Marine Biology (2006) 149: 107–115 DOI 10.1007/s00227-005-0211-2
R ES E AR C H A RT I C L E
Hajime Watanabe Æ Taisen Iguchi
Using ecotoxicogenomics to evaluate the impact of chemicals on aquatic organisms
Received: 13 March 2005 / Accepted: 22 July 2005 / Published online: 17 December 2005 Ó Springer-Verlag 2005
Abstract Toxicogenomics has become an important field in toxicology. Originally, toxicogenomics was intended to be used to evaluate the risks of chemicals to humans, but the recent increase in genetic information has allowed the field to be extended to other organisms. Ecotoxicogenomics is the application of toxicogenomics to organisms that are representative of ecosystems and is used to study the hazardous effects of chemicals on ecosystems as well as individuals. Although, the availability of genomic information about non-model organisms is still very limited, the application of toxicogenomics to a variety of organisms could be a powerful tool for evaluating the effects of chemicals on ecosystems.
Introduction Concern about the environment and animal welfare is increasing and protection of ecosystems has become an important issue. Although abnormalities caused by environmental chemicals have been reported in many organisms in the wild, including rock shells, otters, birds, and alligators (Smith 1981; Mason et al. 1986; Gilbertson et al. 1991; Guillette et al. 1994), many of the mechanisms of these defects remain unclear. This is also true in marine organisms (Allen et al. 1999; Matthiessen et al. 2002). In order to understand the mechanisms and
Communicated by R. Cattaneo-Vietti, Genova Physical and Chemical Impacts on Marine Organisms, a Bilateral Seminar Italy–Japan held in November 2004. H. Watanabe (&) Æ T. Iguchi Okazaki Institute for Integrative Bioscience, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji, Okazaki, Aichi 444-8787, Japan E-mail:
[email protected] Tel.: +81-564-595237 Fax: +81-564-595236
to contribute to animal welfare, development of tools is necessary. At the same time, there has been remarkable progress in genomics, which was accelerated by the sequencing of the human (Lander et al. 2001), mouse (Waterston et al. 2002), and rat genomes (Gibbs et al. 2004) as well as other species (Hillier et al. 2004). Genome sequencing and expression sequence tag (EST) analysis (Okazaki et al. 2002) created a new area of biological study, the ‘‘genome-wide analysis’’. In traditional molecular biology, specific genes, or proteins are analyzed, but a genome-wide analysis investigates the expression of a wide selection of genes and proteins all at once, reducing the bias generated by selecting genes to study. In parallel with the increase of gene information, technologies related to genome-wide analysis have been developed, including DNA microarrays to examine the transcriptome, 2-dimensional polyacrylamide gel electrophoresis-mass spectrometry (2D-PAGE-MS) to examine the proteome (Persidis 1998), and nuclear magnetic resonance (NMR) to examine the metabolome (Nicholson et al. 1999; Robertson et al. 2000). These methodologies contribute to the understanding of biology as a system and enable the identification of genes that participate in particular biological functions, even if their participation was not expected. They are known as genomics or ‘‘-omics’’ studies and many disciplines have rapidly adopted genome-wide analysis. Toxicology is a field that is suitable for genome-wide analyses, because the adverse effects of chemicals are broad and it is hard to specify the responsible genes. Toxicogenomics, the subfield of toxicology, was first devoted to the study of genome-wide gene expression (Nuwaysir et al. 1999; Lovett 2000) and now it includes not only transcriptomics, but also proteomics and metabolomics (Aardema and MacGregor 2002; Reo 2002; Viant 2003). Toxicogenomics has the potential to identify sensitive biomarkers that can predict toxicity. It may also clarify the molecular mechanisms that lead to the hazardous effects of toxins.
