Stacking transgenic event DAS-Ø15Ø7-1 alters maize composition less than traditional breeding.

The impact of crossing ('stacking') genetically modified (GM) events on maize-grain biochemical composition was compared with the impact of generating nonGM hybrids. The compositional similarity of seven GM stacks containing event DAS-Ø15Ø7-1, and their matched nonGM near-isogenic hybrids (iso-hybrids) was compared with the compositional similarity of concurrently grown nonGM hybrids and these same iso-hybrids. Scatter plots were used to visualize comparisons among hybrids and a coefficient of identity (per cent of variation explained by line of identity) was calculated to quantify the relationships within analyte profiles. The composition of GM breeding stacks was more similar to the composition of iso-hybrids than was the composition of nonGM hybrids. NonGM breeding more strongly influenced crop composition than did transgenesis or stacking of GM events. These findings call into question the value of uniquely requiring composition studies for GM crops, especially for breeding stacks composed of GM events previously found to be compositionally normal.

Maize hybrids derived from GM positive and negative segregant inbreds are compositionally equivalent: any observed differences are associated with conventional backcrossing practices.

In this study, we show that compositional differences in grain harvested from genetically modified (GM) maize hybrids derived from near-isogenic trait-positive and trait-negative segregant inbreds are more likely related to backcrossing practices than to the GM trait. To demonstrate this, four paired GM trait-positive (NK603: herbicide tolerance) and trait-negative near-isogenic inbred male lines were generated. These were crossed with two different females (testers) to create a series of trait-positive and trait-negative hybrid variants. The hypothesis was, that compositional variation within the hybrid variants would reflect differences associated with backcrossing practices and provide context to any observed differences between GM and non-GM hybrids. The F1 hybrids, as well as corresponding conventional comparator hybrids, were grown concurrently at four field sites across the United States during the 2013 season. Grain was harvested for compositional analysis; proximates (protein, starch, and oil), amino acids, fatty acids, minerals, tocopherols (α-, δ-, γ-), ß-carotene, phytic acid, and raffinose were measured. Statistical analysis showed that within each hybrid tester set, there were very few significant (p < 0.05) differences between the paired trait-positive and trait-negative hybrids or between the conventional comparators and the trait-positive or trait-negative hybrids. Assessments of the magnitudes of differences and variance component analysis highlighted that growing location, and the tester used in hybrid formation, had a markedly greater effect on composition than did the GM trait. Significantly, for each tester set, compositional differences within the trait-positive and trait-negative hybrid variants were greater than differences between the GM and non-GM comparators. Overall, GM trait insertion is not intrinsically a meaningful contributor to compositional variation, and observed differences between GM and non-GM comparators typically reflect incidental changes associated with conventional breeding practices. These results contribute to ongoing discussions on the relevance of negative segregants as comparators in GM assessments.

Compositional differences between near-isogenic GM and conventional maize hybrids are associated with backcrossing practices in conventional breeding.

Here, we show that differences between genetically modified (GM) and non-GM comparators cannot be attributed unequivocally to the GM trait, but arise because of minor genomic differences in near-isogenic lines. Specifically, this study contrasted the effect of three GM traits (drought tolerance, MON 87460; herbicide resistance, NK603; insect protection, MON 89034) on maize grain composition relative to the effects of residual genetic variation from backcrossing. Important features of the study included (i) marker-assisted backcrossing to generate genetically similar inbred variants for each GM line, (ii) high-resolution genotyping to evaluate the genetic similarity of GM lines to the corresponding recurrent parents and (iii) introgression of the different GM traits separately into a wide range of genetically distinct conventional inbred lines. The F1 hybrids of all lines were grown concurrently at three replicated field sites in the United States during the 2012 growing season, and harvested grain was subjected to compositional analysis. Proximates (protein, starch and oil), amino acids, fatty acids, tocopherols and minerals were measured. The number of statistically significant differences (α = 0.05), as well as magnitudes of difference, in mean levels of these components between corresponding GM variants was essentially identical to that between GM and non-GM controls. The largest sources of compositional variation were the genetic background of the different conventional inbred lines (males and females) used to generate the maize hybrids and location. The lack of any compositional effect attributable to GM suggests the development of modern agricultural biotechnology has been accompanied by a lack of any safety or nutritional concerns.

