Profiling technologies have confirmed on a case-by-case basis the compositional "equivalence" of GM crops to their conventional near-isogenic comparators (Ricroch et al., 2011). Profiling technologies are, however, unlikely to provide immediately interpretable data in safety assessments that would provide added value to, or otherwise enhance, rigorously quantitative assessments of known nutrients and anti-nutrients that comprise foodstuffs. In the case of metabolic profiling, this can be directly attributable to i) the intrinsically safe nature of food itself, ii) inconsistencies in metabolite coverage versus quantitative capabilities afforded by different data acquisition technologies, iii) the ubiquitous and innocuous nature of small molecule metabolites identified in profiling as well as extreme variability in metabolite levels even within homozygous genotypes, and iv) the "chasm" between the large number of data generated in profiling experiments and the ability to interpret them in a way that is meaningful to nutrition and food safety. We now expand on these observations and further emphasize that a clear distinction between

1. *Domesticated crops have been selected to serve human needs and have an extensive history of safe consumption. Extensive information on levels of nutrients and crop-specific antinutrients is available. These can be measured through highly quantitative assays to provide interpretable*

Of over 250,000 plant species, only 7000 are considered as foodstuffs (Khoshbahkt and Hammer, 2008), and even fewer, 150, supply over 90% of all plant food. Three major crops, *i.e.* maize, wheat, and rice, supply over 66%. Choices made in crop domestication and breeding have enabled food and feed qualities that serve human needs. Numerous path changes between wild and domesticated plants are known and include *e.g.* loss of spontaneous shattering of seed head on ripening and greater uniformity of seed ripening and germination, both of which facilitate human harvest. A key path change however is reduction and even loss of toxic compounds in the edible parts of domesticated crops. In other words, human selection has resulted in crop phenotypes and compositions that distinguish domesticated varieties from their natural counterparts, are more suited to human diets and needs, and are safer and more nutritious. Interestingly, one review (Jones, 1998) asked "Why are so many food plants cyanogenic?" and concluded that "Cyanogenesis by plants is not only a surprisingly effective chemical defence against casual herbivores, but it is also easily overcome by careful pre-ingestion food processing, this latter skill being

"substantial equivalence" and food safety should be promoted.

*data of direct relevance to food nutrition and safety.*

known toxicants (Chassy, 2010).

**2. Key challenges for the omics**

almost exclusive to humans." In other words, because "cyanogenic plants are surprisingly well protected from herbivory and yet can be readily detoxified by food processing, … early farmers fortuitously chose these plants above all others for cultivation."

Of course, many modern foodstuffs are still associated with "ancestral" secondary metabolites that may confer nutritional or safety concerns at elevated levels. Classic examples include glycoalkaloids in potato (NIEHS, 1998), β-N-oxalyl-L-α,βdiaminopropionic acid (ODAP) in *Lathyrus sativus* (Bell, 2003), psoralens in celery (Beier and Oertli, 1983), and gossypol in cotton (Sunlkumar et al., 2006). Targeted measurement of these components as opposed to broad-based compositional screening is recommended by Herman et al. (2009); in other words, compositional assessments should focus on molecules explicitly associated with safety concerns. This is consistent with the observation that in the very few examples where a new plant variety has caused toxic effects it has been attributable to well-know toxicants associated with conventionally bred crops and not to a hitherto undetected metabolite (Chassy, 2010).

It is noteworthy that such targeted assessments could easily facilitate a partnership with omics researchers conducting semi-targeted profiling on pathways associated with toxic metabolites to support both early development and commercialization of nutritionally enhanced products. Such a partnership could, at least in principle, mitigate the current regulatory burden imposed on new GM crops (Graff et al., 2009; Potrykus, 2010) and promote the application of omics within modern agricultural biotechnology.

2. *Information on compositional variation in conventional crops with respect to their responsiveness to environmental and genetic factors is necessary to provide context to evaluations of new GM crops. The need to assess natural variation is also true for metabolomics yet little information on the impact of conventional breeding on metabolite profiles is available. The inconsistent coverage of metabolites offered through different data acquisition platforms may provide challenges in establishing a coherent literature in this area.*

Ironically, as mentioned earlier, continued confirmation that conventional breeding, environment, and germplasm contribute to compositional variation more than transgene insertion has coincided with increased interest in the use of 'omics technologies. This paradox is compounded by the fact that results from these technologies have only further highlighted the equivalence of GM crops to their conventional counterparts and reaffirmed the substantial effect of environment and germplasm on compositional and biochemical variability (see Ricroch et al., 2011). Although there are complexities in the interpretation of data generated through modern profiling technologies (Broadhurst and Kell, 2006; Lay et al., 2006) including the fact that the data is not quantitative and there is no standardized framework for comparisons, the lack of variation between GM crops and their conventional comparators at the transcriptomic, proteomic, and metabolomic level has, nonetheless, been independently corroborated.

Challenges for Metabolomics as a Tool in Safety Assessments 337

An often overlooked aspect of the Catchpole et al. (2005) paper is their demonstration that levels of glycoalkaloids (-chaconine and -solanine) were normal in the GM potatoes, a result that is easily interpretable from a food and feed perspective. Our understanding of nutrients and anti-nutrients forms the basis of attempt to modify crops through conventional breeding or agricultural biotechnology. It has allowed crops to be developed by conventional breeding that are deliberately non-equivalent to their parental progenitors in a wide range of nutritional (and agronomic) characteristics.. As Rischer et al. (2006) point out "For centuries, conventional plant breeding programs have produced new traits, higher yields and improved quality. However, little attention has been paid to metabolic changes occurring in successive generations. The issue has gained importance only recently in the context of defining thresholds for safety assessments of GM crops." It is not immediately obvious why these hitherto neglected metabolites should now be at the center of such attention. Indeed, there are few studies on small molecule metabolite changes in crops where macro-molecular composition has been deliberately changed through conventional breeding (e.g. high oil and high protein corn,

Catchpole et al. (2005) in their demonstration of the compositional equivalence of GM potatoes to conventional lines also remark on the large metabolite variation in conventional potato as follows; "These significant differences [between conventional cultivars] were never sought as desired traits in traditional breeding programs, and overall composition has not given cause for public safety concerns". Overall, however, experimental designs that will both account for natural variation and have enough power to identify differences that can be attributed to transgene insertion will offer opportunities to maximize the value of omics technologies as tools in plant breeding and the

3. *Metabolomics offers opportunities to generate data on large numbers of metabolites. Most of these metabolites will be low in abundance and levels will be highly variable. They are also more likely to include central (and hence ubiquitous) metabolites such as sugars, organic acids, and free amino acids; metabolites that are not immediately associated with safety or nutritional*

Compositional assessments of new foodstuffs generally focus on the article of commerce, most typically harvested seed or grain. This material is generally characterized by high levels of starch, protein, fat, and fibers, with the small metabolite pool being low in abundance. For example, approximately 95-98% of maize grain is comprised the aforementioned materials; the small metabolite pool in grain, is of low abundance (~2-5% of grain biomass) and its levels are highly dependent on changes in the macromolecular pool. Soybean seed is comprised 40% protein, 20% fat, and 15% fiber. The residual 15% is comprised mainly of sugars (e.g. sucrose, raffinose, stachyose, glucose, galactose, fructose) of which the principal two, raffinose and stachyose, are measured in regulatory assessments. The fact that the small molecule metabolite pool in seed or grain is of low abundance and influenced by levels of the major macromolecular nutrients accounts for its extensive

Skogerson et al. (2010) sought to assess genetic and environmental impacts on the metabolite composition of corn grain. Their data acquisition technology (gas

high oil soybean).

development of new crops.

variability (Skogerson et al., 2010; Harrigan et al., 2007).

*relevance.*

As with the compositional studies reported above, results from many of the 'omics studies emphasize the need to understand natural variation in levels of endogenous metabolites in providing biological context to pair-wise differences in any recorded profiles (see Figure 1). Levels of compositional components are sensitive to environmental conditions. This has been established for, for example, protein and oil in key crops (Panthee et al., 2005; Lam et al., 2010). Protein levels in soybean seed generally average ~40% dry weight (dwt), with values reported in the USDA soybean germplasm collection, for example, ranging from 34.1 to 56.8% dwt (Wilson, 2004). In a recent meta-analysis of environmental effects on soybean composition, Rotundo and Westgate (2009) observed that water stress, temperature, and/or nitrogen supply all affected protein levels measured in mature seed.

Variability is even greater for lower abundance small molecule metabolites. Vitamin E ( tocopherol) is typically only a minor component in soybean but is known to be important in maintaining oxidative stability of soybean oil. Levels in soybean seed are affected by environment and germplasm. For example, Britz et al. (2008) showed a greater than 2-fold variation in levels across three locations in the U.S. over a period of four years. Levels in soybean seed harvested from six different locations in Eastern Canada over a single year ranged from 0.87 to 3.32 mg/100g dwt (Seguin et al. 2009). Seguin et al. (2010) point out that environmental factors associated with variability in vitamin E levels include drought, temperature, and even crop management systems. The "overwhelming variability" of isoflavones was mentioned in the introduction (see Figure 1). As will be discussed later, this "overwhelming variability" can be considered to apply to levels of small molecule metabolites in harvested seed and grain of most crops.

Encouragingly, many comparative profiling studies on GM and non-GM crops have been designed to include at least one element of genotypic or environmental variability. This is exemplified in the following two examples, both of which reaffirm the need to provide biological context to pairwise-differences between two comparators.

In Baker et al. (2006) NMR-based metabolic profiles of three GM wheat varieties and the corresponding parents were generated. The incorporated transgenes encoded highmolecular weight subunits of the storage protein, glutenin. The wheat varieties were grown at two different sites over three different growing seasons (1999 -2001). Differences between the GM and parental lines were within the same range as the differences between the control lines grown on different sites and in different years. Analogous to the approach adopted in targeted compositional analyses adopting OECD recommendations, this study emphasized the importance of data from multiple years and multiple sites and that environmental variation influences metabolome composition.

In Catchpole et al. (2005) two GM potato varieties modified in fructan chemistry were grown over two different seasons (2001, 2003). Metabolic profiles of the GM and five conventional crops were generated using flow-injection MS (FIE-MS), GC-MS, and LC-MS. These demonstrated that differences between the GM and conventional potatoes were due to the intended metabolic changes, but aside from these targeted changes, the GM crops were "substantially equivalent to traditional cultivars". A major finding recognized by the authors was the large variation in the metabolic profiles of the conventional crops and, as such, the study emphasized the importance of understanding genotypic variability in assessments of compositional changes.

