidly (Kornfield and Smith, 2000). Second, estimations based on 675 duplicated genes of Tetraodon suggest increased rate of evolution in them (Brunet et al., 2006). Third, conserved non-coding elements have evolved faster in teleost fish than in tetrapods (Lee et al., 2011). Fourth, the killifish Fundulus heteroclitus has shown heritable responses to pollution (Whitehead et al., 2010). Fifth, our study of hypoxia-inducible factor sequence variation indicates that the structures of these particular genes have evolved more rapidly in teleost fish than in mammals (Rytkonen et al., 2008, 2011). One factor facilitating genomic changes, which is associated with the genomewide gene duplication, is the polyploidy common to, e.g., cyprinids and salmonids (Le Comber and Smith, 2004). Presently, genomic sequences at different stages of refinement are available for the following toxicologically much used species: zebrafish (www.zfin.org), medaka (Takeda and Shimada, 2010) and three-spined stickleback (www.ensembl.org/ Gasterosteus aculeatus/Info/Index). While the genomic information of these model organisms is valuable, another important aspect is to be able to utilize non-model organisms. There are close to 30,000 species of fish, inhabiting virtually all aquatic environments, so from environmental and toxicological point of view the organisms with limited genomic information make up an immense majority. Kassahn et al. (2007) have evaluated the utility of zebrafish microarrays to some coral reef fishes with encouraging results. Gracey and Cossins (2003) have evaluated more generally the utility of microarray technology to non-model species. With regard to toxicologically relevant aquatic invertebrates, the genomic sequence of Daphnia pulex has recently been published (Colbourne et al., 2011). In terms of aquatic toxicology, two points are significant: first, more than one third of the genes does not have counterpart in other so far sequenced genomes and, second, especially the Daphnia-specific genes respond rapidly to environmental disturbances (Colbourne et al., 2011). Taken together, the genome data on fish and Daphnia suggest both rapid evolution and rapid development of genetic responses to environmental contamination at least in some aquatic species. These observations have significant bearing to studies in aquatic toxicology. As an example, the aryl hydrocarbon receptor and its associated molecules show more genetic variability in teleost fish than in, e.g., mammals (Hansson et al., 2004; Hahn et al., 2006). The traditional genome sequencing platforms are characterized both with relatively high cost and limits to the sequencing speed. Consequently, whole genome sequences of most organisms relevant to aquatic toxicology could not be envisioned with firstgeneration sequencing. However, massively parallel sequencing has both cut the costs and increased sequencing speed, so that one is now attempting to generate genome sequences from 10,000 vertebrates (Genome 10K Community of Scientists, 2009). When successful, the consortium will obtain genome sequences from 15 to 20% of all vertebrates. The major limitation of the massively parallel sequencing is the short sequences that can be analyzed at one go: the length is maximally only 1/5th of the traditional Sanger sequencing sequence lengths. As a consequence, the final sequences are error-prone (Alkan et al., 2011), and if repeated

