Making Sense Of Metabolites In Living Systems

Combining computational algorithms with mass spectrometry, scientists in Japan have devised a technique to speed up the identification and characterization of natural products.

AsianScientist (Apr. 10, 2019) – A team of scientists in Japan has developed a computational mass spectrometry system that could help scientists identify useful natural compounds in plants and other organisms. They published their finding in the journal Nature Methods.

Numerous drugs used in clinics today are derived from nature. For example, aspirin and penicillin were derived from plants and mold respectively. Currently, scientists have only identified about five percent of all natural products.

In the present study, researchers led by Dr. Hiroshi Tsugawa at the RIKEN Center for Sustainable Resource Science (CSRS) in Japan have developed a technique that can identify entire sets of metabolites in living systems.

The method combines computational algorithms with mass spectrometry, and the researchers used it to predict the molecular formula of metabolites in plants labeled with carbon-13, an alternative form of carbon. The software can also classify the metabolites by type and predict the substructure of unknown metabolites. This is important because metabolite structure is often related to function.

“While no software can comprehensively identify all the metabolites in a living organism, our program incorporates new techniques in computational mass spectrometry and provides ten times the coverage of previous methods,” said Tsugawa.

Using their system, the researchers were able to characterize a class of antibiotics—benzoxazinoids—in rice and maize, as well as a class of compounds known as glycoalkaloids, which exhibit anti-inflammatory and antibacterial properties, in the common onion, tomato and potato. The team also identified compounds such as triterpene saponins in soy beans and licorice and beta-carboline alkaloids in a coffee-related plant, which have anticancer properties.

The scientists further noted that their method is not limited to the analysis of plant metabolomes.

“I believe that computationally decoding metabolomic mass spectrometry data is linked to a deeper understanding of all metabolism. Our next goal is to improve this methodology to facilitate global identification of human and microbiota metabolomes as well. Newly found metabolites can then be further investigated via genomics, transcriptomics, and proteomics,” Tsugawa explained.

The article can be found at: Tsugawa et al. (2019) A Cheminformatics Approach to Characterize Metabolomes in Stable Isotope-labeled Organisms.


Source: RIKEN; Photo: Pixabay.
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