Cao, S; He, X; Xu, W; Luo, Y; Yuan, Y; Liu, P; Cao, B; Shi, H; Huang, K
IUBMB Life. 2012 March. 64(3):242–250
Link to full text (open access, freely available)
PMID: 22215564 DOI: 10.1002/iub.601 ISSN: 1521-6543
(GM) organisms are the most important barriers to their promotion. We aimed to establish a new in vivo evaluation model for genetically modified foods by using metabonomics and bacterial profile approaches. T1c-19 rice flour or its transgenic parent MH63 was used at 70% wt/wt to produce diets that were fed to rats for ∼ 90 days. Urine metabolite changes were detected using (1)H NMR. Denaturing gradient gel electrophoresis and real-time polymerase chain reaction (RT-PCR) were used to detect the bacterial profiles between the two groups. The metabonomics was analyzed for metabolite changes in rat urine, when compared with the non-GM rice group, where rats were fed a GM rice diet. Several metabolites correlated with rat age and sex but not with GM rice diet. Significant biological differences were not identified between the GM rice diet and the non-GM rice diet. The bacteria related to rat urine metabolites were also discussed. The results from metabonomics and bacterial profile analyses were comparable with the results attained using the traditional method. Because metabonomics and bacterial profiling offer noninvasive, dynamic approaches for monitoring food safety, they provide a novel process for assessing the safety of GM foods.
Cao, S, X He, W Xu, Y Luo, Y Yuan, P Liu, B Cao, H Shi, K Huang. "Safety assessment of transgenic Bacillus thuringiensis rice T1c-19 in Sprague-Dawley rats from metabonomics and bacterial profile perspectives." IUBMB Life 64.3 (2012): 242–250. Web. 24 Sep. 2018.
Cao, S., He, X., Xu, W., Luo, Y., Yuan, Y., Liu, P., Cao, B., Shi, H., & Huang, K. (2012). Safety assessment of transgenic Bacillus thuringiensis rice T1c-19 in Sprague-Dawley rats from metabonomics and bacterial profile perspectives. IUBMB Life, 64(3), 242–250. doi:10.1002/iub.601
Please verify citations before use, citations are automatically generated based on information stored within the GENERA database and therefore may or may not be correct.