Mystery Rays from Outer Space

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

April 29th, 2010

Influenza variations, part II

Mutation NationAbout 15 minutes after I wrote my last article on influenza variation, I was reading the Journal of Virology  and ran across another paper1 on the same thing, that at least partly addresses some of the missing points in the earlier ones.

To brutally truncate my earlier comments: influenza should generate a huge number of mutants as it replicates; but in the few studies that have been done, not all that many variants have actually been detected.

One of the points I raised was that the influenza variation was sampled at the end-point of the infection — after the patient had died, in the paper I talked about the other day.2 Even though the virus had been through the maximum number of replication cycles, it had also experienced the maximal selection pressure, potentially reducing the number of surviving mutants. Is it possible that more variants arose earlier in the infection, but died off before they were detected?

This new paper1  actually looked at exactly that. They used canine influenza as their model, so they could deliberately infect their patients and track through the infections from the beginning through the end. Even though they used a technique that is much less sensitive to mutations (and is probably more error-prone as well) they found tons of variation, and the pattern they found is fascinating:

Mutations arose readily in the infected animals and reached high frequencies in some vaccinated dogs, but they were mostly transient and often were not detected on subsequent days. Hence, CIV populations are highly dynamic and characterized by a rapid turnover of likely deleterious mutations. ((Hoelzer, K., Murcia, P., Baillie, G., Wood, J., Metzger, S., Osterrieder, N., Dubovi, E., Holmes, E., & Parrish, C. (2010). Intrahost Evolutionary Dynamics of Canine Influenza Virus in Naive and Partially Immune Dogs Journal of Virology, 84 (10), 5329-5335 DOI: 10.1128/JVI.02469-09))

(My emphasis) This (assuming it holds true in other studies) beautifully resolves much of the difference between the expected level of variation, and the level that’s observed at any one time point of infection. The explanation is that the variation does indeed appear, but it doesn’t persist.  There is variation is over time as well as at any one time point.

Hoelzer 2010 Fig 1
Figure 1. Variation between challenge influenza virus (yellow) and virus isolated from two naïve dogs 2 to 4 days after infection 1  

There are a lot of very cool things about this study that I’m not going to talk about (differences between vaccinated and unvaccinated animals, evidence for antigenic escape) but there are two things that I thought were particularly exciting.

First is the question of why the mutations seem to be so transient. Part of that could just be chance, part of it is probably selection against deleterious mutants.

But it’s also worth keeping in mind that the viruses are replicating in a dynamic, rapidly-changing environment. The virus enters a host whose immune system is at rest but that immediately recognizes viral infection and ramps up interferons,  then other cytokines, then innate antiviral systems that build up and spill over into an adaptive immune response  …  a whole range of inflammation whose mediators and effectors change from hour to hour. Is this changing environment selecting for mutations that are briefly beneficial, and that then become deleterious as the situation changes a few hours later?

Second – when we think about viruses that are able to jump from one species to another, we think usually of mutants, virus that may be less fit in their “proper” hosts but adequately fit in some other species. (In fact canine influenza itself is a great example of this, a virus that jumped from horses into dogs six or seven years ago.  It is essentially equine influenza, but compared to the equine version it has a half-dozen variants that make it more suitable for replication in dogs.)

If we look at any particular time point we may not find any of these potential emergent mutants. But if we look at all the time points, as in this study, perhaps these potential species-jumping mutants are popping up all the time, but only for a few hours at a time:

This observation suggests that mutations that facilitate adaptation to a new host species might occur transiently in the donor host despite any associated fitness costs and provide a transient reservoir of preadapted mutations1

(My emphasis) There’s also theoretical and experimental work that probably addresses how this sort of pressure could drive population-level robustness.  For example, while heterogeneity is linked to fitness in HIV,3 Claus Wilke says:

Virus strains with a history of repeated genetic bottlenecks frequently show a diminished ability to adapt compared to strains that do not have such a history.4

I don’t know that work as well as I’d like to, but I think it’s probably relevant when considering local and global evolutionary pressures on the virus.

