Literature DB >> 16186495

Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops.

Gareth S Catchpole1, Manfred Beckmann, David P Enot, Madhav Mondhe, Britta Zywicki, Janet Taylor, Nigel Hardy, Aileen Smith, Ross D King, Douglas B Kell, Oliver Fiehn, John Draper.   

Abstract

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.

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Year:  2005        PMID: 16186495      PMCID: PMC1242293          DOI: 10.1073/pnas.0503955102

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  21 in total

1.  Technical advance: simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry.

Authors:  U Roessner; C Wagner; J Kopka; R N Trethewey; L Willmitzer
Journal:  Plant J       Date:  2000-07       Impact factor: 6.417

Review 2.  Metabolomics--the link between genotypes and phenotypes.

Authors:  Oliver Fiehn
Journal:  Plant Mol Biol       Date:  2002-01       Impact factor: 4.076

Review 3.  Comparative safety assessment for biotech crops.

Authors:  Esther J Kok; Harry A Kuiper
Journal:  Trends Biotechnol       Date:  2003-10       Impact factor: 19.536

4.  Simultaneous determination of the main metabolites in rice leaves using capillary electrophoresis mass spectrometry and capillary electrophoresis diode array detection.

Authors:  Shigeru Sato; Tomoyoshi Soga; Takaaki Nishioka; Masaru Tomita
Journal:  Plant J       Date:  2004-10       Impact factor: 6.417

5.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms.

Authors: 
Journal:  Neural Comput       Date:  1998-09-15       Impact factor: 2.026

6.  Metabolite profiling for plant functional genomics.

Authors:  O Fiehn; J Kopka; P Dörmann; T Altmann; R N Trethewey; L Willmitzer
Journal:  Nat Biotechnol       Date:  2000-11       Impact factor: 54.908

7.  Metabolite fingerprinting: detecting biological features by independent component analysis.

Authors:  M Scholz; S Gatzek; A Sterling; O Fiehn; J Selbig
Journal:  Bioinformatics       Date:  2004-04-15       Impact factor: 6.937

8.  Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks.

Authors:  R Goodacre; E M Timmins; R Burton; N Kaderbhai; A M Woodward; D B Kell; P J Rooney
Journal:  Microbiology       Date:  1998-05       Impact factor: 2.777

9.  High-throughput classification of yeast mutants for functional genomics using metabolic footprinting.

Authors:  Jess Allen; Hazel M Davey; David Broadhurst; Jim K Heald; Jem J Rowland; Stephen G Oliver; Douglas B Kell
Journal:  Nat Biotechnol       Date:  2003-05-12       Impact factor: 54.908

10.  Assessment of 1H NMR spectroscopy and multivariate analysis as a technique for metabolite fingerprinting of Arabidopsis thaliana.

Authors:  Jane L Ward; Cassandra Harris; Jennie Lewis; Michael H Beale
Journal:  Phytochemistry       Date:  2003-03       Impact factor: 4.072

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  70 in total

1.  Metabolic profiling based on LC/MS to evaluate unintended effects of transgenic rice with cry1Ac and sck genes.

Authors:  Yuwei Chang; Chunxia Zhao; Zhen Zhu; Zeming Wu; Jia Zhou; Yanni Zhao; Xin Lu; Guowang Xu
Journal:  Plant Mol Biol       Date:  2012-01-22       Impact factor: 4.076

2.  Transcriptome and metabolome profiling of field-grown transgenic barley lack induced differences but show cultivar-specific variances.

Authors:  Karl-Heinz Kogel; Lars M Voll; Patrick Schäfer; Carin Jansen; Yongchun Wu; Gregor Langen; Jafargholi Imani; Jörg Hofmann; Alfred Schmiedl; Sophia Sonnewald; Diter von Wettstein; R James Cook; Uwe Sonnewald
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-22       Impact factor: 11.205

3.  Combining genetic diversity, informatics and metabolomics to facilitate annotation of plant gene function.

Authors:  Takayuki Tohge; Alisdair R Fernie
Journal:  Nat Protoc       Date:  2010-06-10       Impact factor: 13.491

4.  Natural variation explains most transcriptomic changes among maize plants of MON810 and comparable non-GM varieties subjected to two N-fertilization farming practices.

Authors:  Anna Coll; Anna Nadal; Rosa Collado; Gemma Capellades; Mikael Kubista; Joaquima Messeguer; Maria Pla
Journal:  Plant Mol Biol       Date:  2010-03-27       Impact factor: 4.076

5.  Proteomic analysis of MON810 and comparable non-GM maize varieties grown in agricultural fields.

Authors:  Anna Coll; Anna Nadal; Michel Rossignol; Pere Puigdomènech; Maria Pla
Journal:  Transgenic Res       Date:  2010-10-23       Impact factor: 2.788

6.  Response diversity of Arabidopsis thaliana ecotypes in elevated [CO2] in the field.

Authors:  Pinghua Li; Allan Sioson; Shrinivasrao P Mane; Alexander Ulanov; Gregory Grothaus; Lenwood S Heath; T M Murali; Hans J Bohnert; Ruth Grene
Journal:  Plant Mol Biol       Date:  2006-08-29       Impact factor: 4.076

7.  Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals.

Authors:  David P Enot; Manfred Beckmann; David Overy; John Draper
Journal:  Proc Natl Acad Sci U S A       Date:  2006-09-21       Impact factor: 11.205

8.  Rhizosphere communities of genetically modified zeaxanthin-accumulating potato plants and their parent cultivar differ less than those of different potato cultivars.

Authors:  Nicole Weinert; Remo Meincke; Christine Gottwald; Holger Heuer; Newton C M Gomes; Michael Schloter; Gabriele Berg; Kornelia Smalla
Journal:  Appl Environ Microbiol       Date:  2009-04-17       Impact factor: 4.792

9.  Nutritionally improved agricultural crops.

Authors:  Martina Newell-McGloughlin
Journal:  Plant Physiol       Date:  2008-07       Impact factor: 8.340

10.  Comparative compositional analysis of transgenic potato resistant to potato tuber moth (PTM) and its non-transformed counterpart.

Authors:  Hassan Rahnama; Amir Bahram Moradi; Seyed Hamid Mirrokni; Foad Moradi; Mohammad Reza Shams; Mohammad Hossein Fotokian
Journal:  Transgenic Res       Date:  2018-05-04       Impact factor: 2.788

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