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sequences occur in the genome, the likelihood of detecting them is compromised. With regard to teleost fish with their inherent polyploidy, this is a major drawback. While one is aiming at improving the accuracy using new data analysis methods (Gnerre et al., 2011), the probable methodological improvement involves “real time DNA sequencing” which both cuts the amount of DNA needed, and markedly increases the sequence lengths that can be analyzed at one go (Eid et al., 2009). 3. Gene ontology – bias towards systems with most information combining sequences and function The gene ontology initiative (www.geneontology.org) aims at helping to integrate functional and gene sequence information. While the initiative helps in predicting the functions of different gene products of all different organisms, the information is biased towards organism groups with much information combining sequences and functions. Because this is not the case for many organisms important in aquatic toxicology or for phylogenetically related organisms to them, the functional predictions may not be accurate, if the functions of gene products differ from much studied organisms. This may be caused by neofunctionalization or unique functions of gene products of the organism in question. As discussed above, both factors may be involved in toxicologically relevant aquatic animals. Furthermore, some of toxicologically studied organisms such as corals are phylogenetically very far removed from any organisms for which detailed information is available, which makes any inference of function uncertain unless one actually studies the gene product. 4. Transcription – regulation by transcription factors and RNAs The protein-coding portion of human and other studied mammalian genomes is only 1–2% of the genome. The present estimate of protein-coding genes in man is about 21,000 (Clamp et al., 2007). Much of the variability of cellular proteins is generated by alternative splicing which appears to occur in more than 90% of the mammalian protein-coding genes (Lander, 2011) so that the number of different proteins in humans is close to 100,000. It is currently not known, if non-mammalian vertebrates or invertebrates differ from mammals in the frequency of protein splice variants. In the studied mammals there are approximately 10 times more different transcripts than there are protein-coding genes (Kapranov et al., 2002; Carninci et al., 2005). Presently, the nature of the noncoding RNAs, and their possible functions, are poorly known. Some of them undoubtedly represent only noise, but others are likely to be evolutionarily significant and control genome function (Lander, 2011). With regard to transcriptional regulation, two major components can be indicated, transcription factors, and regulatory RNAs. Transcription factors are proteins, which by binding to the promoter/enhancer sites of genes affect their consecutive transcription. It is beyond the scope of this summary to describe transcription factor function in detail. Instead, the readers should consult the many textbooks available, e.g. Alberts et al. (2008). Some transcription factors are toxicologically important: these include aryl hydrocarbon receptor(s), heat shock factor(s), transcription factors binding to the metal response or oxidation/reduction response elements, and hypoxia-inducible factor(s) (Thiele, 1992; Otsuka et al., 1994; Cotto and Morimoto, 1999; Diamond et al., 1999; Gius et al., 1999; Dalton et al., 2000; Hahn, 2001; Furness et al., 2007). Many of the environmentally relevant transcription factors belong to the bHLH-PAS protein family; structural features of the major environmentally regulated bHLH-PAS

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Fig. 1. Schematic representations of some bHLH-PAS domain transcription factors, indicating the major structural domains. Group 1: AhR, aryl hydrocarbon receptor; AhRR, aryl hydrocarbon receptor repressor; HIFa, hypoxia-inducible factor ␣; IPAS, inhibitory PAS domain protein; SIM, single minded; CLOCK, circadian locomoter output cycles protein kaput. Group 2: ARNT, aryl hydrocarbon receptor nuclear translocator (also called HIF␤); BMAL, brain and muscle aryl hydrocarbon receptor nuclear translocator (ARNT)-like. Usually group 1 proteins form dimers with group 2 proteins to yield active products (e.g. CLOCK and BMAL form a heterodimer involved in the regulation of biological rhythms).

factors are given in Fig. 1 (see also Gu et al., 2000). Toxicologically the most studied bHLH-PAS transcription factor is the aryl hydrocarbon receptor. After dimerization with ARNT (aryl hydrocarbon receptor nuclear translocator; another bHLH-PAS protein) it binds to the xenobiotic response elements (XRE) in the genome, and induces gene transcription (Swanson and Bradfield, 1993; Hahn, 1998; Beischlag et al., 2008). Notably, although the aryl hydrocarbon receptor function has not been much studied from rhythm perspective, it is affected by light cycles (Rannug and Fritsche, 2006), and generation of rhythms usually involves another bHLH-PAS transcription factor, CLOCK (and its dimerization partner BMAL) (Wang, 2009). Another point is that the dimerization partner ARNT is also used by other bHLH-PAS transcription factors, such as HIF (hypoxia-inducible factor), as dimerization partner (Kewley et al., 2004). Thus, AhR and HIF may influence each other’s function by competing for the same dimerization partner, as has been suggested in some studies (Gu et al., 2000; Nie et al., 2001; Hofer et al., 2004; Kinoshita et al., 2004). The possibility of interactions is described in Fig. 2. Possibly the variation in the ARNT isoforms, observed between species, is related to their affinities to the different dimerization partners, i.e. HIF and AhR, and will affect xenobiotic and hypoxic responses and their interaction (Powell et al., 1999; Powell and Hahn, 2000, 2002). The variation of major ARNT forms is pronounced in fish, which can, e.g., influence their responses to environmental contamination (Powell et al., 2000; Powell and Hahn, 2000). In contrast to this information of transcription factor function, very little is known about how RNAs regulate transcription in aquatic toxicological context. 5. Transcription – pitfalls in quantification Transcriptional responses are usually studied using microarray or quantitative PCR methodology. In the former method a large number of transcripts or even the whole genome is studied. In the latter, a few transcripts are normally looked at. Recently PCR microarrays have been developed for use in evaluating pathways

