Mystery Rays from Outer Space

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

September 30th, 2009

Viruses and icebergs


Metagenomics is a rapidly-expanding field that repeatedly tells us how little we know.

Metagenomics is basically the process of surveying genomes in the environment.  By going to genome analysis as directly as possible, this reduces the issues of isolation and culture. If you can isolate or grow bacteria or viruses, you probably already have a fairly decent idea of what you’re looking for.  Metagenomics lets you see what’s actually there, not what you think should be there or what you happen to be able to work with.  And it seems that wherever the metagenomists go looking, there are vast numbers of viruses1 hiding, unculturable or unidentifiable. We’ve been looking at the tips of the icebergs, thinking that the little bumps and valleys we’ve mapped are the whole story; and now we have to start looking at the hidden part.

This is true whether the samples are from what we think of as the “environment” (lakes, oceans, soil) or from animals and people.  Pretty typically, most of the genomes that get turned up — well over half of them — don’t look like anything we know about.  Just to put a little context on that, over 2000 viruses have had their genomes completely sequenced, and there are over 1,000,000 sequences in GenBank tagged “virus”;  yet if you go and look pretty much anywhere, most 0f the viruses there are completely new to us, so different that we can’t even detect the most distant relationship to anything we know about.

For example, in Lake Needham, in Maryland, “a large majority (~66%) of these assemblies had no significant homology to any known sequences of viral, bacterial, eukaryotic and archaeal origin …but appeared to be most likely derived from novel viruses“.  2.  In reclaimed water, “Over 50% of the viral metagenomic sequences (both DNA and RNA) identified in reclaimed water metagenomes had no significant similarity to proteins in GenBank“;3 In ocean samples, “On average, >91% of the sequences were not significantly similar to those in the extant databases.” 4


That might not be too surprising — there hasn’t been long-standing, intense interest in viruses in lakes, so you’d expect to find a lot of new stuff.  But even in us, a good half of our viral inhabitants are unknown.5 For example, in stool samples scanned for viruses, “Most of the sequences were unrelated to anything previously reported.6

Most of these unknown viruses are probably harmless.  Many are probably just hitch-hikers, traveling through our intestines only because we ate, say, the pepper that they were infecting. 7  But metagenomics has recently also started turning up new pathogens (or at any rate, viruses that may be pathogens), of humans as well as other species.5 Some of these are really new: A virus isolated from sea turtles, that potentially is involved in the fibropapilloma disease that’s spreading in them, “may represent a new viral genus of the Circoviridae family or possibly even a new viral family.8

In the next few years, there’s going to be yet another data explosion, as metagenomics turns up new things in astronomical numbers.  Clinical research is going to have to scramble to understand what these mean — which of these are pathogens, which are irrelevant as far as disease and health?  We’re going to need new tools to understand and screen these things.  It should be interesting to see what happens.

