Abstract
Glyphosate tolerant genetically modified (GM) maize NK603 was assessed as ‘substantially equivalent’ to its isogenic counterpart by a nutrient composition analysis in order to be granted market approval. We have applied contemporary in depth molecular profiling methods of NK603 maize kernels (sprayed or unsprayed with Roundup) and the isogenic corn to reassess its substantial equivalence status. Proteome profiles of the maize kernels revealed alterations in the levels of enzymes of glycolysis and TCA cycle pathways, which were reflective of an imbalance in energy metabolism. Changes in proteins and metabolites of glutathione metabolism were indicative of increased oxidative stress. The most pronounced metabolome differences between NK603 and its isogenic counterpart consisted of an increase in polyamines including N-acetyl-cadaverine (2.9-fold), N-acetylputrescine (1.8-fold), putrescine (2.7-fold) and cadaverine (28-fold), which depending on context can be either protective or a cause of toxicity. Our molecular profiling results show that NK603 and its isogenic control are not substantially equivalent.
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Introduction
The application of genetic engineering (GE) to modify edible crops is often advocated as one of the most important scientific advances to improve farming systems and feed the world in a more sustainable manner1. GE has been used to create crops adapted to abiotic stress, resistant to pathogens, with a longer shelf life, or with enhanced nutritional properties. However, commercialization of these traits is currently minor. Agricultural genetically modified (GM) crops are dominated by plants engineered to tolerate application of a herbicide or/and to produce their own insecticides2. A total of 180 million hectares of GM crops are currently cultivated worldwide on around 1.5 billion hectares constituting approximately 10% of global arable land3. Approximately 80% of GM crops have been modified to tolerate application of and thus accumulate glyphosate-based herbicide residues without dying in order to facilitate weed management.
Regulations for the release of genetically modified organisms (GMOs) of any kind in a country are covered by the national biosafety regulations of that nation. Guidance on risk assessment (RA) aim at identifying and avoiding adverse effects by early detection and proper evaluation of intended and potential unintended changes in a GMO. These should be detected and identified at early stages of RA, often referred to as “hazard identification”. Hazard identification is essential to the RA process as it sets the foundation of what is considered or observed in later steps in the risk assessment process4. In the US, the Food and Drug Administration considers GM technology as an extension of conventional breeding and GMO crops are deregulated once nutritional and compositional “substantial equivalence” is demonstrated5. The set of parameters and analyses necessary to declare a GMO as substantially equivalent to its conventional counterpart is still vague and focuses on a restricted set of compositional variables, such as the amounts of protein, carbohydrate, vitamins and minerals. GMOs are then declared substantially equivalent when sufficient similarities appear for those selected variables6. Remarkably, while a majority of GMO crops have been modified to withstand and thus accumulate a herbicide without dying, analysis for residues for such pesticides are neglected in compositional assessment7.
Recent technologies used to ascertain the molecular compositional profile of a system, such as transcriptomics, proteomics, metabolomics, epigenomics and mirnomics, collectively referred to as “omics technologies”, are used extensively in basic and applied science8. Comparative omics analyses have been performed comparing GMO crops and their isogenic counterpart. A number of them have shown metabolic disturbances from potential unintended effects of the GM transformation process in Bt maize9,10,11,12, glyphosate-tolerant soybean13,14,15, potato16, cotton17 and rice18. However, these studies do not report consistent or coherent results, which can be explained by the use of a variety of genetic backgrounds and/or different growth conditions, as well as variations in the technologies and threshold levels applied19. Indeed, the majority of authors of these types of studies conclude that the statistically significant changes observed between the conventional and the GM varieties are not biologically significant because they fall into the range of variations obtained in the comparisons between different conventionally-bred varieties, and under different environmental conditions11. However, other authors conclude that observed differences could reflect biologically significant, GM transformation process induced changes in protein profiles12 or metabolism20 when appropriate near-isogenic controls were applied and test crops grown at the same time and location to avoid differences brought about by variable environmental conditions20. Currently, no regulatory authority requires mandatory untargeted molecular profiling omics analysis to be performed but some acknowledge their potential relevance for food and feed derived from GM plants with specific metabolic pathways modified, or in situations where a suitable comparator is not available4,21.
Despite being declared to be ‘substantially equivalent’, off target effects have been observed in non-target species for Bt toxin-producing GMO crops22,23,24. Additionally, laboratory animal feeding trials performed with some GM plants in comparison to the non-GM counterpart have been proposed to provide evidence of ill-health effects. Several laboratory studies consisting of 90-day feeding trials in rodents have been conducted to evaluate the safety of GMO crop consumption25,26. These investigations have frequently resulted in statistically significant differences in parameters reflective of disturbances in various organ systems and in particular liver and kidney biochemistry, but with interpretation of their biological significance, especially with respect to health implications, being controversial27,28,29. Such differences in outcome in such laboratory animal feeding studies could have multiple sources including the presence of GMO-associated pesticide residues30,31.
In an effort to provide insight into the substantial equivalence classification of a Roundup tolerant NK603 GM maize, we have performed proteomics and metabolomics analyses of NK603 (sprayed or unsprayed with Roundup) and isogenic maize kernels (Fig. 1). We used a TMT10plex™ isobaric mass tag labelling method and quantified proteins by Liquid chromatography-tandem mass spectrometry (LC-MS/MS). The metabolome profile was determined by ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS). Altogether, our integrative analysis shows that the GM transformation process used to generate NK603 maize caused deep alterations in the proteome and metabolome profiles of this crop and results in marked metabolic changes. We conclude that NK603 maize is not compositionally equivalent to its non-GM isogenic counterpart as previously claimed.
Flowchart of the experimental procedure.
Harvested grains from NK603 GM maize cultivations, sprayed (NK603 + R) or not (NK603) with Roundup, were compared to their nearest isogenic non-transgenic control (Isogenic) grown under similar normal conditions. Two biological replicates were obtained by performing two cultivations at the same location in different years. Maize grains were analyzed by different mass spectrometry methods to determine proteome and metabolome profiles in 3 technical replicates.
Results
The objective of this investigation was to obtain a deeper understanding of the biology of the NK603 GM maize by molecular profiling (proteomics and metabolomics) in order to gain insight into its substantial equivalence classification. We began by undertaking an unsupervised exploratory analysis of variance structure. We integrated metabolome and proteome profiles of the NK603, cultivated either with or without Roundup, and its isogenic counterpart, into a two-step multiple co-inertia analysis (MCIA) process. First, a one-table ordination method transforms each multidimensional dataset (hyperspaces) separately into comparable lower dimensional spaces by finding axes maximizing the sum of the variances of the variables. The resulting variance structure can be described by a PCA (Additional file 3). The results show a clear separation of each feed type (NK603, NK603+Roundup and control) in both platforms. Control samples had the most distinct proteome and metabolome profiles as observed in PCA plots.
In a second step, the variance structure analyses from metabolome and proteome profiles were combined into a single analysis (Fig. 2). This aims to find new axes on which the two hyperspaces are projected by maximizing the square covariance. Figure 2A shows the projection of metabolome and proteome profiles onto the first two principal components of MCIA. Absolute eigenvalues of these components are given by a bar plot (Fig. 2B). The transgenic feed samples NK603 and NK603+Roundup are separated from the non-transgenic control (Isogenic) along the first component (horizontal axis). This clustering accounts for most of the variation (percentage of explained variance of 56.7%). The NK603 maize sprayed with Roundup separates from the unsprayed NK603 maize on the second component (vertical axis, percentage of explained variance of 16.6%). The lines connecting the different dots are proportional to the divergence between the different variables of the dataset. A relatively high correlation is depicted by the short edges. It shows similar trends in metabolome and proteome profiles, and also between the two cultivations, indicating that the most variant sources of biological information were similar. The projection of individual protein or metabolites on a 2-dimensional space (Fig. 2C) showed a mix pattern indicating that no particular subsets of variables are driving the clustering of groups. Finally, Fig. 2D shows the pseudo-eigenvalues space. The proteome samples (blue and green dots) are highly weighted on the horizontal axis indicating that this dataset is the highest contributor of the clustering of the transgenic feed samples from the control. By contrast, the differences between the NK603 maize sprayed with Roundup and the unsprayed NK603 maize are mostly due to the composition of the metabolome since the latest has a high weight on the vertical axis (red and black dots) of the pseudo-eigenvalues space. The fold changes observed in the comparisons of the NK603 maize sprayed with Roundup, the unsprayed NK603 maize and the isogenic control corn were highly correlated between the two cultivations performed during two different growing seasons (Additional file 4). Overall, the MCIA shows that the GM transformation process was the major contributor to variation in the protein and metabolite profiles rather than environmental factors such as the spraying of a pesticide or the growing season.
Integration of metabolome and proteome profiles of the NK603 maize and its near-isogenic counterpart into a multiple co-inertia analysis projection plot.
(A) The first two axes of MCIA represent metabolome and proteomic datasets. Different shapes represent the different variables which are connected by lines, the length of these lines is proportional to the divergence between the data. Lines for each sample are joined at a common point at which the covariance derived from the MCIA analysis is maximal. (B) Pseudo-eigenvalue space showing the percentage of variance explained by each of the MCIA component. Each barplot represents the absolute eigenvalues. (C) Protein or metabolites (colored dots) are projected on a 2-dimensional space. In this panel, a protein or a metabolite that is particularly highly expressed in a maize variety will be located on the direction of this variety. (D) Pseudo-eigenvalues space of all datasets, indicating how much variance of an eigenvalue is contributed by the proteome or the metabolome for cultivations 1 and 2.
We next conducted a statistical evaluation of the biological differences resulting from the GM transformation process, as well as from the spraying of Roundup, by pairwise comparisons in order to identify proteins and metabolites associated with possible metabolic alterations. The list of proteins and metabolites having their levels significantly disturbed is given in Additional files 5 and 6, respectively. Figure 3 shows the statistical significance of differential protein/metabolite levels by volcano plots along with respective fold changes. While only one protein is newly produced as a result of the transgene insertion, a total of 117 proteins and 91 metabolites have been altered in maize by the genetic transformation process and insertion of the EPSPS-CP4 cassette (Isogenic vs NK603 panel, Fig. 3). One protein (B4G0K5) and 31 metabolites had their expression significantly altered by the spraying of the Roundup pesticide (NK603 vs NK603+Roundup (R) panel, Fig. 3).
Volcano plots of the maize proteome and metabolome profiles.
Volcano plots show the log 2 fold changes and the −log10 adjusted p-values in protein or metabolite level induced by the GM transformation process (isogenic vs NK603, isogenic vs NK603 + R) or by the pesticide spraying (NK603 vs NK603 + R). Data were selected at the cut off values adj-p < 0.05 and fold change >1.5. Red dots represent protein or metabolites having their level significantly altered in the different samples.
The NK603 maize has been engineered to express a modified version of the Agrobacterium tumefaciens strain EPSPS-CP432. Two peptides (lAGGEDVADLR and gLGNASGAAVATHLDHR) from EPSPS-CP433 were detected and quantified by undertaking a specific targeted data analysis (Fig. 4). Their location on EPSPS-CP4 is shown by Fig. 4C. Reporter ion intensities for EPSPS-CP4 peptides in the NK603+Roundup and the NK603 were on average respectively 7 and 10 times higher than in the isogenic control. The observed signal for the non-transgenic corn probably represents non-specific background noise since it does not contain the EPSPS-CP4 gene. This would be caused by the co-isolation of other peptides in the corresponding MS/MS experiment, which gives rise to low intensity reporter ions in the control channels.
Mass spectrometric detection of CP4 EPSPS in the NK603 genetically modified maize.
Two different peptides from Agrobacterium sp. 3-phosphoshikimate 1-carboxyvinyltransferase have been detected (gLGNASGAAVATHLDHR and lAGGEDVADLR) in all different samples allowing semi-quantitation (A) Reporter intensity ion values pertaining for CP4 EPSPS in the different samples of the two cultivations. (B) Localization of the peptides on the CP4 EPSPS (in grey) (C) Spectrum from the detection of the lAGGEDVADLR pertaining to Agrobacterium CP4 EPSPS (cultivation 1 of NK603).
We analysed the biological information contained in proteome profiles from the NK603 and its isogenic counterpart to see if they bear a signature representative of metabolic disturbances caused by the insertion of the transgene cassette and/or the expression of bacterial EPSPS-CP4. Among different pathway enrichment analysis software tested, String was chosen due to its in-house predictions and homology transfers, as well as its connection to many fine external database resources, and thus its ability to identify a larger number of proteins. Nevertheless, our interpretation remained limited by the quality of protein annotation in such databases. A total of 42.7% (50/117) and 35% (55/156) of the proteins respectively disturbed in the comparison to the unsprayed or the sprayed NK603 maize were uncharacterized or not annotated in the databases (Additional file 5).
Pathway enrichment analysis of differentially expressed proteins in NK603 and NK603+Roundup feed samples was mainly assigned to carbohydrate and energy metabolism (Table 1). Most of the proteins, including enzymes, associated with these pathways were overexpressed in GM samples (Additional file 5). An increased expression of some proteins involved in glycolysis (FDR adjusted p-value = 4.2e-7), and in particular in the synthesis of pyruvate from D-glyceraldehyde 3-phosphate can be indicative of an increased demand for energy. Among them, pyruvate kinase (B4F9G8), enolase (ENO1), and three glyceraldehyde-3-phosphate dehydrogenases (GAPC1, GAPC2, GAPC3) had their levels increased in NK603 maize. Interestingly, gene ontology terms related to metabolic responses to stress were enriched (FDR adjusted p-value = 1.5e-6) and some heat shock proteins (e.g., HSP82) have been overexpressed.
The comparison between Roundup-sprayed NK603 and control samples revealed a similar pattern to that observed in unsprayed samples. However, glutathione metabolism (KEGG ID 480) showed a significant alteration in sprayed NK603. The proteins assigned to that pathway, glutathione S-transferase 1 and 6-phosphogluconate dehydrogenase (P12653 and B4FSV6 respectively) were more abundant in sprayed samples while another glutathione transferase isoform GST-5 (A0A0B4J3E6) was less abundant. Additionally, the 1-Cys peroxiredoxin PER and the peroxidase were overexpressed. Although only one protein was statistically significantly altered in a pairwise comparison between NK603+Roundup and NK603 as the effect of Roundup herbicide spray alone, the protein B4G0K5 that has an identified conserved domain of Ricin-type β-trefoil lectin. The Ricin-type β-trefoil is a carbohydrate-binding domain found in a variety of molecules serving diverse functions such as enzymatic activity, inhibitory toxicity and signal transduction34.