Fig. 2. Representation of how AhR-mediated and HIF-mediated transcription may interact. 1. The AhR ligands, of which dioxins and other planar multiring aromatic hydrocarbons are best known, activate the receptor. Activation is characterized by removal of HSP90 and phosphorylation of the protein. The activated protein is transported to the nucleus. 2. Reduced oxygen tension stabilizes HIF␣ which is transported to the nucleus. 3. The activated AhR and 4. HIF␣ form a dimer with ARNT. Because of this, competition for a common dimerization partner can occur. The affinities of proteins for each other and their concentrations may determine which dimer is formed. 5 and 6. The dimer binds to the XRE (xenobiotic response element) or HRE (hypoxia response element) in the enhancer/promoter regions of the affected genes, respectively, with the consequence that the transcription of these genes is induced (7 and 8).

with a limited number of genes. Regardless of the method an important component of studies is to be able to quantify transcript amount. In quantitative PCR, the most exact normalization can be done if a known amount of externally added mRNA to the sample(s) is analyzed (e.g., Ellefsen et al., 2008). In this case, the exact copy number of the transcripts can be analyzed. However, this kind of external quantification has not yet been extensively used. Instead, most studies use the internal reference gene approach, which evaluates a change in the amount of transcript in relation to a control. In its use the outset is that changes of the transcript levels of the target genes are compared to the transcript level of a gene which is transcribed equally both in control and treatment (=reference gene). Thus, checking that constant transcription takes place for reference genes should be a must, and constant transcription is the most important component of a reference gene. Naturally, the same kind of treatment with the same tissue should give the same constant transcription. There have been a number of articles written about reference genes for use in quantitative rt-PCR in fish (Jorgensen et al., 2006; Tang et al., 2007; Olsvik et al., 2008; Small et al., 2008; Bower and Johnston, 2009; Rytkonen et al., 2010). As shown in Fig. 3, different reference genes give different quantifications for transcripts of target genes. Consequently, validation and normalization of quantitative PCR results (using reference genes) needs to be done for every experiment, and the publication of pure reference gene articles will undoubtedly be decreasing in the future (Bustin et al., 2010).

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0.7 0.6

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Arbitrary units

0.5 0.4 0.3 0.2 0.1 0.0

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Fig. 3. An example of how the used reference gene can influence the results of quantitative PCR analysis. Epaulette sharks were exposed to 2 h hypoxia (5% of air saturation), and HIF1 mRNA of cerebral samples determined by quantitative PCR using two different reference genes: on the left HIF-values were normalized to the transcript of myosin phosphatase-rho interacting protein (mrip) gene and on the right to the transcript of DNA J subfamily A2 (heat shock protein 40) (dnaja2) gene. While no change was observed for values on the left, HIF1 mRNA appeared significantly reduced (**P < 0.01, Mann–Whitney U-test) in hypoxia on the right (N = normoxic values, 10 animals; H = hypoxic values, 10 animals, bars indicate SEM). Data from Rytkönen et al. (2010) and unpublished data of K.T. Rytkönen, G.M. Renshaw and M. Nikinmaa.

6. Post-transcriptional processing – studies of mRNA expression do not necessarily reflect protein expression The results giving mRNA level changes give a picture of transcriptional regulation. A change in the transcript level often indicates a change in the level of gene product, but also other alternatives are possible, as given in Fig. 4. For example, environmental contaminants may affect the translational efficiency (Pytharopoulou et al., 2008), or mRNA or protein stability all of which affect the level of the gene product, which together with the cellular conditions and cell-to-cell interactions determine organismic functions. A major mechanism of post-transcriptional processing involves microRNAs (miRNAs). They regulate both translational efficiency and mRNA stability (He et al., 2011). Thus,

