Single-Event Transgene Product Levels Predict Levels in Genetically

Aug 21, 2017 - The concentration of transgene products (proteins and double-stranded RNA) in genetically modified (GM) crop tissues is measured to sup...
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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 is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

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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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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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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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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

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shown as insert within each figure panel.

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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

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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

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

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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

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products (2mEPSPS, AAD-12, Cry1Ac, Cry1F, PAT) 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 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.

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=

= cotton

= leaf V5,

=

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

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

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products are represented by the following symbols: :

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Cry1A.105,

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DvSnf7 RNA, and

= Cry1F,

= Cry2Ab2,

= AAD-1,

= Cry34Ab1,

= CP4 EPSPS,

= Cry35Ab1, .

= PAT.

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=

= Cry3Bb1,

=

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Table 1: GM events and corresponding transgenic gene products.

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GM Event

Transgenic Gene Products

Trait (s)

DAS-Ø15Ø7-1 maize

Cry1F, PAT

Insect resistance, Herbicide tolerance

DAS-59122-7 maize

Cry34Ab1, Cry35Ab1, PAT

Insect resistance, Herbicide tolerance

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

Insect resistance, Herbicide tolerance

DAS-24236-5 cotton

Cry1F, PAT

Insect resistance, Herbicide tolerance

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

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Figure 3

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Figure 4

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Figure 5

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