Mystery Rays from Outer Space

A paper in the latest issue of PLoS Pathogens¹ makes a provocative suggestion, summarized in their title: Immunodominant Tuberculosis CD8 Antigens Preferentially Restricted by HLA-B.² This is another paper that offers reasonably exhaustive mapping of T cell targets of a particular pathogen — a genre that’s becoming more and more common.

Just a few days ago I commented on a similar paper that mapped influenza epitopes. In this case, the pathogen is Mycobacterium tuberculosis, and they screened overlapping peptides from eight Mtb proteins. (It’s still, of course, prohibitively expensive and time-consuming to test the entire Mtb genome, the way some viral genomes are being screened.) There’s not much new or different about this paper, for the most part (it’s a useful technical contribution). There’s the obligatory comment on epitope prediction, which should seem familiar to anyone who’s been reading my earlier posts:

Because much work on human CD8+ T cell responses to Mtb has relied upon the use of HLA prediction algorithms, as each epitope was defined we asked whether or not the epitopes would have been predicted by these approaches. Given the prevalence of HLA-B alleles and 10-mer and 11-mer epitopes, it is perhaps not surprising that many of these epitopes were not ranked strongly (unpublished data)

So I’ll skip over almost all their results and move on to the “provocative statement”:

All but one of the epitopes that have been mapped to date are restricted by HLA-B molecules. … we speculate that Mtb antigens may preferentially bind to HLA-B molecules, that Mtb preferentially interferes with HLA-A processing and presentation, that infection with Mtb leads to selective upregulation of HLA-B, or that HLA-B is preferentially delivered to the Mtb phagosome.

They identified 12 epitopes, and 11 of them were restricted to HLA-B (various alleles). They take this as evidence of skewing toward HLA-B as opposed to HLA-A, and speculate as to the cause of the skewing. Yeah, well. Maybe. But I’d like to suggest some other possibilities.

M. tb genome

First of all, there are lots of non-HLA-B-restricted Mtb epitopes in the literature. My databases³ contain 24 human Mtb epitopes, of which 9 are HLA-A restricted. That’s far from definitive, because many of those come from experiments specifically screening HLA-A2, but it certainly demonstrates that there’s no hard and fast effect.

Second, there are two possible explanations they didn’t mention:

1. Chance. Even if the epitopes are “really” distributed evenly between HLA-A and HLA-B,⁴ an 11/12 or 12/12 distribution (either way — HLA-A or B) will appear about 0.625% of time. ⁵ The appearance of strong skewing (10 or more out of 12) will appear ~4% of the time by chance. Both of those are under the tradition 5% cutoff — but that’s only for hypothesis testing! The skewing was not an a priori hypothesis, it was a post facto observation, and these sorts of p-values are not applicable. It’s probable that something odd would appear in their results, whether it’s the number of epitopes that have alanine in P2 or whatever. You can’t focus on one oddity after the fact and declare that it’s significant.
2. Peptide length. They commented specifically on how many long peptides they found in their output (6 of the 12 epitopes are longer than the canonical 9 amino acid epitope length). Well, they screened with 15mers. If the HLA-B alleles they were dealing with are more likely to bind long peptides,⁶ then they’re skewing to HLA-B right there.

My bet is that this HLA-B skewing is purely chance, and that further epitope mapping in Mtb will find a bunch of HLA-A-restricted epitopes — revert to the mean. That’s not to say their suggestions are biologically implausible. Several of the viral immune evasion molecules, for example, preferentially target either HLA-A or HLA-B, though the effects are usually not black and white — which is consistent with what they’re seeing here. Still, I really think the likeliest explanation is simply chance.

For the record, here are the human and mouse MTb epitopes I know of:

1. Since the PLoS papers just continually conveyer-belt out their papers, do they really have “issues”?[↩]
2. Immunodominant Tuberculosis CD8 Antigens Preferentially Restricted by HLA-B. Lewinsohn, D. A., Winata, E., Swarbrick, G. M., Tanner, K. E., Cook, M. S., Null, M. D., Cansler, M. E., Sette, A., Sidney, J., and Lewinsohn, D. M. (2007). PLoS Pathog 3, e127. [↩]
3. Just compilations of the curated on-line databases and a couple other sources[↩]
4. HLA-C is kind of the red-headed stepchild of classical antigen presentation, and we’ll leave it out of the question[↩]
5. I think.[↩]
6. I don’t know if they are or not; in my databases the HLA-B alleles they used, and HLA-B in general, are not so biased, but the numbers are small[↩]

OK, maybe it’s not quite as important as a cure for cancer. But it’s close.