  1. And bacteria, but bacteria aren’t very interesting, are they[]
  2. Djikeng A, Kuzmickas R, Anderson NG, Spiro DJ (2009) Metagenomic Analysis of RNA Viruses in a Fresh Water Lake. PLoS ONE 4(9): e7264. doi:10.1371/journal.pone.0007264[]
  3. Rosario, K., Nilsson, C., Lim, Y., Ruan, Y., & Breitbart, M. (2009). Metagenomic analysis of viruses in reclaimed water Environmental Microbiology DOI: 10.1111/j.1462-2920.2009.01964.x[]
  4. Angly, F., Felts, B., Breitbart, M., Salamon, P., Edwards, R., Carlson, C., Chan, A., Haynes, M., Kelley, S., Liu, H., Mahaffy, J., Mueller, J., Nulton, J., Olson, R., Parsons, R., Rayhawk, S., Suttle, C., & Rohwer, F. (2006). The Marine Viromes of Four Oceanic Regions PLoS Biology, 4 (11) DOI: 10.1371/journal.pbio.0040368[]
  5. Victoria, J., Kapoor, A., Li, L., Blinkova, O., Slikas, B., Wang, C., Naeem, A., Zaidi, S., & Delwart, E. (2009). Metagenomic Analyses of Viruses in Stool Samples from Children with Acute Flaccid Paralysis Journal of Virology, 83 (9), 4642-4651 DOI: 10.1128/JVI.02301-08[][]
  6. Breitbart, M., Hewson, I., Felts, B., Mahaffy, J., Nulton, J., Salamon, P., & Rohwer, F. (2003). Metagenomic Analyses of an Uncultured Viral Community from Human Feces Journal of Bacteriology, 185 (20), 6220-6223 DOI: 10.1128/JB.185.20.6220-6223.2003[]
  7. Zhang, T., Breitbart, M., Lee, W., Run, J., Wei, C., Soh, S., Hibberd, M., Liu, E., Rohwer, F., & Ruan, Y. (2006). RNA Viral Community in Human Feces: Prevalence of Plant Pathogenic Viruses PLoS Biology, 4 (1) DOI: 10.1371/journal.pbio.0040003[]
  8. Ng, T., Manire, C., Borrowman, K., Langer, T., Ehrhart, L., & Breitbart, M. (2008). Discovery of a Novel Single-Stranded DNA Virus from a Sea Turtle Fibropapilloma by Using Viral Metagenomics Journal of Virology, 83 (6), 2500-2509 DOI: 10.1128/JVI.01946-08[]
September 21st, 2009

Starting an immune response: Find your dance partner

Far Side "Just gotta be me"At the peak of an immune response, hundreds of thousands of identical T cells are scampering about, searching out the pathogen and doing their own special T cell things to try to get rid of it. We know that these hundreds of thousands of cells weren’t there at the onset of infection; the whole T cell schtick involves rapid expansion of very, very rare cells. Only a very few T cells are able to recognize any particular antigen; but within a few days, the progeny of those rare cells are now common, and all retain their ability to recognize the same antigen. 1

In the past few years, we’ve learned a little more, quantitatively, just how dramatic this expansion phase is. Delicate work has established that there are maybe 20 to 1000 potentially-reactive T cells in a mouse, before infection (see my discussion here and here). Those few cells are the precursors of the huge numbers of T cells a week or so after infection.

If you think about it, it’s pretty remarkable that these few T cells, hidden within millions upon millions of other, irrelevant, T cells, ever get the signal to divide. That signal is carried by specialist antigen-presenting cells, most often dendritic cells. A simplified overview goes something like this: A dendritic cell is hanging out somewhere in the body — let’s say in the skin. It’s constantly filter-feeding, sampling the surrounding environment. Mostly, this surrounding environment is innocuous; there are only normal self antigens in it. If the DC finds evidence of an infection, like, say, viral RNA, then it grinds into action; it migrates to a local lymph node and shows the local T cells everything it (the DC) has been exposed to over the past 24 hours or so. Early in an infection, there usually aren’t a lot of pathogens present, and so there aren’t very many of these activated DC; maybe a few hundred or a thousand.

Meanwhile, the naive T cells are also roaming around, from lymph node to lymph node, scanning as many DC as they can. If there’s nothing they recognize in the DCs of one lymph node, off they go to the next for more scanning.

So for a T cell to get the go signal, a tiny number of specific T cells (behaving identically to the vast number of irrelevant T cells) have to make contact with a tiny number of specific DC (hidden within the vast expanse of all the body’s lymphoid tissue), just by randomly bumping into each other.

Now, here’s a question: When you get that large pool of activated T cells a week after infection, are these the product of all (let’s say) 500 precursors, or are they all the progeny of one or two lucky precursors that managed to bump into the right DC? In other words, how efficient is the bumping-into-DC process? It’s remarkable enough that it works at all; could it, even more remarkably, be so efficient that all the possible precursors manage to find a partner in the first day?

