Mystery Rays from Outer Space

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

April 10th, 2010

Immune databases and hypotheses

The folks associated with the IEDB (Immune Epitope Database) have published a very nice and useful guide to all the serious contenders in the immune database field.  1 If you have a particular need, this is an excellent starting point for choosing the appropriate starting point.  (It’s an open access article, too.)

They’ve obviously looked in a lot more depth than I have, but they make a few comments that my more limited assessment strongly supports:

… our survey highlights clear shortcomings in the predictive tools available. Namely, MHC class II and B cell epitope predictive tools merit improvement, both in terms of predictive performance and, for MHC class II, in terms of coverage of species and alleles currently available. 1

They comment that most (80%) of citations of the databases are attributable to “practical applications”, which I take to mean direct use of the prediction tools (identification of epitopes in new flu strains, for example), construction of new tools (e.g. better prediction of epitopes), and maybe papers that review the databases (which is rather circular, I think).

Hearn et al 2009 FIgure 8
FIGURE 8. Aminopeptidases influence amino acid frequency N-terminal of naturally presented MHC I epitopes. Regions N-terminal to naturally processed MHC I epitopes, or selected randomly from protein pre- cursors, were identified as described in Materials and Methods. A, Probability of divergence occuring randomly (Chi2 test) vs position relative to epitope start site. B, Observed amino acid frequencies at position 1 (P1) relative to epitope start vs no divergence from back- ground (45-degree line). The amino acids that diverge +/-2 SDs from background frequency are indicated.2

The other 20% of citations are, I guess, using the databases to generate and test hypotheses.   This seems high, to me.  I don’t think I’ve seen very much basic science in immunology that builds on this sort of resource.  I think we’re reaching the point where these databases are usable to test and develop new hypotheses, though, and I hope to see more of this in the near future.

One example is our recent paper,2  where I used the IEDB to ask what influence ER aminopeptidases have on MHC class I epitopes (see the Figure to the left). (If you care, we concluded that aminopeptidases were probably most important for trimming N-terminal extensions of up to three residues, and that there was a global preference for a half-dozen amino acids and a bias against valine and, of course, proline — proline is resistant to aminopeptidase trimming in general, so that finding supported the approach.)

We weren’t the first to use this general approach (Schatz et al3 came up with the same idea independently and published before we did) but we used the IEDB, instead of the SYFPEITHI database, and were able to identify many more epitopes.   (My last run at the database coincided with the database being revised and half the search tools I needed stopped working, which was annoying, but the manager [Randi Vita] was very helpful and we managed to grind through the queries, albeit in slow motion compared to earlier runs.)

  1. Salimi, N., Fleri, W., Peters, B., & Sette, A. (2010). Design and utilization of epitope-based databases and predictive tools Immunogenetics, 62 (4), 185-196 DOI: 10.1007/s00251-010-0435-2[][]
  2. Hearn, A., York, I., & Rock, K. (2009). The Specificity of Trimming of MHC Class I-Presented Peptides in the Endoplasmic Reticulum The Journal of Immunology, 183 (9), 5526-5536 DOI: 10.4049/jimmunol.0803663[][]
  3. Schatz MM, Peters B, Akkad N, Ullrich N, Martinez AN, Carroll O, Bulik S, Rammensee HG, van Endert P, Holzhütter HG, Tenzer S, & Schild H (2008). Characterizing the N-terminal processing motif of MHC class I ligands. Journal of immunology (Baltimore, Md. : 1950), 180 (5), 3210-7 PMID: 18292545[]
January 22nd, 2010

A flood of DRiPs

"Untitled (Green Silver)” - Jackson Pollock
“Untitled (Green Silver)” – Jackson Pollock

In the past few weeks not only did I post a short update on the DRiPs hypothesis here, but coincidentally a bunch of papers on DRiPs have also been published. I’ll probably cover some of these in more detail at some point, but here are some of the recent papers and my brief comments.

Just as a reminder: the DRiPs (“Defective ribosomal products”) hypothesis proposes that most of the peptides presented to cytotoxic T lymphocytes don’t come from the actual proteins that we normally measure — rather, the immunologically relevant peptides come from deformed and defective proteins that are mis-read and misfolded during their translation. (More explanation of DRiPs here and here; more explanation of how T cells recognize cells and where peptides come in, here.)

Jon Yewdell’s insight,1 which is still somewhat controversial, was that defective proteins may actually be very common. Instead of being rare and abnormal events, he argued, protein production is a highly error-prone business, and a large fraction of newly synthesized proteins are broken. These defective products are very rapidly recycled into peptides and amino acids, and because of this rapid recycling they are the major source of peptides for T cell recognition.

On his original publication I had no problem with the underlying concept, but wasn’t overwhelmed by the data, and felt that there were too many counterexamples; since then he, and others, have put forward more and more examples, and I think it’s also fair to say that Jon has softened a little on the original hypothesis.2 I’m more or less convinced that DRiPs are one important source of peptides, though I remain dubious that they are the only, or (and here I get very uncertain) even the major source.

Anyway, in the past few weeks, we’ve seen these papers:

  • The Synthesis of Truncated Polypeptides for Immune Surveillance and Viral Evasion3

This is from Nilabh Shastri, and it’s not a big conceptual departure from some of his previous work. He’s argued for quite a while that aberrant proteins are major sources of T cell targets (see my posts here and here, for examples). Here he extends the argument to the EBNA1 protein from Epstein-Barr virus. This is a remarkably interesting protein for many reasons, one of which is that there’s reason to believe that DRiPs must be the only real source of T cell targets from EBNA1. Here, Shastri shows that in fact DRiPs (in the forms of truncated synthesis products) are in fact targets for T cells (“Thus, translation of viral mRNAs as truncated polypeptides is important for determining the antigenicity of virus proteins“). (I don’t know if it’s fair to generalize to all viral mRNAs from this very unusual protein, though.)  Very intriguingly, he also shows that DRiPs seem to be specifically blocked by EBNA1 mRNA!

Regulating production of DRiPs at the level of mRNA translation may serve as an immune evasion strategy for latent viruses. …  It is tempting to speculate that episome maintenance proteins, found in herpesviruses of various species, might have evolved to inhibit pMHC I presentation by interfering with production of DRiPs.

Is this a new viral immune evasion mechanism? And if so, how widespread is it? I know Nilabh (or someone from his lab) reads this blog occasionally, and I’d be interested in hearing their ideas on this — is it pure speculation, or do they have reason to extend the observation?

