New method identifies adaptive mutations in complex evolving populations

Anonymous — Mon, 30 Nov 2020 16:55:28 +0000

New method identifies adaptive mutations in complex evolving populations Anonymous (not verified) Mon, 11/30/2020 - 08:55 More News Iqbal Pittalwala | Inside UCR November 30, 2020

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.

John P. Barton. (UCR/Barton lab)

“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. The title of the research paper is “MPL resolves genetic linkage in fitness inference from complex evolutionary histories.”

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.

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NIH grant to physicist focuses on how pathogens evolve

Anonymous — Mon, 10 Aug 2020 19:06:18 +0000

NIH grant to physicist focuses on how pathogens evolve Anonymous (not verified) Mon, 08/10/2020 - 12:06 More News Iqbal Pittalwala August 10, 2020 https://news.ucr.edu/articles/2020/08/10/nih-grant-physicist-focuses-how-pathogens-evolve

For several years, physicist John Barton at the University of California, Riverside, has been working on developing models to understand and predict how pathogens, such as HIV, evolve. This research pursuit has just received support from the National Institutes of Health—the first grant from the institutes to the UC Riverside Department of Physics and Astronomy.

Barton, an assistant professor in the department, has been awarded a Maximizing Investigators’ Research Award for early-stage investigators, allowing his lab to work on developing methods to understand how different mutations affect the ability of organisms to survive and reproduce.

John Barton.

“We are most interested in viruses like HIV, but in principle, the methods that we're working on could be applied to many different types of evolving populations,” Barton said.

The five-year grant of nearly $1.86 million, titled “Methods for quantifying selection in evolving populations,” will support two graduate students and two postdoctoral researchers. Barton will work with Rebecca Lynch at George Washington University to apply some of his lab’s methods to study how HIV evolves to escape from antibody responses within patients.

Barton explained that evolution plays a central role in many public health challenges.

“For example, bacteria can evolve to become resistant to antibiotics, and cancer cells can evolve to hide from the immune system or to resist chemotherapy,” he said. “The methods that we're developing may allow us to get a better understanding of how processes like these take place, which may ultimately lead to the design of new and better treatments.”

The grant will allow Barton to build mathematical models that describe how pathogen populations evolve.

“We will try to understand what the data can tell us about the evolutionary process,” Barton said. “We plan to apply our methods to a few important problems. One application is to understand how HIV-1 evolves to escape from the immune system. We also plan to use our methods to improve the interpretation of high-throughput mutagenesis experiments.”

Barton acknowledged that the data can be complex and difficult to interpret with existing methods, which motivated him to try a new approach to better understand how different changes at the genetic level were driving evolution at the population level.

“The question we’re thinking about is one that’s been considered by population geneticists for a long time,” he said. “Two of the more novel features of this project are that we’re developing methods to look specifically at genetic sequence data that’s been collected over time, which is an underexplored area; and we’re using some mathematical techniques that are common in physics but not so well known in population genetics, which makes our approach more computationally efficient than most prior work.”

Barton, an expert in statistical physics, came to UCR from the Massachusetts Institute of Technology in 2018.