Nutrigenomics in aquaculture
Nutrigenomics in aquaculture research: a key in the ‘Aquanomic’ revolution
Published: July 2, 2007
By: EWEN MCLEAN and STEVEN R. CRAIG (Courtesy of Alltech Inc.)
Publication of the human genome draft sequence (Lander et al., 2001; Venter et al., 2001), and those of other species represent major milestones in scientific discovery. This genomic unraveling has helped identify the functions of numerous unknown genes and, in certain cases, has assisted in determining the roles of specific genes in disease processes. The genomic revolution incorporates much more than the decoding of organismal genomes alone, however. Critical to the success of this field have been like innovations in molecular and cellular biology, information technology, analytical sciences and in automation (Witkamp, 2005).
A genome sequence does not itself reveal the functionality of its gene, but rather is a portal to understanding gene and gene product roles, such as expression patterns and locations, protein synthesis and how these processes are modified and regulated. Nor does the genome sequence inform us about the functional processes of a gene or protein, such as their involvement in signal transduction pathways and engagement in transcription, mRNA processing, mRNA stability, translation, or post-translational modifications.
Indeed these tasks are left to the biologist, who must decode and map the active genes, and determine the aforementioned functions. Comprehending this complex genomic blueprint demands a holistic approach. The last decade has witnessed a rapid expansion in the field of functional genomics that represents the confederacy of genomics, proteomics, genotyping, transcriptomics and metabolomics.
The huge data sets generated by the ‘omics’ fields has energized the field of bioinformatics, which has in turn developed new methods by which to acquire, store, share, analyze, present, and manage ‘omic’- derived information, allowing the processing and integration of complex and often dissimilar data sets into seamless and coherent searchable databases (Brazma, 2001; Desiere et al., 2002; van Ommen and Stierum, 2002). These approaches provide a powerful means by which numerous and sometimes negligible changes in a genome can be monitored without the need of prior knowledge of a specific mechanism.
Nutrigenomics
In recognizing that nutrients: 1) modify gene expression, 2) may alter normal metabolism, and 3) affect health status (Corthésy-Theulaz et al., 2005) the scientific community has rallied to define inter-relationships between diet, health and disease processes using molecular techniques (Kaput and Rodriguez, 2004; Davis and Hord, 2005).
This subdiscipline of functional genomics, termed nutritional genomics or nutrigenomics, endeavors to resolve the influence of dietary chemicals upon the genome and to increase our understanding of how dietary constituents influence metabolism (Müller and Kersten, 2003; Mutch et al., 2005). Nutrigenomics employs a wide portfolio of ‘omic’ methods because no single technique is suitable for analyzing all different types of molecule or outcomes.
However, of the 50 or so ‘omic’ terms coined (Mutch et al., 2005), nutrigenomics truely encompasses only four (Table 1), namely transcriptomics, or microarray technologies, which monitors altered mRNA levels for the entire genome (Scheel et al., 2002); proteomics, which encompasses protein structure determination, expression and molecular interactions (Kussmann et al., 2005); metabolomics, which examines changes in metabolites involved in primary and intermediary metabolism (German et al., 2004); and epigenomics, which ascertains DNA methylation patterns, imprinting and DNA packaging (Beck et al., 1999).
Table 1.Some ‘omics’ defined.
GENE EXPRESSION PROFILING
It is now widely appreciated that many metabolic processes are dependent upon an exquisite synchronization of events among several organs, involving sometimes many thousands of genes (Liu-Stratton et al., 2004; Kato et al., 2005). This complexity is enhanced by the involvement of other molecules that include receptors, hormones and enzymes.
Given the aforementioned complexity, and because diets are heterogenous mixtures of chemicals, it is obvious that evaluating the effects of a nutrient, for example, upon health, requires examining more than a single biomolecule or biomarker. The need to access a suite of markers when examining the effect of a nutrient upon a subject is made more intricate because most genes have small sequence differences or polymorphisms and these distinctions may affect proteinprotein or protein-substrate interactions.
Inconsistencies in some epidemiological studies in humans may be explained partly by polymorphisms, as exemplified by those upon folate metabolism and altered risk to colon cancer and responsiveness to hypolipemiant drugs (Davis and Hord, 2005; Ruano et al., 2005).