The composition of the metabolome is shown in Additional file 6. The most pronounced differences between the NK603 GM maize and its isogenic counterpart mostly consisted of an increase in the amounts of numerous polyamines. The levels of N-acetyl-cadaverine (2.9-fold), N-acetylputrescine (1.8-fold), putrescine (2.7-fold) and cadaverine (28-fold) were increased in NK603. The metabolome profile also highlighted an impairment of energy metabolism. While metabolites from the first part of the TCA cycle had their levels increased (α-ketoglutarate by 1.65-fold and citrate by 1.49-fold), metabolites from the second part of the TCA cycle had their levels decreased (malate by 0.59-fold, fumarate by 0.60-fold, succinate by 0.80-fold). Additionally, while proteins associated with glycolysis were overexpressed, carbohydrate metabolism is depleted in several metabolites (glucuronate by 0.63-fold, glucose 1-phosphate by 0.56-fold, maltohexaose by 0.28-fold, maltopentaose by 0.51-fold). Differences due to the pesticide spray were subtle: phenylpropanoid such as 4-hydroxycinnamate (0.63-fold), ferulate (0.59-fold) and sinapate (2.9-fold) were significantly changed. While alterations of the shikimate pathway were not detected, intermediates from aromatic amino acid metabolism (PEP derived) had their level increased (phenyllactate by 1.60-fold, phenylpyruvate by 2.71-fold, N-acetylphenylalanine by 2.24-fold and xanthurenate by 1.82-fold). These changes could be indicative of an increase in amino acid catabolism. However, of note is that PEP itself was not detected in the analysis.
Table 2 provides pathway enrichment analysis of metabolites that were found to be statistically significantly altered in the pairwise comparisons. For the metabolome pathways analysis, the profile of NK603 and NK603+R showed a distinct pattern compared to the profiles observed in the proteome analysis. From the 10 most altered pathways, these two samples shared only five altered pathways and these suggest an alteration due to the GM transformation process. These pathways revealed an alteration in aspartate, pyruvate and phenylalanine amino acid downstream processes. The NK603 metabolome profile seems to differ from sprayed samples by fatty acid related pathways and choline, nicotinate and nicotinamide metabolism while sprayed samples showed alterations in serine metabolism and other sugar related metabolism.
The STITCH tool was used to provide a visualisation of predicted interactions of chemicals and proteins that might have a link to the transgene-associated EPSPS-CP4 pathway. The interaction network reveals that some proteins or metabolites altered in the NK603 maize are interacting with EPSPS (Fig. 5). The network formed by these proteins/metabolites is centred on some TCA cycle intermediates, among them, the α-ketoglutarate. One should note that EPSPS is using an energy metabolism intermediate (phosphenolpyruvate) as substrate. Overall, our data shows that the expression of a heterologous EPSPS in the NK603 maize is causing a deep alteration in the proteome and metabolome profiles of feed samples and thus resulting in a metabolic imbalance.
Interaction network of the metabolic effects resulting from the GM transformation process.
The STITCH (‘Search Tool for Interacting Chemicals’) tool was used to provide a visualisation of the consequence of EPSPS insertion. The proteins and the metabolites which were found commonly deregulated in the two comparisons of NK603 and NK603 + R to its isogenic counterpart were used as input list. The top ten interaction partners with the highest scores as well as the maize EPSPS were included to reveal interactions.
Discussion
In this report we present the first multi-omics analysis of GM NK603 maize compared to a near isogenic non-GM counterpart. Based on analysis conducted by the developer Monsanto Company, NK603 maize was scored as ‘substantially equivalent’ to its isogenic control, which was a major contributor to this product being granted market approval for animal and human consumption in the European Union, United States, Brazil and several other nations. Although NK603 had comparable nutritional and compositional profiles when originally accessed by the developer company upon registration of their product, our analysis at a detailed, in-depth molecular profiling level shows that NK603 grains, with or without Roundup spraying during cultivation, are not equivalent to isogenic non-transgenic control samples (Fig. 2).
The concept of substantial equivalence has long being used in safety testing of GMO crops, but the term and the concept has no clear definition35. In 1993 the Organization for Economic Co-operation and Development (OECD) stated that the “concept of substantial equivalence embodies the idea that existing organisms used as food, or as a source of food, can be used as the basis for comparison when assessing the safety of human consumption of a food or food component that has been modified or is new”36. The vagueness of this term generates conflict among stakeholders to determine which compositional differences are sufficient to declare a GMO as non-substantially equivalent. However, the Codex Alimentarius Commission37 makes it clear that a safety assessment of a new food based on the concept of substantial equivalence “does not imply absolute safety of the new product; rather, it focuses on assessing the safety of any identified differences so that the safety of the new product can be considered relative to its conventional counterpart.” Thus, the concept of substantial equivalence should not be used as a proof of safety. However, it could be used as a first tier in risk assessment to detect any unintended effects of the GM transformation process. Unintended effects can be understood as the effects that go beyond the primary expected effects of the genetic modification, and represent statistically significant differences in the GMO compared with an appropriate control38. Unintended effects during transgenesis include rearrangements, insertion, or deletions during the genetic transformation or during the tissue culture stages of GMO development39,40. A comprehensive characterization of the GM plant at the molecular level could facilitate identification of unintended effects in GMO crops and could be used as a complementary analytical tool to existing safety assessment procedures41,42,43,44.
In general, our study design further highlights the importance of restricting comparison to the GMO crop and non-GMO isogenic comparator and cultivation of the two at the same location and season when the objective is to evaluate the effect of the GM transformation process. This is obligatory in order to reduce effects on plant metabolism arising from differing environmental conditions, which can make it difficult to attribute differences that are observed to the procedure of transgenesis. However, even though our experimental design takes into account the effect of the growing season, further experiments made under different environmental conditions would be needed to determine the full range of effects of the GM transformation process on NK603 phenotype. Indeed, virtually all traits are influenced by genotype–environment interactions. Neither genetic differences nor environmental variations alone can account for the production of a particular phenotypic variation. For example, a study of the expression of the transgene encoding a Bt toxin in the MON810 GM maize under different environmental conditions, has shown that the phenotype resulting from the GM transformation process is influenced by stressful environmental conditions45.
The increasing literature reporting application of omics methods to assess proteome, metabolome and transcriptome profiles in GMO crops shows strong evidence of distinct grain proteomes in other GM maize events, such as MON810 Bt insecticide producing maize11,12,46. Although the majority of studies have focused on insect-resistant maize (e.g., MON810 event) and most likely because this was the first GM maize to enter the food and feed market, there has also been one previous metabolomics study investigating NK603. Metabolite profiling of NK603 maize kernels were analyzed and approximately 3% of the metabolites detected showed statistically significant differences compared to the respective isogenic lines47. Two metabolites (γ-tocopherol and myo-inositol) were less abundant in NK60347. Interestingly, γ-tocotrienol and myo-inositol levels were also found to be significantly reduced in our study, and thus attributable to the genetic transformation. This suggests that some metabolic alterations are consistently reported despite a strong background triggered by environmental influence. In a study of two common MON810/non-GM variety pairs subjected to two farming practices (conventional and low-nitrogen fertilization), it was found that up to 37.4% of the variation was dependent upon the variety, 31.9% were the result of the fertilization treatment, and 9.7% was attributable to the GM character48.
Alterations can also be found in other plant tissues. For example, analysis of leaves of Brazilian varieties of MON810 Bt maize revealed a total of 32 differentially expressed proteins between GM and non-GM samples that were identified and assigned to carbohydrate and energy metabolism, genetic information processing and stress response9.
Our study revealed significant metabolome profile differences between NK603 that was either sprayed or not with Roundup during cultivation (Fig. 2). This was surprising since the single application of this herbicide was prior to development of the maize cobs. In addition, we did not detect glyphosate or AMPA residues in the test maize kernel samples (Additional File 1). This indicates that metabolic differences provoked by an early application of Roundup persisted throughout the life of the maize even in the absence of herbicide residues. At present we can only speculate as to the mechanisms that may explain these effects but they may have their basis in epigenetic programming of gene expression patterns with consequent longer term effects. The spraying of Roundup could have acted as a signal causing an alteration in gene expression patterns in the growing maize. A recent study that demonstrated marked epigenetic (DNA methylation) changes in A thaliana in response to treatment with carbendazim supports this possibility49. In addition, it has been demonstrated that epigenetic (DNA methylation and post-translational histone modification) patterns acquired in one cultivation can be transgenerationally inherited in an A thaliana model system50. However, further research would be needed to determine if epigenetic alterations provoked by pesticide exposure can hamper plant phenotypes across generations.
The maize kernels analysed in this study were previously used to feed laboratory animals that formed part of a chronic (2 year) study looking at potential toxic effects arising from the consumption of this NK603 Roundup-tolerant GM maize. A dry feed was formulated to contain 11%, 22%, or 33% of NK603 maize, cultivated either with or without Roundup application, or 33% of the near isogenic variety. Sprague Dawley rats fed for two years on these diets presented blood/urine biochemical changes indicative of an increased incidence of liver and kidney structure and functional pathology in the NK603-containing diet groups compared to non-GM controls51. Standard biochemical compositional analysis revealed no particular differences between the different maize types tested51. Metabolic disturbances observed in our study may help to understand the negative health effects suggested after the chronic consumption of this GM maize. Alterations in concentrations of metabolites in grains might be directly related to pathogenic effects due to some active compounds that are known to be toxic52. For instance, a soybean glycoprotein allergen (Gly m Bd 28 K fragment) was also found overexpressed in a proteomic study of Roundup Ready GM soybean seeds (MSOY 7575 RR event)13. In our study, cadaverin levels were significantly increased (Log2FC 4.81 for NK603 and 5.31 for NK603+Roundup). Cadaverin plays important roles in lysine biosynthesis53 and also glutathione metabolism54. Other similar biogenic amines, such as N-acetyl-cadaverine, N-acetylputrescine and putrescine were also found to be present at higher levels in NK603 in our investigation. Different polyamines have been reported to have different effects, which depend on various factors such as age, tissue or disease status55. In certain contexts some of these polyamines have been found to be protective whereas in other situations they can be a cause of toxicity. On the one hand, toxicological effects such as nausea, headaches, rashes and changes in blood pressure are provoked by the consumption of foods with high concentrations of polyamines56. Putrescine and cadaverine have been reported as potentiators of the effects of histamine, and both have been implicated in the formation of carcinogenic nitrosamines with nitrite in meat products57. On the other hand, certain polyamines can also have beneficial anti-inflammatory effects and have been found to be beneficial during aging in some rodent model systems58. Noticeably, these polyamines were not measured in the first compositional analysis of NK603 maize performed for regulatory purposes32. Overall, whether the increased levels of cadaverine and putrescine found in the NK603 maize samples can account for the signs of potential negative health effects upon its consumption by rats, as implied by the blood/urine biochemical analysis33, needs to be further analyzed in experiments using more quantitative methods.
Our results suggest that expression of the EPSPS-CP4 transgene alters the oxidative environment in cells, and the increased levels of antioxidant enzymes are likely to be a response to oxidative burst by reactive oxygen species (ROS) in order to maintain proper physiological function. Glutathione metabolism was significantly altered in the NK603 when Roundup was sprayed during cultivation. Glutathione is known to be an important antioxidant in most living organisms, preventing damage to important cellular components caused by several environmental pollutants, including agrochemicals59. Plant glutathione S-transferases (GSTs) are also widely known for their role in herbicide detoxification60. Enzymes involved in combating reactive oxygen species, ascorbate peroxidase, glutathione reductase, and catalase are expressed at a higher level in transgenic soybean seeds14. Levels of ROS and other free radicals in GM food and feed would have to be monitored and quantified by further experiments in order to conclude on their potential impact on the agronomic performances of the plant. Additionally, it is known that polyamines are typically elevated in plants under abiotic stress conditions61. Typically, when cellular polyamine content increases, the levels of hydrogen peroxide also increases, activating antioxidant systems. Unintended effects of the inserted EPSPS-CP4 transgene was linked to energy metabolism disturbances in other studies13,14,15. It can be hypothesized that the plant is searching for a new equilibrium to maintain heterologous EPSPS-CP4 metabolism within levels that can be tolerated by the plant.
Glyphosate, the active ingredient of Roundup herbicide, inhibits the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), which is the sixth enzyme of the shikimate pathway, and plays an essential role in the biosynthesis of aromatic amino acids and other aromatic compounds in plants62. The EPSPS has a binding site for phosphenolpyruvate (PEP) and it could be hypothesized that an overexpression of a heterologous EPSPS could provoke a metabolic imbalance by altering the metabolism of PEP. Alterations in intermediate metabolism are corroborated in our experiment by the fact that the network formed by altered proteins/metabolites is centred on some TCA cycle intermediates (Fig. 5) such as α-ketoglutarate. In fact, it is also known that EPSPS inhibition by glyphosate impairs carbon metabolism, in particular by inducing alternative respiration and aerobic fermentation63. In this latest study, the metabolic switch was explained by an accumulation of pyruvate. Thus, if EPSPS inhibition is able to alter intermediate metabolism, a comparable change in the opposite direction could be expected as a result of EPSPS overexpression.
This study is the first and most detailed multi-omics characterization of a widely commercialized GMO crop and its isogenic counterpart. In conclusion, our integrative statistical and bioinformatics analysis allowed us to suggest a mechanistic link between the proteome and metabolome alterations observed and the insertion of a particular transgene. The transformation process and the resulting expression of a transgenic protein cause a general disturbance in the GM plant and it is clear that NK603 maize is markedly different from its non-GM isogenic line at the proteome and metabolome levels. In addition, our data correlates with previous studies, which observed higher amounts of ROS that act as free-radicals promoting oxidative stress in those transgenic plant materials. We also confirm a metabolic imbalance in energy and carbohydrate metabolism. Although a clear mechanistic link between alterations in the GM feed and the possible health effects following long-term consumption of this product remains to be established, the evidence we present clearly shows that NK603 and non-GM isogenic maize are not substantially equivalent and the nutritional quality of GM feed might be hampered by metabolic imbalances related to plant energy and stress metabolism.
Materials and Method
Maize cultivation
The varieties of maize used in this study were DKC 2678 Roundup-tolerant NK603 (Monsanto Corp., USA), and its nearest isogenic non-transgenic control DKC 2675. These two types of maize were grown under similar normal conditions, in the same location and season, spaced at a sufficient distance to avoid cross-contamination. The site of cultivation consisted of an imperfectly drained field with a coarse loam surface texture and fine loam subsoil. A typical soil compositional analysis is provided in Additional File 1. The maize cultivation rows were spaced 75 cm apart, with approximately 30 cm between planted seeds (78,000 seeds/ha). One pass of the seeder included 4 rows of corn. To avoid edge effects in the field, 2 passes (8 rows) of DKC 2575 (isogenic) were planted as a buffer zone. DKC 2678 (NK603) and DKC 2575 (isogenic) were planted ~85 m apart. Half of the DKC 2678 received the treatment with Roundup WeatherMax.
Fertilization was performed with 26 T/ha liquid dairy manure, 100 kg/ha of 30-0-10 fertilizer was broadcast at planting, and 150 kg/ha of 18-46-0 fertilizer banded with the seed. The corn was harvested when the moisture content was less than 30%. All corn varieties were hand harvested by collecting ears in large tote bags to avoid cross contamination. The corn pickers were instructed to pick every ear of corn so as to avoid any risk of quality differentiation. Each corn variety was shelled (kernels removed from the cob) using a small threshing machine designed for this purpose. Each variety was dried in separate bulk drying bins to avoid any risk of cross contamination. The corn was dried at a low temperature (<30 °C) to avoid drying too rapidly and affecting feed quality. The corn was dried to <14% moisture before bagging.
