New Computational Analysis Forecasts New Targets to Control HIV

Adding a new approach to the research on finding a cure for HIV, computational scientists have forecasted the presence of numerous human proteins required by the human immunodeficiency virus (HIV) to replicate itself.

These discoveries constitute a powerful resource for experimentalists who desire to discover new targets for human proteins that can control the spread of HIV, noted study authors T.M. Murali, a computer scientist, and Brett Tyler of the Virginia Bioinformatics Institute.

Co-authored by Michael G. Katze, a microbiologist and associate director of the Washington National Primate Research Center at the University of Washington, the study was published in the September issue of PLoS Computational Biology, a journal published by the Public Library of Science.

The study is based on the impact of three other studies published in 2008 that systematically discovered hundreds of HDFs. HDFs are genes that are necessary for HIV virus to survive and replicate. The belief is based on the recent line of research that examines whether human proteins can be targeted to cure HIV.

The research also follows the understanding that since HIV viruses and the like have very small genomes, they need to exploit the cellular machinery of the host to spread. Therefore, by disrupting the activity of selected host proteins, it may help to obstruct these viruses. Because human proteins evolve at a much slower rate than HIV proteins, human proteins that are targeted by drugs do not always mutate, making these drugs ineffective in targeting HIV-like viruses.

When a person contracts HIV, it causes the progressive failure of the body's immune system, with the onset of life threatening infections and diseases such as cancer. Over 25 years of intensive research have failed to create a vaccine for preventing HIV. Moreover, drugs used to cure HIV become rapidly ineffective because HIV is able to develop mutations against drugs, Murali said.

The researchers used an algorithm called SinkSource developed by Murali and Tyler. The analogy states, We treated the human protein network as if it were a system of tanks connected by pipes carrying water. This arrangement allowed us to study the flow of predictive information (water) from proteins we are certain about (full tanks) to those we are uncertain about (empty tanks).

The further you get from the full tanks, the weaker the trickle, and the less water accumulates in the bottom of the tank. Mathematically you can show that, over time, every empty tank accumulates some stable level of water. At the end of the analysis, tanks accumulating lots of water were judged to be good predictions, explained Tyler.

Their research used an analysis of HDF activities in two primate species that respond differently to Simian Immunodeficiency Virus (SIV). One species, the African green monkey, does not develop disease when infected by SIV, in contrast to the other species, the pig-tailed macaque.

Using data already published by Katze, the authors showed that predicted HDFs had very different patterns of expression in the two species, especially in lymph nodes and within 10 days after infection with the virus.

They also showed that predicted HDFs participated in human cellular processes that are known to be subverted by the virus, including gene transcription and translation, energy production, protein degradation and transport across the nuclear membrane. Moreover, many predicted HDFs themselves directly interacted with proteins in HIV.

From these results, Murali, Tyler and Katze concluded that existing genomic screens are incomplete and many HDFs are yet to be discovered experimentally. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of Acquired Immune Deficiency Syndrome (AIDS) development.