These recognized deviations from the norm in people have led to the development of the concept of ‘personalized medicine’, wherein microarray gene expression profiling (GEP) is used to provide elite treatment regimens based upon molecular classification of gene subtypes (Chin et al., 2004; Jain, 2004) - molecular diagnoses and medical interventions that will no doubt be refined further by miniaturization with the advent of nanotechnology.
In nutrition research GEP may be used for three distinct purposes (Müller and Kersten, 2003):
1) To assist in the identification and characterization of basic molecular pathways that may be impacted, either positively or negatively, by nutrients;
2) To provide insights upon specific mechanisms that trigger such beneficial or negative effects and;
3) To identify specific genes altered by nutrients that might prove valuable as molecular biomarkers or nutrient sensors and in gene discovery.
All three areas of application have already provided high bounty especially in the clinical and pharmacological sciences and all indications suggest that this will continue unabated (Müller and Kersten, 2003; Nugent, 2004; Kaput and Rodriguez, 2004; van Ommen, 2004; Chin et al., 2004; Witkamp, 2005; Mutch et al., 2005; Gibney et al., 2005).
In contrast to the successes achieved in the biomedical arena however, applying the same biotechnologies to the animal production sector has yet to provide like rewards. Clearly, fundamental differences exist between clinical and animal production settings. These distinctions will drive the specific applications of functional genomic technologies in the agribusiness sector, especially with regard to meat and egg production, milk quality and even in developing models for xenotransplantation (Rothschild, 2004; Burt, 2005; Womack, 2005).
As in terrestrial animal production, aquaculture too will concentrate research in areas that are most likely to provide economic advantage. The following will center upon the application of GEP in aquaculture research.
Aquanomics
In functional genomic investigations in aquaculture, or ‘aquanomics’ research, the in vivo model will represent the gold standard in studies designed to evaluate the effects of particular dietary component(s) upon production characteristics. This introduces a number of complications that must be addressed during experimental design.
Species selection will be necessarily contingent upon the availability of specific homologous microarray systems. This in itself becomes problematic since although many thousands of expressed sequence tags are available for carp, catfish, trout and salmon, complete genome sequences are only offered for zebrafish, tetradon or puffer fish, fugu and medaka (Crollius and Weissenbach, 2005).
The development of Atlantic salmon microarrays progresses at pace. This is the only species of aquaculture significance for which commercial microarrays are available. The use of heterologous microarrays requires rigorous validations although excellent correlations have been observed from human gene chips hybridized with distantly-related mammalian species (bovine, porcine and canine), the Arabidopsis chip for cross-species studies and a cichlid chip for research with various cichlid species, a salmonid, poeciliid and cyprinid fish (Becher et al., 2004; Ji et al., 2004; Renn et al., 2004).
Other than species selection, decisions must be made regarding the choice of strain, ploidy status, age, sex and environmental and dietary conditioning. The acceptance of a tissue type, or part thereof, to be examined depends upon the postulated effect of a specific nutrient, dietary component or other treatment upon the target animal; the latter of which may be affected ultimately by dose and route of administration (McLean and Craig, 2003).
Essential foci for GEP in aquaculture will be animal health and welfare- and nutrition-related and for selective breeding. This is because on a global basis disease causes billions of dollars in losses and consumers demand meat products untainted by chemicals and antibiotics. Therefore, development of feed-based immunoprotectants, especially those that are naturally occurring, represents a rational approach to combating disease and a substantial reduction of reliance upon chemicals.
On the other hand, feed represents the single most important operational variable in aquaculture, often exceeding 50% of a facility’s annual budget. Development of methods that reduce feed costs, optimize nutrient utilization or provide a means of adding value to final products (e.g., functionality, organic products) would be of high economic value to the industry.
Another area of concern for the sustainable development of mariculture is the supply of high quality juveniles. A more comprehensive understanding of developmental nutritional physiology, nutritional requirements and the potential beneficial effects of feed additives upon the survival and weaning of larval fishes would have immense implications for aquaculture.
BIOMARKERS
Because microarray technologies provide exquisite empirical screening tools, clearly the conformist dogma of hypothesis-driven research no longer remains valid. Studies can be undertaken without a starting hypothesis and, following acquisition of GEPs, one may convert back to more banal hypothesis-based research. Once raw hybridization intensities have been obtained from microarray studies, and following background correction and normalization, a multitude of genes may be identified as being overexpressed.
