2. Equine influenza (horse flu)
RNA virus with segmented genome (mutation and
reassortment)
Influenza A of H3N8 subtype circulating
Acute viral infection that lasts on average 3 days
(range 1 to 6 days or more)
Spread by contact, aerosols or fomites
300
Frequency
200
EI in 2007 in Australia
100
1 2 3 4 5 6 7 8 9
Distance of spread (km)
Davis J et al. Transbound. Emerg. Dis. 2009;56:31-38
4. EIV evolutionary dynamics at
different scales
Epidemic
(Weeks/Months)
Global level (Years)
Single animal (Days)
Multiple animals (Days)
5. Evolutionary history of H3N8
Predivergence
European
Worldwide distribution
Currently two
divergent lineages American
circulating
Murcia P R et al. J. Virol. 2011;85:5312-5322
6. In vivo studies
Murcia et al. J. Virol. 2010;84:6943-6954
Murcia et al. J. Virol. 2013 (published)
7. Linking EIV evolution
at different scales
Epidemic
(Weeks/Months)
Global level (Years)
Single animal (Days)
Multiple animals (Days)
9. Aims: Spatiotemporal spread of
EI virus during an outbreak
Intra-host diversity versus consensus
Genetic diversity in Newmarket
Inter-host transmission (bottlenecks and
mixed infection)
Inter-yard transmission (virus population
structure)
Using the transmission network to infer
epidemiological characteristics
12. Large viral loads sequenced
60
50
Number of swabs
40
30
20
10
0
1 <100 100-1000 1000-10000 10000-100000 >100000
Copy number (copies/ul of swab)
13. Intra-host diversity
N = 40 N = 108
17 unique 37 unique
Newmarket/5/2003
Clonal amplicon sequencing
using Sanger for 48 horses from
17 yards
14. Lack of resolution from a
consensus sequence
N = 35 Coloured according
to their training
yard
N = 14
Non-synonymous
Synonymous
Statistical parsimony of consensus sequences
15. Random mutation along the gene
2361 sequences of 903bp compared to the
reference (most frequently found sequence)
161 synonymous (155
different sites)
332 non-synonmous
(307 different sites)
dN/dS=0.890068
(Estimated 95% CI =
[0.806365,0.979389])
16. Increase in genetic diversity over
time
600 0.0013
0.0012
Cumulative number of new mutations
500 TotalMutationsObserved
0.0011
mean pairwise
Mean Pairwise distance
400
0.001
300 0.0009
0.0008
200
0.0007
100
0.0006
0 0.0005
17. G230
38 horses from 19 yards
Two lineages
co-circulating
15 horses in Newmarket
from
6 yards A230
18. Lineage dynamics
200
180
160
140
Number of sequences
120
100
G at 230
80 A at 230
60
40
20
0
21. Removal of homoplasy
Removal of 242 sequences that exhibited
81 mutations linked to A230 or G230
Editor's Notes
Equine influenza in an important disease caused by Equine influenza virus which is a segmented RNA virus which evolves through mutations due to the lack of proof-reading mechanisms and reassortment. For reassortment to occur, an individual needs to be infected with two or more different viruses and the virus needs to infect the same cell. There are two subtypes of EI, H7N7 which is believed to be extinct and H3N8 currently circulating3) Equine influenza causes an acute viral infection that lasts on average 3 days (but with a large range)4) It is spread by contact, aerosols or fomites 3) 2003 outbreak in the UK well characterized by Newton et al.
No risks direct risk to humnas: Although equine influenza is thought not to be a risk to humans, outbreaks in the horse populationhave caused periodic disruption to the training schedules of vaccinated thoroughbreds in individual yards 2) Following a large influenza outbreak in the UK in 1989 (Livesay et al 1993), smaller outbreaks of influenza occurred periodically among vaccinated racehorses in Newmarket between 1990 and 2002, but these were limited to either single premises or a handful of yards in close proximity3)With changes in the influenza virus due to antigenic drift, EI has caused outbreaks even in populations with high vaccination coverage. Anitgenic drift is the process by which viral genomes are constantly mutating, some mutations will be at antigenic sites resulting in new antigens
Studies on the phylodynamics of EIV across different scales – from the individual to the population - are essential for devising effective measures to predict and prevent or contain influenza emergence.
Maximum likelihood phylogenetic tree for HA1 segment clones from a natural case of EIV H3N8H3N8 has a worldwide distribution.There are two lineages currently circulating, the Eurasian and the American, whilst the american lineage has made it across the atlantic, the European lineages has not been found circulating in AmericaCurrently, there are two American clades Florida clade 1 and Florida clade 2 co-circulating
In vivo studies with designed transmission chains and natural transmissionthe intra- and inter-host evolution of equine influenza virus in vaccinated horses and naïve horses have shown inter-horse bottlenecks are loose
Phylogenetic studies and intrahost sequence diversity have contributed to a better understanding of the emergence, spread and evolution of equine influenza, but sampling of genetic data has never been detailed enough to allow mapping of the spatiotemporal spread of EI viruses during a single epidemic. The Newmarket outbreak is a great opporutnity to link the evolutionary dynamics at difference scales by looking at an epidemic at the scale of weeks. The data is also sufficiently dense to integrate epidemiological processes with evolution at the scale of individual hosts.