StupendousMan’s wins prediction for Yankees and Red Sox, showing one-sigma uncertainty, as the 2007 season progressed.

(Last night the Boston Red Sox clinched their first American League East Division Championship¹ since 1995.)

1. That would be baseball.[↩]

I’ve talked several times (for example, here, here, and here) about predicting cytotoxic T lymphocyte (CTL) epitopes, and emphasized how hard it is (or, at least, how poor the tools are). Here’s an example of why it’s difficult.

(Quick review: CTL recognize virus-infected cells by screening small peptides that are bound to the class I major histocompatibility complex [MHC class I]. The peptides are created by destruction of proteins in the target cell. There’s a handy guide to antigen presentation here, if that helps put things into context.)

In my previous post on the subject, I listed a bunch of different factors that need to be incorporated in the predictions. Number 7 was “Binding to the MHC complex in the ER”, and I commented that peptide binding to MHC class I is probably the second-best understood step in the pathway (behind TAP transport, if you’re keeping score at home).

A paper from earlier this year¹ tried to identify CTL epitopes in influenza viruses. Lots of papers do this, but most don’t follow up with actual, complete tests — too expensive and difficult. Wang et al did the follow through.

They started by looking simply at binding to MHC class I alleles. Without going into details (they were looking for conserved epitopes that matched HLA supertypes, if anyone cares) they identified 167 peptides that they predicted should bind to the various MHC class I alleles; and then they tested them to see if they actually did bind. (They used NetMHC 3.0 ² to predict binding.)

Of the 167 predicted binders, 39 failed to bind altogether, and another 39 only bound very weakly. That leaves 89 peptides (just 53% of their tested pool) that were authentic binders.

Influenza viruses infecting cells of the trachea

Then, they tested to see if their peptides actually reacted with CTL from healthy donors. (They assumed that their healthy donors were immune to a influenza A — reasonable, but not a guarantee, so this is a particularly conservative test, I think.) Just 13 of their peptides were positive by this test (7.8% of their total predicted pool). Unexpectedly, two peptides that were non-binders triggered a response. Wang et al speculated that the very low affinity binding was enough for the CTL, but I wonder if this represented a contamination issue — CTL are famously sensitive, and it’s well known that tiny contaminating peptides in a synthetic prep are enough to trigger CTL, even if they’re barely detectable by other means.

The paper I’ve thought of as the record-holder for accuracy (if I’m being generous with their denominator) is Kotturi et al,³ whose prediction was correct for 25 of 160 potential peptides — about twice as good as the influenza predictions here. But Kotturi et al were dealing with just two MHC class I alleles, H-2D^b and H-2K^b, and those are very intensively-studied alleles. Wang et al. are not only looking at multiple alleles, they were using supertype approaches that allow them to cover almost all (>99%) of the population — a much more difficult prediction. To me, then, their predictions are remarkably successful.

But still: Just over 7% of their predictions were correct. And even limiting to prediction to a single step in the complex pathway — just looking at MHC class I binding of the peptides — they’re barely above 50% accuracy.

It’s a hard job. But I have to say that the field is progressing with impressive speed; these predictions are much more accurate than I would have expected five years ago.

1. Wang, M., Lamberth, K., Harndahl, M., Roder, G., Stryhn, A., Larsen, M. V., Nielsen, M., Lundegaard, C., Tang, S. T., Dziegiel, M. H., Rosenkvist, J., Pedersen, A. E., Buus, S., Claesson, M. H., and Lund, O. (2007). CTL epitopes for influenza A including the H5N1 bird flu; genome-, pathogen-, and HLA-wide screening. Vaccine 25, 2823-2831. [↩]
2. NetMHC is based on these three references — which I’m including as a note to myself: (1) Nielsen, M., Lundegaard, C., Worning, P., Hvid, C. S., Lamberth, K., Buus, S., Brunak, S., and Lund, O. (2004). Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 20, 1388-1397 . (2) Nielsen, M., Lundegaard, C., Worning, P., Lauemoller, S. L., Lamberth, K., Buus, S., Brunak, S., and Lund, O. (2003). Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 12, 1007-1017 . (3) Buus, S., Lauemoller, S. L., Worning, P., Kesmir, C., Frimurer, T., Corbet, S., Fomsgaard, A., Hilden, J., Holm, A., and Brunak, S. (2003). Sensitive quantitative predictions of peptide-MHC binding by a ‘Query by Committee’ artificial neural network approach. Tissue Antigens 62, 378-384. [↩]
3. 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 [↩]

