Mystery Rays from Outer Space

Meddling with things mankind is not meant to understand. Also, pictures of my kids

June 4th, 2009

On predicting immunogenicity

… the overall analysis emphasizes the naiveté of believing that researchers could look at the sequence of a single wild-type protein, predict—using bioinformatics—its capacity to bind one or several common HLA molecules, and know with assurance exactly which protein sequence to incorporate into a favorite immunogenic vector.

–Margulies DH (2009) Antigen-processing and presentation pathways select antigenic HIV peptides in the fight against viral evolution. Nat Immunol 10:566–568. doi:10.1038/ni0609-566

(referring to
Tenzer S, Wee E, Burgevin A, Stewart-Jones G, Friis L, Lamberth K, Chang CH, Harndahl M, Weimershaus M, Gerstoft J et al. (2009) Antigen processing influences HIV-specific cytotoxic T lymphocyte immunodominance. Nat Immunol 10:636–646. doi:10.1038/ni.1728 )

January 20th, 2009

Immunodominance: Not so much?

The Nervous System (Fritz Kahn (1888-1968))
The Nervous System (Fritz Kahn (1888-1968))

Is “immunodominance” just what you get when you measure the wrong place?

Usually, when you look at T cell immune responses to a virus, they’re pretty strongly biased. That is, although the T cells are theoretically, and often observably, able to recognize a wide range of target peptides, the immune response is strongly focused on just a handful of these peptides, while the remaining pool of potential targets is either ignored altogether or given a cursory glance by a handful of T cells. This phenomenon is known as “immunodominance“, and it’s seen with  immune responses to all sorts of pathogens. In some cases — such as for HIV — it’s likely that a strongly immunodominant response is harmful, because it makes it easier for the infecting virus to mutate away from immune control. But in the vast majority of cases the immune response, be it never so immunodominant, does a perfectly good job of controlling the virus; which is why we’re able to easily control most of the viruses that we’re exposed to.

Usually when you measure an immunodominant response, you’ll take lymphocytes from the most abundant, easily-accessed place you can find. That would be blood, in humans; in mice you’d probably take a spleen or lymph nodes.  Some viruses like to hang out in these places, and these include some of the more popular research viruses.

But most of the viruses we’re exposed to don’t infect blood or secondary lymphoid organs; they infect the lungs, or the skin, or neurons, or some other tissue. When we measure the blood response, we believe we’re measuring a good approximation of the real response ongoing in the infected tissue, but that’s mostly been an assumption, not a demonstrated fact.

Recently there’s been some work starting to feel out how similar the tissue response is to the blood/lymphoid organ response. For example, I talked here about work establishing the timing of immune responses in the lungs, vs. the blood. In this case, the overall patterns were similar, though the details were somewhat different.

But that was only really looking at a fairly big picture — overall patterns. What about specifics of target recognition? In particular, is the immunodominance we measure in the blood what actually happens on the battlefield?

I’m only aware of a couple of studies that look at this at all, and those were mainly as asides, noticed in passing. Yewdell’s group has shown in a couple of paper that  infecting mice with poxviruses by different routes leads to differences in immunodominance:1

The latter point is underscored by our observation that the ID hierarchy varies with the route of infection, the first observation of its kind to our knowledge. It will be of great interest to determine the underlying mechanism. 2

I’ve been told of unpublished data that show different immunodominant responses between lung and spleen, as well; also with a poxvirus.

But in those few examples, the epitopes were all known ones.  Known epitopes moved up or down a notch or two in the immunodominance hierarchy. A recent paper from Bob Hendricks’ group3 shows that T cells in the tissues can recognize things that are apparently not seen at all in the blood or spleen.

Baines HSV
Electron tomogram of HSV4

Here they used herpes simplex virus (HSV) in C57BL/6 mice, which have long been believed to almost entirely focus their CD8 T cell response on a single peptide. Hendricks’ group has been looking at the immune response to HSV in the brain, where the virus sets up a latent infection  (I’ve talked about some of his findings here and here).  Contrary to more traditional concepts, it’s now becoming clear (from Hendricks’ work, and that of others) that T cells in the brain are important in controlling latent HSV infection.