Some of these may represent potential biomarkers of a specific process or treatment effect – that is, they provide characteristic signatures associated with a particular physiological status. In fact, a major issue requiring resolution is the current lack of validated biomarkers that are specific, sensitive, predictable and quantifiable. Once potential biomarkers are revealed by microarray analyses, their applicability and value must be validated using other methods such as quantitative real-time reverse-transcriptasepolymerase chain reaction (qRT-PCR), Northern or Western blotting or via quantification of protein levels by enzyme-linked immunosorbent (ELISA) or similar assays.
HOW CAN AQUACULTURE BENEFIT FROM THE ‘OMICS’ REVOLUTION?
Already, several studies have employed the microarray-based technology to examine various aspects of fish biology. Many of these have used the zebrafish model (Crollius and Weissenback, 2005), due to the commercial availability of a microarray for this species.
Although not of aquaculture importance, work with zebrafish nevertheless provides crucial information, since GEPs following various manipulations have established biomarkers that may be appropriate for research with aquaculturally important species (Table 2). These include biomarkers for disease processes such as mycobacteriosis (Meijer et al., 2005), sex determination and reproductive function (Alberti et al., 2005; Wen et al., 2005), larval development (Ton et al., 2002; Lo et al., 2003; Linney et al., 2004), hypoxia (Ton et al., 2003) and temperature stress (Malek et al., 2004).
Microarray technology has also been applied in salmon studies (Rise et al., 2004a; 2004b; von Schalburg et al., 2005). In-house microarray technology has been developed and used to examine the early development of gilthead sea bream (Sarropoulou et al., 2005) and to evaluate the impact of a recombinant VHS vaccine upon immunity in the olive flounder (Byon et al., 2005).
Studies with Atlantic salmon and olive flounder incorporated RT-PCR verification of several maker genes (Table 2). However, it is equally noteworthy that signature biomarkers, for example, for different disease processes vary, suggesting that universal signatures for disease may be difficult to acquire. Nevertheless, diagnostic microarrays that examine a suite of genes that are intimately engaged in disease processes will undoubtedly appear in the future. Such microarrays would provide the means to assess the overall health of farmed stock and perhaps provide an advanced-warning system based upon early-stage homeostatic dysfunctions detected at the molecular level.
Table 2. Marker genes for reproduction, disease processes, oxidative stress and hypoxia in various species of fish for which validation has been performed following microarray identification.
Likewise, microarray technologies will prove invaluable in assessing the effectiveness of various immunoprotectants. A number of commercial aquafeed product lines incorporate various immunostimulants including the mannan oligosaccharide product Bio-Mos®.
Electron microscopic examinations have established that the latter provide benefit in terms of optimizing intestinal integrity (Dimitroglou et al., 2005). The same might also have been assessed using marker genes associated with gut cell cycling (Figure 1), including claudin 6, keratin 4, myosin heavy chain 11 and others.
Mannan oligosaccharides also modulate the immune response of animals (Bland et al., 2004) and, in trout and carp, can positively affect growth, survival, and feed efficiency, while enhancing the activity of lysozyme and complement proteins (Staykov et al., 2005). Innate immunity-associated genes, including those for complement and lysozyme, have already been employed as biomarkers in mammalian and fish-based microarray research (Machado et al., 2005; Table 2). These clearly represent excellent candidates as immune markers in the aquaculture arena.
Other dietary ingredients that might be used during aquaculture, both to the benefit of the cultured organism and consumer, include various micronutrients. For example, the health benefits of selenium (Se), especially with respect to cancer risk, and its ability to withstand viral insults, are already established (Hill and Burk, 2001) and the potential exists to increase fish fillet selenium levels as a method for enhancing dietary selenium intake in humans.
Hyperaccumulation of selenium in fish fillets can be attained through dietary manipulation (Cotter et al., 2005), but discrete biomarkers are still required to monitor selenium absorption and effects at different concentrations both in the host and target organism. Microarray technology has demonstrated Se-driven up- and downregulation of a broad variety of genes, including those related to detoxification, Se-binding proteins, some apoptotic genes, and genes engaged in cell proliferation (El-Bayoumy and Sinha, 2005).