Amazingly enough, the answer is yes, all the precursors do find a partner. A paper from Ton Schumacher’s group2 has showed that there are virtually no wallflowers. Recruitment into the activated state is close to perfect (they demonstrate 95%). They go on to calculate how many random interactions you need, to get this level of recruitment, and suggest that there have to be over 50 million interactions to get this level of recruitment — but this is still in the right ballpark from what we know about rate of interactions:

Assuming that naïve antigen-specific CD8+ T cells are present at a frequency of ~1:100,000 within a CD8+ T cell pool of ~20 x 106 cells, it would require around 59 x 106 T-DC interactions to achieve 95% recruitment, a number that is largely independent of variations in precursor frequency within the physiological range. It has been estimated that DCs are able to interact with at least 500 different T cells/hour; thus, a pool of <2000 antigen-presenting DCs could suffice to achieve this near-complete recruitment. 2

A couple of groups have made movies of T cell/DC interactions, and when you look at those the figures seem more plausible.  Here3 is a movie from the Cahalan lab.4  (Go to their lab page for more fascinating immunology movies, by the way.) This is two-photon microscopy, taken in the lymph node of a live mouse.  DC are green, T cells are, hmm, sort of an orangey red.  The left panel shows T cells interacting with DC that don’t have the appropriate antigen; the T cells charge in, take a quick look at the DC, bounce off, and move on to the next one.  (This movie is time-compressed, of course; we’re looking at a couple of hours here.)  On the right, we see DC that do have the appropriate target for the T cells.  Here the T cells have bumped into the DC and immediately stopped looking further; they just sort of hang with the DC, soaking up the information, and preparing to move off into the activated T cell program.

The frenetic action on the left gives a sense of the rate of interaction, and 500 T cells per hour doesn’t seem too far off.

  1. And then, as part of the same program that triggered their massive expansion, the cast majority of the expanded T cells die off again, restoring the immune system almost to the original status quo.[]
  2. van Heijst, J., Gerlach, C., Swart, E., Sie, D., Nunes-Alves, C., Kerkhoven, R., Arens, R., Correia-Neves, M., Schepers, K., & Schumacher, T. (2009). Recruitment of Antigen-Specific CD8+ T Cells in Response to Infection Is Markedly Efficient Science, 325 (5945), 1265-1269 DOI: 10.1126/science.1175455[][]
  3. If I’ve embedded this properly[]
  4. Miller, M. (2002). Two-Photon Imaging of Lymphocyte Motility and Antigen Response in Intact Lymph Node Science, 296 (5574), 1869-1873 DOI: 10.1126/science.1070051[]
September 18th, 2009

Beautiful tumors

A new technique called optical frequency domain imaging (OFDI) provides amazing images of tumors (especially their blood vessels) in situ.

3D tumor vasculature with OFDI
“(a) OFDI images of representative control and treated tumors 5 d after initiation of antiangiogenic VEGFR-2. The lymphatic
vascular networks are also presented (blue) for both tumors. (b) Quantification of tumor volume and vascular geometry and
morphology in response to VEGFR-2 blockade.”

Vakoc, B., Lanning, R., Tyrrell, J., Padera, T., Bartlett, L., Stylianopoulos, T., Munn, L., Tearney, G., Fukumura, D., Jain, R., & Bouma, B. (2009). Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging Nature Medicine DOI: 10.1038/nm.1971

September 17th, 2009

Stealth influenza

"Avoid influenza, gargle daily"
“How to avoid influenza: Gargle Daily”

Every virus that infects a vertebrate, has to be able to deal with the vertebrate immune system. The virus’s ancestors that infected vertebrates must have been able to deal with the vertebrate immune system. Those viruses that couldn’t handle an immune response are extinct.

Some of the ways viruses handle immunity, we don’t think of as really “specific”. Rapid replication, for example, has benefits for the virus that extend past just beating the immune system to the punch. But just about every virus, even the smallest ones, also have some form of specific immune evasion gene — some way of blocking, dodging, diverting, or confusing the immune system.

In spite of this nearly universal presence, we don’t really have a good grasp of precisely what viral immune evasion genes do, as far as supporting viral pathogenesis. (For that matter, it’s only for a handful of viruses that we really have much understanding of the pathogenesis in general.) Some viruses have a huge number of genes that are clearly immune evasion genes, others apparently only have one or two. Sometimes you can knock out an immune evasion gene and virtually destroy the virus’s ability to infect; sometimes the knockout only has a modest effect; sometimes there’s no effect at all, or it may even make the virus more, rather than less, virulent.

