研究課題
Through machine-learning-based analysis of aromatic compound degradation genes across the prokaryotic domains, novel genes in catabolism of xenobiotics were identified. The analysis was focused on several phyla known to utilize aromatic compounds. Xenobiotics-degrading genes were often clustered together in genomes. They were often modular, being paired with different genes depending on the natural habitat of the organism. Interestingly, they also displayed recognizable trends in their evolutionary history, suggesting that this may be an effective tool in predicting novel genes supporting xenobiotics catabolism.
1: 当初の計画以上に進展している
The research is proceeding as planned.
In the coming year, the analysis will be expanded to genes involved in other high-profile metabolisms and catabolic pathways that may be unique to a specific domain (bacteria vs archaea). Targets include other genes for degrading non-aromatic xenobiotics and high-priority contaminants (detected frequently and persistent) with unknown genes and catabolic pathways that differentiate/distinguish bacteria and archaea. Bacteria and Archaea are distinct phylogenetic lineages, yet we understand very little about how they differ in physiology and way of life. Machine-learning-based approaches will be a novel approach to tackling this fundamental question in biology.
すべて 2020 2019
すべて 雑誌論文 (4件) (うち査読あり 4件、 オープンアクセス 1件)
mBio
巻: 11 ページ: e00408-20
10.1128/mBio.00408-20
Nature Ecology & Evolution
巻: 4 ページ: 534～542
10.1038/s41559-020-1125-6
Nature
巻: 577 ページ: 519～525
10.1038/s41586-019-1916-6
Scientific Reports
巻: 9 ページ: na
10.1038/s41598-019-46388-1