Other potential selenium biomarkers have already been established for fish (Thisse et al., 2003) using microarrays, and include glutathione peroxidase, thioredoxin reductase and the cell cycle regulator p53. These and other biomarkers can be used to assess selenium status rapidly as well as to further unravel the molecular mechanisms of action of this micronutrient. Similarly, GEP can identify useful marker genes to assess micronutrient status and requirements, as well as identifying hitherto unknown beneficial (i.e., health) and negative effects of dietary nutrient concentrations.
Since approximately 40 micronutrients are required in the diets of growing animals, the savings in time, cost and animal use, and thence equipment and facilities for traditional research that are offered by transcriptomics cannot be undervalued. The same is true for macronutrient research.
Fewer than 10% of all aquacultured organisms have experienced any form of selective breeding (Gjedrem, 2005) and in general, selection programs have centered attention upon growth rates, feed efficiencies, and resistance to disease.
In contrast, selection programs for flesh quality are rare. This likely reflects the inherent difficulties and costs associated with sampling potential breeders and may be due to the fact that heritability for some important flesh quality attributes are moderate to low (0.1-0.2; Quinton et al., 2005), which likely results from polygenic effects: that is, quality traits are influenced by a large number of genes that each impart minor effects.
An alternative to traditional breeding programs is to elucidate the biological processes that control flesh quality using a markerbased approach. Improvements to flesh quality include not only manipulating traits such as tenderness or coloration, but also increasing product uniformity. This is not a trivial matter since fish flesh is processed and employed in a wide variety of ways.
Furthermore, it is not beyond the realm of possibility that in the future processors and retailers will demand the presence/up-regulation or absence/down-regulation of a series of health- and quality-related gene sets. Already the use of biomarkers to authenticate seafood product origin is employed (Piñeiro et al., 2003). Since flesh quality traits are influenced by genetic potential, environment, and feed, then a nutrigenomic approach clearly represents the most cost-effective method for enhancing product quality through rapid screening and identification of genes that influence desirable characteristics.
Figure 1.A study designed to compare the effect of low and high energy (lipid) diets upon tilapia performance revealed that both diets influenced gene expression within the gut. Here, functional gene categories affected by the high lipid diet are illustrated for the anterior intestine. Noteworthy was that most upregulated genes were of unknown function. Approximately 33% of genes with known or inferred functions expressed lipid-induced up-regulation. In particular, apolipoprotein receptor E gene expression increased.
Apolipoproteins and their receptors play a central role in lipid metabolism, mediate the absorption of lipoproteins, regulate the cholesterol content of peripheral tissues through the reverse cholesterol transport pathway, and are intimately involved in maintenance of the nervous system and in cellular signaling. From this study, a number of genes were identified as holding potential as biomarkers, including the baculoviral IAP repeat-containing 5B (e.g., birc5b; survivin 2), which was strongly expressed in tilapia fed on high lipid diets. Birc5b is strongly associated with apoptosis.
Nutrigenomic approaches clearly herald a new era for the aquafeed ingredient industry. This area provides enabling technologies that permit development, refinement and modification of bioactive and natural compounds that more accurately target specific diseases or provide other health benefits both to cultured animal and consumer.
Nevertheless, it should be reiterated that great care must be taken in the analysis of generated data sets as well as in the use of biomarkers. Since biological processes are rarely monogenetic, the predictive value of specific biomarkers may be undermined by the action of many hundreds or thousands of different genes, environmental and/or nutritional history (see Blanck et al., 2003; Marshall, 2003; Potischman, 2003; Potischman and Freudenheim, 2003).
Even so, it is clear that the power of this technology will, when fully engaged, affect all aspects of aquaculture production, its sustainability and economic viability. As well, development of diagnostic arrays will provide exquisite techniques for establishing point of origin of farmed product as well as safety for the consumer. It is rare indeed for aquaculture to be on a level playing field with the more established and better-funded animal production sciences. The use of this aquanomic technology catapults the discipline to the forefront of animal nutritional research.
Acknowledgements
The authors would like to recognize Johanna C. Craig and Eric M. Hallerman for insightful discussions. We are also indebted to JCC for her careful editorial comments.
