Mystery Rays from Outer Space

My friend Lauredhel, at the Hoyden About Town blog, made an interesting point about risk factors for swine-origin influenza virus (SOIV), and the perception of those risk factors in the press.   The press has made a big deal of the putative link between obesity and risk of severe SOIV.  But, as she pointed out back in June, the data¹ at that point showed no such link — in fact the percent of obese people with severe SOIV was if anything lower than the frequency of obesity in the general population.  However, the press picked up on this because of a reply to a question at the May CDC press briefing, and the headlines were all over this (actually non-existent) link.

Lauredhel made the same point when a recent paper in the Medical Journal of Australia reviewed cases of SOIV. ²  Once again, Lauredhel points out, in this series of patients, obesity was actually lower in severely affected people than in the general population:

Around 18% of the Australian population is obese. Around 7% of people severely ill with H1N1 flu are obese. ³

However, I don’t think the story is that simple.  I made a few comments there, which I’ll review here.  The summary of the exchange, I think (Lauredhel might disagree) is that some, but not all, studies have found a connection between obesity and risk of severe SOIV; the largest studies do show a connection, but when looking at the overall picture, it’s not a strong connection.  The press has, however, vastly overstated this link, focusing on it apparently because of one comment in a press briefing, but ignoring several attempts to clarify and downplay the observation.

My comments:

The largest series I’m aware of is the recent JAMA study. ⁴ (The eMJA paper came about about simultaneously, so they can be forgiven for claiming theirs is the largest study.) Here 48% (of hospitalized or fatal cases, where data were recorded) were obese. The rate was slightly higher in fatal cases (66%, of 110 cases total) than non-fatal (52%, of 212 cases; these are cases over 18 years, and the non-fatal rate would be lower if we included younger people). The fatal cases, especially, were disproportionately in the highest BMI cases — 50% of fatal cases had a BMI over 40, if I’m reading their Table 2 right.

I don’t know what the relevant population rates of obesity are, so we don’t know relative risk. In the US generally, I believe obesity is in the 30% range. The authors say “Of adults with BMI data available, more than half were obese and one-quarter were morbidly obese. As a point of reference, the percentage of adults who are morbidly obese in the United States is 4.8%”.⁵

(An important concern is that this may be distorted. It looks as if data weren’t recorded for obesity on the majority of patients. I would worry about a recording bias, with information on obese patients being recorded more readily than for non-obese. Still, even if not one of the non-recorded patients had BMI over 40, the case rate is higher than the 4.8% background.)

In smaller studies, there seems to be a similar picture. In a Michigan survey (June ‘09) 9 of the 10 patients with swine-origin H1N1 hospitalized with ARDS were obese; in a European survey, 8 of 13 fatal cases were obese.

The numbers are still quite small, and they’re not all consistent, but from what I see here, I wouldn’t dismiss obesity as a risk factor.

Also, I see a survey in Australia⁶ where the relative risk of obesity and “morbid obesity” (BMI > 40) is worked out. Just as you note, the relative risk of “obesity” for death is less than 1 (0.6), but probably 1 is easily in the 95% confidence interval. But the RR for obesity of ICU admission is up (1.7) and for morbid obesity is 4.4; death RR for the latter is 2.4.

These data aren’t entirely consistent with the eMJA data, but I don’t have time to try to resolve the differences. One point that’s raised by the JAMA study I quoted above is whether BMI is specifically recorded in these cases. The eMJA study only notes 8 patients that were obese, but unlike the JAMA study they don’t explicitly give a denominator — that is, they don’t specifically say that the remaining 104 patients had a BMI recorded at all. Could the BMI simply not be available for some of these remaining patients? I don’t know, I’m asking.

Lauredhel said in the comments:

I saw the CDC and other ‘experts’ at the beginning deciding that obesity was obviously a major risk factor based on early data that appeared to show the complete opposite

I replied:

For what it’s worth, neither the CDC MMWR article from May, nor the eMJA paper that just came out,⁷ say that obesity is a factor. The CDC report includes obesity last in a list of underlying medical conditions, and never says that it’s a risk factor per se. The eMJA paper only mentions obesity when they define it, show the rate in a table, and don’t mention it in their discussion at all.

