Stemming the spread of HIV by accurately predicting its spread

By Thomas Leitner, Ph.D.

Examining evolutionary relationships in HIV’s genetic code allows researchers to evaluate how HIV is transmitted.

One of the challenges with stemming the spread of HIV lies in understanding how it is spread. Because HIV mutates so rapidly, it has historically been difficult — if not impossible — to trace exactly who transmitted the virus to whom. Without that understanding, it’s easy for the disease to run unfettered through a population — with devastating results. Each year, HIV infects approximately 1.8 million people worldwide. All told, nearly 37 million people are currently estimated to be living with HIV/AIDS. But that might be changing.

In a study published this week in the journal Nature Microbiology, my colleagues and I demonstrate that computer simulations can accurately predict the transmission of HIV across populations, which could aid in preventing the disease.

The simulations were consistent with actual DNA data obtained from a global public HIV database, developed and maintained by Los Alamos National Laboratory. The archive has more than 840,000 published HIV sequences for scientific research.

We looked for special genetic patterns that we had seen in the simulations, and we can now confirm that these patterns also hold for real data covering the entire epidemic. This is ground-breaking news — and something we hope will change the way we track the disease.

It is particularly interesting — and difficult — to study the genetic patterns of HIV because the virus mutates rapidly and constantly within each infected individual. But the changing “genetic signatures” of its code provide a path that we can follow in determining the origin and time frame of an infection, and the computer simulations are now proven to be successful in tracking and predicting the virus’s movements through populations.

How did we figure this out? We used phylogenetic methods, examining evolutionary relationships in the virus’s genetic code, to evaluate how HIV is transmitted. We found that certain phylogenetic “family tree” patterns correlated to the DNA data from 955 pairs of people, in which the transmitter and recipient of the virus were known. These HIV transmissions had known linkage based on epidemiological information such as partner studies, mother-to-child transmission, pairs identified by contact tracing, and criminal cases.

Now that we are confident that we can trace the infection pathway, we are collaborating with the health agencies of Colorado and Michigan to develop public health computational tools. These tools will help track the disease and allocate resources for targeted prevention campaigns in hopes that they will hinder new infections in the future.

Furthermore, the usefulness of these modeling tools isn’t limited to HIV. They can also be used to predict patterns of other rapidly evolving infectious diseases such as hepatitis C, influenza, and Ebola. If we can pinpoint how the disease is spreading from one individual to the next, it might be possible for public health workers to stem the tide — and save countless lives.

Thomas Leitner, Ph.D., is a computational biologist at Los Alamos National Laboratory and lead author of the study Consistent phylogenetic patterns recover HIV epidemiologic relationships and Reveal common transmission of multiple variants in known pairs. The study was funded by the National Institutes of Health.

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