The conundrum of influenza A virus diversity and host immune responses – lessons from a vaccination experiment in Mallards

The influenza A virus is in an interesting virus. It exists in many subtypes and can infect a range of hosts, but most of the variation in subtypes and lineages is restricted to wild waterfowl, especially dabbling ducks. Contrary to humans and other mammals, the virus doesn’t normally cause disease in ducks and these viruses are said to be low-pathogenic. The traditional explanation for the evolution of subtypes is that they have evolved to be sufficiently antigenically different that infection with one subtype does not incur protection to another one. Hence, antibodies raised against a H1 virus would do poorly with an H7 infection, and vice versa, but work well against an infection with a homologous virus, i.e. another H1 virus.

The latter is called homosubtypic immunity, and has been shown in a range of studies of Mallards (our favorite bird), using both experimental infections and studies conducted in the field, and although serum antibodies in Mallards seem to wane with time, immunity does seem to be long-lasting (see for instance Tolf et al. 2013).

A few years ago, we identified the existence of heterosubtypic immunity in wild Mallards. We analyzed infection histories of individuals recaptured during their stopover stay at Ottenby and investigated patterns of subtype occurrence compared to what would be if infection order was non-structured. In essence, what we could see was that heterosubtypic immunity was frequent, most strongly observed at hemagglutinin (HA) clade level, but also detectable at the HA group level. In contrast, there was no effect of the neuraminidase subtype (see Latorre-Margalef et al. 2013). The strength of this pattern was rather surprising, and has sparked follow-up studies.

Lately, a number of studies have used experimental infections to investigate heterosubtypic immunity further, either as a cause of understanding how highly-pathogenic viruses can be maintained in waterfowl, or for assessing immunity patterns in low-pathogenic avian influenza infections. Two nice, recent articles are by Segovia et al. 2017 investigating H3N8, H4N6, H10N7 and H14N5 infections in a balanced design, and by Latorre-Margalef et al. 2017 assessing protection of H3 antibodies against a range of other virus subtypes. Collectively, these studies suggest that the order of infections are important for future disease dynamics, both at the individual level but also at the population level. In other words: the order of outbreaks in a population will govern the fate of other subtypes in the population later; a competition among subtypes over susceptible hosts. This is very interesting, and something we currently try to model with infection history data of captured and recaptured wild Mallards at our study site.

The principle of immunity is that previous infections will render the bird immunity to reinfection with the same virus subtype, so called homosubtypic immunity, as long as the antigenic properties of the two strains are similar. A heterosubtypic immunity is when infection with one subtype provides full or partial protection against other subtypes, and it is expected that this is more common in phylogenetically related subtypes. (Illustration by M. Wille)

However, field and lab are two different things, and a couple of years ago we wanted to use the duck trap at Ottenby to study immune processes. As we cannot infect and release birds in the trap we used vaccination as a means of simulating previous infection. We prepared two vaccines, one against H3 and one against H6 (and one sham), immunized birds and followed them to make sure they developed serum antibodies (against NP) and neutralizing antibodies against the HA, after which we released them into the duck trap and followed their natural infections in the wild. As often is the case, our experiment didn’t really go as intended. First of all, there were no H6 infections in the wild population at the time of the experiment, thus no H6 infections recorded in any of the groups of our experiment so we couldn’t analyze the protectiveness of H6 vaccination. Quite surprisingly, all three groups were infected with H3 viruses – including the group that had received the H3 vaccine.

There are two possible explanations for the failed homosubtypic response. One is that immunization didn’t result in protective immunity, and the other that the viruses were antigenically different. We did detect neutralizing antibodies against H3 viruses in the ducks, suggesting these ducks did raise a specific immune response against the vaccine. Interestingly, the ducks didn’t raise a similar response against H3 infections after being in the duck trap. Investigating the latter we could show that the vaccine strain and the outbreak strain differed by a number of substitutions close to the receptor binding site. Going back to our virus neutrilizations, we could see differences in in the strength of the antibody response against different H3 viruses, including differences between the strain we used to vaccinate and the strain that was circulating during our experiment. Sufficiently different to suggest antigenic difference. The paper is just out (Wille et al. 2017). H3s are quite interesting, as they have been the focus in much of human infection research, especially because there seems to be two antigentically different lineages and after infection with one of these H3 lineages humans may not be protected against the other. Antigenic cartography has identified the importance of a few sites in or at the receptor binding site for immune evasion in human H3N2, and it is possible that this is what we see also in avian H3s.