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When toxicogenomics first emerged as a field, transcriptomics was the main approach (Nuwaysir et al. 1999). DNA microarrays were rapidly developed and were successfully used to monitor the effects of chemicals on the transcriptional system. The emerging field of toxicogenomics was combined with ecotoxicology in order to evaluate the risk of environmental chemicals to ecosystems and to understand their modes of action. Toxicogenomics is based on human and model animals and is therefore supported by genomic information resources, such as gene annotation, gene function, biological response, and metabolism, together with accumulated biological knowledge and observation. Although the application of these ‘‘omics’’ technologies to the non-model organisms that make up an ecosystem does not have the same extent of information resources, the development of ecotoxicogenomics will contribute to the protection of ecosystems and to assessing the risk of chemicals that are suspected to be hazardous, on a scale ranging from individuals to ecosystems (Fig. 1). Actually, many genomic challenges have been started in non-model organisms and they are expected to expand (Gracey et al. 2001; Crump et al. 2002; Larkin et al. 2003; Williams et al. 2003; Brown et al. 2004; Volz et al. 2005). As the framework of ecotoxicogenomics is already well described in other reviews (Snape et al. 2004), this review addresses more practical issues.
Species selection Proper species selection is critical for the application of genomics to the study of ecotoxicological effects. Some species that have been extensively studied in many aspects including genomics, such as sea urchins and ascidians are possible candidates. Other spices that show characteristic features in response to environmental
Fig. 1 In ecotoxicogenomics, information from genomes, transcriptomes, proteomes, and metabolome is integrated over individuals, populations, communities, and ecosystems, whereas the integration ends at the level of the individual in studies of model organisms
toxicants such as rock shells should provide important information. In general, species that can satisfy the following criteria will be good models for ecotoxicogenomics analyses. These species should be important in their ecological settings, easy to sample, and, hopefully, able to be cultivated in laboratories so that controlled studies of chemical exposure can be performed. The last criteria may be a critical point for the selection of species at least for the development for ecotoxicogenomics, because it is difficult to elucidate the cause of gene expression changes if the conditions of exposure cannot be controlled. Even if the organisms have been sampled from both contaminated and uncontaminated areas, it is sometimes difficult to discriminate effects of contaminants from effects of genetic background and others. Genome size is also a critical factor for selection of species. Although, as is mentioned below, genomic sequences are not essential, it can provide great information to investigate ‘‘-omics’’. The time and cost of genomic sequencing are directly dependant on the genome sizes and they are diverse among species (Table 1). From a practical point of view, efficient and reproducible RNA preparation should be considered to select species. If a specific tissue is to be used for transcriptome analysis, efficient isolation of certain amount of tissue is desirable. Tissues that are difficult to isolate may result in transcriptome analyses with inconsistent results because of contamination or degradation. For small organisms, it is possible to isolate RNA from total body, but in this case, it should be noticed that tissue-specific gene expression changes are significantly underestimated because of large amounts of RNAs from non-target tissues. In this context, development of biopsy for cDNA preparation may be important in order to apply a genomic approach to many species in ecosystems (Veldhoen and Helbing 2001). Acquisition of parameters related to samples may be important for aquatic organisms. For example, population of heterogeneity,
109 Table 1 Genome database sources for aquatic organisms Name
Database
URL
Sea Urchin Ascidian Ascidian Fugu Zebrafish Medaka Extremophiles Model organisms
The sea Urchin genome resource Ciona intestinalis cDNA resources Ciona savignyi database The Fugu genomics project The Zebrafish information network Medaka genome project ExtremoBase Ensembl Joint genome institute National Center for Biotechnology Information
http://www.sugp.caltech.edu/intro/ http://www.ghost.zool.kyoto-u.ac.jp/indexr1.html http://www.broad.mit.edu/annotation/ciona/ http://www.fugu.hgmp.mrc.ac.uk/ http://www.zfin.org/ http://www.dolphin.lab.nig.ac.jp/medaka/ http://www.jamstec.go.jp/jamstec-j/XBR/db/exbase/exbase.html http://www.ensembl.org/ http://www.jgi.doe.gov/ http://www.ncbi.nih.gov/Genomes/
age or developmental stages of samples, genetic backgrounds of populations may affect interpretation of genomics data. In case of micro-organisms, identification and isolation of specific organisms is difficult. Thus application of ‘‘-omics’’ studies that are widely adopted in eukaryotes may still be difficult. Instead, a newly emerging metagenomic approach (Handelsman et al. 1998) that deals with a genome as mixed microbial populations, may be useful to evaluate toxic effect to micro-organisms (Sebat et al. 2003; Steele and Streit 2005).
