According to a recent study, evolution is not as random as previously
believed. This finding may help scientists investigate which genes may be
helpful in treating diseases, antibiotic resistance, and climate
change.
The study, which is
published
in the Proceedings of the National Academy of Sciences (PNAS), disproves the
conventional wisdom regarding the unpredictable nature of evolution by
discovering that a genome's evolutionary history may have more influence
over its course than random events and a variety of other factors.
Dr. Alan Beavan and Professor James McInerney from Nottingham Trent
University's School of Life Sciences and Dr. Maria Rosa Domingo-Sananes from
the University of Nottingham led the study.
Lead author Professor McInerney commented, "The implications of this
research are nothing short of revolutionary." "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."
In order to determine if evolution is predictable or if genomes'
evolutionary pathways are contingent on their past and thus unpredictable in
the present, the researchers analyzed the pangenome, or the whole collection
of genes inside a particular species.
The researchers used a dataset of 2,500 whole genomes from a single
bacterial species and a machine learning technique called Random Forest to
process many hundred thousand hours of data in order to answer the
issue.
Upon loading the data into their high-end computer, the group initially
created "gene families" using every gene in every genome.
Dr. Domingo-Sananes stated, "In this way, we could compare like-with-like
across the genomes."
After identifying the families, the group examined the pattern of these
families' presence in some genomes and absence from others.
"We found that some gene families never turned up in a genome when a
particular other gene family was already there, and on other occasions, some
genes were very much dependent on a different gene family being
present."
Essentially, what the researchers found is an unseen ecosystem in which
genes may interact with one other or work against each other.
"These interactions between genes make aspects of evolution somewhat
predictable and furthermore, we now have a tool that allows us to make those
predictions," says Dr. Domingo-Sananes.
"With this work, we can start investigating which genes'support' an
antibiotic resistance gene, for example," stated Dr. Beavan. Thus, in
addition to focusing on the focal gene, we may also target the genes that
support it if our goal is to eradicate antibiotic resistance.
"With this method, we may create novel genomic constructions that may be
utilized to create novel medications or vaccinations. Numerous new
discoveries have become possible as a result of our current
understanding."
The research has broad ramifications that might result in:
Novel Genome Design offers a blueprint for the predictable manipulation of
genetic material and enables scientists to create synthetic genomes.
Fighting Antibiotic Resistance: By identifying the "supporting cast" of
genes that enable antibiotic resistance, tailored therapies may be developed
by having a better understanding of the relationships between genes.
Climate Change Mitigation: The study's findings may help develop microbes
that are designed to absorb carbon dioxide or break down contaminants, which
would aid in the fight against climate change.
Applications in Medicine: By offering new measures for illness risk and
treatment effectiveness, the predictability of gene interactions has the
potential to completely transform personalized medicine.
Provided by
University of Nottingham