  • Viral adaptation to immune selection pressure by HLA class I–restricted CTL responses targeting epitopes in HIV frameshift sequences4
HIV-1 frameshift inducing element
HIV-1 frameshift inducing element

These authors looked at proteins produced by reading frame shifts from HIV.  Although HIV does a lot of frame-shifting “deliberately”, here we’re looking at frame-shifts that are (probably) not “real”.  That is, while it’s possible that some of these proteins have a biological function, for the most part they’re probably nonsense proteins, the product of incorrect selection of reading frames by the ribosome, and therefore you’d expect them to be recognized as improper proteins by the quality-control system and rapidly destroyed. In that sense they fit into the “DRiPs” concept. This fits neatly with Shastri’s previous work on frame-shifting, as well as providing modest support of the DRiPs concept.

The interesting thing here is that this paper offers evidence for large-scale immunological importance of peptides from frame-shifted proteins.  Shastri has previously shown convincing evidence that peptides derived from frame-shifted proteins can be recognized by T cells, but I always wondered if that was just a test-tube novelty. In this paper, though, Berger et al. argue that these frame-shifted potential targets show evidence of evolutionary selection, suggesting that they are recognized often enough to be a significant factor in the viral life-cycle.

  • CD8 T cell response and evolutionary pressure to HIV-1 cryptic epitopes derived from antisense transcription. 5

And this is a very similar paper, showing the same thing for antisense-derived peptides. Like the frame-shifted proteins discussed above, these antisense proteins would probably be nonsense and rapidly degraded — defective ribosomal products, in other words — and again, there’s some evidence that these are under immunological selection, suggesting that this recognition is a real-world phenomenon.

These findings indicate that the HIV-1 genome might encode and deploy a large potential repertoire of unconventional epitopes to enhance vaccine-induced antiviral immunity.5

  • The antiviral factor APOBEC3G improves CTL recognition of cultured HIV-infected T cells. 6

This is a particularly cool paper.7 We know that APOBEC3G — a host protein that evolved, apparently, to provide protection against infection with retroviruses such as HIV — acts by driving hypermutation of infecting retroviral genomes. HIV resists this effect through its protein vif, which in turn drives rapid degradation of several APOBECs.

But in spite of this vif-mediated protection, it’s probably true that APOBECs still have some effect on HIV, especially very early in an infection before vif can take them out; so there’s a background of mutation in HIV driven by APOBECs. This paper shows that APOBEC-driven mutation improves T cell recognition of HIV-infected cells, and the effect is probably because the mutations force HIV to make even more defective proteins, so that there are more T cell targets. This was done in rather an artificial system (mainly by either eliminating vif altogether, or by cranking up the levels of APOBEC3G artificially), so it’s not clear how important it would be in a natural infection.

I also wonder if this argues against the notion that DRiPs are normally a big factor, because if so the background of DRiP-derived peptides should be quite high and increasing it might not be a big factor; but that’s a quantitative issue that’s hard to deal with. Still, an interesting take on antiviral effects.

  • Defective Ribosomal Products Are the Major Source of Antigenic Peptides Endogenously Generated from Influenza A Virus Neuraminidase 8
"Drips" (Inger Taylor)
“Drips” (Inger Taylor)

This is the paper that most explicitly tests DRiPs, which is not surprising, since it comes from Jon Yewdell himself.9 The paper starts with quite a fair summary of the hypothesis’s status, including some of the problems with previous experiments:

In all of these studies, we used recombinant vaccinia viruses (VVs) to express SIINFEKL-containing source Ags. It is possible that we grossly overestimated the contribution of DRiPs to Ag processing in these studies due to the use of VV to express non-VV genes. We recently showed that differences in viral translation mechanisms can greatly increase the fraction of DRiPs; expression of influenza A virus (IAV) nuclear protein by an Alphavirus vector resulted in the defective translation of >50% of nuclear protein recovered from cells. VV expression is known to modify the Ag processing pathway of some inserted viral gene products compared with their natural infection context. Further, the fusion of multiple genes to create chimeric proteins can greatly decrease the fidelity of protein synthesis or protein folding …8

In an attempt to get around some of these problems, they tried to come up with a more natural system.  What they built is more natural, but still is fairly artificial (as they acknowledge); still, their findings did add more support to the basic idea. (As a sign that Jon has softened his position some in the past decade, their comment “Although DRiPs are clearly a major source of antigenic peptides, it is important to recognize that peptides are also generated from natural protein turnover” is one that I think all but the most hardened anti-DRiPers would agree with; it’s coming down to a question of quantitation, of what “major” actually means, rather than absolutes.)

I still suspect that there are cases where DRiPs are critical, and cases where they’re not particularly important, and I don’t have a good sense for how many instances of each there are. My gut feeling is about half and half, but it’s not something I’d defend with my life.

  1. Yewdell, J. W., Aton, L. C., and Benink, J. R. (1996). Defective ribosomal products (DRiPs): A major source of antigenic peptides for MHC class I molecules? J. Immunol. 157, 1823-1826[]
  2. Which has made it a bit of a moving target when it comes to disproving it, unfortunately[]
  3. Cardinaud, S., Starck, S., Chandra, P., & Shastri, N. (2010). The Synthesis of Truncated Polypeptides for Immune Surveillance and Viral Evasion PLoS ONE, 5 (1) DOI: 10.1371/journal.pone.0008692[]
  4. Berger, C., Carlson, J., Brumme, C., Hartman, K., Brumme, Z., Henry, L., Rosato, P., Piechocka-Trocha, A., Brockman, M., Harrigan, P., Heckerman, D., Kaufmann, D., & Brander, C. (2010). Viral adaptation to immune selection pressure by HLA class I-restricted CTL responses targeting epitopes in HIV frameshift sequences Journal of Experimental Medicine, 207 (1), 61-75 DOI: 10.1084/jem.20091808[]
  5. Bansal, A., Carlson, J., Yan, J., Akinsiku, O., Schaefer, M., Sabbaj, S., Bet, A., Levy, D., Heath, S., Tang, J., Kaslow, R., Walker, B., Ndung’u, T., Goulder, P., Heckerman, D., Hunter, E., & Goepfert, P. (2010). CD8 T cell response and evolutionary pressure to HIV-1 cryptic epitopes derived from antisense transcription Journal of Experimental Medicine, 207 (1), 51-59 DOI: 10.1084/jem.20092060[][]
  6. Casartelli, N., Guivel-Benhassine, F., Bouziat, R., Brandler, S., Schwartz, O., & Moris, A. (2009). The antiviral factor APOBEC3G improves CTL recognition of cultured HIV-infected T cells Journal of Experimental Medicine, 207 (1), 39-49 DOI: 10.1084/jem.20091933[]
  7. I’m presenting this one on Friday in the Immunology Journal Club I run here.[]
  8. Dolan, B., Li, L., Takeda, K., Bennink, J., & Yewdell, J. (2009). Defective Ribosomal Products Are the Major Source of Antigenic Peptides Endogenously Generated from Influenza A Virus Neuraminidase The Journal of Immunology, 184 (3), 1419-1424 DOI: 10.4049/jimmunol.0901907[][]
  9. Interestingly, it looks as if Jon has turned his attention back to influenza viruses in the past year — he cut his teeth on influenza, quite a number of years back, but it hasn’t been his main focus for a while. I guess H1N1 gave him the excuse he needed to move back that way.[]
May 22nd, 2009

On antigen processing

As my regular readers1 may have noticed, updates are a little sparse the last week or so.  The explanation is that I have an NIH grant application due at the beginning of June.  I hope to mostly finish it this weekend, but until I’m done struggling with paperwork and online submissions and budget predictions, the posts here are likely to be on the short side.

