Genetically modified biotechnology is the fastest-adopted technology in the history of modern agriculture. In 1996 there were 1.7 million hectares of GM crops; since then that number has increased to 148 million hectares—an 87-fold increase. But, there is some concern surrounding the potential side effects of randomly inserting exogenous genes in plant genomes. Mainly, an insertion of exogenous genes could produce modified biochemical processes, new proteins, or other secondary pleiotropic effects. Evaluating the substantial equivalence of GM groups to traditional crops is therefore essential to guarantee the safe use of GM crops and alleviate the fears consumers have about GM food. Here, the researchers evaluated the effects of transgenes on rice seed proteomes by 2-D differential in-gel electrophoresis (2D-DIGE) combined with mass spectrometry (MS). The study found that GM events do not substantially alter proteome profiles as compared with conventional genetic breeding and natural genetic variation. Specifically, mass spectrometry revealed 234 proteins differentially expressed in the 6 materials (BAR68-1, D68, 2036-la, MH86, MH63, ZH10), which are involved in different cellular and metabolic processes. This finding suggests that metabolism, protein synthesis and destination, and defense response in seeds are important in differentiating rice cultivars and varieties.
Gong, Chun Yan, et al. “Proteomics insight into the biological safety of transgenic modification of rice as compared with conventional genetic breeding and spontaneous genotypic variation.” Journal of Proteome Research 11.5 (2012): 3019-3029. [GSSS gong proteomics insight rice]
Studies of diverse plant species have demonstrated that changes in transcript levels are not fully followed by the same changes in protein levels. Proteins are the key players in gene function and are directly involved in metabolism and cellular development or have roles as toxins, antrinutrients, or allergens. Therefore, comparisons of GM proteomes and control lines are of necessary importance when trying to determine GM crop safety. The results of these experiments can reveal molecular differences in varieties produced by conventional genetic breeding and natural genetic variation and help researchers better assess the safety of a GM crop. Here, the researchers evaluated the effects of transgenes on rice seed proteomes by 2-D differential in-gel electrophoresis (2D-DIGE) combined with mass spectrometry (MS). Two sets of GM indica rice and controls were used: Bar68-1 transformed with herbicide resistant gene bar and its non transgenic control indica variety D68, and 2036-la transformed with insect-resistant genes cry1Ac/sckand its nontransgenic control indicavariety MingHui 86 (MH86). In addition to these, the researchers used MH63, which is a parental line used for breeding MH86, and japonica rice Zhonghua 10 (ZH10). In summary, the experimental design included GM rice and controls, different indica varieties (parental and filial), and indica and japonicacultivars.
First, the researchers confirmed that their transgenic lines were successfully transformed. PCR with specific primers revealed BAR68-1 had one detectable DNA fragment with a size of 568 bp, which corresponded to the bar gene for herbicide resistance. 2036-la line had 2 detectable DNA fragments of 1709 bp and 358 bp corresponding to cry1Ac and sck genes, respectively (coding for insect resistance).
Next, a 2D-DIGE with pH 4–7 strips analyzed seed proteomes from different rice lines and detected about 2250 protein sports in each image. A principal component analysis (PCA) was conducted to investigate similarities in the proteomes of the 6 rice lines. Much less variation was found in the proteomes between transgenic lines and their controls than between different indica varieties or between indica and japonica cultivars. In addition to this, the researchers analyzed differentially expressed proteins (DEPs) from the seed proteomes of the 6 lines which revealed 423 (Student’s t test) and 443 (ANOVA) protein spots, respectively, with statistically significant differences in expression. The largest differences of protein expression were found between indica (varieties NH63, D68, and MH86) and japonica (ZH10) cultivars. A smaller difference was found between the 3 indicavarieties, an even smaller difference between NH63 and MH86, and the least between transgenic lines and controls (Bar68-1 vs D68; 2036-la vs MH86). Compared with conventional genetic breeding and natural genetic variation, rice seed proteomes were largely unchanged with transgenic modification.
In addition to the principal component analysis, mass spectrometry was used to analyze 264 differentially expressed proteins (DEPs) selected on the basis of (1) 1.2-fold changes in expression and (2) significant difference by both Student’s ttest and ANOVA. These proteins were classified into eight functional categories: metabolism, protein synthesis and destination, defense response, cell growth and division, pyruvate orthophosphate dikinases (PPDKs), signal transduction, transcription, and transporters. Most of the DEPs were involved in metabolism (31.2%), protein synthesis and destination (25.2%), and defense response (22.4%). In summary, proteins implicated in central carbon metabolism, starch synthesis, protein folding and modification, and defense response showed altered expression in response to natural genetic variation, conventional breeding, and transgene modification.
To examine the identified DEPs in more detail, the authors analyzed the expression patterns of 218 DEPs using GeneCluster 2.0. The DEPs were grouped into 6 clusters: c0, c1, c2, c3, c4, c5. The 6 clusters were further grouped into three antagonistic pairs (clusters pairs: c1 vs c3, c1 vs c4, c2 vs c5). Proteins in c0c3 sets were assumed to contribute to the variability between D68/MH63 and MH86/ZH10. Proteins grouped into c1c4 sets possibily separated MingHui from D68/ZH10. Furthermore, the c2c5 sets may be the main contributors to the separation between japonica and indica rice. Although there were large changes in expression of these proteins among nontransgenic varieties, their expression was similar between transgenic lines and their respective controls in all clusters.
The authors further performed PCA for the 218 DEPs to estimate the contribution of DEPs to the total variability observed within rice lines and identify proteins responsible for the variability. The results of the PCA confirmed the authors finidings from cluster analysis that proteins in 3 clusters pairs (c1c4, c2c5, and c0c3) may have a distinct contribution to the entire variability of the data set.
Next, the authors evaluated the distribution on 218 DEPs involved in different function categories and subcategories in cluster pairs c0c3, c1c4, and c2c5. The results of this analysis clearly showed different functional categories and subcategories-related proteins distributed heterogeneously in these cluster pairs. For example, metabolism-related proteins were mainly in c1c4 and c2c5, but less so in c0c3. Protein synthesis and destination-related proteins were more often in c0c3 and c2c5 than in c1c4. Most of the defense response-related proteins were in c0c3 and c1c4. Thus, the different functional categories in these clusters confirmed differences in biological processes in all analyzed rice lines.
To investigate the changes in biological processes as a result of natural genetic variation, conventional breeding, and transgene modification, the authors performed expression profile analysis of protein groups associated with nine functional categories and subcategories showing significant contribution to the differences. The expression patterns of proteins involved in glycolysis and starch synthesis were similar, with relatively high levels in 2 nontransgenic MinHui varieties and low levels in D68 and ZH10. The opposite relationship was found with proteins involved in the TCA pathway. All in all, the expression of proteins in transgenic lines and their respective controls was changed, but the changes were similar to those observed between certain non-transgenic varieties.
All these data combined suggested that GM does not significantly alter the rice seed proteomes as compared with natural genetic variation and conventional genetic breeding. Specifically, the integration in rice genomes and expression of bar or cry1Ac/sck do not change the proteome patterns as compared with natural genetic variation and conventional breeding. Apart from the safety conclusions of this experiment, the results also show that the proteins differentially expressed in nontransgenic rice varieties had functions in central carbon metabolism, starch synthesis, protein folding and modification, and defense response. For future experiments, these processes can be further investigated to differentiate and explore rice varieties.