Viruses are so different from each other1 that there are probably few if any general rules for immune evasion. Still, we’re not even at a point yet where we have non-general rules, so the more we learn the more likely we are to see patterns.

Physicians thank influenza (1803)
Physicians expressing their thanks to influenza.
Coloured etching attributed to Temple West, 1803.

Influenza, of course, has its own set of immune evasion genes. The most important one is the NS1 gene.2 NS1 blocks the interferon pathway, and to the extent that we can generalize, it seems that blocking interferon is one of the most critical things any virus can do. Almost every virus has some way of meddling with the interferon pathways, whether by preventing interferon from being triggered or inducing resistance to the effects of interferon. It’s been known for quite a while that NS1 does this — prevents interferon from being turned on — for influenza viruses, and it’s also been known that NS1 is very, very important to the virus. Mutant influenza viruses without NS1 are much, much less virulent than wild-type virus, and even targeting NS1 after an infection has started can help treat influenza.

(A flip side of this is that influenza viruses with a particularly effective NS1 may be more virulent. The 1918 pandemic influenza, which had a very high mortality rate,3 seems to have a particularly effective NS1 that can block interferon in several ways, and it’s been shown that swapping just the NS1 from the 1918 virus can make otherwise mild flu viruses more virulent. See my previous post about that.)

But there’s a bit of a paradox here. We know that NS1, the interferon blocker, is important to influenza virus. But we also know that interferon is very important in controlling influenza virus infections. For example, mice that can’t respond to interferons are much more susceptible to infection with avian influenza.4 So if NS1 works by blocking interferon, why does interferon still protect?

For that matter, one of the major explanations for why some influenza viruses (like avian flu and the 1918 flu) are so virulent, is the “cytokine storm” hypothesis.  (I talked about cytokine storms here and here.)  According to this concept, these viruses are especially lethal because they induce a huge release of cytokines, such as interferon. Yet at the same time the argument is made that these viruses are the ones with especially effective interferon blockers. If they’re really good at blocking interferon, then why do people die of having too much interferon?

It turns out that part of the answer may be timing. A recent paper from Thomas Moran’s group5 shows that in the very earliest stages of influenza virus infection, interferons are not being produced; then, a couple of days in, there’s a sudden big bang of cytokines. Knocking NS1 out of the virus changed this; interferons were produced from the beginning of the infection, and the virus was shut down. They call this phenomenon “stealth replication”:

Our data demonstrate that the initiation of lung inflammation does not begin until almost 2 full days after infection, when virus replication reaches its apex. The migration of lung DCs to lymph nodes and the subsequent priming of naive T cells are similarly subject to this delay. We demonstrate that the delay in the generation of immediate lung inflammation is mediated by the influenza NS1 protein. We propose that the virally encoded NS1 protein establishes a time-limited “stealth phase” during which the replicating influenza virus remains undetected, thus preventing the immediate initiation of innate and adaptive immunity. 5

They point out that in normal human influenza virus infection, symptoms take a couple of days to kick in, which fits because most of the “flu-like symptoms” we talk about are generic effects of cytokines. They also point out that a lot of virus transmission occurs before symptoms — i.e. in the first couple days of infection.

Thus, a stealth phase may also occur in humans and probably functions to maximize the probability of transmission before cytokines such as type I IFNs hamper the normal replicative cycle of influenza virus.5

This also helps make sense of the cytokine storm concept, I think. If avian or 1918 NS1 is especially good at preventing cytokines, then there might be a slightly longer stealth period, during which time the virus can replicate more. Then, when the immune system suddenly does become aware of an infection, there’s a huge amount of virus present, and the cytokine response would be correspondingly huge.

