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A patient gets tested with the OraQuick Rapid
HIV-1 Antibody test at the Whitman-Walker clinic in Washington.
Researchers have developed computer simulations that can accurately
predict HIV transmissions across populations. Photo by Michael
Kleinfeld/UPI | License Photo
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By
Allen ConeUPI
Researchers have developed computer simulations that can accurately predict
HIV transmissions across populations in an effort to prevent the disease's spread.
The simulations, conducted by the U.S. Department of Energy's Los
Alamos National Laboratory, yielded results consistent with actual DNA
data from a public database of more than 840,000 HIV sequences from
throughout the world. The findings were published this week in the journal Nature Microbiology.
"We looked for special genetic patterns that we had seen in the
simulations, and we can confirm that these patterns also hold for real
data covering the entire epidemic," lead author Thomas Leitner, a
computational biologist at Los Alamos, said in a press release
.
HIV, which stands for the human immunodeficiency virus, attacks a
person's immune system. It can lead to acquired immunodeficiency
syndrome, or AIDS, if not treated.
Because the virus mutates rapidly and constantly within each infected
individual, Leitner said HIV is interesting to study this way.
With changing "genetic signatures" of its code, it provides a way for
researchers to follow the origin and timeframe of an infection. In the
past, computer simulations have been successful in tracking and
predicting the virus' movements through populations.
But rapid mutational capability of the virus makes it difficult to disrupt with a vaccine.
Using phylogenetic methods, researchers examined evolutionary
relationships in the virus' genetic code to evaluate how HIV is
transmitted.
A total of 272 phylogenetic "family tree" patterns were correlated to
DNA data from 955 pairs of people. They represented diverse geography,
risk groups, subtypes and genomic regions.
"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," the authors wrote.
The researchers plan to develop public health computational tools for
agencies to track the disease and allocate resources for prevention.
They already are collaborating with Colorado and Michigan state health
agencies.
"We hope these tools will help to hinder new infections in the future," Leitner said.
He said these simulations tools can also be used to predict the patterns of other rapidly evolving infectious diseases.
