Mystery Rays from Outer Space

One 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

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[↩]

Vaccination is one of the (if not the most) important medical advances in history. The problem today is that most of the easy diseases already have vaccines available, and now we’re trying to develop vaccines against the hard ones. Fortunately, I think we’re entering a new golden age of vaccine development, as we begin to understand why immunization works at the molecular level, to the point where we may soon be able to deliberately tweak them for optimal efficacy.

Back in the dark ages, when I was first working with vaccines,¹ adjuvants were a witches’ brew of newts’ eyes and frogspawn, and the ones that worked, just sort of … worked. No one really knew why. But around the time I backed away from vaccines, (partly because of this empirical adjuvant stuff) new theoretical frameworks were being developed that began to explain how and why adjuvants work, and now — some 20 years later — we are at the point where theory is moving solidly toward practice.

I’ve commented several times on the issue of immunodominance. T cell responses to antigens aren’t smoothly distributed over all the possible targets in the antigen; instead, a handful of targets get the lion’s share of the T cell response. Sometimes this is a good thing (for example, it’s a way of getting a screaming hot response to a target, instead of having a bunch of wimpy little responses); sometimes it’s bad (if it’s a moving target, as with rapidly-mutating viruses such as HIV, then your screaming hot response may be to a target that no longer exists, whereas having a bunch of targets at least nearly guarantees that you’ve got something to shoot at.)

In spite of its importance, though, the underlying mechanisms that drive immunodominance aren’t well understood. For example, one possible explanation is that the T cell that ends up becoming dominant, started out as the most abundant clone originally. A paper last year² (I talked about it here) supported that possibility, but a more recent study³ that I talked about earlier this week suggested that while clonal abundance is one factor, there must be other, equally important, influences on the response.

That fits with another paper that came out in May,⁴ looking at the effects of different adjuvants on the immune response. Of course this has been done many times in a quantitative way — which adjuvant gives the biggest response? — but Malherbe et al. asked the question qualitatively: What exactly happens to the T cell response? That is: We know that different adjuvants can cause higher or lower responses to an antigen; but are the different responses made up of the same CTL⁵, or do different adjuvants crank up different sets? Can we drive a T cell response that is qualitatively, as well as quantitatively, better?

I, for one, (and I think most of the field) would have said “No”; no matter what your adjuvant is, the response would be qualitatively the same. Why would one particular CTL precursor clone be stimulated better or worse by a particular adjuvant? That’s the answer that would be predicted from the first study, that suggested that immunodominance is determined mainly by the precursor frequency: You can’t really affect the precursor frequency (that’s set during thymic development), so no matter what you do with your antigen you should get the same relative response (even though the total response may be higher or lower, it would contain the same proportion of T cell clones).

In fact, that’s not what happens. Malherbe et al. compared five different adjuvants, mixed with the same antigen. The adjuvants are known to act through different mechanisms. (That is, while they all act by stimulating innate immune recognition molecules, they stimulate different innate receptors — different TLR molecules, or [as we now know⁶ ] pattern recognition receptors that are different from TLRs altogether.) Then they assessed the subsequent immune response by comparing the immunodominance hierarchies that came out of the immunization. The different adjuvants drove expansion of different T cell clones, so that the response was qualitatively different.

In particular, adjuvants drove expansion of higher-affinity clones:

…adjuvants regulate clonal composition by using a mechanism that alters initial TCR-based selection thresholds and that relies most heavily on blocking the propagation of antigen-specific clonotypes expressing low-affinity TCR. … Thus, adjuvant formulation can modify the TCR-based selection threshold that regulates Th cell clonal composition in response to protein vaccination.

How adjuvants do this remains unknown. It wasn’t related to the antigen dose (which has previously been shown to affect the TcR affinity). Possibilities include differential dendritic cell maturation, altering local antigen contentration (the “depot” effect that has been the classic explanation for alum’s mechanism of action — though that explanation is at least partly rendered obsolete by the recent paper⁷ from Richard Flavell’s group), and direct stimulation of T cell clones — but who knows.

Assuming this holds up for different antigens (they’ve only looked at one, so far) the key thing, in clinical terms, is that it’s possible to alter immunodominance without changing the antigen. We need to understand how this works, because it may be a much simpler way of improving immune responses than altering the antigen itself.

1. It looks as if I may be doing so again; our proposal for a Vaccine Center here has been funded, at least for a few years; although I’m only a small part of the group[↩]
2. Naive CD4(+) T Cell Frequency Varies for Different Epitopes and Predicts Repertoire Diversity and Response Magnitude. Moon JJ, Chu HH, Pepper M, McSorley SJ, Jameson SC, Kedl RM, Jenkins MK. Immunity. 2007 Aug;27(2):203-13.[↩]
3. Obar, J., Khanna, K., LeFrancois, L. (2008). Endogenous Naive CD8+ T Cell Precursor Frequency Regulates Primary and Memory Responses to Infection. Immunity, 28(6), 859-869. DOI: 10.1016/j.immuni.2008.04.010[↩]
4. Malherbe, L., Mark, L., Fazilleau, N., McHeyzer-Williams, L., McHeyzer-Williams, M. (2008). Vaccine Adjuvants Alter TCR-Based Selection Thresholds. Immunity, 28(5), 698-709. DOI: 10.1016/j.immuni.2008.03.014