We might even be able to generalize to other viruses:

The stealth phase concept is not only applicable to influenza virus but can probably be extended to virtually all “real” human viral pathogens that have been shown to have an asymptomatic incubation time. For example, measles and varicella zoster viruses have a substantially prolonged evasion period that can last up to 2 wk. During this asymptomatic phase, these viruses also transmit to other susceptible hosts. Research aimed at interfering with the stealth phase may lead to the development of novel modulators as preventive treatments that target this early immune evasion mechanism. 5

I want to point to a previous post I made here, too, about herpes simplex virus. HSV has a wide range of immune evasion molecules, and we don’t have much understanding of what these things do in a natural infection.Frank Carbone’s group  did experiments with mouse infection that showed that HSV has a very narrow window (less than 24 hours) during which it can move from its original site of infection, in the skin, to neurons where it sets up a life-long infection. If the immune response can control HSV in this window, the virus can’t get into neurons and its life cycle is cut short. I speculated at the time that this might help explain immune evasion by HSV — it wouldn’t have to be super efficient, just keep things under control during that brief, early window. Seems quite similar to the influenza situation: Timing is critical, and perhaps immune evasion is one reason why.

  1. “Virus” isn’t a natural division; it groups together things with very different, and completely unconnected, evolutionary histories[]
  2. “NS” stands for “Non-structural”, meaning that the protein isn’t part of the virion that floats around and infects new cells — rather, the NS1 protein is produced anew in each cell after infection.[]
  3. As influenza infections go — not close to something like smallpox or ebola, but some 20 times higher than normal seasonal flu[]
  4. Szretter, K., Gangappa, S., Belser, J., Zeng, H., Chen, H., Matsuoka, Y., Sambhara, S., Swayne, D., Tumpey, T., & Katz, J. (2009). Early Control of H5N1 Influenza Virus Replication by the Type I Interferon Response in Mice Journal of Virology, 83 (11), 5825-5834 DOI: 10.1128/JVI.02144-08[]
  5. Moltedo, B., Lopez, C., Pazos, M., Becker, M., Hermesh, T., & Moran, T. (2009). Cutting Edge: Stealth Influenza Virus Replication Precedes the Initiation of Adaptive Immunity The Journal of Immunology, 183 (6), 3569-3573 DOI: 10.4049/jimmunol.0900091[][][][]
September 10th, 2009

Predicting norovirus epidemics

Norovirus (from J Virol. 82:2079-2088 (2008))

Noroviruses cause an unpleasant, but rarely serious, diarrhea and vomiting-type disease — “cruise ship flu”1 is one term for it.  As well as cruise ships, nursing homes and hospitals and other more or less closed systems also see outbreaks of norovirus disease fairly regularly, and as you’d expect the elderly and immune-compromised are more at risk from the disease.

Noroviruses have been around for a long time (they were first identified in the early 1970s, as “Norwalk Viruses”), but it’s in the last ten years or less that they really exploded; in 2002 there was an abrupt, worldwide upsurge of norovirus outbreaks, and more epidemics have followed almost every winter. Those outbreaks were all different mutant variants of norovirus; I talked about this earlier.2 Each outbreak3 was associated with a new variant of norovirus, that is no longer controlled by the immunity that controlled the previous outbreak.

A couple of recent papers look at norovirus epidemics more closely. One4 analyzed the different strains involved in global outbreaks. They found that there were eight distinct variants of the GII.4 noroviruses, three of which caused global epidemics. Other strains did become epidemic, but on a more local scale (countries or continents, rather than everywhere).

My first thought was that that’s essentially the strategy that influenza viruses have used, also very effectively; each new flu season sees new variants of influenza virus, and each season’s most abundant viruses are the ones that are less well controlled by the immunity among their target population. This is the notorious “antigenic shift” that beginning virologists learn to parrot in their first class. The parallel to influenza epidemics was also noted by the authors, and they pointed out another parallel: Most of the global norovirus epidemics seem to have originated in Asia, as with influenza A.