A protein structure of the H3 hemagglutinin, where differences between the outbreak and the vaccine strains are mapped. For more information have a look at the paper in Molecular Ecology.

So, what can we learn from this? As always in science, each new study answers some questions but raises many more. First of all, what is the rate of antigenic drift in avian viruses, how do that differ among subtypes, and what does that mean in a functional and evolutionary context? How does this relate to long-term subtype dynamics and the role of herd immunity and heterosubtypic immunity in wild avian hosts? Second, it illustrates our lack of knowledge on the actual mechanisms of immunity –  despite low-pathogenic avian influenza viruses being gastrointestinal infections in waterfowl, we tend to study serum antibodies rather than mucosal antibodies or innate immune responses. Third, we have work to do as regards vaccination as a model for disease – are immune processes the same, and is protection similar?

Stay tuned – we will get back to this subject later.

If you want to read the study, it is available as Open Access:

Wille, M., Latorre-Margalef, N., Tolf, C., Stallknecht, D.E. & Waldenström, J. 2017. No evidence for homosubtypic immunity of influenza H3 in Mallards following vaccination in a natural experimental system. Molecular Ecology. [doi:10.1111/mec.13967]

http://onlinelibrary.wiley.com/doi/10.1111/mec.13967/full

 

Influenza A virus epidemiology – from individual disease histories to disease dynamics

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Mallards on the wing (Photo by Flickr user Bengt Nyman used under a CC-BY 2.0 license)

Wildlife disease studies are challenging. That’s a fact. If you want an easy science life you should choose another path with more instant results. However, challenging is also the opposite of boring, and the rewards of getting your results are even more exhilarating when lots of toil, sweat and tears have been invested. As readers of this blog are aware, wildlife disease studies are what we do, and I have repeatedly written about our ongoing work on influenza A virus ecology and epidemiology in wild migratory Mallards. This week another study from our study site was published, entitled Capturing individual-level parameters of influenza A virus dynamics in wild ducks using multistate models, which can be found on early view in the Journal of Applied Ecology.

The challenges of studying wildlife disease dynamics are that you want to capture a dynamic process influenced both by the host and the pathogen, which in turn is compounded by variation in the environment – both biotic factors, such as food abundance and the occurrence of other potential hosts, and abiotic factors, such as weather and climate. Disentangling these interconnected effects is a little like making a cube out of mercury. In most wildlife disease studies the available data is at the population level, usually in the form of prevalence rates at specific time points. This type of data is ‘fairly easy’ to collect – you head out into the field, sample all animals you can lay your hands on and then use this snapshot in time as a proxy for the true disease dynamic in your system. The more times you are out collecting data, the better your model becomes. However, disease is driven by factors operating at the level of individuals, such as infection risk and recovery rate, and that type of data can only be acquired by repeated sampling of individuals across a suitable timescale. This is rarely achieved because of logistical, practical and monetary reasons.

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Mallards on the wing (Photo by Flickr user Bengt Nyman used under a CC-BY 2.0 license)

We, however, sit on a huge collection of Mallard and flu data gathered at the same study site with similar methods over a period of close to 15 years. Our latest paper, headed by Alexis Avril and with collaboration with colleagues in France, utilizes this dataset to develop individual-based influenza A virus epidemiological models. This proved to a monumental task that stretched over several years and burned the processors of a good number of computers. Part of the difficulty can be attributed to the data itself – capture and disease histories for 3500 individuals collected over 7 seasons, where at each capture occasion axillary data on bird age, sex, condition, infection status and weather were included. But also the patchy nature of recapture probability and the short duration of most influenza virus infections contributed significantly to extensive data crunching.

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The conceptual framework in the multistate CMR model.

The method we used was multistate capture-mark-recapture models, which are extensions of models originally developed to investigate mortality rates from census data, but where one can include the infection state – i.e. infected or not with influenza virus – as a factor in the analyses. Interested readers should head over and read the publication, as I will spear the rest of you any hardcore statistics and model lingo. Parts of the abstract serves as a good summary:

 For most years, prevalence and risk of influenza A virus (IAV) infection peaked at a single time during the autumn migration season, but the timing, shape and intensity of the infection curve showed strong annual heterogeneity. In contrast, the seasonal pattern of recovery rate only varied in intensity across years. Adults and juveniles displayed similar seasonal patterns of infection and recovery each year. However, compared to adults, juveniles experienced twice the risk of becoming infected, whereas recovery rates were similar across age categories. Finally, we did not find evidence that infection influenced the timing of emigration from the stopover site.

