What can 1081 influenza viruses tell you?

By Jonas Waldenström

Today we published a major article in a well-respected journal. The reason why I write major is not to brag (although I am very pleased). No, the reason for that epithet is that the paper is based on such a huge long-term effort. In fact, in this paper, ten years of fieldwork, laboratory work, and statistical analyses are boiled down into nine glossy pages!

As frequent readers of this blog probably know, mallards and flu is our main study system. Through repeated captures, samplings and recaptures of ducks at a migratory stopover site we have built very large datasets that we now can analyze for long-term patterns in virus-host interactions. The title of the current paper is: “Long-term variation in influenza A virus prevalence and subtype diversity in migratory mallards in northern Europe”

Influenza A virus prevalence was in part determined by peaks of mallard migration. Photo by Serget Yeliseev under a CC BY-NC-ND 2.0 license.

Influenza A virus prevalence was in part determined by peaks of mallard migration. Photo by Sergey Yeliseev under a CC BY-NC-ND 2.0 license.

What we did was to screen all 22,229 samples collected in the period 2002-2010 for the presence of influenza A virus RNA. Positive samples were then inoculated in eggs in order to obtain virus isolates. After this process, we had a virus bank consisting of 1081 viruses of 74 different subtypes, ranging from H1N1 to H12N3. As you can see from the figures above, influenza virus research is time-consuming and costly, and the travel from sample to RRT-PCR-positive to characterized virus could be described as a negative logarithmic function. It is all about big numbers! You need a lot of samples to get the statistical power to say something about virus ecology and epidemiology at the level of subtypes. You also need to be stubborn as a mule.

There are three major results that I would like to share with you.

First, we were able to fit a model of how influenza A virus varied with season in the sampled mallard population. The resulting figure very neatly shows how the virus starts low in spring, becomes more or less absent during the breeding season, and how it suddenly increases in frequency in August when the first wave of migrating mallards arrive at Ottenby. The August peak is followed by a second peak in October-November, likely consisting of mallards with a Finnish or Russian origin. Actually, the plot looks like a camel!

Influenza A virus prevalence showed two distinct peaks in autumn, one in August and one in October-November.

Influenza A virus prevalence showed two distinct peaks in autumn, one in August and one in October-November.

However, plotting prevalence rates over time has been done before. The strength with our analysis is that it includes and accounts for the variation in prevalence induced by year effects. Mallards are migratory birds, but their timing of migration is rather flexible. In years characterized by mild autumns they arrive late at our study site, and in years with harsh autumns they are early. The final model accounted for approximately half of the variance in prevalence, which is pretty good all considered.

Second, I would like to stress the incredible diversity of subtypes! The two surface proteins hemagglutinin (16 variants) and neuraminidase (9 variants) sit on two different RNA-segments in the genome and can theoretically be combined in 144 different ways, or subtypes as we call them. We found 74 different HA/NA subtypes. In addition, some subtypes are likely not functional, or would have to include a hemagglutinin (like H14 or H15) that is restricted to areas outside Europe. This plethora of genotypes is a world record from a single site. Or to put it in perspective: more than half of the possible subtypes have been found in mallards trapped in our little duck pond on the southern point of the island Öland, in the SW part of the Baltic Sea, in Northern Europe. A speck in the ocean, but a global diversity of viruses.

Further, the 1081 viruses were not evenly distributed on subtypes. Rather, some subtypes were very common, such as the H4N6, the H1N1, or the H2N3 subtypes. Others were rare, including the famous combinations H5N1 and H7N9, both which were only found once, and not in the pathogenic forms known from elsewhere. Interestingly, the high frequency of certain combination, and a low frequency of other combinations despite the HA and NA being common in other virus constellations suggests that some subtypes have low fitness. Consider for instance H4N3 that was found only 5 times, while the H4 hemagglutinin was found in 291 viruses, and the N3 neuraminidase in 116 viruses.

A cute mallard couple. Photo by Chuq Von Rospach under a CC BY-NC-ND 2.0 license

A cute mallard couple. Photo by Chuq Von Rospach under a CC BY-NC-ND 2.0 license

Third, and perhaps most interestingly, we found a heterosubtypic effect at the virus population level. By grouping viruses in classes depending on their HA relatedness we could see that the different virus classes peaked at different times within an autumn. The virus type that was common in early autumn was rare in late autumn and vice versa. Understanding how individual and herd immunity processes affect influenza A virus dynamics in nature is highly warranted, as that would aid our capacity to predict how the virus population could change over time. Viruses in wild birds remain an important pool from which genotypes could be seeded in domestic animals, and even humans.

Finally, I would like to say how incredibly fortunate I am to have had the opportunity to work in such a hard-working and persistent research group. The work we presented today has been collected by a small army of duck trappers, a score of laboratory staff, a handful PhD-students, a couple of postdocs and a quartet of PIs from Kalmar, Uppsala and Rotterdam. And the most important of all was Dr Neus Latorre-Margalef, who carried this publication from start to finish! Well done!

Link to the article:

Latorre-Margalef, N., Tolf, C., Grosbois, V., Avril, A., Bengtsson, D., Wille, M., Osterhaus, A.D.M.E., Fouchier, R.A.M., Olsen, B. & Waldenström, J. 2014. Long-term variation in influenza A virus prevalence and subtype diversity in migratory Mallards in Northern Europe. Proceedings B, online early.

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One thought on “What can 1081 influenza viruses tell you?

  1. Pingback: How To Die Before Your Time -With A Lot Of Fun | Third News

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