For a long time, many evolutionary biologists have believed that the majority genetic changes that make up genes and proteins are neutral. Under this view, mutations are generally neither helpful nor harmful, allowing them to spread quietly without strong support or rejection by natural selection.
A brand new study from the University of Michigan challenges that long-held assumption and suggests that evolution may fit more otherwise than once thought.
Rethinking the neutral theory of evolution
As a species evolves, random genetic mutations arise. Some of those mutations develop into fixed, meaning they spread until every individual within the population carries the mutation. The neutral theory of molecular evolution argues that the majority mutations that reach this stage are neutral. As evolutionary biologist Jianzi Zhang explains, harmful mutations are eliminated quickly, while helpful ones are considered extremely rare.
Zhang and his colleagues got down to test whether this concept held up upon closer examination. Their findings point to a bigger problem. The researchers found that helpful variations occur way over neutral theory allows. At the identical time, they observed that the general rate at which mutations are fixed in a population is far lower than can be expected if many useful mutations were suppressed.
Why Beneficial Mutations Don't Live Often
To explain this discrepancy, the team proposed a brand new explanation rooted in climate change. A mutation that confers a bonus in a single sequence may develop into deleterious once the conditions change. Because the environment changes so incessantly, many helpful mutations never spread widely enough to develop into fixed.
The research, which was supported by the US National Institutes of Health, was published in
“We're saying that the result was neutral, but the process was not neutral,” said Zhang, a professor of ecology and evolutionary biology at Um. “Our model shows that natural populations don't really adapt to their environment because the environment changes so quickly, and populations are always chasing the environment.”
Zhang calls this framework adaptive tracking with anti-pleiotropy. This idea helps explain why organisms rarely match their environment.
What this implies for humans and other species
Zhang believes these findings have wide-ranging implications, including for humans. Modern environments are dramatically different from those experienced by our ancestors, which can help explain why some genetic traits not serve us in addition to they once did.
“I think this has broad implications. For example, humans. Our environment has changed a lot, and our genes may not be optimal for today's environment because we've been through so many other environments. Some mutations may have been beneficial in our old environment, but not identical to today's.”
How well a population has adapted is determined by how recently its environment has modified, he added.
“Any time you observe a natural population, depending on the last time the environment underwent a major change, the population may be very poorly adapted or it may be relatively well adapted. But we will probably never see a population that is completely adapted to its environment, because a perfect adaptation can be constant to almost any natural environment.”
How Scientists Study Beneficial Mutations
The neutral theory of molecular evolution was introduced within the Sixties, when advances in protein and gene sequencing allowed scientists to check evolution on the molecular level quite than focusing only on physical traits comparable to shape and structure.
To measure how often helpful mutations occur, Zhang and his team analyzed large deep mutational scanning datasets produced by his own lab and others. In these experiments, researchers deliberately created many alternative mutations in the identical gene or a part of the genome in organisms comparable to yeast and E. coli.
They then tracked these organisms over several generations and compared their growth to the wild type, or essentially the most common form present in nature. By measuring developmental differences, researchers can estimate whether a mutation helped or harmed an organism.
The results showed that variations greater than 1% were helpful. This rate is far higher than the neutral theory would predict. If all those helpful mutations were fixed, just about all genetic changes can be helpful and evolution would proceed much faster than scientists actually observe. This realization led the team to query the belief that the environment is constant over time.
Examining evolution in changing environments
To explore the results of environmental changes, the researchers studied two groups of yeast. One group was evolved in a stable environment for 800 generations (each generation lasted 3 hours). A second group was developed for similar species but in a changing environment containing 10 various kinds of media, or growth solutions. Each medium was used for 80 generations before changing to the following.
Yeast exposed to changing conditions showed fewer helpful mutations than those in stable environments. Even when helpful mutations appeared, they rarely lasted long enough before conditions modified again.
“This is where the asymmetry comes in. While we observe a lot of beneficial mutations in a given environment, those beneficial mutations don't have a chance to settle because as their frequency increases to a certain level, the environment changes,” Zhang said. “Those beneficial mutations in the old environment may be detrimental in the new environment.”
Limitations and next steps
Zhang cautioned that the study focused on yeast and E. coli, single-celled organisms where the results of mutations are easier to measure. Data from multicellular organisms can be needed to find out whether the identical patterns apply to more complex life, including humans.
The research team is now planning follow-up studies to raised understand why it takes so long for organisms to completely adapt even when environmental conditions remain stable.
Other authors of the study include former UM graduate students Silyang Song and Xukang Shen and former UM postdoctoral researcher Piaopiao Chen.