The influenza (flu) virus is continually undergoing a technique of evolution and adaptation through acquiring recent mutations.

Scientists at St. Jude Kid’s Research Hospital have added a recent layer of understanding to elucidate why and the way flu viruses change. The “survival of the accessible” model provides a complementary view to the more well known “survival of the fittest” way of evolving. The work was published today in Science Advances.

Viruses undergo a rapid evolutionary flux as a consequence of constant genetic mutations. This rapid flux is why people get a flu shot every yr, as we want to tackle the newest flu variant that has emerged because the dominant strain. We regularly see these mutations within the context of traditional evolutionary considering, where variant fitness determines which mutated virus emerges as a dominant strain in a population. The St. Jude team investigated this theory and defined another evolutionary principle, which they propose is a key driver of evolution, termed “variant accessibility.”

The research, led by Alexander Gunnarsson, Ph.D., and M. Madan Babu, Ph.D., St. Jude Department of Structural Biology and Center of Excellence for Data-Driven Discovery, involved making a model of mutational accessibility to assist predict how and why specific mutations emerge in a population during viral evolution.

The unappreciated role of variant accessibility

The genomic alphabet only has 4 letters representing the nucleotides: (A)denosine, (T)hymine, (G)uanine, and (C)ytosine. Groups of three nucleotides inside a protein-coding gene are called a codon. Codons act like a recipe for assembling proteins, encoding for a particular amino acid. Mutations occur when nucleotides are altered, as an example, during replication. This alteration results in a distinct amino acid getting used to make the protein. But not all mutations are equally more likely to emerge, as Babu and Gunnarsson discovered.

“The technique of genetic replication has inherent biases inbuilt, similar to the relative ease of an A to be mutated to a C fairly than to a G,” Babu explained. “Which means that the pool of mutants with this A-to-C mutation is larger, and surviving variants will predominantly emerge from that individual pool, despite the fact that there could also be a fitter sequence with an A-to-G mutation.”

Using the influenza virus as a case study, Gunnarsson and Babu translated this idea right into a mathematical model. Their model enables researchers to predict the trail of future evolution based on the accessibility of a mutation. Of particular interest was exploring how specific protein sites can gain or lose the power to be modified after acquiring a mutation. They then examined how this gain or loss influenced the protein’s function.

Phosphorylation is an example of such a modification. It occurs when a phosphate molecule is added to specific amino acids of a protein. When it comes to the flu, phosphorylation will help the virus hijack the host molecular pathways for mediating successful infection. Such mutations can have been critical to influenza pandemics of the past, and it’s these datasets that Gunnarsson and Babu used to develop their model.

The importance of jackpot events

The model also helped the researchers higher understand a long-conceptualized mutation property, the jackpot event. These are mutations that occur by likelihood early in the expansion of a population, resulting in a continuous profit seen throughout the descendants. “The more accessible a genotype is, the more frequent these specific jackpot events are since it’s simply a probabilistic event,” Gunnarsson explained. “If a specific gene is 100 times more likely to amass a particular mutation, you will see that jackpot event happening proportionately more regularly. These events are essential in evolution and are driven primarily by how accessible the variants are.”

More accessible mutations are more likely to be predominant in a population despite the fact that they will not be the fittest mutation. “If the probability of acquiring the fittest mutation is one out of a whole lot of trillions,” Gunnarsson said, “the likelihood of it reaching fixation in a population, even when it is the fittest mutation, is low. When you might have multiple instances of jackpot mutations happening, statistically, the prevalence of this variant increases massively, even when it’s less fit in comparison with one other, more healthy but less accessible mutant.”

Furthering our understanding of mutational bias and predicting outcomes in evolving systems

The concept of variant accessibility is elegant in its simplicity, but like most things in nature, it’s a balance of statistical probabilities. From the mutation event and differences within the probability of certain nucleotide changes to codon redundancy (multiple codons for a similar amino acid), it’s a fragile balance between components that drives evolutionary pathways.

Furthering our understanding of biochemical mutational biases (e.g., during replication) in viruses can open up recent directions and possibilities since it’ll give significantly better insights into how a virus is more likely to evolve.”

Madan Babu, PhD, Department of Structural Biology and Center of Excellence for Data-Driven Discovery, St. Jude Kid’s Research Hospital

In reality, the model is being applied to historical data about how the flu virus has modified inside the framework of mutational accessibility to predict viral evolution more accurately.

The flexibility to predict viral evolutionary outcomes based on accessibility has piqued the interest of influenza expert Richard Webby, Ph.D., of St. Jude Department of Host-Microbe Interactions and Director of the World Health Organization Collaborating Centre for Studies on the Ecology of Influenza in Animals and Birds.

“There are a lot of scenarios in public health where we attempt to predict the evolutionary path of influenza viruses, including choosing probably the most appropriate vaccines for future influenza,” Webby said. “The ‘survival of the accessible’ model will empower these predictions and permit us to discover viruses more more likely to tackle worrying traits more confidently.”

This model also applies beyond influenza and even virology and steers further research into mutational biases in several diseases. In cancer, for instance, the model will help answer quite a few questions on pathology, similar to why particular cancer-driving or drug-resistance mutations repeatedly surface.

“Our model might be applied to assist predict whether a specific style of mutation is more likely to emerge as a tumor driver or as a resistant mutation to a particular treatment,” Babu stated. “We hope our work will spur research into characterizing mutational biases driving viral and tumor evolution. If we will quantify and higher understand the biochemical processes contributing to mutational bias, that will probably be invaluable to predict mutational outcomes in evolving genetic systems. The flexibility to predict outcomes before they occur will allow us to be prepared once they eventually unfold.”

Source:

St. Jude Kid’s Research Hospital

Journal reference:

Gunnarsson, P. A & Babu, M. M (2023) Predicting evolutionary outcomes through the probability of accessing sequence variants. Science Advances. doi.org/10.1126/sciadv.ade2903.