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the regulation of gene expression by miRNAs remains undetected by conventional microarray and real time PCR studies. The regulation by the many hundred miRNAs has been shown to be highly important in development of, e.g., zebrafish (Giraldez et al., 2005). We are not aware of studies that would specifically have addressed the question, how miRNAs are involved in aquatic toxicological responses. To be able to relate transcript expression and functional responses, the correspondence between transcript and protein level should be evaluated. The relationship is protein-dependent: while generally an increase in mRNA level is also seen as an increase in the protein level (Buckley et al., 2006), the amount of protein formed per unit amount of mRNA varies between proteins, and, for a given protein, temporally. The same amount of protein can be formed with 30-fold variation of the mRNA level, and with the same mRNA level, the amount of protein formed can vary 20-fold (Gygi et al., 1999). The case of hypoxia-inducible factor illustrates very well why one has to know the relationship between individual mRNAs and proteins, and why an overall relationship between the two cannot be used instead. Often no changes in the production of HIF-1␣ mRNA occur in hypoxia. Such a situation is observed in salmonid fish (Soitamo et al., 2001). If quantitative rt-PCR or cDNA microarrays had been used as methods to reach conclusions about the importance of hypoxia-inducible factor protein in the regulation of gene function during oxygen limitation, one would have had to conclude that hypoxia-inducible factor is not involved in hypoxia responses, as no changes in mRNA (given by the results) would have been observed in hypoxia for HIF-1˛ gene of many species. Furthermore, the results by Mounier et al. (2010) indicate that the mRNA-HIF-1␣ protein level relationship differs between different muscle tissues. Fig. 5 gives an overview of HIF regulation in normoxia and hypoxia. Consequently, one needs to study the proteins and their function in order to draw conclusions about how gene function affects organism, as discussed in detail by Greenbaum et al. (2003) and Feder and Walser (2005). One must always remember that studies at mRNA and protein activity level are independent. This is particularly relevant for toxicological studies, as the same toxicant which inhibits the activity of protein may increase the transcription of the gene coding for the protein. The independence of mRNA level and protein activity is illustrated, e.g., by our observation that while

Fig. 4. A schematic representation of situations, when functional responses do not occur, although genomic responses indicate possibility for such responses. A. Transcription and translation lead to the formation of gene product, but the cellular conditions are such that the product formed is not active. B. The environmental regulatory loop causes a marked decrease in the stability of the functional gene product (or mRNA).

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10

8

*

6

4

2

0 Fig. 6. An example of a situation, where environmental perturbation affects protein activity (enzyme activity) and mRNA level independently (and differently). The effect of 3 h hypoxia (25% air saturation) on the EROD enzyme activity (the two bars on the left; pmol substrate treated/mg protein/min; mean + SEM given, N = 8, * indicates that the activity in normoxia differs from that in hypoxia at 95% probability level, Mann–Whitney U-test) and transcription of Cyp 1A gene (the two bars on the right; arbitrary units, mean + SEM given, N = 18 in normoxia and 16 in hypoxia) in threespine stickleback. Black bars normoxia (air saturation > 80%), grey bars hypoxia; unpublished data of L. Leveelahti, P. Leskinen, W. Waser, E. H. Leder and M. Nikinmaa.

3 h hypoxia did not affect cypIA2 transcription, the EROD enzyme activity was significantly increased (Fig. 6). Discussion of functional responses on the basis of transcriptional data is warranted only when one has ascertained that the formation of gene product is mainly dependent on transcription, i.e., when the correlation between the two is tight, which is not always the case as illustrated above for hypoxia-inducible factor. In addition to mRNA and protein stability, factors affecting the mRNA–protein relationship are usually those affecting the efficiency of translation, which is normally higher in pro- than eukaryotes. The efficiency of translation is affected, e.g., by the RNA structure, codon bias (for amino acids coded for by several nucleotide triplets, the translation efficiency of different triplets is not equal) and the attachment of mRNA to ribosomes (high attachment indicates high translation efficiency; Maier et al., 2009)). Often, abundant proteins tend to have closer relationship to their respective mRNAs than little expressed proteins (Greenbaum et al., 2002). Also, for a given protein the relationship is affected by the amount of pre-existing protein. Thus, early in the development when the protein is first produced, the relationship between mRNA and protein levels differs markedly from the relationship observed in adult animals with new protein formed only to replace the molecules broken down. The relationship in such situation is, in addition different from the one observed at the onset of any response where more protein is produced to change function. Because of the fact that transcription must always precede translation, the time courses of mRNA and protein production will always differ with the former being faster

1.2 1.0

Proportion of maximum

Fig. 5. The principles of hypoxia-inducible factor function. A. Normoxic and B. hypoxic situation. HIF1␣ is normally constitutively produced, but is rapidly broken down in normoxia. In hypoxia the factor is stabilized, translocates to the nucleus where it forms a dimer with ARNT. The dimer binds to the hypoxia response elements of enhancer/promoter sites in oxygen-sensitive genes, whereby the transcription of these genes is induced (adapted from Nikinmaa and Rees, 2005, Am. J. Physiol.-Regul. Integr. Comp. Physiol. 288, R1079–R1090, by permission of the American Physiological Society).