  1. Hoelzer, K., Murcia, P., Baillie, G., Wood, J., Metzger, S., Osterrieder, N., Dubovi, E., Holmes, E., & Parrish, C. (2010). Intrahost Evolutionary Dynamics of Canine Influenza Virus in Naive and Partially Immune Dogs Journal of Virology, 84 (10), 5329-5335 DOI: 10.1128/JVI.02469-09[][][][]
  2. Kuroda, M., Katano, H., Nakajima, N., Tobiume, M., Ainai, A., Sekizuka, T., Hasegawa, H., Tashiro, M., Sasaki, Y., Arakawa, Y., Hata, S., Watanabe, M., & Sata, T. (2010). Characterization of Quasispecies of Pandemic 2009 Influenza A Virus (A/H1N1/2009) by De Novo Sequencing Using a Next-Generation DNA Sequencer PLoS ONE, 5 (4) DOI: 10.1371/journal.pone.0010256[]
  3. Bordería AV, Lorenzo-Redondo R, Pernas M, Casado C, Alvaro T, et al. (2010) Initial Fitness Recovery of HIV-1 Is Associated with Quasispecies Heterogeneity and Can Occur without Modifications in the Consensus Sequence. PLoS ONE 5(4): e10319. doi:10.1371/journal.pone.0010319[]
  4. Novella, I., Presloid, J., Zhou, T., Smith-Tsurkan, S., Ebendick-Corpus, B., Dutta, R., Lust, K., & Wilke, C. (2010). Genomic Evolution of Vesicular Stomatitis Virus Strains with Differences in Adaptability Journal of Virology, 84 (10), 4960-4968 DOI: 10.1128/JVI.00710-09[]
April 27th, 2010

Influenza variations

Mutation comic

Indeed, the amount of HIV diversity within a single infected individual can exceed the variability generated over the course of a global influenza epidemic, the latter of which results in the need for a new vaccine each year. 1

That was said as part of a discussion on HIV vaccines, but let’s think about it from the influenza side.  Why is it true? Why doesn’t influenza have as many variants as HIV?

(Update: Another paper also looks at this question and points to some interesting explanations; I talk about that paper here.)

We know that influenza, like other RNA viruses, is prone to mutation (that is, it has an error-prone polymerase). Depending how you measure it, it’s likely that almost every new influenza genome has at least one mutation in it, meaning that every new infected animal or person should be be generating thousands upon thousands of new influenza variants.

Globally, of course we do see thousands of new flu variants each year.2 But, based on replication fidelity, you’d expect to see a lot more — maybe not quite as many as HIV, but not far from it.

This is also true on a much smaller scale, looking within infected individuals (animals or people).  Even using modern deep-sequencing techniques (like those used in some of the HIV analyses) that should in theory be able to detect large numbers of mutations, there are fewer than you might expect based on the known replication fidelity — far fewer variants than in HIV:

Inasmuch as the mutation rate for type A influenza viruses is estimated at one nucleotide change per 10,000 nucleotide during replication and most infections are caused by as many as 10 to 1000 virions which likely possess varying numbers of nucleotide differences in their genomes, one can expect that each influenza A virion is possibly a quasispecies. However, we identified relatively few quasispecies – probably because the currently available sequence analysis software do not allow robust quasispecies analysis and extensive manual curation is necessary. We believe that with the help of improved bioinformatic tools we would detect more quasispecies populations in our sample sets.  3

H1N1 (swine-origin influenza virus)
H1N1 (swine-origin influenza virus)

I don’t know enough about the computational side to comment on their bioinformatics point.  Another recent paper4 uses a similar approach and (at least at first) seems to reach an more conservative conclusion.  They talk about “quasispecies”, but they seem to be using the term rather loosely, to describe just a handful of distinct genomic sequences. These sequences differ by, for example, a single base (and a single amino acid) in the HA, where one of the sequences was present at about 75% of the sequences, and the other at about 25%. To me that’s not really a “quasispecies” — a quasispecies is something that needs to be defined by an average sequence even though the vast majority of the genomes are different from that average. (Here and here are Vincent Racaniello’s explanations at The Virology Blog.)  Two sequences is just two sequences.

However! The authors do make their data available. I don’t have time to do a detailed look, but from what I think is a very conservative analysis, in one stretch of just 25-40 bases some 5-10% of the genomes have at least one mutation.5 If that’s roughly true across the whole genome, then each genome would have, what, maybe a half-dozen mutations on average. That, to me, really is a quasispecies.