References
A genome sequence does not itself reveal the functionality of its gene, but rather is a portal to understanding gene and gene product roles, such as expression patterns and locations, protein synthesis and how these processes are modified and regulated. Nor does the genome sequence inform us about the functional processes of a gene or protein, such as their involvement in signal transduction pathways and engagement in transcription, mRNA processing, mRNA stability, translation, or post-translational modifications.
Indeed these tasks are left to the biologist, who must decode and map the active genes, and determine the aforementioned functions. Comprehending this complex genomic blueprint demands a holistic approach. The last decade has witnessed a rapid expansion in the field of functional genomics that represents the confederacy of genomics, proteomics, genotyping, transcriptomics and metabolomics.
The huge data sets generated by the ‘omics’ fields has energized the field of bioinformatics, which has in turn developed new methods by which to acquire, store, share, analyze, present, and manage ‘omic’- derived information, allowing the processing and integration of complex and often dissimilar data sets into seamless and coherent searchable databases (Brazma, 2001; Desiere et al., 2002; van Ommen and Stierum, 2002). These approaches provide a powerful means by which numerous and sometimes negligible changes in a genome can be monitored without the need of prior knowledge of a specific mechanism.
Nutrigenomics
In recognizing that nutrients: 1) modify gene expression, 2) may alter normal metabolism, and 3) affect health status (Corthésy-Theulaz et al., 2005) the scientific community has rallied to define inter-relationships between diet, health and disease processes using molecular techniques (Kaput and Rodriguez, 2004; Davis and Hord, 2005).
This subdiscipline of functional genomics, termed nutritional genomics or nutrigenomics, endeavors to resolve the influence of dietary chemicals upon the genome and to increase our understanding of how dietary constituents influence metabolism (Müller and Kersten, 2003; Mutch et al., 2005). Nutrigenomics employs a wide portfolio of ‘omic’ methods because no single technique is suitable for analyzing all different types of molecule or outcomes.
However, of the 50 or so ‘omic’ terms coined (Mutch et al., 2005), nutrigenomics truely encompasses only four (Table 1), namely transcriptomics, or microarray technologies, which monitors altered mRNA levels for the entire genome (Scheel et al., 2002); proteomics, which encompasses protein structure determination, expression and molecular interactions (Kussmann et al., 2005); metabolomics, which examines changes in metabolites involved in primary and intermediary metabolism (German et al., 2004); and epigenomics, which ascertains DNA methylation patterns, imprinting and DNA packaging (Beck et al., 1999).
Table 1.Some ‘omics’ defined.
GENE EXPRESSION PROFILING
It is now widely appreciated that many metabolic processes are dependent upon an exquisite synchronization of events among several organs, involving sometimes many thousands of genes (Liu-Stratton et al., 2004; Kato et al., 2005). This complexity is enhanced by the involvement of other molecules that include receptors, hormones and enzymes.
Given the aforementioned complexity, and because diets are heterogenous mixtures of chemicals, it is obvious that evaluating the effects of a nutrient, for example, upon health, requires examining more than a single biomolecule or biomarker. The need to access a suite of markers when examining the effect of a nutrient upon a subject is made more intricate because most genes have small sequence differences or polymorphisms and these distinctions may affect proteinprotein or protein-substrate interactions.
Inconsistencies in some epidemiological studies in humans may be explained partly by polymorphisms, as exemplified by those upon folate metabolism and altered risk to colon cancer and responsiveness to hypolipemiant drugs (Davis and Hord, 2005; Ruano et al., 2005).
These recognized deviations from the norm in people have led to the development of the concept of ‘personalized medicine’, wherein microarray gene expression profiling (GEP) is used to provide elite treatment regimens based upon molecular classification of gene subtypes (Chin et al., 2004; Jain, 2004) - molecular diagnoses and medical interventions that will no doubt be refined further by miniaturization with the advent of nanotechnology.
In nutrition research GEP may be used for three distinct purposes (Müller and Kersten, 2003):
1) To assist in the identification and characterization of basic molecular pathways that may be impacted, either positively or negatively, by nutrients;
2) To provide insights upon specific mechanisms that trigger such beneficial or negative effects and;
3) To identify specific genes altered by nutrients that might prove valuable as molecular biomarkers or nutrient sensors and in gene discovery.