Diversity in fish

Last week when I brought up the subject of MHC selection, I listed a number of possible explanations for the enormous polymorphism within the population. Let’s quickly brush aside the least likely candidates, so we can concentrate on the potential winners.

Quick review: The major histocompatibility complex region of the genome expresses proteins that are important for protection against parasites, and these proteins are sequence-specific — each allele offers the chance of protecting against a large, but not infinite, number of parasite proteins. The MHC region is also incredibly diverse in almost all vertebrate populations;¹ for example, the human MHC region contains thousands of different alleles.

The question is: What kind of evolutionary forces drive this extraordinary diversity?

The list of possibilities includes two leading candidates: overdominant selection and frequency-dependent selection. Three other explanations that have been put forward are increased mutation rate at the MHC (most recently put forward by JoAnne, in the comments section here); maternal-fetal interactions; and sexual selection. I’ll treat sexual selection separately, even though it’s a distant third in the plausibility races. That leaves maternal-fetal interactions, and increased mutation rates to be eliminated here.

The idea behind the maternal-fetal interaction theory was that somehow, having an MHC that doesn’t match your mother’s improves fetal survival. Although there are a number of papers arguing both sides of this, the simple answer is that birds, amphibians, and fish — which don’t have fetal-maternal interactions at all — also show extreme diversity at the MHC region. For example, 43 MHC class II alleles were identifed among just 74 Lake Trout.² It would take really special pleading to have this explanation work.

What about increased mutation rate? Raw mutation rates can be estimated by looking at the frequency of synonymous vs. non-synonymous changes in a coding region (d_S vs. d_N):

One early hypothesis … was the hypothesis that MHC loci have an unusually high mutation rate. DNA sequence data have made it possible to test this hypothesis rigorously. Because d_S is expected to reflect the mutation rate, d_S values for MHC genes can be compared with those of other genes to assess the comparative magnitude of the mutation rate at MHC loci. Such comparisons have shown that the mutation rates at MHC loci are below average for mammalian genes.³ [My emphasis: IY]

Similarly, what about some special form of mutation at the MHC?

A more recent version of essentially the same hypothesis held that MHC polymorphism was enhanced by interlocus recombination (gene conversion). … However, gene conversion is expected to be an essentially random process; thus it cannot explain the very specific pattern of d_N > d_S in the PBR⁴ codons that characterizes MHC loci. ⁵

In other words, the DNA sequence strongly argues that there’s no increased mutation at the MHC. Rather, some of those mutations that do arise are strongly positively selected.

We’re left with two major hypotheses, plus sexual selection, as to the source of that selection.

1. And since only vertebrates have an MHC region, that’s not a very limiting qualifier[↩]
2. Dorschner, M. O., Duris, T., Bronte, C. R., Burnham Curtis, M. K., and Phillips, R. B. (2000). High levels of MHC class II allelic diversity in lake trout from Lake Superior. J Hered 91, 359-363.[↩]
3. Hughes, A. L., and Yeager, M. (1998). Natural selection at major histocompatibility complex loci of vertebrates. Annu Rev Genet 32, 415-435. [↩]
4. ”PBR”: Peptide-binding regions — regions in the MHC proteins that are functionally critical, and that are much more likely to be varies than other regions.[↩]
5. Hughes and Yeager again.[↩]

“Crow 104″ by Meggan Gould

The other day I heard a fascinating talk from Ned Walker, on the ecology and evolution of West Nile Virus in birds and mosquitos. Hopefully some of Ned’s cooler stuff should be published relatively soon, and I’ll talk about it then. In the mean time, Ned’s seminar reminded me of a really baffling observation I remembered reading about, in the mid 1990s, and prompted me to see what had happened to that story. As far as I can find, at present the state of the art regarding it seems to be (A) a big shrug, and (B) the suggestion that it’s an irrelevant side effect — the sort of thing that should make Larry Moran happy.