As with the compositional studies reported above, results from many of the 'omics studies emphasize the need to understand natural variation in levels of endogenous metabolites in providing biological context to pair-wise differences in any recorded profiles (see Figure 1). Levels of compositional components are sensitive to environmental conditions. This has been established for, for example, protein and oil in key crops (Panthee et al., 2005; Lam et al., 2010). Protein levels in soybean seed generally average ~40% dry weight (dwt), with values reported in the USDA soybean germplasm collection, for example, ranging from 34.1 to 56.8% dwt (Wilson, 2004). In a recent meta-analysis of environmental effects on soybean composition, Rotundo and Westgate (2009) observed that water stress, temperature, and/or nitrogen supply

Variability is even greater for lower abundance small molecule metabolites. Vitamin E ( tocopherol) is typically only a minor component in soybean but is known to be important in maintaining oxidative stability of soybean oil. Levels in soybean seed are affected by environment and germplasm. For example, Britz et al. (2008) showed a greater than 2-fold variation in levels across three locations in the U.S. over a period of four years. Levels in soybean seed harvested from six different locations in Eastern Canada over a single year ranged from 0.87 to 3.32 mg/100g dwt (Seguin et al. 2009). Seguin et al. (2010) point out that environmental factors associated with variability in vitamin E levels include drought, temperature, and even crop management systems. The "overwhelming variability" of isoflavones was mentioned in the introduction (see Figure 1). As will be discussed later, this "overwhelming variability" can be considered to apply to levels of small molecule

Encouragingly, many comparative profiling studies on GM and non-GM crops have been designed to include at least one element of genotypic or environmental variability. This is exemplified in the following two examples, both of which reaffirm the need to provide

In Baker et al. (2006) NMR-based metabolic profiles of three GM wheat varieties and the corresponding parents were generated. The incorporated transgenes encoded highmolecular weight subunits of the storage protein, glutenin. The wheat varieties were grown at two different sites over three different growing seasons (1999 -2001). Differences between the GM and parental lines were within the same range as the differences between the control lines grown on different sites and in different years. Analogous to the approach adopted in targeted compositional analyses adopting OECD recommendations, this study emphasized the importance of data from multiple years and multiple sites and that

In Catchpole et al. (2005) two GM potato varieties modified in fructan chemistry were grown over two different seasons (2001, 2003). Metabolic profiles of the GM and five conventional crops were generated using flow-injection MS (FIE-MS), GC-MS, and LC-MS. These demonstrated that differences between the GM and conventional potatoes were due to the intended metabolic changes, but aside from these targeted changes, the GM crops were "substantially equivalent to traditional cultivars". A major finding recognized by the authors was the large variation in the metabolic profiles of the conventional crops and, as such, the study emphasized the importance of understanding genotypic variability in

all affected protein levels measured in mature seed.

metabolites in harvested seed and grain of most crops.

biological context to pairwise-differences between two comparators.

environmental variation influences metabolome composition.

assessments of compositional changes.

An often overlooked aspect of the Catchpole et al. (2005) paper is their demonstration that levels of glycoalkaloids (-chaconine and -solanine) were normal in the GM potatoes, a result that is easily interpretable from a food and feed perspective. Our understanding of nutrients and anti-nutrients forms the basis of attempt to modify crops through conventional breeding or agricultural biotechnology. It has allowed crops to be developed by conventional breeding that are deliberately non-equivalent to their parental progenitors in a wide range of nutritional (and agronomic) characteristics.. As Rischer et al. (2006) point out "For centuries, conventional plant breeding programs have produced new traits, higher yields and improved quality. However, little attention has been paid to metabolic changes occurring in successive generations. The issue has gained importance only recently in the context of defining thresholds for safety assessments of GM crops." It is not immediately obvious why these hitherto neglected metabolites should now be at the center of such attention. Indeed, there are few studies on small molecule metabolite changes in crops where macro-molecular composition has been deliberately changed through conventional breeding (e.g. high oil and high protein corn, high oil soybean).

Catchpole et al. (2005) in their demonstration of the compositional equivalence of GM potatoes to conventional lines also remark on the large metabolite variation in conventional potato as follows; "These significant differences [between conventional cultivars] were never sought as desired traits in traditional breeding programs, and overall composition has not given cause for public safety concerns". Overall, however, experimental designs that will both account for natural variation and have enough power to identify differences that can be attributed to transgene insertion will offer opportunities to maximize the value of omics technologies as tools in plant breeding and the development of new crops.

3. *Metabolomics offers opportunities to generate data on large numbers of metabolites. Most of these metabolites will be low in abundance and levels will be highly variable. They are also more likely to include central (and hence ubiquitous) metabolites such as sugars, organic acids, and free amino acids; metabolites that are not immediately associated with safety or nutritional relevance.*

Compositional assessments of new foodstuffs generally focus on the article of commerce, most typically harvested seed or grain. This material is generally characterized by high levels of starch, protein, fat, and fibers, with the small metabolite pool being low in abundance. For example, approximately 95-98% of maize grain is comprised the aforementioned materials; the small metabolite pool in grain, is of low abundance (~2-5% of grain biomass) and its levels are highly dependent on changes in the macromolecular pool. Soybean seed is comprised 40% protein, 20% fat, and 15% fiber. The residual 15% is comprised mainly of sugars (e.g. sucrose, raffinose, stachyose, glucose, galactose, fructose) of which the principal two, raffinose and stachyose, are measured in regulatory assessments. The fact that the small molecule metabolite pool in seed or grain is of low abundance and influenced by levels of the major macromolecular nutrients accounts for its extensive variability (Skogerson et al., 2010; Harrigan et al., 2007).

Skogerson et al. (2010) sought to assess genetic and environmental impacts on the metabolite composition of corn grain. Their data acquisition technology (gas

Challenges for Metabolomics as a Tool in Safety Assessments 339

4. *Another challenge in establishing a coherent literature on the impact of conventional and other approaches to breeding on natural variability in metabolite as well as determining a framework to establish nutritional meaning from metabolite analysis is the differential coverage of metabolites offered through the numerous data acquisition platforms available to omics*

As described in numerous articles on metabolomics, (*e.g.* Goodacre et al., 2004; Rischer and Oksman-Caldentey, 2006; Kusano et al., 2011) the large physico-chemical diversity of small molecule metabolites renders comprehensive metabolomic profiling through a single data acquisition technology impossible. A range of technologies associated with different detection capabilities (metabolite coverage and sensitivity), precision, resolution, throughput and reproducibility are now extensively deployed by the research community. Nuclear magnetic resonance spectroscopy (NMR), gas-chromatography mass spectrometry (GC-MS), liquid-chromatography (LC)-MS utilizing different ionization modes, Fouriertransform MS, and capillary electrophoresis (CE)- MS have all been applied in comparative assessments of GM and non-GM crops. MS approaches predominate over NMR analyses given their greater sensitivity and coverage; however this advantage does come at the expense of quantitation (i.e. MS would need an internal standard for every metabolite to be quantitated) and with a large number of unidentified MS signals in any metabolite profile. Whilst it has been suggested that untargeted profiling techniques are unbiased, it is clear that selection of a specific data acquisition technology *is* a bias and that this type of analytical bias would need to be justified by pre-specified experimental hypotheses. This

Recognizing inherent limitations for any given data acquisition technology Kusano et al. (2011) applied a multi-platform approach to an evaluation of transgenic tomato. These authors used a combination of GC-MS, LC-MS, and CE-MS with each technology covering distinct metabolite classes. Free amino acids, sugars and organic acids were covered by GC-MS, larger molecules (e.g. flavonoids) by LC-MS whereas CE-MS measured specific cations and anions. Overall, the data generated 175 unique identified metabolites but a total of 1460 with "no or imprecise metabolite annotation." Of the identified metabolites, only 56 were observed in at least two platforms. A total of 261 peaks showed no correlation with experimental factors (transgene,

It is worth pointing out that two studies that assessed the metabolic profiles of grain from GM maize containing the *Cry1ab* gene and that utilized the same data acquisition platform (NMR) differed in their conclusions on the impact of transgene insertion on levels of free amino acids (Manetti et al., 2006; Piccioni et al., 2009). Manetti et al. (2006) reported that the GM crop included higher levels of sugars (glucose, sucrose, meliobiose), GABA, glutamine, and succinate and decreased levels of alanine, asparagine, and choline. Piccioni et al. (2009) reported lower levels of all amino acids, lower sugars, and lower succinate (and other organic acids). Piccioni et al. (2009) were also able to report on metabolites observed in their NMR profiles but absent in those of Manetti et al. (2006). Key design differences between the two studies include different

Levandi et al. (2008) utilized CE-MS to compare levels of 27 metabolites in three different GM maize lines also containing the Cry1ab gene. Some of these metabolites (e.g., certain free amino acids, choline, GABA) were also recorded in the NMR platforms of Mannetti et al

justification would be critical in a Regulatory environment.

cultivar, tissue type) and had to be removed from statistical analyses.

parental lines, different growth conditions, and sample extraction protocols.

*researchers.*

chromatography-mass spectrometry) measured 119 identified metabolites including free amino acids, free fatty acids, sugars, organic acids, and other small molecules in a range of corn hybrids derived from 48 inbred lines crossed against two different tester lines (from the C103 and Iodent heterotic groups) and grown at three locations in Iowa (Table 1). Different metabolic phenotypes were clearly associated with the two distinct tester populations. Overall, grain from the C103 lines contained higher levels of free fatty acids and organic acids, whereas grain from the Iodent lines were associated with higher levels of amino acids and carbohydrates. In addition, the fold-range of genotype mean values [composed of six samples each (two tester crosses per inbred × three field sites)] for identified metabolites ranged from 1.5- to 93-fold with sugars and polyols being particularly variable. Interestingly, some grain metabolites showed a non-normal distribution over the entire corn population, which could, at least in part, be attributed to large differences in metabolite values within specific inbred crosses relative to other inbred sets.

aThis indicates a statistically significant difference (p<0.0001) between hybrids derived from a cross with one tester (C103 heterotic group) versus another tester (Iodents heterotic group) bThis indicates a statistically significant difference (p<0.0001) across the three locations in this study

Table 1. Variation in Metabolites due to Genotype or Environmental Variation

In an analogous report on the same samples, Harrigan et al. (2007) concluded that, given such variability, measurement of the small metabolite pool, was unlikely to prove useful to a comparative assessment of GM crops unless a given metabolite was an intended nutritional or toxicological endpoint. In fact, it is not immediately obvious how the data generated in Skogerson et al. (2011) could be used to determine which hybrids in this study were the safest.

In its report in 2004 the US National Research Council made pointed remarks about this disconnect as summarized in the following quotes. ".. severe imbalances between highly advanced analytical technologies and limited ability to interpret the results and predict health effects that result from the consumption of food that is genetically modified" and "….inherent difficulties, however, in identifying all of the constituents detected in profiling methods or understanding the activity and potential biological consequence of all genes in an organism severely limit the usefulness of these methods for predictive purposes.." Unable to bridge this gap, many profiling proponents make an assumption of safety on the non-GM comparator and consider statistical differences to equate with unintended effects. This tendency is described later.

chromatography-mass spectrometry) measured 119 identified metabolites including free amino acids, free fatty acids, sugars, organic acids, and other small molecules in a range of corn hybrids derived from 48 inbred lines crossed against two different tester lines (from the C103 and Iodent heterotic groups) and grown at three locations in Iowa (Table 1). Different metabolic phenotypes were clearly associated with the two distinct tester populations. Overall, grain from the C103 lines contained higher levels of free fatty acids and organic acids, whereas grain from the Iodent lines were associated with higher levels of amino acids and carbohydrates. In addition, the fold-range of genotype mean values [composed of six samples each (two tester crosses per inbred × three field sites)] for identified metabolites ranged from 1.5- to 93-fold with sugars and polyols being particularly variable. Interestingly, some grain metabolites showed a non-normal distribution over the entire corn population, which could, at least in part, be attributed to large differences in metabolite

**Metabolite class No. of analytes Affected by Testera Affected by**

others 17 6 1 organic acids 17 6 0

free amino acids 26 14 2

related metabolites 17 5 0 sugar alcohols 18 5 0

trisaccharides 16 1 0 sugar acids 8 0 1 aThis indicates a statistically significant difference (p<0.0001) between hybrids derived from a cross with one tester (C103 heterotic group) versus another tester (Iodents heterotic group) bThis indicates a

In an analogous report on the same samples, Harrigan et al. (2007) concluded that, given such variability, measurement of the small metabolite pool, was unlikely to prove useful to a comparative assessment of GM crops unless a given metabolite was an intended nutritional or toxicological endpoint. In fact, it is not immediately obvious how the data generated in Skogerson et al. (2011) could be used to determine which hybrids in this study were the safest. In its report in 2004 the US National Research Council made pointed remarks about this disconnect as summarized in the following quotes. ".. severe imbalances between highly advanced analytical technologies and limited ability to interpret the results and predict health effects that result from the consumption of food that is genetically modified" and "….inherent difficulties, however, in identifying all of the constituents detected in profiling methods or understanding the activity and potential biological consequence of all genes in an organism severely limit the usefulness of these methods for predictive purposes.." Unable to bridge this gap, many profiling proponents make an assumption of safety on the non-GM comparator and consider statistical differences to equate with unintended effects.

statistically significant difference (p<0.0001) across the three locations in this study Table 1. Variation in Metabolites due to Genotype or Environmental Variation

**Locationb**

values within specific inbred crosses relative to other inbred sets.

sterols, amines, and

free fatty acids and

mono-, di-, and

This tendency is described later.