The genetic nature, as well as the purity of the NK603 maize seeds and harvested material, was confirmed by quantitative PCR analysis of DNA samples. One field of NK603 was sprayed once with Roundup at 3 L ha−1 (WeatherMAX, 540 g/L of glyphosate, EPA Reg. 524–537) whilst another field of NK603 was not treated with Roundup. Test samples were produced by two cultivation cycles performed over two growing seasons. All maize samples were analysed for a total of 423 pesticide residues by SGS Institut Fresenius GmbH (Berlin, Germany), including glyphosate and its metabolite AMPA. No pesticide contaminants were detected in any of the samples (Additional file 2). All samples were maintained at −80 °C until processing for analysis. A schematic overview of our experimental design, sampling strategy and analytical approach is provided in Fig. 1.
Proteome analysis
Sample preparation
Ground maize kernel samples were lysed in 8M lysis buffer (urea, NaCl, Tri-HCl, phosphatase and protease inhibitor) and their protein concentration calculated using a Nanodrop protein assay. Samples in triplicate were run through an SDS-PAGE 4–20% polyacrylamide gradient gel at 150 V. Excised gel bands were reduced with dithiothreitol (Sigma-Aldrich Ltd, Gillingham, Dorset, UK), alkylated with Iodoacetamide (Sigma-Aldrich Ltd) and digested with bovine sequencing grade trypsin (Roche, Penzberg, Germany; ref. 11418475001) at 37 °C for 18 hours. Subsequently extracted peptides were labelled with 60 mM TMT10plex Isobaric Label Reagents (ThermoFisher Scientific, Waltham, MA, USA; ref 90406) and the respective samples combined. Labelled peptides were then purified and extracted using Waters Sep-Pak Vac 3cc 200 mg tC18 cartridges, before being separated into 10 fractions by strong cation exchange (SCX) across an increasing salt concentration. The eluted peptide fractions were purified and extracted once again before being lyophilised for direct analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS).
Liquid chromatography-tandem mass spectrometry
Fractionated samples were resuspended in 100 μl of 50 mM ammonium bicarbonate and 10 μl of each of the 10 fractions was loaded onto a 50 cm EASY-spray column (ThermoFisher Scientific). Quantitative analysis was performed using the Orbitrap Velos-Pro mass spectrometer (ThermoFisher Scientific) in positive ion mode. The peptides were separated by gradient elution, from 5–80% 0.1% trifluoroacetic acid in acetonitrile (5–40% from 0–100 minutes, 40–80% from 100–110 minutes), at a flow rate of 300 nl/min. Mass spectra (m/z) ranging from 400–1600 Daltons was acquired at a resolution of 60,000 and the 10 most intense ions were subjected to MS/MS by HCD fragmentation with 35% collision energy.
Data processing
Protein identification was performed with Proteome Discoverer 1.4. Raw files were imported and searched against the UniProtKB/Swiss-Prot Database using Sequest for Proteome Discoverer. Raw files for all fractions were merged together in a single file search for each of the two TMT10plex sets. Precursor mass tolerance for the searches was set at 20ppm and fragment mass tolerance at 0.8ppm. The taxonomy selected was Zea mays and three enzymatic mis-cleavages were allowed. Dynamic modifications selected on the search were Oxidation/+15.995 Da (M) and Deamidated/+0.984 (N, Q) and static modifications were Carbamidomethyl/+57.021 Da (C), TMT10plex/229.163 Da (K), TMT10plex/229.163 Da (Any N-terminus). Only peptides with TMT reporter ion signal intensities for all ten samples were used for further bioinformatics analysis.
Metabolome analysis
The metabolome analysis was performed by Metabolon Inc. (Durham, NC, USA) as previously described64. Ground maize kernel samples were prepared using the automated MicroLab STAR® system from Hamilton Company (Reno, NV, USA). Several recovery standards were added prior to the first step in the extraction process for QC purposes. In order to remove protein, dissociate small molecules bound to protein or trapped in the precipitated protein matrix, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for 2 min (Glen Mills GenoGrinder 2000) followed by centrifugation. The resulting extract was divided into five fractions: two for analysis by two separate reverse phase (RP)/UPLC-MS/MS methods with positive ion mode electrospray ionization (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, one for analysis by HILIC/UPLC-MS/MS with negative ion mode ESI, and one sample was reserved for backup. Samples were placed briefly on a TurboVap® (SOTAX Corp, Westborough, MA, USA) to remove the organic solvent. The sample extracts were stored overnight under nitrogen before preparation for analysis.
Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS) for metabolome analysis
All methods utilized a Waters ACQUITY ultra-performance liquid chromatography (Waters Corp, Milford, MA, USA) and a ThermoFisher Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyser operated at 35,000 mass resolution. The sample extract was dried then reconstituted in solvents compatible to each of the four methods. Each reconstitution solvent contained a series of standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds. In this method, the extract was gradient eluted from a C18 column (Waters UPLC BEH C18-2.1 × 100 mm, 1.7 μm) using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was also analysed using acidic positive ion conditions, however it was chromatographically optimized for more hydrophobic compounds. In this method, the extract was gradient eluted from the same afore mentioned C18 column using methanol, acetonitrile, water, 0.05% PFPA and 0.01% FA and was operated at an overall higher organic content. Another aliquot was analysed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however with 6.5 mM ammonium bicarbonate at pH 8. The fourth aliquot was analysed via negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2.1 × 150 mm, 1.7 μm) using a gradient consisting of water and acetonitrile with 10 mM ammonium formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slighted between methods but covered 70–1000 m/z.
Metabolome data processing
A quality control value assessment was undertaken to determine instrument variability by calculating the median relative standard deviation (RSD) for the internal standards that were pre-mixed into each sample prior to injection into the mass spectrometer. This yielded a value of 3% for instrument variability. Overall process variability as determined by calculating the median RSD for all endogenous metabolites (that is, non-instrument standards) present in 100% of the samples gave a value of 7%. Raw data was extracted, peak-identified and QC processed using Metabolon’s hardware and software as previously described65. Metabolites were identified by automated comparison and curated by visual inspection for quality control using software developed at Metabolon66. Peaks were quantified using area-under-the-curve.
Integrative bioinformatics analysis
For plotting of results, a Principal Component Analysis (PCA) was first performed. The language and statistical environment R67 together with the ade4 package68 method was employed in order to explore the relationship between GM and non-GM varieties. Second, we performed a Multiple Co-Inertia Analysis (MCIA), using the language and statistical environment R together with the omicade4 package69, in order to integrate multiple omics datasets where the same tissue have been assayed multiple times (in this case, proteomics and metabolomics).
Pairwise Welch’s t-tests were performed, for bothproteomics and metabolomics datasets, for Isogenic vs NK603, Isogenic vs NK603+Roundup and NK603 vs NK603+Roundup comparisons. The resulting p-values were adjusted by the Benjamini-Hochberg multi-test adjustment method for the high number of comparisons. Volcano plots were also constructed in order to visualize the differences in metabolite and protein expression for each of the comparisons. The aforementioned tests and plots were performed using in-house R scripts. Pathway enrichment analysis of the proteomics dataset was conducted using the web tool STRING v10.070. For the metabolomics data, due to a lack of well-annotated metabolome databases for maize, the pathway enrichment analysis was conducted as follows. First, enrichment scores (ES) for each pathways were determined using the following formula: ES = (k/m)/(n/N) where (# of significant metabolites in pathway(k)/total # of detected metabolites in pathway(m))/(total # of significant metabolites(n)/total # of detected metabolites(N)). Then, the statistical significance was assessed using a Fisher one-sided exact test. The STITCH v5.0 beta web tool71 was used to investigate metabolite-protein interactions on maize endogenous pathways. The list of disturbed proteins and metabolites, including the protein EPSPS, was uploaded and the metabolic networks was studied using STITCH v5.0 initial parameters.
Additional Information
How to cite this article: Mesnage, R. et al. An integrated multi-omics analysis of the NK603 Roundup-tolerant GM maize reveals metabolism disturbances caused by the transformation process. Sci. Rep. 6, 37855; doi: 10.1038/srep37855 (2016).
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References
Van Montagu, M. It is a long way to GM agriculture. Annu Rev Plant Biol 62, 1–23 (2011).
Parisi, C., Tillie, P. & Rodriguez-Cerezo, E. The global pipeline of GM crops out to 2020. Nat Biotechnol 34, 31–36 (2016).
James, C. Global Status of Commercialized Biotech/GM Crops: 2015. ISAAA Brief 51 (2015).
AHTEG. Guidance Document on Risk Assessment of Living Modified Organisms. United Nations Environment Programme Convention for Biodiversity. http://www.cbd.int/doc/meetings/bs/mop-05/official/mop-05-12-en.pdf (Date of access:24/10/2016) (2010).
Benbrook, C. Enhancements Needed in GE Crop and Food Regulation in the U.S. Frontiers in public health 4, 59 (2016).
Millstone, E., Brunner, E. & Mayer, S. Beyond substantial equivalence. Nature 401, 525–526 (1999).
Cuhra, M. Review of GMO safety assessment studies: glyphosate residues in Roundup Ready crops is an ignored issue. Environmental Sciences Europe 27, 1–14 (2015).
Heinemann, J. A., Kurenbach, B. & Quist, D. Molecular profiling–a tool for addressing emerging gaps in the comparative risk assessment of GMOs. Environ Int 37, 1285–1293 (2011).
Agapito-Tenfen, S. Z., Guerra, M. P., Wikmark, O. G. & Nodari, R. O. Comparative proteomic analysis of genetically modified maize grown under different agroecosystems conditions in Brazil. Proteome science 11, 46 (2013).
Agapito-Tenfen, S. Z. et al. Effect of stacking insecticidal cry and herbicide tolerance epsps transgenes on transgenic maize proteome. BMC Plant Biology 14, 1–19 (2014).
Barros, E. et al. Comparison of two GM maize varieties with a near-isogenic non-GM variety using transcriptomics, proteomics and metabolomics. Plant biotechnology journal 8, 436–451 (2010).
Zolla, L., Rinalducci, S., Antonioli, P. & Righetti, P. Proteomics as a complementary tool for identifying unintended side effects occurring in transgenic maize seeds as a result of genetic modifications. J Proteome Res 7, 1850–1861 (2008).
Brandao, A. R., Barbosa, H. S. & Arruda, M. A. Image analysis of two-dimensional gel electrophoresis for comparative proteomics of transgenic and non-transgenic soybean seeds. J Proteomics 73, 1433–1440 (2010).
Barbosa, H. S., Arruda, S. C., Azevedo, R. A. & Arruda, M. A. New insights on proteomics of transgenic soybean seeds: evaluation of differential expressions of enzymes and proteins. Anal Bioanal Chem 402, 299–314 (2012).
Arruda, S. C., Barbosa, H. S., Azevedo, R. A. & Arruda, M. A. Comparative studies focusing on transgenic through cp4EPSPS gene and non-transgenic soybean plants: an analysis of protein species and enzymes. J Proteomics 93, 107–116 (2013).
Lehesranta, S. J. et al. Comparison of tuber proteomes of potato varieties, landraces, and genetically modified lines. Plant Physiol 138, 1690–1699 (2005).
Wang, L. et al. Comparative proteomics of Bt-transgenic and non-transgenic cotton leaves. Proteome science 13, 15 (2015).
Gong, C. Y., Li, Q., Yu, H. T., Wang, Z. & Wang, T. Proteomics insight into the biological safety of transgenic modification of rice as compared with conventional genetic breeding and spontaneous genotypic variation. J Proteome Res 11, 3019–3029 (2012).
Ricroch, A. E., Bergé, J. B. & Kuntz, M. Evaluation of Genetically Engineered Crops Using Transcriptomic, Proteomic, and Metabolomic Profiling Techniques. Plant Physiol 155, 1752–1761 (2011).
Manetti, C. et al. A metabonomic study of transgenic maize (Zea mays) seeds revealed variations in osmolytes and branched amino acids. J Exp Bot 57, 2613–2625 (2006).
EFSA. Guidance on selection of comparators for the risk assessment of genetically modified plants and derived food and feed. EFSA J 2149, doi: 10.2903/j.efsa.2011.2150 (2011).
Losey, J. E., Rayor, L. S. & Carter, M. E. Transgenic pollen harms monarch larvae. Nature 399, 214, doi: 10.1038/20338 (1999).
Hilbeck, A. & Schmidt, J. E. U. Another view on Bt proteins – how specific are they and what else might they do? Biopesti Int 2, 1–50 (2006).
Hilbeck, A., Meier, M. & Trtikova, M., Underlying reasons of the controversy over adverse effects of Bt toxins on lady beetle and lacewing larvae. Environmental Sciences Europe 24, doi: 10.1186/2190-4715-24-9 (2012).
Hammond, B., Dudek, R., Lemen, J. & Nemeth, M. Results of a 13 week safety assurance study with rats fed grain from glyphosate tolerant corn. Food Chem Toxicol 42, 1003–1014 (2004).
Hammond, B. et al. Results of a 90-day safety assurance study with rats fed grain from corn rootworm-protected corn. Food Chem Toxicol 44, 147–160 (2006).
Spiroux de Vendomois, J. et al. Debate on GMOs health risks after statistical findings in regulatory tests. Int J Biol Sci 6, 590–598 (2010).
Seralini, G.-E. et al. Genetically modified crops safety assessments: present limits and possible improvements. Environ Sci Eur 23, 10 (2011).
Doull, J. et al. Report of an Expert Panel on the reanalysis by of a 90-day study conducted by Monsanto in support of the safety of a genetically modified corn variety (MON 863). Food Chem Toxicol 45, 2073–2085 (2007).
Mesnage, R. & Séralini, G.-É. The Need for a Closer Look at Pesticide Toxicity during GMO Assessment, in Practical Food Safety: Contemporary Issues and Future Directions (eds Bhat, R. & Gómez-López, V. M. ) (John Wiley & Sons, Ltd, Chichester, UK, doi: 10.1002/9781118474563.ch10. 2014).
Mesnage, R. et al. Transcriptome profile analysis reflects rat liver and kidney damage following chronic ultra-low dose Roundup exposure. Environ Health 14, 70 (2015).
Monsanto. Safety Assessment of Roundup Ready Corn Event NK603 http://www.monsanto.com/products/documents/safety-summaries/corn_pss_nk603.pdf (Date of access: 24/10/2016) (2002).
Ocana, M. F., Fraser, P. D., Patel, R. K., Halket, J. M. & Bramley, P. M. Mass spectrometric detection of CP4 EPSPS in genetically modified soya and maize. Rapid Commun Mass Spectrom 21, 319–328 (2007).
Marchler-Bauer, A. et al. CDD: NCBI’s conserved domain database. Nucleic Acids Res 43, D222–226 (2015).
National Academies of Sciences, E., and Medicine. Genetically Engineered Crops: Experiences and Prospects (Washington, DC: The National Academies Press doi: 10.17226/23395 2016).
OECD. Safety Considerations for Biotechnology: Scale-up of Crop Plants. Paris: OECD. https://www.oecd.org/env/ehs/biotrack/1958527.pdf (Date of access: 24/10/2016) (1993).
Codex Alimentarius Commission Guideline for the Conduct of Food Safety Assessment of Foods Using Recombinant DNA Plants. Doc CAC/GL 45-2003. Rome: World Health Organization and Food and Agriculture Organization. http://www.fao.org/input/download/standards/10021/CXG_045e.pdf (Date of access: 24/10/2016) (2003).