West Nile Virus (WNV) is a member of the flavivirus family, which are small (~11,000 bases) single-stranded RNA viruses that typically infect many species and often have an insect vector. For unknown reasons, in the mid 1990s WNV started to spread out of its traditional geographical regions (which are, you’ll never guess, the western Nile region of Africa) through much of Africa and Europe, and then hopped into North America and now has spread across the continent. It’s dangerous to humans (4269 cases, 177 fatalities in the USA in 2006) and much worse to birds — causing local extinctions of some species,¹ especially crows² (which, by the way, Ned³ thinks have got a bad rap as carriers — he thinks crows may be dead-end species, while many other species may be the actual routes of transmission).

Anyway, all this flavivirus talk reminded me of this previous observation.⁴ The reason I remember it was that it came out when I was particularly obsessed (even more so than I am today, believe it or not) with viral immune evasion; and the paper described exactly the opposite effect of what I was expecting.

Class I major histocompatibility complexes are recognized by cytotoxic T lymphocytes, which are generally agreed to be a major antiviral force; as such, many viruses target MHC class I and thereby block CTL recognition. ⁵ So it’s pretty common for virus-infected cells to show reduced levels of MHC class I.

West Nile Virus

Mullbacher’s paper showed that flaviviruses do the opposite: They specifically up-regulate surface levels of MHC class I. (As it happens, this had been described earlier, though I think only in specific cell types,⁶ but this was the first time I had run into it.) Mullbacher’s group argued, and still argues, that this is because the virus specifically increases peptide transport into the endoplasmic reticulum (in the 1995 paper, they guessed that a general leakiness might be the cause; later, they argue that it’s a specific effect on the TAP peptide transporter.⁷ ) Other groups believe that it’s a transcriptional effect, through several different (interferon-dependent and -independent) pathways.⁸ Still, the phenomenon seems real, significant, and robust.

How come? What’s the benefit to the virus to up-regulate MHC class I, thus making itself a better target to CTL?

Over the years (I found out, once I picked up on this story again last week) a bunch of different explanations have been proposed — resistance to natural killer cells, and so on; but none of them have been very convincing, and more recently, you get the sense that the researchers are just growing tired of it. (“MHC class I up-regulation by flaviviruses: Immune interaction with unknown advantage to host or pathogen.”⁹)

The latest article on the subject I’ve been able to find¹⁰ argues that it’s just a side-effect of the flavivirus life-cycle, and has nothing to do with immunity one way or another:

We propose that the phenomenon of flavivirus-mediated MHC class I upregulation is a by-product of a unique assembly strategy evolved by flaviviruses and therefore did not evolve primarily as an immune escape mechanism for virus growth in the vertebrate host.

Correct or not, it’s a reasonable suggestion, and a useful reminder that not everything we can measure is adaptive. However, if the other groups’ transcriptional arguments are correct, then the phenomenon sounds more like the infected cells’ attempt at host defense — which I would file under the “adaptive” category, even if it’s not actually effective in this case.

1. LaDeau, S. L., Kilpatrick, A. M., and Marra, P. P. (2007). West Nile virus emergence and large-scale declines of North American bird populations. Nature 447, 710-713. [↩]
2. The photo at the top, by the way, is by Meggan Gould[↩]
3. I see that other groups have reached a similar conclusion, especially implicating robins as important transmission species[↩]
4. Mullbacher, A., and Lobigs, M. (1995). Up-regulation of MHC class I by flavivirus-induced peptide translocation into the endoplasmic reticulum. Immunity 3, 207-214.[↩]
5. At least, that’s the accepted wisdom — but see my previous discussions of that.[↩]
6. King, N. J., Maxwell, L. E., and Kesson, A. M. (1989). Induction of class I major histocompatibility complex antigen expression by West Nile virus on gamma interferon-refractory early murine trophoblast cells. Proc Natl Acad Sci U S A 86, 911-915.[↩]
7. Momburg, F., Mullbacher, A., and Lobigs, M. (2001). Modulation of transporter associated with antigen processing (TAP)-mediated peptide import into the endoplasmic reticulum by flavivirus infection. J Virol 75, 5663-5671.[↩]
8. For example, Cheng, Y., King, N. J., and Kesson, A. M. (2004). Major histocompatibility complex class I (MHC-I) induction by West Nile virus: involvement of 2 signaling pathways in MHC-I up-regulation. J Infect Dis 189, 658-668.[↩]
9. Lobigs, M., Mullbacher, A., and Regner, M. (2003). MHC class I up-regulation by flaviviruses: Immune interaction with unknown advantage to host or pathogen. Immunol Cell Biol 81, 217-223.[↩]
10. Lobigs, M., Mullbacher, A., and Lee, E. (2004). Evidence that a mechanism for efficient flavivirus budding upregulates MHC class I. Immunol Cell Biol 82, 184-188. [↩]