4. *Another challenge in establishing a coherent literature on the impact of conventional and other approaches to breeding on natural variability in metabolite as well as determining a framework to establish nutritional meaning from metabolite analysis is the differential coverage of metabolites offered through the numerous data acquisition platforms available to omics researchers.*

As described in numerous articles on metabolomics, (*e.g.* Goodacre et al., 2004; Rischer and Oksman-Caldentey, 2006; Kusano et al., 2011) the large physico-chemical diversity of small molecule metabolites renders comprehensive metabolomic profiling through a single data acquisition technology impossible. A range of technologies associated with different detection capabilities (metabolite coverage and sensitivity), precision, resolution, throughput and reproducibility are now extensively deployed by the research community. Nuclear magnetic resonance spectroscopy (NMR), gas-chromatography mass spectrometry (GC-MS), liquid-chromatography (LC)-MS utilizing different ionization modes, Fouriertransform MS, and capillary electrophoresis (CE)- MS have all been applied in comparative assessments of GM and non-GM crops. MS approaches predominate over NMR analyses given their greater sensitivity and coverage; however this advantage does come at the expense of quantitation (i.e. MS would need an internal standard for every metabolite to be quantitated) and with a large number of unidentified MS signals in any metabolite profile. Whilst it has been suggested that untargeted profiling techniques are unbiased, it is clear that selection of a specific data acquisition technology *is* a bias and that this type of analytical bias would need to be justified by pre-specified experimental hypotheses. This justification would be critical in a Regulatory environment.

Recognizing inherent limitations for any given data acquisition technology Kusano et al. (2011) applied a multi-platform approach to an evaluation of transgenic tomato. These authors used a combination of GC-MS, LC-MS, and CE-MS with each technology covering distinct metabolite classes. Free amino acids, sugars and organic acids were covered by GC-MS, larger molecules (e.g. flavonoids) by LC-MS whereas CE-MS measured specific cations and anions. Overall, the data generated 175 unique identified metabolites but a total of 1460 with "no or imprecise metabolite annotation." Of the identified metabolites, only 56 were observed in at least two platforms. A total of 261 peaks showed no correlation with experimental factors (transgene, cultivar, tissue type) and had to be removed from statistical analyses.

It is worth pointing out that two studies that assessed the metabolic profiles of grain from GM maize containing the *Cry1ab* gene and that utilized the same data acquisition platform (NMR) differed in their conclusions on the impact of transgene insertion on levels of free amino acids (Manetti et al., 2006; Piccioni et al., 2009). Manetti et al. (2006) reported that the GM crop included higher levels of sugars (glucose, sucrose, meliobiose), GABA, glutamine, and succinate and decreased levels of alanine, asparagine, and choline. Piccioni et al. (2009) reported lower levels of all amino acids, lower sugars, and lower succinate (and other organic acids). Piccioni et al. (2009) were also able to report on metabolites observed in their NMR profiles but absent in those of Manetti et al. (2006). Key design differences between the two studies include different parental lines, different growth conditions, and sample extraction protocols.

Levandi et al. (2008) utilized CE-MS to compare levels of 27 metabolites in three different GM maize lines also containing the Cry1ab gene. Some of these metabolites (e.g., certain free amino acids, choline, GABA) were also recorded in the NMR platforms of Mannetti et al

Challenges for Metabolomics as a Tool in Safety Assessments 341

As indicated earlier, compositional assessments of GM crops involve direct comparisons of levels of key nutrients and anti-nutrients in the new crop variety to those of a near-isogenic conventional comparator. Statistical evaluations of the compositional data have typically utilized classical frequentist significance testing. There are, however, several features of significance hypothesis testing that impact its application to compositional comparisons between crops with different agronomic qualities (Lecoutre, et al., 2001). Berger (1985), for example, stated, "We know from the beginning that the point null hypothesis is almost certainly not exactly true, and that this will always be confirmed by a large enough sample. What we are really interested in determining is whether or not the null hypothesis is approximately true." There are many factors that impact crop composition, *including agronomic traits we seek to modify through plant breeding*, (e.g. Scott et al., 2006; Uribelarrea et al., 2004; Dornbos and Mullen, 1992; Hymowitz et al., 1972; Wilcox and Shibles, 2001; Yin and Vyn, 2005) and any compositional changes that accompany enhanced agronomic quality may confound interpretation of results generated through significance testing.

Statistical significance is used only as a first step in comparative assessments. The interpretation of statistical significance from a p-value, *the probability of an observed result or a more extreme result occurring if the null hypothesis were true*, does not imply biological significance (Goodman, 2008). Statistically significant differences do not imply large differences between GM and conventional comparators or that these comparators can be easily distinguished from a biological perspective. In fact, the power of the experimental designs (multiple highly replicated field trials) adopted in current compositional assessments allows statistical significance to be assigned even where there are very small difference in mean values of a given component but where the distribution of component values overlap extensively. As such, significance approaches must be accompanied with further data analysis encompassing discussion of magnitudes of differences, assessments of component ranges, and the sensitivity of component values to environmental factors such as location. This is consistent with the recommendation by Codex Alimentarius (2008, Ch. 44) that "The statistical significance of any observed differences should be assessed in the context of the range of natural variations for that parameter to determine its biological significance." It is further consistent with observations of high variability in crop composition recorded in the scientific literature. The current scientific consensus is that, in most if not all cases, statistically significant differences between GM and near-isogenic conventional controls represent modest and nutritionally meaningless differences in magnitude. For example, a recent review of studies on GM crop composition showed that over 99% of all nutrient and antinutrients comparisons, where significant differences at the 5% level (=0.05) in mean values were observed, had a relative magnitude difference less than 20%. These differences are considerably less than the range of values attributable to

Most metabolic profiling experiments utilize significance testing and Rischer and Oksman-Caldentey (2006) refer to unintended effects as "effects which represent a statistically significant difference (e.g. in chemical composition of the GM plant compared with a suitable non-GM plant)" although they acknowledge that such differences would have to be evaluated in the context of natural variability. One review that endorses the use of omics in safety assessments suggests that "the amount of variation from genetic engineering should be small (~3%)." (Heineman et al., 2011). Whilst this particular number is unrealistic since it

germplasm and environmental factors (Harrigan et al., 2010).

(2006) and Piccioni et al. (2009). No consistent association of these metabolites with the GM trait when assessed over all three GM lines was observed, a conclusion in line with the combined results of Mannetti et al. (2006) and Piccioni et al. (2009). Several of the metabolites reported by Levandi et al. (2006) are more typically associated with other taxonomic groupings, for example, graveolin (*Ruta graveolus*, Rutaceae) and lunarine, (*Lunaria annua*, Brassicaceae). The assignment of peaks to metabolites not typically associated with a genus or family would almost certainly require extensive validation in a Regulatory environment.

Leon et al. (2009) utilized FT-MS on the same samples assessed by Levandi et al. (2008). This allowed coverage of 5500 mass signals of which approximately 1000 could be assigned an elemental composition. Those elemental compositions could be associated (through MasstTRIX) with specific metabolic pathways (KEGG); these associations are referred to as "isomeric hits". This approach would identify any differences in GM and non-GM metabolic profiles, especially where an elemental composition could be assigned, to be tentatively associated with biochemical differences. Overall, it was shown that a greater number of isomeric hits in pathways such as arachidonic acid metabolism, free amino acid metabolism, purine metabolism, and folate biosynthesis were associated with the GM samples. A list of 33 possible compounds that could distinguish the GM and non-GM varieties was generated, of which 12 could be confirmed in an orthogonal assay (CE-MS). The authors then indicated that only four of these could be considered as potential GM "biomarkers"; L-carnitine, apigenidin, 5, 6-dihydroxyindole, and one unidentified metabolite. There is little further literature on levels of these metabolites in maize and the association of these metabolites as GM biomarkers is almost certainly premature. Further, the interpretability of the Levandi et al. (2009) approach is not at all clear; there are fewer isomeric hits associated with inositol phosphate metabolism, yet levels of phytic acid have been well-established to be nearidentical in GM and non-GM maize. The association of isomeric hits for bile acid biosynthesis, which is not typically associated with plant metabolism, is also difficult to interpret.

In summary, different metabolic profiling platforms applied to similar biological questions will yield non-overlapping solutions. This is due to differential metabolite coverage (even within similar data acquisition technologies) and is compounded both by the number of unidentified signals observed in current metabolite profiles and, in some cases, "identification" of metabolites not previously known to be biosynthetically associated with the plant species or genus in question.

#### **3. Equating statistical equivalence with biosafety**

Predetermined criteria would need to be established for any study protocol, data acquisition steps or statistical analyses utilized in a safety assessment. As alluded to earlier, sampling from multiple replicated field sites would be required. Discussions on the number of replicates required to generate meaningful results from omics experiments are available as well as on the potential for bias and over-fitting (Broadhurst and Kell, 2006; Goodacre et al., 2007). Here we focus on the routine misinterpretation of "statistical significance" (Goodman, 2008) and the tendency to associate statistically significant differences between GM and a non-GM comparator as, minimally, an unintended effect, and often to imply the statistical difference raises a question about the safety of a newly evaluated crop.

(2006) and Piccioni et al. (2009). No consistent association of these metabolites with the GM trait when assessed over all three GM lines was observed, a conclusion in line with the combined results of Mannetti et al. (2006) and Piccioni et al. (2009). Several of the metabolites reported by Levandi et al. (2006) are more typically associated with other taxonomic groupings, for example, graveolin (*Ruta graveolus*, Rutaceae) and lunarine, (*Lunaria annua*, Brassicaceae). The assignment of peaks to metabolites not typically associated with a genus or family would almost certainly require extensive validation in a

Leon et al. (2009) utilized FT-MS on the same samples assessed by Levandi et al. (2008). This allowed coverage of 5500 mass signals of which approximately 1000 could be assigned an elemental composition. Those elemental compositions could be associated (through MasstTRIX) with specific metabolic pathways (KEGG); these associations are referred to as "isomeric hits". This approach would identify any differences in GM and non-GM metabolic profiles, especially where an elemental composition could be assigned, to be tentatively associated with biochemical differences. Overall, it was shown that a greater number of isomeric hits in pathways such as arachidonic acid metabolism, free amino acid metabolism, purine metabolism, and folate biosynthesis were associated with the GM samples. A list of 33 possible compounds that could distinguish the GM and non-GM varieties was generated, of which 12 could be confirmed in an orthogonal assay (CE-MS). The authors then indicated that only four of these could be considered as potential GM "biomarkers"; L-carnitine, apigenidin, 5, 6-dihydroxyindole, and one unidentified metabolite. There is little further literature on levels of these metabolites in maize and the association of these metabolites as GM biomarkers is almost certainly premature. Further, the interpretability of the Levandi et al. (2009) approach is not at all clear; there are fewer isomeric hits associated with inositol phosphate metabolism, yet levels of phytic acid have been well-established to be nearidentical in GM and non-GM maize. The association of isomeric hits for bile acid biosynthesis, which is not typically associated with plant metabolism, is also difficult to

In summary, different metabolic profiling platforms applied to similar biological questions will yield non-overlapping solutions. This is due to differential metabolite coverage (even within similar data acquisition technologies) and is compounded both by the number of unidentified signals observed in current metabolite profiles and, in some cases, "identification" of metabolites not previously known to be biosynthetically associated with

Predetermined criteria would need to be established for any study protocol, data acquisition steps or statistical analyses utilized in a safety assessment. As alluded to earlier, sampling from multiple replicated field sites would be required. Discussions on the number of replicates required to generate meaningful results from omics experiments are available as well as on the potential for bias and over-fitting (Broadhurst and Kell, 2006; Goodacre et al., 2007). Here we focus on the routine misinterpretation of "statistical significance" (Goodman, 2008) and the tendency to associate statistically significant differences between GM and a non-GM comparator as, minimally, an unintended effect, and often to imply the statistical

Regulatory environment.

interpret.

the plant species or genus in question.