Our study provides robust empirical estimates of epidemiological parameters for predicting IAV dynamics. However, the strong annual variation in infection curves makes forecasting difficult. Prevalence data can provide reliable surveillance indicators as long as they catch the variation in infection risk. However, individual-based monitoring of infection is required to verify this assumption in areas where surveillance occurs. In this context, monitoring of captive sentinel birds kept in close contact with wild birds is useful. The fact that infection does not impact the timing of migration underpins the potential for mallards to spread viruses rapidly over large geographical scales.

Our findings corroborate much of the earlier works done on IAV in birds from population level data or from infection experiments, but with higher robustness of the conclusions. Importantly, we provide estimates of the most crucial infection parameters and show how they vary in relation to age in different seasons and years. And from a model point of view, we show that MS-CMRs are a potent method for disease dynamic inferences. We hope this paper will be read and cited by people in the IAV field and in general disease dynamic research, and that it will be useful for stakeholders interested in the contribution of wild birds in the epidemiology of IAV in poultry.

Link to the paper:

Avril, A., Grosbois, V., Latorre-Margalef, N., Gaidet, N., Tolf, C., Olsen, B. & Waldenström, J. 2016. Capturing individual-level parameters of influenza A virus dynamics in wild ducks using multistate models. Journal of Applied Ecology, online early.

Flu, ducks and the costs of being infected

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There was light snow this morning, but it has since melted away, leaving small puddles on the streets. Unfortunately, the sun seems to have lost today’s battle with the fog and the low clouds – it is, in essence, an ordinary wet, cold and gloomy February day. But if I peer out through the window, ignoring the construction works in the foreground, there is water on the horizon. And where there is water, there are ducks. And where there are ducks, there is flu. One cannot ask for more.

Over the years I have thought much about ducks and flu. (Some would say too much, but they don’t know what they miss). Although my research group has already produced four PhD theses on this topic, there is so much more that I would like to know. Some of it is  highly specialized knowledge, of interest for a limited set of like-minded scientists with  acquired duck disease tastes. Other things are quite basic, but hard to study, such as the question whether ducks infected with flu suffer from infection or not. That is a pretty important question also for a broader audience, as it has relevance for how well virus can spread with individuals in the environment; especially how ducks may spread virus long distances during migration. So, do they suffer from infections, or not?

Actually, there has been some controversy on this topic – partly stemming from different methods of quantifying disease effects. A field ecologist and a veterinarian have different scales in their toolboxes, one could say. In the latter case, disease signs are determined in  experimental infections in animal house facilities, where individuals can be followed over time. Such experiments in Mallards have not been associated with strong disease signs – as long as we consider the low-pathogenic avian influenza viruses that are naturally occurring in wild avian populations (highly pathogenic AI is a completely different story). Infected Mallards shed viruses, but are otherwise apparently healthy, or only display a very short increase in body temperature. But, the ecologist argues, the artificial environment with plenty of food, controlled temperature and absence of predators is not really mimicking the situation in the wild, where even small reductions in vigilance and movement capacity may end in the death from a raptor’s claw. Absence of overt disease is not equal to absence of ecological costs, the ecologist would conclude.

The field studies so far have been a mixed bag, ranging from large effects to negligible effects depending on study and the species considered. The largest effect was seen in a study of Bewick’s swans in the Netherlands, where infected birds had poorer condition and migrated slower than uninfected swans. Such large effects have not been seen in other species, and one can not conclusively rule out other underlying factors, as the swan study was based on a limited number of birds. When it comes to Mallards – the most glorious of all avian influenza reservoir species – previous population studies from our group have suggested infected birds to weigh on average less than uninfected birds at capture.

Averages and populations are all and well, but to get to a mechanistic understanding one is better off with experiment conducted on a set of individuals. However, a problem is that we can not infect birds and release them in the field; in fact, we are not allowed to do so – there is a reason infection experiments are conducted in biosafety labs, after all. What to do, then? Well, we approached this question via GPS and accelerometer loggers attached to two groups of birds caught during the ongoing surveillance at our study site: one group of 20 Mallards with natural avian influenza infection at the time of capture, and another group of 20 Mallards that were negative for influenza at the time of capture.

The benefit of these data loggers is that they record such a wealth of information. From the GPS fixes we can follow the birds in the landscape and quantify their movements at spatial and temporal scales; from the accelerometer we can get metrics that describe activity, defined as movements in the x, y, z-dimensions. We predicted that infection would significantly hamper movement, and that with time the difference between infected and uninfected birds would level off (see figure below); hence the analyses need to take time in to account, too.