Yeah, so this is getting a little repetitious. The new E1 I noted back in June, and then again in August, has been reported one more time.

Whining aside,¹ the new paper² is a nice addition to the pack, because it offers a quick look at a knockout mouse, and a new binding partner.

The significance of a new E1, if you don’t want to look at my previous posts, is that E1s are the upstream-most enzyme involved in the ubiquitin cascade,³ which is critical to all kinds of cellular function starting from (but not limited to) regulated proteolysis . It had been believed that there was only a single E1 enzyme in most mammalian genomes, although the downstream enzyme families E2 and E3 are much more variable (a couple dozen genes, and close to a thousand, respectively). Jin et al⁴ and Pelzer et al⁵ both identified a new E1 (Jin et al called it UBA6; Pelzer et al stuck to an established name and called it UBE1L2) and showed that it is capable of charging ubiquitin but not any of several other ubiquitin-like substrates. Jin et al took it a little further, showing, for example, that the E1 did have a specific E2.

Chiu et al call the gene E1-L2, which is near as dammit to the authentic name but just different enough that I had to compare sequences to make sure it was the same.⁶ As well as ubiquitin, they do find a different substrate for E1-L2: the ubiquitin-like molecule FAT10, which had been specifically ruled out by the other groups (Chiu et al explain it as a techinical problem with the system,⁷ which I don’t know well enough to comment on).

The knockout mouse is embryo-lethal, which was already known though not, I think, published (thanks to Jianping Jin for sending me some unpublished info a while back). This is in contrast to FAT10 knockout mice, which are viable and not grossly abnormal,⁸ so E1-L2/UBE1L2/UBA6 does something else — consistent with a role for this thing in authentic ubiquitination pathways.

The connection with FAT10 makes this thing even more interesting to me, because FAT10 is encoded in the major histocompatibility complex — a region that contains many genes important in immunity — and FAT10 has been linked to some aspects of the immune response.

1. But before I stop whining, doesn’t Molecular Cell use DOIs? I can’t find one associated with the paper. Get with the new millennium, guys![↩]
2. E1-L2 Activates Both Ubiquitin and FAT10. Yu-Hsin Chiu, Qinmiao Sun, and Zhijian J. Chen. Molecular Cell, Vol 27, 1014-1023, 21 September 2007 [↩]
3. (The figure at top, taken from VanDemark, A. P., and Hill, C. P. (2005). E1 on the move. Mol Cell 17, 474-475. , shows a model of E1’s mechanism of action.[↩]
4. Dual E1 activation systems for ubiquitin differentially regulate E2 enzyme charging. Jianping Jin, Xue Li, Steven P. Gygi & J. Wade Harper. Nature 447, 1135-1138 (28 June 2007) [↩]
5. UBE1L2, a Novel E1 Enzyme Specific for Ubiquitin. Christiane Pelzer, Ingrid Kassner, Konstantin Matentzoglu, Rajesh K. Singh, Hans-Peter Wollscheid, Martin Scheffner, Gunter Schmidtke, and Marcus Groettrup. J. Biol. Chem., Vol. 282, Issue 32, 23010-23014, August 10, 2007 [↩]
6. They make it clear in their discussion, so I should have read the whole paper before grumbling.[↩]
7. ”We believe it is important to remove the GST tag or replace it with a smaller tag such as His6 in order to observe the activation of FAT10 by E1-L2.”[↩]
8. FAT10/diubiquitin-like protein-deficient mice exhibit minimal phenotypic differences. Canaan, A., Yu, X., Booth, C. J., Lian, J., Lazar, I., Gamfi, S. L., Castille, K., Kohya, N., Nakayama, Y., Liu, Y. C., Eynon, E., Flavell, R., and Weissman, S. M. (2006). Mol Cell Biol 26, 5180-5189. [↩]

I’m no expert on evolutionary theory, but I do think it’s pretty cool, especially when applied to the major histocompatibility complex; so when Razib pointed out a paper on the evolution of MHC I figured I should write a post about that paper.