I’ll try to stall with this small version of the Nature Reviews Immunology antigen processing (link here [pdf], but I don’t know if it’s open-access) from Wearsch and Cresswell.

Nature Reviews Immunology Antigen Processing poster

  1. Who know what’s supposed to go in this footnote[]
December 12th, 2008


Langerhans cells in the skin
Dendritic cells in the skin (Langerhans cells) form a dense network of “sentinels” that act as first line of defense of the immune system.1

What happens when a pathogen invades us? Well, lots of things happen, of course. Early on, there are innate immune responses; generic pathogen-like aspects of the pathogen trigger a relatively stereotyped immune response. Parts of this innate immune response then connect the pathogen features to the adaptive immune response (T cells and B cells), and in a few days there should be a much larger and more focused (pathogen-specific) immune response.

This link between the innate and the adaptive immune response is most often made by dendritic cells (DC). DC hang out in tissues all over the body, forming a net that constantly filters stuff in the tissues (see the image to the left). Almost all the time (one hopes) these DC don’t run into any pathogen signatures; and in that case, they just continue to hang out and filter some more. When a DC does run into something that’s associated with a pathogen (such as, say, lipopolysaccharide, LPS, a part of some bacterial cell walls) then the DC changes and enters a new program designed to efficiently interact with T cells.

There’s been no obvious reason to suspect that antigen presentation is connected to movement of the dendritic cell. But a paper in today’s issue of Science2 shows that in fact the two are tightly linked, because the same protein helps regulate both of them. This protein is the invariant chain (also known as Ii), and it’s been known for years that it’s important in antigen presentation; the details of that are well worked out. The new, and really surprising, finding is that Ii also helps control movement of dendritic cells (and probably other cells, such as B cells, that also have Ii), by interacting with myosin II. The authors show that Ii acts as a brake on DC movement, and this brake is released when (as a part of its normal antigen presentation function) Ii is partially destroyed.

The use of common regulators for Ag processing and cell motility provides a way for DCs to coordinate these two functions in time and space. In immature DCs that patrol peripheral tissues, the periodic low motility phases induced by Ii may enable DCs to efficiently couple Ag uptake and processing to cell migration, facilitating the sampling of the microenvironment.  2

Dendritic cellThe concept makes sense; the DC would want to look more closely for antigens in an area they’d just arrived in, rather than in somewhere they’ve already sampled for a while. One interesting implication, I think, is that antigen presentation, like the movement that they show, may be episodic, happening in bursts rather than in a continuous conveyer belt. We already knew that the conveyer belt was jerky on a larger scale, but I think this suggests that it’s on and off on a much finer scale than has been previously shown (as far as I know). I have some interesting data on a different type of antigen presentation that would fit with this model, so I’ve been wondering for a while about looking for jerkiness in antigen presentation anyway, and maybe this reinforces that notion.

By the way, the paper has some cute movies of dendritic cells in little runways, chugging down the lines like little trains, including the DC’s occasional stops and reversals like a train that’s passed the passenger loading area and has to back up.

  1. Tolerogenic dendritic cells and regulatory T cells: A two-way relationship. (2007) Karsten Mahnke, Theron S. Johnson, Sabine Ring and Alexander H. Enk.  J of Derm Sci 46:159-167 doi:10.1016/j.jdermsci.2007.03.002 []
  2. Gabrielle Faure-André, Pablo Vargas, Maria-Isabel Yuseff, Mélina Heuzé, Jheimmy Diaz, Danielle Lankar, Veronica Steri, Jeremy Manry, Stéphanie Hugues, Fulvia Vascotto, Jérôme Boulanger, Graça Raposo, Maria-Rosa Bono, Mario Rosemblatt, Matthieu Piel, Ana-Maria Lennon-Duménil (2008). Regulation of Dendritic Cell Migration by CD74, the MHC Class II-Associated Invariant Chain Science, 322 (5908), 1705-1710 DOI: 10.1126/science.1159894[][]
May 21st, 2008

Microevolution and bottlenecks: HIV transmission

HIV (Wellcome Images)“All politics is local” may be a cliche; but “All evolution is local” is at least equally true, and is a more interesting concept to geeks like me. A mutated virus may spread through a population, but it starts somewhere. What are the bottlenecks, what are the resources that evolution can draw on, what are the checks or drivers of transmission and spread?

It’s only relatively recently, though, that the techniques to properly measure microevolution of viruses have become generally available; genomic sequencing of reasonably large chunks of virus has become fast and cheap enough that very small changes in virus sequence can be used to track evolution over short times and distance. I talked about using this to track the foot and mouth disease outbreak in England in 2007. Two recent studies look at microevolution to analyze bottlenecks and transmission, in quite different contexts: Dengue virus circulation in schools, and HIV transmission between individuals. I’ll talk about the dengue study some other time; here I only have room for the Keele et al paper.1

I’ve talked a fair bit (like here and here) about the evolution in situ that HIV undergoes in each of its hosts, evolving rapidly in response to local conditions such as immune responses. Much of the variation in the infected individual is selected, of course; selected in response to local conditions — those local conditions being the genetics of the host, and to a large extent the host’s immune system. The immune system puts tremendous pressure on the virus, and the only way it can escape from the prison is to cripple itself. HIV immune escape variants are usually relatively defective viruses, because the mutations that allow them to become invisible to the immune system, damage the virus’s ability to replicate and spread.

HIV assembling in a macrophage
HIV infecting a macrophage2

Immune systems are idiosyncratic; yours is different than mine. When HIV is transmitted from one person to another the virus moves from one selection immune landscape to a very different one. The mutations that saved it in the first person are now probably no longer protective, yet the damage that those mutations were doing is still very much present – a double whammy. Fortunately for HIV (less so for humanity) it is still able to mutate its way back to a functional virus. If you examine HIV in one individual and then in the next step in the transmission chain, the new host’s virus will probably have started to revert back to the platonic essence of HIV; less, of course, the mutations that the new host’s immune system imposes on it.

A question is: What does the virus have to work with in this process? We speak of HIV as a quasi-species, traveling around as a cloud of related but different viruses. Within that cloud is variation that can be immediately selected. But is that true in transmission? How many viruses actually take that gigantic leap from one host to the next?