Genome The genome sequence is fundamental to genomic studies, and the acquisition of information from non-model organisms is a rate-limiting step in ecotoxicogenomics because genome sequencing takes a substantial amount of time. Generally, many of genome projects have been performed by a consortium or national. For example, Marine Genomics Europe (http://www.marine-genomics-europe.org), The Institute for Genomic Research (TIGR) (Kirkness and Kerlavage 1997) (http://www.tigr.org/), Sanger Institute (http://www.sanger.ac.uk/) and the Joint Genome Institute in US (Pruitt 1998) (http://www.jgi.doe.gov/) are good examples of consortiums that are promoting genome or EST projects of many species. Although number of species are still limited, genome databases of marine organisms are increasing (Table 1). However, genomic sequences are not essential for the fabrication of DNA microarrays. At a minimum, it is essential to have EST information for target species, and annotated genomic sequences are preferable. The other rate-limiting step that may be associated with the development of ecotoxicogenomics is gene annotation. In order to annotate genes, some specific motifs or similarities to other genes are necessary. For this purpose, the whole genome sequence and a large number of EST sequences of model organisms can be used as landmarks for gene annotation. In particular, if genomic sequences are available, EST sequences can be assigned to the genome and whole genes and coding regions can be elucidated (Curwen et al. 2004). This can improve the quality of EST information because EST
sequences derived from alterative splicing can easily be distinguished from artificial chimeric sequences that sometimes happen during cDNA library preparation. In addition, gene functions can be assigned from synteny with other model organisms (Wei et al. 2002) and their annotations can be more reliable than simple comparison of the sequences. In case of some marine organisms, even when EST sequences are obtained from an isolated organism, it may be difficult to annotate the function of genes if they show only low similarity to other known genes. Without functions, EST sequences cannot contribute to the integration of genomic analysis. This is the case especially in some invertebrates that are important candidates in marine organisms, because they are developmentally diverse but genomic information is very limited. Thus, systematic genomic sequencing of organisms spread over the ‘‘tree of life’’ or ecosystem would contribute to the development of ecotoxicogenomics. This underscores the importance of the selection of species for the understanding of ecotoxicogenomics.
Transcriptome DNA microarray technology developed rapidly with the increase of human genetic information. In the traditional method of quantifying the content of a specific mRNA in tissue, total RNA is fixed onto a membrane, and radiolabeled DNA with a sequence complimentary to that of the target gene is hybridized to the RNA. The converse is true with DNA microarrays: the DNA is fixed onto glass and the RNA is labeled and hybridized to it. By inverting the labeling, the expression of a number of genes can be detected at one time. In the first DNA microarrays, amplified cDNAs (Schena et al. 1995) were used and then oligonuleotides were used depending on systems. In addition, in situ oligonucleotide synthesis (Lockhart et al. 1996) was developed and many other methodologies related to fixation, labeling, and scanning have also been developed (Stoughton 2005). There are now many platforms for DNA microarray analysis (Table 2). When cDNA is used for a DNA microarray, they are amplified by PCR using a cloning vector site or specific primers to amplify a representative (unique) region of
110 Table 2 DNA microarray platforms DNA probes
Length
Supports
Spotting
Target
Labeling
PCR-amplified Pre-synthesized In situ synthesized
102 to 103 bp 50–80 base 24–60 base
Nylon/glass Glass Glass/other
Pin Pin/ink-jet In situ
cRNA/cDNA cRNA/cDNA cRNA
Radio isotope fluorescence Fluorescence Fluorescence
General platforms are indicated. In addition to this list, algorithms for DNA probe design, glass coating protocol, shapes of spotting rods, target preparation protocols, labeling protocols, scanner types, data acquisition algorithms may be differ each other. Consequently, there are many variations in DNA microarray platforms
the gene. This method has advantages when gene information is limited, because once a cDNA library is obtained, a DNA microarray can be made without sequence information. This also means that the quality of the microarray depends directly on the quality of the cDNA. Generally, the cDNAs on a DNA microarray are 500 bp to 2 kbp in length and can hybridize similar sequences. This system risks detecting the expression of similar genes by cross-hybridization, particularly if the clone has a gas chromatography (GC)-rich sequence or a specific sequence that is similar to other genes. Use of other probes or other techniques is necessary to confirm the results. But, in ecotoxicogenomics, this crosshybridization can be advantageous for detecting paralogs of genes in related species, because small amounts of mismatch can occur between populations and species but it can be ignored in this system. This may be useful because there is limited genetic information and the diversity of the genomic sequences within specific organisms is not clear. In addition to conventional cDNA libraries that are made from isolated tissues or whole bodies of organisms, specific cDNA libraries can be constructed by subtracting (Rubenstein et al. 1990) or normalizing (Patanjali et al. 1991) the cDNA libraries. These techniques can increase the chance to clone low copy number of genes. For example, if the