Bayesian statistical approaches to compositional analyses of transgenic crops 2. Application and validation of informative prior distributions.

Bayesian approaches to evaluation of crop composition data allow simpler interpretations than traditional statistical significance tests. An important advantage of Bayesian approaches is that they allow formal incorporation of previously generated data through prior distributions in the analysis steps. This manuscript describes key steps to ensure meaningful and transparent selection and application of informative prior distributions. These include (i) review of previous data in the scientific literature to form the prior distributions, (ii) proper statistical model specification and documentation, (iii) graphical analyses to evaluate the fit of the statistical model to new study data, and (iv) sensitivity analyses to evaluate the robustness of results to the choice of prior distribution. The validity of the prior distribution for any crop component is critical to acceptance of Bayesian approaches to compositional analyses and would be essential for studies conducted in a regulatory setting. Selection and validation of prior distributions for three soybean isoflavones (daidzein, genistein, and glycitein) and two oligosaccharides (raffinose and stachyose) are illustrated in a comparative assessment of data obtained on GM and non-GM soybean seed harvested from replicated field sites at multiple locations in the US during the 2009 growing season.

Assessing compositional variability through graphical analysis and Bayesian statistical approaches: case studies on transgenic crops.

New transgenic (GM) crops are subjected to extensive safety assessments that include compositional comparisons with conventional counterparts as a cornerstone of the process. The influence of germplasm, location, environment, and agronomic treatments on compositional variability is, however, often obscured in these pair-wise comparisons. Furthermore, classical statistical significance testing can often provide an incomplete and over-simplified summary of highly responsive variables such as crop composition. In order to more clearly describe the influence of the numerous sources of compositional variation we present an introduction to two alternative but complementary approaches to data analysis and interpretation. These include i) exploratory data analysis (EDA) with its emphasis on visualization and graphics-based approaches and ii) Bayesian statistical methodology that provides easily interpretable and meaningful evaluations of data in terms of probability distributions. The EDA case-studies include analyses of herbicide-tolerant GM soybean and insect-protected GM maize and soybean. Bayesian approaches are presented in an analysis of herbicide-tolerant GM soybean. Advantages of these approaches over classical frequentist significance testing include the more direct interpretation of results in terms of probabilities pertaining to quantities of interest and no confusion over the application of corrections for multiple comparisons. It is concluded that a standardized framework for these methodologies could provide specific advantages through enhanced clarity of presentation and interpretation in comparative assessments of crop composition.

Introduction to Bayesian statistical approaches to compositional analyses of transgenic crops 1. Model validation and setting the stage.

Statistical comparisons of compositional data generated on genetically modified (GM) crops and their near-isogenic conventional (non-GM) counterparts typically rely on classical significance testing. This manuscript presents an introduction to Bayesian methods for compositional analysis along with recommendations for model validation. The approach is illustrated using protein and fat data from two herbicide tolerant GM soybeans (MON87708 and MON87708×MON89788) and a conventional comparator grown in the US in 2008 and 2009. Guidelines recommended by the US Food and Drug Administration (FDA) in conducting Bayesian analyses of clinical studies on medical devices were followed. This study is the first Bayesian approach to GM and non-GM compositional comparisons. The evaluation presented here supports a conclusion that a Bayesian approach to analyzing compositional data can provide meaningful and interpretable results. We further describe the importance of method validation and approaches to model checking if Bayesian approaches to compositional data analysis are to be considered viable by scientists involved in GM research and regulation.

Assessing the natural variability in crop composition.