(Do note that this is not the mutation frequency for any individual residue. No single point [with the two or three exceptions that the authors focused on] is mutated at much more than one in a thousand, and most probably more like one in many thousand, which is about what you’d expect. )

There are a myriad of complicating factors separating the error frequency in these genomes from the raw error rate of the viral polymerase. A couple of huge ones: These viruses had undergone a bunch of replications in the host – this isn’t the error rate per replication cycle, it’s the cumulative error rate after many cycles. The virus was from a patient who had died with (and probably of) the virus, and though we don’t know how many time the original infecting virus had replicated it was at least a half dozen cycles, perhaps two or three times that.

Influenza virion
Influenza virion

On the other hand, during those replication cycles, many mutations (quite likely even the great majority of them) would have been deleterious or outright defective, so most of the mutations would have never propagated but would have just silently disappeared and not been counted at the end.

The most interesting point is that these mutations aren’t arising in a vacuum. Thinking now about which mutations survive and get detected, not the baseline rate of mutation formation: The variants are forming are in an environment that’s designed to be very hostile to viruses. Mutations are going to undergo selection by the immune system.

This is one place where influenza is going to experience a very a different set of pressures than HIV. HIV persists in the presence of the adaptive (T cell and antibody-based) immune response, whereas as the adaptive response kicks in for flu the virus gets evicted. HIV therefore not only have a much longer period (years instead of days) to throw out mutations, it also is shaped by the immune response. By comparison flu would probably only have a couple of replication cycles in the presence of an adaptive response.

Changes in the virus that accumulate over the handful of replication cycles would reflect a strong selection pressure. The vast majority of mutations, even those that aren’t completely defective, are going to be less fit than the original virus and won’t accumulate. Knowing which mutations do accumulate should be very interesting because it may tell us what the virus is going through in the host.  That’s what the authors of this paper focused on — the one particular site that had a much, much higher variant  frequency, more like 25% of the genomes.  The assumption is that this arose during the infection and was positively selected for. 6

The variants that replicate best in a host may be quite different from those that are effectively transmitted. That is, there may be multiple sources of selective pressure, of which we have previously mainly only seen transmission pressure (because that’s the main one that will accumulate in a population, because transmission represents a bottleneck in the virus’s evolution [link to The Virology Blog]).  The particular HA variant that was picked up here (that apparently accumulated during the infection) is rare globally. Is that a version of the HA that’s more efficient within a host, but that doesn’t transmit as well?

I think a major reason for the difference between HIV and influenza variant accumulation is the difference between within-host and between-host (transmission) selection.  HIV spends long, long periods within a single host, thousands of replication cycles, accumulating mutations.  The transmission bottlenecks come at much longer intervals and have a much larger accumulated population to work with.  Influenza has a comparatively brief period within the host, only a handful of replications before a new transmission bottleneck hits. 7

This sort of deep sequencing experiment on influenza will probably be improved over the next few years, and I’ll be very interested to see just how much variation there really is within on flu-infected host.

  1. Walker, B., & Burton, D. (2008). Toward an AIDS Vaccine Science, 320 (5877), 760-764 DOI: 10.1126/science.1152622[]
  2. More correctly, I suppose, we infer the presence of thousands of new variants based on the hundreds of them that we see, and knowing that we are only examining a tiny fraction of all the flu cases that are out there.[]
  3. Ramakrishnan, M., Tu, Z., Singh, S., Chockalingam, A., Gramer, M., Wang, P., Goyal, S., Yang, M., Halvorson, D., & Sreevatsan, S. (2009). The Feasibility of Using High Resolution Genome Sequencing of Influenza A Viruses to Detect Mixed Infections and Quasispecies PLoS ONE, 4 (9) DOI: 10.1371/journal.pone.0007105[]
  4. Kuroda, M., Katano, H., Nakajima, N., Tobiume, M., Ainai, A., Sekizuka, T., Hasegawa, H., Tashiro, M., Sasaki, Y., Arakawa, Y., Hata, S., Watanabe, M., & Sata, T. (2010). Characterization of Quasispecies of Pandemic 2009 Influenza A Virus (A/H1N1/2009) by De Novo Sequencing Using a Next-Generation DNA Sequencer PLoS ONE, 5 (4) DOI: 10.1371/journal.pone.0010256[]
  5. I extracted the FASTQ data containing the short sequence reads matching influenza sequences from the supplemental PDF, converted it to FASTA, and used xdformat to move it into a BLAST database. Then I grabbed 40 bases from the genbank sequence CY045951.1, the PB2 segment of the closest-match influenza strain, choosing a region (positions 2151-2190) with very high coverage, and BLASTed this sequence against the short sequence data, using parameters such that I retrieved sequences that match at least 25 of 40 positions.  Of the  ~2050 hits I retrieved, about 120 had at least one internal mismatch. I can’t distinguish these from sequencing errors, but I think it’s much higher than you’d expect from sequencing error.  And I hope that my conservative approach (for example, I would have discarded mismatches at the ends of the hits) would balance out that source of confusion. []
  6. One point, by the way, that the authors didn’t cover was the possibility that this patient had actually been initially infected with more than one viral sequence.  We do know that a significant number of flu cases are doubly infected. The fact that the minor variant is a very unusual strain makes this less likely, but not impossible.[]
  7. And I think it’s fair to say that the global population-based HIV variation — the transmission-selected amount of variation, as opposed to the vast within-individual variation — is rather more comparable to that of influenza.[]
April 22nd, 2010