AHTEG Final Report of the Ad Hoc Technical Expert Group on Risk Assessment and Risk Management under the Cartagena Protocol on Biosafety. UNEP/CBD/BS/AHTEG-RA&RM/4/6. http://www.cbd.int/doc/meetings/bs/bsrarm-04/official/bsrarm-04-06-en.pdf (Date of access: 24/10/2016) (2012).
Latham, J. R., Wilson, A. K. & Steinbrecher, R. A. The mutational consequences of plant transformation. Journal of biomedicine & biotechnology 2006, 25376 (2006).
Fonseca, C. et al. In vitro culture may be the major contributing factor for transgenic versus nontransgenic proteomic plant differences. Proteomics 15, 124–134 (2015).
Garcia-Canas, V., Simo, C., Leon, C., Ibanez, E. & Cifuentes, A. MS-based analytical methodologies to characterize genetically modified crops. Mass spectrometry reviews 30, 396–416 (2011).
Simó, C., Ibáez, C., Valdés, A., Cifuentes, A. & García-Cañas, V. Metabolomics of Genetically Modified Crops. International Journal of Molecular Sciences 15, 18941–18966 (2014).
Heinemann, J. A. & El-Kawy, O. A. Observational science in the environmental risk assessment and management of GMOs. Environ Int 45, 68–71 (2012).
D’Alessandro, A. & Zolla, L. We are what we eat: food safety and proteomics. J Proteome Res 11, 26–36 (2012).
Trtikova, M., Wikmark, O. G., Zemp, N., Widmer, A. & Hilbeck, A. Transgene Expression and Bt Protein Content in Transgenic Bt Maize (MON810) under Optimal and Stressful Environmental Conditions. PLoS ONE 10, e0123011 (2015).
Vidal, N., Barbosa, H., Jacob, S. & Arruda, M. Comparative study of transgenic and non-transgenic maize (Zea mays) flours commercialized in Brazil, focussing on proteomic analyses. Food chemistry 180, 288–294 (2015).
Frank, T., Rohlig, R. M., Davies, H. V., Barros, E. & Engel, K. H. Metabolite profiling of maize kernels–genetic modification versus environmental influence. J Agric Food Chem 60, 3005–3012 (2012).
Coll, A. et al. Natural variation explains most transcriptomic changes among maize plants of MON810 and comparable non-GM varieties subjected to two N-fertilization farming practices. Plant Mol Biol 73, 349–362 (2010).
Li, Z., Wang, Z. & Li, S. Gene chip analysis of Arabidopsis thaliana genomic DNA methylation and gene expression in response to carbendazim. Biotechnology letters 37, 1297–1307 (2015).
Hauser, M.-T., Aufsatz, W., Jonak, C. & Luschnig, C. Transgenerational epigenetic inheritance in plants. Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms 1809, 459–468 (2011).
Seralini, G.-E. et al. Republished study: long-term toxicity of a Roundup herbicide and a Roundup-tolerant genetically modified maize. Environmental Sciences Europe 26, 14 (2014).
Esco Working Group, M. EFSA Compendium of botanicals that have been reported to contain toxic, addictive, psychotropic or other substances of concern. EFSA Supporting Publications 6, doi: 10.2903/j.efsa.2012.2663 (2009).
Tomar, P. C., Lakra, N. & Mishra, S. N. Cadaverine: a lysine catabolite involved in plant growth and development. Plant signaling & behavior 8, doi: 10 4161/psb 25850 (2013).
Simon-Sarkadi, L., Ludidi, N. & Kocsy, G. Modification of cadaverine content by NO in salt-stressed maize. Plant signaling & behavior 9, e27598 (2014).
Minois, N., Carmona-Gutierrez, D. & Madeo, F. Polyamines in aging and disease. Aging (Albany NY) 3, 716–732 (2011).
Álvarez González, M. Á., Calles-Enríquez, M., Fernández García, M. & Ladero Losada, V. M. Toxicological Effects of Dietary Biogenic Amines. Curr Nutr Food Sci 6, 145–156 (2010).
Nebelin, E., Pillai, S., Lund, E. & Thomsen, J. On the formation of N-nitrosopyrrolidine from potential precursors and nitrite. IARC scientific publications 183–193 (1980).
Soda, K., Dobashi, Y., Kano, Y., Tsujinaka, S. & Konishi, F. Polyamine-rich food decreases age-associated pathology and mortality in aged mice. Exp Gerontol 44, 727–732 (2009).
Couto, N., Wood, J. & Barber, J. The role of glutathione reductase and related enzymes on cellular redox homoeostasis network. Free Radical Biology and Medicine 95, 27–42 (2016).
Milligan, A. S., Daly, A., Parry, M. A. J., Lazzeri, P. A. & Jepson, I. The expression of a maize glutathione S-transferase gene in transgenic wheat confers herbicide tolerance, both in planta and in vitro. Molecular Breeding 7, 301–315 (2001).
Minocha, R., Majumdar, R. & Minocha, S. C. Polyamines and abiotic stress in plants: A complex relationship. Frontiers in Plant Science 5 (2014).
Boocock, M. R. & Coggins, J. R. Kinetics of 5-enolpyruvylshikimate-3-phosphate synthase inhibition by glyphosate. FEBS Letters 154, 127–133 (1983).
Armendariz, O., Gil-Monreal, M., Zulet, A., Zabalza, A. & Royuela, M. Both foliar and residual applications of herbicides that inhibit amino acid biosynthesis induce alternative respiration and aerobic fermentation in pea roots. Plant Biol (Stuttg) 18, 382–390 (2016).
Evans, A. M. et al. High Resolution Mass Spectrometry Improves Data Quantity and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High-Throughput Profiling Metabolomics. Metabolomics 4, 132 (2014).
DeHaven, C. D., E., A., Dai, H. & Lawton, K. A. Software Techniques for Enabling High-Throughput Analysis of Metabolomic Datasets. Metabolomics, Dr Ute Roessner (Ed.), ISBN: 978-953-51-0046-1, InTech, doi: 10.5772/31277 (2012).
Dehaven, C. D., Evans, A. M., Dai, H. & Lawton, K. A. Organization of GC/MS and LC/MS metabolomics data into chemical libraries. Journal of cheminformatics 2, 9 (2010).
Team, R. C. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2015. URL http://www.R-project.org/ (Date of access: 24/10/2016) (2015).
Dray, S. & Dufour, A.-B. The ade4 Package: Implementing the Duality Diagram for Ecologists. 2007 22, 20 (2007).
Meng, C., Kuster, B., Culhane, A. C. & Gholami, A. M. A multivariate approach to the integration of multi-omics datasets. BMC bioinformatics 15, 1–13 (2014).
Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43, D447–452 (2015).
Szklarczyk, D. et al. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44, D380–384 (2016).
Acknowledgements
We thank Pr. Nigel Crawford (USCD) for careful reading of the manuscript and helpful comments and suggestions. This work was funded by the Sustainable Food Alliance (USA), whose support is gratefully acknowledged.
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R.M. and S.Z.A. interpreted the data, and drafted the manuscript. V.V. performed the statistical analysis. G.R. and M.W. conducted the proteome experiment. R.O.N. assisted with data interpretation. G.E.S. conceived the animal feeding trial and provided maize samples for analysis. M.N.A. and G.E.S. conceived the study. M.N.A. coordinated the investigation and drafted the manuscript. All authors reviewed the manuscript.
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Mesnage, R., Agapito-Tenfen, S., Vilperte, V. et al. An integrated multi-omics analysis of the NK603 Roundup-tolerant GM maize reveals metabolism disturbances caused by the transformation process. Sci Rep 6, 37855 (2016). https://doi.org/10.1038/srep37855
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Gord Bestwick
Nature, you are publishing things that have Seralini attached to them?
Come on, you know better than this.
George B
Seriously Nature, a Seralini paper? Only ever republished on non-predatory journals to discuss 'methodological controversies'.
mudskipper5 Replied to George B
My thoughts as well. Wondering on the peer review process on this. It will be interesting over the next few weeks to see this reviewed by the scientific community and see how it stands up to scrutiny by those not in the anti-GMO camp.
mem_somerville
Am I reading the protein data right--the top protein difference they find in both the Isogenic x NK603, untreated and R treated, is a corn rot fungus tubulin?
Here's their spreadsheet: http://www.screencast.com/t...
Here's the protein: http://www.uniprot.org/unip...
If they have infected corn, then all the protein changes that they claim could be related to that and have absolutely nothing to do with their conclusions.
Claire Replied to mem_somerville
Certainly troubling that the control (isogenic) is very enriched for Gibberella ear rot proteins, and additionally for multiple pathogenesis-related proteins including trypsin inhibitors, chitinases, and beta-1,3-glucanase, all of which are characterized responses to gibberella infection.
See
1. "Proteomic profiling of two maize inbreds during early gibberella ear rot infection." https://www.ncbi.nlm.nih.go... and
2. "Greenhouse and field testing of transgenic wheat plants stably
expressing genes for thaumatin-like protein, chitinase and glucanase
against Fusarium graminearum." https://www.ncbi.nlm.nih.go....
Gibberella zeae = Fusarium graminearum = ear rot.
Mary Mangan Replied to Claire
Yeah, look at how many genes change with a fungus challenge. It's certainly something to be careful about in these kinds of studies.
Ena Valikov Replied to Mary Mangan
How many aliases are you posting under Mem_Sommerville / Mary Mangan ?
Mary Mangan Replied to Ena Valikov
Yes, Ena, it's all a giant conspiracy to bother you.
Ena Valikov Replied to Mary Mangan
You aren't bothering me, Mary. My sister passed on some great pointers after doing her rotation through the psych ward at USC. When a mentally unhinged person believes they are Napoleon, arguing them out of it is an exercise in futility.
patzagame Replied to mem_somerville
I'm trying to understand where you are going with this,if you don't mind,Mary,mem from somerville. Are you suggesting that all the corn samples are contaminated by corn rot fungus? or just the engineered samples? And the spreadsheet does list that tubulin,but is that really the "top" protein difference?
Mary Mangan Replied to patzagame
If the plants that are in this study are battling an infection of fungus, it could change their gene expression.
Are you a biologist--or do you need me to explain that in more detail?
patzagame Replied to Mary Mangan
I'm not a biologist,just looking for some clarity. So are all the plants infected including the iso-gene parent? and is it mentioned in the study that there was an infection present?
Ena Valikov Replied to patzagame
Pat, think about it. The only way a plant can be grown without any environmental microorganisms is to be grown in an autoclaved sterile environment. Presence of minute traces of components of microorganisms (protein, tubulin) isn't the least bit surprising, nor relevant, for a plant grown under normal conditions OUTSIDE, where fungi are ubiquitous., Remember, the analytical methods used are ultra sensitive, HPLC-MS detects parts per billion!
Lets see Mary post a proteomics study on this corn entirely free of all environmental contaminants.
patzagame Replied to Ena Valikov
Sure,exposure to contaminates happens naturally, I was just wondering however if the presence of that protein necessitates the fact that the the plants were infected,or does that protein presents itself naturally as part of the corn plants protein profile. Is it possible that the disturbed proteins and metabolites will be explained away by blaming it on the corn rot fungus and is this the angle that will be used by industry champions?
Ena Valikov Replied to patzagame
Based on our previous experience with frivolous criticisms, this is the talking point industry will use, instead of replicating this study. Mere presence of traces of a protein, doesn't mean an actual INFECTION.
This is what these plants would look like if they were infected...do you think these researchers would sample corn that looked like this...?
http://www.krugerseed.com/A...
I don't.
Don't you think the study would report presence of mycotoxins this fungus produces?
I do. Yet, they aren't reported!
Kangaface Replied to Ena Valikov
Ena you are correct, and also this corn was tested for mycotoxins and none were found above regulatory levels--and the levels were the same in non-GMO and GMO corn. So it looks as if Mary's speculations are futile.
mem_somerville Replied to Kangaface
Oh, where's the mycotoxin data? As Ena noted, they aren't reported. I didn't see it. Are you one of the researchers Kangaface? When you say it wasn't found above regulatory levels--are you saying it was found, though?
Could be early stages of infection and a plant would still be mounting a response. Regulatory levels aren't the same as what a plant might be detecting.
And the protocol said all ears were required to be picked, so Ena is, typically, wrong on that point too.
So, Kanga, how do you explain the highest fold changes for the plant pathogen protein?
First Officer Replied to mem_somerville
Let's say, for sake of argument, the lack of fungal proteins is due to the DNA change made by GE techniques. (through, as yet, unknown mechanism). This puts Seralini and other anti-gmo'ers in the position of advocating we stay with corn that contains the higher quantity of these non-corn proteins.
Ena Valikov Replied to First Officer
You just made word soup. There are at least two trolls who interpreted it to mean something.
Ena Valikov Replied to First Officer
You are once again making word soup. Please post your name, scientific credentials and institutional affiliation.
mem_somerville Replied to Ena Valikov
This is why nobody asks Ena to do research. She's not too good with protocols.
Craig Babington Replied to Ena Valikov
Wrong Ena. Plants can be infected and not bother the ear of corn . You might want to brush up on your knowledge of corn plant diseases.
Mary Mangan Replied to patzagame
So are you saying you'd believe that the changes in protein levels matter, but only if it matches your narrative?
patzagame Replied to Mary Mangan
I'm not saying anything of the sort,why are you putting words in my mouth?
Mary Mangan Replied to patzagame
So you'd accept if the "disturbed" proteins were disturbed because the plants were battling a fungus. Super.
patzagame Replied to Mary Mangan
If that is the cause,sure. But I'd want to know why it was effecting the GE plants and not the isogene.
Mary Mangan Replied to patzagame
Let's hope that the researchers will have an explanation for the differences.
Dustin Replied to Mary Mangan
Perhaps the plants were battling a fungus due to the modification process. Is it possible the genetic engineering or glyphosate application are having unintended effects on disease immunity due to changes in the plants or soil?
Ena Valikov Replied to Dustin
There is no perhaps. There was no fungus....an organism, just traces of a single protein-tubulin. Never mind any evidence of an infection, no matter how many times anonymous pesticide trolls repeat this silly lie.
Rod Herman Replied to Dustin
Anything is possible, but without evidence from replicated plots, it is not likely. If by chance the GM line was better protected from disease, what is the concern?
patzagame Replied to Mary Mangan
Can I ask you a question,Mary,since I know you like to science? If the transformation process of genetically engineered plants does alter their proteins and metabolites,why wouldn't you want to investigate that further? Why pass it off on a corn virus?
Mary Mangan Replied to patzagame
I came to look at this data to see what it showed. Because that's how it works. And it's interesting to see why the results of groups varied from all the previous work. I've read plenty of papers that examine this type of data. Pro-tip: I'm not the one passing it off. I'm fascinated with their data.
What you do then is you evaluate the strains, the conditions, etc. I promise you I was shocked to find out that the biggest change in protein levels was a protein not even in the corn. I didn't expect that at all. Isn't science fascinating?
PS: It's not a virus.
patzagame Replied to Mary Mangan
I meant fungus,my mistake.
patzagame Replied to Mary Mangan
Science is fascinating,and one thing for sure is its never settled.
Claire Replied to patzagame
Hi patzagame. I'm not Mary as another commenter suggested, but I'd like to jump in and put my personal concerns with this paper in plain English for other non-biologists who might stumble on this discussion. First off, a major part of my day-to-day job is doing the same type of protein difference calculations that are done in this paper. I work in an academic lab, and have no stake in anything, really, except that things are accurate.