The number of evaluations of the nutrient composition of food and feed crops has increased over the past 15years due to the introduction of new crops using the tools of modern biotechnology. The composition of these crops has been extensively compared with conventional (non-transgenic) controls as an integral part of the comparative safety assessment process. Following guidelines outlined in the Organization of Economic Co-operation and Development (OECD) Consensus Documents, most of these studies have incorporated field trials at multiple geographies and a diverse range of commercially available varieties/hybrids that are analyzed to understand natural variability in composition due to genetic and environmental influences. Using studies conducted in the US, Argentina and Brazil over multiple growing seasons, this report documents the effect of geography, growing season, and genetic background on soybean composition where fatty acids and isoflavones were shown to be particularly variable. A separate investigation of 96 different maize hybrids grown at three locations in the US demonstrated that levels of free amino acids, sugars/polyols, and molecules associated with stress response can vary to a greater degree than that observed for more abundant components. The International Life Sciences Institute (ILSI) crop composition database has proven to be an important resource for collecting and disseminating nutrient composition data to promote a further understanding of the variability that occurs naturally in crops used for food and feed.

Characterization of proteomic and metabolomic responses to dietary factors and supplements.

Over the past decade there has been a renewed interest in research and development of both dietary and nutritional supplements. Significant advancements have been made in the scientific assessment of the quality, safety, and efficacy of these products because of the strong interest in and financial support of these projects. As research in both fields continues to advance, opportunities to use new and innovative research technologies and methodologies, such as proteomics and metabolomics, are critical for the future progress of the science. The purpose of the symposium was to begin the process of communicating new innovative proteomic and metabolomic methodologies that may be applied by researchers in both the nutrition and the natural product communities. This symposium highlighted 2 proteomic approaches, protein fingerprinting in complex mixtures with peptoid microarrays and top-down mass spectrometry for annotation of gene products. Likewise, an overview of the methodologies used in metabolomic profiling of natural products was presented, and an illustration of an integrated metabolomics approach in nutrition research was highlighted.

Chemical composition of glyphosate-tolerant soybean 40-3-2 grown in Europe remains equivalent with that of conventional soybean (Glycine max L.).

The composition of glyphosate-tolerant (Roundup Ready) soybean 40-3-2 was compared with that of conventional soybean grown in Romania in 2005 as part of a comparative safety assessment program. Samples were collected from replicated field trials, and compositional analyses were performed to measure proximates (moisture, fat, ash, protein, and carbohydrates by calculation), fiber, amino acids, fatty acids, isoflavones, raffinose, stachyose, phytic acid, trypsin inhibitor, and lectin in grain as well as proximates and fiber in forage. The mean values for all biochemical components assessed for Roundup Ready soybean 40-30-2 were similar to those of the conventional control and were within the published range observed for commercial soybean. The compositional profile of Roundup Ready soybean 40-3-2 was also compared to that of conventional soybean varieties grown in Romania by calculating a 99% tolerance interval to describe compositional variability in the population of traditional soybean varieties already on the marketplace. These comparisons, together with the history of the safe use of soybean as a common component of animal feed and human food, lead to the conclusion that Roundup Ready soybean 40-3-2 is compositionally equivalent to and as safe and nutritious as conventional soybean varieties grown commercially.

Metabolite analyses of grain from maize hybrids grown in the United States under drought and watered conditions during the 2002 field season.

Understanding natural variation in the composition of conventional crop germplasms is critical in establishing a baseline for comparison of biotechnology-derived crops. This is particularly relevant to such traits as tolerance to drought stress. Thus, there is both a need to understand the contribution of stress conditions to natural variation in plant nutritional components and to determine whether levels of small molecule metabolites such as osmoprotectants and stress metabolites are also affected. As a first step in developing such information for maize, seven conventional hybrids were grown under different moisture regimens and the impact of moisture on composition was assessed. The regimens included well-watered conditions, water restriction during the vegetative phase, and water restriction during grain fill. Compositional analyses of the harvested grain included assessments of the levels of proximates (moisture, protein, oil, starch) and small molecule metabolites such as fatty acids, free amino acids, organic acids, sugars, total glycerol, glycine betaine, and abscisic acid. Ranges for these analytes were determined across all moisture regimens, and the effect of the different water regimens on these analytes was also evaluated. The number and type of grain analytes that showed statistically significant differences in levels between different water regimens differed quite markedly by maize hybrid. However, the magnitude of mean differences between well-watered and water-restricted samples was typically small, and statistically significant differences for any given analyte were typically observed in only one to three of the seven maize hybrids. Only two analytes, free glutamine and free proline, showed a significant drought-induced difference in at least four maize hybrids.