So I’m not sure you can blame the scientists here. The overwrought press coverage, as far as I can see, entirely arose out of a comment by Anne Schuchat in a press briefing. (Schuchat would not be one of the authors of the article.). Her comment certainly was misleading, but it’s not quoting the scientists who did the work; it seems to come out of nowhere. Not excusing her here, but I would bet her comment was in response to a specific question from the press, not something she raised herself, and she seems to have only been referring to “severe cases” (not all the cases in that MMWR report), which at that time would have been a tiny subset of a tiny subset of cases.

And she corrected herself, at least partially, later on. If you look at a subsequent press briefing (in July) she specifically says that the difference is not there, especially accounting for other underlying conditions, and “They [obese people] would not be a targeted group.” That didn’t get any press, as far as I can find.

So, not surprisingly, the press has done a poor job of covering this, jumping on the comment from Schuchat without checking the figures. The experts, at least those who are actually doing the work, aren’t making the connection except in those studies that actually do see a disproportionate number (the JAMA study and others).

Lauredhel said:

Schuchat’s original statements weren’t a single throwaway remark

I replied:

That’s true, but in fact the context of her explanation was exactly the point you are making — that the frequency of obesity in SOIV patients wasn’t necessarily higher than that in the population (”So it’s hard for us to say at this point to say whether the number of patients with reported obesity is significantly higher than we would expect”). In other words, I’d say that “her perceptions of obesity and risk” were pretty much what you’re saying.

That was the May press briefing, the one that led to the press rampage. Now, reading the press conference transcript, the point she tried to make didn’t come across very well, because she started off sounding as if she agreed with the obesity issue and didn’t make the qualifications until several questions later. She clearly recognized that she hadn’t been clear, because she tried to clarify the point in each of the subsequent briefings (in June and in July). But I’m not seeing this as the experts making assumptions — quite the opposite, in fact. The expert was carefully not making the assumption, but the press didn’t pick up on the qualifiers that she explicitly presented. She could have presented this better, but I’m inclined to put it down to imperfect communication, not jumping to conclusions.

1. Centers for Disease Control and Prevention (CDC) (2009). Hospitalized patients with novel influenza A (H1N1) virus infection – California, April-May, 2009. MMWR. Morbidity and mortality weekly report, 58 (19), 536-41 PMID: 19478723[↩]
2. Justin T Denholm, Claire L Gordon, Paul D Johnson, Saliya S Hewagama, Rhonda L Stuart, Craig Aboltins, Cameron Jeremiah, James Knox, Garry P Lane, Adrian R Tramontana, Monica A Slavin, Thomas R Schulz, Michael Richards, Chris J Birch, & Allen C Cheng (2010). Hospitalised adult patients with pandemic (H1N1) 2009 influenza in Melbourne, Australia The Medical Journal of Australia, 192, 1-3[↩]
3. Obesity Still Dramatically Decreases Risk of Severe H1N1 Flu?[↩]
4. Louie JK, Acosta M, Winter K, Jean C, Gavali S, Schechter R, Vugia D, Harriman K, Matyas B, Glaser CA, Samuel MC, Rosenberg J, Talarico J, Hatch D, & California Pandemic (H1N1) Working Group (2009). Factors associated with death or hospitalization due to pandemic 2009 influenza A(H1N1) infection in California. JAMA : the journal of the American Medical Association, 302 (17), 1896-902 PMID: 19887665[↩]
5. Louie JK, Acosta M, Winter K, Jean C, Gavali S, Schechter R, Vugia D, Harriman K, Matyas B, Glaser CA, Samuel MC, Rosenberg J, Talarico J, Hatch D, & California Pandemic (H1N1) Working Group (2009). Factors associated with death or hospitalization due to pandemic 2009 influenza A(H1N1) infection in California. JAMA : the journal of the American Medical Association, 302 (17), 1896-902 PMID: 19887665[↩]
6. New South Wales public health network (2009). Progression and impact of the first winter wave of the 2009 pandemic H1N1 influenza in New South Wales, Australia. Euro surveillance : bulletin europeen sur les maladies transmissibles = European communicable disease bulletin, 14 (42) PMID: 19883546[↩]
7. Justin T Denholm, Claire L Gordon, Paul D Johnson, Saliya S Hewagama, Rhonda L Stuart, Craig Aboltins, Cameron Jeremiah, James Knox, Garry P Lane, Adrian R Tramontana, Monica A Slavin, Thomas R Schulz, Michael Richards, Chris J Birch, & Allen C Cheng (2010). Hospitalised adult patients with pandemic (H1N1) 2009 influenza in Melbourne, Australia The Medical Journal of Australia, 192, 1-3[↩]