In these experiments, you start with at least two samples that you hope only differ in one way. This paper was trying to test protein levels in control (isogenic) vs. genetically modified wheat. However, the proteomics enrichment in the supplementary table strongly strongly hint that there is something else that is different between the two - the control (normal, unmodified maize) looks infected.
This type of enrichment analysis isn't really measuring mere presence or absence of a contaminant - it's saying that there is way more of something in one sample or another. If this was just a normal environmental contaminant, you'd find it in about the same proportion in both samples, and it wouldn't show up as enriched. The ear rot protein could easily be in all samples - but I see way more of it in the control. The single biggest difference between the control and the genetically modified maize is that the control appears to have a ton more of a corn fungal protein.
This sets off alarms, because among the other large differences between the two groups are another fungal protein and several other proteins associated with plant defense against the corn fungal protein. It says that the control not only has way more proteins from a fungus, it also has way higher levels of proteins associated with infection from that fungus. All in all, the control looks infected. Since you can't compare infected to non-infected and still measure the difference between control and genetically modified, this is a serious concern for the conclusions of this paper.
This is the analysis I would give to anyone with any sample who sent me this enrichment data to interpret. But without the raw data, there's only so much that can be inferred.
Ena Valikov Replied to Claire
If the control was "sick"/ infected- mycotoxins would have been reported. They aren't.
As it stands, they detected traces of a fungal protein, which does not mean the plant was infected, just contaminated, which is unsurprising!
So, yes, if you want to repeat it, please do. I'd love to see a proteomic /metabolomic analysis on a perfectly sterile plant.
Second off--this is highly doubtful for an anonymous untreaceable unaccountable account: "First off, a major part of my day-to-day job is doing the same type of protein difference calculations that are done in this paper. I work in an academic lab, and have no stake in anything, really, except that things are accurate."
Post your name and the university that employs you. Thanks.
Ena Valikov Replied to Claire
There is no evidence of you being a biologist qualified to interpret this very well done study that speaks for itself. Post your name and the institution where your identity can be verified.
Mary Mangan Replied to patzagame
The way they show the data it's a comparison between samples. All we can see is that the biggest fold change that they provide is a fungal gene of a known maize pathogen.
So we'll have to see how they explain it.
patzagame Replied to Mary Mangan
So we really don't know if the plants were diseased then.
grinninglibber
So the "substantial equivalence" meme is a LIE.
Surprise surprise.....
How many Big-GMO operatives can you count here?
Rightbiotech
The point of the paper is the magnitude of differences between two near isogenic lines of corn grown in the same place at the same time. The study does not come to different conclusions than other studies, because there are no other studies using these new and powerful techniques. It is new science passed through rigorous anonymous peer-review by subject experts.
The results indicate where NK603 differs from its non-modified parent and provides those responsible for the safety of this product (in my view the developer but also the regulators approving its use) with specific hypotheses to test for confirmation that NK603 is as safe as its conventional counterpart.
Mary Mangan Replied to Rightbiotech
So, Jack, if you think fold differences matter--then they matter whether it is the proteins that you want to be different or not, right? You can't make the case that the protein differences matter until it's an inconvenient protein and then pretend they don't.
How do you think the reviewers missed that the fungal protein changes were so apparent?
Rightbiotech Replied to Mary Mangan
That isn't what I said, Mary.
Mary Mangan Replied to Rightbiotech
You said the magnitude of the differences matter, right?
Rightbiotech Replied to Mary Mangan
I was referring to number of differences, not magnitude if individual differences.
Mary Mangan Replied to Rightbiotech
But these could certainly be a result of a pathogen's presence and the plant's response to it. In fact, many of the changes described perfectly characterize the response to such an assault by fungal pathogens, wouldn't you say?
Rod Herman Replied to Rightbiotech
Many replicated composition studies have been done with this GM event because this GM event has been bred (stacked) with other GM events. These previous studies included field-plot replicates within each location and multiple locations. For example the European Food Safety Authority requires 4 replicates per location and 8 locations for GM composition studies (32 plots per treatment). A single study without field-plot replication (as this study seems to be) is not even suitable for publication in most scientific journals because the "noise" in the data cannot be measured, so the "signal" cannot be attributed to anything beyond "noise". Field-plot replication is a basic requirement in this type research to be able to attribute any differences to the treatment groups rather than experimental error. The potential for field-plot location to result in a greater level of fungal disease in one grain sample compared with another (as seems likely based on specific protein levels observed in the subject study) provides an excellent example of why plot replication and separate plot compositional analysis is needed in such experiments. Every journal makes mistakes and publishes papers that slip through the peer-review process. Respected journals correct this problem with a retraction once the greater scientific community reviews the work. This is how the process works. It is not perfect, but it is self correcting.
Rightbiotech
The talk of the conventional corn being infected by a maize pathogen is speculation. The protein has a strong alignment with a fungal pathogen, but also with a protein found in a worm. It takes more than finding a sequence to diagnose a disease.
Some commentators are discussing the issue of 'burden of proof', attempting to shift it from those who develop and sell NK603 to the authors of a paper describing the best available science evaluating prior claims about the product. The burden of proof solidly remains with those who profit from the product and its reputation. Rhetorical dismissals of this work are not the same as evidence of safety.
Mary Mangan Replied to Rightbiotech
Yes, the authors should definitely address this possibility. It's very odd that they didn't in the body of the paper.
And that's not the only fungus protein. Both lists have more than one example of that plant pathogen's proteins.
I think we should address the issues with this paper here. Although there is prior data from the developers of NK603 that you'll certainly want to read up on: https://www.ncbi.nlm.nih.go...
Benjamin Edge Replied to Rightbiotech
Increased protein levels in the non-GM line as a result of insect feeding would be just as damning of this study as fungal proteins.
Rightbiotech Replied to Benjamin Edge
Not damning at all. It is a study of the differences & detection of differences would only further validate its methodology. But no reason to think insect feeding would be different because this maize is herbicide resistant, not insecticidal, and the conventional and NK603 grown in the same location at the same time.
Rod Herman Replied to Rightbiotech
One would expect different feeding damage in two different single plots since insects do not necessarily infest different field plots at the same intensity. This is just one environmental factor that is expected to vary, and is the reason field experiments should include replicated field plots that are analyzed independently for any endpoint.
Mary Mangan Replied to Rightbiotech
My previous reply was suppressed, so I'll try again.
Yes, Jack, I know you are on mailing lists with the authors. Could you please ask them to come by and address the speculation? We really should hear from them why the top protein change was a fungal protein.
Unless you actually want to call into question the methods and the accuracy of the protein calls. There are plenty of reasons to question how this work was done, I agree.
But as I posted before, you are going to want to have a look at the NK603 data in this paper, which people posting here want to pretend doesn't exist. I know it's inconvenient for you.
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.
https://www.ncbi.nlm.nih.go...
mem_somerville
There's a lot of nonsense drama below now, but I want to hear from the authors (Robin Mesnage asked me to post here, but I can't see if he's responding):
1. What is your explanation for the fact that top fold-change proteins in your data set are fungal proteins (and it's a known maize pathogen)?
2. Are you aware that fungal contamination could result in similar changes in regards to the pathway changes that you describe? Did you consider this at all? Why didn't you address this in your paper?
3. If you wish to dismiss your own top reported proteins, how can you stand by the importance of the fold-change claims you are making about other proteins?
Thanks for your guidance on this. It's very perplexing.
Rod Herman
Before deeply considering the importance of the analytical results, I would think one would look at the experimental design of the field experiment from which the grain samples came. Can someone please explain how grain samples from unreplicated field plots can be used to determine anything about the effects of the crop genetics or production method? Am I missing something? Is it now scientifically acceptable to evaluate crop varieties from single unreplicated plots at one location?
Ena Valikov Replied to Rod Herman
Hi Rod. There were replications....two separate growing seasons. Can you please cite the animal feeding trials in the peer reviewed literature on this commercialized corn... that were replicated twice? Furthermore, it is the feeding trials conducted on this crop that used a very flawed experimental design by comparing effects on rats fed GMOs grown in one location to controls fed crops grown in unrelated locations...instead of using proper comparators-fed corn grown SIDE BY SIDE, for two seasons, if you so desire-- to eliminate the influence of environmental factors on the composition of the corn.
Rod Herman Replied to Ena Valikov
I am confused by your comment. Are you claiming two replicates are reported and gave enough consistency to see a significant treatment effect compared to the replicate to replicate variation? Where does the paper claim replicate plots were analysed?
Ena Valikov Replied to Rod Herman
I am confused by you placing a different burden of proof on this study compared to feeding studies.
How many replicates of the feeding trial on this corn are in the published literature, Rod? The answer is-zero. When Seralini extended it to a full two years rather than the standard 90 days, increased morbidity with renal and hepatic disease and increased mortality was reported. It has not been replicated.
Furthermore, the study clearly explains why a proper comparison has to be on crops grown SIDE BY SIDE, unlike the corn in the diets in the feeding trials.
Rod Herman Replied to Ena Valikov
This was not a feeding study, but rather a grain composition study. The standard is a minimum of 8 sites with each site having 4 replicates. That is a minimum of 32 samples, not 1. You seem to be informed, so you are clearly trying to deceive.
Ena Valikov Replied to Rod Herman
No kidding?! Do you mean like this "feeding study" on this very corn,
Rod Herman of Dow AgriChemicals ?
Oh, by the way, why didn't you disclose your obvious conflict of interest, Rod Herman of Dow Agrichmicals...surely not to decieve? http://beachvetlbc.blogspot...
Rod Herman Replied to Ena Valikov
Are you referring to the feeding study retracted because conclusions were not supported by the data? While you seem a bit confused, are you questioning my ethics because I post under my actual real name? Do you expect a conflict of interest statement for very post? I certainly include one in my scientific publications - do you?
https://www.reddit.com/r/GM...
Ena Valikov Replied to Rod Herman
No, Rod. I am referring to the feeding study that SHOULD HAVE BEEN retracted and wasn't - by Hammond--discussed very thoroughly in my link.
Curiously every one of my comments asking for your COI disclosure was removed...pure coincidence, must be. Yes, I do expect you to disclose your affiliation with Dow.
I am a clinician and I have NO CONFLICTS OF INTEREST.
Rod Herman Replied to Ena Valikov
You are a an anti-GMO activist based on your extensive and focused posts. If you were knowledgeable about the safety assessment of GM crops then you would know who I am from my scientific publications. In science, one does not rely on blogs for critiques of scientific papers. Valid critiques are routinely published in the scientific literature. You can find these by using Google Scholar. It sifts out the crackpot posts. These comment sections are a great way to lead interested folks to the actual scientific literature, and point out the professional posters with a ideology that cannot be altered with evidence. Thank you for the opportunity to point this out in response to a classic anti-something poster:)
Ena Valikov Replied to Rod Herman
I am a licensed veterinary doctor who has decades of CLINICAL experience with these mutants present in most pet foods, who has NO CONFLICTS OF INTEREST--UNLIKE YOU, whose income depends on commercializing & defending these substandard, allergenic, unsafe crops.
Your scientific publications on behalf of Dow? Blatantly biased agenda-driven propaganda, in other words- worthless.
Rod Herman Replied to Ena Valikov
I would not ask a molecular biologist or biochemist to treat my dog for fleas, nor ask a veterinarian to interpret a crop composition study. I apologize to other veterinarians for the embarrassment caused by these posts. I know that a farm boy that studies agricultural science and then spends a career trying to bring improved tools to farmers is the devil in your mind. My last quarter of a century restoring wildlife habitat is equally evil...
Ena Valikov Replied to Rod Herman
Unfortunately, my patients eat the products of your junk science- whom you aren't licensed to diagnose or treat. And this isn't by choice, since Pesticide Corporations you and Mary work for spent millions to fight labeling them.
Most people realize that someone who works for Dow is using the word "scientist" very loosely.
You can do the grandest charity of all by actually being a scientist rather than an overpaid hack-but the money paid to sell your soul & publish garbage is just too good.
Keep on keeping on with your insults...they wash off me like water off a duck.
Rod Herman Replied to Ena Valikov
You reveal yourself when you assume that everyone that works for a large corporation leaves their ethics behind, or when you assume that someone intelligent enough to become a veterinarian is not superstitious or paranoid? As a matter of fact, mental illness is more common in intelligent people.
Ena Valikov Replied to Rod Herman
I revealed myself when I dissected your junk science which you evaded like the plague.
When you throw mud at Seralini, it comes flying right back at you.
http://beachvetlbc.blogspot...
You have one gear, and that is ad hominem. It isn't surprising.
Rod Herman Replied to Ena Valikov
Cite a reputable scientific journal, and I would be glad to follow your link, but I do not get my facts from blog spots. That is a difference between us.
Ena Valikov Replied to Rod Herman
Rod, clinicians do what you aren't licensed to do--diagnose and treat patients. We aren't required nor incentivized to publish, but we most certainly are required to keep up with the scientific literature- none of which was published by you.
As far as my blog, it explains very clearly why the GMO ( this particular NK603 mutant) you are defending is allergenic, toxic, substandard junk that I wouldn't recommend for kitty litter.
Rod Herman Replied to Ena Valikov
I am glad that my veterinarian bases her diagnosis and treatments on facts and evidence. As far as the safety of GMOs, I'll go with the most respected scientific organizations on the planet, not your blog.
https://matthespian.wordpre...
Ena Valikov Replied to Rod Herman
Not a single credible veterinary organization endorses safety of these foods for animals. Neither do millions of physicians. The only propaganda industry managed to squeeze out is an aged position statement from the AMA that represents less than 15% of physicians-- which recommended mandatory safety testing.
What ethical medical practitioner not riddled with conflicts of interest endorses GMO junk food? No one.
Nothing you post in your conflict ridden Dow AgriChemical BLOG will change that.
Rod Herman Replied to Ena Valikov
My link allows readers to go directly to the web sites of the most respected scientific organizations in the world. Thank you for the posts that allow me to send folks to the actual scientists! Without your help, many would not have access to these facts.
Ena Valikov Replied to Rod Herman
I am sure smart readers will be reading a blog by a Dow Chemical pesticide merchant just as clearly as they will be feeding their beloved family your line of pesticides, Round Up sprayed on this corn, PCBs, Agent Orange and DDT, propaganda merchant.
Ena Valikov Replied to Rod Herman
The most informative link is to your LinkedIn account that shows you lack a graduate science degree and work for Dow AgriChemicals.
Rod Herman Replied to Ena Valikov
You seem to have a reading comprehension problem.
Ena Valikov Replied to Rod Herman
I have no difficulty with reading or comprehension You are projecting, it might be your lack of a graduate science education...a measly BS & MS in entomology?!..you are just a glorified exterminator, Mr Dow Chemical.
And your image on LindedIn is precious.
I have a serious question- I would have hoped with all the money you are paid to sell poisons you'd be able to afford a shave. When did you last have a hair cut.... in 1980? https://gmoanswers.com/expe...
Mike Replied to Ena Valikov
Side by side but 85 meters apart
.
Rod Herman Replied to Ena Valikov
This was a composition study, not a feeding study. The feeding study that you cite was retracted from the scientific journal that initially published it because the conclusions were not supported by the data. One can read about this by Googling "Seralini affair". Feeding studies are kept small for humanitarian reasons. The grain used in these studies must be shown to be compositionally equivalent before it is used in feeding studies. The subject study seems to show that the grain samples were not suitable for a feeding study based on the conclusions of the authors of non-equivalence. It is good that these data were made public even if the conclusions from the data are different than stated by the authors. It appears to suggest that the retracted feeding study had another major flaw.