What surprised me originally about the norovirus equivalent of antigenic shift was that at the time, conventional wisdom had it that immunity doesn’t play a big part in controlling seasonal norovirus outbreaks; immunity to noroviruses is weak and short-lived, and I had not thought that immunity from the previous winter would be a factor in controlling outbreaks this winter. The previous paper I talked about showed evidence, though, that immunity is a major factor in determining norovirus epidemics, and the other paper I have here5 looks at this in much more detail. I won’t go into their work in any detail6 but what they’re doing is building predictive models for norovirus epidemics. Very briefly, their overall conclusion is:

These results point to a complex interplay between host, viral and climatic factors driving norovirus epidemic patterns. Increases in norovirus are associated with cold, dry temperature, low population immunity and the emergence of novel genogroup 2 type 4 antigenic variants.5

The “new variant” part matches the first paper’s description of epidemics — mostly, but not always, they’re driven by a new version of the virus, but new variants don’t necessarily explode globally. It seems that a new variant may often be necessary for an outbreak, but isn’t sufficient; and in some cases, other factors may mean new variants aren’t absolutely necessary. Cool, dry weather supports an epidemic (and this is probably a big part of the highly seasonal pattern of norovirus infections, as well; it’s charmingly called “Winter vomiting disease” by some). And epidemics are possible when population immunity to a particular strain of norovirus drops under a certain level.

Norovirus outbreak prediction
“Daily norovirus laboratory reports (grey circles) and
predicted values (red line) from full model including
temperature, relative humidity, immunity, new variants
and autoregressive terms and other confounders.

The authors point out that new variants are selected by population immunity, so two of these factors are not strictly independent. However, “Despite this, these two factors had significant effects after controlling for the other”;5 perhaps there’s some immunity even to variant strains of norovirus. Since immunity to norovirus does drop very quickly,7 perhaps a year is enough to open a window for new strains, but not for the same one; particularly if the weather cooperates. Or perhaps the arrow is going the other way — population immunity at the end of one season chokes out the prevalent strain, and only new strains that are relatively resistant survive to cause the next epidemic once the weather cooperates.

At any rate, from these parameters the authors derived a predictive model. Applied retrospectively, it looks pretty impressive (see the figure to the right; click for a larger version). 8  It will be interesting to see how well it actually predicts new outbreaks.

By the way, regular readers may have noticed that this is two weeks in a row with only one new post — I usually aim for two or three per week, but what with my kids starting their school this week, and my teaching schedule9 kicking in, I’m scrambling some to keep up.  Hopefully I’ll be in more control soon, but I make no promises.

  1. It’s not flu![]
  2. Referring to this paper: Lindesmith, L.C., Donaldson, E.F., LoBue, A.D., Cannon, J.L., Zheng, D., Vinje, J., Baric, R.S. (2008). Mechanisms of GII.4 Norovirus Persistence in Human Populations . PLoS Medicine, 5(2), e31. DOI: 10.1371/journal.pmed.0050031[]
  3. Except for the 2007/08 outbreak, which was mainly the same strain as the previous year’s[]
  4. Siebenga, J., Vennema, H., Zheng, D., Vinjé, J., Lee, B., Pang, X., Ho, E., Lim, W., Choudekar, A., Broor, S., Halperin, T., Rasool, N., Hewitt, J., Greening, G., Jin, M., Duan, Z., Lucero, Y., O’Ryan, M., Hoehne, M., Schreier, E., Ratcliff, R., White, P., Iritani, N., Reuter, G., & Koopmans, M. (2009). Norovirus Illness Is a Global Problem: Emergence and Spread of Norovirus GII.4 Variants, 2001–2007 The Journal of Infectious Diseases, 200 (5), 802-812 DOI: 10.1086/605127[]
  5. Lopman, B., Armstrong, B., Atchison, C., & Gray, J. (2009). Host, Weather and Virological Factors Drive Norovirus Epidemiology: Time-Series Analysis of Laboratory Surveillance Data in England and Wales PLoS ONE, 4 (8) DOI: 10.1371/journal.pone.0006671[][][]
  6. There are intimidating equations and everything[]
  7. Though it’s not known exactly how quickly[]
  8. By the way, while looking around for images to illustrate this norovirus post, I came across a lot of images of people hurling, and worse.  I decided to stick with obscure graphs instead.  No need to thank me.[]
  9. A half-dozen classes in graduate immunology, a half dozen veterinary virology, and a dozen in veterinary immunology this year; plus a couple of guest lectures, where I’ll talk about immunity to viruses, probably focusing on swine-origin influenza virus as a particularly topical example[]
September 2nd, 2009

Measles deaths, pre-vaccine

Update: This post may be confusing because of differences between measles cases (which didn’t drop until vaccination) and measles deaths (which did drop dramatically before vaccination was introduced).  I’ve written much more about the drop in measles deaths, starting here: Measles Week, Part I.