The number of influenza cases this year seems to have peaked and started to drop in the last few weeks, according to both the CDC surveillance data and Google Flu Trends (which updates more in real-time).  Does that mean swine-origin influenza virus is gone for good? We don’t know, of course, but I was struck by the resemblance of this year’s caseload to charts I’ve seen of the 1918 influenza mortality rates:

I don’t have the original data that were used to make the 1918 data¹ (black traces; these are mortality data, rather than cases; some sources suggest that this would underestimate the case number for the spring/summer peak of 1918, if the virus increased its virulence before the fall outbreak) , but I think I have the chart aligned with the CDC’s weeklies (red) for this year’s pandemic. ² They’re not identical, but they’re similar so far.  In particular, there was a surge in the summer, a big peak early in the fall — much earlier than standard seasonal influenza, which doesn’t usually get going until January or February — and then the fall cases dropped dramatically.  The 1918 influenza was followed by a third, smaller, peak, in winter, around the usual period for influenza.

In 1957, when a new pandemic influenza struck the US, the pattern of three waves — smallish in summer, very large in fall, followed by a slightly smaller but still major wave in early winter — was also broadly similar.³ Again, there was a spring/summer wave throughout the US, starting in June and peaking, maybe, in August.⁴  The fall outbreak started in September and peaked in late October, there was a lull, and then there was a new surge early in 1958, this time perhaps peaking a little later in the winter than the 1918 version.

1957/1958 influenza cases, by week5

The next pandemic was 1968/1969, when H3N2 moved in and supplanted the H2N2.  The pattern here seems different: There was little or no spring/summer wave,⁶ and while the outbreak did start a little earlier than usual, it was only by a few weeks:

The first outbreaks in the civilian population developed in Puerto Rico and Alaska in late September and early October. The first outbreak in  civilian population in the continental USA did not develop until the third week of October, when the small desert city of Needles, Calif., reported an influenza-like illness involving 35%-40% of the of the population. ⁷

(By comparison, by the third week in October this year, the pandemic seems to have just about peaked.)  Along with the late start in 1968, there was no lull; the influenza kept building to its peak, in December, and then quickly dropped down and stayed down. It was really more like a very large seasonal epidemic, very different from the patterns of 1918, 1957, or 2009.  (There’s actually a more impressive bump in the following summer, 1969, so this might be simply because the virus didn’t reach the US in time, and carried over into the summer rather than presaging the pandemic.)

1968/1969 influenza cases, by week, compared to previous years7

The next pandemic was 1977-1978, when H1N1 returned.  I don’t think we have good data for that, because the main measure was death, and that pandemic strain didn’t impact mortality significantly. ^{8 ,9}  However, as far as I can tell, that was mainly a winter-only wave. So the common claim that pandemics come in several waves is not universally true — just two of the four 20th century pandemics acted that way — but it does seem to be true for the 2009 pandemic.

In any case, based on previous pandemic patterns where the disease started this early, I’m guessing that we’re already past the very worst of the 2009 SOIV outbreak, and we’re going to enter a bit of a pause; but it’s going to come back early in 2010 — perhaps not to the same levels as we’ve been seeing in late October/early November, but not too far off.

1. Taubenberger JK, & Morens DM (2006). 1918 Influenza: the mother of all pandemics. Emerging infectious diseases, 12 (1), 15-22 PMID: 16494711[↩]
2. http://www.cdc.gov/flu/weekly/index.htm[↩]
3. This was the introduction of an H2N2 strain that replaced the previously-circulating H1N1 virus[↩]
4. I haven’t seen the data for the summer of 1957, so this is based on comments in the descriptive papers, and I don’t know how big the summer wave was[↩]
5. D. A. Henderson, Brooke Courtney, Thomas V. Inglesby, Eric Toner, & Jennifer B. Nuzzo (2009). Public Health and Medical Responses to the 1957-58 Influenza Pandemic Biosecurity and Bioterrorism, 7 (3), 1-9[↩]
6. You can see a little bump in the summer of 1968, which could conceivably have been the summer wave, but it’s definitely much less dramatic than the 1957 or the 2009 summer.[↩]
7. Robert G. Sharrar (1969). National Influenza Experience in the USA, 1968-69 Bull Wld Hlth Org, 41, 361-366[↩][↩]
8. Lui, K., & Kendal, A. (1987). Impact of influenza epidemics on mortality in the United States from October 1972 to May 1985. American Journal of Public Health, 77 (6), 712-716 DOI: 10.2105/AJPH.77.6.712[↩]
9. The 1977-78 H1N1 was almost certainly a laboratory accident, a release of an earlier strain from pre-1957.  Accordingly, most people over their mid-20s were already immune to the pandemic virus.  Since older people are usually the ones with the highest mortality rates, the 1977-78 pandemic didn’t trtanslate into increased mortality, and doesn’t show up in mortality rates.[↩]

