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mzGroupAnalyzer--predicting pathways and novel chemical structures from untargeted high-throughput metabolomics data

Doerfler, Hannes ; Sun, Xiaoliang ; Wang, Lei ; Engelmeier, Doris ; Lyon, David ; Weckwerth, Wolfram Vertes, Akos

PloS one, 2014-05, Vol.9 (5), p.e96188-e96188 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
mzGroupAnalyzer--predicting pathways and novel chemical structures from untargeted high-throughput metabolomics data
Autor: Doerfler, Hannes ; Sun, Xiaoliang ; Wang, Lei ; Engelmeier, Doris ; Lyon, David ; Weckwerth, Wolfram
Vertes, Akos
Assuntos: Algorithms ; Analytical chemistry ; Antioxidants ; Arabidopsis ; Arabidopsis - genetics ; Arabidopsis - metabolism ; Arabidopsis thaliana ; Biochemistry ; Bioflavonoids ; Bioinformatics ; Biology ; Biology and Life Sciences ; Chromatography ; Cold ; Cold treatment ; Computer and Information Sciences ; Feature extraction ; Flavones ; Flavonoids ; Gene sequencing ; Genomes ; Genomics ; Genotypes ; Graphical user interface ; Ions ; Light effects ; Mass spectrometry ; Metabolism ; Metabolites ; Metabolome ; Metabolomics ; Metabolomics - instrumentation ; Metabolomics - methods ; Natural products ; Oxidative stress ; Pathways ; Pattern recognition ; Phenotypes ; Physical Sciences ; Plant protection ; Research and Analysis Methods ; Scientific imaging ; Transformations ; User-Computer Interface
É parte de: PloS one, 2014-05, Vol.9 (5), p.e96188-e96188
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
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Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: HD XS WW. Performed the experiments: HD XS. Analyzed the data: HD XS WW. Contributed reagents/materials/analysis tools: XS LW DE DL WW. Wrote the paper: HD XS LW DE DL WW.
Descrição: The metabolome is a highly dynamic entity and the final readout of the genotype x environment x phenotype (GxExP) relationship of an organism. Monitoring metabolite dynamics over time thus theoretically encrypts the whole range of possible chemical and biochemical transformations of small molecules involved in metabolism. The bottleneck is, however, the sheer number of unidentified structures in these samples. This represents the next challenge for metabolomics technology and is comparable with genome sequencing 30 years ago. At the same time it is impossible to handle the amount of data involved in a metabolomics analysis manually. Algorithms are therefore imperative to allow for automated m/z feature extraction and subsequent structure or pathway assignment. Here we provide an automated pathway inference strategy comprising measurements of metabolome time series using LC- MS with high resolution and high mass accuracy. An algorithm was developed, called mzGroupAnalyzer, to automatically explore the metabolome for the detection of metabolite transformations caused by biochemical or chemical modifications. Pathways are extracted directly from the data and putative novel structures can be identified. The detected m/z features can be mapped on a van Krevelen diagram according to their H/C and O/C ratios for pattern recognition and to visualize oxidative processes and biochemical transformations. This method was applied to Arabidopsis thaliana treated simultaneously with cold and high light. Due to a protective antioxidant response the plants turn from green to purple color via the accumulation of flavonoid structures. The detection of potential biochemical pathways resulted in 15 putatively new compounds involved in the flavonoid-pathway. These compounds were further validated by product ion spectra from the same data. The mzGroupAnalyzer is implemented in the graphical user interface (GUI) of the metabolomics toolbox COVAIN (Sun & Weckwerth, 2012, Metabolomics 8: 81-93). The strategy can be extended to any biological system.
Editor: United States: Public Library of Science
Idioma: Inglês

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mzGroupAnalyzer--predicting pathways and novel chemical structures from untargeted high-throughput metabolomics data

Doerfler, Hannes ; Sun, Xiaoliang ; Wang, Lei ; Engelmeier, Doris ; Lyon, David ; Weckwerth, Wolfram Vertes, Akos

United States: Public Library of Science

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