   Commentary at:
   Immunity 28:602-604 (16 May 2008) doi:10.1016/j.immuni.2008.04.008
   Preview: Taking a Toll Road to Better Vaccines
   Sharon Celeste Morley and Paul M. Allen[↩]

5. CTL: Cytotoxic T lymphocytes[↩]
6. Eisenbarth, S.C., Colegio, O.R., O'Connor, W., Sutterwala, F.S., Flavell, R.A. (2008). Crucial role for the Nalp3 inflammasome in the immunostimulatory properties of aluminium adjuvants. Nature DOI: 10.1038/nature06939[↩]
7. Eisenbarth, S.C., Colegio, O.R., O’Connor, W., Sutterwala, F.S., Flavell, R.A. (2008). Crucial role for the Nalp3 inflammasome in the immunostimulatory properties of aluminium adjuvants. Nature. DOI: 10.1038/nature06939[↩]

Immunodominance is one of the many critical, yet poorly understood, phenomena associated with antiviral immunity. Why is it that one particular viral peptide may be recognized by as many as 1% of all the cytotoxic T lymphocytes (CTL) in the body, while a different epitope may be recognized only by 0.001%? There are obvious implications for vaccine design and development; yet we really have little idea of the causes. People have proposed all kinds of explanations — kinetics of peptide presentation, kinetics of T cell response, number of T cell clones, amount of peptide presented — and each of the suggestions has some support but doesn’t seem to explain every instance.

One of the problems is the technical difficulty involved. Accurately quantifying the minute, highly localized amounts of peptide involved, or the tiny handful of cells that could respond, has simply been out of our reach; until very recently.

About a year back, Marc Jenkins’ group described a new technique for measuring very small numbers of T cells in mice¹ and came up with some interesting numbers. They looked at three epitopes (for CD4, T helper, T cells, not CTL; but guesses have been that the two groups of T cells have similar numbers of precursors) and concluded that the epitopes had 190, 20, and 16 precursor T cells specific for them. What’s more, the more T cell precursors there are, the higher the ultimate T cell response to the epitope — the more immunodominant that epitope is.

I commented on the paper at the time and said “It’s an interesting suggestion, and their data certainly are suggestive. I’m sure there will be more epitopes examined by this technique over the next little while, so we’ll see how well it holds up.” Now, a year later, we’re seeing the first followup, and it turns out to hold up pretty well; although there are, not surprisingly, some added complexities.

The followup is from Leo Lefrançois’s group in UConn.² I will skip over their controls, except to say that they did a bunch of ingenious controls to demonstrate that they really were looking at what they claim to be.

First, they looked at a half-dozen known epitopes and asked how many precursor CTL there were for each. Their numbers were in the same ballpark as the CD4 precursors measured earlier; they came up with a range from 80 to 1200 CTL (average, 120-160) specific for their various epitopes. This is somewhat larger than the Moon et al. estimates I mentioned earlier, but I think that these epitopes were all, or almost all, fairly abundant to start with, so it’s pretty consistent.

They also used the technique for an extremely cool experiment. They infected mice with viruses, and then tracked through the number of CTL present each day. This way they were able to ask the exciting question, When is the immunodominance hierarchy set?

Moon et al. last year suggested that immunodominance hierarchies are set on day 0; that the number of T cell precursors present determines the size of the response to an epitope. I was a little dubious about that, saying “I think it’s equally likely that while the size of the naive precursor pool is one factor, you can also get different T cell responses out of the same number of precursors, for any of a variety of reasons.”

Here’s Obar  et al.’s conclusion:

Although the M45:Db- and VSV-N:Kb-specific responses differed kinetically, they were of similar overall magnitude, even though their initial precursor frequencies differed on average by 4-fold… These data suggested that interclonal competition for resources (i.e., APC interactions, growth factors, or costimulatory molecules) prior to the peak of the response was important in modulating overall clonal expansion.

(My emphasis)

So the bad news, I guess, is that there may not be a single simple explanation for immunodominance, at least for CTL. However, precursor frequency does seem to be one factor — and an important one — in setting CTL immunodominance hierarchies, and knowing the timing of other factors (hierarchies are set around day 3, Obar et al. determined) should be a big help in narrowing down possibilities.