Impact of genetics and environment on nutritional and metabolite components of maize grain.

The Organization for Economic Co-operation and Development (OECD) recommends the measurement of specific plant components for compositional assessments of new biotechnology-derived crops. These components include proximates, nutrients, antinutrients, and certain crop-specific secondary metabolites. A considerable literature on the natural variability of these components in conventional and biotechnology-derived crops now exists. Yet the OECD consensus also suggests measurements of any metabolites that may be directly associated with a newly introduced trait. Therefore, steps have been initiated to assess natural variation in metabolites not typically included in the OECD consensus but which might reasonably be expected to be affected by new traits addressing, for example, nutritional enhancement or improved stress tolerance. The compositional study reported here extended across a diverse genetic range of maize hybrids derived from 48 inbreds crossed against two different testers. These were grown at three different, but geographically similar, locations in the United States. In addition to OECD analytes such as proximates, total amino acids and free fatty acids, the levels of free amino acids, sugars, organic acids, and selected stress metabolites in harvested grain were assessed. The major free amino acids identified were asparagine, aspartate, glutamate, and proline. The major sugars were sucrose, glucose, and fructose. The most predominant organic acid was citric acid, with only minor amounts of other organic acids detected. The impact of genetic background and location was assessed for all components. Overall, natural variation in free amino acids, sugars, and organic acids appeared to be markedly higher than that observed for the OECD analytes.

Dicamba-Tolerant Soybeans (Glycine max L.) MON 87708 and MON 87708 × MON 89788 Are Compositionally Equivalent to Conventional Soybean.

Herbicide-tolerant crops can expand both tools for and timing of weed control strategies. MON 87708 soybean has been developed through genetic modification and confers tolerance to the dicamba herbicide. As part of the safety assessment conducted for new genetically modified (GM) crop varieties, a compositional assessment of MON 87708 was performed. Levels of key soybean nutrients and anti-nutrients in harvested MON 87708 were compared to levels of those components in a closely related non-GM variety as well as to levels measured in other conventional soybean varieties. From this analysis, MON 87708 was shown to be compositionally equivalent to its comparator. A similar analysis conducted for a stacked trait product produced by conventional breeding, MON 87708 × MON 89788, which confers tolerance to both dicamba and glyphosate herbicides, reached the same conclusion. These results are consistent with other results that demonstrate no compositional impact of genetic modification, except in those cases where an impact was an intended outcome.

High-throughput screening, metabolomics and drug discovery.

The science of metabolomics has the potential to deliver wide-reaching benefits to the currently embattled pharmaceutical industry. Current applications for this field center around toxicological profiling and biomarker studies; however, the ability of metabolomics to quantitatively assess pharmacologically induced changes in biological systems at the phenotype level suggests that there would be value in its adoption at much earlier phases of the drug-discovery process. As is argued herein, this approach could be coupled with a re-organization of early drug-discovery paradigms to reduce both the rates of attrition and the costs of bringing a drug to market.

Evaluation of metabolomics profiles of grain from maize hybrids derived from near-isogenic GM positive and negative segregant inbreds demonstrates that observed differences cannot be attributed unequivocally to the GM trait.