This is not that post.

First, as I started to review the background — the various possible kinds of evolutionary drives for MHC diversity — things got a bit too complicated for one post, and I decided I’d be better off splitting things up into several background posts. (Especially since, as I say, I’m no expert, and I can take advantage of a more leisurely review.) Second, as I poked around the literature, I ran across a particularly neat paper¹ that I couldn’t resist talking about.

The major histocompatibility complex region is the most polymorphic region in the genome, for most (if not all) vertebrates; MHC class I and II genes evolve with dramatic speed (”During the timeframe of mammalian evolution, the lifetimes of a functional class I locus are short and those of individual alleles even shorter.”² ) (To the right is an illustration of that, a phylogenetic tree showing relationship of the known alleles for a single MHC class I locus, HLA-A — not even the most diverse locus [the IMGT/HLA Database lists 580 HLA-A alleles, compared with 921 HLA-B] . This was made from the dbMHC’s data. Click on the figure for a larger view.)

The question is: Why is it so diverse? As I understand it — and if any readers know more than me, which wouldn’t be all that hard, feel free to jump in and correct me — the general term for this situation (in which there is a drive to maintain multiple alleles in a population) is balancing selection, and the most common explanations for the phenomenon in MHC are heterozygote advantage (which is not quite the same as overdominance), frequency-dependent selection, disease-specific selection, sexual selection, maternal-fetal interactions, and drift. (As I understand it, none of those possibilities are mutually exclusive.) The paper Razib talked about was looking at heterozygote advantage, and concluded that overdominant selection was not a factor — though, as I observed in the comments there, the literature is really back and forth on that, and there are some instances where overdominance may be important. I will try to give an overview of that some other time.

This brings me to the San Nicolas Island fox (Urocyon littoralis dickeyi). Foxes colonized six of the Channel Islands off California between 16000 and 800 years ago (see the map to the left, click for larger version). These small, isolated populations are extraordinarily homogenous; in particular, the San Nicolas Island foxes, with an estimated population of 247, had no variation whatsover in any of the alloenzymes, minisatellites, or microsatellites examined (”for which the probability of genetic identity is commonly <1 in several million“). This was because of a recent genetic bottleneck, as well as earlier ones:

Initial simulations clearly suggested a severe bottleneck (to an effective size of 10 individuals or fewer for one or two generations, followed by 12 generations of population growth) was necessary to explain near monomorphism at the 18 loci. … This extreme scenario is consistent with the recent population crash of island foxes on the east end of Santa Catalina Island where the population was reduced from >1,000 to 10 individuals in a single generation due to a canine distemper epidemic in 1999. A similar event may have occurred on San Nicolas Island …

Of course, there’s an exception to the homogeneity, and of course it turns out to be the MHC. Within the MHC region these foxes are diverse: “These values of heterozygosity are similar to those in larger populations of island foxes and suggest the action of intense balancing selection over a sizable genomic interval within the fox MHC class II.“

So in spite of a drastic bottleneck that virtually eliminated genetic diversity, balancing selection has maintained, or generated, diversity within this specific locus (and perhaps only this locus; but the authors point out that the same effect may be true on other fitness-related genes, though probably not to the same extent).

This does not, I think, speak to one particular mechanism for the balancing selection, but it does tell us just how much selective pressure gets applied.

Given our demographic scenario for San Nicolas Island foxes, we found that periodic selection coefficients for the microsatellite loci as high as 0.5-0.95 are required to maintain heterozygosity values near 0.62 at microsatellite locus FH2202 and 0.36 at the DRB locus. These selection coefficients are much larger than those reported in natural (range: 0.05-0.15) and human populations (range: 0.19-0.39) for a locus under balancing selection.

