2023年1月13日
Machine learning enables prediction of metabolic system evolution in bacteria
Science Advances
- ,
- 巻
- 9
- 号
- 2
- 記述言語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1126/sciadv.adc9130
- 出版者・発行元
- American Association for the Advancement of Science (AAAS)
Evolution prediction is a long-standing goal in evolutionary biology, with potential impacts on strategic pathogen control, genome engineering, and synthetic biology. While laboratory evolution studies have shown the predictability of short-term and sequence-level evolution, that of long-term and system-level evolution has not been systematically examined. Here, we show that the gene content evolution of metabolic systems is generally predictable by applying ancestral gene content reconstruction and machine learning techniques to ~3000 bacterial genomes. Our framework, Evodictor, successfully predicted gene gain and loss evolution at the branches of the reference phylogenetic tree, suggesting that evolutionary pressures and constraints on metabolic systems are universally shared. Investigation of pathway architectures and meta-analysis of metagenomic datasets confirmed that these evolutionary patterns have physiological and ecological bases as functional dependencies among metabolic reactions and bacterial habitat changes. Last, pan-genomic analysis of intraspecies gene content variations proved that even “ongoing” evolution in extant bacterial species is predictable in our framework.
- ID情報
-
- DOI : 10.1126/sciadv.adc9130
- eISSN : 2375-2548
- ORCIDのPut Code : 126322901