Evolution is not as unpredictable as previously believed, according to a study published in the Proceedings of the National Academy of Sciences. The authors suggest this would allow scientists to explore which genes could be useful for tackling antibiotic resistance and climate change.
A team from the University of Nottingham, UK, found that the evolutionary trajectory of a genome is influenced by its evolutionary history rather than outside factors and historical accidents. “The implications of this research are nothing short of revolutionary,” said Professor McInerney, from the School of Life Sciences at the University of Nottingham and lead author of the study. “By demonstrating that evolution is not as random as we once thought, we’ve opened the door to an array of possibilities in synthetic biology, medicine, and environmental science.”
To carry out this work, the team conducted an analysis of the pangenome — the complete set of genes for any given species — to find out whether evolution is predictable or whether the evolutionary paths of genomes are dependent on their history and not predictable. Using a machine learning called Random Forest and a dataset of 2,500 complete genomes from a single bacterial species, the team carried out several hundred thousand hours of computer processing to answer this question.
The first step was to make “gene families” to compare like-with-like across the genomes. Then, they analysed patterns in these families, looking at which genes were present in some genomes and absent in others.
Results showed that some gene families never appeared in a genome when a particular other gene family was already there, while others only appeared when another specific gene family was present. The authors believe they’ve unveiled an invisible ecosystem where genes can cooperate or can be in conflict with one another. “These interactions between genes make aspects of evolution somewhat predictable, and furthermore, we now have a tool that allows us to make those predictions,” adds Dr. Domingo-Sananes.
“From this work, we can begin to explore which genes “support” an antibiotic resistance gene, for example. Therefore, if we are trying to eliminate antibiotic resistance, we can target not just the focal gene but we can also target its supporting genes,” added Dr Beavan. “We can use this approach to synthesise new kinds of genetic constructs that could be used to develop new drugs or vaccines. Knowing what we now know has opened the door to a whole host of other discoveries.”
Some of the implications for this study include:
- Allowing researchers to design synthetic genomes, providing a way to manipulate genetic material in a predictable manner
- Understanding the connections between genes to identify genes that make antibiotic resistance possible
- “engineering” microorganisms to capture carbon and pollutants
- Revolutionising personalised medicine and developing new treatments
Beavan AJS, Domingo-Sananes MR, McInerney JO. Contingency, repeatability, and predictability in the evolution of a prokaryotic pangenome. Proc Natl Acad Sci U S A. 2024 Jan 2;121(1):e2304934120. doi: 10.1073/pnas.2304934120.