In this paper, he found that the immune response in the brain is much more diverse, fairly strongly recognizing at least one  peptide other than the known dominant job.  Because the “normal” (that is, non-neuronal) immune response is so focused, this almost certainly means that the active immune response, down at the pointy end where the T cells are actually working, are responding to altogether different peptides.

It’s generally been assumed, as I say, that the easily-accessed blood or secondary lymphoid tissue is a reasonable approximation of what’s going on in the actual sites of action, in the peripheral tissues — in other words, the idea has been that there’s more or less equal flow of cells between the tissues and the blood and lymph. The recent work on timing and kinetics that I mentioned here sort of supported that assumption, but now we have to wonder whether in fact there’s some kind of filter that keeps some sets of T cells from entering, or staying in, the blood.

We also have to wonder if the whole “immunodominance” paradigm is what we think it is. Could immunodominance represent the filter between blood and tissues, rather than the actual formation of responses? I actually don’t think that would explain immunodominance in general (for one thing, we see strong immunodominance for viruses of lymphocytes, where the blood is the site of infection, so there shouldn’t be a filter) but it’s something to factor in.


  1. D. C. Tscharke (2006). Poxvirus CD8+ T-Cell Determinants and Cross-Reactivity in BALB/c Mice Journal of Virology, 80 (13), 6318-6323 DOI: 10.1128/JVI.00427-06
    D. C. Tscharke (2005). Identification of poxvirus CD8+ T cell determinants to enable rational design and characterization of smallpox vaccines Journal of Experimental Medicine, 201 (1), 95-104 DOI: 10.1084/jem.20041912[]
  2. D. C. Tscharke (2005). Identification of poxvirus CD8+ T cell determinants to enable rational design and characterization of smallpox vaccines Journal of Experimental Medicine, 201 (1), 95-104 DOI: 10.1084/jem.20041912[]
  3. B. S. Sheridan, T. L. Cherpes, J. Urban, P. Kalinski, R. L. Hendricks (2008). Reevaluating the CD8 T cell response to HSV-1: Involvement of CD8 T cells reactive to subdominant epitopes Journal of Virology DOI: 10.1128/JVI.01699-08[]
  4. Electron tomogram of a HSV nucelocapsid completing envelopment , from Baines, J. D., C. E. Hsieh, E. Wills, C. Mannella, and M. Marko. 2007. Electron tomography of nascent herpes simplex virus virions. J Virol 81: 2726-2735.[]
July 24th, 2008

HIV and immunodominance, again

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[]
June 19th, 2008

Adjuvants: Quality as well as quantity

Jenner vaccination bookVaccination 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?

Smallpox vaccine vialI, 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[]
June 15th, 2008

Immunodominance: When is it set?

T cell activation

T cell activation

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 Lefrancois’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.

T cell - dendritic cell interactions

T cell and dendritic cell interactions

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 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[]
March 26th, 2008

Redirecting killers

Mouse splenocytes (T cells, B cells, dendritic cells)

Normal mouse spleen: B cells (red), CTL (green), dendritic cells (blue)

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.

HIV budding from a T cellIf 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[]
August 29th, 2007

Snowflakes in a blizzard: Counting T cells

Bentley snowflakeThere are maybe 1011 naive T cells in a human body. How many of those T cells can recognize any particular antigen?

About twenty.

OKTHXBYE!

… Well, if you want a little more expansion of that (and all the weasel words that go along with it) …

T cells have to recognize the entire universe of all possible pathogens, and they generally manage to do so; it’s not often that people are infected with a pathogen that simply doesn’t elicit a T cell response. On the other hand, for all the possible antigens present in Generic Joe Pathogen, T cells typically only recognize a handful of them; you don’t see a massive upwelling of T cells that recognize every possible epitope in the pathogen.

Floating around your body, there are somewhere between, let’s say, 108 (if you’re a mouse) and 1011 (if you’re a human) naive T cells. 1 It’s that population that must be prepared to take on our hypothetical universe of pathogens. In other words, the largest number of antigens you could possibly recognize is 108 – 1011, if each naive T cell recognized a distinct antigen.

Is there redundancy among T cell specificities? If so, how many T cells typically recognize an individual antigen? And therefore, how may distinct antigens can your body detect?