Modeling disease and epidemics

Blyuss & Kyrychko, Fig. 5
Fig. 5.  Boundary of the Hopf bifurcation of the endemic steady state … 1

I don’t pretend to be a mathematician or to understand the more complex disease models that are out there, but I do think modeling is an essential way of understanding how to effectively deal with diseases.  A recent paper1 looks at epidemic diseases and seems to reach some interesting conclusions (though I will cheerfully admit that I don’t even remotely understand this paper, which is heavily mathematical).

The authors have built on models of infectious disease that incorporate immunity to the disease, and incorporated the assumption that immunity to the disease can wane over time, as opposed2 to the simpler, but less realistic, assumption that the immunity is either on or off.  I don’t think they are the first to do this, and I don’t understand any of the details of how their techniques differ from other models,3 but what I think they’re saying is that temporary immunity can lead to disruption of a simple, constant level of infection, and can actually drive periodic epidemics:

[W]hen the temporary immunity period is within a certain range, there will be periodic outbreaks of epidemic, and the disease will not be eradicated from the population. … The main feature is that temporary immunity leads to a possible destabilization of endemic steady state, and an interesting open question is what effects would vaccination have on the dynamics of an epidemic in such situation. 1

(My emphasis)  If I’m understanding this correctly, it leads to the possibility that where vaccines lead to relatively short-term immunity compared to the natural infection,4 it’s conceivable that vaccination could actually shift the disease from a steady state to an epidemic mode.  Obviously, if this can happen, it would be nice to be able to predict it.

Offhand, I can’t think of any examples where this might have happened in real life.  The most notorious epidemics, like influenza and norovirus, both tend to have fairly short-term immunity to start with. Something like Marek’s Disease of chickens would be an interesting case study, but the logistics of the poultry agribusiness is going to have a bigger impact than the vaccine (I would think).  The chicken-pox vaccine is the best example I can think of for a vaccine with relatively short-term immunity where the disease was endemic before the vaccine, and we’re not seeing any sign, that I know of, that chicken-pox is entering an epidemic situation.

The more relevant situation, I think, is for the natural epidemics.  As I say, both influenza and norovirus are well known for short-term immunity from natural infection, so maybe this is a factor there.  On the other hand, measles, which is spectacularly epidemic, has pretty long-term immunity from both the vaccine and the natural disease, and I don’t see any sign that the vaccine changed the personality of measles epidemics qualitatively (though of course, quantitatively the epidemics are much smaller now).

On the other other hand, of course it’s entirely possible that I completely misunderstand this paper, so if someone has a better grasp than I do please feel free to correct me.

  1. Blyuss, K., & Kyrychko, Y. (2009). Stability and Bifurcations in an Epidemic Model with Varying Immunity Period Bulletin of Mathematical Biology, 72 (2), 490-505 DOI: 10.1007/s11538-009-9458-y[][][]
  2. I think[]
  3. “For numerical bifurcation analysis of system with weak and strong kernels, we use a Matlab package traceDDE, which is based on pseudo-spectral differentiation and allows one to find characteristic roots and stability charts for linear autonomous systems of delay differential equations … “[]
  4. This is true for some vaccines, though not all[]
April 20th, 2010

Rotavirus vaccine and herd immunity

Rotaviruses are one of the most common causes of gastroenteritis in children.  A new rotavirus vaccine was introduced a few years ago; what impact has it had on disease?