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Theoretical predictions of the influence of infection on movement metrics. If infection affects spatial behaviour, infected (blue) and uninfected (red) birds should behave differently at the time of release. We postulate that, at this time, movement metrics for infected birds should be lower than for uninfected birds, which would be revealed as different intercepts of the regression of the movement metrics against time for uninfected (β0) and infected birds (β0+βInf). As infected birds recover with time, their movement metrics will approach and eventually meet the values for uninfected birds. This happens when the slope of the regression of the movement metrics against time for infected individuals (βT.aft.Rel*inf) reaches the slope for uninfected birds (βT.aft.Rel), which is expected to be null.

The full paper is freely accessible at Royal Society Open, and I hope readers with a more heavy interest in movement ecology download and read it. There is a lot of data crunching and statistics there that most of you are likely not that interested in – if you are, go read the original publication – but remember even easy questions may be hard to answer. Okay, with that said, what where the results?

Well. There were no effects of infection on the movement parameters measured, at all. Yes, there were differences among individuals, and between night and day, but infection status did not explain much of the variation in movement metrics. This means that under the natural situation in this study, conducted during stopover in autumn migration, infected ducks moved as much as uninfected ducks. This also means they likely are not impaired by infection during active migration, and could therefore carry LPAI viruses on the wing as they depart the stopover site.

Is this, then, the last nail in the ‘cost of infection’ coffin for low-pathogenic influenza in ducks? Probably not, because one could argue that non all viruses behave the same (in fact, there should be a variation for virulence), and that some viruses may have adapted to infect non-mallard-birds, and hence be spillover infections in Mallards (and then potentially be at less than optimal virulence). Moreover – and perhaps a stronger argument – there may be differences in outcome depending on whether it is a primary infection, or subsequent infection; where the first infection in a naïve bird could be believed to carry a larger cost. Or there may be effects seen only at certain environmental conditions.

All these ‘but, or, perhaps, mayhaps’ are classic scientist disclaimers… My personal belief, these days, is that also the ecological costs of infection are slim. But I am happy to be proven wrong – out you go now and study.

There is water at the horizon still. And questions aplenty.

 

Link to the article:

Bengtsson, D., Safi, K., Avril, A., Fiedler, W., Wikelski, M., Gunnarsson, G., Elmberg, J., Tolf, C., Olsen, B. & Waldenström, J. 2016. Does influenza A virus infection affect movement behaviour during stopover in its wild reservoir host? Royal Society Open Science 3: 150633.

 

From mothers to eggs – maternal anti-influenza antibody transfer in Mallards

A Mallard nest. Photo by Flickr user nottsexminer, used under a CC BY-SA 2.0 license.

A Mallard nest. Photo by Flickr user nottsexminer, used under a CC BY-SA 2.0 license.

By Jonas Waldenström

I have spent the last two days at home nursing one of my offspring that has been down with a cold. Actually, since this is the oldest daughter, nursing generally involves providing her with unlimited access to her mother’s iPad and all the sandwiches she cares to eat.

The added benefit is that I have had more time to read and think than I usually have, far away from the office turmoil. So while I have time, I thought I could toss in yet another blog post.

A few months ago I served as an external examiner on Jacintha van Dijk’s PhD thesis in the Netherlands. That was an enjoyable experience – because of the quality of the thesis, and the unfamiliar and ancient procedures of a Dutch defense. I got to wear a funny hat and toga, there was a whole lot of ceremonial ‘all rise’, some marching in and out of rooms in predetermined processions, and other strange things that we don’t do in Sweden.

Anyway, most of Dr van Dijk’s papers are now published, and today I reread a story on maternal antibodies against avian influenza virus in Mallards, published in PLOS ONE this November.

For animals, the energy put into rearing offspring is a substantial investment. Generally, the more you invest the better chances the offspring has to reach reproductive age. However, as all things in ecology, energy isn’t endless, and animal needs to trade-off investments in one life history parameter to those of other parameters. For instance, in animals with several breeding seasons, current reproduction needs to be balanced with survival.

The last 15 years, ornithologists have looked into allocations of maternal antibodies between mothers and offspring. When an egg is laid some of the antibodies of the mother can pass over to the yolk, providing the hatching chick a kick-start of antibodies to fight infections. Such maternal antibodies do not last more than a few weeks, but may nevertheless be important in the early stage of a chick’s life. For instance, this is seen in commercially reared chickens, where maternal antibodies against Campylobacter can protect the chicks from colonization up to two weeks. It has been argued that the mother can choose how much antibodies different eggs receive, thereby modifying the future prospects of her offspring.