This is important for a couple of reasons. The big one is vaccination. One of the huge obstacles to HIV vaccination is variation; not only within a population, but within an individual. Let’s say you are vaccinated and protected against the most common HIV variant. If you are infected only with a handful of viruses, you will probably shut them down. But if you’re infected with a cloud of many different viruses, somewhere in that cloud will be a resistant virus; the chance of protection have gone way down. Which of those is actually what happens?

Keele et al 2008 Fig 2That’s the question that Keele1 (and a cast of thousands, or at any rate another 36 authors; particle phsyics authorship on a biology paper) asked, examining thousands of virus samples (just one gene, not the whole genome; but based on single virus genomes, not pooled genomes from the virus cloud) from over 100 patients shortly after they were newly infected with HIV. They used a mathematical model (which I am for now going to accept on faith; with two grants due in the next two weeks, I don’t have time to sit down and work through the math) to consider three different possibilities:

  1. A cloud of viruses is transmitted;
  2. A limited number of viruses is transmitted out of the cloud;
  3. A cloud of viruses is transmitted, but is rapidly thinned down to become a limited number of viruses, due to selection within the new host.

They concluded that in fact the second possibility was true: Only a very small number of viruses manage to establish a foothold in the hostile new terrain:

we found that 78 (76%) had evidence of infection by a single virus or virus-infected cell and that 24 others (24%) had evidence of infection by at least two to five viruses. Aside from early selection of CTL escape variants found in several subjects, there was no suggestion of virus adaptation to a more replicative variant or bottlenecking in virus diversity preceding peak viremia. … we interpret the findings of low multiplicity infection and limited viral evolution preceding peak viremia to suggest a crucial but finite window of potential vulnerability of HIV-1 to vaccine-elicited immune responses.1

(My emphasis)

They also noted particular characteristics of the successful viruses, though I think their study, not being specifically designed for this, wasn’t able to come up with any surprises. But of course this opens the door to this further question: What’s special about these tiny few viruses that are successful transmitters, compared to the thousands or millions of other variants circulating in the original host? Are they just lucky little viruses, or are these the only ones that have some unique qualification for spread? And if so, can we take advantage of that quality to block spread?

  1. Keele BF, Giorgi EE, Salazar-Gonzalez JF, Decker JM, Pham KT, Salazar MG, Sun C, Grayson T, Wang S, Li H et al. (2008) Identification and characterization of transmitted and early founder virus envelopes in primary HIV-1 infection. Proc Natl Acad Sci U S A doi:10.1073/pnas.0802203105[][][]
  2. Gross, L., 2006. Reconfirming the Traditional Model of HIV Particle Assembly. PLoS Biology, 4(12), p.e445 EP []
April 7th, 2008

Non-cytotoxic cytotoxic T lymphocytes

Hepatitis (Wellcome)

(Spending a few days in Toronto with my kids, so I can go to the Darwin exhibit at the Royal Ontario Museum.1 Depending on when I get back, my next blog post may be a little delayed.)

Even though cytotoxic T lymphocytes are called “cytotoxic”, it was no surprise when new techniques in the mid-90s suggested that CTL might have other strings to their bows. (I talked about this the other day.) Some experiments were also pointing the same way. For example, Frank Chisari’s group offered evidence that CTL might be able to control hepatitis B virus without actually being cytotoxic.

Hepatitis B virus was a particularly difficult case for study in general, because it’s pretty much a human-specific virus. (HBV is a pain in tissue culture. There are a couple of animal models, but they’re hardly convenient. I mean, ducks? Woodchucks?) From studies of the natural disease, it seemed pretty clear that natural HBV infection was often handled by CTL; people who control HBV show a strong CTL response, those in whom HBV progresses and becomes chronic usually do not. But testing the function of CTL in HBV infection wasn’t easy without an animal model.

So Chisari made an animal model. He made transgenic mice that express the HBV genome (that is, the mouse genome contained the HBV genome) under a liver-specific promoter.2 There you go, hepatitis B virus in the mouse liver. It can’t spread and infect new cells as would normally happen in humans, but on the other hand you’re expressing the virus in essentially all the liver cells anyway, so you don’t need to infect new cells.

Hepatitis B virus-transgenic mice

Of course, the “virus” is “self” under those conditions, meaning that the immune system doesn’t react to it. So Chisari’s group immunized other mice to raise HBV-specific CTL, and transferred the CTL into the transgenic mice.3 These HBV-specific CTL did two things: They apparently controlled the virus “infection”, and they damaged the liver.4 The presumption was that the two findings were the same effect, and the CTL were trying to eliminate virus by killing liver cells: “Our data show that antigen-specific immune effector mechanisms can destroy HBV-positive hepatocytes in vitro and in vivo … “.

Hepatitis viruses (Wellcome)
Hepatitis viruses

Six years later, though, they were suggesting the opposite, demonstrating that in fact CTL were controlling the “virus” through a non-cytolytic mechanism.5 (Actually, they showed very similar data a couple of years earlier than that,6 but the 1996 paper went further with the mechanisms and was overall more solid.) Essentially, they demonstrated that CTL were shutting down virus expression in the liver by releasing interferon and other cytokines. There is some liver damage, but it’s not necessarily because of direct cytotoxity; it’s a side-effect of the cytokines, not directly related to the viral clearance. (Recently, it’s been proposed that the CTL make a decision which route to go — cytotoxicity vs. cytokine-mediated control — based on the amount of virus antigen. 7 This might offer a way to manipulate this and drive the response to the most effective system in other infections, though it’s far from trivial to adjust amount of viral antigen.)

Non-cytotoxic control of HBV

So what? What difference does it make how the CTL are clearing virus, so long as they do clear it? Here are some reasons to care:

  • This way a few CTL can affect many target cells. There are some 1011 hepatocytes, and hepatitis viruses can infect a lot of them, very fast. There are different estimates for how long it takes for a CTL to deliver a lethal hit when it’s being cytotoxic, but let’s say something like 30 – 60 minutes per cell. That’s a long, long time for CTL to kill off all the infected cells. If all they have to do is release interferon, they can probably pick off many cells at once and move on. This is a faster way to control viruses, and it doesn’t take as many CTL. Guidotti et al transferred 5 x 106 HBV-specific CTL into the transgenic mice; within 24 hours, all of the virus had been shut down (remember. these are transgenic mice expressing “virus” in every liver cell).
  • Again: There are some 1011 hepatocytes, and hepatitis viruses can infect a lot of them, very fast. If CTL have to kill all the virus-infected cells, what’s left of the liver to do it’s usual liverly duties? If the infection can be shut down without killing the cells, you’ve saved your liver. Those 5 x 106 CTL shut down the virus without killing more than 10% of the liver cells.
  • Potentially, any inflammation in the liver can lead to protection. If you have HBV, and you’re infected by lymphocytic choriomeningitis, (well, first you’d likely be a mouse), the inflammation induced by the LCMV might shut down the HBV, as a handy side effect. This cross-protection from other viruses has actually been seen both in mice8 and — maybe — in humans as well.9

So the finding that CTL are capable of shutting down virus replication without having to kill the infected cells, fit very nicely with the new technology showing that CTL commonly have these mechanisms available.