This study confirms on a national scale that the 2008 rotavirus season among children aged <5 years was dramatically reduced compared to pre-RV5 seasons.  …  Based on the observed decrease during the 2008 season, we estimated that ~55,000 acute gastroenteritis hospitalizations were prevented during the 2008 rotavirus season in the United States. A decrease of this magnitude would translate into the elimination of 1 in every 20 hospitalizations among US children aged <5 years.1

(My emphasis)

Here’s what that looks like:

Rotavirus vaccine vs. gastroenteritis

Monthly acute gastroenteritis and rotavirus-confirmed hospitalization rates.  The rotavirus vaccine was introduced in 2006; in 2007 about 3% of children were completely vaccinated; in 2008 about 33% were vaccinated 1

Interestingly, the reduction in gastroenteritis wasn’t only in vaccinated children:

In 2008, acute gastroenteritis hospitalization rates decreased for all children aged <5 years, including those who were either too young or too old to be eligible for RV5 vaccination. …These findings … raise the possibility that vaccination of a proportion of the population could be conferring indirect benefits (ie, herd immunity) to nonvaccinated individuals through reduced viral transmission in the community1

(My emphasis, again)

Assuming this continues to hold up (and similar studies2 have found similar large reductions) it’s a striking example of herd immunity.

(Added later: The vaccine this paper looked at was RotaTeq.  This is not the vaccine that was recently found to be contaminated with porcine circovirus genomic fragments; that was the other rotavirus vaccine, Rotarix.)3

(Second update: RotaTeq apparently also is contaminated with porcine circovirus genomic fragments.)

  1. Curns, A., Steiner, C., Barrett, M., Hunter, K., Wilson, E., & Parashar, U. (2010). Reduction in Acute Gastroenteritis Hospitalizations among US Children After Introduction of Rotavirus Vaccine: Analysis of Hospital Discharge Data from 18 US States The Journal of Infectious Diseases DOI: 10.1086/652403[][][]
  2. For references see
    Weinberg, G., & Szilagyi, P. (2010). Vaccine Epidemiology: Efficacy, Effectiveness, and the Translational Research Roadmap The Journal of Infectious Diseases DOI: 10.1086/652404[]
  3. I haven’t talked about the Rotarix withdrawal because I think it’s been widely and very well covered on other blogs.  (I have 536 papers in my list of things I want to talk about here some time, so I usually don’t bother blogging about findings other places cover in detail.)  Vincent Racaniello at the Virology Blog has his usual high-quality commentary on it here.  He also made an important point on his podcast, This Week In Virology (either number 75 or number 77, I don’t remember which), which I don’t see explicitly on the post: The circovirus-containing vaccine went through all the safety trials, and no problems were seen.

    Obviously circovirus genomes aren’t supposed to be in the vaccine and they’ve got to go.  But (1) we don’t know if the genomes are infectious, or just fragments; (2) there’s no evidence, in spite of centuries of exposure to porcine circovirus, that it has any effects in humans; (3) the vaccines were shown to be safe, at least in the short term.

    On a larger scale, we’re entering a new era of analysis.  I suspect more of this sort of contamination will turn up as the sensitivity of our screening techniques improve, much like chemical detection: As we improve chemical detection to the parts-per-billion and parts-per-trillion level there needs to be better understanding of safety levels. Is this true for biologics? There are good arguments that there may be no safe level for some biologics, and any detection should lead to withdrawal, but on the other hand there clearly is a safe level for other biologics.  Human poop is loaded with vast amounts of viruses of peppers, for example; now that we know that should we regulate pepper mottle virus?

    I don’t have answers, which is why I relegate this to a footnote, albeit a long a rambling footnote.[]

April 15th, 2010

Living in the future: Mouse TcR clones

T cell receptor (top) interacting with MHC

It would be nice if I could claim that advances in biology are driven by pure intellectual processes, by hermits on mountaintops achieving new theories through mediation and  deep, pure thoughts. Of course, that’s not the case.  I think its fair to say that many, if not most, of the advances in immunology and virology are driven by new technology. Every so often, some lab comes up with some new way of looking at cells (say, multicolor intracellular staining and flow cytometry) or measuring something about the cells (MHC tetramers, maybe), and we go back to look again at the problems we’ve been struggling, using this new approach, and sometimes the new approach cracks open the problem (usually revealing new and even more interesting problems inside, but that’s why we do this, right?)