In this paper, the Dutch team investigated deposition of anti-influenza antibodies in Mallard eggs. They collected eggs from free-living Mallard nests – which is quite an achievement, since the nests are often tucked away and camouflaged. They also investigated eggs from captive ducks, more conveniently situated in a pen just outside the research institute.

Antibody concentrations were determined in both egg yolk and in the blood of the mothers, and they controlled for egg size, embryo sex, egg laying order, and female body condition. However, first they needed to check whether the incubating female indeed was mother to all the eggs in the clutch, since mallards are notorious egg dumpers.

Association between the AIV antibody concentration in egg yolk and female serum from (A) the field study and (B) the captive study. Note: axes are minuslog10-scaled. (From the original publication doi:10.1371/journal.pone.0112595.g001)

Association between the AIV antibody concentration in egg yolk and female serum from (A) the field study and (B) the captive study. Note: axes are minuslog10-scaled.
(From the original publication doi:10.1371/journal.pone.0112595.g001)

Indeed, maternal anti-influenza antibodies were found in Mallard eggs from antibody positive females, similar to earlier studies conducted on gulls. There was a positive correlation between antibody concentrations in the eggs and the concentration in the females, but there was no effect of any of the other investigated factors, including the body condition of the female. One more thing, though: there seemed to be an increasing concentration of antibodies with egg laying order; thus, later laid eggs had higher concentrations of antibodies than the early eggs in the clutch.

It remains to show whether this maternal transfer confers protection against influenza virus infections in young mallards, but it is an interesting finding. If maternal antibodies do protect, that could potentially affect local perpetuation patterns of flu, by temporarily reducing the general number of susceptible animals. Or if the antibodies are specific to only those subtypes that infected the mother (which is likely), it could potentially affect subtype distributions in the population, favoring subtypes that are antigenically different. However, if maternal antibodies wane after a few weeks, these effects, if any, should be transient.

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Ducks of the corn

Our research shows that there are many scary things lurking in the cornfields. (The Mallard head source, under a CC BY 2.0 license; rearranged by M. Wille).

Our research shows that there are many scary things lurking in the cornfields. (The Mallard head source under a CC BY 2.0 license; rearranged by M. Wille).

By Jonas Waldenström

The Mallard is the most widespread and abundant duck in the world. It inhabits almost any type of water body, from the shores of great open lakes, to the smallest ponds. Most people don’t really notice them; they are just there. (One exception here on the eco-evolutionary dynamics’ blog).

Our negligence of Mallards should not be taken as they are unimportant. On the contrary, they are important parts of the food web, as consumers of invertebrates and seeds, and providers of juicy dinners for aerial raptors such as Peregrine falcons and White-tailed Sea-eagles. They make good food for mammals too – many a fox has dined on Mallards eggs, and we ourselves shoot them in large numbers. Very large numbers.

In our research we are interested in Mallards as hosts for diseases, especially the influenza A virus. We have studied virus carriage in great detail over the last 12 years and are now trying to connect the various pieces to a coherent epidemiological picture. However, one part of the puzzle has been largely neglected until now: their stopover ecology.

Yes, Mallards are migratory, especially the populations that live on northern latitudes . The ducks we trap at our field site originate from the Baltic states, Finland and Russia, and migrate predominately to southern Denmark and northern Germany. Daniel Bengtsson in my lab works on Mallard ecology, focusing on what the ducks do when they are on stopovers during migration.

Mallards are relatively heavy birds, with an average body mass of just below one kilogram. Migration is taxing and requires a lot of energy, and with the heavy bulk the Mallards don’t fly the whole distance at once; rather they make several bouts of migration interspersed with longer stays at suitable stopover sites.

So what do the ducks do all day, and all night? A seemingly straightforward question. During daytime we can watch them with binoculars and note their behaviors. But it turns out they don’t do very much. A typical Mallard in an autumn day in November does pretty much nothing more than sleeps, poops, and preens its plumage. Pretty dull. Occasionally it may dabble a little in the murky water, but on average they are just chilling.

This makes sense from a prey perspective: if you are a juicy meal you should stay in groups and divide the predator scouting among individuals. The talons, beaks and jaws are always ready to take a piece of you. However, at the same time a duck needs to replenish the energy stores in order to finish migration. So if you don’t feed much at day, you have to do it at night.