INTRODUCTION: Past studies on plant metabolomes have highlighted the influence of growing environments and varietal differences in variation of levels of metabolites yet there remains continued interest in evaluating the effect of genetic modification (GM). OBJECTIVES: Here we test the hypothesis that metabolomics differences in grain from maize hybrids derived from a series of GM (NK603, herbicide tolerance) inbreds and corresponding negative segregants can arise from residual genetic variation associated with backcrossing and that the effect of insertion of the GM trait is negligible. METHODS: Four NK603-positive and negative segregant inbred males were crossed with two different females (testers). The resultant hybrids, as well as conventional comparator hybrids, were then grown at three replicated field sites in Illinois, Minnesota, and Nebraska during the 2013 season. Metabolomics data acquisition using gas chromatography-time of flight-mass spectrometry (GC-TOF-MS) allowed the measurement of 367 unique metabolite features in harvested grain, of which 153 were identified with small molecule standards. Multivariate analyses of these data included multi-block principal component analysis and ANOVA-simultaneous component analysis. Univariate analyses of all 153 identified metabolites was conducted based on significance testing (α = 0.05), effect size evaluation (assessing magnitudes of differences), and variance component analysis. RESULTS: Results demonstrated that the largest effects on metabolomic variation were associated with different growing locations and the female tester. They further demonstrated that differences observed between GM and non-GM comparators, even in stringent tests utilizing near-isogenic positive and negative segregants, can simply reflect minor genomic differences associated with conventional back-crossing practices. CONCLUSION: The effect of GM on metabolomics variation was determined to be negligible and supports that there is no scientific rationale for prioritizing GM as a source of variation.

Metabolomic Assessment of Key Maize Resources: GC-MS and NMR Profiling of Grain from B73 Hybrids of the Nested Association Mapping (NAM) Founders and of Geographically Diverse Landraces.

The present study expands metabolomic assessments of maize beyond commercial lines to include two sets of hybrids used extensively in the scientific community. One set included hybrids derived from the nested association mapping (NAM) founder lines, a collection of 25 inbreds selected on the basis of genetic diversity and used to investigate the genetic basis of complex plant traits. A second set included 24 hybrids derived from a collection of landraces representative of native diversity from North and South America that may serve as a source of new alleles for improving modern maize hybrids. Metabolomic analysis of grain harvested from these hybrids utilized gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) and (1)H nuclear magnetic resonance spectroscopy ((1)H NMR) techniques. Results highlighted extensive metabolomic variation in grain from both hybrid sets, but also demonstrated that, within each hybrid set, subpopulations could be differentiated in a pattern consistent with the known genetic and compositional variation of these lines. Correlation analysis did not indicate a strong association of the metabolomic data with grain nutrient composition, although some metabolites did show moderately strong correlations with agronomic features such as plant and ear height. Overall, this study provides insights into the extensive metabolomic diversity associated with conventional maize germplasm.

Global Adoption of Genetically Modified (GM) Crops: Challenges for the Public Sector.

Advances in biotechnology continue to drive the development of a wide range of insect-protected, herbicide-tolerant, stress-tolerant, and nutritionally enhanced genetically modified (GM) crops, yet societal and public policy considerations may slow their commercialization. Such restrictions may disproportionately affect developing countries, as well as smaller entrepreneurial and public sector initiatives. The 2014 IUPAC International Congress of Pesticide Chemistry (San Francisco, CA, USA; August 2014) included a symposium on "Challenges Associated with Global Adoption of Agricultural Biotechnology" to review current obstacles in promoting GM crops. Challenges identified by symposium presenters included (i) poor public understanding of GM technology and the need for enhanced communication strategies, (ii) nonharmonized and prescriptive regulatory requirements, and (iii) limited experience with regulations and product development within some public sector programs. The need for holistic resistance management programs to enable the most effective use of insect-protected crops was also a point of emphasis. This paper provides details on the symposium discussion and provides background information that can be used in support of further adoption of beneficial GM crops. Overall, it emphasizes that global adoption of modern agricultural biotechnology has not only provided benefits to growers and consumers but has great potential to provide solutions to an increasing global population and diminishing agricultural land. This potential will be realized by continued scientific innovation, harmonized regulatory systems, and broader communication of the benefits of the high-yielding, disease-resistant, and nutritionally enhanced crops attainable through modern biotechnology.

Medicinal chemistry, metabolic profiling and drug target discovery: a role for metabolic profiling in reverse pharmacology and chemical genetics.

Comprehensive analysis of the metabolome can contribute to mechanism of action studies for small molecules discovered in phenotypic screens. Examples are presented in this overview of the rapidly developing field of "metabolic profiling." These examples include the use of NMR in gene function analysis, GC-based studies on the identification of metabolic pathways affected by PPAR-gamma agonists, applications of Fourier-transform MS and the use of stable isotope-based metabolic profiling (SIDMAP) to investigate metabolic adaptive changes induced by effective anticancer agents.

