New method identifies adaptive mutations in complex evolving populations


Date:
November 30, 2020
Source:
University of California - Riverside
Summary:
A scientist has developed a method to study how HIV mutates to
escape the immune system in multiple individuals, which could
inform HIV vaccine design.



FULL STORY ==========================================================================
A team co-led by a scientist at the University of California, Riverside,
has developed a method to study how HIV mutates to escape the immune
system in multiple individuals, which could inform HIV vaccine design.


==========================================================================
HIV, which can lead to AIDS, evolves rapidly and attacks the body's
immune system. Genetic mutations in the virus can prevent it from being eliminated by the immune system. While there is no effective cure for
the virus currently available, it can be controlled with medication.

"Understanding the genetic drivers of disease is important in the
biomedical sciences," said John P. Barton, an assistant professor
of physics and astronomy at UCR, who co-led the study with Matthew
R. McKay, a professor of electronic and computer engineering and
chemical and biological engineering at the Hong Kong University of
Science and Technology. "Being able to identify genomic rearrangements
is key to understanding how illnesses occur and how to treat them."
Barton explained that notable examples of genetic drivers of disease
include mutations that allow viruses to escape from immune control,
while others confer drug resistance to bacteria.

"It can be difficult, however, to differentiate between real, adaptive mutations and random genetic variation," he added. "The new method
we developed allows us to identify such mutations in complex evolving populations." Evolutionary history, he added, contains information about
which mutations affect survival and which simply reflect random variation.



========================================================================== "However, it is computationally difficult to extract this information from data," he said. "We used methods from statistical physics to overcome
this computational challenge. Our method can be applied generally to
evolving populations and is not limited to HIV." McKay explained the new method provides a means to efficiently infer selection from observations
of complex evolutionary histories.

"It enables us to sort out which genetic changes provide an evolutionary advantage from those that offer no advantage or have a deleterious
effect," he said. "The method is quite general and could be potentially
used to study diverse 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 systems that were beyond the reach of existing methods." Some well-known diseases that have known genetic causes
are cystic fibrosis, sickle cell anemia, Duchenne muscular dystrophy, colorblindness, and Huntington's disease.

"In the case of HIV, an understanding of the genetic mutations that lead
to HIV resistance could help researchers determine the most appropriate treatment for patients," Barton said. "Our approach isn't limited to HIV,
but there are a few reasons why we focused on HIV as a test system. HIV
is highly mutable and genetically diverse. It also mutates within humans
to escape from the immune system. Understanding the details of how HIV
evolves could therefore help to develop better treatments against the
virus." Barton was supported by a grant from the National Institutes
of Health. Study results appear in Nature Biotechnology.

Barton and McKay were joined in the study by Muhammad Saqib Sohail and
Raymond H. Y. Louie of Hong Kong University of Science and Technology
and the University of New South Wales.


========================================================================== Story Source: Materials provided by
University_of_California_-_Riverside. Original written by Iqbal
Pittalwala. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Muhammad Saqib Sohail, Raymond H. Y. Louie, Matthew R. McKay,
John P.

Barton. MPL resolves genetic linkage in fitness inference from
complex evolutionary histories. Nature Biotechnology, 2020; DOI:
10.1038/s41587- 020-0737-3 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/11/201130131408.htm

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