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Genomic Science Program


Labeling the Thale Cress Metabolites
Published: January 27, 2021
Posted: August 22, 2021

New data pipeline identifies metabolites following heavy isotope labeling.

Analysis of individual metabolites in different types of Arabidopsis (thale cress) plants using the PODIUM tool. [Image courtesy Clint Chapple]

The Science

Plants synthesize hundreds to thousands of different chemicals. These chemicals, called metabolites, help plants adapt to their natural environments. Scientists use a method called mass spectrometry to detect and measure the abundances of many chemicals in a plant or another sample. However, it is more challenging to identify individual metabolites in a mixed sample and their origin. Researchers need improved methods for such chemical analysis. These tools would help researchers understand the origin of the metabolites that plants synthesize and would also accelerate the identification of those metabolites and the genes responsible for their synthesis.

The Impact

The hundreds of thousands of metabolites found in flowering plants influence a huge range of characteristics, such as the scent and color that flowers use to attract bees and other pollinators. One method of studying the pathways in plants related to their synthesis of metabolites is to introduce precursors labeled using radioactivity or isotopes. Precursors are chemicals or compounds that are a step before the target chemical in a pathway. Scientists can then track these precursors as they move through plants’ synthesis pathways. Previous labeling studies used these approaches to track just one or a few substances involved with plant metabolites. However, these approaches did not allow researchers to follow all other substances that might have incorporated the label. This new research developed an easy-to-use computational tool called PODIUM. This tool locates all the substances that incorporate 13carbon-labeled precursors in an experiment.

Summary

Researchers developed an isotopic labeling approach that established a library of metabolites produced from the amino acid phenylalanine in the stems of Arabidopsis plants (also called thale cress). By varying genotype, using both loss-of-function mutants and natural variants, the researchers enhanced their understanding of how soluble phenylpropanoids are affected by mutations in genes encoding steps in these metabolic pathways. In addition, the researchers identified phenylalanine-derived mass spectrometry features that associate with natural polymorphisms that contribute to plant chemical diversity in the wild. This process located several new gene candidates that affect soluble phenylpropanoids and identified novel phenylalanine-derived metabolites.

Contacts
BER Program Manager
Cathy Ronning
U.S. Department of Energy Office of Science, Office of Biological and Environmental Research
Biological Systems Science Division (SC-33.2)
Foundational Genomics Research and Environmental Genomics
catherine.ronning@science.doe.gov

Principal Investigators
Clint Chapple
Department of Biochemistry and Center for Plant Biology
Purdue University,
chapple@purdue.edu

Brian Dilkes
Department of Biochemistry and Center for Plant Biology
Purdue University
bdilkes@purdue.edu

Funding

This work was supported by the Office of Biological and Environmental Research, within the U.S. Department of Energy (DOE) Office of Science. One of the researchers was supported in part by a U.S. Department of Agriculture National Institute of Food and Agriculture postdoctoral grant.

Publications

Simpson, J.P. et al. “Metabolic source isotopic pair labeling and genome-wide association are complementary tools for the identification of metabolite–gene associations in plants.” The Plant Cell  33(3), March 2021, Pages 492–510 (2021). [DOI:10.1093/plcell/koaa046]

Related Links

Purdue University news feature: Purdue scientists develop methods to improve molecular identification in plants

Topic Areas:

  • Research Area: Genomic Analysis and Systems Biology
  • Research Area: Computational Biology, Bioinformatics, Modeling
  • Research Area: Research Technologies and Methodologies

Division: SC-33.2 Biological Systems Science Division, BER

 

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