When a T cell is allowed to exit the thymus, where it matures, it has a T cell receptor (TcR). That TcR is what interacts with, say, a viral antigen, and what allows the T cell to respond in its specific and (hopefully) appropriate way. TcRs are formed by genomic rearrangement, shuffling a moderate handful of possible segments to form, by combinatorial multiplication, a very large number of possible sequences. (If you want a mechanism, see any introductory immunology text, or Wikipedia. ) How large is a “very large number of possible sequences”? In theory, it could be as many as 1015 different TcRs2, but in practice it’s probably more like 108.3 (And that’s 108 possible clones — precise TcR sequences. There’s more than one way to skin a virus: TcRs with different sequences can recognize the same epitope.)

At any rate, it seems TcR diversity is, very roughly, on the same order of magnitude as naive T cell abundance; or perhaps a little less. We would expect maybe up to a thousand, maybe a few more, maybe a lot fewer, T cells per epitope. That doesn’t help us all that much with the question; we’re left with having to measure directly.

Directly measuring the frequency of naive T cells is, as you can imagine, very difficult. You’re looking for an event with a frequency of at most 1/100,000, with the positives spread out among an entire mouse (or an entire human). Several groups have tried, and have published their results to widespread raised eyebrows. Just recently, Marc Jenkins’ group has taken another run at the problem,4 and this time there’s more of the thoughtful nodding and less of the skeptical frowns.

The paper is almost entirely technical, so I won’t go into any details. Suffice it to say that they show fairly convincingly that they are counting what they say they are, and that they’re not missing many of them. (Mark Davis has a commentary5 on the paper, in which he points out some caveats and cautions — though I agree with his points, I don’t think they’re likely to throw the estimates way out of whack. For now, let’s accept the numbers but mentally add some grey fuzz to the upper side.)

Here’s what they found. They looked at three T cell epitopes. One yields a large, one a medium, and the other yields a smallish T cell response when you infect with the appropriate conditions. For the “large” epitope, they estimated their mice contained 190 naive T cells specific for it; the “medium”, about 20; the “small”, about 16.

Sixteen T cells, swimming about among the vast pool of irrelevant T cells and distributed randomly through the body’s lymphoid tissue, are capable of generating an immune response that, in less than 6 days, will expel invading pathogens.

Moon et al Fig 5The next cool thing was the link to the ultimate T cell response. Over the first 6 days of an immune response, the “large” epitope response went from around 190 T cells to around 80,000; the medium, from 20 to 5000; the small, from 16 to 3000 cells. (The figure at right shows the cell counts for each T cell group, over time. Note that it’s a log Y axis.) The expansion is quite similar for all three epitopes: 400-fold, 250-fold, and 200-fold. Here I’m going to quibble with the Moon et al interpretation. They call these all “about 300” (fair enough, I suppose) and argue that each ultimate response was proportional to the number of naive cells. While I can see that for the biggest response, I’m skeptical that 20 is actually different from 16 — though the error bars aren’t spelled out, they clearly overlap a lot — and I’m also skeptical that 400-fold is the same as 200-fold. Also, of course, this is just three epitopes. 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.

(Of course, this is a part of the immunodominance equation that I’ve touched on before.)

Still, 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.

Incidentally, it’s been stated (I don’t know the data well enough to judge how accurately) that after naive T cell clones6 leave the thymus, they divide a little bit — just ticking over, compared to the vast expansion after they meet their antigen, but enough to expand each clone up to maybe 10-fold or so. If so, the two smaller naive populations here may have originated with just a handful of T cell clones. Jenkins’ group actually looked at TcR sequences, and their findings are roughly consistent with this idea. Certainly these small pools had a very limited number of TcR clones within them, and the larger pool had a lot more T cell clones, but there wasn’t enough material to tweeze it down much finer than that.


  1. “Naive” means they haven’t encountered their cognate antigen yet. After they encounter antigen, they’ll typically divide and multiply immensely.[]
  2. T-cell antigen receptor genes and T-cell recognition. Davis, M. M., P. J. Bjorkman. 1988. Nature 334:395. []
  3. T. P. Arstila et al., Science 286, 958 (1999).[]
  4. 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. []
  5. The αβ T Cell Repertoire Comes into Focus. Davis MM. Immunity. 2007 Aug;27(2):179-80. []
  6. A clone being a T cell with a specific TcR sequence[]
August 2nd, 2007

Immunodominance, Part II: Why care?

