HIV modelOne of the reasons HIV can persist in infected people, in spite of a powerful and effective cytotoxic T cell immune response against the virus, is that the virus mutates rapidly. Because CTL each only target a short stretch of the genome (say, 9 amino acids) and a single amino acid change may allow the virus to escape recognition by a particular CTL clone, it may not take long for a viral mutant to arise that is invisible to the dominant CTL population in a particular individual.

It’s been suggested that immunodominance is one of the factors that determines the rate at which HIV can escape from a particular immune response. In a highly immunodominant response, most of the CTL specific for the virus all target a single peptide epitope. If the virus manages to mutate this peptide, it has escaped the bulk of the immune response, and the new mutant virus can explode unchecked (until a new CTL response arises).

On the other hand, if the CTL response isn’t dominated by a single epitope — that is, if the response is broad, targeting many peptides — the virus has to simultaneously mutate several regions of its genome, which is exponentially less probable than single mutations. On the other hand, typically a broad CTL response would have fewer cells attacking each individual epitope, so perhaps the overall control might not be as good during the peak response.

Directly analyzing these questions is a huge task. Identifying CTL epitopes isn’t easy even when there are a few of them; looking at HIV changes isn’t easy even when there’s a concrete starting point; and in an infected patient you would need to track CTL recognition and HIV changes at short intervals, and over a long period; a task even more complicated by all the variables of a massively diverse starting population, replication and fitness issues … just an overwhelming problem.

A paper in PLoS Computational Biology1 tries to model these possibilities.

Organic computer
Organic computer

I don’t feel competent to assess the model here, in any technical way. As with most bench scientists, I suspect, I’m at best cautious, and more often outright skeptical, about computer modeling of biological problems, especially when they’re as complex as these ones. For example, the authors list a dozen parameters they took from various sources — maximal CTL proliferation rate, natural death rate of CD4 cells, and so on. (Not to mention assumptions that aren’t explicit.) Lots of these parameters are offered as single numbers: 0.01 d-1 as the death rate of CD4 target cells. Naturally, each of those numbers would have error bars in the original, and probably weren’t all measured in comparable ways, and so on. I doubt anyone would be much surprised if any of those parameters was off by 50% or more; perhaps much more. Cumulatively, how much error is in there? Or do we count on having all the errors more or less cancel out?

Still (again, probably typical of bench scientists) I’m always intrigued by computer modeling, and I’m willing to accept that modeling might well open up a problem enough to suggest new approaches. Encouragingly, the model here fits observation reasonably well; escape variants pop up intermittently over a couple of years, CTL clones decline as their targets mutate away. The model looks rather similar, in some ways, to the study a couple of years ago on a pair of identical twins infected with HIV. 2

One interesting observation from the model is that escape variants are mostly all present within a couple of years of infection, though they may later reappear as if they are new as CTL pressure varies:

After about two years, the virus population stabilizes as the ‘easy’ escapes have been done, the replicative capacity is partially restored and only few escapes are expected to appear later during infection. … If an escape is found to happen late it does not necessarily mean that it had not been selected earlier during infection

An observation and prediction arising from this is that CTL may actually become more effective later in infection (all other things being equal, of course), as further attempts by the virus to escape bump up against more severe fitness costs for the virus.

Another observation is the effect of immunodominance. A highly immunodominant CTL response results in more escape variants, as predicted by other studies. However, since escape variants are usually less fit than the Platonic essence of HIV, even though there are more cells infected with virus, that virus is less fit; so even a highly immunodominant response may be surprisingly (to me) effective, by forcing the virus into an unfit state.

A higher degree of immunodominance leads to more frequent escape with a reduced control of viral replication but a substantially impaired replicative capacity of the virus.

Presumably (I don’t think the authors of this model addressed this directly) the effectiveness (quantitatively) of an immunodominant response would depend on the fitness cost — in other words, an immunodominant response that could be escaped with only a small loss in fitness would be ineffective, whereas one that forces a big hit in fitness to escape would be effective. That would reflect what we know about the connection between elite suppressors and particular MHC class I alleles associated with immunodominant epitopes.

I’ve been rather unimpressed by highly immunodominant responses to HIV, but if this model is accurate, such responses may not as bad as I thought; though broad responses are probably still more desirable.

  1. Althaus CL, De Boer RJ (2008) Dynamics of Immune Escape during HIV/SIV Infection. PLoS Comput Biol 4(7): e1000103. doi:10.1371/journal.pcbi.1000103[]
  2. Draenert R, Allen T, Liu Y, Wrin T, Chappey C, et al. (2006) Constraints on HIV-1 evolution and immunodominance revealed in monozygotic adult twins infected with the same virus. J Exp Med 203: 529-39[]