Together with colleagues in Germany and Sweden we have started to tap into the whereabouts and behaviors of Mallards during migration. At our aid we have modern telemetry gadgets that help us track the birds remotely. Last week we published a paper in PLOS ONE with the latest results.

A Mallard ready for departure (Photo D. Bengtsson)

A Mallard ready for departure (Photo D. Bengtsson)

We equipped wild-caught Mallards with small GPS-transmitters, fastened on the back of the birds as little rucksacks. These devices took a GPS fix every 15 minutes, thereby allowing us to see where the birds moved, and when. However, in order to get the data we first needed to find the birds in the field and then download the data with a receiver on a radio link. The best way of doing this was to make daily flights in a light airplane, gently soaring amidst the clouds until the receiver made a ‘beep’, signaling that it located a duck. On days with bad weather the plane could not be used; instead we trudged through kilometers of shoreline on foot.

In the paper we analyzed movement patterns and habitat use of 16 individuals followed across a couple of weeks. During daytime the ducks behaved just as I said before – they didn’t move much at all. But when dusk fell all birds got on their wings and flew inland, sometimes quite long distances. Each duck seemed to follow its own nightly routines, but it was also evident that some ducks followed other ducks around. A typical night would consist of a first flight from the coast to an agricultural field, most often harvested cornfields. In the fields they would settle for a short while, often less than an hour, likely stuffing their crops full of leftover corn, before embarking on another flight out to various inland water bodies. In these ponds and wetlands they spent the reminder of the night. Just before dawn, the flight would go in the opposite direction, including a short stay in the cornfield, before settling down on the coast again.

An airial view of the study site. Ducks chill by the coast at day and fly out in the agricultural landscape at night (Photo D. Bengtsson)

An airial view of the study site. Ducks chill by the coast at day and fly out in the agricultural landscape at night (Photo D. Bengtsson)

Example of typical mallard movements between frequently used sites on southeast Öland, Sweden, October – December 2010. Inset A: Orange ovals = coastal meadows; yellow ovals = maize fields; red ovals = flooded areas; blue ovals = coastal day-roosts; green ovals = coastal reefs used as day-roosts; grey circle = duck trap location. Inset B: Yellow oval (1) = maize field visited during dawn and dusk; red ovals (2) = various small (flooded) wetlands on alvar steppe (the upper one reaching into a maize field), visited at night; green oval (3) = coastal reef used as day-roost. Inset C: Yellow oval (1) = two maize fields frequently visited, mostly during dawn and dusk; red oval (2) = flooded area (stream) visited most nights; light purple oval (3) = flooded pasture visited during two consecutive nights (probably for feeding); blue oval (4) = most frequented day-roost. From the article in PLOS ONE, under CC Attribution License.

Example of typical mallard movements between frequently used sites on southeast Öland, Sweden, October – December 2010. Inset A: Orange ovals = coastal meadows; yellow ovals = maize fields; red ovals = flooded areas; blue ovals = coastal day-roosts; green ovals = coastal reefs used as day-roosts; grey circle = duck trap location. Inset B: Yellow oval (1) = maize field visited during dawn and dusk; red ovals (2) = various small (flooded) wetlands on alvar steppe (the upper one reaching into a maize field), visited at night; green oval (3) = coastal reef used as day-roost. Inset C: Yellow oval (1) = two maize fields frequently visited, mostly during dawn and dusk; red oval (2) = flooded area (stream) visited most nights; light purple oval (3) = flooded pasture visited during two consecutive nights (probably for feeding); blue oval (4) = most frequented day-roost. From the article in PLOS ONE, under CC Attribution License.

A postdoc in our lab summarized this study as “ducks like water, and food”. Although correct, I think we could say a number of other things. Firstly, we could estimate how large home ranges the ducks were utilizing, and that they very selectively chose cornfields (which comprised only a minority of the arable land in the study area). Secondly, we could show that the omnivorous Mallard is flexible in diet selection, as corn (intended for winter fodder for cattle) is a new crop in the area, and that this happens already in inexperienced juveniles during first migration – in fact, corn isn’t a much grown crop in the recruitment areas. Thirdly, our data suggests that the ducks balanced intake of corn with predation risk, trying to minimize the time spent in open fields. Knowing these things adds to our understanding of the connection between wildlife and agriculture, especially important for the Mallard,  a prime reservoir for influenza A virus that can infect domestic poultry.