**3. Equating statistical equivalence with biosafety**

difference raises a question about the safety of a newly evaluated crop.

As indicated earlier, compositional assessments of GM crops involve direct comparisons of levels of key nutrients and anti-nutrients in the new crop variety to those of a near-isogenic conventional comparator. Statistical evaluations of the compositional data have typically utilized classical frequentist significance testing. There are, however, several features of significance hypothesis testing that impact its application to compositional comparisons between crops with different agronomic qualities (Lecoutre, et al., 2001). Berger (1985), for example, stated, "We know from the beginning that the point null hypothesis is almost certainly not exactly true, and that this will always be confirmed by a large enough sample. What we are really interested in determining is whether or not the null hypothesis is approximately true." There are many factors that impact crop composition, *including agronomic traits we seek to modify through plant breeding*, (e.g. Scott et al., 2006; Uribelarrea et al., 2004; Dornbos and Mullen, 1992; Hymowitz et al., 1972; Wilcox and Shibles, 2001; Yin and Vyn, 2005) and any compositional changes that accompany enhanced agronomic quality may confound interpretation of results generated through significance testing.

Statistical significance is used only as a first step in comparative assessments. The interpretation of statistical significance from a p-value, *the probability of an observed result or a more extreme result occurring if the null hypothesis were true*, does not imply biological significance (Goodman, 2008). Statistically significant differences do not imply large differences between GM and conventional comparators or that these comparators can be easily distinguished from a biological perspective. In fact, the power of the experimental designs (multiple highly replicated field trials) adopted in current compositional assessments allows statistical significance to be assigned even where there are very small difference in mean values of a given component but where the distribution of component values overlap extensively. As such, significance approaches must be accompanied with further data analysis encompassing discussion of magnitudes of differences, assessments of component ranges, and the sensitivity of component values to environmental factors such as location. This is consistent with the recommendation by Codex Alimentarius (2008, Ch. 44) that "The statistical significance of any observed differences should be assessed in the context of the range of natural variations for that parameter to determine its biological significance." It is further consistent with observations of high variability in crop composition recorded in the scientific literature. The current scientific consensus is that, in most if not all cases, statistically significant differences between GM and near-isogenic conventional controls represent modest and nutritionally meaningless differences in magnitude. For example, a recent review of studies on GM crop composition showed that over 99% of all nutrient and antinutrients comparisons, where significant differences at the 5% level (=0.05) in mean values were observed, had a relative magnitude difference less than 20%. These differences are considerably less than the range of values attributable to germplasm and environmental factors (Harrigan et al., 2010).

Most metabolic profiling experiments utilize significance testing and Rischer and Oksman-Caldentey (2006) refer to unintended effects as "effects which represent a statistically significant difference (e.g. in chemical composition of the GM plant compared with a suitable non-GM plant)" although they acknowledge that such differences would have to be evaluated in the context of natural variability. One review that endorses the use of omics in safety assessments suggests that "the amount of variation from genetic engineering should be small (~3%)." (Heineman et al., 2011). Whilst this particular number is unrealistic since it

Challenges for Metabolomics as a Tool in Safety Assessments 343

of any "gaps" in our compositional knowledge that might compromise safety and in fact, our current understanding of plant anti-nutrients and toxicants, allows GM solutions to enhancing food safety (e.g. Sunilkumar et al., 2006). The last 25 years of research on GM plants and 15 years of commercial experience planting GM crops without harm or incident suggest that no difference in safety that would require further analysis exists between GM and crops bred by other strategies. All breeding induces genetic changes and these changes

We consider that metabolic profiling could increase its value in food safety science as well as

1. *Improved compositional analysis*. One potential target for future research could be to develop metabolic screening methods that afford a comprehensive compositional assessment in a single suite of determinations rapidly and at lower cost than traditional targeted analysis. It is known that the metabolites in a cell form a large, complex and interconnected network; one possible approach would be elucidation of key metabolic compound whose determination might provide insight into the global concentrations of numerous other metabolites. If such a validated analytical method could be developed it would great aid research and development and would be particularly valuable in assessments of nutritionally enhanced crops where changes in a specific pathway are sought. However, metabolomic technologies are not able to supply this kind of analysis

2. *Detection of novel toxicants*. Targeted analysis is inherently incapable of assessing levels of metabolites that are not selected (targeted) for analysis. Proponents of metabolic profiling have argued that profiling might detect the emergence of previously unknown novel toxicants presumably created by the breeding process. However, the abundance of a few macro-components (protein, fiber, carbohydrate, lipids) and numerous minor metabolites leaves little compositional "space" for novel toxicants. If wholly new molecules were created by the spontaneous evolution of a new pathway or pathways necessary for its biosynthesis, the chances that sufficient quantities would be present to exert an adverse effect are small indeed. Perhaps this is why such effects have not yet been observed by science or why coherent hypotheses as to how a novel toxicant would

be generated by a specific breeding process appear to be sparse in the literature. 3. *Detection of unintended effects*. Proponents of metabolic profiling often suggest that a profile itself may be an indicator that unintended changes had occurred. Methods to draw safety conclusions based on differences in metabolic profiles do not yet exist, and certainly as we have discussed above, no reason to assume that differences in profiles imply a safety concern; in fact, by any objective measure, there is no such technique as metabolomic profiling. What we have today is a series of distinct and emerging powerful scanning techniques each of which surveys a slightly different molecular landscape with variable degrees of resolution. Clearly, the number of metabolites present in crops is very large and the power of targeted metabolic profiling will become increasingly useful in analyzing the chemical complexity of prospective commercial

releases as they progress through initial research and development phases.

Metabolomics is an expanding and exciting field of research. The rapidly expanding scope of the metabolomic profiling technologies tempts us to test their applicability to a wide

give rise to transcriptomic, proteomic and metabolomic alterations.

in the development of nutritionally enhanced crops as follows;

and data.

falls well within the natural variability of metabolite levels and is even less than typical experimental error, setting a universal threshold for relative magnitude of differences as a trigger for further safety assessments of GM crops has been considered. In 2000, the Nordic Council of Ministers recommended that if a component in a GM crop differed from the conventional control by ±20% in relative magnitude, additional analyses of the GM crop were warranted (cited in Hothorn and Oberdoerfer, 2006). This concept was refined to account for the nutritional relevance of a component and the experimental precision of its measurement (Hothorn and Oberdoerfer, 2006). Threshold ranges for GM components were suggested as follows; 0.833-1.20 of the conventional control for "nutritionally very relevant" components (minerals, vitamins, anti-nutrients, bioactives, essential amino acids, and fatty acids), 0.769-1.30 for "relevant" (non-essential amino and fatty acids), and 0.667-1.50 for components of "less relevance" (proximates, fiber). Suggestions for the use of limits and triggers of this kind have been criticized for their failure to fully account for the role and contributions of the specific crop in the human diet; and with GM crops in particular since they are often not eaten as such but are used as a source of macronutrients such as oil, starch and protein (Chassy, 2008; Chassy, 2010). As noted previously, most plant foods in the human diet make significant contributions to the total intake of just a few macro- and micronutrients and therefore even large compositional changes in a single crop plant might produce little impact on the nutritional value of the overall diet. Chassy (2010) has observed that composition cannot be viewed in isolation since the composition of the diet is far more important than the composition of a single variety of a single crop. Strictly numerical approaches have not been adopted in compositional studies and there is no reason they would be relevant to profiling experiments.

At least one profiling study has attempted to apply statistical equivalence testing but again falls prey to the dubious association of equivalence with safety. Kusano et al. (2011) compared a GM-tomato (a miraculin protein expressor) to not only to the parental line but to a panel of conventional reference varieties. The statistical design (described by the authors as a proof-of-safety test) involved comparing the difference between test and control and the determining whether these differences fell within equivalence limits established by the reference varieties. However such a design makes more of a statement about the selection of the reference substances and the control to which the GM-trait is introgressed, and not about the effect of transgene insertion; the same test-to-control differences can be equivalent or non-equivalent contingent on whether a limited or diverse range of genotypes is available. The overall conclusion from the study however was that "miraculin overexpressors are remarkably similar to the control line".

In summary, there are no defined data analyses strategies currently being consistently applied to profiling data that would facilitate interpretability of data.

#### **4. Conclusion**

There are clearly divergent views about the utility of 'omics sciences in food safety assessments. This paper has discussed some of the reasons metabolic profiling technologies are, however, unlikely to provide immediately interpretable data in safety assessments that would otherwise enhance rigorously quantitative assessments of known nutrients and antinutrients that comprise foodstuffs. Indeed, it is not clear to the present authors that any new types of data are in fact necessary to judge GM or other foods as safe. We are also unaware

falls well within the natural variability of metabolite levels and is even less than typical experimental error, setting a universal threshold for relative magnitude of differences as a trigger for further safety assessments of GM crops has been considered. In 2000, the Nordic Council of Ministers recommended that if a component in a GM crop differed from the conventional control by ±20% in relative magnitude, additional analyses of the GM crop were warranted (cited in Hothorn and Oberdoerfer, 2006). This concept was refined to account for the nutritional relevance of a component and the experimental precision of its measurement (Hothorn and Oberdoerfer, 2006). Threshold ranges for GM components were suggested as follows; 0.833-1.20 of the conventional control for "nutritionally very relevant" components (minerals, vitamins, anti-nutrients, bioactives, essential amino acids, and fatty acids), 0.769-1.30 for "relevant" (non-essential amino and fatty acids), and 0.667-1.50 for components of "less relevance" (proximates, fiber). Suggestions for the use of limits and triggers of this kind have been criticized for their failure to fully account for the role and contributions of the specific crop in the human diet; and with GM crops in particular since they are often not eaten as such but are used as a source of macronutrients such as oil, starch and protein (Chassy, 2008; Chassy, 2010). As noted previously, most plant foods in the human diet make significant contributions to the total intake of just a few macro- and micronutrients and therefore even large compositional changes in a single crop plant might produce little impact on the nutritional value of the overall diet. Chassy (2010) has observed that composition cannot be viewed in isolation since the composition of the diet is far more important than the composition of a single variety of a single crop. Strictly numerical approaches have not been adopted in compositional studies and there is no reason they

At least one profiling study has attempted to apply statistical equivalence testing but again falls prey to the dubious association of equivalence with safety. Kusano et al. (2011) compared a GM-tomato (a miraculin protein expressor) to not only to the parental line but to a panel of conventional reference varieties. The statistical design (described by the authors as a proof-of-safety test) involved comparing the difference between test and control and the determining whether these differences fell within equivalence limits established by the reference varieties. However such a design makes more of a statement about the selection of the reference substances and the control to which the GM-trait is introgressed, and not about the effect of transgene insertion; the same test-to-control differences can be equivalent or non-equivalent contingent on whether a limited or diverse range of genotypes is available. The overall conclusion from the study however was that "miraculin over-

In summary, there are no defined data analyses strategies currently being consistently

There are clearly divergent views about the utility of 'omics sciences in food safety assessments. This paper has discussed some of the reasons metabolic profiling technologies are, however, unlikely to provide immediately interpretable data in safety assessments that would otherwise enhance rigorously quantitative assessments of known nutrients and antinutrients that comprise foodstuffs. Indeed, it is not clear to the present authors that any new types of data are in fact necessary to judge GM or other foods as safe. We are also unaware

would be relevant to profiling experiments.

expressors are remarkably similar to the control line".

**4. Conclusion**

applied to profiling data that would facilitate interpretability of data.

of any "gaps" in our compositional knowledge that might compromise safety and in fact, our current understanding of plant anti-nutrients and toxicants, allows GM solutions to enhancing food safety (e.g. Sunilkumar et al., 2006). The last 25 years of research on GM plants and 15 years of commercial experience planting GM crops without harm or incident suggest that no difference in safety that would require further analysis exists between GM and crops bred by other strategies. All breeding induces genetic changes and these changes give rise to transcriptomic, proteomic and metabolomic alterations.