–Patterson KD, & Pyle GF (1991). The geography and mortality of the 1918 influenza pandemic. Bulletin of the history of medicine, 65 (1), 4-21 PMID: 2021692

(Click on the image for a larger version)

Persons who were born before 1957 had a reduced risk of infection …  Persons who were born between 1957 and 1975 were at intermediate risk for infection. ¹

In Ontario, people over 53 years old had about 1/6 the chance² of getting the new H1N1; the those between about 33 and 53 had a little more than half the chance (odds ratios of about .15 and .42, respectively).

1. David N. Fisman, Rachel Savage, Jonathan Gubbay, Camille Achonu, Holy Akwar, David J. Farrell, Natasha S. Crowcroft, & Phil Jackson (2009). Older Age and a Reduced Likelihood of 2009 H1N1 Virus Infection The New England Journal of Medicine, 361, 2000-20001[↩]
2. I realize that odds ratios don’t quite say this, but it’s close enough[↩]

Influenza virion

At this point in 2009, I think most people probably have a general grasp of influenza virus infection patterns.  At the simplest level, a few strains of virus circulate every year, with relatively small changes year-to-year.  Every so often, a new strain, with larger changes, appears and spreads globally, often becoming the dominant “base” strain for a while (that is, circulating annually with smallish changes each year) until it in turn is replaced by a new strain.

A particularly interesting “new” influenza strain appeared in 1977.  At that time the dominant circulating strains didn’t include any H1N1 strains; H1N1 had gone extinct in humans around 1957. (H1N1 was still circulating in swine, though.) An H3N2 strain, the tail end of the 1968 pandemic influenza, was the major strain. ¹ But in 1977, H1N1 returned, first isolated in China and subsequently in Russia, then rapidly spreading throughout the world and remaining endemic since then, circulating in parallel with H3N2 strains since then.  (That’s why influenza vaccines are trivalent — they cover both an H1N1 and an H3N2 strain of influenza A, plus an influenza B strain.)

It was quickly discovered that the “new” H1N1 was not new at all.  Even without modern genomic sequencing systems, Peter Palese’s group was able to show that the 1977 H1N1 was actually an old strain,² described in 1950, that had re-appeared without any evidence of evolution throughout the dozen intervening years. Although Palese didn’t outright say it at the time, this almost certainly was a laboratory strain of influenza that had escaped back into the wild.

Finally, it is possible that a 1950 influenza virus was truly frozen in nature or elsewhere and that such a strain was only recently reintroduced into man. ²

(My emphasis.)

A couple of side notes before I continue.  There are several other claims in the literature that various influenzas since 1977 are lab escapees.  Most, if not all, of those claims are almost certainly wrong, and represent a different kind of lab contamination — reference strains of influenza that have contaminated the test strain, within the lab, rather than a lab strain actually circulating in the wild. ³ This form of lab contamination with reference strains seems to be a relatively common source of error,⁴ and it’s certainly a problem, but isn’t an actual direct threat to the population.

In particular, the swine-origin H1N1 is not a lab escapee (and, in spite of the rumors among bloggers who don’t know anything about viruses, but who dearly love a conspiracy theory, it’s not an artificial construct).  Although the details of the SOIV origins are fuzzy, its parents and family tree is pretty clear by now.

That said, lab escapes are not unheard of.  Notoriously, the last case of smallpox was a lab escape, and the 2007 outbreak of foot and mouth disease in the UK was almost certainly an escaped lab strain from the Pirbright Research Center. ⁵ The problem with the conspiracy theories⁶ isn’t that virologists say lab escape is impossible.  We know perfectly well that lab escapes can, and do, happen.  The reason the conspiracy theories are wrong is that escapes have happened and been promptly detected and reported.  There’s no conspiracy.

