Single-Event Transgene Product Levels Predict Levels in Genetically

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Single-event transgene-product levels predict levels in genetically modified breeding stacks Satyalinga Srinivas Gampala, Brandon J. Fast, Kimberly A Richey, Zhifang Gao, Ryan Christopher Hill, Bryant Wulfkuhle, Guomin Shan, Greg A Bradfisch, and Rod A. Herman J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b03098 • Publication Date (Web): 21 Aug 2017 Downloaded from http://pubs.acs.org on August 22, 2017

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Journal of Agricultural and Food Chemistry

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Single-event transgene-product levels predict levels in genetically modified breeding stacks

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Satyalinga Srinivas Gampala*, Brandon J. Fast, Kimberly A. Richey, Zhifang Gao, Ryan Hill,

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Bryant Wulfkuhle, Guomin Shan, Greg A. Bradfisch, Rod A. Herman

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Dow AgroSciences, Indianapolis, Indiana, USA

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*Corresponding Author:

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Satyalinga Srinivas Gampala

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Dow AgroSciences, Building 312, 9330 Zionsville Road, Indianapolis, IN 46268 USA

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Telephone: 1-317-337-3830

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Email: [email protected]

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1 ACS Paragon Plus Environment


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Abstract:

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The concentration of transgene products (proteins and double-stranded RNA) in genetically

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modified (GM) crop tissues is measured to support food, feed, and environmental risk

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assessments. Measurement of transgene-product concentrations in breeding stacks of previously

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assessed and approved GM events is required by many regulatory authorities to evaluate

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unexpected transgene interactions that might affect expression. Research was conducted to

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determine how well concentrations of transgene products in single GM events predict levels in

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breeding stacks composed of these events. The concentrations of transgene products were

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compared between GM maize, soybean, and cotton breeding stacks (MON-87427 × MON-89034

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× DAS-Ø15Ø7-1 × MON-87411 × DAS-59122-7 × DAS-40278-9 corn; DAS-81419-2 × DAS-

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44406-6 soybean; DAS-21023-5 x DAS-24236-5 x SYN-IR102-7 × MON-88913-8 × DAS-

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81910-7) and their component single events (MON-87427, MON-89034, DAS-Ø15Ø7-1, MON-

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87411, DAS-59122-7, DAS-40278-9 corn; DAS-81419-2, DAS-44406-6 soybean; DAS-21023-

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5, DAS-24236-5, SYN-IR102-7, MON-88913-8, DAS-81910-7). Comparisons were made

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within a crop and transgene product across plant tissue types, and were also made across

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transgene products in each breeding stack for grain/seed. Scatter plots were generated

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comparing expression in the stacks to their component events, and the percent of variability

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accounted for by the line of identity (y = x) was calculated (coefficient of identity, i2). Results

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support transgene concentrations in single events predicting similar concentrations in breeding

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stacks containing the single events. Therefore, food, feed, and environmental risk assessments

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based on concentrations of transgene products in single GM events are generally applicable to

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breeding stacks composed of these events.

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Keywords:

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Transgene expression; Genetically Modified (GM) breeding stacks; Genetically Modified

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Organisms (GMO), food, feed, and environmental risk assessment; safety assessment, coefficient

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of identity

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Introduction:

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As part of the safety assessment for genetically modified (GM) crops, the transgene products

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(proteins or double-stranded RNA) are assessed for safety. Risk is a product of hazard (toxicity)

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and exposure. There have been no hazards identified for commercialized transgene products in

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the context of food and feed safety1, 2, and existing transgene products have been issued an

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exemption from the requirement for a tolerance in pesticidal GM crops regulated by the US

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Environmental Protection Agency (EPA). Therefore exposure measurements should not be

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necessary for food and feed safety assessments of these products.

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Insecticidal transgene products may present a potential hazard to a subset of non-target insects,

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typically taxonomically related to target pests. Thus, an exposure assessment for these transgene

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products is required as part of the assessment of the environmental risk of pesticidal transgene

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products to beneficial (or otherwise valued) non-target organisms. For pesticidal transgene

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products where sensitive non-target organisms might be exposed to crop tissues, it is important

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to characterize the concentrations of transgene products in the tissues to which exposure is

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expected.

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In practice, the concentrations of all transgene products in commercialized GM crops have been

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characterized in multiple plant tissues at various crop stages irrespective of any identified hazard.

