Introduction:
A groundbreaking study challenges the belief that evolution is completely incompatible. The researchers found that the evolutionary trajectory of the genome may be influenced by its evolutionary history, providing implications for synthetic genome design, antibiotic resistance confrontation, climate change mitigation, and personalized medicine.
Key Facts:
The researchers challenged the inadmissibility of evolution and proposed the idea that evolution was influenced by genomic history.
The study has provided practical applications for various fields by understanding the interaction between genes.
Potential benefits include novel genome designs, targeted antibiotic resistance**, climate change solutions, and improved personalized medicine.
**: University of Nottingham
A groundbreaking study has found that evolution is not as difficult as previously thought**, making it possible for scientists to explore which genes can be used to solve real-world problems, such as antibiotic resistance, disease, and climate change.
The study, published in the Proceedings of the National Academy of Sciences (PNAS), challenges long-held beliefs about the inadmissibility of evolution and finds that the evolutionary trajectory of the genome may be influenced by its evolutionary history, rather than by numerous factors and historical contingencies.
The study was led by Professor James McKinney and Dr Alan Biven from the School of Life Sciences at the University of Nottingham and Dr Maria Rosa Domingo-Sanans from Nottingham Trent University.
The significance of this study is no less revolutionary," said Professor McKinney, lead author of the study, "By demonstrating that evolution is not as random as we once thought, we have opened a door of possibilities for synthetic biology, medicine and environmental science." ”
The team analyzed the pangenome – a complete set of all genes within a given species – to answer a key question: is evolution possible, or is the evolution path of the genome dependent on its history and therefore not today.
Using a machine learning method called random forest, combined with a dataset of 2,500 complete genomes from a single bacterial species, the team conducted hundreds of thousands of hours of computer processing to solve the problem.
After inputting the data into a high-performance computer, the team first made "gene families" from each gene in each genome.
In this way, we can make similarity comparisons between genomes," says Dr. Domingo-Sanans.
Once these families were identified, the team analyzed the patterns of their presence in some genomes and deletion in others.
"We found that certain gene families never appear in one genome when another particular gene family already exists, while in other cases, certain genes are very dependent on the presence of another gene family. ”
In fact, researchers have discovered an unseen ecosystem where genes can cooperate or conflict with each other.
These interactions between genes make certain aspects of evolution possible, and we now have a tool that can help us make them, Dr. Domingo-Sanans adds.
"With this work, we can begin to explore which genes "support" antibiotic resistance genes, for example. So if we're trying to eliminate antibiotic resistance, we can target not only focus genes, but also their support genes. ”
We can use this approach to synthesize novel genetic constructs that can be used to develop new drugs or vaccines. Knowing what we know now has opened a door to other discoveries for us. ”
The implications of this study are far-reaching and could lead to:
Novel genome design enables scientists to design synthetic genomes that provide a roadmap to the manipulation of genetic material.
Fight antibiotic resistanceUnderstanding the dependencies between genes can help identify the "supporting role" genes that make antibiotic resistance possible, paving the way for targeted **.
Climate change mitigationInsights from the research may help design engineered microorganisms that can capture carbon or degrade pollutants to contribute to the fight against climate change.
Medical applicationsThe availability of genetic interactions has the potential to revolutionize personalized medicine by providing new indicators of disease risk and efficacy.