Dengue virus

What may be the latest example of this was just reported in PLoS One.⁷  In the course of analyzing Dengue virus strains circulating in Brazil and Columbia, an unexpected strain was detected: It is a strain that was present in Asia some 20 years ago, almost unchanged since.  Like influenza, dengue viruses mutate and evolve fairly rapidly; this kind of stability (35-fold lower than expected) would be extraordinary in a virus that’s been circulating for two decades.  What’s more, these viruses don’t show geographic clustering. Normally, dengue viruses circulate locally and develop local sub-strains; this older virus, though, is very similar in Brazil and Columbia, and doesn’t group with local viruses:

DENV-3, when introduced to a new area, evolves locally, resulting in geographically-associated clusters closely related to other virus recently circulating in other region.  Interestingly, we have shown in this study that viruses recently circulating in Brazil and Colombia form a monophyletic cluster together with viruses isolated in Asia more than two decades ago.⁷

There are two obvious possible explanations.  One is that this represents lab error — the strains they were analyzing were somehow contaminated with this older strain.  The other is that this is a lab escapee:

Could some how this strain escape from the laboratory and started to infect humans, thus explain the close relationship of the new viruses with genotype V strains?⁷

I’d like to see this work repeated by an independent lab to make sure it’s not lab contamination (and the authors clearly want the same thing: “Therefore, more studies are needed to confirm the origin of American genotype V viruses”) but it certainly seems like a plausible explanation.

I’m not a Dengue expert by any means, but I don’t think there’s anything especially hazardous about the “new” (old) strain — Dengue is already widely present in these areas, and I don’t think having one more strain in circulation adds to the general population risk.  But I’d like to see an expert’s opinion on this; interactions between Dengue strains are important in the disease.

In any case, it reinforces (if reinforcement was necessary) the importance of proper lab procedures and security.

1. A nice review is Morens, D., Taubenberger, J., & Fauci, A. (2009). The Persistent Legacy of the 1918 Influenza Virus New England Journal of Medicine, 361 (3), 225-229 DOI: 10.1056/NEJMp0904819[↩]
2. Recent human influenza A (H1N1) viruses are closely related genetically to strains isolated in 1950.
   Nakajima K, Desselberger U, Palese P.
   Nature. 1978 Jul 27;274(5669):334-9.[↩][↩]
3. Worobey, M. (2008). Phylogenetic Evidence against Evolutionary Stasis and Natural Abiotic Reservoirs of Influenza A Virus Journal of Virology, 82 (7), 3769-3774 DOI: 10.1128/JVI.02207-07[↩]
4. I’ve previously cited
   Krasnitz, M., Levine, A., & Rabadan, R. (2008). Anomalies in the Influenza Virus Genome Database: New Biology or Laboratory Errors? Journal of Virology, 82 (17), 8947-8950 DOI: 10.1128/JVI.00101-08
   and see also

   Li, J., Dohna, H., Miller, J., Cardona, C., & Carpenter, T. (2009). Identifying errors in avian influenza virus gene sequences and implications for data usage of public databases Genomics DOI: 10.1016/j.ygeno.2009.09.005 [↩]

5. Cottam, E.M., Wadsworth, J., Shaw, A.E., Rowlands, R.J., Goatley, L., Maan, S., Maan, N.S., Mertens, P.P., Ebert, K., Li, Y., Ryan, E.D., Juleff, N., Ferris, N.P., Wilesmith, J.W., Haydon, D.T., King, D.P., Paton, D.J., Knowles, N.J. (2008). Transmission Pathways of Foot-and-Mouth Disease Virus in the United Kingdom in 2007. PLoS Pathogens, 4(4), e1000050. DOI: 10.1371/journal.ppat.1000050[↩]
6. Apart from the fact that most of them are batshit crazy[↩]
7. Aquino, V., Amarilla, A., Alfonso, H., Batista, W., & Figueiredo, L. (2009). New Genotype of Dengue Type 3 Virus Circulating in Brazil and Colombia Showed a Close Relationship to Old Asian Viruses PLoS ONE, 4 (10) DOI: 10.1371/journal.pone.0007299[↩][↩][↩]

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[↩]

“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 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[↩][↩][↩][↩]

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.