First Officer
I'm curious what kind of result there would be if this was done between non HT sunflowers and HT sunflowers, both non-gm.
First Officer
I get the feeling that what is called substantially equivalent or not in the conclusions was determined by what they found to be different between these two samples and not what is typically found between a body of samples with both negative and positive controls. Further, i find this fails to explain how differences they did find would have been, "different",if the same trait differences were affected by means other than GE. We could, in principle, create the same DNA base pairs via other methods and selection. We'd just have to wait for the right combination to be occur via directed chance.
Ena Valikov
Dear Nature. In the interest of science integrity and transparency, representatives of Pesticide Corporations like Rod Herman who works for Dow AgriChem need to disclose their conflicts of interest instead of flagging and removing responses of independent scientists to tilt this forum in industry's interests.
Claire
I wanted to add something that has been bothering me about the detected fungal proteins in the maize samples.
In proteomics analyses like this, you don’t just accidentally identify proteins from non-target species. Protein identification is based on matching mass spectra to predicted protein masses, and all possible proteins from all species is too large a search space. Instead, proteins are identified from a user-created database of possible proteins, usually the proteome of the species you’re looking at. And that’s what the methods say happened: “The taxonomy selected was Zea mays”. But yet, the enriched proteins list includes proteins from Gibberella and Colletotrichum. How did these species make it into the protein sequence database? It might end up being a small thing, but this is a discrepancy between the described methods and the presented data that I'd like to hear a response to.
Damian
If the question here is whether the inserted allele in NK603 caused a phenotypic difference (at proteome and metabolic levels), isn't the inactivated complement the correct control, and not the "closest isogenic line" ?
Chris Preston Replied to Damian
If you look at the paper, the stated intention was to address the issue of substantial equivalence: "In an effort to provide insight into the substantial equivalence classification of a Roundup tolerant NK603 GM maize". However, the authors do not address substantial equivalence as interpreted by regulatory agencies, instead they address something much closer to what you suggest.
The control used should have been the one that addressed the hypothesis put forward. In the case of substantial equivalence, what regulatory agencies look at is whether there is evidence that the crop has a composition that might be outside the range of what humans are already exposed to in their diet, as that may indicate the need for more testing. Therefore, you will see most substantial equivalence tests address not only the non-transformed isogenic line and/or a null transformant, but also the range of known compositions for that crop.
If the authors truly wanted to address substantial equivalence, they should have tested the NK603 maize against a range of maize cultivars common in diets. If the idea was to address whether the specific transformation caused differences, then a null transformant would have been more appropriate. There are likely to be a reasonable number of differences between DKC 2678 and DKC 2675 irrespective of the transgene, so the experimental design used would be unable to address that question adequately.
In short, this study was flawed from the beginning as it wasn't set up to test the hypothesis that the authors claimed they were testing. This is irrespective of the issue with Gibberella moniliformis infection.
Chris Preston Replied to Damian
I posted this response before, but it was deleted as well as all my other comments about the paper on this site. It is almost as if someone doesn't want there to be any criticism of this work.
If you look at the paper, the stated intention was to address the issue of substantial equivalence: "In an effort to provide insight into the substantial equivalence classification of a Roundup tolerant NK603 GM maize". However, the authors do not address substantial equivalence as interpreted by regulatory agencies, instead they address something much closer to what you suggest.
The control used should have been the one that addressed the hypothesis put forward. In the case of substantial equivalence, what regulatory agencies look at is whether there is evidence that the crop has a composition that might be outside the range of what humans are already exposed to in their diet, as that may indicate the need for more testing. Therefore, you will see most substantial equivalence tests address not only the non-transformed isogenic line and/or a null transformant, but also the range of known compositions for that crop.
If the authors truly wanted to address substantial equivalence, they should have tested the NK603 maize against a range of maize cultivars common in diets. If the idea was to address whether the specific transformation caused differences, then a null transformant would have been more appropriate. There are likely to be a reasonable number of differences between DKC 2678 and DKC 2675 irrespective of the transgene, so the experimental design used would be unable to address that question adequately.
In short, this study was flawed from the beginning as it wasn't set up to test the hypothesis that the authors claimed they were testing. This is irrespective of the issue with Gibberella moniliformis infection that means the conclusions drawn by the authors are unsafe. The presence of significantly more of these proteins in the non-GM corn, despite the authors running a screen just for the the maize proteome, means that it is impossible to tell whether any differences between the two samples were due to fungal infection or the insertion of the gene.
The multiple flaws in this study mean that you really cannot conclude anything from the results.
Andrew McDavid
Is the proteomics and metabolomics data released? It appears some sort of mixed model would be more appropriate to analyze the differences in these measures. It would be interesting to see if this effects the conclusions at all.
Ena Valikov Replied to Andrew McDavid
I suggest you actually read the study and look forward to reading yours. When are you planning on publishing it and have you already submitted it to Nature?
Ena Valikov Replied to Andrew McDavid
Interesting. Have you published any studies specific to transgenic plants or in the GMO bio-safety literature, Andrew?
Are you planning on refining and publishing an improved study or just doing Monday morning quarterbacking to stir up confusion?
Sorry if I sound blunt. I have to ask after Rod Herman's of Dow Chemical and Mary Mangan's /Rob Fraley/ Monsanto's *fungal infection*- mania ceased.
mem_somerville
This journal is not moderating comments properly, unfortunately. A comment of mine to the authors from days ago has been moved away. I will repeat it here because Robin Mesnage asked me to post--but obviously the site is not suited to proper scientific discussion.
Here's the original commment: http://screencast.com/t/I8d...
Luckily, we have a place where it can be discussed without a persistant lunatic fringe. I would encourage researchers to go to the PubMed page for the paper. Serious discussion of this paper won't be able to happen here, as trolls have down-rate options that will remove comments that do not align with the paper author's narrative.
My previous comment, reposted:
There's a lot of nonsense drama below now, but I want to hear from the authors (Robin Mesnage asked me to post here, but I can't see if he's responding):
1. What is your explanation for the fact that top fold-change proteins in your data set are fungal proteins (and it's a known maize pathogen)?
2. Are you aware that fungal contamination could result in similar changes in regards to the pathway changes that you describe? Did you consider this at all? Why didn't you address this in your paper?
3. If you wish to dismiss your own top reported proteins, how can you stand by the importance of the fold-change claims you are making about other proteins?
Thanks for your guidance on this. It's very perplexing.
mem_somerville Replied to mem_somerville
Here is the PubMed page: https://www.ncbi.nlm.nih.go...
Please leave scientific comments for the authors over there in the hope that they'll see them. They won't be able to see them here.
Farmer with a Dell Replied to mem_somerville
Stick to your guns Mary. Don't let the troll baastids wear you down.
mem_somerville Replied to Farmer with a Dell
I have the questions saved at PubMed. I will repost them every time they vanish.
Mary Mangan
Here are more of the comments that the troll team has suppressed. We've provided many links to appropriate literature in the field, including other multi-omics comparisions of GMO and isoline maize, illustrations of the fact that fungal presence triggers polyamines, and information about the fungal proteins in the list. But those are gone now. I know there have been as many as 130 comments before. But the facts aren't sitting well with folks who don't want to see them.
http://screencast.com/t/QRS...
Kanga claimed to have mycotoxin data about this work, but refused to provide it.
Ena Valikov Replied to Mary Mangan
You are the Monsanto Troll, Mary Mangan. Get out of here.
Eric Bjerregaard Replied to Mary Mangan
Thanks for taking the time to try to spread some truth.
Ena Valikov Replied to Eric Bjerregaard
Monsanto Propaganda
Paul Vincelli
By my reading of this paper, the three corn plantings that represented the source of grain samples for the three experimental treatments studied were not spatially randomized/replicated in the field. Therefore, the statistical effect of treatment is confounded with the effect of planting position, making it seemingly impossible to statistically separate one effect from the other. Even in such highly controlled environments as growth chambers, plants in different positions can produce significant differences in growth. In the field, position effects are even more likely, as variation in the physical, chemical, and biological environment can be substantial even in sites which appear to be superficially similar. Therefore, the conclusions about the treatment effects reported here are called into question. I know of no statistical analysis that can overcome this design flaw. Others have independently identified this as a major concern, as well (http://www.sciencemediacent....
The paper seems not to discuss -omics effects of conventional plant breeding approaches, which are substantial (Sustainability 2016, 8(5), 495; doi:10.3390/su8050495).
For the record, I report no conflicts of interest in the topic of genetically
engineered crops (GMOs) (http://out-of-the-box-vince....
Paul Vincelli Replied to Paul Vincelli
Here is a re-post of the link in the first paragraph:
http://www.sciencemediacent...
Ena Valikov Replied to Paul Vincelli
I wouldn't trust your scientific opinion in arithmetic, Paul Vincelli after you interpretation of Alison's livestock study. Bottom line is we need blinded life-long animal studies designed by experts --much unlike you, to assess health effects of these obviously metabolically & proteomically, as well as herbicide-residue-laden crops on animals, in whom NONE of you are experts...unless of course you want to eat all this untested corn yourselves. My patients will most certainly not be Guinea pigs in your reckless experiment.
Claire
This is comment is a synthesis of my previous missing comments on this paper. I'm only commenting on the proteomics and metabolomics data as presented in good faith, though noting that quantitative proteomics is incredibly sensitive and must be tightly controlled to give biologically informative differences.
This paper claimed to test protein levels in control vs. genetically modified wheat. However, the proteomics enrichment in the supplementary table strongly strongly hint that there is something else that is different between the two - the control (normal, unmodified maize) looks like its dealing with a fungal infection. The single biggest difference between the control and the genetically modified maize is that the control has high levels of a corn fungal protein. This sets off alarms, because among the other large differences between the two groups are another fungal protein and several other proteins associated with plant defense against the corn fungal pathogen. It says that the control not only has way more proteins from a fungus, it also has higher levels of proteins associated with infection from that fungus. These are multiple pathogenesis-related proteins including trypsin inhibitors, chitinases, and beta-1,3-glucanase, all of which are characterized responses to gibberella infection.
See 1. "Proteomic profiling of two maize inbreds during early gibberella ear rot infection." https://www.ncbi.nlm.nih.go... & 2. "Greenhouse and field testing of transgenic wheat plants stably expressing genes for thaumatin-like protein, chitinase and glucanase against Fusarium graminearum."https://www.ncbi.nlm.nih.go...
note: Gibberella zeae = Fusarium graminearum = ear rot.
Without absolute amounts in the metabolomics data, and normal ranges of
variation of cadaverine in maize, it's hard to draw any conclusions here. But cadaverine concentration is apparently very variable. "However, [cadaverine's] concentration varies widely between species, organs, and even between different developmental stages of the same plant (Felix 1987)" https://www.ncbi.nlm.nih.go...
Ena Valikov Replied to Claire
You have once again failed to prove a fungal infection in this corn, nor posted your real name & institutional affiliation. Anonymous posters have very little credibility in serious science circles because it is impossible to validate neither their scientific credentials nor industry bias.
mem_somerville
There's a good summary of the issues with this paper here: http://sciblogs.co.nz/code-...
Comments there are likely to be sane and stay around. Add your thoughts there too.
Rod Herman Replied to mem_somerville
It is very disturbing the way these comments are being moderated. I think that Nature Publishing should audit the way this has been handled as it possibly calls into question either the ethics or competence of the moderator(s).
Ena Valikov Replied to Rod Herman
I think the most disturbing to me is your deletion of my comments that link to your LinkedIn account, Rod Herman with a BS/MS in entomology & a life long career at Dow Chemical- pesticide manufacturer with a huge conflict of interest.
mudskipper5 Replied to Rod Herman
I agree. First of all, I was surprised to see comments permitted at all on a scientific journal article. But then to have someone treating the comments sections as if we were on a 4Chan discussion is very disturbing. If this is a scientific discussion, rude, non-productive comments should be removed.
Ena Valikov Replied to mem_somerville
Thanks for a link to a pesticide industry blog disguised as science.
Are you really as delusional as you appear, Mary Mangan?
You think people don't recognize industry propaganda?
Mike
First, some of the language in the paper bothered me. They repeatedly said the different enzyme and metabolite levels mean the plant's metabolism is "disturbed," and "impaired" by the EPSPS transgene. I think "different" is a better term since they didn't say the plants were suffering. I also could not find the actual levels anywhere, just the amount of variance.
Second, the varieties were planted 85 meters apart. They say this was to avoid cross-contamination but that much of a buffer invites some environmental effects.Since they weren't producing seed for planting they would have been better off using a randomized block design with smaller buffers; hand-pollinate and bag the ears, or just risk some contamination. It would have also been good if in the second crop they switched the cultivar positions but if they did they didn't mention it.
Third, they say this was the corn used to feed the rats in the widely-denounced Seralini study (two-year study published in 2012 and subsequently retracted), so the corn was sitting around for a few years I assume in separate bins. One of the differences observed was the levels of protein from a fungal corn rot pathogen--Giberella moniliformis (additional file 6, top of the list). Just the presence of that fungus can mess up metabolite levels.
Finally they make a big deal about the polyamines putrescine and cadaverine levels so I googled those terms + corn. Some people think elevated levels of those compounds are good for you: http://www.sciencedirect.co...
This got too long. In summary, there are differences but they don't seem detrimental and I don't think they proved it was due to the transgene. I'd also be very interested in seeing such a detailed analysis that included a few other cultivars.
Ena Valikov Replied to Mike
You obviously haven't a clue of the standards used in testing these crops in animals. The Seralini toxicity study reported increased morblidiy- liver and kidneys, as well as increased moratlity.
If you want to eat a crop that's been shown to increase rates of chronic diseases to your kids, be my guest. I don't even recommend that it's used for cat litter, yet.
This study shows convincingly that genetic engineering altered these crops radically and that they contain elevated levels of polyamines which aggravate allergies.
Until these are assessed by professional immunologists it is reckless to expose animals, never mind humans to these experimental crops.
It is long past time for these corporations to replicate animal feeding studies on these thus untested crops--in blinded life-long animal studies designed by independent animal medical experts & allergy specialists, as well as internal medicine specialists, because experimenting on people is unethical, and neither is suggesting that people eat this altered experimental corn, especially without obtaining their informed consent. Ethics 101.
Ena Valikov
The animal feeding study on this crop is highly suggestive of kidney and liver disease, as well as raises questions about allergies. When Seralini replicated the animal feeding study for 2 years, higher mortality due to kidney and liver disease was reported. It is time to perform well designed, blinded, life-long animal feeding studies on this corn to answer these questions with very significant impacts on animal and human health. These crops are not safe for animals or people until rigorous studies are published in peer reviewed open access journals, meeting stringent scientific standards.
Ena Valikov
Happy New Year Everyone. If you are unable to post under your real name and make disclosures-- your scientific credentials, as well as industry/ institutional affiliation, which can be checked and verified, rational scientist or member of the public, should not take anything you say seriously. Skepticism is most definitely warranted of any comments that are left by people who wish to keep their identities, biases and financial conflicts hidden.