So, that’s how effective balancing selection is on MHC. What’s the cause (or causes)?

More on that later.

1. Aguilar, A., Roemer, G., Debenham, S., Binns, M., Garcelon, D., and Wayne, R. K. (2004). High MHC diversity maintained by balancing selection in an otherwise genetically monomorphic mammal. Proc Natl Acad Sci U S A 101, 3490-3494. [↩]
2. Parham, P. (1994). The rise and fall of great class I genes. Semin Immunol 6, 373-382. [↩]

A week after I talked about toll-like receptors (TLRs) and their role in signaling “danger”, here’s another beautiful real-world example.

TLRs, remember, are pattern-recognition molecules that identify “pathogen-associated molecular patterns” (PAMP). There are a dozen or so of them in humans and mice, and they recognize things like bacterial cell walls, viral DNA, and double-stranded RNA (which is associated with a lot of viral infections). The downstream effects that TLRs trigger after they recognize a PAMP are various, but typically do things like activating dendritic cells and inducing interferon release.

Herpes simplex viruses, like most herpesviruses, are ancient and immensely successful viruses. They’re very common infections (HSV type 1 infects something like 60-80% of North Americans; HSV-2, around 25%), rarely cause any diseases at all, and most of the diseases they are associated with, are fairly mild. But occasionally, HSVs do cause serious disease, most dramatically herpes simplex encephalitis (HSE). HSE isn’t common, but it has a 70% mortality rate, and the survivors often have long-term neurologic problems. Why do some two out of a million people have such major problems, while the vast majority breeze along without even knowing they’re infected?

I think it’s fair to say that we don’t know much about immunity to HSV. We know that cytotoxic T lymphocytes are abundant and may help prevent recurrence, but don’t eliminate the infection. (HSVs do have an immune evasion molecule, ICP47,¹ that purportedly provides resistance to CTL, but it’s not clear how important that is in actual infection — as is true of most viral anti-CTL immune evasion molecules.) Natural killer cells are probably involved in resistance, but we don’t have a detailed understanding of that either. For that matter, only a handful of humans lacking NK function have been found, and while a couple of them have been susceptible to herpesvirus infections² it’s been varicella, rather than herpes simplex, that was the major problem.³

One reason we don’t fully understand immunity to HSV is that, like most herpesviruses, HSVs are very host-specific. Although they will infect mice, the course of infection there is probably quite different than in humans, and we don’t see many humans lining up for the chance to be infected with mutant herpes simplex viruses. On the other hand, without wanting to be too vulture-ish about it, one advantage of a human-specific virus is that humans are a huge population that is naturally riddled with mutations, and those mutations that are associated with disease tend to get examined carefully. An example of one such mutation is in the 14 September issue of Science.⁴

In this paper, they looked at two children who had herpes simplex encephalitis. Both children, though they are unrelated, turned out to have the same point mutation in their TLR3. TLR3 is the TLR that recognizes double-stranded RNA. Herpes simplex virus produces dsRNA during infection, and although other PAMP receptors also recognize dsRNA, apparently only TLR3 recognizes the form that HSV produces, because it seems that TLR3 is essential for protection against encephalitis caused by herpes simplex.⁵ It’s not a universal effect: TLR3 does not seem to be important for recognizing several other viruses, including some with double-stranded RNA genomes,⁶ but is important in protecting against another herpesvirus, murine cytomegalovirus.⁷

As a side note, TLR3 was the first (and I believe only) TLR that’s been crystallized,⁸ so we know a bit about its structure. (I made the figure at top, using YASARA, from the Choe et al structure.) The mutation is in a region proposed to be associated with dsRNA binding and with receptor dimerization. The mutation here turns out to be dominant-negative (heterozygotes are unresponsive to dsRNA through TLR3), and this (at least to me) hints that perhaps dimerization is intact but binding is screwed up.

This paper builds on a previous study from the same group that found that HSE was associated with a mutation in UNC-93B,⁹ a molecule involved in signalling from TLR7, TLR8, TLR9, and, yes, TLR3. However, not everyone with these mutations gets HSE:

Interestingly, five of the seven TLR3-deficient individuals and one of the three UNC-93B-deficient individuals did not develop HSE after HSV-1 infection. The incomplete clinical penetrance of TLR3 and UNC-93B deficiency is consistent with the typically sporadic, as opposed to familial, occurrence of HSE. Multiple factors may affect clinical penetrance, including age at infection with HSV-1, the viral inoculum, and human modifier genes.