Chemical and biological integrity in natural products screening.

Due to pressure from combinatorial chemistry and the streamlining of the drug discovery process through automated high-throughput screening technologies, pharmaceutically based natural products programs are under increasing scrutiny. However by taking advantages of technologies originally developed for high-throughput screening and combinatorial chemistry and applying them to processes considered as bottlenecks in classical natural products chemistry (purification, structure elucidation, sample availability) it is our opinion that natural products can still contribute to the effective discovery of novel bioactive and pharmaceutically relevant metabolites. We describe here several such strategies that if universally implemented, will demonstrate i) whether chemical diversity is truly being accessed, ii) that novel metabolites can be formatted in a manner appropriate for modern screening paradigms, and iii) that natural products can be rapidly identified not only for novelty and pharmaceutical relevance but to assess their true biological origin.

Metabolic biomarker and kinase drug target discovery in cancer using stable isotope-based dynamic metabolic profiling (SIDMAP).

Tumor cells respond to growth signals by the activation of protein kinases, altered gene expression and significant modifications in substrate flow and re-distribution among biosynthetic pathways. This results in a proliferating phenotype with altered cellular function. These transformed cells exhibit unique anabolic characteristics, which includes increased and preferential utilization of glucose through the non-oxidative steps of the pentose cycle for nucleic acid synthesis but limited de novo fatty acid synthesis and TCA cycle glucose oxidation. This primarily non-oxidative anabolic profile reflects an undifferentiated highly proliferative aneuploid cell phenotype and serves as a reliable metabolic biomarker to determine cell proliferation rate and the level of cell transformation/differentiation in response to drug treatment. Novel drugs effective in particular cancers exert their anti-proliferative effects by inducing significant reversions of a few specific non-oxidative anabolic pathways. Here we present evidence that cell transformation of various mechanisms is sustained by a unique disproportional substrate distribution between the two branches of the pentose cycle for nucleic acid synthesis, glycolysis and the TCA cycle for fatty acid synthesis and glucose oxidation. This can be demonstrated by the broad labeling and unique specificity of [1,2-(13)C(2)]glucose to trace a large number of metabolites in the metabolome. Stable isotope-based dynamic metabolic profiles (SIDMAP) serve the drug discovery process by providing a powerful new tool that integrates the metabolome into a functional genomics approach to developing new drugs. It can be used in screening kinases and their metabolic targets, which can therefore be more efficiently characterized, speeding up and improving drug testing, approval and labeling processes by saving trial and error type study costs in drug testing.

Compositional assessments of key maize populations: B73 hybrids of the Nested Association Mapping founder lines and diverse landrace inbred lines.

The present study provides an assessment of the compositional diversity in maize B73 hybrids derived both from the Nested Association Mapping (NAM) founder lines and from a diverse collection of landrace accessions from North and South America. The NAM founders represent a key population of publicly available lines that are used extensively in the maize community to investigate the genetic basis of complex traits. Landraces are also of interest to the maize community as they offer the potential to discover new alleles that could be incorporated into modern maize lines. The compositional analysis of B73 hybrids from the 25 NAM founders and 24 inbred lines derived from landraces included measurements of proximates (protein, fat, ash, and starch), fibers, minerals, amino acids, fatty acids, tocopherols (α-, γ-, and δ-), ß-carotene, phytic acid, and raffinose. Grain was harvested from a replicated trial in New York, USA. For each data set (NAM and landrace) canonical discriminant analysis allowed separation of distinct breeding groups (tropical, temperate, flint, mixed/intermediate) within each data set. Overall, results highlighted extensive variation in all composition components assessed for both sets of hybrids. The variation observed for some components within the landraces may therefore be of value for increasing their levels in modern maize lines. The study described here provided significant information on contributions of conventional breeding to crop compositional variation, as well as valuable information on key genetic resources for the maize community in the development of new improved lines.