HIV budding from a lymphocyteHIV and hepatitis C virus (HCV) are the two best-known chronic infections of humans. Both of them seem to persist at least partly by throwing out immune escape variants.

To expand that a bit: These are viruses that continue to infect people in spite of a specific immune response: People infected with either virus, generate cytotoxic T lymphocytes (CTL) that recognize and destroy infected cells. CTL recognize short peptides, say 9 amino acids long, that are derived from viral proteins. If you monitor which viral peptides that CTL are recognizing, and track those peptides over time, what you often (but not invariably) find is that the peptides in the dominant virus in the body changes sequence over time. As a result, CTL regularly lose the ability to recognize the virus. Each time (at least for a while) the virus mutates away from the CTL, new CTL pop up that recognize the new version of the virus, but each time the virus has a window to bump up its replication for a while as CTL control is reduced. 1

This sort of immune escape occurs in HCV infections as well2 although it’s not as clear that it’s critical to HCV persistence:3

Although it is clear that CTL escape mutations occur in HCV genomes, the relevance of this mechanism to viral persistence is an open question. Mutations usually occur within the first 3-4 months of infection …. Such observations are compatible with release from early immune selection pressure as viral escape is established, and perhaps suggest a role for CTL escape mutations in the genesis of chronic infection.

Boat wakePicture the virus as motorboat, roaring through the T cell ocean, leaving behind it a wake of failed CTL that can no longer recognize the viral epitopes. The problem with this image is that to keep ahead, the virus has to continually change its sequence4 and changing a protein’s sequence usually means losing some functionality. It’s been shown that immune escape is often associated with a reduction in viral fitness.5 From any particular viral sequence, there are probably a limited number of directions the virus can move without losing its ability to replicate effectively: “The stereotypic nature of acquired mutations provides support for biochemical constraints limiting HIV-1 evolution and for the impact of CD8 escape mutations on viral fitness.”6

So it’s not as effortless as it seems for the persistent virus to keep on mutating away from the controlling T cells; the virus takes a pretty big hit to do so. The amount of fitness the virus is “willing” to lose in order to escape from CTL recognition tells us just how effective CTL must be in controlling the virus, so CTL must be pretty good at the job. How can we help CTL control the virus? How can we keep the virus from escaping from CTL control?

This is where the concept of immunodominance comes in (see? I had a point after all!). Immunodominance, if you missed the last post on the subject, is the observation that (for reasons that are not well understood) immune responses often focus on a very limited number of epitopes; there may be many peptides that are recognized to some extent, but the vast majority of CTL recognize only two or three of those peptides. If a CTL response is “broad”, meaning that many viral epitopes are recognized well (with no clear immnodominant epitope), then to escape from CTL control the virus quasispecies must throw out multiple mutations at the same time. That’s much harder (less likely) than throwing out a single mutation; and it’s much harder than sequentially throwing out single escape mutants, with periods in between of efficient replication (unchecked by CTL) in which the quasispecies can establish compensatory mutations and become set for a new mutation.

In this context, then, immunodominance may be a bad thing. It’s been suggested7 that some individuals who can control HIV for a long time, do so at least partially because of their subdominant CTL response. If we could manipulate the CTL response during vaccination or initial infection, then, perhaps we could reduce the response to an immunodominant epitope and increase the responses to multiple subdominant epitopes, and perhaps this would help control HIV infection.

Is there a context in which immunodominant responses are good things?

More later.