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These experiments are commonly referred to as expression studies. Furthermore, most

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regulatory authorities require expression studies to be completed for GM breeding stacks (single

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GM events bred together using traditional breeding) to bridge the environmental risk assessments

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of single GM transformation events to these breeding stacks.3 When breeding stacks express


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transgene products at levels similar to those expressed in single GM events, the environmental

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risk assessments done for the single events apply to the breeding stack.

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Regulatory authorities also consider expression in food/feed safety assessments of GM breeding

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stacks despite the lack of hazard associated with the transgene products. Furthermore, some

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geographies regulate the efficacy and/or durability of pesticidal GM crops (which can be

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dependent on transgene expression levels), but this evaluation is related to product performance

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rather than safety.4 It is noteworthy that, to ensure market success, the phenotype and trait

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performance of new crop varieties and hybrids are routinely evaluated by plant breeders

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independent of government regulation or whether the trait originated from GM or traditional

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breeding.

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Since transgenes in GM events are introgressed into many different commercialized varieties or

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hybrids with many different genotypes, the requirement to reassess expression in GM breeding

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stacks appears to be based on the hypothesis that transgenes are more likely to interact with each

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other than with the many new endogenous genotypes into which they are introgressed. To our

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knowledge, there is no scientific evidence to support this hypothesis. The requirement to

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reassess transgene product levels in breeding stacks also does not take into consideration whether

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a hazard for these products has been identified. Furthermore, consideration is often not taken as

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to whether the margins of exposure previously afforded by empirical toxicology tests are

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insufficient to protect against harm if expression in the stack was to increase within biologically-

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plausible limits.

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Here we report empirical transgene-expression data for breeding stacks compared with their

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individual component events across multiple crops and tissue types. We plot the expression level

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of each transgene product in each tissue/crop-stage for the single events against the stack, and 5 ACS Paragon Plus Environment


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quantify the predictive capability for the single-event expression to predict stack expression by

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calculating the percent of variation (coefficient of identity) captured by the line of identity (y =

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x).5, 6 Because of the importance of grain/seed in trade and the food and feed safety assessment,

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we also plot the grain/seed expression levels for each transgene product for the single events

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against the breeding stacks across transgene products, and calculate the coefficient of identity (I2)

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for these relationships in each crop/stack. These results help visualize and quantify the

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predictability of expression in the breeding stacks based on expression in the single events, and

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evaluate the usefulness of transgene expression data for breeding stacks in their safety

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assessment.

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Materials and Methods:

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GM breeding stacks:

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The largest Dow AgroSciences (Indianapolis, IN) maize, soybean, and cotton GM breeding

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stacks were chosen for investigating the transgene product expression levels compared with

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expression in their component single events. These breeding stacks included MON-87427 ×

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MON-89034 × DAS-Ø15Ø7-1 × MON-87411 × DAS-59122-7 × DAS-40278-9 maize

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expressing CP4 EPSPS, Cry1A.105, Cry2Ab2, Cry1F, PAT, Cry3Bb1, Cry34Ab1, Cry35Ab1,

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and AAD-1 proteins, and DvSnf7 double stranded RNA; DAS-81419-2 × DAS-44406-6 soybean

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expressing Cry1Ac, Cry1F, PAT, AAD-12, and 2mEPSPS proteins; and DAS-21023-5 x DAS-

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24236-5 x SYN-IR102-7 × MON-88913-8 × DAS-81910-7 cotton expressing the Cry1Ac,

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Cry1F, PAT, Vip3Aa19, APH4, CP4 EPSPS, and AAD-12 proteins (Table 1). DAS-21023-5

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and DAS-24236-5 cotton events were never commercialized as separate events, and accordingly


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expression data were not generated for the single events in these studies; therefore the two-event

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breeding stack of these events was treated as a single event for the purposes of this analysis. The

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Bacillus thuringiensis crystal (Cry) and vegetative (Vip) proteins and the DvSnf7 double

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stranded RNA confer insect resistance, and the CP4 EPSPS, PAT, AAD-1, AAD-12, and

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2mEPSPS proteins confer herbicide tolerance (Table 1).