“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[↩]

“Virons le virus” (Institut Merieux Benelux, 1991)

One of the important drivers of influenza virus evolution is mixed infection: Infection of the same individual with two different strains of virus, which can then reassort to generate brand-new viral genomes. This presumably what happened, for example, with the recent swine-origin influenza virus (SOIV): some pig was simultaneously infected with North American swine flu and a Eurasian swine flu, the two reassorted so that two of the Eurasian virus’s segments joined with 6 of the North American segments, and the new virus thus produced turned out, just by chance, to be good at infecting humans.

Reassortment, notoriously, can generate rapid large changes in the personality of the virus. Pandemic influenzas have been reassortants, unrecognized by the population’s immune systems. But that’s not the only possible outcome; reassortants between closely-related viruses can lead to small changes, reassortants between two circulating strains would still be recognized by the immune response, and so on. Reassortment per se isn’t inevitably devastating, the big concern is reassortment between widely-differing viruses — human and avians strains being the major issue today.

I’ve tended to think of multiple infection and reassortment as quite a rare phenomenon. Reassorted influenza viruses appear and circulate relatively often, but not, you know, daily;¹ and those are the product of millions upon millions of infected individuals. On the other hand, most reassortments are probably either dead on arrival (their different segments are simply not compatible) or at best very unfit (their different segments make them easily outcompeted by the wild flu that’s already well adapted to the individuals in question). That means we don’t know the frequency of reassortants, because most of them would be invisible to us.

I’ve also tended to think, perhaps naively, that multiple infections would be a little unusual, because the timing would have to be fairly precise. Viruses generally rely on a couple of days of relative peace (immunologically speaking) to quickly replicate and bank a virus load that then keeps pace with the increasing immune response. If Virus B tries to infect you a couple of days after Virus A is already present, Virus B is going to run right into the thick of the immune response to Virus A, never have that chance to bank its progeny virus, and you’d expect it to be quickly overwhelmed. So you probably need nearly simultaneous infections to get a real multiple infection.

“Influenza is prevalent” (Chicago, 1918)

But this is all speculation. A recent paper² went out and actually looked for evidence of mixed infection in humans.³ They used previously-collected samples, and this is going to greatly underestimate the extent of mixed infection,⁴ but they did detect evidence of several mixed infections in their collection of over 1000 influenza samples. A plausible number they offer is about 0.5% of their samples — half a dozen individuals — were potentially mixed infections.⁵

(Later they suggest, as unpublished data, that the number may be as high as 3%. An important caution, that they don’t mention here, is that the 3% number is from influenza database analysis, and we know that these databases are not high quality — see On the accuracy of the influenza databases and the paper referenced therein⁶ — in fact, about 3% of the samples in the database are contaminated, so I don’t know if the present authors took this into account when interpreting evidence for mixed infections.)

However, sticking with the 0.5% figure — which is still remarkably high, and would represent tens of thousands of cases per year — they were able to look more closely at several of these samples and confirmed that they did, in fact, represent true mixed infections. This is another spinoff of the rapid, high-throughput sequencing that’s now becoming widely available. One patient, from New Zealand, was simultaneously infected with two viruses:

…one closely related to viruses cocirculating in New Zealand during 2004 and a second lineage that clustered with A/H3N2 viruses that became dominant in the following (2005) influenza season in the southern hemisphere ²

Another, in New York, was infected with two different influenza strains that are antigenically distinct — that is, viruses that would require different vaccines for protection. Remember that influenza vaccines are customized, year by year, to match up against the dominant circulating virus of that particular year. This patient would have needed two distinct vaccines to get adequate protection from his two infections.

A third, “even more dramatic” example was another New Yorker who was infected with two viruses that were not merely antigenically different, but that came from two distinct, broad groups — influenza A and influenza B viruses. I don’t think A and B can reassort, or at least the progeny would be very unlikely to be fit, but it illustrates that very mixed infection is quite possible.

It’s important to note that they were looking for mixed infection, not reassortment. Reassortment woud be much less common than mixed infection — you need mixed infectio nfor reassortment, but it’s not inevitable following mixed infection. Still, the background of mixed infection seems to be rather higher than I thought it would be.

