Mystery Rays from Outer Space

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[↩]

There 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.

The 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[↩]

HIV 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.

Picture 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.[↩]

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)

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.)

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:

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[↩]