A simple method for estimating biosynthetic genes for secondary metabolites
September 27, 2017
Copy-number variation genes promote secondary metabolite diversity
Researchers from RIKEN CSRS and the Kyushu Institute of Technology have collaboratively developed a convenient, accurate method to estimate gene clusters involved in the synthesis of secondary metabolites based on an analysis method that merged data from multiple plant genome data sources.
Secondary metabolites produced by plants are used to make dyes, perfumes, pharmaceuticals and other useful products. Artificial production of such secondary metabolites requires the identification of the underlying gene clusters, while clarification of the mechanisms of evolution are needed for greater production efficiency. A simple method was called for that could identify biosynthetic genes for large amounts of secondary metabolites.
The research group was able to develop its method for data on 1) metabolomes, for identifying secondary metabolites in a comprehensive way, 2) transcriptomes, for checking the expression of all genes, and 3) SNP data for showing base diversity in the same species. Using this method, the researchers were able to estimate that 5,654 genes were involved in the biosynthesis of 1,335 secondary metabolites in the model plant Arabidopsis thaliana. Furthermore, the results revealed that copy-number variation genes play a large role in secondary metabolite diversity in plants.
Utilization of these findings is expected to lead to control of secondary metabolite biosynthesis and more efficient production of secondary metabolites.
- Original article
- Molecular Biology and Evolution doi:10.1093/molbev/msx234
- K. Shirai, F. Matsuda, R. Nakabayashi, M. Okamoto, M. Tanaka, A. Fujimoto, M. Shimizu, K. Shinozaki, M. Seki, K. Saito, K. Hanada,
- "A highly specific genome-wide association study integrated with transcriptome data reveals the contribution of copy number variations to specialized metabolites in Arabidopsis thaliana accessions".
- Kousuke Hanada
- Visiting Scientist
- Gene Discovery Research Group