It’s not spelled out in the paper if the HSE patients here were selected for further study for some specific reason, or if the authors had methodically tested every case of HSE in France, or if in fact this mutation is a common cause of HSE, so that most patients with HSE would have something wrong with TLR3 or its signalling pathway. If I’m reading that quote right, though, the authors are at least implying that these mutations are the major underlying cause of herpes simplex encephalitis. If that’s the case (and even if it’s not), I think it’s a remarkable link between innate immunity and a real-world disease.

1. York, I. A., Roop, C., Andrews, D. W., Riddell, S. R., Graham, F. L., and Johnson, D. C. (1994). A cytosolic herpes simplex virus protein inhibits antigen presentation to CD8+ T lymphocytes. Cell 77, 525-535.[↩]
2. Biron, C. A., Byron, K. S., and Sullivan, J. L. (1989). Severe herpesvirus infections in an adolescent without natural killer cells. N. Eng. J. Med. 320, 1731-1735. and A. Etzioni, C. Eidenschenk, R. Katz, R. Beck, J.L. Casanova and S. Pollack. (2005) Fatal varicella associated with selective natural killer cell deficiency, J Pediatr 146, 423-425.[↩]
3. And Dr Biron has told me that she knows of at least one NK-deficient person who hasn’t been written up because she, or he, is living perfectly normal, herpes-free life.[↩]
4. Zhang, S.-Y., Jouanguy, E., Ugolini, S., Smahi, A., Elain, G., Romero, P., Segal, D., Sancho-Shimizu, V., Lorenzo, L., Puel, A., Picard, C., Chapgier, A., Plancoulaine, S., Titeux, M., Cognet, C., von, B., Horst, Ku, C.-L., Casrouge, A., Zhang, X.-X., Barreiro, L., Leonard, J., Hamilton, C., Lebon, P., Heron, B., Vallee, L., Quintana-Murci, L., Hovnanian, A., Rozenberg, F., Vivier, E., Geissmann, F., Tardieu, M., Abel, L., and Casanova, J.-L. (2007). TLR3 Deficiency in Patients with Herpes Simplex Encephalitis. Science 317, 1522-1527. [↩]
5. Though, perhaps, not for other aspects of HSV infection?[↩]
6. This paper, and also Edelmann, K. H. (2004). Does Toll-like receptor 3 play a biological role in virus infections? Virology 322, 231-238. [↩]
7. Tabeta, K., Georgel, P., Janssen, E., Du, X., Hoebe, K., Crozat, K., Mudd, S., Shamel, L., Sovath, S., Goode, J., Alexopoulou, L., Flavell, R. A., and Beutler, B. (2004). Toll-like receptors 9 and 3 as essential components of innate immune defense against mouse cytomegalovirus infection. Proc Natl Acad Sci U S A 101, 3516-3521.[↩]
8. Choe, J., Kelker, M. S., and Wilson, I. A. (2005). Crystal Structure of Human Toll-Like Receptor 3 (TLR3) Ectodomain. Science 309, 581-585. and Bell, J. K., Askins, J., Hall, P. R., Davies, D. R., and Segal, D. M. (2006). The dsRNA binding site of human Toll-like receptor 3. Proc Natl Acad Sci U S A 103, 8792-8797. [↩]
9. Casrouge, A., Zhang, S.-Y., Eidenschenk, C., Jouanguy, E., Puel, A., Yang, K., Alcais, A., Picard, C., Mahfoufi, N., Nicolas, N., Lorenzo, L., Plancoulaine, S., Senechal, B., Geissmann, F., Tabeta, K., Hoebe, K., Du, X., Miller, R. L., Heron, B., Mignot, C., de, V., Thierry Billette, Lebon, P., Dulac, O., Rozenberg, F., Beutler, B., Tardieu, M., Abel, L., and Casanova, J.-L. (2006). Herpes Simplex Virus Encephalitis in Human UNC-93B Deficiency. Science 314, 308-312. [↩]