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Field trials:

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Field trials were conducted at multiple locations with four replicate blocks at each location

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arranged in a randomized complete block design. MON-87427 × MON-89034 × DAS-Ø15Ø7-1

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× MON-87411 × DAS-59122-7 × DAS-40278-9 maize was grown during 2015 at eight sites

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(Kimballton, IA; Cresco, IA; Richland, IA; Stewardson, IL; Pickard, IN; Kirksville, MO; York,

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NE; Germansville, PA); DAS-81419-2 × DAS-44406-6 soybean was grown during 2012 at nine

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sites (Richland, IA; Atlantic, IA; Carlyle, IL; Wyoming, IL; Sheridan, IN; Kirksville, MO; Fisk,

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MO; York, NE; Germansville, PA); and DAS-21023-5 × DAS-24236-5 × SYN-IR102-7 ×

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MON-88913-8 x DAS-81910-7 cotton was grown during 2013 at five sites (Tallassee, AL;

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Sycamore, GA; Greenville, MS; Cedar Grove, NC; East Bernard, TX). All plots within a study

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location were treated with the same maintenance chemicals and agronomic practices. Plant

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tissue types and the crop stage at collection are indicated in figure captions.

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Analytical Methods:

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Proteins were quantified using ELISA methods validated under EPA Good Laboratory Practice

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(GLP) standards. CP4 EPSPS, Cry2Ab2, Cry34Ab1, AAD-1 and PAT in maize, PAT in

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soybean and CP4 EPSPS, AAD-12, and PAT in cotton were quantified using validated Dow

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AgroSciences methods with kits purchased from Acadia, LLC (Portland, ME) or EnviroLogix



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(Portland, ME). Cry1F and Cry35Ab1 in maize, Cry1Ac and Cry1F in soybean, and Cry1Ac,

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Cry1F, and Vip3A in cotton were also quantified using validated Dow AgroSciences methods

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with kits purchased from Acadia, LLC (Portland, ME) or Romer Labs (Newark, DE).

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Cry1A.105 was quantified using a validated method following a protocol provided by Monsanto

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(St. Louis, MO). Cry3Bb1 and APH4 were quantified using a validated method with ELISA kits

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produced at Dow AgroSciences or Acadia, LLC (Portland, ME). AAD-12 and 2mEPSPS in

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soybean were quantified using a validated method with ELISA kits produced at Acadia

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(Portland, ME) or at Dow AgroSciences. The double stranded RNA transgene product, DvSnf7,

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was quantified in using the QuantiGene Plex 2.0 RNA assay platform from Affymetrix (Santa

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Clara, CA) as previously described.7 All ELISA methods were fully validated under GLP (Good

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Laboratory Practices) according to ICH guidelines which include evaluation of recovery, limit of

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detection, and precision.

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Data Analysis:

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Mean expression levels (expressed as ng/mg dry-weight of tissue for proteins and ng/g dry-

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weight for DvSnf7) within each single event for each transgene product were plotted against the

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relevant breeding stack across crop tissue types. For grain/seed, mean expression levels within

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each single event were plotted against the relevant breeding stack across transgene products.

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Where the same transgene product is present in multiple single events found in the breeding

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stack (i.e. CP4 EPSPS protein in maize, and PAT protein in maize, soybean, and cotton),

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expression levels were summed to represent total expression in the component single events.

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The amount of variation in the data accounted for by the line of identity (I2) was calculated in a

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manner analogous to how the coefficient of determination (R2) is calculated for a regression line5

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. The I2 is calculated as follows: 8 ACS Paragon Plus Environment

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∑ ( − ) =1− ∑ ( − )

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It is noteworthy that the I2 will never exceed the R2 for any given dataset because, unlike a

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regression line that is fit to the data, the line of identity is fixed (y = x). Analyses were

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conducted in both the natural and base-10 logarithmic (Log10) scales. Analyses in the Log10

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scale weights lower expressing proteins more heavily than in the natural scale.

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Results and Discussion:

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With the exception of APH4, which was undetectable in most cotton tissues, the other transgenes

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products expressed at detectable levels in sampled plant tissues. The plots of single-event

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transgene expression levels against stack expression levels, across crop tissue types, show

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limited scatter around the line of identity (Figures 1-4). The variation observed between the

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single and stack transgene expression levels across crops and crop tissue types from single-year

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multi-site field trials is well accounted for by the line of identity for all transgene products across

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all breeding stacks (I2 > 0.86; I2 > 0.93 in Log10 scale) with the exception of Cry1Ac expression

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in soybean (I2 = 0.14; I2 = 0.85 in Log10 scale; Figure 3) and marginally for Cry1F in cotton (I2

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= 0.71; I2 = 0.84 in Log10 scale; Figure 2).

