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In the recently published article ‘Transgenes in Mexican maize: molecular evidence and methodological considerations for GMO detection in landrace populations’ by Piñeyro-Nelson et al. (2009), the authors report the finding of transgenes in Oaxacan maize landraces and make several claims to which we feel compelled to respond. The authors maintain that testing of leaf tissues in general, and in particular at Genetic ID (GID), is inclined to false negative results and that the presence of metabolites in landraces could lead to the reporting of false negatives. Much of the data reported by Piñeyro-Nelson et al. is from end point PCR analyses of leaf material from single plants. We suggest and provide evidence that, for a study of such importance, conclusions must be drawn from real time qPCR analyses. Genetically any given plant should be either non-transgenic or transgenic, therefore for leaf tissue of a single transgenic plant, a GMO level close to 100% is expected. In their study, the authors chose to classify leaf samples as transgenic despite GMO levels of ∼0.1%. We contend that results such as these are incorrectly interpreted as positive and are more likely to be indicative of contamination in the laboratory.

The authors claim that analysis of lyophilized leaf tissue can lead to reporting of false negative results. Real time PCR analysis of dried leaf tissue samples from three transgenic plants (MON810, NK603, Bt11) verified that transgenic corn can be detected in such samples. Dried leaves were stored at GID for 3 years at room temperature prior to testing. DNA was extracted from 10 mg of leaf material, and the transgenic events were successfully detected using endpoint PCR. Subsequent analysis of the DNA by real time qPCR confirmed the expected GMO value of ∼100% for all three of the transgenic plants for the 35S promotor, NOS terminator and the specific event. Identical results were obtained from a MON810 dried leaf tissue positive control which the authors sent to us in a blinded fashion in 2005 (i.e. ∼100% GMO value for the 35S promoter target gene and correct identification of the MON810 event).

The authors sent to us a dried leaf tissue in blinded fashion in 2006 that was considered by them to be an NK603 positive control. They state that we failed to positively identify this sample as NK603. As the authors stated, using real time qPCR we detected only ∼0.1% for the NOS terminator and were unable to detect the 35S promoter or NK603. However, we detected high values for the maize reference gene. This indicated that the DNA in this sample was amplifiable by PCR and that there was an ample amount of DNA present to allow clear and definitive detection of NK603 sequences, if they were present. In light of these observations, namely that strong signals were observed using a primer set recognizing maize specific reference sequences, but not for a primer set specific for NK603, and that we were able to easily and successfully detect both the NK603 and MON810 events in dried corn leaves as well as the MON810 blinded dried leaf positive control that was provided by the authors, we are confident that our assay was accurate. We would argue that the leaf sample provided by the authors did not contain the claimed NK603 event and, furthermore, does not contain material from any commercialized transgenic single plant.

The authors claim that either metabolites in transgenic landraces or inefficient primer binding due to event-specific modifications could have obscured the testing and led to reporting of false negative results. Several lines of evidence argue that the negative results observed by GID for the samples in question cannot be explained by either of these mechanisms. GID includes at several points in its analytical procedures controls that would detect the kinds of problems cited by the authors and therefore ensure accurate reporting of results. For example, PCR inhibition tests are routinely conducted to rule out the presence of compounds (‘metabolites’) that could interfere with PCR amplification. In the case of the samples that GID analysed on behalf of Piñeyro-Nelson et al. (2009), no inhibition was observed. GID routinely analyses and quantifies GMOs in processed food and feed samples as well as a diversity of other plant materials (including fermented tobacco leaves), which contain a broad range of complex metabolites. An indicator for the successful analysis of such samples is for GID, as for any testing laboratory, the accuracy of results in proficiency tests.