We consider that metabolic profiling could increase its value in food safety science as well as in the development of nutritionally enhanced crops as follows;

Metabolomics is an expanding and exciting field of research. The rapidly expanding scope of the metabolomic profiling technologies tempts us to test their applicability to a wide

Challenges for Metabolomics as a Tool in Safety Assessments 345

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It seems clear to the present authors that there is no role for metabolic profiling in food safety assessment. We agree that modern targeted metabolic profiling technologies can rapidly identify pathway perturbations and, if judiciously applied and interpreted, might enhance food safety science, although traditional analytical methods can still be used to assess if changes in pathways and metabolite pools have occurred. If incorporated into the early selection stages of a prospective new trait targeted metabolic profiling may greatly aid in the selection of metabolites that need to be considered during the compositional phase of a risk assessment. To quote Larkin and Harrigan (2007) "However, it should be self-evident that GM crops ought not to be considered a single monolithic class that is either good or bad for the economy, agriculture or the environment. Each novel crop should be considered on its own merits and demerits. If we ever get to that point we will have achieved something positive out of the GM controversy." It is our hope that colleagues will take this as a challenge to further metabolic profiling in the advancement of food safety and nutritional enhancement of crops.

#### **5. Acknowledgements**

Figure 1 was prepared by Jay Harrison of the Statistics Technology Center, Monsanto Company.

#### **6. References**

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Figure 1 was prepared by Jay Harrison of the Statistics Technology Center, Monsanto

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Kusano, M., Redestig, H., Hirai, T., Oikawa, A., Matsuda, F., Fukushima, A., Arita, M.,

Watanabe, S., Yano, M., Hiwasa-Tanase, K., Ezura, H. & Saito, K. 2011. Covering chemical diversity of genetically-modified tomatoes using metabolomics for

plant-derived foods. Regul Toxicol Pharmacol, 50, 98-113.

approval of new biotech crops. Nat Biotechnol, 25, 509-11.

objective substantial equivalence assessment. PLoS, 6, e16989.

Jones, D.A. 1998. Why are so many food plants cyanogenic? Phytochem, 47, 155-62.

crops: an idea whose time has passed. Trends Biotechnol, 27, 555-7.

wild type wheat. Transgenic Res, 14, 887-905.

crops. Biotechnol Genet Eng Rev, 28, 1-18.

transgenesis. Nat Biotechnol, 28, 402-4.

varieties. Plant Mol Biol, 65, 645-54.

Sci U S A, 102, 1779-84.

Crop Evol, 55, 925-28

Genet, 119, 1069-83.

Food Chem, 55, 6177-85.

93.

analysis of gene expression profiles during grain development in transgenic and

Nguyen, H. T. & Sleper, D.A. 2009. Genetic control of soybean seed isoflavone content: importance of statistical model and epistasis in complex traits. Theor Appl

graphical analysis and Bayesian statistical approaches; case studies on transgenic

Macisaac, S., Halls, S. C., Orth, R., Smith, R. G., Wen, L., Brown, W. E., Welsch, M., Riley, R., Mcfarland, D., Pandravada, A. & Glenn, K. C. 2007. Impact of genetics and environment on nutritional and metabolite components of maize grain. J Agric

W. P. & Glenn, K. C. 2010. Natural variation in crop composition and the impact of

emerging gaps in the comparative risk assessment of GMOs. Environ Int, 37, 1285-

K. & Sautter, C. 2007. Flavonoid profiling among wild type and related GM wheat

S. 2005. Metabolic engineering of dhurrin in transgenic Arabidopsis plants with marginal inadvertent effects on the metabolome and transcriptome. Proc Natl Acad

**15**

*USA*

**Metabolomics Approach for Hazard**

**Environmental Chemicals**

Suryanarayana V. Vulimiri1, Brian Pachkowski2, Ambuja S. Bale1 and Babasaheb Sonawane1

**Identification in Human Health Assessment of**

Exposure to xenobiotics induces complex biochemical responses in mammalian cells resulting in several perturbations in cellular toxicity pathways. Within the context of systems biology, such biochemical perturbations can be studied individually using "omics" approaches such as toxicogenomics, transcriptomics, proteomics and metabolomics (Heijne et al., 2005). The objective of this chapter is to examine how the metabolomics approach can be used in identifying the risk posed by environmental chemicals to human health using selective examples of organ toxicity. Metabolomics is a medium-to-high throughput technique employing predominantly mass spectrometry (MS) and nuclear magnetic resonance (NMR) technology (Roux et al., 2011) for the identification and characterization of endogenous metabolites of low molecular weight (<1800 Da) arising from different biochemical pathways either as primary or secondary metabolites (Idle & Gonzalez, 2007). The sum total of all small metabolites is referred to as the "metabolome". Metabolomics has also been applied to the identification of low molecular weight, exogenous metabolites of xenobiotics (Roux et al., 2011; Rubino et al., 2009). With these capabilities, metabolomics represents a relatively quick and informative approach for assessing the physiological

Chemicals in the environment could pose potential risks to human health. In order to inform the assessment of risks from chemical exposures, the U.S. National Research Council (NRC) published a report entitled, "*Risk Assessment in the Federal Government: Managing the Process,"* more commonly known as the "Red Book" (NRC, 1983), which has been widely accepted and endorsed by the U.S. Environmental Protection Agency (U.S. EPA) and other federal agencies. This risk assessment process consists of four steps: hazard identification, doseresponse assessment, exposure assessment, and risk characterization. The focus herein is on hazard identification, which has been defined as "identification of the contaminants that are

**1. Introduction**

response to environmental chemicals.

**2. Human health risk assessment**

*1U.S. Environmental Protection Agency, Office of Research and Development,*

*2Oak Ridge Institute for Science and Education Postdoctoral Fellow, National Center for Environmental Assessment, Washington DC,*

## **Metabolomics Approach for Hazard Identification in Human Health Assessment of Environmental Chemicals**

Suryanarayana V. Vulimiri1, Brian Pachkowski2, Ambuja S. Bale1 and Babasaheb Sonawane1 *1U.S. Environmental Protection Agency, Office of Research and Development, 2Oak Ridge Institute for Science and Education Postdoctoral Fellow, National Center for Environmental Assessment, Washington DC, USA*

#### **1. Introduction**

348 Metabolomics

Ricroch, A. E., Berge, J. B. & Kuntz, M. 2011. Evaluation of genetically engineered crops

Rischer, H. & Oksman-Caldentey, K.-M. 2006. Unintended effects in genetically modified

Rotundo, J.L. & Westgate, M. E. 2009. Meta-analysis of environmental effects on soybean

Scott, M. P., Edwards, J. W., Bell, C. P., Schussler, J. R. & Smith, J. S. 2006. Grain composition

Seguin, P., Turcotte, P., Tremblay, G., Pageau, D., & Liu, W. 2009. Tocopherols concentration and stability in early maturing soybean genotypes. Agron. J, 101, 1153-59. Seguin, P., Tremblay, G., Pageau, D. & Liu, W. 2010. Soybean tocopherol concentrations are

Senti, F.R. & Rizek, R. R.1974. An overview of GRAS regulations and the effect from the

Skogerson, K., Harrigan, G. G., Reynolds, T. L., Halls, S. C., Ruebelt, M., Iandolino, A.,

Uribelarrea, M., Below, F. E. & Moose, S.P. 2004. Grain composition and productivity of

Yin, X.H. & Vyn, T. J. 2005. Relationships of isoflavone, oil, and protein in seed with yield of

Wakasa, K., Hasegawa, H., Nemoto, H., Matsuda, F., Miyazawa, H., Tozawa, Y., Morino, K.,

Wilson, R. F. 2004. Seed composition, In H.R. Boerma and J.E. Specht (ed.) Soybeans: Improvement, production and uses, 3rd ed. ASA, CSSA, SSA, Madison, WI. Zhou, J., Berman, K. H., Breeze, M. L., Nemeth, M. A., Oliveira, W. S., Braga, D. V., Berger,

Zhou, J., Harrigan, G. G., Berman, K. H., Webb, E. G., Klusmeyer, T. H. & Nemeth, M. A.

breeding germplasms. J Agric Food Chem, 59, 8822-28.

agronomic traits and seed metabolite profile. J Exp Bot, 57, 3069-78 . Wilcox, J.R. & Shibles, R. M. 2001. Interrelationships among seed quality attributes in

crops: revealed by metabolomics? Trends Biotechnol, 24, 102-4.

affected by crop management. J Agric Food Chem, 58, 5495-5501.

toxic gossypol. Proc Natl Acad Sci U S A, 103, 18054-59.

nitrogen supply. Crop Sci, 44, 1593-1600.

soybean. Agron J, 97, 1314-21.

soybean. Crop Sci, 41, 11-4.

Food Chem, 59, online

seed composition. Field Crops Res, 110, 147-56.

maize varieties. Maydica, 51, 417-23.

Physiol, 155, 1752-61.

Madison, WI.

using transcriptomic, proteomic, and metabolomic profiling techniques. Plant

and amino acid content in maize cultivars representing 80 Years of commercial

viewpoint of nutrition, In Hanson C. H. (ed) CSSA Special Publication. Vol. 5. SSA,

Pandravada, A., Glenn, K. C. & Fiehn, O. 2010. Impact of genetics and environment on the metabolite composition of maize grain. J Agric Food Chem, 58, 3600-10. Sunilkumar, G., Campbell, L. M., Puckhaber, L., Stipanovic, R. D. & Rathore, K. S. 2006.

Engineering cottonseed for use in human nutrition by tissue-specific reduction of

maize hybrids derived from the Illinois protein strains in response to variable

Komatsu, A., Yamada, T., Terakawa, T. & Miyagawa, H. 2006. High-level tryptophan accumulation in seeds of transgenic rice and its limited effects on

G. U. & Harrigan, G. G. 2011a. Compositional variability in conventional and glyphosate-tolerant soybean varieties grown in different regions in Brazil. J Agric

2011b. Stability of the compositional equivalence of grain from insect-protected corn and seed from herbicide-tolerant soybean over multiple seasons, locations and Exposure to xenobiotics induces complex biochemical responses in mammalian cells resulting in several perturbations in cellular toxicity pathways. Within the context of systems biology, such biochemical perturbations can be studied individually using "omics" approaches such as toxicogenomics, transcriptomics, proteomics and metabolomics (Heijne et al., 2005). The objective of this chapter is to examine how the metabolomics approach can be used in identifying the risk posed by environmental chemicals to human health using selective examples of organ toxicity. Metabolomics is a medium-to-high throughput technique employing predominantly mass spectrometry (MS) and nuclear magnetic resonance (NMR) technology (Roux et al., 2011) for the identification and characterization of endogenous metabolites of low molecular weight (<1800 Da) arising from different biochemical pathways either as primary or secondary metabolites (Idle & Gonzalez, 2007). The sum total of all small metabolites is referred to as the "metabolome". Metabolomics has also been applied to the identification of low molecular weight, exogenous metabolites of xenobiotics (Roux et al., 2011; Rubino et al., 2009). With these capabilities, metabolomics represents a relatively quick and informative approach for assessing the physiological response to environmental chemicals.

#### **2. Human health risk assessment**

Chemicals in the environment could pose potential risks to human health. In order to inform the assessment of risks from chemical exposures, the U.S. National Research Council (NRC) published a report entitled, "*Risk Assessment in the Federal Government: Managing the Process,"* more commonly known as the "Red Book" (NRC, 1983), which has been widely accepted and endorsed by the U.S. Environmental Protection Agency (U.S. EPA) and other federal agencies. This risk assessment process consists of four steps: hazard identification, doseresponse assessment, exposure assessment, and risk characterization. The focus herein is on hazard identification, which has been defined as "identification of the contaminants that are

Metabolomics Approach for Hazard Identification in

experimental animals to humans.