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A single tissue type (leaf at V5) drove the variability (low I2 value) seen between the soybean

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breeding stack (8.7 ng Cry1Ac/mg) and the single event (25.4 ng Cry1Ac/mg) (Figure 4);

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however, the stack sprayed with the trait-relevant herbicides (2,4-D + glyphosate + glufosinate)

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expressed similar levels (20.7 ng Cry1Ac/mg) as the single event (25.4 ng Cry1Ac/mg) in the

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same tissue and study. Furthermore, the expression in a later-stage leaf tissue (V10-12) was



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similar for the single event (23.7 ng Cry1Ac/mg) and the stack (22.6 ng Cry1Ac/mg). This

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suggests that the lower expression in the stack for a single plant tissue for one GM protein in a

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single crop is likely an anomaly due to the large number of transgene products measured across

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several plant tissues in three different crops (115 comparisons). Regardless, the magnitude of

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reduction in Cry1Ac level observed for the stack in this single early stage leaf tissue does not

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raise any safety concerns.

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The marginally lower capacity of the single cotton event to predict the stack for Cry1F (I2 =

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0.71) compared with other transgene products across the three crops (I2 > 0.86) was driven by

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slightly lower expression in the breeding stack compared with the single event across plant

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tissues (Figure 3). As with the Cry1Ac level in V5 leaf, the magnitude of reduction in Cry1F

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level observed for the stack in these leaf tissue does not raise any safety concerns.

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The results expressed in the Log10 scale weight lower-expressing tissues more heavily than in

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the natural scale and thus balance the contribution of the different tissues more evenly than in the

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natural scale. It is noteworthy that the variation observed between single and stack transgene

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expression levels across crops and crop tissue types is well accounted for by the line of identity

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for all transgene products across all breeding stacks in the Log10 scale (I2 > 0.83).

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Considering the weight of evidence from these results, expression of transgene products in single

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events predicts expression in stacks very well. Since crop-tissue expression levels are used to

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estimate exposure in environmental risk assessments, these data strongly support the

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applicability of environmental risk assessments of single events to breeding stacks where hazard

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has not increased (no biologically significant synergism among pesticidal transgene products in

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the breeding stack).


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Plots of single-event transgene expression levels against stack expression levels in grain/seed,

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across all transgene products, also show limited scatter around the line of identity (Figure 5).

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The variation observed between the single and stack transgene expression levels in grain/seed

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across transgene product types was well accounted for by the line of identity (I2 > 0.96 across all

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breeding stacks in both the natural and Log10 scales). While transgene products in currently

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commercialized crops have shown no hazard in the context of a food and feed risk assessment, if

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a transgene product were to demonstrate some level of hazard in a future GM crop, then

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expression levels in grain could be used to estimate worst-case exposure in humans and

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livestock. It is noteworthy that transgene products in current commercial GM commodity crops

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are all inactivated and/or degraded by the processing of the GM grain/seed to produce food, and

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thus exposure is dramatically lower than predicted from raw grain/seed (8). The results of this

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analysis show that the transgene expression levels in grain/seed for single GM events predict the

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levels in GM breeding stacks very well across different transgene products within the same

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breeding stack. Thus, food and feed safety assessments for single events should apply to

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breeding stacks containing these same GM events.

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The results of this investigation indicate that transgene expression levels of single GM events

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predict expression levels in GM breeding stacks very well across crop tissues and among

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transgene products within a crop tissue (grain/seed) for the three major GM crops investigated

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(maize, soybean, and cotton). The analysis reported here avoids some of the pitfalls of

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conducting pairwise statistical comparisons between expression levels in single GM events and

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stacks using difference tests (ANOVA). These weaknesses include: 1) false positives due to

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many individual comparisons (multiplicity), 2) differences in transgene expression due to slight

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genotype differences between single events and stacks (common when traits from different



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developers are combined in stacks due to different initial transformant germplasm) 9, 3 detection

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of statistically significant differences that are very small in magnitude and have no biological

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relevance in the context of a risk assessment. The profiling plots used here put the magnitude of

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observed expression levels into the context of differences among crop tissues and among

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transgene products.

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Transgene expression can help inform risk assessments, and the results reported here provide

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strong indication that stacking individual GM events through traditional breeding has a

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negligible effect on transgene expression. This finding is consistent with both the expectation

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and observation that transgenes are no more likely to interact with each other than with unrelated

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endogenous genes.5, 10-13 While genotype and environment certainly can affect the expression of

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endogenous proteins, and thus expected to affect transgenes in a similar manner, previous

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investigations have demonstrated that environmental factors typically have the greatest impact

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on the production of endogenous proteins, as recently exemplified by proteinaceous soybean

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allergens.14, 15 Since transgenes have been observed to behave the same as endogenous genes 16,

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plant breeders evaluate GM trait performance in each new crop variety or hybrid under diverse

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environmental conditions (as they do for non-GM traits) before their commercial release to

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ensure consistency across the geographies where they will be grown. In the context of

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agricultural production and risk assessment, the measurement of transgene expression for single

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GM events under diverse environmental conditions provides a robust evaluation of potential

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exposure to a GM event alone or in breeding stacks.

