Catchpole, GS; Beckmann, M; Enot, DP; Mondhe, M; Zywicki, B; Taylor, J; Hardy, N; Smith, A; King, RD; Kell, DB; Fiehn, O; Draper, J
Proceedings of the National Academy of Sciences of the United States of America. 2005 October. 102(40):14458-62
Link to full text (journal may charge for access)
PMID: 16186495 DOI: 10.1073/pnas.0503955102 ISSN: 1091-6490
There is current debate whether genetically modified (GM) plants might contain unexpected, potentially undesirable changes in overall metabolite composition. However, appropriate analytical technology and acceptable metrics of compositional similarity require development. We describe a comprehensive comparison of total metabolites in field-grown GM and conventional potato tubers using a hierarchical approach initiating with rapid metabolome "fingerprinting" to guide more detailed profiling of metabolites where significant differences are suspected. Central to this strategy are data analysis procedures able to generate validated, reproducible metrics of comparison from complex metabolome data. We show that, apart from targeted changes, these GM potatoes in this study appear substantially equivalent to traditional cultivars.
Catchpole, GS, M Beckmann, DP Enot, M Mondhe, B Zywicki, J Taylor, N Hardy, A Smith, RD King, DB Kell, O Fiehn, J Draper. "Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops ." Proceedings of the National Academy of Sciences of the United States of America 102.40 (2005): 14458-62. Web. 22 Oct. 2018.
Catchpole, GS., Beckmann, M., Enot, DP., Mondhe, M., Zywicki, B., Taylor, J., Hardy, N., Smith, A., King, RD., Kell, DB., Fiehn, O., & Draper, J. (2005). Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops . Proceedings of the National Academy of Sciences of the United States of America, 102(40), 14458-62. doi:10.1073/pnas.0503955102
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.