Human Health Assessment of Environmental Chemicals 351

EPA, 2005). *In vitro* systems (e.g. cell cultures) and *in vivo* models (e.g. experimental animals or human population studies) have identified several of the early key events such as oxidative stress, inflammation, genotoxicity, and cytotoxicity that occur from toxicant exposure. Since metabolomics measures biological response at the molecular level, this approach can identify the metabolites associated with the sequence of 'key events' and the processes inherent to the mechanism(s) of xenobiotic toxicity. The metabolomics approach could generate several mode(s) of action hypotheses using a nontargeted approach. The individual MOA hypothesis thus generated could be tested in targeted approaches (e.g. measuring glutathione reduction from oxidative stress) using more conventional assays. Metabolomics data could be used to further inform the mode(s) of action in experimental animals associated with carcinogenicity or with noncancer health outcomes, which may help to confirm the relevancy of the observations in

**4. Metabolomics approach in investigating environmental chemical exposure**  Environmental chemicals act through multiple toxicity pathways via a multitude of mechanisms (Guyton et al., 2009). To date, very limited toxicogenomics information has been applied to the field of risk assessment (Boverhof & Zacharewski, 2006; Mortensen & Euling, 2011). There is a paucity of relevant metabolomic information for application to human health risk assessment of environmental chemicals. To date, the available literature suggests an informative role of metabolomics in understanding the mode of action of

In general, human data are relatively sparse for many environmental chemicals with respect to both non-cancer and cancer health outcomes. Most human data are occupational, where exposure levels are generally higher than those encountered in the environment. In many cases, there are not any environmental or occupational human exposures that could be used to corroborate the animal data. In these cases, animal data are generally used to develop non-cancer and cancer risk values for human health assessments thereby raising issues of uncertainty associated with interspecies extrapolation. Metabolomic data could be used to fill in such data gaps. For example, *in vitro* metabolomic assays with human cells may be developed to compare with animal metabolomic profiles to determine if there are potentially similar mechanisms of toxicity or for identifying toxicity pathways. As a result, this characterization of biochemical mechanisms of toxicity would inform hazard

**5. Ability of metabolomics to differentiate gender, phenotypic, and genetic**

extrapolation between species as well as within species (i.e. human) variability.

Since metabolomics analyzes endogenous and exogenous (xenobiotic-derived) low molecular weight metabolites, this approach has been applied to the differentiation of metabolic profiles between phenotypes and genotypes. As briefly discussed below, metabolomics has the ability to inform gender, genetic, and phenotypic differences as well as organ-specific effects. For understanding the toxicity of environmental agents, the utility of such information would clarify toxicodynamic uncertainty associated with the

environmental xenobiotics (Vulimiri et al., 2011; Vulimiri et al., 2009).

identification for use in the human health risk assessment process.

**differences, and organ-specific effects**

suspected to pose health hazards, quantification of the concentrations at which they are present in the environment, a description of the specific forms of toxicity (i.e. neurotoxicity, carcinogenicity, etc.) that can be caused by the contaminants of concern, and an evaluation of the conditions under which these forms of toxicity might be expressed in exposed humans" (NRC, 1994).

For human health assessment of chemicals, non-cancer or cancer risk values are derived based on the selection of a critical endpoint of toxicity or several endpoints (e.g. biochemical, pathological, physiological, and behavioral abnormalities) of adverse health outcomes. Uncertainty factors are applied to the lowest dose associated with the critical health outcome(s) in order to derive the resulting exposure level for non-cancer toxicity. These uncertainty factors attempt to account for exposure duration, pharmacokinetic, and pharmacodynamic data gaps associated with inter- and intra-species extrapolation.

The U.S. EPA and the International Agency for Research on Cancer (IARC) evaluate the evidence for carcinogenesis in humans from epidemiological, experimental animal, and mechanistic data to determine the qualitative cancer classification for humans. In addition, the U.S. EPA evaluates exposure-response relationships and develops quantitative cancer risk values based on the observed tumors that correspond to a unit exposure (U.S. EPA, 2005). Uncertainties with cancer risk values are presented and are generally associated with the mode of action (MOA) for carcinogenicity.

One of the major concerns with cancer risk assessment is false-positive animal tumor findings. Having an understanding of the mechanism(s) leading to carcinogenicity would help in developing a better perspective of whether a carcinogen in experimental animals is likely to be a carcinogen in humans. For example, correlating a metabolomic profile of a suspected carcinogen between human exposures (environmental or occupational) and experimental animal exposure studies would be highly useful. If similar biochemical markers were to appear across the human and animal metabolomic profiles, that information would help in informing similarities or differences in interspecies mechanisms. Further, if the chemical was demonstrated to be a carcinogen in animals through a traditional two-year animal bioassay, but there was inconclusive epidemiological evidence, the similarity in metabolomic data could be used along with other mechanistic data (e.g. mutagenicity/genotoxicity assays, cell proliferation findings, oxidative stress, epigenetics, etc.) to support or refute human carcinogenicity. In this regard, metabolomics information could be used to support mechanistic data to augment the animal and human findings.

#### **3. The potential of "omics" data to inform mode of action of environmental chemicals**

In developing a human health evaluation for environmental chemical hazard identification, it is ideal to have information on the key mechanistic events leading to an adverse health outcome. In this regard, mode of action (MOA) is an important part of hazard identification. MOA can be defined as "a sequence of key events and processes, starting with interaction of an agent with a cell, proceeding through operational and anatomical changes, and resulting in cancer formation" (U.S. EPA, 2005). A 'key event' is defined as "an empirically observable precursor which is by itself a necessary component of the MOA or is a biologically-based measurable marker for such a component" (U.S.

suspected to pose health hazards, quantification of the concentrations at which they are present in the environment, a description of the specific forms of toxicity (i.e. neurotoxicity, carcinogenicity, etc.) that can be caused by the contaminants of concern, and an evaluation of the conditions under which these forms of toxicity might be expressed in exposed

For human health assessment of chemicals, non-cancer or cancer risk values are derived based on the selection of a critical endpoint of toxicity or several endpoints (e.g. biochemical, pathological, physiological, and behavioral abnormalities) of adverse health outcomes. Uncertainty factors are applied to the lowest dose associated with the critical health outcome(s) in order to derive the resulting exposure level for non-cancer toxicity. These uncertainty factors attempt to account for exposure duration, pharmacokinetic, and

The U.S. EPA and the International Agency for Research on Cancer (IARC) evaluate the evidence for carcinogenesis in humans from epidemiological, experimental animal, and mechanistic data to determine the qualitative cancer classification for humans. In addition, the U.S. EPA evaluates exposure-response relationships and develops quantitative cancer risk values based on the observed tumors that correspond to a unit exposure (U.S. EPA, 2005). Uncertainties with cancer risk values are presented and are generally associated with

One of the major concerns with cancer risk assessment is false-positive animal tumor findings. Having an understanding of the mechanism(s) leading to carcinogenicity would help in developing a better perspective of whether a carcinogen in experimental animals is likely to be a carcinogen in humans. For example, correlating a metabolomic profile of a suspected carcinogen between human exposures (environmental or occupational) and experimental animal exposure studies would be highly useful. If similar biochemical markers were to appear across the human and animal metabolomic profiles, that information would help in informing similarities or differences in interspecies mechanisms. Further, if the chemical was demonstrated to be a carcinogen in animals through a traditional two-year animal bioassay, but there was inconclusive epidemiological evidence, the similarity in metabolomic data could be used along with other mechanistic data (e.g. mutagenicity/genotoxicity assays, cell proliferation findings, oxidative stress, epigenetics, etc.) to support or refute human carcinogenicity. In this regard, metabolomics information could be used to support mechanistic data to augment the animal and human findings.

**3. The potential of "omics" data to inform mode of action of environmental**

In developing a human health evaluation for environmental chemical hazard identification, it is ideal to have information on the key mechanistic events leading to an adverse health outcome. In this regard, mode of action (MOA) is an important part of hazard identification. MOA can be defined as "a sequence of key events and processes, starting with interaction of an agent with a cell, proceeding through operational and anatomical changes, and resulting in cancer formation" (U.S. EPA, 2005). A 'key event' is defined as "an empirically observable precursor which is by itself a necessary component of the MOA or is a biologically-based measurable marker for such a component" (U.S.

pharmacodynamic data gaps associated with inter- and intra-species extrapolation.

humans" (NRC, 1994).

**chemicals**

the mode of action (MOA) for carcinogenicity.

EPA, 2005). *In vitro* systems (e.g. cell cultures) and *in vivo* models (e.g. experimental animals or human population studies) have identified several of the early key events such as oxidative stress, inflammation, genotoxicity, and cytotoxicity that occur from toxicant exposure. Since metabolomics measures biological response at the molecular level, this approach can identify the metabolites associated with the sequence of 'key events' and the processes inherent to the mechanism(s) of xenobiotic toxicity. The metabolomics approach could generate several mode(s) of action hypotheses using a nontargeted approach. The individual MOA hypothesis thus generated could be tested in targeted approaches (e.g. measuring glutathione reduction from oxidative stress) using more conventional assays. Metabolomics data could be used to further inform the mode(s) of action in experimental animals associated with carcinogenicity or with noncancer health outcomes, which may help to confirm the relevancy of the observations in experimental animals to humans.

#### **4. Metabolomics approach in investigating environmental chemical exposure**

Environmental chemicals act through multiple toxicity pathways via a multitude of mechanisms (Guyton et al., 2009). To date, very limited toxicogenomics information has been applied to the field of risk assessment (Boverhof & Zacharewski, 2006; Mortensen & Euling, 2011). There is a paucity of relevant metabolomic information for application to human health risk assessment of environmental chemicals. To date, the available literature suggests an informative role of metabolomics in understanding the mode of action of environmental xenobiotics (Vulimiri et al., 2011; Vulimiri et al., 2009).

In general, human data are relatively sparse for many environmental chemicals with respect to both non-cancer and cancer health outcomes. Most human data are occupational, where exposure levels are generally higher than those encountered in the environment. In many cases, there are not any environmental or occupational human exposures that could be used to corroborate the animal data. In these cases, animal data are generally used to develop non-cancer and cancer risk values for human health assessments thereby raising issues of uncertainty associated with interspecies extrapolation. Metabolomic data could be used to fill in such data gaps. For example, *in vitro* metabolomic assays with human cells may be developed to compare with animal metabolomic profiles to determine if there are potentially similar mechanisms of toxicity or for identifying toxicity pathways. As a result, this characterization of biochemical mechanisms of toxicity would inform hazard identification for use in the human health risk assessment process.

#### **5. Ability of metabolomics to differentiate gender, phenotypic, and genetic differences, and organ-specific effects**

Since metabolomics analyzes endogenous and exogenous (xenobiotic-derived) low molecular weight metabolites, this approach has been applied to the differentiation of metabolic profiles between phenotypes and genotypes. As briefly discussed below, metabolomics has the ability to inform gender, genetic, and phenotypic differences as well as organ-specific effects. For understanding the toxicity of environmental agents, the utility of such information would clarify toxicodynamic uncertainty associated with the extrapolation between species as well as within species (i.e. human) variability.

Metabolomics Approach for Hazard Identification in

**5.3.1 Hepatotoxicity (Carbon tetrachloride)**

mechanisms of toxic response.

many are nonspecific for the selected organ toxicities.

base-pair substitutions (Barbin, 2000; Chung et al., 1999).

that can be detected using a metabolomic approach (Wang et al., 2009).