  1. Also I promise it’s a complete coincidence that my Red Sox were playing the Blue Jays here last few days. The Jays gave them a whuppin’, but William and I had a fine time at the ballpark anyway.[]
  2. Chisari, F. V., Pinkert, C. A., Milich, D. R., Filippi, P., McLachlan, A., Palmiter, R. D., and Brinster, R. L. (1985). A transgenic mouse model of the chronic hepatitis B surface antigen carrier state. Science 230, 1157-1160.[]
  3. As you can see, the system is pretty elaborate, and I’ve never really felt all that comfortable with it — just too Rube Goldberg-ish for my liking — even though there’s nothing specific I can point to; and Chisari’s group is conscientious about their controls.[]
  4. Moriyama, T., Guilhot, S., Klopchin, K., Moss, B., Pinkert, C. A., Palmiter, R. D., Brinster, R. L., Kanagawa, O., and Chisari, F. V. (1990). Immunobiology and pathogenesis of hepatocellular injury in hepatitis B virus transgenic mice. Science 248, 361-364.[]
  5. Guidotti, L. G., Ishikawa, T., Hobbs, M. V., Matxke, B., Schreiber, R., and Chisari, F. V. (1996). Intracellular inactivation of the hepatitis B virus by cytotoxic T lymphocytes. Immunity 4, 25-36.[]
  6. Guidotti, L. G., Ando, K., Hobbs, M. V., Ishikawa, T., Runkel, L., Schreiber, R. D., and Chisari, F. V. (1994). Cytotoxic T lymphocytes inhibit hepatitis B virus gene expression by a noncytolytic mechanism in transgenic mice. Proc Natl Acad Sci U S A 91, 3764-3768.
    Tsui, L. V., Guidotti, L. G., Ishikawa, T., and Chisari, F. V. (1995). Posttranscriptional clearance of hepatitis B virus RNA by cytotoxic T lymphocyte-activated hepatocytes. Proc Natl Acad Sci U S A 92, 12398-12402.[]
  7. Gehring, A. J., Sun, D., Kennedy, P. T., Nolte-‘t Hoen, E., Lim, S. G., Wasser, S., Selden, C., Maini, M. K., Davis, D. M., Nassal, M., and Bertoletti, A. (2007). The level of viral antigen presented by hepatocytes influences CD8 T-cell function. J Virol 81, 2940-2949.[]
  8. Guidotti, L. G., Borrow, P., Hobbs, M. V., Matzke, B., Gresser, I., Oldstone, M. B., and Chisari, F. V. (1996). Viral cross talk: intracellular inactivation of the hepatitis B virus during an unrelated viral infection of the liver. Proc Natl Acad Sci U S A 93, 4589-4594.[]
  9. Thio, C. L., Netski, D. M., Myung, J., Seaberg, E. C., and Thomas, D. L. (2004). Changes in hepatitis B virus DNA levels with acute HIV infection. Clin Infect Dis 38, 1024-1029.[]
March 17th, 2008

Controlled TReg production

Saints Cosmas and Damian performing a miraculous cure by transplantation of a leg/The Master of Los Balbases.I’ve previously posted on regulatory T cells (TRegs) and their potential role in transplants. Briefly, TRegs are capable of specifically shutting off immune responses to particular antigens; they’re normal components of an immune system. TRegs can be damaging in some contexts — for example, in cancer, where it seems that TRegs often shut off immune responses to tumors, so that the tumor can escape immune clearance; and they can be beneficial in other context — for example, in some persistent virus infections, where a chronic immune response would be damaging, TRegs apparently modulate the immune response so that the virus persists but doesn’t cause severe damage.

There are a couple of obvious scenarios where it would be nice to be able to control TRegs. There’s a lot of interest in reducing TReg activity in cancer, such as with CTLA4 antagonists. There’s also a lot of interest in increasing TReg activity in organ transplants, and there have actually been a couple of cases where it’s seemed to have worked.

A recent paper in PNAS1 offers steps toward a more general procedure, that could in theory lead to controlled, planned generation of TRegs for any antigen.

A key aspect of TRegs is that they are antigen-specific. They don’t randomly suppress immune responses; they identify particular antigens that should be tolerated, and shut off immunity to those antigens. That allows fine control over the response, but it also makes it harder to catch a TReg; T cells (not just TRegs) that recognize any particular antigen are very rare events, hiding in a blizzard of other specificities. What if you could force T cells for an antigen you choose to enter the TReg pathway?

Regulatory T cells (J Clin Invest cover)This has already been done, in fact, but in a very artificial system — in mice with transgenic T cell receptors. These mice overwhelmingly express a single TcR in all of their T cells — there’s no snowflake in a blizzard problem, because the entire blizzard is made of identical flakes. Harold von Boehmer’s group has shown that you can drive these transgenic T cells into the TReg pathway by offering very, very low levels of antigen, under defined conditions, over a long period. 2 The recent paper1 shows that you can do the same thing in normal, non-transgenic, mice; and by doing this you can force graft tolerance. (They used female mice and drove tolerance to the male antigen H-Y antigen. The tolerized female mice then became tolerant of male grafts, while the control female mice rejected the male grafts.)

The key, at least for this particular protocol, seems to be to use very low dose antigen and “suboptimal” conditions (where “optimal” refers to conditions that drive conventional immune responses. The vocabulary of immune responses is really kind of misleading, because it’s focused on easily-measured responses like protection against viruses or graft rejection. Regulatory T cell responses are just as active, and probably are just about as common and important, but it’s hard to talk about them without giving the impression that they’re somehow passive, or abnormal, or defective).

One problem with moving this into the clinic is that you would need to know what the target antigen is, which in an outbred population like humans you do not know a priori. However, as bioinformatic and experimental techniques for identifying antigen peptides improve, it may become more practical to run this for patients before their transplants. The potential payoff would be very high, because you might be able to remove immunosuppression altogether:

If a procedure as simple as peptide infusion, which permits de novo induction of Tregs from mature T cells, prevents transplant rejection or GVHD, it could offer a realistic opportunity to induce tolerance to a variety of antigens such as allergens, transplantation antigens, and antigens causing autoimmunity while minimizing undesirable side effects often associated with general immunosuppression.