All three areas of application have already provided high bounty especially in the clinical and pharmacological sciences and all indications suggest that this will continue unabated (Müller and Kersten, 2003; Nugent, 2004; Kaput and Rodriguez, 2004; van Ommen, 2004; Chin et al., 2004; Witkamp, 2005; Mutch et al., 2005; Gibney et al., 2005).
In contrast to the successes achieved in the biomedical arena however, applying the same biotechnologies to the animal production sector has yet to provide like rewards. Clearly, fundamental differences exist between clinical and animal production settings. These distinctions will drive the specific applications of functional genomic technologies in the agribusiness sector, especially with regard to meat and egg production, milk quality and even in developing models for xenotransplantation (Rothschild, 2004; Burt, 2005; Womack, 2005).
As in terrestrial animal production, aquaculture too will concentrate research in areas that are most likely to provide economic advantage. The following will center upon the application of GEP in aquaculture research.
Aquanomics
In functional genomic investigations in aquaculture, or ‘aquanomics’ research, the in vivo model will represent the gold standard in studies designed to evaluate the effects of particular dietary component(s) upon production characteristics. This introduces a number of complications that must be addressed during experimental design.
Species selection will be necessarily contingent upon the availability of specific homologous microarray systems. This in itself becomes problematic since although many thousands of expressed sequence tags are available for carp, catfish, trout and salmon, complete genome sequences are only offered for zebrafish, tetradon or puffer fish, fugu and medaka (Crollius and Weissenbach, 2005).
The development of Atlantic salmon microarrays progresses at pace. This is the only species of aquaculture significance for which commercial microarrays are available. The use of heterologous microarrays requires rigorous validations although excellent correlations have been observed from human gene chips hybridized with distantly-related mammalian species (bovine, porcine and canine), the Arabidopsis chip for cross-species studies and a cichlid chip for research with various cichlid species, a salmonid, poeciliid and cyprinid fish (Becher et al., 2004; Ji et al., 2004; Renn et al., 2004).
Other than species selection, decisions must be made regarding the choice of strain, ploidy status, age, sex and environmental and dietary conditioning. The acceptance of a tissue type, or part thereof, to be examined depends upon the postulated effect of a specific nutrient, dietary component or other treatment upon the target animal; the latter of which may be affected ultimately by dose and route of administration (McLean and Craig, 2003).
Essential foci for GEP in aquaculture will be animal health and welfare- and nutrition-related and for selective breeding. This is because on a global basis disease causes billions of dollars in losses and consumers demand meat products untainted by chemicals and antibiotics. Therefore, development of feed-based immunoprotectants, especially those that are naturally occurring, represents a rational approach to combating disease and a substantial reduction of reliance upon chemicals.
On the other hand, feed represents the single most important operational variable in aquaculture, often exceeding 50% of a facility’s annual budget. Development of methods that reduce feed costs, optimize nutrient utilization or provide a means of adding value to final products (e.g., functionality, organic products) would be of high economic value to the industry.
Another area of concern for the sustainable development of mariculture is the supply of high quality juveniles. A more comprehensive understanding of developmental nutritional physiology, nutritional requirements and the potential beneficial effects of feed additives upon the survival and weaning of larval fishes would have immense implications for aquaculture.
BIOMARKERS
Because microarray technologies provide exquisite empirical screening tools, clearly the conformist dogma of hypothesis-driven research no longer remains valid. Studies can be undertaken without a starting hypothesis and, following acquisition of GEPs, one may convert back to more banal hypothesis-based research. Once raw hybridization intensities have been obtained from microarray studies, and following background correction and normalization, a multitude of genes may be identified as being overexpressed.
Some of these may represent potential biomarkers of a specific process or treatment effect – that is, they provide characteristic signatures associated with a particular physiological status. In fact, a major issue requiring resolution is the current lack of validated biomarkers that are specific, sensitive, predictable and quantifiable. Once potential biomarkers are revealed by microarray analyses, their applicability and value must be validated using other methods such as quantitative real-time reverse-transcriptasepolymerase chain reaction (qRT-PCR), Northern or Western blotting or via quantification of protein levels by enzyme-linked immunosorbent (ELISA) or similar assays.
HOW CAN AQUACULTURE BENEFIT FROM THE ‘OMICS’ REVOLUTION?