For the un-initiated, there are two well known Pesticide Propaganda agents manipulating the comments. Dow Chemical Corporate "Scientists" with a BS & MS in Entomology, Rod Herman (easy to check online) as well as Monsanto (Mem_Somerville aka Mary Mangan)-likewise very well known to anyone who has been on Twitter and social media for any time at all.
Sink Chicken
So, when the gene structure is changed you get a different organism?
Chimps + genes = Humans?
tommy tunes
Could these GMO foods weaken people's immune systems and make antibiotics less effective in the Metabolic reaction to pathogens ?
This Review is extremely concerning ,
http://www.academia.edu/542...
Liv Langberg Replied to tommy tunes
The commersialized GMO foods have been proven safe to eat. The review you refer to seems to be an opinion piece. (comment)
Kangaface Replied to Liv Langberg
Incorrect--the only way you can prove that something is safe to eat is by long-term toxicity testing in animals (followed up by dose escalation trials in humans) yet this has not been done with commercialized GM crops.
Rod Herman Replied to Kangaface
By that standard, we have never proven any food safe. GM foods are the most thoroughly tested foods in history and that is why the most respected scientific organizations on the planet have concluded they are safe. The link below contains links to their websites (you are free to ignore the text on this actual page, as it is just a comprehensive collection of links to the scientific organizations and their statements).
https://matthespian.wordpre...
Ray Kinney Replied to Rod Herman
How long of a time period have feeding studies continued for? Chronic low dose accumulative effects need realistic and accurate assessment for toxicologic risk.
Rod Herman Replied to Ray Kinney
You question is a common tactic employed by those wanting to cast doubt on the safety of GM food. Please post a link to a feeding study that has ever been conducted with any food that would satisfy you about the safety of a GM food. Those trained in food safety and toxicology have conclude that the current evidence on GM food is sufficient to conclude safety. The link that I provided above verifies this.
Ray Kinney Replied to Rod Herman
Yes, it is a common tactic of intelligence wanting to learn more of the reality of our world... not simply casting doubt.
Rod Herman Replied to Ray Kinney
Many intelligent people are superstitious (believe in things not supported by evidence). Research scientist spend their lives learning about the reality of our world.
Ray Kinney Replied to Rod Herman
Isn't that the definition of NOT being intelligent? And, don't a lot of researchers suffer from not getting paid to do due diligence assessment because the industry does not pay for unsupportive research as well as supportive research?
Rod Herman Replied to Ray Kinney
The consensus concerning the safety of commercialized GM crops is not restricted to industrial scientists.
https://matthespian.wordpre...
http://www.siquierotransgen...
And hundreds of millions of dollars have gone into non-industry independent research.
http://europa.eu/rapid/pres...
Revisit your definition of intelligence and your position on GM food in light of all that research.
Ray Kinney Replied to Rod Herman
I'm not anti GE, it holds great potential for improving our food future. I am concerned with the potential gaps in our toxicologic knowledge and unintended consequences. It is NOT a 'done deal' QED situation of all GE foods being deemed 'safe' by currently accumulated consensus of researchers as you claim. Yes, it most probably IS a very substantial degree of safety, but that is a moving target, and science is an ongoing process. Historically we have had great toxicologic harm from new products and practices that were at first deemed to have been studied well enough that 'consensus' was achieved in the medical world, later to be shown to not only not having had consensus, but there was ignored research that had brought up considerable question (DES and other drug research, for example). Safety of products is complicated, and always subject to new scientific information needing to be considered.
Rod Herman Replied to Ray Kinney
Brussels sprouts and Lima beans are what keep me up at night! Where are the long-term, multi-generational, evolutionary, studies proving that they are safe:)
Well_Actualy Replied to Rod Herman
What kind of long term studies would satisfy you?
Because I could refer you to this systematic review of 12 long-term studies and 12 multigenerational studies (from 2 to 5
generations), and concluded that "GM plants are nutritionally equivalent to their non-GM counterparts and can be safely used in food and feed." (Snell et al. Assessment of the health impact of GM plant diets in long-term and multigenerational animal feeding trials: A literature review. Food and Chemical Toxicology. Volume 50, Issues 3–4, , pages 1134–1148)
or to the study by Van Eenennaam and Young, which looked at the health data of millions of cows over the last 30 years (i.e. from before GE crops were grown to when they were common in animal feed) who between them had eaten billions of portions of GE food, and found no alterations in the health of animals before and after GE was introduced into the food chain (Van Eenennaam and Young, Prevalence and impacts of genetically engineered feedstuffs on livestock populations. J Anim Sci. 2014 Oct;92(10):4255-78)
And there are plenty more safety studies out there.
But the real question is this - what is exclusive or unique about GMOs that merits such rigorous testing, yet excludes any other crop modification techniques? Why no requests for "long-term, multi-generational, evolutionary, studies" proving that crops produced by radioactive mutagenesis, or forced hybridisation, or protoplast fusion, or from the artificial creation of polyploids are safe? If your argument is that GMOs are made by scientists in a lab and that's why they're riskier, the same applies to seedless watermelons (also lab created). If the fact that GMOs have genes from other species is supposed to somehow make them intriniscally more dangerous, then you have to apply the same argument to sweet potatoes which have genes from bacteria naturally introduced thousands of years ago. If the issue is that we've had thousands of years to co-evolve with other crops, but not GMOs - I'm Western European Caucasian with some Eastern European and Dutch Huguenot roots. My ancestors didn't have access to bananas, or passion fruit, or squash, or pecans, or chillies, or macadamia nuts. They didn't co-evolve and adapt to any of them. But I eat them just fine nonetheless.
Sweet potatos, seedless watermelons, bananas, pecans etc did not undergo any testing, animal or human before becoming part of the food chain. Given this, why the continual insistence that all GE crops, regardless of trait should undergo animal and human testing?
Rod Herman Replied to Well_Actualy
I think you may have erroneously replied to my post?
Well_Actualy Replied to Rod Herman
(Was replying to a thread regarding long term testing of GE crops. This seems to be it, so guessing the right place)
Ray Kinney Replied to Rod Herman
So, after reading all of that research, how can you say that human bodies are not subject to adverse effects of glyphosate, because humans do not have the shikamate pathway that is targeted by this pesticide??? Our bodies obviously DO have beneficial bacterial assemblages that feature some with the shikamate pathway, that could also be targeted by glyphosate residues.
Rod Herman Replied to Ray Kinney
Everything we eat results in changes to our gut biota. All changes are not adverse. Why would you single out a fleetingly small residue of a synthetic herbicide when 99.99% of the pesticides we eat are produced by the plants themselves, and half of these natural pesticides have been shown to cause cancer in rats? The likely answer is superstition. We take antibiotics when we get sick which wipe out most our gut bacteria. How can you sleep at night when taking penicillin?
http://www.pnas.org/content...
Ray Kinney Replied to Rod Herman
Antibiotics are a sledgehammer effect that is episodic, and is a radical change. If the spread of increasing GMO use of glyphosate on and in our food supply as residues becomes a chronic low dose accumulative effect on the shikamate bacteria, it may be a longer term change than that of penicillin, an anthropogenic shift in our physiology. It may be that glyphosate IS replacing far worse pesticide use than the GMO uses now, and perhaps we are better off because of this, but it bothers me when AG promoters of GMO science make statements that say "Actually, people are naturally RoundUp tolerant because they do not have the target enzyme that the herbicide inactivates to kill plants, although it can cause eye irritation." but do not seem to allow for such potential or real changes... and speak in absolutist terms about food 'safety' when many data gaps and more in-depth research appears to be important to continue to add to our understanding about the reality of our status and trends toward food safety. That is all I'm saying, our work IS NOT done... It is not a QED situation...the science continues... hopefully utilizing the whole scientific method more consistently.
Rod Herman Replied to Ray Kinney
Of course more research can and is being done, but diverting limited resources toward fleetingly small risks is dangerous. Two food risks dominate our lives. The first is food poisoning due to pathogens, and the second is eating too much. We have seen some high profile food poisonings in restaurant and supermarket chains that have focused on imagined risks rather than preventing known dangers. This is what happens when the public is mislead by scare mongers.
Ray Kinney Replied to Rod Herman
Well, maybe I am a scare monger? But why are there 'limited resources' that concern our food paradigm science? Why do we not prioritize resources better (say by limiting wars just a little bit more) to allow for better science of things we eat... in a much more comprehensive practice? Why can't we figure out how to have less-limited resources? Perhaps we could better fund the full scientific method to be used more responsibly within the industrial AG product development process than is done currently... and still have ample profit to be taken AFTER reducing much of the externalized costs onto society. Perhaps we could question better, and find more comprehensive answers to move us toward an even more sane food future. Is this talk also 'scaremonger talk' ?
Rod Herman Replied to Ray Kinney
We will always have limited resources. That is a fact of life. I suggest that with limited resources, we prioritize these resources to focus on the greatest risks. One thing is certain, we need more food for our growing population, unless we want folks to starve. Producing more food on less land leaves more land in natural habitat. While more food does not guarantee this food will get to all that need it, insufficient food guarantees people will go hungry. We need to increase this food production while reducing the adverse environmental effects. GM technology can help us do this, but its applications, especially to help poor farmers, is being hampered by scare mongers.
Ray Kinney Replied to Rod Herman
Yes, AND we could go a LONG way by just cutting food loss of about 50% waste currently done, plenty of food if we just figure out better how to care for it after it is produced.
Rod Herman Replied to Ray Kinney
Once again, you point to a tangential issue as a distraction. Experts agree that we must increase food production dramatically to feed our growing population. These experts understand the magnitude of post-harvest losses. In addition to producing more food, scientists are working to reduce these post harvest losses. It is not either/or as you suggest, but both. The indication of a fungal toxin contaminating the corn samples in the subject paper is an excellent example. Insect tolerant GM corn has been shown to decrease mycotoxin contamination by reducing the feeding damage where the fungus grows, thus increasing yield, decreasing post-harvest losses, and reducing the toxic effects of these toxins on humans and livestock.
mem_somerville
There's another very thorough analysis of the flaws in this work here: http://biobeef.faculty.ucda...
Mary Mangan
https://twitter.com/mem_som...
Michael Antoniou
For reasons unknown, this comment by the project lead and communicating author of this study was removed from the page. It is here resubmitted in slightly edited form.
In the comments thread Mem_somerville claims that the pathogen proteins found in our proteomics dataset might indicate a fungal infection in non-GM isogenic grain and thus could be the reason for the observed changes in the metabolism of GM grains compared to control samples.
We were aware that two proteins (out of more than 150 maize proteins that had their levels significantly altered by the GM transformation process) detected in our proteomics analysis, matched a protein in Gibberella moniliformis, a known maize pathogen.
Our finding of fungal proteins in our maize samples is not surprising since the crops were grown under open field, not sterile, conditions, which were intended to mimic real world farming scenarios of cultivated maize that will enter the human and animal feed supply. Thus it is expected there will be environmental interaction with the plants.
Mem_somerville suggests that fungal infection as indicated by the presence of the G. moniliformis proteins could have caused the major metabolic alterations seen between GM and non-GM samples. This is incorrect for two reasons:
1.No other proteins related to pathogenic response have been observed in the entire dataset as would be expected, based on observations in other proteomic investigations looking at changes in plants in response to pathogen infection (Pechanova and Pechan, 2015).
2.In the case of a pathogen infection in control samples, these samples would be expected to show enzymes and metabolites related to an increase in oxidative stress or feedback enzymes to fight high oxidative cell environments. This is because the recognition of any invading pathogen results in a coordinated activation of plant defence mechanisms, including a strong and rapid oxidative response (Campo et al, 2004; Pechanova and Pechan, 2015). However, we observed the opposite – our results suggest that the GM samples, not the control samples, had increased oxidative stress.
Contrary to Mem_somerville’s suggestion, we did consider the two pathogen proteins in our dataset. In fact, we raised concerns about any other mycotoxin contamination even before we performed the proteomics analysis. We performed a mycotoxin analysis but did not include this data in the manuscript because it was outside the scope of the work presented. This analysis revealed the presence of the fumonisin group of mycotoxins, but at only trace amounts (less than 200 µg/kg of fumonisin B1 and B2) in the corn samples. Levels did not differ between varieties. The fumonisin class of mycotoxin is produced by G. moniliformis, also known as Fusarium verticillioides or F. moniliforme. As only very low levels of the fumonisins were found to be present this in turn implies low levels of contamination with this fungal pathogen.
There is indeed evidence that the presence of this pathogen can change polyamine metabolism in plants. However, such changes occur at levels of contamination, as measured by fumonisin levels, at least 1,000 times higher than the levels we detected (Campos-Bermudez et al, 2013). Moreover, these studies show that the higher the levels of pathogen infection, the higher the levels of polyamine compounds. However, again, we observed the opposite. There were higher levels of mycotoxin in the control (non-GM) corn than in GM corn and yet the non-GM corn had lower levels of polyamines. Therefore the elevated levels of polyamines in the GM corn samples were not caused by the presence of the fungal pathogen.
Thus Mem_somerville’s theory is incorrect.
In summary, our results clearly show that the two pathogen proteins referred to by Mem_somerville, which were found in our dataset of more than 150 proteins whose levels were significantly altered in the GM compared to the non-GM isogenic corn samples, did not contribute to the observed metabolic changes. Hence we are confident that our molecular profiling results obtained by application of state-of-the-art proteomics and metabolomics analyses of NK603 GM corn and its isogenic control clearly and unequivocally reveal that they are not substantially equivalent.
References
Pechanova O, Pechan T. Maize-Pathogen Interactions: An Ongoing Combat from a Proteomics Perspective. Int J Mol Sci. 2015 Nov 30; 16(12):28429-48.
Campo S, Carrascal M, Coca M, Abián J, San Segundo B. The defense response of germinating maize embryos against fungal infection: a proteomics approach. Proteomics. 2004 Feb;4(2):383-96.
Campos-Bermudez VA, Fauguel CM, Tronconi MA, Casati P, Presello DA, Andreo CS. Transcriptional and Metabolic Changes Associated to the Infection by Fusarium verticillioides in Maize Inbreds with Contrasting Ear Rot Resistance. PLoS ONE. 2013 8(4): e61580.
Mary Mangan Replied to Michael Antoniou
Thanks for coming by. But this doesn't quite cover the problems with your data. It's helpful of you to confirm that you knew about this, and decided to ignore it in your analysis and discussion. But it still doesn't explain why the highest fold change protein in your list is a fungus product. Can you please be clearer on why you think the top fold change protein is fungal?
Other matters:
1. There are 3 proteins in your short lists that are non-maize proteins and are from plant pathogens. UniProtID = W7LNM5 (top fold change), W7M8H2, E3QAG4. This is not a theory. This is your data.
2. If you claim to know about them, did you include them in your PCA and MCIA and other analyses? The journal should make you re-do these as maize-only, without the fungal proteins if they were included.
3. If your claim is that no other fungal proteins or response proteins were found, I think you need to release your full raw data for everyone to see. I'm not willing to take your word on your analysis, because from the data we have seen your analysis wasn't very careful, or you chose to ignore things. Please deposit it at a public proteomics/metabolomics repository for everyone to access. But we still don't understand why the highest fold change protein is a fungal pathogen product. And in your current data I see trypsin inhibitors including P01088, which seems to be resistance related. And there are other resistance-related proteins in your list (Pechanova and Pechan, 2015).