(I’m not trying to say that all the advances in the field are technique-driven. Charlie Janeway’s “Dirty Little Secrets” essay didn’t rely on new techniques, and neither did the concept of cross-priming, or lots of others. I’m just saying that new techniques do have a huge influence.)

A particularly cool new technique was just described by Hidde Ploegh, in association with Rudi Jaenisch. 1 Basically, it’s a new way of making TcR-transgenic mice.  TcR transgenics have been around for a long time2 and have led to a quite a few advances in immunology  — they’re now just another tool that’s used in lots of basic research.

thymocytes in the hthymus
Thymocytes developing in the thymus

But making a TcR transgenic mouse is a fair bit of work.  You need to find the T cell you’re interested in, clone out both TcR chains, clone them into the right transgenic vector and transfer them into a stem cell, then make a mouse from that and usually backcross it to a RAG knockout for a dozen generations before you can actually use it. And then you can ask whatever question you had, a couple of years ago when you started all this.

(If that didn’t mean much to you: The TcR is the T cell receptor. It’s what makes T cells specific. Each T cell as it comes out of the thymus has its own, distinct receptor.  It’s distinct at the protein level, and the reason its distinct is that the genome of the T cell is also unique.  The genome of T cells gets sliced and diced and glued back together in a unique way.  If you want to get a duplicate of that receptor you grab that DNA, for both halves of the receptor [that is, the alpha and the beta chains] and plunk it back into other t cells, and hen those t cells will all recognize the same thing.  This is wildly oversimplified, of course, but it’s close enough.)

Ploegh’s group figured a way around most of this, just by cloning a mouse straight from the T cell.

(By the way, saying “just cloning a mouse” — I know we’re living in the future right now. 3  It reminds me of when I was in Worcester, in the early 2000s when Advanced Cell Technology was cloning cattle, seeing a group of protesters at the corner of my street holding signs protesting cloning.  “Ban cloning! No to flying cars! Martians go home!”)

T cell receptor
T cell receptor

Anyway, here in the future, we, or at least Jaenisch, have reached a point where they can quite routinely clone mice from somatic cells; that is, from skin cells or from, say, T cells.  So that’s what they did. They took a T cell that recognized the specific antigen they were interested in,4 and used that to clone out a mouse.  Since the T cell had already undergone its genome rearrangement (and since that can only happen once), all the cells in the new, cloned, mouse ended up with the properly rearranged DNA.  That means the TcR in these mice is fixed, so all the T cells in these mice will recognize the antigen you want, instead of several trillion different antigens.

Essentially these are TcR transgenics, only faster and better.  Better, because, for example, there’s no endogenous TcR to eliminate, so no back-crossing to RAG mice — though they did have to do some back-crossing — and the TcR gene is in the right place under all the right regulation and so on.  They also point out that the standard procedures for making TcR has to start with activated T cells that have been through repeated rounds of stimulation, whereas this approach lets you start with naive cells.  I’m not quite sure this is a huge factor, but I won’t argue the point.

It’s one of those things that seems fairly obvious once it’s done, but (at least to me) was not at all obvious until they actually did it.  I have a feeling that it’s probably not quite as easy as they made it sound, but is still doable by most labs if they really want to do it; so I think it’s something we’re going to see quite a bit of in the next few years. It should help move the mouse field into testing more relevant and accurate systems.

The time-consuming generation of transgenic mouse models has led to the widespread use of a limited number of surrogate antigens, such as ovalbumin (recognized by the OT-I and OT-II transgenic mice) to study the immunobiology of infectious disease. Pathogens engineered to produce fragments of ovalbumin, and the immune reaction against it, are unlikely to capture all essential aspects of the physiological response. 1

  1. Kirak O, Frickel EM, Grotenbreg GM, Suh H, Jaenisch R, & Ploegh HL (2010). Transnuclear mice with predefined T cell receptor specificities against Toxoplasma gondii obtained via SCNT. Science (New York, N.Y.), 328 (5975), 243-8 PMID: 20378817[][]
  2. I think the first one was von Boehmer’s, in 1988:
    Kisielow P, Blüthmann H, Staerz UD, Steinmetz M, & von Boehmer H (1988). Tolerance in T-cell-receptor transgenic mice involves deletion of nonmature CD4+8+ thymocytes. Nature, 333 (6175), 742-6 PMID: 3260350[]
  3. Also, living in the future-wise, I wrote this a first draft of this on my iPad while sitting at my son’s soccer practice. (The iPad turns out to be fine for typing, but not so much for WordPress input — links and images are a problem.)  []
  4. This is the new part — previous clones have been made from lymphocytes, but the target was unknown.[]
April 12th, 2010

iPad for science? Not yet

Yeah, I got an iPad.