(Buckley et al., 2006; Wang et al., 2010). Whereas mRNA production peaks in minutes to hours, normally hours to days are required for protein formation to peak. The exact time difference between mRNA and protein production depends on the protein in question, and cellular conditions. An example of difference in the time course of mRNA and protein production is drawn in Fig. 7, using gill HSP70 data of Buckley et al. (2006). The main reason for using mRNA level as a proxy for protein level (and thus not considering translational or post-translational effects in gene expression) is that while good high-throughput methods are available for measuring mRNA, commonly available protein identification and quantification methods can reliably be used with abundant proteins, but for many biologically interesting molecules, present only in small numbers in the cells, reliable evaluation may not be possible at present (Fu et al., 2007). Poor correlation between mRNA and protein level may result either from the fact

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protein mRNA

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Time (h) Fig. 7. The time courses of HSP70 gene transcription (mRNA production) and HSP70 protein production in the gills of the goby Gillichthys mirabilis exposed to 32 ◦ C (after acclimation to 18 ◦ C). Maximal protein and mRNA value is 1, and other values are fractional values indicating the proportion of the level compared to maximum. Data from Buckley et al. (2006).

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that the level of many proteins is posttranscriptionally regulated or from methodological inaccuracies in determining both mRNA and protein contents (Greenbaum et al., 2003). If the poor correlation between mRNA and protein levels is caused by the associated biological factors, it cannot be improved. If, on the other hand, mainly methodological inaccuracies affect the mRNA–protein correlation, then increased correlation will be obtained with refinement of the determination methods, including mathematical and statistical analysis of the data (Hack, 2004; Nie et al., 2007). 7. Epigenetics in aquatic toxicology Epigenetic effects can be defined as inherited changes in gene expression caused by mechanisms other than changes in the underlying DNA sequence (for a detailed discussion see, e.g., Ho and Burggren, 2010). The best studied epigenetic mechanisms are those involving alterations in DNA or histone methylation, or histone acetylation. Changes in the methylation/acetylation status of histones and DNA affect the coiling of chromosomes and the accessibility of genes for transcription. As an example of how environmental conditions may affect histone modification, hypoxic conditions affect Jumonji protein (JMJD2) activity. The enzyme activity affects histone methylation (Krieg et al., 2010). While it is not clear if this particular modification persists for more than one generation, epigenesis as a result of DNA methylation or histone modification has received much experimental attention in biomedical studies. There are nowadays several commercially available kits to study methylation changes of human DNAs. In contrast, studies addressing aspects of aquatic toxicology are scarce: the term epigenetic has been mentioned in approximately 30 Aquatic Toxicology articles in 30 years. Notably, 20 of the articles have been published after 2008. The possibility for epigenetic effects via changes in methylation exists both in the studied fish and Daphnia. For example, Stromqvist et al. (2010) have studied how DNA methylation of/in the vicinity of the vitellogenin gene in zebrafish responds to estrogen exposure. Their results suggest decreased methylation, if the fish are treated with estrogen. Similarly, Wang et al. (2009) observed that tributyltin exposure was associated with hypomethylation. Naturally, methylation effects as such do not show that transgenerational (epigenetic) effects would have taken place. In fact, the term epigenetics has sometimes been used loosely to describe any environmentally caused change in DNA methylation or histone modification without assessing if the changes are inherited or not. So far, the few articles available have not been able to show the existence of transgenerational effects related to methylation status in either Fundulus (Aluru et al., 2011) or Daphnia (Vandegehuchte et al., 2010). 8. How environmental adaptations, toxicological responses and genomic effects interact Recently, especially the climate change (and associated ocean acidification), eutrophication and increased UV-radiation as a result of depletion of stratospheric ozone have emerged as major environmental concerns. These environmental questions are often interconnected and associated with chemicalization of the environment. For example, an increase in temperature accelerates eutrophication which both increases the depth of hypoxia and the length of hypoxic periods. Increased UV-radiation is associated with an increased reactive oxygen species (ROS) level and many chemicals including metals with variable valency states of their ions exert their influence via oxidative stresses which are characterized by an increase of ROS. Consequently, looking at interactions between natural environmental parameters and toxicant