Human Health Assessment of Environmental Chemicals 353

chemicals. The extent of the metabolomic datasets varies for each type of organ toxicity; however, discussion focuses on how metabolomic investigations have contributed to some understanding of mechanisms of toxicity. Some of these metabolic changes are specific but

Carbon tetrachloride (CCl4) is a well-studied hepatotoxicant and carcinogen that has been used as a fumigant (Manibusan et al., 2007). The mechanisms underlying CCl4 toxic responses are fairly well understood. In rats, CCl4 exposure results in mild to severe centrilobular necrosis of the liver with elevation of serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities suggestive of hepatotoxicity. In addition, CCl4 induces lipid peroxidation, oxidative stress, and regenerative proliferation, eventually leading to hepatocarcinogenesis. The following brief discussion focuses on how metabolomics has been used to identify metabolites associated with these known

Metabolomic analyses have identified several aldehydes (e.g. formaldehyde, acetaldehyde, propanal, butanal, pentanal, hexanal and malondialdehyde) excreted in urine (De Zwart et al., 1997) or in exhaled breath (Gee et al., 1981) from rodents dosed with CCl4. Similarly, short-chain hydrocarbons (e.g. pentane and ethane) generated during lipid peroxidation have been reported in the breath of humans suggestive of CCl4-induced oxidative stress (Hwang & Kim, 2007). These reactive aldehydes arising from lipid peroxidation have been shown to be quenched by reduced glutathione (GSH), causing GSH depletion which leads to cirrhosis in the liver of rats (Cabre et al., 2000). Further, GSH depletion causes a perturbation of prooxidant-antioxidant balance eventually leading to oxidative stress (Ichi et al., 2009). Metabolomic studies have provided support that oxidative stress induced by CCl4 generates reactive oxygen species (ROS) capable of causing DNA damage as shown by an increase in 8-oxodeoxyguanosine (8-oxodG) and malondialdehyde-deoxyguanosine (M1G) adducts (Beddowes et al., 2003). Also, lipid peroxidation products, such as 4-hydroxynonenal, can form etheno adducts, which are capable of inducing mutations in critical genes through

Further, CCl4-induced oxidative stress inhibits energy metabolism as observed by changes in levels of Krebs cycle intermediates (i.e. citric acid, 2-oxoglutarate, and succinate), which has an overall effect on glutathione metabolism eventually leading to hepatotoxicity (Huang et al., 2008; Kim et al., 2008; Lin et al., 2009; Robertson et al., 2000). These earlier cellular perturbations further affect Ca+2 homeostasis, cause protein and phospholipid degradation, and potentially lead to cytotoxicity (Manibusan et al., 2007), apoptosis and necrosis of the liver tissue (Sun et al., 2001). Furthermore, membrane phospholipid degradation leads to the release of high levels of *o*-phosphoethanolamine, a well-known biomarker of cytotoxicity

CCl4 toxicity also leads to an inflammatory response characterized by the release of arachidonic acid, which is the precursor for a cascade of events leading to prostaglandin and leukotriene synthesis involving cyclooxygenases and lipoxygenases, respectively (Basu, 2003), and other newer inflammatory biomarkers, such as resolvins, protectins, and maresins (Liu et al., 2009; Serhan, 2009). Following cytotoxicity, liver tissue undergoes

#### **5.1 Gender differences**

Metabolomics approach can differentiate between gender-dependent differences in the metabolic profiles of untreated or control experimental animals. In a study aimed at the identification of novel biomarkers of effect using chemicals with diverse modes of action, one research group (van Ravenzwaay et al., 2007) pooled the metabolomic data (i.e. plasma profiles) from eleven individual experiments involving 670 male and female control Wistar rats over a span of one year. After principal component analysis (PCA), the authors found that the metabolic profiles were clustered into separate groups for males and females suggesting a stable metabolome of the control rats during the study period. This observation highlights the ability of metabolomics to potentially identify unique gender responses to chemical exposure.

#### **5.2 Genetic differences and phenotypic effects**

The metabolomics approach can be further used for studying the relationship between the genotype and phenotype of the organism. Genetic polymorphisms in human genes are known to modify exposure to environmental health hazards and are a source of uncertainty when assessing risk from environmental chemicals (Ginsberg et al., 2009; Kelada et al., 2003). Genetic differences have been shown to reflect changes in the metabolite profiles of individuals. In a human population, genetically determined variants (e.g. those associated with fatty acid metabolism) in metabolic phenotype (metabotype) have been identified by simultaneously detecting single-nucleotide polymorphisms (SNPs) in a genome-wide association study (GWAS) and endogenous serum metabolites (Gieger et al., 2008).

This is also evident in the field of functional genomics where a change in phenotype is observed due to gene-related alterations (reverse-genetics) such as deletions or insertions leading to silent mutations as in yeasts (Raamsdonk et al., 2001). Specific gene mutations can also be evaluated using metabolic footprinting (Szeto et al., 2010). Such metabolomic data could provide basic information regarding gene product function, particularly in the context of environmental exposure.

Also, the genotypes of animals, as in genetically manipulated animal models (e.g. gene knockout and transgenic mice), have been used effectively for understanding the metabolism of toxicants mediated by cytochrome P450 (CYP) isozymes, in order to further elucidate mechanisms of toxicity. For example, MS-based approaches were able to distinguish between the metabolic profiles for *Cyp2e1*-null, CYP1A2-humanized, and wildtype mice after exposure to the ubiquitous dietary carcinogen 2-amino-1-methyl-6 phenylimidazo[4,5-b]pyridine (Chen et al., 2007) or the hepatotoxic agent acetaminophen (Chen et al., 2008). As a result, the metabolomics approach could be used to identify mechanistic changes stemming from genetic differences.

#### **5.3 Organ-specific effects**

Metabolomics approach has been utilized in identifying specific profiles that are altered in different organs in response to toxicant injury. The following represents a brief discussion of three select organ toxicities associated with exposure to a given chemical or mixture of

Metabolomics approach can differentiate between gender-dependent differences in the metabolic profiles of untreated or control experimental animals. In a study aimed at the identification of novel biomarkers of effect using chemicals with diverse modes of action, one research group (van Ravenzwaay et al., 2007) pooled the metabolomic data (i.e. plasma profiles) from eleven individual experiments involving 670 male and female control Wistar rats over a span of one year. After principal component analysis (PCA), the authors found that the metabolic profiles were clustered into separate groups for males and females suggesting a stable metabolome of the control rats during the study period. This observation highlights the ability of metabolomics to potentially identify unique gender responses to

The metabolomics approach can be further used for studying the relationship between the genotype and phenotype of the organism. Genetic polymorphisms in human genes are known to modify exposure to environmental health hazards and are a source of uncertainty when assessing risk from environmental chemicals (Ginsberg et al., 2009; Kelada et al., 2003). Genetic differences have been shown to reflect changes in the metabolite profiles of individuals. In a human population, genetically determined variants (e.g. those associated with fatty acid metabolism) in metabolic phenotype (metabotype) have been identified by simultaneously detecting single-nucleotide polymorphisms (SNPs) in a genome-wide association study (GWAS) and endogenous serum metabolites

This is also evident in the field of functional genomics where a change in phenotype is observed due to gene-related alterations (reverse-genetics) such as deletions or insertions leading to silent mutations as in yeasts (Raamsdonk et al., 2001). Specific gene mutations can also be evaluated using metabolic footprinting (Szeto et al., 2010). Such metabolomic data could provide basic information regarding gene product function, particularly in the context

Also, the genotypes of animals, as in genetically manipulated animal models (e.g. gene knockout and transgenic mice), have been used effectively for understanding the metabolism of toxicants mediated by cytochrome P450 (CYP) isozymes, in order to further elucidate mechanisms of toxicity. For example, MS-based approaches were able to distinguish between the metabolic profiles for *Cyp2e1*-null, CYP1A2-humanized, and wildtype mice after exposure to the ubiquitous dietary carcinogen 2-amino-1-methyl-6 phenylimidazo[4,5-b]pyridine (Chen et al., 2007) or the hepatotoxic agent acetaminophen (Chen et al., 2008). As a result, the metabolomics approach could be used to identify

Metabolomics approach has been utilized in identifying specific profiles that are altered in different organs in response to toxicant injury. The following represents a brief discussion of three select organ toxicities associated with exposure to a given chemical or mixture of

**5.1 Gender differences**

chemical exposure.

(Gieger et al., 2008).

of environmental exposure.

**5.3 Organ-specific effects**

**5.2 Genetic differences and phenotypic effects**

mechanistic changes stemming from genetic differences.

chemicals. The extent of the metabolomic datasets varies for each type of organ toxicity; however, discussion focuses on how metabolomic investigations have contributed to some understanding of mechanisms of toxicity. Some of these metabolic changes are specific but many are nonspecific for the selected organ toxicities.

#### **5.3.1 Hepatotoxicity (Carbon tetrachloride)**

Carbon tetrachloride (CCl4) is a well-studied hepatotoxicant and carcinogen that has been used as a fumigant (Manibusan et al., 2007). The mechanisms underlying CCl4 toxic responses are fairly well understood. In rats, CCl4 exposure results in mild to severe centrilobular necrosis of the liver with elevation of serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities suggestive of hepatotoxicity. In addition, CCl4 induces lipid peroxidation, oxidative stress, and regenerative proliferation, eventually leading to hepatocarcinogenesis. The following brief discussion focuses on how metabolomics has been used to identify metabolites associated with these known mechanisms of toxic response.

Metabolomic analyses have identified several aldehydes (e.g. formaldehyde, acetaldehyde, propanal, butanal, pentanal, hexanal and malondialdehyde) excreted in urine (De Zwart et al., 1997) or in exhaled breath (Gee et al., 1981) from rodents dosed with CCl4. Similarly, short-chain hydrocarbons (e.g. pentane and ethane) generated during lipid peroxidation have been reported in the breath of humans suggestive of CCl4-induced oxidative stress (Hwang & Kim, 2007). These reactive aldehydes arising from lipid peroxidation have been shown to be quenched by reduced glutathione (GSH), causing GSH depletion which leads to cirrhosis in the liver of rats (Cabre et al., 2000). Further, GSH depletion causes a perturbation of prooxidant-antioxidant balance eventually leading to oxidative stress (Ichi et al., 2009). Metabolomic studies have provided support that oxidative stress induced by CCl4 generates reactive oxygen species (ROS) capable of causing DNA damage as shown by an increase in 8-oxodeoxyguanosine (8-oxodG) and malondialdehyde-deoxyguanosine (M1G) adducts (Beddowes et al., 2003). Also, lipid peroxidation products, such as 4-hydroxynonenal, can form etheno adducts, which are capable of inducing mutations in critical genes through base-pair substitutions (Barbin, 2000; Chung et al., 1999).

Further, CCl4-induced oxidative stress inhibits energy metabolism as observed by changes in levels of Krebs cycle intermediates (i.e. citric acid, 2-oxoglutarate, and succinate), which has an overall effect on glutathione metabolism eventually leading to hepatotoxicity (Huang et al., 2008; Kim et al., 2008; Lin et al., 2009; Robertson et al., 2000). These earlier cellular perturbations further affect Ca+2 homeostasis, cause protein and phospholipid degradation, and potentially lead to cytotoxicity (Manibusan et al., 2007), apoptosis and necrosis of the liver tissue (Sun et al., 2001). Furthermore, membrane phospholipid degradation leads to the release of high levels of *o*-phosphoethanolamine, a well-known biomarker of cytotoxicity that can be detected using a metabolomic approach (Wang et al., 2009).

CCl4 toxicity also leads to an inflammatory response characterized by the release of arachidonic acid, which is the precursor for a cascade of events leading to prostaglandin and leukotriene synthesis involving cyclooxygenases and lipoxygenases, respectively (Basu, 2003), and other newer inflammatory biomarkers, such as resolvins, protectins, and maresins (Liu et al., 2009; Serhan, 2009). Following cytotoxicity, liver tissue undergoes

Metabolomics Approach for Hazard Identification in

gliosis.

uncertainty factors.

and cellular cofactors.