  1. Verginis, P., McLaughlin, K.A., Wucherpfennig, K.W., von Boehmer, H., Apostolou, I. (2008). Induction of antigen-specific regulatory T cells in wild-type mice: Visualization and targets of suppression. Proceedings of the National Academy of Sciences, 105(9), 3479-3484. DOI: 10.1073/pnas.0800149105[][]
  2. Kretschmer, K., Apostolou, I., Hawiger, D., Khazaie, K., Nussenzweig, M. C., and von Boehmer, H. (2005). Inducing and expanding regulatory T cell populations by foreign antigen. Nat Immunol 6, 1219-1227.
    Apostolou, I., and von Boehmer, H. (2004). In vivo instruction of suppressor commitment in naive T cells. J Exp Med 199, 1401-1408.[]
February 17th, 2008

Classic paper: Presentation from ER proteins

Endoplasmic reticulum I have a very sporadic and idiosyncratic series in which I talk about “classic papers”, and in my idiosyncratic series Vic Engelhard’s paper on tyrosinase processing counts as a classic paper. It was one of the early indications that proteins in the ER must be degraded in the cytosol, and as such it’s one of a number of ways that antigen presentation has helped fundamental understanding of cell biology; but I think it hasn’t received as much recognition as it could have.

But perhaps I should begin at the beginning.

Proteolysis is a normal part of cell function. Proteins that are damaged, misfolded, or mis-translated, as well as proteins that have simply reached the end of their useful life, are degraded and converted to amino acids that can be recycled into new proteins. In the early- to mid-1990s, there were three general systems that were known to degrade proteins, depending on which subcellular compartment they were in:

  • In the cytosol and nucleus, proteins are predominately degraded by proteasomes.
  • Proteins taken up from the exterior of the cell can be degraded in acidic lysosomes
  • Mitochondrial proteins are degraded by a number of proteases within the mitochondrion1

Endoplasmic reticulum (pancreas cell)That leaves an obvious gap. What happens to proteins that are in the endoplasmic reticulum (ER)? This is particularly relevant because the ER is a ferociously active site of protein synthesis, folding, and assembly; when any of those steps goes awry, the protein is supposed to be degraded, a process known as “quality control”. It was clear in the 1980s that proteins that failed quality control in the ER were degraded; in human cells, a well-known example was the cystic fibrosis transmembrane conductance regulator (CFTR), which folds inefficiently and is rapidly degraded2. But it was not clear where the degradation happened (in the ER? The cytosol? Somewhere else?), and what proteases were responsible.

At first it was believed that “what happens in the ER stays in the ER” — ER proteins were degraded in the ER, by ill-defined proteases in that compartment. But I don’t think there was much enthusiasm for that belief, and basically, the field was a mess, as you can see from this introductory paragraph from the time:3

Other membrane proteins are also known to be degraded at the ER, but the process is poorly understood, and the responsible enzymes have not been identified. For example, some of these proteolytic events are ATP dependent, but some are not; some occur within the lumen while others take place on the cytoplasmic side; some exhibit inhibitor sensitivities characteristic of serine proteases, whereas others do not.

This was relevant to me as an immunologist, because viral glycoproteins (which, of course, are synthesized in the ER) are popular targets for cytotoxic T lymphocyte (CTL) recognition (a quick review of MHC class I antigen presentation is here). We knew in the early 1990s that most if not all CTL targets — even those derived from ER proteins — were generated in the cytosol. As I wrote in a 1996 review:4

Proteins targeted into the ER by signal sequences can also be presented on MHC I molecules. Since these molecules are cotranslationally transported into the endoplasmic reticulum, they might be expected to bypass hydrolysis in the cytosol. However, where analyzed, the presentation of most of these antigens is dependent on the TAP-transporter and on proteasome activity, and therefore the presented peptides are probably being generated in the cytosol.

The three obvious possible explanations were that either the putative glycoprotein never made it into the ER and was degraded as a mistargeted protein (Jon Yewdell would call that a “DRiP”; I can’t remember exactly when I heard him propose that first, but it was around that time); that the glycoprotein went in to the ER, got degraded there, and the peptides were first transfered to the cytosol; or that the glycoprotein was transfered from the ER to the cytosol and degraded there.

The simplest explanation, at the time, seemed to be the first one –  proteins never made it in to the ER, and were degraded in the cytosol. However, it wasn’t an explanation that we liked very much, for various reasons. Vic Engelhard’s paper (Remember Engelhard’s paper? This here’s a post about Engelhard’s paper) cleared that question up, at least for one epitope.

Skipper, J. C., Hendrickson, R. C., Gulden, P. H., Brichard, V., Van Pel, A., Chen, Y., Shabanowitz, J., Wolfel, T., Slingluff, C. L., Jr., Boon, T., Hunt, D. F., and Engelhard, V. H. (1996). An HLA-A2-restricted tyrosinase antigen on melanoma cells results from posttranslational modification and suggests a novel pathway for processing of membrane proteins. J. Exp. Med. 183, 527-534.

His finding is simple enough to describe, although it relied on a technically very difficult mass spec analysis:

  1. A peptide presented on MHC class I was derived from an ER protein (which they knew from its sequence);
  2. the protein had actually gone into the ER, because it had been N-glycosylated, which only happens in the ER;
  3. yet the peptide itself was probably generated in the cytosol, because enzymes that modified it are mainly found in the cytosol.

Mumps virus protein (turquoise) in endoplasmic reticulumThe most surprising and exciting part was the second point: Clear evidence that the protein had actually gone into the ER before the peptide was generated. 5 This wasn’t by any means definitive proof that ER proteins are degraded in the cytosol (a follow-up paper in 19986 took it a bit further) but it certainly was suggestive.

If Vic’s paper had come out a year or two earlier, it would probably have made much more of a splash than it did, but in February of 1996 it was only a nose ahead of several more focused papers. The field had started to clear up in the mid-1990s, with some observations in yeast in 1993 and 1994 7 and around 1995 moving into mammalian cells with (among others) the Jensen et al. paper I quoted above. 8  And later in 1996, the iceberg tipped over altogether, with a whole bunch of almost simultaneous papers that showed quite clearly that ER degradation wasn’t done by ER proteases at all, but was in fact performed by proteasomes. 9 All the papers demonstrated that there’s an export step before degradation: ER proteins that fail quality control are shunted out into the cytosol, where the proteasomes can grab onto them and chop them up. (The export step is still not all that well understood in molecular detail, though in 2007 it started to open up some, I think.)

Though Engelhard’s 1996 paper is reasonably widely cited (256 citations as I write this) it clearly didn’t have the impact on cell biology in general that it did on me, probably because it came out around the same time as a bunch of more specific papers. This blogpost is an attempt to give a bit more retroactive credit to a very nice example of logical reasoning from indirect evidence.

