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Biochem Mol biol J
ISSN: 2471-8084
Volume 3, Issue 2
Metabolomics Conference 2017
August 29-30, 2017 Prague, Czech Republic
9th International Conference and Exhibition on
Metabolomics and Systems Biology
Notes:
Page 35
Mapping the Arabidopsis metabolic landscape
by untargeted metabolomics at different
environmental conditions
Si Wu
Max Planck Institute of Molecular Plant Physiology, Germany
Metabolic genome-wide association studies (mGWAS),
whereupon metabolite levels are regarded as traits,
can help unravel the genetic basis of metabolic networks.
Aiming to increase the discovery of true metabolite–gene
associations, we applied abiotic stress to Arabidopsis
thaliana using an integrative approach combining
mGWAS and metabolite-transcript correlation-network
analysis. 309 natural accessions were grown under two
independent environmental conditions (control and stress)
and subjected to untargeted LC-MS-based metabolomics;
levels of the obtained hydrophilic metabolites were used
in GWAS, followed by integration with network-derived
metabolite-transcript correlations using a time-course
stress experiment. Our two-condition-based GWAS for
~2,000 semi-polar metabolites resulted in the detection
of numerous highly resolved mQTL, many of which
environment-specific. We show increased discovery of
causal genes for well-characterized secondarymetabolites
by applyingGWASunder , moreover, discovered
a large number of hitherto uncharacterized metabolite-
gene associations, serving as a rich reservoir for further
gene-characterization efforts. Of these, we identified 93
key candidate associations between structural genes and
metabolites. We then experimentally validated-using loss-
of-function mutants-eight of the novel associations, two
of them showing differential genetic regulation in the two
environments studied. Our study thus demonstrates the
power of combining large-scale untargeted metabolomics-
based GWAS with time-course-derived networks, when
both approaches are performed under different abiotic
environments, to facilitate the identification of metabolite-
gene associations. Additionally, it also provides new global
insights into the metabolic landscape of Arabidopsis using
a strategy that could readily be adapted for other plant
species.
Biography
Si Wu conducted MS-based untargeted metabolomics study to investigate
the pathophysiology of complex metabolic disease – hypothyroidism and
therapeutic effects of traditional Chinese medicine. She published four
metabolomics-related scientific papers as the first author during the Master
degree. She worked as an Intern at Agilent Technologies (Shanghai, P.R.
China) to conduct Drug Quality Standard Test of Chinese Pharmacopoeia
(2010 Edition). At present, she is a PhD candidate waiting for the defense at
Max Planck Institute of Molecular Plant Physiology to carry on an integrative
research of combining Genome Wide Association Study (GWAS) and
network analysis to identify novel genes involved in secondary metabolism in
Arabidopsis.
Si Wu, Biochem Mol biol J, 3:2
DOI: 10.21767/2471-8084-C1-002