Already, several studies have employed the microarray-based technology to examine various aspects of fish biology. Many of these have used the zebrafish model (Crollius and Weissenback, 2005), due to the commercial availability of a microarray for this species.
Although not of aquaculture importance, work with zebrafish nevertheless provides crucial information, since GEPs following various manipulations have established biomarkers that may be appropriate for research with aquaculturally important species (Table 2). These include biomarkers for disease processes such as mycobacteriosis (Meijer et al., 2005), sex determination and reproductive function (Alberti et al., 2005; Wen et al., 2005), larval development (Ton et al., 2002; Lo et al., 2003; Linney et al., 2004), hypoxia (Ton et al., 2003) and temperature stress (Malek et al., 2004).
Microarray technology has also been applied in salmon studies (Rise et al., 2004a; 2004b; von Schalburg et al., 2005). In-house microarray technology has been developed and used to examine the early development of gilthead sea bream (Sarropoulou et al., 2005) and to evaluate the impact of a recombinant VHS vaccine upon immunity in the olive flounder (Byon et al., 2005).
Studies with Atlantic salmon and olive flounder incorporated RT-PCR verification of several maker genes (Table 2). However, it is equally noteworthy that signature biomarkers, for example, for different disease processes vary, suggesting that universal signatures for disease may be difficult to acquire. Nevertheless, diagnostic microarrays that examine a suite of genes that are intimately engaged in disease processes will undoubtedly appear in the future. Such microarrays would provide the means to assess the overall health of farmed stock and perhaps provide an advanced-warning system based upon early-stage homeostatic dysfunctions detected at the molecular level.
Table 2. Marker genes for reproduction, disease processes, oxidative stress and hypoxia in various species of fish for which validation has been performed following microarray identification.
Likewise, microarray technologies will prove invaluable in assessing the effectiveness of various immunoprotectants. A number of commercial aquafeed product lines incorporate various immunostimulants including the mannan oligosaccharide product Bio-Mos®.
Electron microscopic examinations have established that the latter provide benefit in terms of optimizing intestinal integrity (Dimitroglou et al., 2005). The same might also have been assessed using marker genes associated with gut cell cycling (Figure 1), including claudin 6, keratin 4, myosin heavy chain 11 and others.
Mannan oligosaccharides also modulate the immune response of animals (Bland et al., 2004) and, in trout and carp, can positively affect growth, survival, and feed efficiency, while enhancing the activity of lysozyme and complement proteins (Staykov et al., 2005). Innate immunity-associated genes, including those for complement and lysozyme, have already been employed as biomarkers in mammalian and fish-based microarray research (Machado et al., 2005; Table 2). These clearly represent excellent candidates as immune markers in the aquaculture arena.
Other dietary ingredients that might be used during aquaculture, both to the benefit of the cultured organism and consumer, include various micronutrients. For example, the health benefits of selenium (Se), especially with respect to cancer risk, and its ability to withstand viral insults, are already established (Hill and Burk, 2001) and the potential exists to increase fish fillet selenium levels as a method for enhancing dietary selenium intake in humans.
Hyperaccumulation of selenium in fish fillets can be attained through dietary manipulation (Cotter et al., 2005), but discrete biomarkers are still required to monitor selenium absorption and effects at different concentrations both in the host and target organism. Microarray technology has demonstrated Se-driven up- and downregulation of a broad variety of genes, including those related to detoxification, Se-binding proteins, some apoptotic genes, and genes engaged in cell proliferation (El-Bayoumy and Sinha, 2005).
Other potential selenium biomarkers have already been established for fish (Thisse et al., 2003) using microarrays, and include glutathione peroxidase, thioredoxin reductase and the cell cycle regulator p53. These and other biomarkers can be used to assess selenium status rapidly as well as to further unravel the molecular mechanisms of action of this micronutrient. Similarly, GEP can identify useful marker genes to assess micronutrient status and requirements, as well as identifying hitherto unknown beneficial (i.e., health) and negative effects of dietary nutrient concentrations.
Since approximately 40 micronutrients are required in the diets of growing animals, the savings in time, cost and animal use, and thence equipment and facilities for traditional research that are offered by transcriptomics cannot be undervalued. The same is true for macronutrient research.
Fewer than 10% of all aquacultured organisms have experienced any form of selective breeding (Gjedrem, 2005) and in general, selection programs have centered attention upon growth rates, feed efficiencies, and resistance to disease.