question being investigated is which genes are specifically expressed following chemical treatment, cDNA libraries can be made from organisms or tissues exposed to the chemical and control. In the subtraction process, treated and control singlestranded cDNAs are hybridized, and only those cDNAs that remain single-stranded—that is, that differ between the two cDNAs—are cloned as subtracted cDNA library. The information gathered from these techniques is rather limited compared to an actual genome-wide transcriptome analysis, but it can be used to evaluate the effects of chemicals on organisms and could also be used to understand the mechanisms of the effects. In correlation with the increase of genomic information, application of another platform based on oligonucleotids has increased. The length of the oligonucleotide depends on the system, but generally is 25–70 bp. Oligonucleotide DNA microarrays have some advantages compared to cDNA microarrays. For example, DNA microarray can be obtained from a database without construction and analysis of cDNA
libraries. Selection of oligonucleotide sequences of representative genes that have similar melting points is much easier than selection of cDNA fragments; furthermore, as oligonucleotide synthesis becomes a more established technology, larger amounts of high-quality oligonucleotides will reproducibly be available based on information from nucleotide sequence databases. Thus, higher reproducibility can be expected from an oligobased DNA microarray. On the other hand, amplification of cDNA using universal primers always carries a risk of contamination or amplification of undesirable DNA fragment. Consequently careful quality control of the amplified DNA is necessary. As can be imagined, different DNA microarray platforms do not always produce identical results (Shippy et al. 2004). Although, it is not possible to normalize all data across many laboratories, use of normalization or quality control methods is necessary for sharing and comparing DNA microarray data across platforms and laboratories. For this purpose, common annotation of microarray data, called Minimum Information About a Microarray Experiment (MIAME), has been proposed (Brazma et al. 2001). After normalizing the data, some gene sets can be selected for further analysis. They are selected based on reliability that is represented in signal intensities and reproducibility. The selected gene sets can be analyzed depending on the question (Draghici 2003). For example, hierarchical clustering (Eisen et al. 1998), selforganizing maps (Tamayo et al. 1999), and principal component analysis (Raychaudhuri et al. 2000) have been used to interpret gene expression profile. These methods are also used in other ‘‘-omics’’ studies such as proteomics and metabolomics.
Proteome Proteomics (Wasinger et al. 1995; Wilkins et al. 1996) is the study of the full set of proteins encoded by a genome (that is, of the proteome). Proteomic analysis has two steps, separation of proteins and identification of proteins. After isolating a tissue or organ, proteins are solubilized and then subjected to 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE). After the separation of the proteins, each spot is excised and digested by proteases that recognize specific amino acid residues. The molecular weights of the digested peptides
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are measured by matrix-assisted laser disorption/ionization (MALDI)-mass spectrometry (MS) (Karas and Hillenkamp 1988) or liquid chromatography (LC)-MS (Fenn et al. 1989). The isolated protein can generally be identified by the fingerprinting pattern (Henzel et al. 1993) or MS/MS ions search (Burlingame et al. 1998). In these methods, protein sequence database is searched for measured masses. Thus, efficient proteomics ideally requires the all amino acid sequence information in the entire genome to rapidly identify fingerprinting. In ecotoxicogenomics, conventional 2D-PAGE separation can be used as the first step to separate proteins. By comparing the 2D-PAGE pattern between two or more conditions, one can select specific proteins whose expression differs significantly between the conditions and the proteins can then be isolated from the gel. But because of limited genomic information of non-model organisms, LC-MS based fingerprinting is generally difficult and de novo sequencing by LC-MS/MS may be necessary. Different from LC-MS, LC-MS/MS can break inonized molecule into fragments and measure molecular weights of the fragments. As a result, de novo sequence but not only fingering pattern can be obtained. Although, de novo sequencing of differentially expressed protein is far from a total understanding of the proteome, it can be helpful for finding specific biomarkers that indicate exposure to chemicals. Although 2D-PAGE is the most popular technique for proteome analysis, it has several problems. For example, not all proteins can be solubilized in order to be separated on 2D-PAGE; this is especially true for membrane proteins. To overcome this disadvantage, proteins can be separated using high-performance liquid chromatography (HPLC) (Opiteck et al. 1998). In addition, the dynamic range of 2D-PAGE is much narrower than that of DNA microarrays. Thus, it is difficult to detect changes in the small amounts of protein such as regulatory proteins among changes in structural proteins that are abundant in cells. In order to get high resolution, 2D-PAGE must be run on a large scale, which requires a large amount of protein, but unlike the amplification of nucleic acids (Phillips and Eberwine 1996) in analyses of the transcriptome, there is no way to amplify proteins. Thus, improvement of separation and identification of small amount of protein is necessary.