Link to the article:

Bengtsson, D., Avril, A., Gunnarsson, G., Elmberg, J., Söderquist, P., Norevik, G., Tolf, C., Safi, K., Fiedler, W., Wikelski, M., Olsen, B. & Waldenström, J. 2014. Movements, Home-Range Size and Habitat Selection of Mallards during Autumn Migration. PLOS ONE 10.1371/journal.pone.0100764

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Front or back – which end of the duck should you swab?

By Jonas Waldenström

Simple questions can have simple or advanced answers. The question in the title – which end of a bird you should sample for flu – will be answered in due time, but first I need to take you on an odyssey of duck flu history. Let’s start in Italy:

A Blue rock thrush view of Colloseum (Photo from Flickr, T. van Ardenne under a CC BY-NC-ND 2.0 license)

A Blue rock thrush view of Colloseum (Photo from Flickr, T. van Ardenne under a CC BY-NC-ND 2.0 license)

It was a beautiful day in Rome. The sun poured down on Colloseum and the hordes of tourists that meandered through the old remnants of the Roman empire. A Blue rock thrush fluted melodiously from the ruins, ignorant of the people below.

Meanwhile, in a concrete slab on the other side of Forum Romanum, the world’s leading experts on influenza A virus had gathered at the OIE headquarters. The acronym stands for Office International des Epizooties, but as no one could remember that name it has since been renamed the World Organization for Animal Health, a UN body devoted to fight disease in food production animals.

This was back in 2006, and the reason we were all there was the looming threat of highly pathogenic avian influenza H5N1 – or the bird flu as it was called in the media. This particular virus had crossed species barriers from the wild waterfowl reservoir into poultry and mutated into a highly pathogenic form that spread rapidly in southern China. This is in itself wasn’t too worrying – as outbreaks of highly pathogenic avian influenza emerge from time to time in poultry – but the unprecedented events were that H5N1 also spilled back into wild birds and that it infected and killed humans. Thus, it was a virus of substantial concern for the poultry industry and one which also had the potential to become a new human pandemic virus. The spread had been slow at start, with outbreaks mainly regionally in Asia but then the virus took a sudden leap across Russia into Turkey, and then further into Europe and Africa. People were scared, authorities had no proper preparedness plans, and research on avian flu was still limited.

Inside the big hearing room sat a mix of researchers, health care professionals, vets, and officials from various governments (and yours truly, in the back row, perplexed and overwhelmed of it all). The atmosphere was tense. What did we know? How could we learn more? What did we need to know to stop it? Questions without answers at the time.

A particular problem was the elusive nature of the virus. If it infected a poultry farm we would see it – it was easily detected due to its high mortality in chickens. If your chickens were dying in heaps, chances were that H5N1 had made a visit. But unlike most other previous poultry outbreaks of highly pathogenic avian influenza it also popped up in wild birds. The most covered outbreak in Europe was the dead swans on the shores of Rügen in Germany, but swans and diving ducks were found dead in several other places too, including Sweden during the winter 2005/2006. At the same time, no one found any infected dabbling ducks, despite thousands of samples being tested. Where was this bloody virus?

And it was then Ron Fouchier, a virologist from the Erasmus Medical Center in Rotterdam, dropped the bomb. In his talk he showed data from infection experiments showing that H5N1 was not primarily a gastrointestinal virus in ducks (as they usually are). Rather, the highest titers of virus were found in samples from the upper airways, suggesting an all-together different epidemiology. He had a punch line in his PowerPoint presentation that said (if I remember correctly) “Are we sampling the wrong end of the bird?” It was a deafening silence while it sank in, and then a forest of hands was raised for questions. These results, later repeated by other laboratories, changed the recommendations for wild bird H5N1 sampling more or less over night.

Let’s get back to 2014 again. We know more today than we did in 2006, but still the influenza A virus research field is full of surprises. Adding the sampling of the respiratory tract (or rather the oropharyngeal cavity or the pharynx) did not yield many H5N1 positive samples from wild birds in the EU – most found cases were still from dead birds, especially swans. The virus disappeared from Europe, and the interest from European authorities and media vaned with time. However, the question regarding front or rear remained among researchers. Not so for highly pathogenic forms of influenza A virus – which at least for H5N1 is clearly different in pathogenesis – but for the more normal, waterfowl-adapted low pathogenic precursor viruses.

The old dogma was that the low pathogenic viruses primarily infected the lower gastrointestinal tract, but that it could be found at lower frequency also in the respiratory tract. Indeed, this is often what you find in field studies; at our sampling location oropharyngeal swabs normally peak at 2-5% prevalence, compared to up to 30-40% in fecal/cloacal samples. But detection is not the same as infection, as the viruses detected there may be contaminants from feces or water. In infection experiments with low pathogenic viruses, replicating virus is sometimes found in the respiratory tract, but this may be an aberrant result due to large infection doses and mode of inoculation, where some virus may be washed down where it normally would not go.