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Notes:

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All authors are employed by Dow AgroSciences LLC, a wholly owned subsidiary of Dow

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Chemical Company, which develops transgenic crops.

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Acknowledgement:

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We thank Nick Storer and Chandrashekar Aradhya for reviewing the manuscript and providing

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input.

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246 247



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Figure captions:

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Expression in single events is plotted on X-axis and expression in breeding stacks is plotted on

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Y-axis. Results were also expressed in the Log10 scale, and coefficient of identity (I2) was

300

shown as insert within each figure panel.

301

Figure 1. Maize single-event versus breeding-stack expression (ng/mg dw) of AAD-1, CP4

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EPSPS, Cry1A.105, Cry1F, Cry2Ab2, and Cry34Ab1 across tissue types showing line of

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identity. Tissue types are represented in the plots by the following symbols:

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= leaf V9,

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Figure 2. Maize single-event versus breeding-stack expression (ng/mg dw) of Cry35Ab1,

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Cry3Bb1, DvSnf7 RNA (ng/gm), and PAT across tissue types showing line of identity. Tissue

307

types are represented in the plots by the following symbols:

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leaf R1,

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Figure 3. Cotton single-event versus breeding-stack expression (ng/mg dw) of transgene

310

products (AAD-12, CP4 EPSPS, Cry1Ac, Cry1F, PAT and Vip3Aa19) across tissue types

311

showing line of identity. Tissue types are represented in the plots by the following symbols:

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leaf BBCH 14,

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seed BBCH 99.

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Figure 4. Soybean single-event versus breeding-stack expression (ng/mg dw) of transgene

315

products (2mEPSPS, AAD-12, Cry1Ac, Cry1F, PAT) across tissue types showing line of

316

identity. Tissue types are represented in the plots by the following symbols:

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leaf V10-12,

= leaf R1,

= root,

= root R1,

= forage, and

= leaf BBCH 60,

= forage R3,

= forage R5, and

= leaf V2-V4,

= grain R6.

= leaf V2-V4,

= leaf V9,

=

= grain.

= leaf BBCH 80,

= root R3, and

= root BBCH 86, and

= grain R8.


=

= cotton

= leaf V5,

=

Page 17 of 25


318

Figure 5. Maize, soybean, and cotton single-event versus breeding-stack grain/seed expression

319

(ng/mg dw) of transgene products across transgene products within each stack. Transgene

320

products are represented by the following symbols: :

321

Cry1A.105,

322

DvSnf7 RNA, and

= Cry1F,

= Cry2Ab2,

= AAD-1,

= Cry34Ab1,

= CP4 EPSPS,

= Cry35Ab1, .

= PAT.

323


=

= Cry3Bb1,

=


324

Table 1: GM events and corresponding transgenic gene products.

325

GM Event

Transgenic Gene Products

Trait (s)

DAS-Ø15Ø7-1 maize

Cry1F, PAT

Insect resistance, Herbicide tolerance

DAS-59122-7 maize

Cry34Ab1, Cry35Ab1, PAT


DAS-4Ø278-9 maize

AAD-1

Herbicide tolerance

MON-89Ø34 maize

Cry1A.105, Cry2Ab2

Insect resistance

MON-87427 maize

CP4 EPSPS

Herbicide tolerance

MON-87411 maize

Cry3Bb1, DVsnf7, CP4

Insect resistance, Herbicide

EPSPS

tolerance

Cry1F, Cry1Ac, PAT

Insect resistance, Herbicide

DAS-81419-2 soybean

tolerance DAS-44406-6 soybean

AAD-12, 2mEPSPS, PAT

Herbicide tolerance

DAS-21023-5 cotton

Cry1Ac, PAT


DAS-24236-5 cotton

Cry1F, PAT


SYN-IR102-7 cotton

Vip3Aa19, APH4

Insect resistance

MON-88913-8 cotton

CP4 EPSPS

Herbicide tolerance

DAS-81910-7 cotton

AAD-12, PAT

Herbicide tolerance


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Figure 1

329 330 20 ACS Paragon Plus Environment

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Figure 2

332 333

334

335

336



337

Figure 3

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Figure 4

340



341

Figure 5


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343


TOC Graphic

344


Single-Event Transgene Product Levels Predict Levels in Genetically

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