1. Naive CD4(+) T Cell Frequency Varies for Different Epitopes and Predicts Repertoire Diversity and Response Magnitude. Moon JJ, Chu HH, Pepper M, McSorley SJ, Jameson SC, Kedl RM, Jenkins MK. Immunity. 2007 Aug;27(2):203-13.
   Commentary on the paper, by Mark Davis, here: The αβ T Cell Repertoire Comes into Focus. Davis MM. Immunity. 2007 Aug;27(2):179-80. [↩]
2. Obar, J., Khanna, K., LeFrancois, L. (2008). Endogenous Naive CD8+ T Cell Precursor Frequency Regulates Primary and Memory Responses to Infection. Immunity, 28(6), 859-869. DOI: 10.1016/j.immuni.2008.04.010[↩]

We know that HIV can be controlled by an appropriate immune response. Cytotoxic T lymphocytes (CTL) are capable of very effectively suppressing HIV; in fact, in a standard HIV infection, the virus typically spends most of its early phase being controlled by a T cell response. In most people, unfortunately, the control is temporary; since HIV replication is sloppy, the virus throws off mutants at regular intervals, and eventually one of the mutants will be invisible to the dominant CTL response. That mutant replicates rapidly (probably damaging the immune response as it does so) until a new CTL response brings that virus under control, only for other variants to arise again.

Some people are apparently able to hold the virus under control for very long periods — the long-term non-progressor HIV patients. Some of these people seem to have T cell responses against part of the virus that has very precise sequence requirements; if the virus mutates away from CTL recognition, the virus is crippled and can’t replicate effectively. Other people seem to have a broad T cell response, one that recognizes several parts of the virus at once. The odds of successfully mutating all of the targeted areas simultaneously are exponentially lower than of mutating a single region.

Obviously, either of these are states that vaccine designers want as outcomes. That’s not all that easy. People are variable, and there don’t seem to be general rules that you can use to force an immune response to the target of one’s choice. ¹ Wouldn’t it be nice if there was a way of bypassing the whole messy immunization step, and just moving straight on to the desired finale of CTL specific for the target of one’s choice?

A paper in the March ‘08 issue of Journal of Virology² does just that.

When you induce T cell-mediated immunity, whether through a vaccine or a real infection, what you’re actually doing is expanding a pool of T cells whose receptor recognizes your special antigen. There are a huge number of potential T cell receptors (TcRs); under normal conditions, any particular antigenic target might have only 20 or 100 T cells that can recognize it, scattered among the millions of T cells with irrelevant specificities. Once a T cell finds its antigen, though,³ that T cell clone divided and expands enormously, as much as 100,000 times. The next time that antigen rides through town, it finds hundreds of sheriffs awaiting it, not just one or two.

If the TcR is all you need for specific recognition, can you bypass the whole annoying specific recognition and expansion step? Why not take the TcR from a previous clone, that you already know is useful (perhaps one from another individual altogether) and swap it into generic, non-specific T cells? In fact, that’s been done in a number of cases, and it actually seems to work.⁴

Joseph et al. tried this with a TcR specific for a HIV antigen. They swapped this known TcR into ordinary generic T cells from a normal blood donor, and turned those boring old plain T cells into CTL that specifically killed HIV-infected cells.

OK, their system is very artificial, involving transformed target lines and a Rube Goldbergesque mouse system to test “in vivo activity”, so it’s not really possible to draw any conclusions about clinical potential. In an actual infection, you’d presumably want to do this with multiple TcRs simultaneously, to target many HIV antigens at once and reduce the risk of immune escape (otherwise, just putting in one chimeric TcR is not different from getting a strong CTL response to HIV — which we know is not sufficient in the long run). I don’t think we know what would happen in that situation; would there be competition between the different TcRs to the point that most would be outcompeted and swamped, ending up with a de facto single target after all? ⁵

Another question I have is whether the original TcRs might cause mischief — if the T cell has two TcRs, stimulation through one might lead to reactivity with the other, and if the other, original, TcR happens to react with a self antigen you might get the mother of all autoimmune diseases. So my guess is that this is mostly a cute idea that will never go anywhere (for HIV; I think it has much more potential in tumor treatment).

Still, it really is a neat concept, and I hope some of my questions get addressed.

1. There are some approaches that can do this, but they also have drawbacks.[↩]
2. Joseph, A., Zheng, J.H., Follenzi, A., DiLorenzo, T., Sango, K., Hyman, J., Chen, K., Piechocka-Trocha, A., Brander, C., Hooijberg, E., Vignali, D.A., Walker, B.D., Goldstein, H. (2008). Lentiviral Vectors Encoding Human Immunodeficiency Virus Type 1 (HIV-1)-Specific T-Cell Receptor Genes Efficiently Convert Peripheral Blood CD8 T Lymphocytes into Cytotoxic T Lymphocytes with Potent In Vitro and In Vivo HIV-1-Specific Inhibitory Activity. Journal of Virology, 82(6), 3078-3089. DOI: 10.1128/JVI.01812-07[↩]
3. assuming appropriate conditions for activation and so forth[↩]
4. E.g. for tumors; Morgan, R. A., Dudley, M. E., Wunderlich, J. R., Hughes, M. S., Yang, J. C., Sherry, R. M., Royal, R. E., Topalian, S. L., Kammula, U. S., Restifo, N. P., Zheng, Z., Nahvi, A., de Vries, C. R., Rogers-Freezer, L. J., Mavroukakis, S. A., and Rosenberg, S. A. (2006). Cancer regression in patients after transfer of genetically engineered lymphocytes. Science 314, 126-129.[↩]
5. Some models for immunodominance predict this, in fact[↩]