  1. I think the first paper showing evidence for HIV immune escape was Human immunodeficiency virus genetic variation that can escape cytotoxic T cell recognition. Rodney E. Phillips, Sarah Rowland-Jones, Douglas F. Nixon, Frances M. Gotch, Jon P. Edwards, Afolabi O. Ogunlesi, John G. Elvin, Jonathan A. Rothbard, Charles R. M. Bangham, Charles R. Rizza & Andrew J. Mcmichael. Nature 354, 453 – 459 (12 December 1991) []
  2. The outcome of hepatitis C virus infection is predicted by escape mutations in epitopes targeted by cytotoxic T lymphocytes. Erickson AL, Kimura Y, Igarashi S, Eichelberger J, Houghton M, Sidney J, McKinney D, Sette A, Hughes AL, Walker CM. Immunity. 2001 Dec;15(6):883-95. []
  3. Mutational escape from CD8+ T cell immunity: HCV evolution, from chimpanzees to man. David G. Bowen and Christopher M. Walker. J Exp Med 201: 1709-1714 (6 June 2005) []
  4. To be a little more accurate, there’s no single “virus”, but rather a cloud of viruses with slightly varying sequences — a quasispecies; within that cloud, the majority may have the immune-escape sequence.[]
  5. For example: Rapid viral escape at an immunodominant simian-human immunodeficiency virus cytotoxic T-lymphocyte epitope exacts a dramatic fitness cost. Fernandez CS, Stratov I, De Rose R, Walsh K, Dale CJ, Smith MZ, Agy MB, Hu SL, Krebs K, Watkins DI, O’connor DH, Davenport MP, Kent SJ. J Virol. 2005 May;79(9):5721-31.[]
  6. Selective escape from CD8+ T-cell responses represents a major driving force of human immunodeficiency virus type 1 (HIV-1) sequence diversity and reveals constraints on HIV-1 evolution. Allen TM, Altfeld M, Geer SC, Kalife ET, Moore C, O’sullivan KM, Desouza I, Feeney ME, Eldridge RL, Maier EL, Kaufmann DE, Lahaie MP, Reyor L, Tanzi G, Johnston MN, Brander C, Draenert R, Rockstroh JK, Jessen H, Rosenberg ES, Mallal SA, Walker BD. J Virol. 2005 Nov;79(21):13239-49.[]
  7. For example, Subdominant CD8 T-Cell Responses Are Involved in Durable Control of AIDS Virus Replication . Thomas C. Friedrich, Laura E. Valentine, Levi J. Yant, Eva G. Rakasz, Shari M. Piaskowski, Jessica R. Furlott, Kimberly L. Weisgrau, Benjamin Burwitz, Gemma E. May, Enrique J. Leon,Taeko Soma, Gnankang Napoe, Saverio V. Capuano III, Nancy A. Wilson,and David I. Watkins. J Virol, Apr. 2007, p. 3465-3476 Vol. 81, No. 7 doi:10.1128/JVI.02392-06; and Control of human immunodeficiency virus replication by cytotoxic T lymphocytes targeting subdominant epitopes. Frahm N, Kiepiela P, Adams S, Linde CH, Hewitt HS, Sango K, Feeney ME, Addo MM, Lichterfeld M, Lahaie MP, Pae E, Wurcel AG, Roach T, St John MA, Altfeld M, Marincola FM, Moore C, Mallal S, Carrington M, Heckerman D, Allen TM, Mullins JI, Korber BT, Goulder PJ, Walker BD, Brander C. Nat Immunol. 2006 Feb;7(2):173-8.[]
June 25th, 2007

Immunodominance: Part I (Some background)

Cytotoxic T lymphocytes (CTL) recognize peptides that are about 9 amino acids long. There are lots of constraints on which peptides can possibly be presented; the most important factor is whether the peptide can bind to one the MHC class I alleles that the host expresses. Still, a generic virus will have hundreds or more likely thousands of peptides that are reasonable CTL targets. Of those peptides, how many are actually recognized by CTL? Of those that are recognized by CTL, how many are recognized effectively (enough to trigger a detectable response)? Does it make any difference which, and how many, are recognized? And — most interestingly — why are so few peptides recognized?

There are technical problems with this question. One huge problem is just how to identify the peptides that are recognized. Typically, you’d have to synthesize peptides from the viral genome, mix them with CTL from an immune host, and figure out which of the peptides activate the CTL. However, if you try to synthesize all the possible peptides from a viral genome, you’ll have many thousands of peptides: Expensive, to say nothing of the work involved in screening.

People have tried to get around this in two ways. One is to use longer peptides. Traditionally, screening has used 15mers rather than 9mers. Using overlapping 15mers instead of every possible 9mer can cut your screening down into a relatively manageable range — a couple thousand or fewer. Still a big job, but practical. One problem with this, of course, is that 15mers shouldn’t work at all for MHC class I! MHC class I alleles (in contrast to MHC class II) rarely bind peptides anywhere near that long; rarely much more than 11 or so amino acids long. So what you’re counting on, with your 15mers, is that either they’re contaminated with incomplete synthesis products (a common situation), or that they’re partially degraded in the medium when you add them to your cells. In either case, you really don’t have a good idea what your actual coverage of the viral proteome is.