  1. I believe that some, though not all, of the mitochondrial proteases were identified in the late 1980s/early 1990s, and that the broad outline of mitochondrial proteolysis was understood in the early 1980s (Desautels, M. and Goldberg, A. L. (1982) Liver mitochondria contain an ATP-dependent, vanadate-sensitive pathway for the degradation of proteins. Proc Natl Acad Sci USA 79 , pp. 1869-1873.). I don’t know all that much about mitochondrial proteolysis, though, so if someone wants to correct me, please do so.[]
  2. For example, Ward C, Kopito R (October 14, 1994) Intracellular turnover of cystic fibrosis transmembrane conductance regulator. Inefficient processing and rapid degradation of wild-type and mutant proteins. J. Biol. Chem. 269.:25710-25718[]
  3. Taken from Jensen TJ, Loo MA, Pind S, Williams DB, Goldberg AL, et al. (October 6, 1995) Multiple proteolytic systems, including the proteasome, contribute to CFTR processing. Cell 83:129-35. with references removed[]
  4. York, I. A., and Rock, K. L. (1996). Antigen processing and presentation by the class I major histocompatibility complex. Annual review of immunology Annu Rev Immunol 14, 369-396.[]
  5. Technical explanation: The mass spec analysis showed that the asparagine that is encoded in the DNA was actually an aspartic acid in the presented peptide; deglycosylating enzymes that were believed to only be present in the cytosol remove carbohydrates from Asn to generate Asp.[]
  6. Mosse, C. A., Meadows, L., Luckey, C. J., Kittlesen, D. J., Huczko, E. L., Slingluff, C. L., Shabanowitz, J., Hunt, D. F., and Engelhard, V. H. (1998). The class I antigen-processing pathway for the membrane protein tyrosinase involves translation in the endoplasmic reticulum and processing in the cytosol. J Exp Med 187, 37-48.[]
  7. (Sommer T, Jentsch S (September 9, 1993) A protein translocation defect linked to ubiquitin conjugation at the endoplasmic reticulum. Nature 365.:176-9.
    Kölling R, Hollenberg CP (July 15, 1994) The ABC-transporter Ste6 accumulates in the plasma membrane in a ubiquitinated form in endocytosis mutants. EMBO J 13.:3261-71.[]
  8. Jensen TJ, Loo MA, Pind S, Williams DB, Goldberg AL, et al. (October 6, 1995) Multiple proteolytic systems, including the proteasome, contribute to CFTR processing. Cell 83:129-35. []
  9. I may be missing some:
    Hampton RY, Gardner RG, Rine J (December 1996) Role of 26S proteasome and HRD genes in the degradation of 3-hydroxy-3-methylglutaryl-CoA reductase, an integral endoplasmic reticulum membrane protein. Mol Biol Cell 7.:2029-44.
    Werner ED, Brodsky JL, McCracken AA (November 26, 1996) Proteasome-dependent endoplasmic reticulum-associated protein degradation: an unconventional route to a familiar fate. Proc Natl Acad Sci U S A 93.:13797-801.
    Hiller MM, Finger A, Schweiger M, Wolf DH (September 20, 1996) ER degradation of a misfolded luminal protein by the cytosolic ubiquitin-proteasome pathway. Science 273.:1725-8.
    Qu D, Teckman JH, Omura S, Perlmutter DH (September 13, 1996) Degradation of a mutant secretory protein, alpha1-antitrypsin Z, in the endoplasmic reticulum requires proteasome activity. J Biol Chem 271.:22791-5.
    Wiertz EJ, Jones TR, Sun L, Bogyo M, Geuze HJ, et al. (March 8, 1996) The human cytomegalovirus US11 gene product dislocates MHC class I heavy chains from the endoplasmic reticulum to the cytosol. Cell 84.:769-79.[]
November 9th, 2007


Ephemeroptera (Mayfly)

One of the questions in antigen processing is what happens to peptides between the time they’re generated, and the time the they bind to MHC class I.

(The reason we care about peptides and MHC is that antiviral lymphocytes react with a complex of peptides and MHC class I, so this is a central point for antiviral immunity. Peptides are formed as a byproduct of normal protein degradation; an outline of the process, should you care, can be found here.)

In general, the peptides we’re interested in are produced by proteasomes. A protein (say, 500 amino acids long) enters the proteasome, the protein is chopped up, and peptides (between 3 and 30 amino acids long) come out. Almost all of those peptides are further chopped up, to produce amino acids – recycling and replenishing the amino acid pool for new protein synthesis. A small fraction of the peptides (perhaps between 0.01% and 1%), though, escape destruction and manage to bind to MHC class I. We would like to know more about that fraction of peptides, because they drive the lymphocyte attack on virus-infected cells. Why are they not destroyed — is it pure chance, or is there something special about the peptides that are not destroyed? How do they reach the MHC — is it chance again, just random diffusion, or is there some kind of specialized shuttle system that ferries the peptides to the proper subcellular location? Is there any active process modifying the peptides, to make them more (or less) suitable for binding MHC? And so on.

The problem is that it’s really hard to look at those peptides. Ideally, we’d like to grab samples of peptides at every point in the process: Exiting the proteasome, in transit, being degraded and processed, and so on. Then we could analyze what they’re like at each step, and develop a time course of modifications, interactions, and so on. But we can’t do that (yet), because it’s really difficult to measure peptides within a living cell.

A couple of years ago Jacques Neefjes (who always turns out cool papers) put some numbers on just how difficult is is.1

Blogging on Peer-Reviewed ResearchThere were a whole bunch of really cool things about this paper, but just focusing on one: Neefjes’ group came up with a way of measuring the rate of peptide destruction in living cells. They added a fluorescent tag to peptides in such a way that it would only fluoresce when the peptide was degraded; injected the tagged peptides into single cells; and measured (again in single cells) the rate at which the fluorescence appeared.

The injected peptides were destroyed with a half-life of 7 seconds. That is, a single cell can destroy hundreds of thousands, or millions, of peptides within a few seconds. (Most of this destruction, by the way, is performed by aminopeptidases, which are very abundant in cytosol.)

That’s not a long time, and it doesn’t give any individual peptide much chance to find its potential MHC binding partner. “A peptide will thus diffuse through the entire cell in 6 s and has to find TAP within this short period for translocation into the ER lumen.”

Why so fast? Why is the cell so worried about letting peptides hang about? Well, we presume this is because peptides are potentially very toxic. These peptides are generated, pretty much randomly, from active proteins. The peptides will therefore include short chunks of active protein domains, separated from any regulatory context; they could conceivably have biological activities by themselves. Also, you’d get hydrophobic chunks that could cluster into degradation-resistant clumps, if you let them accumulate, and it’s believed that such degradation-resistant complexes are themselves toxic. So you need to get rid of peptides fast, before they accumulate to form dangerous side-effects.

As a result, we antigen processing guys have to pretty much guess and use roundabout, indirect methods to measure peptides. Keeps us off the streets, I guess.