Metabolome Unlike proteomics, metabolomics is not directly linked to genomic information. However, the metabolomics has characteristic advantages compare to other ‘‘-omics’’ studies. One of the advantages is the metabolites are generally identical from species to species. Once metabolite of interest can be identified, the changes of quantity of the metabolites can be easily evaluated among species. In this context, the metabolome is more
applicable to ecotoxicogenomics than proteomics. In addition, transcriptomics and proteomics can predict what will happen, but metabolomics can tell what is actually happening downstream of DNA. On the other hand, there is difficulties to determine structures of metabolites because molecular structure is more diverse than nucleic acids and proteins. Two methodologies have been developed to analyze the metabolome. One is based on NMR (Reo 2002; Viant 2003), and the other is based on MS. MS-based metabolomics is further classified into three methods, which are GC-MS (Halket et al. 1999), LC-MS (Ito et al. 2000), and capillary electrophoresis (CE)-MS (Soga et al. 2003; Tolstikov et al. 2003). The metabolomics is generally investigated by analyzing biological fluids such as urine and blood. In order to apply this methodology to aquatic organisms, especially small organisms, it is necessary to improve the efficiency of metabolite extraction and pre-purification (Maharjan and Ferenci 2003). It should also be noted that identification of all metabolites obtained from NMR is not complete even in model organisms and that many of the identified metabolites are chemicals related to energy metabolism and major metabolism. Thus, changes of small amounts of biologically active substances such as hormones are still difficult to identify and evaluate. It is also necessary to link the metabolomic data to the other ‘‘-omics’’ studies to totally understand the effects of chemicals on aquatic organisms (Bono et al. 2003). Consortium for Metabonomic Toxicology was established in UK (Lindon et al. 2003); a similar consortium for ecosystem studies would be desirable.
Integration Once ‘‘-omics’’ data are obtained, integration of these data is very important. One important point of data integration is phenotype anchoring, in which specific phenotypes caused by environmental factors are combined with ‘‘-omics’’ data. From this combination of information, the gene networks that lead to the phenotypes can be identified, contributing to the understanding of the molecular mechanisms of environmental effects on organisms. In the case of ecotoxicogenomics, comparative analysis is also important and it will be helpful to integrate the data. For example, comparative genomics can be applied to define conserved genomic responses across species. Comparative transcriptomics (Su et al. 2002) and metabolomics (Raamsdonk et al. 2001; Smedsgaard and Nielsen 2005) will be helpful to extract common gene networks and metabolic pathways. Finding common mechanisms conserved in species can contribute to the development of ecotoxicognomics. Once common mechanisms can be identified, they can be used to assess risks to ecosystems.
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Alternatively, species-specific change can also be identified in ecotoxicogenomics. If a species that plays an important role in ecosystem is affected specifically by an environmental chemical, the ecosystem as a whole may suffer the effects. Understanding of mechanisms underlying toxic effect of environmental chemicals from ‘‘-omics’’ studies would contribute protection of ecosystem.
Conclusion Genomics has begun to be successfully introduced into the field of toxicology, though it needs further development. Genomics and related ‘‘-omics’’ fields can be applied to organisms within ecosystems including aquatic organisms, which can potentially lead to the protection of the ecosystem from environmental toxins. Integration of genomics, environment chemistry, ecology, and toxicology will lead to a new understanding of ecotoxicology.
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