Another approach is to try to cut down your required peptides, by trying to predict which ones could possibly bind to your MHC class I and only (or mainly) synthesizing those. The problem here is that for all the progress in understanding MHC class I binding motifs, there are lots of high-affinity peptides for various MHC class I alleles that don’t even come close to matching the putative binding motif. Your coverage is only as good as your predictions, and your predictions will miss some genuine epitopes.

(Another possible problem with both of these approaches is that they’ll miss peptides that are not part of the viral proteome. That includes things like spliced peptides (see my previous post on that), out-of-frame peptides,1 and post-translationally modified peptides that don’t match the encoded sequence — the most famous example probably being glycosylation sites where the carbohydrate is stripped off the Asn in the cytosol to leave a non-templated Asp.2 )

This brings me to Kotturi et al, a paper I’ve mentioned here before:

The CD8 T-Cell Response to Lymphocytic Choriomeningitis Virus Involves the L Antigen: Uncovering New Tricks for an Old Virus

Maya F. Kotturi, Bjoern Peters, Fernando Buendia-Laysa, Jr., John Sidney, Carla Oseroff, Jason Botten, Howard Grey, Michael J. Buchmeier, and Alessandro Sette

Journal of VIrology, May 2007, p. 4928–4940 (doi:10.1128/JVI.02632-06)

Arenavirus

Lymphocytic choriomeningitis virus (invariably abbreviated to LCMV for obvious reasons) is one of the classic models of viral immunity. One of its many nice qualities3 is that it induces a tremendous (i.e. easily measured) immune response. At the peak of the immune response, 6 to 8 days after infection, some 80 to 95% of a mouse’s CD8 +ve T cells4 may be reactive with LCMV. That makes it relatively easy to detect individual components of the response. In other words, you can readily define individual peptide epitopes within the CTL response to LCMV. Another nice thing about the virus is that it’ usually cleared, if you infect an adult mouse, so you can then move on to analyze memory responses, but I won’t get into that today. (The image on the left is of an arenavirus [LCMV is in the arenavirus family] from Michael Buchmeier’s lab at Scripps.)

LCMV peptides

Because LCMV has been studied for a while, and because the CTL response is so large, there have been a bunch of viral epitopes defined; in the commonly-used C57BL/6 mouse, 7 peptides were known to induce CTL reactivity since 1998.5 Seven epitopes is actually a fair number — most viruses don’t have that many defined epitopes for just two MHC class I alleles — but three more epitopes were added earlier this year6 bringing the total to 10 defined epitopes that bind to the B6 mouse MHC class I alleles. (The image on the right shows two of the best-recognized peptides from LCMV glycoprotein, in the shape they assume when bound to particular MHC class I alleles. Taken from: A structural basis for LCMV immune evasion: subversion of H-2D(b) and H-2K(b) presentation of gp33 revealed by comparative crystal structure analyses. Achour A, Michaëlsson J, Harris RA, Odeberg J, Grufman P, Sandberg JK, Levitsky V, Kärre K, Sandalova T, Schneider G. Immunity. 2002 Dec;17(6):757-68.)

However, these 10 epitopes only account for around 80% of the CTL response to LCMV — that is, if you take all the CTL that light up in response to an authentic LCMV-infected cell, about a fifth of those will not light up in response to any of the known epitopes. What are those remaining guys reacting to? Kotturi et al went looking for the missing triggers.

They used both of the approaches I’ve mentioned here. They not only screened with overlapping 15mers covering much of the LCMV proteome, they used MHC prediction programs to identify particular candidates for CTL epitopes and screened those particularly. All in all, they looked at 1064 peptides: “A total of 400 Kb and Db algorithm-selected peptides, along with a set of 664 15-mer peptides, overlapping by 10 amino acids, spanning the entire LCMV proteome, were synthesized.”

Now, remembering that this is an intensively-studied virus, one that’s been a workhorse of immunology for decades, how many new epitopes do you think they turned up? Ten are already known. Kotturi et al turned up another 19 — they nearly tripled the number of MHC class I epitopes for LCMV. That’s the first remarkable thing; it suggests that probably most claims for the number of viral peptides that are recognized are drastic underestimates. (It also suggests that cross-reactive T cells are not common, but that’s another story.)

