New method identifies adaptive mutations in complex evolving populations

New method identifies adaptive mutations in complex evolving populations

The top left panel shows frequency ranges of all mutant alleles, the bottom three panels on the left show the ranges of beneficial / neutral / harmful mutants in blue / orange / red. The right panel shows their estimated fitness values ​​using the suggested inference method. Vertical lines indicate actual fitness values. Credit: HKUST

A research team, co-led by a scientist from the Hong Kong University of Science and Technology (HKUST), has developed a method to study how HIV mutates to escape the immune system in multiple patients, which could help the design of HIV vaccines. to influence.

HIV, which can lead to AIDS, develops quickly and affects the body’s immune system. Genetic mutations in the virus allow it to evade immune responses caused by T cells and antibodies, making it all the more difficult to design an effective solution. While there is currently no effective cure for the virus, it can be controlled with medication.

Now the international research team has come up with a new method from conventional statistical physics to reveal selection patterns in the evolution of HIV using 14 patient data sets, providing a means to efficiently distinguish between the mutations that help the virus. escape the immune system and those that alone are random variations.

“Our new method allows us to find out which genetic changes provide an evolutionary advantage over those that offer no benefit or have a detrimental effect,” said Prof. Matthew McKay, a professor in the departments of electronic and computing technology and chemistry. and biological technology. , who led the study along with Prof. John Barton, an assistant professor of physics and astronomy at the University of California, Riverside.

“The method is quite general and could be used to study various evolutionary processes, such as the evolution of drug resistance of pathogens and the evolution of cancers. The accuracy and high efficiency of our approach enable the analysis of selection in complex evolutionary processes. systems that are beyond the scope of existing methods, ”he added.

“Understanding the genetic causes of diseases is important in the biomedical sciences,” says Prof. Barton. “Identifying genomic rearrangements is key to understanding how diseases arise and how to treat them.” Well-known examples of genetic causes of disease include mutations that allow viruses to escape immune control, while other bacteria confer drug resistance.

“However, it can be difficult to distinguish between true, adaptive mutations and random genetic variation,” he added. “The new method we have developed allows us to identify such mutations in complex evolving populations.”

“However, it is computationally difficult to extract this information from data,” he said. “We have used statistical physics methods to overcome this computational challenge. Our method can be widely applied to evolving populations and is not limited to HIV.”

Some well-known diseases with known genetic causes are cystic fibrosis, sickle cell anemia, Duchenne muscular dystrophy, color blindness, and Huntington’s disease.

“Our approach is not limited to HIV, but there are a few reasons why we have focused on HIV as a testing system,” said Prof. McKay. “HIV has an extraordinary ability to mutate within humans in order to escape the immune system. However, the details of these immune escape dynamic are not well understood. If we can get a clearer picture of how HIV evolves within a person, it could help to develop better treatments against the virus. “

Their findings have just been published in a high-impact scientific journal Nature Biotechnology on November 30, 2020.

Virus evolution could undermine a COVID-19 vaccine, but it can be stopped

More information:
MPL resolves genetic linkage in fitness inference from complex evolutionary histories, Nature Biotechnology (2020). DOI: 10.1038 / s41587-020-0737-3,

Provided by Hong Kong University of Science and Technology

Quote: New Method Identifies Adaptive Mutations in Complex Evolving Populations (2020, November 30) Retrieved November 30, 2020 from

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