IC4R007-Metabolomics-2009-19233746

Project Title

Figure 1. Effect of silylation conditions on the number of peaks, total relative peak area and the peak areas of ten randomly selected peaks

Metabolic profiling of transgenic rice with cryIAc and sck genes: An evaluation of unintended effects at metabolic level by using GC-FID and GC–MS

The Background of This Project

• Oryza sativa L. is a major food crop consumed by a half of the world’s population. During the growth period, disease, insect pest and abiotic stress (drought, heat, cold, salt etc.) are main fac- tors causing the yield reduction. To solve the above-mentioned problems, modern biotechnology is applied in rice breeding which introduces improved agronomic characteristics. Two kinds of insecticidal genes including Bacillus thuringiensis gene and cowpea trypsin inhibitor gene (Bt and CpTI genes) are widely used for the production of insect-resistant plants. Bt gene from Bacillus thuringiensis strains encoding toxin proteins (Cry or Cyt protein) has been introduced into crop plants for its activity against lepidoptera pests but not mammals [29–32]. Bt insecticidal protein has a rapid and strong effect but relatively narrow spectrum of insect-resistant and it is easy to induce insect tolerance. Sck gene, a modified cowpea trypsin inhibitor gene has enough resistance to lepidoptera and part of the coleopteran pests which is often used together with cry gene [33–35].

• The double transgenic rice with sck and cryIAc genes combined different insect-resistant mechanism, exhibited wider anti-insect spectrum and higher resistance against the pests than the single transgenics [36]. Some work has been carried out to assess the changes of proteins, metabolites, genes and physicochemical properties of the GM rice [21,37–43]. Most of the studies on metabolism only paid attention to the key nutri- ents and anti-nutrients such as proximates, fiber compounds, total amino acids, total fatty acids, micronutrients, phytic acid, trypsin inhibitors, lectins and so on. Researches of the global, untargeted metabolite profile of transgenic rice were relatively few.

Plant Materials

• The rice (O. sativa L.) for study was provided by the Key Laboratory of Agriculture Genetic Engineering, Fujian Academy of Agricultural Sciences (Fuzhou). The transgenic rice was developed by Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. M86 was used as the host for the cryIAc and sck genes. The marker-free transgenic samples (N6: N6080, N6130 and N6188) integrated with two insecticidal genes were grown side- by-side with the wild parent (M86-C) in the same period (sowed on Nov. 25, 2005). Additionally, there were other wild M86 samples from different sowing dates or sites including M86-D1 (sowed on Dec. 15, 2005), M86-D2 (sowed on Dec. 25, 2005), and M86-F (sowed on May 25, 2005). All samples but M86-F sowed in Fuzhou City, Fujian Province, China were planted in and obtained from the experimental field of Hainan Province, China. Detailed sample information was listed in Table 1.

Research Findings

Figure 2. Typical gas chromatograms obtained from different BSTFA/pyridine ratios (v/v, ␮L): (a) 30/140; (b) 90/80; and (c) 130/40.

• The derivatization and instrument analysis conditions were optimized using a wild sample (M86). Several factors which have strong effects on derivatization efficiency were investigated, including the temperature, time and ratio of derivatization reagent to solvent. The final method parameters were chosen according to both the total peak areas (divided by internal standard area) and the number of peaks detected. The relative peak areas to IS for 10 randomly selected peaks were given in Fig. 1 which displayed the change tendency of individual peaks along with varied derivatization conditions.
• The effect of BSTFA to pyridine (Py) ratio on derivatization efficiency was studied. When the volume ratio was changed to about 1:1, peak number and total peak area achieved optimum combination (Fig. 1a). Similarly, most of the relative peak areas for 10 randomly selected peaks reached the maximum at this point (Fig. 1b). The derivatization at the ratio of 30:140 was incomplete because of very small relative peak area. Typical gas chromatograms corresponding to 30/140, 90/80, 130/40 of BSTFA to pyridine are given in Fig. 2.
• Changing the derivatization temperatures from 45 ◦ C to 75 ◦ C markedly improved peak responses (Fig. 1c and d). Unstable metabolites might degrade when the derivatization temperature was too high, so 75 ◦ C was more suitable than 90 ◦ C to prevent thermal decomposition. Duration time for derivatization also caused changes in both peak number and peak area (Fig. 1e and f). The peak number and area showed no further increase when the reaction time was longer than 45 min. To shorten time, 45 min was selected for derivatization.
• The stability of sample after silylation was tested for the batch analysis. A derivatized sample was placed on the autosampler and injected every 12 h during 72 h period. It can be observed that the total peak area only had a little change from 0 h to 24 h but sig- nificantly decreased after 36 h, therefore, the derivatized samples should be analyzed within 24 h. The relative standard derivations (RSDs) of relative peak areas of 10 random peaks to IS were lower than 11% at the end of 24 h besides the peak at 27.68 min with a 15.29% RSD. (Table 2).

Labs working on this Project

• Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, The Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China
• Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
• National Institute for Nutrition and Food Safety, Chinese CDC, 100050 Beijing, China

Corresponding Author

• Guowang Xu (E-mail: xugw@dicp.ac.cn)