**5.3.3 Pulmonary toxicity (Cigarette smoke)**

Human Health Assessment of Environmental Chemicals 355

were used for this analysis. The identified putative biomarkers were γ-aminobutyric acid (GABA), choline, glutamine, creatine, and spermine. The authors were able to speculate potential mechanisms of methylmercury from results of traditional biochemical assays (Eskes et al., 2002; Monnet-Tschudi et al., 1996) where the enzymatic activities of neuronal enzymes including glutamine synthesis, choline acetyltransferase, and glutamic acid decarboxylase were reported to be significantly decreased. In essence, the decreased levels of GABA and choline in the *in vitro* metabolomic study may help to explain these decreases in enzymatic activity which then leads to neuron-specific toxicity. This information correlates to the observed increases in apoptotic cell death associated with methylmercury. Similarly, the increased creatine levels that were reported from methylmercury treatment were correlated to gliosis (proliferation of astrocytes in the central nervous system) by the authors. Creatine is generally linked to brain osmoregulation (Bothwell et al., 2001) and increased creatine levels would lead to increased activity of glial cells that could result in

Thus, a very limited metabolomic dataset for methylmercury adds to the mechanistic profile of this compound and helps establish a temporality of the key events leading to cytotoxicity in the brain. In the case of using the metabolomic data for methylmercury, the observed changes in the neuronal metabolites provide supportive, early evidence of later stage events leading to brain cytotoxicity. Metabolomic data from *in vivo* studies or from incidental human exposure would demonstrate any pharmacodynamic differences and if so, how such differences could be quantified through the available data rather than the default use of

Cigarette smoking is one of the major etiological reasons for pulmonary toxicity and lung cancer. Cigarette smoke contain at least 4000 chemicals from different chemical classes, of which at least 60 are well-established carcinogens in experimental animals (Hecht, 2006). Cigarette smoke has been shown to induce oxidative stress and an inflammatory response in lungs, and chronic exposure is known to cause cancer. While cigarette smoke is most deleterious to the smoker, environmental or passive exposure to smoke (i.e. second-hand smoke) can lead to adverse health effects to bystanders (U.S. EPA, 1992). Using the metabolomics approach in A549 human lung epithelial cells it has been shown that several biochemical pathways are altered by either the whole smoke (WS) or its component phases i.e. wet total particulate matter (WTPM) or gas/vapor phase (GVP) (Vulimiri et al., 2009). Exposures to the different phases lead to unique biochemical alterations in A549 cells. For example, WTPM exposure resulted in changes in the Krebs cycle and urea cycle metabolites, whereas GVP exposure resulted in alterations in hexosamines and in lipid metabolism. These investigators found that exposure to cigarette smoke was associated with perturbations in the metabolism of glutathione, amino acids, lipids, and nucleotides. Alterations were also seen in the urea cycle, Krebs cycle, and the production of polyamines

Oxidative stress is an important consequence associated with cigarette smoke. The oxidants produced or found in cigarette smoke can react with and damage cellular macromolecules including proteins, DNA, and lipid membranes. Additionally, these oxidants can act as signaling molecules that can influence cell proliferation (Faux et al., 2009). However, cells

regenerative proliferation and activation of the urea cycle characterized by polyamine biosynthesis, the latter being a hallmark of cellular proliferation and differentiation (Heby, 1981). Detection of increased levels of urea cycle metabolites such as putrescine, ornithine, spermine, and spermidine (Pegg et al., 1981) in CCl4–exposed rats by metabolomics approach gives an early indication of regenerative cell proliferation, which has been detected at a later stage of disease progression using conventional toxicity assays.

Overall, metabolomics studies have identified important biomarkers for CCl4–induced toxicity including: aldehydes (lipid peroxidation); GSH/GSSG ratio (oxidative stress); 8 oxodG, M1G, and etheno adducts (genotoxicity); *o*-phosphoethanolamine (cytotoxicity); arachidonic acid and prostaglandins (inflammation); and polyamines (regenerative cell proliferation), which are also observed in conventional assays (Vulimiri et al., 2011). This brief discussion further supports the use of metabolomics for investigating toxicity associated with environmentally related chemicals.

#### **5.3.2 Neurotoxicity (Methylmercury)**

Methylmercury is a chemical that was originally used as a fungicide for agricultural purposes in the early 20th century. In one of the earliest studies of methylmercury toxicity, occupational exposure to this chemical resulted in detriments in neurological functions in factory workers and changes in brain morphology with specificity to the cerebellar area. Additionally, in one of the most noted disasters in the 20th century, methylmercury exposure through consumption of fish was determined to be the causative agent for producing neurological deficits in fishermen and neurodevelopmental abnormalities in children in Minamata Bay, Japan (Ekino et al., 2007).

Traditional approaches in hazard characterization have concluded that the most sensitive (critical) effect observed from methylmercury exposure is neurobehavioral changes in children [as reviewed in (U.S. EPA, 2005) and Agency for Toxic Substances and Disease Registry (ATSDR, 1999)]. An uncertainty that was highlighted in both of these human health assessment evaluations was that of pharmacodynamic variability in the human population. One approach to address pharmacodynamic variability is to have an understanding of the key mechanism(s) that results in the sensitive effect. There have been several reviews on the neurological mechanisms associated with methylmercury (Ekino et al., 2007; Myers et al., 2009). One of the primary mechanisms associated with methylmercury neurotoxicity in both the adult and developing brain is cytotoxicity. Methylmercury has been demonstrated to induce oxidative stress in the nervous system. Additionally, *in vitro* studies have reported that methylmercury treatment results in apoptosis in glial cells, cerebellar granule cells, and in neuronal cell lines such as differentiated PC12 cells and neuroblastoma cells. Methylmercury exposure in rats has also resulted in apoptosis in the cerebellum as well as loss of astrocytes in monkeys. The cell death pathway for methylmercury has been highly characterized as reviewed recently (Ceccatelli et al., 2010). In summary, several studies have demonstrated that methylmercury induces caspase-dependent apoptosis in primary neuronal cell cultures and cell lines.

Metabolomic analysis for methylmercury was conducted using an *in vitro* methodology proposed by the European Commission Joint Research Centre in 2008 (van Vliet et al., 2008). Dynamic reaggregating neuronal cultures prepared from 16-day old rat fetal telencephalon

regenerative proliferation and activation of the urea cycle characterized by polyamine biosynthesis, the latter being a hallmark of cellular proliferation and differentiation (Heby, 1981). Detection of increased levels of urea cycle metabolites such as putrescine, ornithine, spermine, and spermidine (Pegg et al., 1981) in CCl4–exposed rats by metabolomics approach gives an early indication of regenerative cell proliferation, which has been

Overall, metabolomics studies have identified important biomarkers for CCl4–induced toxicity including: aldehydes (lipid peroxidation); GSH/GSSG ratio (oxidative stress); 8 oxodG, M1G, and etheno adducts (genotoxicity); *o*-phosphoethanolamine (cytotoxicity); arachidonic acid and prostaglandins (inflammation); and polyamines (regenerative cell proliferation), which are also observed in conventional assays (Vulimiri et al., 2011). This brief discussion further supports the use of metabolomics for investigating toxicity

Methylmercury is a chemical that was originally used as a fungicide for agricultural purposes in the early 20th century. In one of the earliest studies of methylmercury toxicity, occupational exposure to this chemical resulted in detriments in neurological functions in factory workers and changes in brain morphology with specificity to the cerebellar area. Additionally, in one of the most noted disasters in the 20th century, methylmercury exposure through consumption of fish was determined to be the causative agent for producing neurological deficits in fishermen and neurodevelopmental abnormalities in children in

Traditional approaches in hazard characterization have concluded that the most sensitive (critical) effect observed from methylmercury exposure is neurobehavioral changes in children [as reviewed in (U.S. EPA, 2005) and Agency for Toxic Substances and Disease Registry (ATSDR, 1999)]. An uncertainty that was highlighted in both of these human health assessment evaluations was that of pharmacodynamic variability in the human population. One approach to address pharmacodynamic variability is to have an understanding of the key mechanism(s) that results in the sensitive effect. There have been several reviews on the neurological mechanisms associated with methylmercury (Ekino et al., 2007; Myers et al., 2009). One of the primary mechanisms associated with methylmercury neurotoxicity in both the adult and developing brain is cytotoxicity. Methylmercury has been demonstrated to induce oxidative stress in the nervous system. Additionally, *in vitro* studies have reported that methylmercury treatment results in apoptosis in glial cells, cerebellar granule cells, and in neuronal cell lines such as differentiated PC12 cells and neuroblastoma cells. Methylmercury exposure in rats has also resulted in apoptosis in the cerebellum as well as loss of astrocytes in monkeys. The cell death pathway for methylmercury has been highly characterized as reviewed recently (Ceccatelli et al., 2010). In summary, several studies have demonstrated that methylmercury induces caspase-dependent apoptosis in primary

Metabolomic analysis for methylmercury was conducted using an *in vitro* methodology proposed by the European Commission Joint Research Centre in 2008 (van Vliet et al., 2008). Dynamic reaggregating neuronal cultures prepared from 16-day old rat fetal telencephalon

detected at a later stage of disease progression using conventional toxicity assays.

associated with environmentally related chemicals.

**5.3.2 Neurotoxicity (Methylmercury)**

Minamata Bay, Japan (Ekino et al., 2007).

neuronal cell cultures and cell lines.

were used for this analysis. The identified putative biomarkers were γ-aminobutyric acid (GABA), choline, glutamine, creatine, and spermine. The authors were able to speculate potential mechanisms of methylmercury from results of traditional biochemical assays (Eskes et al., 2002; Monnet-Tschudi et al., 1996) where the enzymatic activities of neuronal enzymes including glutamine synthesis, choline acetyltransferase, and glutamic acid decarboxylase were reported to be significantly decreased. In essence, the decreased levels of GABA and choline in the *in vitro* metabolomic study may help to explain these decreases in enzymatic activity which then leads to neuron-specific toxicity. This information correlates to the observed increases in apoptotic cell death associated with methylmercury. Similarly, the increased creatine levels that were reported from methylmercury treatment were correlated to gliosis (proliferation of astrocytes in the central nervous system) by the authors. Creatine is generally linked to brain osmoregulation (Bothwell et al., 2001) and increased creatine levels would lead to increased activity of glial cells that could result in gliosis.

Thus, a very limited metabolomic dataset for methylmercury adds to the mechanistic profile of this compound and helps establish a temporality of the key events leading to cytotoxicity in the brain. In the case of using the metabolomic data for methylmercury, the observed changes in the neuronal metabolites provide supportive, early evidence of later stage events leading to brain cytotoxicity. Metabolomic data from *in vivo* studies or from incidental human exposure would demonstrate any pharmacodynamic differences and if so, how such differences could be quantified through the available data rather than the default use of uncertainty factors.

#### **5.3.3 Pulmonary toxicity (Cigarette smoke)**

Cigarette smoking is one of the major etiological reasons for pulmonary toxicity and lung cancer. Cigarette smoke contain at least 4000 chemicals from different chemical classes, of which at least 60 are well-established carcinogens in experimental animals (Hecht, 2006). Cigarette smoke has been shown to induce oxidative stress and an inflammatory response in lungs, and chronic exposure is known to cause cancer. While cigarette smoke is most deleterious to the smoker, environmental or passive exposure to smoke (i.e. second-hand smoke) can lead to adverse health effects to bystanders (U.S. EPA, 1992). Using the metabolomics approach in A549 human lung epithelial cells it has been shown that several biochemical pathways are altered by either the whole smoke (WS) or its component phases i.e. wet total particulate matter (WTPM) or gas/vapor phase (GVP) (Vulimiri et al., 2009). Exposures to the different phases lead to unique biochemical alterations in A549 cells. For example, WTPM exposure resulted in changes in the Krebs cycle and urea cycle metabolites, whereas GVP exposure resulted in alterations in hexosamines and in lipid metabolism. These investigators found that exposure to cigarette smoke was associated with perturbations in the metabolism of glutathione, amino acids, lipids, and nucleotides. Alterations were also seen in the urea cycle, Krebs cycle, and the production of polyamines and cellular cofactors.