In your list of Isogenic x NK603+R, item 83 is "Glutathione S-transferase 1 OS=Zea mays GN=GST1".
4. Because you don't provide absolute levels of proteins, but only the thing readers can conclude is that there is a large difference in the samples and they cannot be compared without considering the fact that one of them may be impacted by fungus. Maybe the conclusion should be that the GMO corn is more resistant to fungus and what we see is in fact an effective response. What you can't claim is that it's the transformation event that caused this. Strain variation could easily cause this as well, as the Campos-Bermudez paper showed.
5. The Campos-Bermudez paper says nothing about what low level infection gene expression fold changes look like--it only shows what they found in their experimental protocol. You can have gene expression changes without visual evidence of infection.
6. I'm confused by these statements: "Levels did not differ between varieties." Followed by "There were higher levels of mycotoxin in the control (non-GM) corn than in GM corn". Care to try that again?
So let's see the full data set in a public repository so we can look further. Thanks for your compliance on this. I was under the impression it's a requirement of this journal.
Dubhslaine Replied to Michael Antoniou
Except you did not use isogenic lines. You ordered seed from a catalog and called it good. Isogenic lines need to be made and cannot be purchased. I thought you had a plant breeder as an author on this paper. Surely they know that isogenic lines have to be made by backcrossing to a recurrent parent, and not just purchasing lines with similar numbers from a seed catalog. This is standard in the field and basic information that is taught to undergraduates. Your whole paper hinges on you having used isogenic lines, without that your conclusions are wrong and your paper is in need of retraction. Can you provide the pedigrees of the lines you used to show that they are isogenic?
mem_somerville Replied to Michael Antoniou
Can you please explain how the same protein can be both an increase and a decrease as a result of the transformation? And how you handed these cases in the pathway enrichment analysis? That's not really clear to me.
PS: the same one appears again a little lower, so it's actually there 3 times.
https://twitter.com/mem_som...
Claire
It appears that there is a major issue with the protein quantification data presented in this paper. The authors describe quantifying proteins, but they seem to have actually quantified individual peptides. Proteins are made up of multiple peptides, and quantification is done at the level of proteins unless explicitly stated. The major statistical problem in this paper is that if any single peptide in a protein is found enriched, the full protein has been counted as enriched, even if all other peptides of that protein are found at equivalent or opposite levels between the two samples.
For a clear demonstration of why individual peptide abundances don’t necessarily translate to full protein abundance, see P49106 14-3-3-like in isogenic vs. NK603+R, with one peptide recorded as over 2 fold enriched and the other over 2 fold depleted (Supplementary File 5).
The authors describe 156 differentially expressed ‘proteins’ in isogenic vs. NK603+R, but they’ve might have just found 156 differential peptides, which map to 105 individual maize proteins. This is not enough information to know if any of the full proteins made up of multiple peptides are statistically enriched or depleted. If there are no significant protein differences, it's not acceptable use peptide differences as if they represented protein differences.
To add evidence to this, the 'Mass (Da)' column in Supp. file 5 lists masses in the range of normal peptide size (hundred of daltons), not protein masses (kiloDaltons). To the authors: What do the masses in this column represent?
Also, pointing out that 6 observed peptides of the same Glyceraldehyde-3-phosphate dehydrogenase 1 (P08735) are recorded as 6 proteins in the total count of 156 proteins with levels "altered by the transformation process" in isogenic vs. NK603+R.
Mary Mangan Replied to Claire
I noticed that some proteins had 2 entries, right next to each other, and I wasn't clear on whether they had counted them separately or they had collapsed them into a single increase/decrease somehow. But yeah, some of them are both increases and decreases which kind of confuses everything.
This would have consequences for other aspects of their analyses as well, right?
Claire Replied to Mary Mangan
Yes, at least the pathway enrichment. Calculations on a list of proteins that had at least one peptide enriched/depleted are not equivalent to calculations on proteins that are statistically enriched/depleted.
Later Replied to Claire
If subatomic particles are different.. While the question re lines http://fafdl.org/gmobb/what... appears interesting, a layman's question would be whether any other line would have polyamines at a similar level to N-acetyl-cadaverine (2.9-fold), N-acetylputrescine (1.8-fold), putrescine (2.7-fold) and cadaverine (28-fold).
Also, do any lines of non transgenic maize have the newly produced protein? I quote, 'While only one protein is newly produced as a result of the transgene insertion..'
Michael Antoniou
Again for reasons unknown the following comment was removed shortly after it had been posted. Here it is again.
In response to Mary Mangan/MEM_somerville, we are aware that there are three rather than two fungal proteins present in our proteomics analysis, whose levels statistically differ between the maize samples analysed. In our previous comment we were referring to the two proteins associated with G. moniliformis/Fusarium verticillioides, which were the focus of the discussion.
In addition, we included these fungal-associated proteins in our list because we wanted to be transparent about all proteins identified in our dataset. We could easily have deleted them from our list of proteins but if we had deleted them from the exploratory principal component analysis (PCA), this would have resulted in unacceptable bias, since PCA is done with normalized raw data and not only statistically significantly altered proteins, so as to explore all kinds of variation (including experimental variation) in the dataset.
Mary Mangan/MEM_somerville seems to have failed to fully understand and appreciate the nature of the data generated by a proteomics analysis. In addition, she has neglected to fully take on board the importance of the core message of our last comment, regarding the consequences arising from the fact that our mycotoxin analysis revealed only trace amounts of fungal infestation in all of the maize samples.
Proteomics analysis accurately highlights differences in protein profiles between samples under scrutiny. Results obtained are expressed in terms of fold differences between test and control samples. As such it is largely a qualitative rather than quantitative evaluation procedure. Thus although our proteomics revealed, not surprisingly, the presence of fungal proteins at differing levels between the maize samples, it does not accurately inform on the absolute quantity of fungal contamination present. Thus the proteomics finding of lower levels of G. moniliformis-/Fusarium verticillioides-associated proteins in the GM compared to the non-GM maize samples may at face value appear contradictory to the results from the mycotoxin analysis, but it is in fact quantitatively less accurate.
In contrast, the mycotoxin analysis we conducted on the maize samples, which we referred to and which we will happily make available to colleagues in academia upon request, does inform on the absolute levels of fungal contamination that are present. Our results clearly show that these are very low, as only trace amounts of mycotoxins including those derived from Gibberella moniliformis are present, with little or no detectable variation in all maize samples.
Thus the presence of the three fungal proteins present in our proteomics dataset is in the final analysis irrelevant, as they are derived from a degree of fungal infestation, as revealed by the mycotoxin analysis, which is 1000 times below what has been found to alter plant biochemistry.
Therefore our conclusion remains valid; the differential presence of fungal, especially Gibberella moniliformis, infestation cannot account for the proteomics and metabolomics differences we detected between the GM NK603 maize and its non-GM counterpart and that the observed alterations are due to the GM transformation process.
Mary Mangan Replied to Michael Antoniou
Please provide your raw data to a public repository so your new handwaving claims can be checked.
Also, I'd like you to explain this: "we will happily make available to colleagues in academia upon request". Please add it for all of us to see as a supplement for this paper--it obviously affects everyone's assessment of your work as it's become an issue. Why this would be restricted to academics is rather bizarre.
And we know your team works with homeopathy companies for funding some of your Roundup research, so we all know you don't actually believe that only 1000x level changes would have impact on a system.
That said, strain issues are very important in pathogen response, and as others have noted your strain choices are not appropriate for the comparisons you are making.
Let's have access to the data.
Mary Mangan Replied to Michael Antoniou
My comment--a call to submit the raw data to a public repository--has been removed by trolls. This site really does a terrible job of comment moderation.
https://www.screencast.com/...
mem_somerville
Just to summarize, for those who have come along later in the discussion and have lost access to comments because of poor moderation at this site:
1. Authors admit that the top fold protein change in their data is a protein from a pathogen of maize, which could affect polyamine levels in maize.
2. Authors admit that they knew about this, and despite this declined to address it in their paper in any manner.
3. Authors claim to have mycotoxin data which they will share with academics (but not everyone, despite it being potentially influential in this analysis). They do not address the fact that mycotoxin production does not directly correlate with the amount of fungal infection present, so still doesn't tell us much, even if they would provide this for everyone to see.
4. Authors admit that they included non-maize proteins in their analysis to illustrate the differences in maize proteins.
5. Authors used peptide-level data to incorrectly make claims about protein level changes, without supporting evidence. This has consequences for subsequent analysis steps as well.
6. The claims of the suitability of their isogenic line are not supported by evidence. The conclusions about the differences arising from the "transformation event" are not supported by this work.
7. Poor study design means that any statistical assessments are flawed right from the beginning. However, with proper design, some of the other items might have been addressed.
8. More than one request for submission of the raw data to a public repository has been made, and authors have not complied with this request.
Anything else? It may be time to take this up with the editors of this publication.
Jason Replied to mem_somerville
So, what you're saying is..... all in all, a pretty solid piece of work? ;-)
Mary Mangan Replied to Jason
It is a piece of work. I stand by that.
Amy Replied to mem_somerville
Good luck taking it up with the editors. I emailed a letter of concern to the Scientific Reports publication board and their reply was basically (I paraphrase) thanks but no thanks; we don't publish letters to the editor/brief communications arising, so go comment on the website. My letter laid out the most glaring issues with study design, field test design, lack of replicates, unsupported claims of isogenicity, etc. etc. etc. I was truly disheartened by their response. I wasn't looking for them to publish my letter, I was trying to alert them to fundamental flaws that in my opinion should have been addressed during peer review.
Mary Mangan Replied to Amy
Hmm. That's disturbing. But thanks for letting me know. And their website moderation is just dreadful.
Can you either 1) post your concerns on the PubMed site, where the journal has no control over the comments or 2) Post your issues here where I am crowd-sourcing issues for a letter to them, or some other effort if that fails? I trust that comment there won't disappear. http://sciblogs.co.nz/code-...
Michael Antoniou
In her last posting mem_somerville (Mary Mangan) states:
“Authors used peptide-level data to incorrectly make claims about protein level changes, without supporting evidence. This has consequences for subsequent analysis steps as well.”
“The major statistical problem in this paper is that if any single peptide in a protein is found enriched, the full protein has been counted as enriched, even if all other peptides of that protein are found at equivalent or opposite levels between the two samples.”
These statements seem to reflect a fundamental lack of understanding of how a contemporary proteomics analysis, such as we present in our publication, is undertaken.
The proteomics approach used in our study constitutes a standard mass spectrometry analysis. In brief, mass spectrometry not only allows the precise determination of the molecular mass of peptides and the proteins from which they are derived, but also the determination of their sequence, especially when used with tandem mass techniques, such as we employed. Therefore, fragmentation of peptides and proteins is necessary to give sequence information that can be used for protein identification, de novo sequencing, and identification and localization of post-translational or other covalent modifications. There is absolutely no misinterpretation of peptide data for the identification of proteins. Information about mass spectrometry principles and applications can be easily obtained online or from this book: Mass Spectrometry: Principles and Applications, 3rd Edition. Edmond de Hoffmann, Vincent Stroobant. ISBN: 978-0-470-03310-4.
As explained before, such a mass spectrometry approach also allows the detection of post-translational or other covalent modifications. This is the reason why peptides with the same amino acid sequence can have a different mass and thus are found separately in our dataset. Because of these differing post-translational modifications, which can lead to different enzymatic and other functions, they cannot be treated as belonging to the same protein and thus their fold changes cannot be grouped together into a single entity, as wrongly suggested by some commentators.
Although we have controlled the rate of false positives by applying the Benjamini-Hochberg procedure, some statistically significant differences could be due to chance. As stated in our paper “P-values calculated by a pairwise Welch's t-tests and adjusted by the Benjamini-Hochberg multi-test adjustment method for the high number of comparisons were below 5%”. This means that 5% of the results indicating alterations in levels of proteins could be false positives. However, this level of uncertainty is standard for this type of statistical analysis dealing large numbers of comparisons. Therefore, the finding of inconsistencies at the level of a few individual proteins does not invalidate the conclusions drawn from findings based on the overall proteome.
Our interpretation of the data obtained follows basic standard procedures for mass spectrometry proteomics analysis, which have been developed and evolved over decades and are now held to be correct. We therefore encourage critics of our study who believe we have used the wrong analytical methods, to publish their views in specialized peer-reviewed journals for evaluation by experts in the field.
mem_somerville Replied to Michael Antoniou
You are really terrible at this response thing. One of those quotes is not mine--it's from someone who does contemporary proteomics analysis every day.
But that said--please, we implore you again--and I know multiple requests have been issued to you, put all the raw data in a public repository. Your handwaving nonsense continues to evade the real issues in this work. We've waited over a month already. Your time is running out.
Mary Mangan Replied to Michael Antoniou
And once again trolls remove the request for the raw data. I'm sure it's important that they hide that--but that request will keep coming.
As I said before in the disappeared comment--your time is running out Michael. Deposit the raw data in a public repository. I know you have received multiple requests for this now, and are in violation of the journal's policies.
KareemAbdul Replied to Mary Mangan
His "time is running out".
Sounds pretty threatening. Violent talk stems from frustration.
It's unprofessional for a scientist to come out in public to speak that way.
You call people "trolls" for what could be a computer glitch, a browser or server flaw, or just about anything else, but did you ever consider that through ad hom attacks people might consider you a troll?
I understand you sound frustrated but a scientist should have only the highest ethical standards, full stop.
mem_somerville Replied to KareemAbdul
It's incredibly irresponsible of you to suggest violence. Right after we've seen a violent act against a plant breeding facility, no doubt based on nonsense peddled by people who make wild claims about maize that are not supported by their data. http://www.sciencemag.org/n...
You clearly don't understand how this works. We have attempted numerous routes to request the data from this team and they have declined every request. So we have to go to the editors.
Misinformation has real consequences, and the editors are going to hear about this.
Mary Mangan Replied to Michael Antoniou
Here's the previous reply from 3 days ago now, which trolls have downvoted away: https://www.screencast.com/...
Claire Replied to Michael Antoniou
Sorry, but you've misunderstood the issue in my (not Mary Mangan's) comment. In this paper, you've taken the peptide-level differences and represented them as if they were biologically relevant protein-level quantifications. This is a fundamental misunderstanding of how protein expression differences are calculated from peptide data in any proteomics experiment. The combined measures of multiple peptides in a TMT experiment gives statistical support for differences in a protein's expression.
Proteome Discoverer's TMT workflow (which you used according to the methods) gives both peptide-level and protein-level changes
between conditions on two different tabs in the interface. You can see this clearly on page 3 of this Thermo manual https://t.co/ytTaCtgkpR
, which also states "A more biologically relevant representation of
these [peptide] metrics is the data for individual proteins (Table 2), as
expression differences for individual proteins is most of interest to
biologists." So you took the top scoring different peptides for this paper. Did the protein quantification show any significant differences? If there weren't, then there are no significant differences in protein expression between these maizes.
Some reading to give background of pep vs prot quantification: https://www.ncbi.nlm.nih.go... https://www.ncbi.nlm.nih.go...
It's also absolutely fine to combine peptides w/ different PTMs from the same protein when calculating protein differences.
mem_somerville
For anyone still following along on this discussion, you should see the EFSA report on the work. They describe it as having "severe shortcomings". Ouch.
http://onlinelibrary.wiley....