Inefficient primer-binding due to event-specific variation in transgenic sequences is not a viable explanation of the results reported by GID. The primers used in the tests performed by GID on behalf of Piñeyro-Nelson et al. (2009) have been extensively characterized and have demonstrated, through standardized validation procedures using all transgenic corn events and species commercialized to date, to be effective and consistent in detecting the sequences of interest. In addition, primers are designed such that amplicon sizes are similar for GM and for corn species target genes and are small enough to detect transgenic sequences even in partially processed samples where partial DNA degradation is common. The authors point to their results in figure 1a as an example of the kind of variability that they observe in signal intensity among samples that are known to contain transgenes. To attribute the variability observed in this figure to mutagenesis, as Piñeyro-Nelson et al. do, is highly implausible as this would require mutagenesis of the very short PCR target sequence to occur at a frequency many fold higher than would be expected on the basis of the known and accepted norms of genetics (Drake et al. 1998).

We contend that neither interference of plant metabolites nor inefficient primer binding led to reporting of false negative results for these samples. Instead, we would argue that the leaf samples derived from a single plant can only be scored as transgenic if the GMO level is close to 100% (using a reference of similar zygocity and copy number as the transgenic reference sequence). It is our observation that levels such as ∼0.1% can derive from contamination of the DNA by cloning and/or sequencing activities. As cloning was admittedly conducted in the author’s laboratory, we contend that these low level signals are due to contamination and should not be construed as indicating transgene sequences in the maize samples studied.

The authors sent what they considered to be 10 positive and 10 negative leaf samples to GID. GID scored several of the ‘positive’ samples as ‘Not detected’ despite what the author’s state as ‘clear’ amplification of the expected fragment. The amplification efficiency was considered by GID to be much lower than would be expected for a 100% (homozygous) or 50% (heterozygous) transgenic plant. The authors also argue that GID’s quantitative analytical results are not valid because GID did not routinely use a control containing 100% transgenic material. GID routinely includes controls that contain quantities of GM material relevant to the samples provided by the customer. All but one of the samples considered by the authors to be ‘positive’ had signals at low GM levels (∼0.01–0.1%). This was within the range of the controls used (figure 1d of Piñeyro-Nelson et al. 2009). Samples with GM levels >0.1% are re-analysed by real time qPCR using the appropriate controls (100% GMO). For example, GID scored sample 5 as >0.1% by endpoint PCR (figure 1d). We then subjected this sample to real time qPCR and detected the 35S promoter at a 100% level. Curiously, however, although 35S was quantified at 100%, in the same sample, NOS terminator was detected at ∼0.1% (figure 1d) and this discrepancy was not addressed by the authors. We again would argue that the low level of NOS terminator detected is most likely a result of contamination, possibly during processing of the corn leaves at the author’s laboratory facilities.

Proper use of controls is critical for accurate and consistent data interpretation. Therefore we are surprised to find that the gel image that the authors show in the upper part of fig 1d was generated from samples (considered as ‘positive’ by the authors) sent to us and analysed in 2004 whereas the gel image shown in the lower part of fig 1d was generated from samples (considered as ‘negative’ by the authors) sent to us and analysed in 2006. Furthermore, only three of the ten ‘negative’ samples are shown. For samples of such importance, it is our opinion that a comprehensive side by side analysis would be essential to make accurate conclusions.

The authors claim either sampling effects or reporting of false negatives as the most likely source of differing detection results between their study and that of Ortiz-García et al. (2005). Our interpretation leads to the conclusion that Piñeyro-Nelson et al. (2009) essentially came up with negative results in their survey of Oaxaca for transgenic maize. Although sample 5 appears to be positive, it is hard to conclude from the provided data whether this is a true positive result as the authors provided neither confirmatory Southern blot data nor information regarding the specific corn event. This interpretation is consistent with the conclusions reported by Ortiz-García et al. (2005). We contend that the sample number was too small in both the study (Ortiz-García et al. and Piñeyro-Nelson et al.) and that sampling was not representative of the total Oxacan maize population. Therefore, our conclusion from both publications on this topic is that results obtained to date are not sufficient to ascertain whether introgression of transgenic traits into the Mexican maize population has or has not taken place.

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