There’s a claim running around the anti-vaccination circles that measles vaccination didn’t do anything because the disease had already dropped by 95% (or 98%) before vaccination was introduced.  That claim is false, of course, and I don’t really expect that debunking it will make any difference, but here it is anyway.

I’m not going to dignifiy the claim with a link.  The author shows a chart with measles incidence dramatically dropping in the early 1900s, and offers a list of references for the chart.  That chart is a flat lie; the references he cites don’t show numbers that bear any relation to the chart he has made up.  I encourage anyone who sees that to check out the references he links — clearly, he’s assuming that people are gullible enough to believe his claim without checking.  Also, of course, I encourage people to check my data.  I’ve taken my numbers from publically-accessible source (thanks to Google Books) and they’re from publications that preceded the vaccine, so there can’t be any claim that the numbers were manipulated by vaccine propnents.  (Unless, of course, the international vaccine conspiracy also has time machines and a vast transdimensional publishing and replacement arm.)

The post-vaccine data I’ve already shown, and I’ll repeat it here.  This is measles in the US, after 1950. (Click for a larger version.) You can easily see where the vaccine was introduced.

Measles cases and deaths after 1950
Measles incidence and deaths in the USA, after 1950

The anti-vaccine claim is that by 1950 measles had already dropped by 95%.  But public health data are available back to at least 1912, and there’s no support for that claim.  Here are the data from the US census (PDF link; measles incidence, expressed as rate per 100,000 population):

US Measles cases, 1912 on
Measles cases in the US, 1912-1997

The specific antivaccine claim is made for the UK.  Fortunately, again, death rates for England and Wales are well documented back into the 19th century.  Here’s what happened to death rates for measles from 1838 to 1937 in the UK (pre-vaccine, obviously).  This is taken from the Annual report of the Registrar-General for England and Wales, Volume 70 By Great Britain. General Register Office (the data up to 1890), from the Parliamentary papers, Volume 13 By Great Britain. Parliament. House of Commons (to 1920), and the 1937 data are from a text entitled “Anomalies and curiosities of medicine” By George Milbry Gould.  (I believe measles stopped being a reportable disease in the UK in 1921, and that’s why the Parliamentary reports stopped including it.)

Measles deaths in England and Wales, 1838-1937
Measles deaths in the UK, 1838-1937

Obviously, there’s no 95% drop. Measles deaths were pretty much constant for over 100 years, until the vaccine was introduced. (EDIT: This is wrong. I meant to write “measles incidence has been pretty much constant … ” not death.  Measles death rate did in fact drop, in some places rather markedly, in the first 50 years of the 20th century (see my long answer to Peter in the comments below for reasons).  The extent of the reduction in death rate (ie. frequency of deaths per case of measles) was very dependent on region — today it’s still 1900-level or so in third-world countries, for example — and depended heavily on nutrition and sanitation.  But the death rate had pretty much plateaued by the early 1950s, and it wasn’t until vaccination spectacularly reduced the incidence and took the deaths away with that.  Nevertheless the death rate drop was nowhere near the 95% claimed by antivaccine loons, as the charts here show.  Check the original sources.)

One important point is that measles is very much an epidemic disease.  It sweeps across a country (in standing waves that originate from cities), and then drops drastically until a new population of susceptible children are born.  That means if you want to fake your data you could draw from peaks and valleys of an epidemic and make it show whatever you want.  Here’s a more detailed illustration of measles — weekly number of cases — in England and Wales (from Benjamin Bolker at hte University of Florida, who compiled these from public reports):

Weekly measles cases 1945-1965
Weekly measles cases in England and Wales

As I say, I don’t really expect this to convince the loons, but I know there are lots of people who are not loons, but who may be puzzled (or fooled) by the loons’ lies.  Don’t take anyone’s word; check the original references.