Paraffin-embedded Mallard tissue samples waiting to be analyzed. Photo by Michelle Wille

Paraffin-embedded Mallard tissue samples waiting to be analyzed. Photo by Michelle Wille

A week ago we published a paper in Veterinary Research that aimed to test the ‘to be, or not to be’-hypothesis of natural respiratory infections. During November, the peak the flu season in ducks, we trapped and sampled roughly 125 Mallards at our field site. Instead of letting the birds go, we kept them in the trap a couple of hours until the test results were back from the lab. Four birds with positive RRT-PCR results from the oropharyngeal swab, and one which was negative, were sacrificed for assessing presence, or absence of virus infection in different body tissues; all other birds were released. The decision to kill a bird for science isn’t one to take lightly, but in this case we believed the benefit of mapping the pathogenesis of the virus justified the study (and procedures, sample size etc. were assessed by an ethical committee and approved by different authorities).

The method we used to look for replicating virus particles is called immunohistochemistry. Fresh tissue specimens are preserved in formalin, and then embedded in paraffin. Very thin slides of tissue (3 micrometer thick) are then mounted on glass slides and treated in such a way that cells infected with influenza A virus will appear red under the microscope. We (Michelle Wille from my lab, and Peter van Run and Thijs Kuiken from the Erasmus Medical Centre in Rotterdam) screened a large number of slides from the full lengths of the respiratory and gastrointestinal tracts. None of the slides from respiratory tract were positive, in contrast to several from the intestinal tract.

Figure 1 from the paper. Selected tissues of the respiratory and gastrointestinal tracts of Mallard following immunohistochemical staining to detect nucleoprotein of influenza A virus. Tissues from the respiratory tract did not show virus antigen expression, such as (A) the respiratory epithelium of the nasal cavity and (B) air sac epithelium. In contrast, some tissues from the gastrointestinal tract such as (C) the epithelium lining the jejunal villi of the gastrointestinal tract, and (D) surface epithelium of the cloacal bursa did show virus antigen expression. Virus antigen expression is visible as diffuse to granular red staining, which is usually darker in the nucleus than in the cytoplasm. The tissues are counterstained blue with hematoxylin. Arrows have been included to illustrate positive cells.  [Used under a CC 2.0 license]

Figure 1 from the paper. Selected tissues of the respiratory and gastrointestinal tracts of Mallard following immunohistochemical staining to detect nucleoprotein of influenza A virus. Tissues from the respiratory tract did not show virus antigen expression, such as (A) the respiratory epithelium of the nasal cavity and (B) air sac epithelium. In contrast, some tissues from the gastrointestinal tract such as (C) the epithelium lining the jejunal villi of the gastrointestinal tract, and (D) surface epithelium of the cloacal bursa did show virus antigen expression. Virus antigen expression is visible as diffuse to granular red staining, which is usually darker in the nucleus than in the cytoplasm. The tissues are counterstained blue with hematoxylin. Arrows have been included to illustrate positive cells. [Used under a CC 2.0 license]

Immunohistochemistry is a very sensitive method for assessing infections. While PCR-based methods are extremely sensitive for detecting presence of virus RNA, they do not say which cells that are infected, and whether they are associated with pathological changes. In our study, the Mallards were positive for influenza A virus in the oropharyngeal cavity by RRT-PCR, and in some cases we were even able to culture the virus, but none of them had any infected cells in the respiratory tract. If we cant find it in these birds (which belong to the main reservoir species), during the peak of infection in autumn, I think it is unlikely that respiratory infections are commonplace. The fact that you may find PCR-positive samples from the front end of the duck is then not due to infection in this site, but from virus received through feeding, drinking or preening.

And this is important, as the pathogenesis of a pathogen is the basis for transmission. Without knowing which parts of an animal that are infected we cannot fully understand the epidemiology and make informed decisions. Thus, if you sample for low pathogenic avian influenza in wild waterfowl go for the rear end of the bird.

And in Colloseum, the Blue rock thrush continues to sing, regardless.

Link to the paper:

Wille, M., van Run, P., Waldenström, J. & Kuiken, T. 2014. Infected or not: are PCR-positive oropharyngeal swabs indicative of low pathogenic influenza A virus infection in the respiratory tract of Mallard Anas platyrhynchos? Veterinary Research 45: 53.

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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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