During this work we adopted two approaches: one exploratory; to investigate the presence of genetic modification in tested plant samples and the other confirmatory to determine the specificity and reliability of the obtained results.
For the purpose of screening GM crops the conventional PCR assay was employed. The universal primers for Cauliflower mosaic virus promoter (P-35S; 195 bp) were successfully amplified in most of the tested samples (Table 2). Generally, fragments of the frequently used promoter or terminator are used for the detection of genetically modified plants (Duijn et al. 1999; Kok et al. 2000; Oraby et al. 2005). Twenty four different regions on the CaMV Promoter were previously reported (Wu et al. 2014) as detection methods for GMOs. Primers for GT88 segment (Oraby et al. 2014) used in the present work is targeting a new region (7117–7206) on the CaMV P-35S promoter (accession no. emb|V00141.1|). This new set of primers (GT88) was amplified in 86.1% of the collected samples, whereas P-35S (195 bp) was amplified in 83.3%. Amplification results of GT88, in the present work not only supported the presence of CaMV-35S promoter in these samples, but also suggested the use of these primers as an additional method for screening GMOs since it is targeting a new region in the CaMV-35S promoter.
The conventional PCR assay was also applied for exploring the presence of fragments from antibiotic resistance marker genes; nptII (173 bp) and aadA (284 bp) in the collected plant samples. Primers for nptII and aadA were successfully amplified in 72.2% and 61.1% respectively of the collected samples. Fragments from both genes were detected together in 50% of the screened samples.
GM plants usually contain bacterial antibiotic resistance (AR) genes which are used as selectable marker genes in the early laboratory stages during their development. The bacterial aadA gene, coding for aminoglycoside 3″ adenyl transferase, is under the control by its own bacterial promote (Miki and McHugh 2004) which renders it inactive in plants thus it is not expressed in GM plants. The nptII gene on the other hand has mostly been used as selectable marker genes (Vidhya et al. 2012) under the control of plant promoter in transgenic plants. For more efficient selection methods, nptII in some cases is used in combination with aminoglycoside phosphotransferase gene rather than using it alone (Kumar et al. 2004; Tabatabaei et al. 2017). This was confirmed in our work where the presence of both genes (nptII and aphIV) was detected in some of the investigated samples when applying the conventional-PCR and the fluorescent-based detection approaches.
Some authors (Miki and McHugh 2004) reported that such markers are routinely eliminated prior to plant transformation. They claimed that markers conferring resistance to hygromycin or other antibiotics have been used in plant research (Day 2003), but do not appear in GM-plants. Our results showed evidence for the presence of these ARM genes in most of the screened plant and diet samples. Adugna and Mesfin (2008) also used nptII gene as screening element for detection and quantification of GM crops. In addition, presence of nptII was reported in transgenic pigeon pea plants (Surekha et al. 2005) and transgenic cotton samples (Vidhya et al. 2012). It has also been reported that nptII gene was used for the production of most citrus transgenic plants (Ballester et al. 2008).
To overcome the limitation of detection of GMOs using conventional PCR the fluorescent-based detection approach using a DNA binding dye (SYBR green 1) in presence of aphIV primers as target sequence was also implemented as a confirmatory approach for detection of GMOs. This approach was followed by the melt curve analysis to estimate the specificity of the amplified products based on their melting characteristics of the double stranded DNA (dsDNA) during heating (Farrar and Wittwer 2017). Due to its dependence upon the length of the product and the type of its nucleotides component this assay allowed for the differentiation between the target specific amplicon and any non-specific amplicons (Nolan et al. 2013). The gathered information presented in Table 3 showed that primers for aphIV were positively amplified in all selected samples. These results reflect the importance of applying this assay as a complementary approach to the conventional PCR for the detection of GM plants since, screening results using the conventional PCR approach showed that some of the plant samples (14, 16, 20, 25, 30 and 32) were found to be negative to one or more of the other investigated primers (Table 2). These results also supported the suggestion of Anklam et al. (2001) that the absence of one or two of the screening elements in tested plant samples do not signify that these samples are not modified, it rather recommends using more than one primer for screening plant samples for the presence of genetic modification. As it is well known that no one method can detect all commercially available transgenic events (Wu et al. 2014) due to the different methods used for the construction of plant transformation. In the present work using different DNA-based methods for screening the investigated plant samples also, confirmed the presence of genetic modifications in these samples.