The next interesting point about their paper is where they got their hits — from their predicted epitopes, or from their 15mers? Well, the predictions did pretty well:

The 15-mer approach including truncated peptide sets required synthesis and testing of 1,2147 peptides and identified approximately 65.2% of the overall response. By contrast, the predictive approach required synthesis and testing of 400 peptides (or 160 if only the top 1.2%8 from each allele would have been synthesized) and identified approximately 88.9% of the total response.

But the predictions did miss several true epitopes; some of the genuine MHC class I epitopes just don’t look like things that are supposed to bind to H-2Kb. If you want to pick up on things that are not, as yet, predictable, you still need a brute-force approach.

So of the hundreds or thousands of potential LCMV epitopes, there are 29 that actually get recognized.9 That’s a fair number of epitopes. But here’s the next part (in fact, this is the whole point of this post). Look at the distribution of CTL responses to each peptide. Here’s what it looks like as a fraction of the total CTL response to LCMV:

Immunodominance

The top 2 peptides of the 29 cover 25% of the response; the top 4, 50%. You need to put the bottom 18 peptides together to catch up to the first two and make up 25% of the response!10 This, ladies and gentlemen, is what we call immunodominance. The top handful of peptides are immunodominant — in a C57BL/6 mouse, those peptides will invariably be the targets of the vast majority of the CTL response.11 The other peptides will cause a response that, while detectable, is much lower than that to the dominant peptides.

Why?

Well, we don’t know, but at least we think we know some of the possible explanations. More in a later post.


  1. Nilabh Shastri has probably been the strongest supporter of this concept. See, for example, Constitutive display of cryptic translation products by MHC class I molecules. Schwab SR, Li KC, Kang C, Shastri N. Science. 2003 Sep 5;301(5638):1367-71. I’m not yet convinced that this is as common as he argues, but it clearly can happen.[]
  2. There are a number of examples of this now. The first demonstration that it can happen was: An HLA-A2-restricted tyrosinase antigen on melanoma cells results from posttranslational modification and suggests a novel pathway for processing of membrane proteins. Skipper JC, Hendrickson RC, Gulden PH, Brichard V, Van Pel A, Chen Y, Shabanowitz J, Wolfel T, Slingluff CL, Boon T, Hunt DF, Engelhard VH. J Exp Med. 1996 Feb 1;183(2):527-34.[]
  3. for an immunologist, anyway[]
  4. Quantitating the magnitude of the lymphocytic choriomeningitis virus-specific CD8 T-cell response: it is even bigger than we thought. J Virol. 2007 Feb;81(4):2002-11. Masopust D, Murali-Krishna K, Ahmed R[]
  5. van der Most, R. G., K. Murali-Krishna, J. L. Whitton, C. Oseroff, J. Alexander, S. Southwood, J. Sidney, R. W. Chesnut, A. Sette, and R. Ahmed. 1998. Identification of Db- and Kb-restricted subdominant cytotoxic T-cell responses in lymphocytic choriomeningitis virus-infected mice. Virology 240: 158–167.[]
  6. Masopust, D., K. Murali-Krishna, and R. Ahmed. 2007. Quantitating the magnitude of the lymphocytic choriomeningitis virus-specific CD8 T-cell response: it is even bigger than we thought. J. Virol. 81:2002–2011. Yes, same reference as before, but I can’t bear to struggle with these footnotes any more.[]
  7. The 664 was their starting pool of 15mers; to actually find the epitopes, they had to synthesize sub-peptides from within the positive 15mers.[]
  8. They broke down the success rate by the rank of the prediction and found that in fact they could have covered most of their hits by using fewer peptides from the most confident predictions[]
  9. There may even be a handful of others; Kotturi et al. don’t account for a few percent of CTL responses even with all the known epitopes. But that may be a sensitivity issue, so let’s assume that the 29 cover everything[]
  10. So, even if the missing few percent of responses are real, one would expect that it would be divided up among many — dozens? Hundreds? — of individual peptides, perhaps all below the limits of sensitivity for present assays.[]
  11. As a side note, even though the predictions did reasonably well — surprisingly well, to me — within their predictions the rank wasn’t a good correlation of immunodominance. For example, the most dominant peptides (50% of the total response) ranked 2, 25, 14, and 28 as predicted epitopes, whereas the three peptides with the highest prediction rank only covered 3.5% of the total response all together[]
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