In sum, we propose that mixed infection of diverse influenza viruses, a necessary precursor to reassortment, is a common occurrence during seasonal influenza in humans and will in turn accelerate the rate of adaptive evolution in this virus. In addition, intrahost populations of influenza virus will harbor genetic diversity generated by de novo mutation, which we have not assessed in the current study. As a consequence, we urge that intrahost sequencing be more routinely employed to assess the degree of genotypic and phenotypic diversity in populations of acute RNA viruses. With the advent of high-throughput next-generation sequencing platforms, viral variants are being much more explicitly revealed within specimens, and this type of data can be made available on a routine basis.²

1. Offhand, actually, I don’t know how often reassortants have been identified. I’ll try to find that[↩]
2. Ghedin, E., Fitch, A., Boyne, A., Griesemer, S., DePasse, J., Bera, J., Zhang, X., Halpin, R., Smit, M., Jennings, L., St. George, K., Holmes, E., & Spiro, D. (2009). Mixed Infection and the Genesis of Influenza Virus Diversity Journal of Virology, 83 (17), 8832-8841 DOI: 10.1128/JVI.00773-09[↩][↩][↩]
3. It would probably be more interesting to look for mixed infection in swine, or wild ducks, but it’s only humans that have enough close attention to detect these relatively rare events.[↩]
4. Most samples of a mixed infection are simply going to pick up the more abundant of the viruses present[↩]
5. This comes with a large helping of caveats; it could over- or under-estimate the frequency. But it’s a reasonable starting point and they did confirm some of them.[↩]
6. Krasnitz, M., Levine, A., & Rabadan, R. (2008). Anomalies in the Influenza Virus Genome Database: New Biology or Laboratory Errors? Journal of Virology, 82 (17), 8947-8950 DOI: 10.1128/JVI.00101-08[↩]

“Some years travels into divers parts of Africa and Asia the Great” R. Everingham for R. Scot, etc.London 1677

Hepatitis C virus (HCV), one of the classic intravenous-spread viruses, was only identified about 20 years ago.  Where and when did it originate, and how did it spread?

A recent paper¹ estimates that the common ancestor of the present world-wide HCV strains was in Guinea-Bissau, around 1470.  From there:

… infections moved to the New World via Benin–Ghana, even when they originated from Guinea–Gambia. … It is therefore likely that the slave trade has played a historical role in the global dissemination of HCV genotype 2. A similar role has previously been proposed for the transcontinental transmission of yellow fever virus prior to mass global travel. ¹

The pattern of HCV spread matches the flow of the slave trade.

There’s another very interesting historical finding from this epidemiology.  HCV epidemiology is very different in Cameroon vs. Guinea-Bissau.  In Cameroon, HCV exploded in the early to mid-20th century; whereas in Guinea-Bissau, HCV spread in the 20th century was slower.  The authors here suggest that this reflects different styles of health care in the two countries — aggressive treatment vs. limited treatment.  But it’s an indirect consequence of treatment of other diseases, and the effects on HCV were the opposite of what you’d expect:

We suggest that the differential epidemic histories of HCV genotype 2 in the two countries probably result from historical differences in the large-scale administration of intravenous antimicrobial drugs, decades before the risk of transmission of blood-borne viruses was understood. After World War I, medical care in Cameroun Français was provided mostly by military doctors, and public-health interventions aimed to cover the whole population … In contrast, the health system before the mid-1940s in Portuguese Guinea (now Guinea-Bissau) was more directed towards protecting the health of the European colonists and their Guinean employees. …Thus, the 25 year delay in organizing public-health interventions in Portuguese Guinea, combined with a lower incidence of yaws and trypanosomiasis in this drier land, resulted in a much lower proportion of the population receiving intravenous injections than in Cameroun Français, and a reduced opportunity for iatrogenic HCV transmission. ¹

In other words, the aggressive treatment of diseases in Cameroon probably dramatically reduced the frequency of many diseases, but because it involved injections with non-sterile needles, the treatment also managed to spread HCV.  The more lackadaisical attitude in Portuguese Guinea may have let other diseases flourish, but accidentally restricted the spread of IV contaminants like HCV as well.

1. Markov, P., Pepin, J., Frost, E., Deslandes, S., Labbe, A., & Pybus, O. (2009). Phylogeography and molecular epidemiology of hepatitis C virus genotype 2 in Africa Journal of General Virology, 90 (9), 2086-2096 DOI: 10.1099/vir.0.011569-0[↩][↩][↩]