My main use for an iPad is for the things it’s very good at: My kids watch videos and play games on it, I read books and news and watch baseball games. It’s good for email, great for internet, and you already know all that.

One of the things I had hoped, though, is that I could use it for “real” work: Word processing, spreadsheets, and presentations, using Apple’s iWork apps.  Having taken a look at two of the three: Not so much.

Don’t expect the iPad versions of Pages or Keynote to be very useful.  These are not the full-fledged versions that Jobs sold them as; they’re stripped down mobile versions, missing critical features.  They have a limited number of fonts. (Pages doesn’t have Times New Roman, which is about as basic as you can find. I take it back; Times New Roman is there. But when I imported a document in, I got an error message saying that it didn’t have the font Times New Roman.  No idea why. Symbol is not there, which is a definite problem for people working with alpha-herpesviruses or alpha-beta T cells and so on.) They can’t handle some media types. I don’t know which ones, but a simple presentation I imported into Keynote was riddled with question marks where it couldn’t render images.

(I see that Chris Anderson at The Unofficial Apple Weblog reaches the same conclusion about Keynote.)

The various ways for syncing documents are clumsy and awkward.  I am still hoping that there will be some kind of shared folder eventually that, say, Dropbox can use; but for now you have to drag things back and forth individually.

If you start a document on the iPad, I’m sure it will look fine.  But if you have some documents on your computer, and you’re thinking you’ll be able to use the iPad to work on them down at the coffee shop or on your deck, don’t be too confident.

It’s not a dealbreaker for me, but if you need an iPad for real work, don’t count on this release of iWorks.

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[]
April 9th, 2010

Immune evasion versus superinfection

HCMV from J Virol
Human cytomegalovirus-infected cell

A number of viruses, especially herpesviruses, block the MHC class I antigen presentation system. It’s been widely assumed that this is for the obvious reason and that it allows the virus to avoid T cell recognition and elimination. But there’s been an awkward lack of experimental support for that assumption, to the point that I’ve begun to question it (and, more productively, to develop experimental systems with which to test it).   (See the list of posts below for some of my earlier comment on the subject.)

Now, at last, Klaus Fruh offers actual evidence that this assumption may be correct. 1

This deserves a long post, which it’s not going to get today.2 Briefly, Klaus’s group used a herpesvirus of monkeys (rhesus cytomegalovirus; rhCMV) to test this. This is closely related to the human herpesvirus human cytomegalovirus, which is a ubiquitous virus; the vast majority of humans have it, are infected with it as toddlers, remain infected with it throughout their lives, and don’t suffer any problems with it. It’s a rare cause of a mono-like disease, and it’s a concern in immune-suppressed people (especially transplant recipients), but mainly it seems to be a pretty innocuous hitchhiker.

Previous posts on MHC class I immune evasion

Immune evasion does work
Herpesvirus immune evasion: An emerging theme?

Immune evasion: Who needs it?

Viral T cell evasion in vivo: The vanishing evidence

Immune evasion: What is it good for?

The CMV family of herpesviruses carry a particularly impressive arsenal of anti-MHC class I immune evasion genes. (MHC class I is the target that antiviral T cells, also known as cytotoxic T lymphocytes or CTL, recognize. There’s an outline of the process that permits that recognition here.) Whereas herpesviruses like herpes simplex, or chicken-pox virus, and so on, seem to use only one gene to block MHC class I, CMVs seem to use three or four. This would suggest that this sort of immune evasion is really important for these viruses, but when Ann Hill actually tested that notion in mice3 removing these immune evasion genes had only a very small impact.

Fruh’s group has now done something similar using his rhesus model, and looked at an unusual characteristic of CMVs: They are able to repeatedly superinfect the same host. That is, someone4 can be infected with CMV, can have an apparently effective immune response to CMV, and yet can be infected by a new CMV virus. This is pretty unusual, of course. You’d expect that there would be a vaccine-type effect, in which the natural infection would drive a protective immune response. As far as I know, you don’t often see this sort of superinfection even with other herpesviruses, which i