Evolution Is Not As Random As It Seems

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Evolution Is Not As Random As It Seems

A groundbreaking new study shows that evolution is not as unpredictable as previously thought. The findings could help scientists discover which genes could be useful in addressing important issues such as antibiotic resistance, disease and climate change.

The study, published in the Proceedings of the National Academy of Sciences (PNAS), challenges the long-held belief that evolution is unpredictable and suggests that the trajectory of a genome may be influenced by its evolutionary history, rather than being determined by a multitude of factors and historical accidents.

The study was led by Professor James McInerney and Dr. Alan Beavan from the School of Life Sciences at the University of Nottingham, as well as Dr. Maria Rosa Domingo Sananes from Nottingham Trent University.

“The results of this study are completely revolutionary,” says lead author Professor McInerney. “By showing that evolution is not as random as we thought, we have opened the door to many possibilities in the fields of synthetic biology, medicine and environmental science.”

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To answer the questions of whether evolution is predictable or whether the evolutionary trajectories of genomes depend on their history, the researchers analyzed the pangenome, which contains the entire gene set of a given species.

Using a machine learning algorithm known as Random Forest, along with a dataset of 2,500 complete genomes from a single bacterial species, the researchers used several hundred thousand hours of computing to uncover the answer.

Transferring the collected data to their high-performance computers, the scientists first created “gene families” from each gene of each genome.

“In this way, we were able to compare similar structures in the genomes,” says Dr. Domingo Sananes.

Once these families were identified, the researchers conducted a pattern analysis of how these families were found in some genomes but not in others.

“He found that some gene families don’t show up in the genome at all if a different gene family is already present, and in other cases, some genes are very dependent on the presence of a different gene family.”

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In fact, the researchers have discovered an invisible ecosystem where genes can cooperate or conflict with each other.

“These interactions between genes make some aspects of evolution somewhat predictable, and what’s more, we now have a tool that allows us to make these predictions,” adds Dr. Domingo Sananes.

Dr. Beavan explains: “With this study, we can start to investigate, for example, which genes ‘promote’ an antibiotic resistance gene. So if we are trying to eliminate antibiotic resistance, we can target not only the focal gene but also the genes that support it.

“Using this approach, we can synthesize new types of genetic constructs that can be used to develop new drugs or vaccines. What we learn opens the door to many other discoveries.”

Although the implications of the research are extensive, the developments it will lead to can be listed as follows;

New Genome Design: By enabling scientists to design artificial genomes, it could provide a roadmap for the predictable manipulation of genetic material.

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Tackling Antibiotic Resistance: Understanding the dependencies between genes can help identify the ‘co-players’ in genes that enable antibiotic resistance, paving the way for targeted therapies.

Mitigating Climate Change: Insights from the study could inform the design of engineered microorganisms to capture carbon and degrade pollutants, thereby contributing to efforts to combat climate change.

Medical Applications: The predictability of gene interactions could revolutionize the field of personalized medicine by providing new measures of disease hazard and treatment efficiency.


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