To guarantee traceability of the GMOs, several strategies have been developed to detect GMOs in food/feed samples by using different technologies. In most cases, GMO screening approaches also apply quantitative methods for detecting the presence of GM material in food and feed samples (Barbau-Piednoir et al. 2014). Many countries have imposed different biosafety laws (De Jong 2010) and GMOs labeling policies with a threshold of tolerance varying between 0 and 5% which are controlled by their competent authorities (Kamle and Ali 2013).
In the present work the standard curve approach using the Fluka certified reference material (Fluka CRM) was chosen for GMO quantification. This approach is based on absolute quantification rather than relative quantification approach (Weighardt et al. 2004) which based on the use of reference gene for normalization.
A series of parameters has to be considered to validate and verify the accuracy and the performance characteristics of the quantification method applied. One of these parameters is the squared correlation coefficient (R2) of the constructed standard curve. For a well-optimized reaction the R2 value should be close to 1 and greater than 0.98 (Nolan et al. 2013). In our case R2 value was 0.999. The dynamic range of concentrations; over which the method performed in a linear manner, is also another important performance characteristic parameter. In the present work the dynamic range of the standard curve showed a linear increase from 0.49 to 4.87%. It is worthy of note here that the dynamic range of concentrations should not exceed five times the permissible concentration (0.9%) of genetic modification (Del Gaudio et al. 2012).
Figure 5 showed that 92% reaction efficiency of the constructed standard curve indicating high efficiency and repeatability of the method employed as predicted from the line of best fit (slope) for the standard curve (− 3.526) in the present work. It has been reported that when a tenfold serial dilution is performed, the amplification plots for each dilution should be 3.3 cycles apart. In our case (Table 4) the amplification plots ranged from 2.26 to 3.56 cycles (Ct) apart. This difference in assay performance could be a result of using different dilutions (0.5, 2, and 5%) for the construction of standard curve in the present work or as suggested previously by Nolan et al. (2013), that it could even be related to different syntheses of the primer pair. Others (Morisset et al. 2009) reported that this difference could also be due to mismatches in the inserted sequences that have arisen during plant crossing.
Further and according to trueness the method applied here showed no bias (Table 5), since the absolute difference (∆m) between mean measured values (Cm) and certified value (CCRM) were smaller than expanded uncertainty (U∆) of difference between result and certified value (Trapmann et al. 2014).
Additionally, Table 5 presented along with the calculation of measurement uncertainty (MU), the estimated enforcement level for each plant samples to ensure compliance with the EU 0.9% legislation. The calculated difference between the reported GM concentrations (Cm) and the expanded uncertainty (U∆) value for all samples did not exceed the permissible concentration threshold (0.9%). Since it is not easy to avoid contamination during storage or transport of GM crops, these results indicated that the tested plant crops were considered not violating the European legislation for GMOs labeling. It also reflects the sensitivity of this approach to detect even traces of GM content in DNA of plant samples.
The validation study necessitates covering all the steps in the method to ensure evaluation of all parameters that may influence the result. One of the important parameters related to method validation as proposed by Holest-Jensen and Berdal (2004) is validation of DNA extraction procedures from different sample matrixes. It is well known that isolation and purification of DNA is a crucial step in DNA molecular techniques used in plant studies for the assessment of food safety (Sönmezoğlu and Keskin 2015), especially with the increase of the global cultivation area of genetically modified (GM) crops. For reliable results extraction of the DNA from all tested plant samples was performed, in the present work applying the same DNA extraction protocol to avoid any possible different composition or substances that may affect the efficiency of the PCR assays. This protocol is a modified CTAB-based method specially developed in our laboratory (Aboul-Maaty and Oraby 2019) for isolation of high quality and purity DNA from different plant orders.