For those working to develop an HIV therapeutic, the virus’ ability to rapidly mutate has consistently been one of the biggest roadblocks to success. Now one researcher has developed a computational model that may offer clues on how to overcome this problem.
David Schaffer is an assistant professor at the University of California, Berkeley, whose lab focuses on using molecular and cellular engineering approaches to solve biomedical problems, particularly in the gene therapy field. About four years ago, when RNAi was revealed to occur within mammalian systems, he expanded his research to include the gene-silencing technology, he said.
“Since RNAi has come out, it has revealed itself as an incredibly powerful way to knock down gene expression, and therefore is a potentially very powerful therapy … for viruses,” Schaffer told RNAi News this week. “We’d been working with HIV and HIV-derived gene-delivery vehicles for the past seven years or so, [and] it just made sense to examine the possibility that antiviral RNAi could be used as a therapy for HIV.”
Schaffer said that while his lab is working on gene-therapy delivery approaches, most of their RNAi work is focused on developing an agent that could be used to treat HIV. “In order for an [RNAi] therapy to succeed, you need a good vehicle … [but you also] need good cargo,” he said. “The work we’re doing … is more focused on the cargo because we think we have some good vehicles” already. Schaffer’s lab is currently designing delivery systems based on both lentiviral and adeno-associated viral vectors, he noted.
When Schaffer began working on an RNAi-based treatment for HIV, he said, he was already aware that viral escape would be a problem. “We were very cognizant of, and had read quite a bit in the literature about the dynamics of viral evolution,” he said. “As the scientific field knows quite well, there’s no single HIV virus — it’s literally million or billions of sequences distributed throughout the world, where each one is mutated away from some parent strain. So designing a single RNAi that only spans 20 base pairs or so of the target sequence is probably not going to work because the virus will rapidly mutate.”
According to Schaffer, his lab began to explore its options for analyzing “how best to design an RNAi strategy to get around that evolution problem. We felt that a very good tool to analyze this … was computation.”
The key to this computation strategy lay in the work of German biophysicist Manfred Eigen, who won the Nobel Prize in chemistry in 1967.
“He came up with this idea of an RNA quasi-species … [which] is simply a collection of related RNA molecules that are mutated versions of each other and dynamically replicate,” Schaffer said. “This idea has been applied to viruses such as HIV, and we simply simulated an HIV quasi-species.”
Schaffer said that his computational model tracks the evolution of a large collection of HIV genomes to which it then applies RNAi pressure. The RNAi inhibits “the replication of HIV genomes when there was a great or perfect match between the small hairpin sequence and the HIV target,” he said
“We began to see that initially if you have a great match between a single RNAi molecule and a target HIV sequence, the knock down of the HIV replication is quite good,” he said. “But eventually HIV is going to mutate around [the RNAi molecule], and one HIV variant is going to get lucky and acquire a mutation that will allow it to escape from the RNAi pressure.” The result is “full-blown viremia,” Schaffer said.
Additional experimentation revealed that a better approach to tackling HIV was to keep the total concentration of RNAi molecules constant, but target a number of different parts of the virus.
Reinforcing the opinion held by many others working on RNAi-based HIV treatments, Schaffer’s model indicated that “the more targets you developed, the better the therapy became,” he said.
He said that the model also yielded some insights into “how to space the different targets.
“It’s known that HIV, in addition to mutating, recombines,” Schaffer said. “So let’s say you actually made the mistake of developing two targets but on opposite ends of the genome. You might then end up with two independent HIVs that got lucky and each introduced a mutation into one or the other of those target sequences. If they recombine with each other, they could combine those mutations and very effectively escape the RNAi therapy.”
As a result, Schaffer said that he and his colleagues came up with some design rules showing that the spacing of the targets is key, and that having them close together is optimal.
Additionally, the model indicated that if a delivery vehicle is not extremely efficient, and does not get an RNAi treatment into every cell in which HIV can replicate, “then it can simply replicate in the unprotected cell population and gradually take over the system again. We showed what [level of] efficiency you need for your vehicle … in order for the RNAi to work optimally.”
Schaffer’s findings were published in the February issue of The Journal of Virology.
Schaffer said that his lab is now in the process of experimentally testing the various predictions put out by the model in vitro. He said that his lab is also working on coding a user interface that would make the computational model more “user-friendly.” He said he expects the model to be ready for posting on his lab’s website before the end of the year.