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responses is assuming increasing importance. Such studies necessarily involve genomic components, among them temperature- and hypoxia-dependent transcriptional regulation. With regard to global warming, the temperature has increased during recent past in 61 out of 63 marine areas. The global warming causes eutrophication and contributes to ocean acidification (an increase in carbon dioxide load shifts the equilibrium between carbon dioxide and carbonates towards carbon dioxide acidifying the medium; see e.g. Portner and Peck, 2010). These effects are pronounced in the Baltic Sea; for example, its temperature increase has been the greatest of any marine area (Belkin, 2004). While transcriptional regulation by heat shock factors has been studied in detail (Basu et al., 2002), also gene regulation by hypoxia-inducible factor (HIF) may play a role in temperature acclimation of poikilothermic animals (Treinin et al., 2003; Rissanen et al., 2006). Both the level and the DNA binding of HIF are influenced by temperature, as is the interaction between HSP90 and HIF (Rissanen et al., 2006). Against the background of the global temperature change, the poikilothermic nature of most organisms, and the fact that the role of genomic components have already been demonstrated in temperature responses, it is quite surprising that temperature is seldom used as a variable in estimating the rates of different control steps of gene expression. As an example, the rate of transcription is normally measured using the transcriptional run-on assay at a constant temperature, normally 30 ◦ C. Notably, the method instructions of Sambrook and Russell (2001) give the method for transcriptional rate measurement at a high and constant temperature. Usually studies on temperature acclimation of also poikilothermic animals have used these instructions and thus measured the rate of transcription at a constant temperature which is far higher than that experienced by the organisms in temperate aquatic habitats. As a result, very little information exists currently on any temperature-dependent effects on transcription and their disturbances by pollutants. Another environmental and organismic variable which has a demonstrated genomic component is rhythmicity. Day length- and season-dependent rhythmicity is a common property of organisms. Aquatic organisms encounter hypoxia regularly, and largely rhythmically (e.g., Nikinmaa and Rees, 2005). As discussed above, transcriptional regulation of rhythms, control of hypoxia responses, and regulation of responses caused by aryl hydrocarbon receptoractivating pollutants are all interconnected. Thus, it is possible that a circadian or seasonal rhythmicity of hypoxia responses could be disturbed by environmental pollution. However, we are not aware of studies which would have investigated the interrelationships of rhythmic environmental responses and environmental pollution in aquatic milieu. In addition to temperature and oxygen variations, the aquatic environments are also characterized by large variations in salinity and pH. These environmental variables affect gene expression, by, e.g., affecting translation, as demonstrated for the crustacean, Artemia (Eads and Hand, 1999; Van Breukelen et al., 2000; Eads and Hand, 2003). In any environmental response one always needs to consider the time course of changes as often the cellular conditions, e.g. pH, change so rapidly, in seconds or minutes (Nikinmaa et al., 1990), that functional changes must necessarily occur without transcriptional and translational changes.

9. Future challenges While studies combining genomic and functional aspects are rare also in aquatic toxicology, the same researchers who are knowledgeable with methodology required for functional studies are also doing ecotoxicogenomic work. Thus, their contribution in

