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For decades, many evolutionary biologists have believed that most genetic changes shaping genes and proteins are neutral. Under this view, mutations are usually neither helpful nor harmful, allowing them to spread quietly without being strongly favored or rejected by natural selection.
A new study from the University of Michigan challenges that long-standing assumption and suggests evolution may work very differently than once thought.
Rethinking the Neutral Theory of Evolution
As species evolve, random genetic mutations arise. Some of these mutations become fixed, meaning they spread until every individual in a population carries the change. The Neutral Theory of Molecular Evolution argues that most mutations that reach this stage are neutral. Harmful mutations are quickly eliminated, while helpful ones are assumed to be extremely rare, explains evolutionary biologist Jianzhi Zhang.
Zhang and his colleagues set out to test whether this idea holds up when examined more closely. Their results pointed to a major problem. The researchers found that beneficial mutations occur far more often than the Neutral Theory allows. At the same time, they observed that the overall rate at which mutations become fixed in populations is much lower than would be expected if so many helpful mutations were taking hold.
Why Beneficial Mutations Often Do Not Last
To explain this contradiction, the team proposed a new explanation rooted in environmental change. A mutation that provides an advantage in one setting may become harmful once conditions shift. Because environments change frequently, many beneficial mutations never spread widely enough to become fixed.
The study, which was supported by the U.S. National Institutes of Health, was published in Nature Ecology and Evolution.
“We’re saying that the outcome was neutral, but the process was not neutral,” said Zhang, a professor of ecology and evolutionary biology at U-M. “Our model suggests that natural populations are not truly adapted to their environments because environments change very quickly, and populations are always chasing the environment.”
Zhang calls this framework Adaptive Tracking with Antagonistic Pleiotropy. The idea helps explain why organisms are rarely perfectly matched to their surroundings.
What This Means for Humans and Other Species
Zhang believes the findings have wide-ranging implications, including for humans. Modern environments differ dramatically from those our ancestors experienced, which may help explain why certain genetic traits no longer serve us as well as they once did.
“I think this has broad implications. For example, humans. Our environment has changed so much, and our genes may not be the best for today’s environment because we went through a lot of other different environments. Some mutations may be beneficial in our old environments, but are mismatched to today,” Zhang said.
He added that how well a population is adapted depends on how recently its environment has changed.
“At any time when you observe a natural population, depending on when the last time the environment had a big change, the population may be very poorly adapted or it may be relatively well adapted. But we’re probably never going to see any population that is fully adapted to its environment, because a full adaptation would take longer than almost any natural environment can remain constant.”
How Scientists Studied Beneficial Mutations
The Neutral Theory of Molecular Evolution was introduced in the 1960s, when advances in protein and gene sequencing allowed scientists to study evolution at the molecular level rather than focusing only on physical traits like shape and structure.
To measure how often beneficial mutations occur, Zhang and his team analyzed large deep mutational scanning datasets generated by their own lab and others. In these experiments, researchers deliberately created many different mutations within a single gene or section of the genome in organisms such as yeast and E. coli.
They then tracked these organisms over many generations and compared their growth to the wild type, or the most common form found in nature. By measuring growth differences, the researchers could estimate whether a mutation helped or harmed the organism.
The results showed that more than 1% of mutations were beneficial. This rate is far higher than what the Neutral Theory predicts. If all those helpful mutations became fixed, nearly all genetic changes would be beneficial and evolution would proceed much faster than what scientists actually observe. That realization led the team to question the assumption that environments stay constant over time.
Testing Evolution in Changing Environments
To explore the effects of environmental shifts, the researchers studied two groups of yeast. One group evolved in a stable environment for 800 generations (each generation lasted 3 hours). The second group evolved for the same number of generations but in a changing environment made up of 10 different types of media, or growth solutions. Each medium was used for 80 generations before switching to the next.
The yeast exposed to changing conditions showed far fewer beneficial mutations becoming established than those in the stable environment. Even when advantageous mutations appeared, they rarely lasted long enough to spread before conditions changed again.
“This is where the inconsistency comes from. While we observe a lot of beneficial mutations in a given environment, those beneficial mutations do not have a chance to be fixed because as their frequency increases to a certain level, the environment changes,” Zhang said. “Those beneficial mutations in the old environment might become deleterious in the new environment.”
Limits and Next Steps
Zhang cautioned that the study focused on yeast and E. coli, single-celled organisms where the effects of mutations are easier to measure. Data from multicellular organisms will be needed to determine whether the same patterns apply to more complex life, including humans.
The research team is now planning follow-up studies to better understand why it takes so long for organisms to fully adapt even when environmental conditions remain stable.
Other authors of the study include former U-M graduate students Siliang Song and Xukang Shen and former U-M postdoctoral researcher Piaopiao Chen.