  1. Reits, E., Griekspoor, A., Neijssen, J., Groothuis, T., Jalink, K., van Veelen, P., Janssen, H., Calafat, J., Drijfhout, J. W., and Neefjes, J. (2003). Peptide diffusion, protection, and degradation in nuclear and cytoplasmic compartments before antigen presentation by MHC class I. Immunity 18, 97-108 .[]
October 30th, 2007

Rube Goldberg and hypersensitivity: Frame-shifting, part II

Rube Goldberg machineAntigen processing is not only interesting and important in itself,1 but it’s been used extensively to tease apart fundamental cell biology — things like protein folding, intracellular proteolysis, protein trafficking, and ER-associated degradation have been identified or studied via antigen processing. There are a bunch of reasons why MHC has been such a Swiss army knife of cell biology. One of the reasons is that MHC can amplify a tiny, tiny signal into a blatant, unmistakable readout.

That’s because cytotoxic T lymphocytes recognize MHC/peptide combinations, recognize it incredibly well, and respond with easily-observed events. CTL can recognize as few as 10 (maybe fewer) specific peptides per cell, even though for every one of those peptide/MHC complexes there are ten thousand other complexes with other peptides, smothering it. And CTL respond by destroying the cell, which gives you a simple, black-and-white, binary outcome.

It’s obviously useful to have a highly sensitive2 readout. But it’s a curse as well as a blessing. In particular, because the outcome is binary (alive or dead) it’s really hard to get quantitative information out of the system. Once you’re over the very low threshold, everything is positive.

What this means is that detecting something with a CTL readout doesn’t tell you if that something is common, unusual, rare, or sui generis. CTL readouts over the years have demonstrated the existence of events that (I believe) are really very unusual — they aren’t representative of “normal” cell biologic processes, but rather represent the far end of the curve, things that, yeah, can happen, but have to be pushed. For example, there’s proteasome splicing : biochemically a really cool phenomenon, that got picked up by CTL readouts — but it’s really not likely that it happens very often, or is a real player in the normal function of the cell.3

On the other hand, of course, some things that have turned out to be common and important processes were identified up in the same ultrasensitive way. For example, exactly this sort of thing turned out to be a very early demonstration of ER-associated degradation,4 which is now known to be a major and critical pathway.

HIV-1 frameshift inducing element
HIV-1 frameshift inducing element

So — following on from my post earlier this week — the immediate question that comes to mind when Nilabh Shastri’s group publishes about frame-shifted epitopes5 is whether this is a major, common phenomenon, or it is the end-product of a Rube Goldbergesqe sequence of events that isn’t going to happen very often?

Shastri has long been a fan of the idea that frame-shifting — reading proteins from abnormal start sites, or by hiccups during translation — could be a common source of antigenic peptides (epitopes). In his latest paper, he demonstrates a frame-shift epitope from HIV; he and some other groups have demonstrated frame-shift epitopes before, but those were mostly fairly minor, and were easy to ignore. This example seems to be a relatively potent epitope, and is harder to ignore. Are frame-shifts common in the cell? Are they common sources of CTL epitopes?

ResearchBlogging.orgInterestingly, they identified the epitope by bioinformatic analysis of a known frame-shift product. (In other cases, the identification went the other way around, from identifying the epitope sequence to the frame-shifted precursor.) This raises one point: If frame-shifted proteins really are common sources of CTL epitopes, then for one thing the task of the bioinformatician is six times harder, because they will have to survey all six reading frames, not just the known proteins, of a viral genome, to look for epitopes. But (for all the criticism I’ve leveled at epitope prediction software) that doesn’t seem to be a major factor; predictions do find epitopes (however inefficiently) and they find them in true proteins.

In the small handful of cases where a full CTL response to a virus has been analyzed fairly completely — that is, where almost all the epitopes recognized the CTL have been identified — they almost all have been identifiably from authentic viral proteins.6 That said, there are some that haven’t been identified yet; for example, in mouse cytomegalovirus a number of epitopes remain unmapped,7 and might be from frame-shifted precursors.

Proponents of the unconventional precursors argue that many MHC-associated peptides (identified by mass spectrometry, for example) don’t have an authentic protein precursor in the various databases. I think that’s true, but far more are identifiable (I don’t know the ratio of identifiable to unidentifiable, though), and most of the anonymous ones probably represent, say, un-sequenced alleles or something like that.

Overall, I think the bulk of the findings from epitope identification really argue that things like frame-shifted epitopes, or proteasome-spliced epitopes, or non-ATG-initiated epitopes — things that we think should be rare, based on what we know about cell biology — really are rare. The fact that they do appear and can be captured by this exquisitely sensitive8 system, probably goes to show that there is more slop in the system than is often believed — more aberrant, defective products sneak through into RNA and protein than is really appreciated, and in all likelihood error correction is just as important as error prevention in normal cell function.

  1. And people who research antigen processing are invariably suave, attractive, and charming. Well-known fact![]
  2. I’m desperately trying to avoid saying “exquisitely sensitive” here, because every paper and review on the subject calls it “exquisitely sensitive”[]
  3. I reserve the right to deny I ever said this, if proteasome splicing ever turns out to be important.[]
  4. Skipper, J. C., Hendrickson, R. C., Gulden, P. H., Brichard, V., Van Pel, A., Chen, Y., Shabanowitz, J., Wolfel, T., Slingluff, C. L., Jr., Boon, T., Hunt, D. F., and Engelhard, V. H. (1996). An HLA-A2-restricted tyrosinase antigen on melanoma cells results from posttranslational modification and suggests a novel pathway for processing of membrane proteins. J. Exp. Med. 183, 527-534.[]
  5. Maness, N. J., Valentine, L. E., May, G. E., Reed, J., Piaskowski, S. M., Soma, T., Furlott, J., Rakasz, E. G., Friedrich, T. C., Price, D. A., Gostick, E., Hughes, A. L., Sidney, J., Sette, A., Wilson, N. A., and Watkins, D. I. (2007). AIDS virus specific CD8+ T lymphocytes against an immunodominant cryptic epitope select for viral escape. J Exp Med 204:2505-2512 []
  6. Kotturi, M. F., Peters, B., Buendia-Laysa, F. J., Sidney, J., Oseroff, C., Botten, J., Grey, H., Buchmeier, M. J., and Sette, A. (2007). The CD8+ T-cell response to lymphocytic choriomeningitis virus involves the L antigen: uncovering new tricks for an old virus. J Virol 81, 4928-4940. []
  7. Munks, M. W., Gold, M. C., Zajac, A. L., Doom, C. M., Morello, C. S., Spector, D. H., and Hill, A. B. (2006). Genome-wide analysis reveals a highly diverse CD8 T cell response to murine cytomegalovirus. J Immunol 176, 3760-3766.[]
  8. Couldn’t keep it up[]