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combining the two approaches appropriately could make aquatic toxicologists forerunners in functional genomics as a whole, not just its toxicological applications. Aquatic toxicologists could also make an important contribution to studies in evolutionary and environmental biology in investigations looking at how responses to variations in the physicochemical environment are influenced by environmental contamination and how these responses are genetically transmitted to future generations. A question which we think will become important is how contamination affects reactive oxygen species (ROS)-dependent signalling. While the toxic effects of large ROS changes have commonly been acknowledged and reviewed also from aquatic toxicology point of view (Lushchak, 2011), it has recently become obvious that ROS are utilized in normal cellular signalling (Halliwell and Gutteridge, 2007). Consequently, ROS changes which are much smaller than the ones required to cause structural changes in the cells may be causing toxicologically relevant responses. Genomically, the major responses that could be affected involve redox-regulated genes. The transcription of these has been recently reviewed (Haddad, 2002; Liu et al., 2005). In addition, while mRNA expression does not, as such, give functional information, transcriptional findings can be good biomarkers of exposure, because they commonly change in response to contaminants and high-throughput methods are available for their measurement. Studies developing new exposure biomarkers will thus be a valuable avenue of work, especially if the biomarker can be linked to a functional disturbance. Acknowledgements The authors’ work is supported by the Centre of Excellence grants from the Academy of Finland and University of Turku. References Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., Walter, P., 2008. Molecular biology of the cell, 5th edition. Garland Science, New York, 1268 pp. Alkan, C., Sajjadian, S., Eichler, E.E., 2011. Limitations of next-generation genome sequence assembly. Nat. Meth. 8, 61–65. Aluru, N., Karchner, S.I., Hahn, M.E., 2011. Role of DNA methylation of AHR1 and AHR2 promoters in differential sensitivity to PCBs in Atlantic Killifish, Fundulus heteroclitus. Aquat. Toxicol. 101, 288–294. Basu, N., Todgham, A.E., Ackerman, P.A., Bibeau, M.R., Nakano, K., Schulte, P.M., Iwama, G.K., 2002. Heat shock protein genes and their functional significance in fish. Gene 295, 173–183. Beischlag, T.V., Luis, M.J., Hollingshead, B.D., Perdew, G.H., 2008. The aryl hydrocarbon receptor complex and the control of gene expression. Crit. Rev. Eukaryot. Gene Expr. 18, 207–250. Belkin, I.M., 2004. Rapid warming of Large Marine Ecosystems. Progr. Oceanogr. 81, 207–213. Bower, N.I., Johnston, I.A., 2009. Selection of reference genes for expression studies with fish myogenic cell cultures. BMC Mol. Biol. 10, art. 80. Brunet, F.G., Roest, C.H., Paris, M., Aury, J.M., Gibert, P., Jaillon, O., Laudet, V., Robinson-Rechavi, M., 2006. Gene loss and evolutionary rates following wholegenome duplication in teleost fishes. Mol. Biol. Evol. 23, 1808–1816. Buckley, B.A., Gracey, A.Y., Somero, G.N., 2006. The cellular response to heat stress in the goby Gillichthys mirabilis: a cDNA microarray and protein-level analysis. J. Exp. Biol. 209, 2660–2677. Bustin, S.A., Beaulieu, J.F., Huggett, J., Jaggi, R., Kibenge, F.S., Olsvik, P.A., Penning, L.C., Toegel, S., 2010. MIQE precis: practical implementation of minimum standard guidelines for fluorescence-based quantitative real-time PCR experiments. BMC Mol. Biol. 11, art. 74. Carninci, P., Kasukawa, T., Katayama, S., Gough, J., Frith, M.C., Maeda, N., Oyama, R., Ravasi, T., Lenhard, B., Wells, C., Kodzius, R., Shimokawa, K., Bajic, V.B., Brenner, S.E., Batalov, S., Forrest, A.R.R., Zavolan, M., Davis, M.J., Wilming, L.G., Aidinis, V., Allen, J.E., Ambesi-Impiombato, A., Apweiler, R., Aturaliya, R.N., Bailey, T.L., Bansal, M., Baxter, L., Beisel, K.W., Bersano, T., Bono, H., Chalk, A.M., Chiu, K.P., Choudhary, V., Christoffels, A., Clutterbuck, D.R., Crowe, M.L., Dalla, E., Dalrymple, B.P., de Bono, B., Gatta, G.D., di Bernardo, D., Down, T., Engstrom, P., Fagiolini, M., Faulkner, G., Fletcher, C.F., Fukushima, T., Furuno, M., Futaki, S., Gariboldi, M., Georgii-Hemming, P., Gingeras, T.R., Gojobori, T., Green, R.E., Gustincich, S., Harbers, M., Hayashi, Y., Hensch, T.K., Hirokawa, N., Hill, D., Huminiecki, L., Iacono, M., Ikeo, K., Iwama, A., Ishikawa, T., Jakt, M., Kanapin, A., Katoh, M., Kawasawa, Y., Kelso, J., Kitamura, H., Kitano, H., Kollias, G., Krishnan, S.P.T., Kruger, A., Kummerfeld, S.K., Kurochkin, I.V., Lareau, L.F., Lazarevic,

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