A pioneering collaborative study has discovered how the HIV virus evades the human body's immune system. The research collaborative - involving scientists from the British Columbia Centre for Excellence in HIV/AIDS, Massachusetts General Hospital (MGH), Microsoft Research and Los Alamos National Laboratory - used highly computer-intensive, cutting-edge statistical research methods to investigate how the HIV virus mutates to escape the body's immune system.
Specifically, "HLA class 1" is a controlling part of the human immune response. The ability of HIV to escape recognition by HLA class 1 leaves the body incapable of finding and fighting the virus.
The study, published in the July issue of PLoS Pathogens, is the largest population-based investigation of how natural variations in HLA class 1 can influence HIV genetic sequence, as well as the first characterization of changes in multiple HIV genes in response to HLA-associated evolutionary pressure.
Researchers successfully mapped sites within particular HIV genes where variations can improve the virus's ability to escape immune recognition, showing this is predictable based upon the HIV patient's individual HLA class 1 profile.
"This is a novel and advanced description of how the human immune system attacks the virus, and how it responds" says Richard Harrigan, PhD, director of the Centre's Research Laboratories and study co-author. "While we always knew the body attacks the virus and the virus changes to dodge pressure, we're now more exact in knowing how this happens in people."
While the study is valuable in helping the scientific community understand how immune pressure impacts HIV, these findings hold tremendous promise in terms of global HIV efforts, says Zabrina Brumme, PhD, the study's lead author. "Achieving a more in-depth understanding of the ways in which HIV mutates to avoid the human immune system will help with the design of an HIV vaccine," says Brumme, who is now with the Partners AIDS Research Center at MGH.
Data were collected from the British Columbia HOMER cohort, a large group of chronically HIV-infected, treatment-na´ve individuals for whom HLA class-1 typing and HIV RNA genotyping were performed.
Microsoft Research provided personnel and advanced software tools to perform highly sophisticated statistical analysis. Algorithms developed by David Heckerman, lead researcher of the Machine Learning and Applied Statistics Group at Microsoft Research and study co-author, and his team allowed for more in-depth analysis of the data sets. "We created the software tools to help researchers exploit the power of computing to more quickly and accurately identify the crucial elements of an effective HIV vaccine," said Heckerman.
The original idea for the development of these statistical methods came from Bette Korber, PhD, at Los Alamos National Laboratory. Korber and co-researchers Tanmoy Bhattacharya, PhD, and Marcus Daniels worked with Heckerman in further developing the cutting-edge statistical approach.
Study results demonstrate that population-based approaches could complement smaller functional studies by providing a whole-gene or whole-virus picture of immune escape. Previous B.C. Centre research published in The Journal of Infectious Diseases investigated the role of HLA class 1 variation on response to anti-HIV therapy. "Moving forward, we'll be expanding our genetic research to other HIV genes. We'll also be investigating the role of drug therapy," says Harrigan.
Note: This story has been adapted from a news release issued by