In contrast, selection programs for flesh quality are rare. This likely reflects the inherent difficulties and costs associated with sampling potential breeders and may be due to the fact that heritability for some important flesh quality attributes are moderate to low (0.1-0.2; Quinton et al., 2005), which likely results from polygenic effects: that is, quality traits are influenced by a large number of genes that each impart minor effects.
An alternative to traditional breeding programs is to elucidate the biological processes that control flesh quality using a markerbased approach. Improvements to flesh quality include not only manipulating traits such as tenderness or coloration, but also increasing product uniformity. This is not a trivial matter since fish flesh is processed and employed in a wide variety of ways.
Furthermore, it is not beyond the realm of possibility that in the future processors and retailers will demand the presence/up-regulation or absence/down-regulation of a series of health- and quality-related gene sets. Already the use of biomarkers to authenticate seafood product origin is employed (Piñeiro et al., 2003). Since flesh quality traits are influenced by genetic potential, environment, and feed, then a nutrigenomic approach clearly represents the most cost-effective method for enhancing product quality through rapid screening and identification of genes that influence desirable characteristics.
Figure 1.A study designed to compare the effect of low and high energy (lipid) diets upon tilapia performance revealed that both diets influenced gene expression within the gut. Here, functional gene categories affected by the high lipid diet are illustrated for the anterior intestine. Noteworthy was that most upregulated genes were of unknown function. Approximately 33% of genes with known or inferred functions expressed lipid-induced up-regulation. In particular, apolipoprotein receptor E gene expression increased.
Apolipoproteins and their receptors play a central role in lipid metabolism, mediate the absorption of lipoproteins, regulate the cholesterol content of peripheral tissues through the reverse cholesterol transport pathway, and are intimately involved in maintenance of the nervous system and in cellular signaling. From this study, a number of genes were identified as holding potential as biomarkers, including the baculoviral IAP repeat-containing 5B (e.g., birc5b; survivin 2), which was strongly expressed in tilapia fed on high lipid diets. Birc5b is strongly associated with apoptosis.
Nutrigenomic approaches clearly herald a new era for the aquafeed ingredient industry. This area provides enabling technologies that permit development, refinement and modification of bioactive and natural compounds that more accurately target specific diseases or provide other health benefits both to cultured animal and consumer.
Nevertheless, it should be reiterated that great care must be taken in the analysis of generated data sets as well as in the use of biomarkers. Since biological processes are rarely monogenetic, the predictive value of specific biomarkers may be undermined by the action of many hundreds or thousands of different genes, environmental and/or nutritional history (see Blanck et al., 2003; Marshall, 2003; Potischman, 2003; Potischman and Freudenheim, 2003).
Even so, it is clear that the power of this technology will, when fully engaged, affect all aspects of aquaculture production, its sustainability and economic viability. As well, development of diagnostic arrays will provide exquisite techniques for establishing point of origin of farmed product as well as safety for the consumer. It is rare indeed for aquaculture to be on a level playing field with the more established and better-funded animal production sciences. The use of this aquanomic technology catapults the discipline to the forefront of animal nutritional research.
Acknowledgements
The authors would like to recognize Johanna C. Craig and Eric M. Hallerman for insightful discussions. We are also indebted to JCC for her careful editorial comments.
References
Alberti, M., U. Kausch, S. Haindl and M. Seifert. 2005. Gene expression analysis for exposure to estrogenic substances. Acta Hydrochim. Hydrobiol. 33:38-44.
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Authors: EWEN MCLEAN1,2 and STEVEN R. CRAIG1,3
1 Virginia Tech Aquaculture Center, Blacksburg, Virginia;
2 Department of Large Animal Clinical Sciences, Virginia Polytechnic Institute and State University, Virginia -Maryland Regional College of Veterinary Medicine, Blacksburg, Virginia;
3 Department of Fisheries and Wildlife Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia
1 Virginia Tech Aquaculture Center, Blacksburg, Virginia;
2 Department of Large Animal Clinical Sciences, Virginia Polytechnic Institute and State University, Virginia -Maryland Regional College of Veterinary Medicine, Blacksburg, Virginia;
3 Department of Fisheries and Wildlife Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia
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