Between March and May 2003, equine influenza virus infection was confirmed as the cause of clinical respiratory disease among both vaccinated and non-vaccinated horses of different breeds and types in 12 different locations in the UK.The largest outbreak affect racehorses in at least 21 Throroughbred training yards in Newmarket300 horses out of 900 tested racehorses where found to have been infected (approx 1300 horses are thought to have been exposed in Newmarket)
In this study I will describeUsing Intra-host diversity, what is intrahost diversity versus consensusSequence diversity in Newmarket, Lineage dynamicsReconstructing the transmission network from intra-host sequencesInter-yard transmission (Structure at the yard level)Using the transmission network to infer epidemiological dataTogether these could provide valuable insight for the implementation of efficient control strategies
The sequencing approach was done by clonal amplicon sequencing. This involves, eluting the swab, titration, amplification of the hemaglutining gene, running out on a gel, cloning, picking colonies and sequencing using Sanger sequencing.
We determined the viral shedding load from 200 nasal swabs using qPCR.Here, I show the timing of the different yard infections and the sum of the copy numbers found in each yard per day.The period during which horses from different yards are shedding overlapped in particular between the 7th of April and the 29th of April where 10 yards were concurrently infected.
Samples with copy numbers above 1E+3 were sequenced. The viral copy numbers in the samples ranged from 1 copy per ul to more than 100,000. Only swabs with >1000 copies/ul could be sequenced
Here I show a couple of examples of the diversity of sequences seen in a horse in the form of median joining networks. Whilst one sequence is found in large copy numbers, we also detect sequences with one to three mutational difference relative to this sequence. The within host diversity varied from host to host with the number of unique sequences ranging from 3 to 46. Clonal amplicon sequencing using Sanger for 48 horses (16%) from 17 yards (74%)90% of infected yards are sequencedNetwork 4.611
These are the results we would have obtained if a single consensus was used.3 different consensus sequencesOne yard which has two different consensus sequencesThe mutation A230G is also found at the epidemiological level.T69C is a mutation also found in Kally (maynard yard, location unknow)Watch_me (armstrong yard,guilford?)
The mutations occurred across the HA gene with no apparent hotspot of mutations2) The number of nonsynonymous mutation was greater than the number of synonymous mutations at most sites (Figure 2, 161 total number of synonymous mutations and 332 nonsynonymous mutations), 3) except for position C69T and A384T, which had markedly high frequency of synonymous mutations. 4) One non-synonymous mutation (site 230) was found in more than 600 sequences5) Indicating purifying selection but close to one which is what you expect when you are sampling random mutations that have not yet undergone selection. (correcting for multiple substitutions)So how was this genetic diversity generated over time.
Looking at the increase in diversity over timeThe number of new mutations increases in three phases. From the 13/03 to the 25/03, the growth in number of new mutations is low, between the 15/03 and 22/04 there is a steep increase in the number of new mutations at a rate of 15 new mutations per day.This large amount of genetic diversity generated is a result of variation generated in a large number of hosts during this period.the number of new mutations then levels off. The mean pairwise diversity follows a similar pattern and this increase in mutation is correlated with the number of horses infected.
Phylogenetic reconstruction usingRaxML with a dot according to the yards in which they were sampledSequences shared by different yards but most just found within a single yard or single horseThe most commonly found sequence found in 1235 copies in 38 horses from 19 yards is identical to a strain circulating in Rome in 2003 and which continued to circulate in France in 2004 (Newmarket/5/2003)3) This sequence is also tmrca of the currently circulating EIVs (Ahmedabad 2009 India descendent from the most commonly found sequence)4) Second consensus was also found in large numbers but in a smaller number of yards: 15 horses from 6 yards
How does the frequency of these lineages change over the course of the outbreakThis illustrates how lineages can vanish from a given locality due to unfavourable conditions such as temperature, low concentration of uninfected hosts and changes in the immunity status of the population during the outbreak.
To reconstruct the transmission dynamics from this dataset, we used a graph approach put forward by Jombart because we have densely sampled dataset which contains the ancestral sequences. Hypothetical genealogy of 6 isolates, three of which were sampledClassical phylogenetic reconstructionTree which takes into account the sampling date as in the BEAST software (Drummond and Rambaut, 2007)Direct ancestry reconstruction as in SeqTrackAssumptions:In the absence of recombination (reassortment), an isolate has only one ancestorAncestors precede their descendents in timeThe likelihood of an ancestor can be inferred from the amount of genetic differentiationAmong equally parsimonious ancestors, the closest geographically is selected
However, homoplasy is known to influence the results of SeqTrack. Homoplasy is the result of similarity in the mutation of different ancestry that is the result of convergent evolutionHere we have a number of mutations linked both to A230 and G230Network 4.611
Using all unique sequences from each horse,seqtrack will output a sequence level transmission graph which can easliy be transformed to a horse level network based on the provenance of each sequence1) seqtrack allows us to reconstruct the genealogy of each sequence, we can then use the knowledge as to which horse the sequence has collected from to reconstruct a plausible transmission network
Although the horses might not have directly transmitted, it might suggest that there were intermediate horses unsampled. : represents plausible transmission pathways2) Each node represents a horse, the size correspond to the genetic diversity of sequences within that horse and the color represents the training yard of the horse3) Arrows between node represents a inferred transmission of a variant , dashed lines refer to transmission of the reference sequence for which we have lower confidence4) Here, I show an example of three mutations being shared between two horses providing evidence of wide bottlenecks
In a large number of cases, horses have received variants from more than one horse => MIXTE infections appear to be common in the field From experimental studies, we have found mixte infections and now this shows that it happens in the field.
This network also enables us to look at particular characteristics such as :1) Determining the centrality or influence of the various horses. Closeness centrality measures how many steps is required to access every other individuals in the network.2) Articulation points in a network are those which are critical to transmission to certain individuals: for an articulation point, all paths between certain nodes have to pass through this point.Additional analyses of the network revealed that horse E10 wasmost central (relative betweenness=0.19, average=0.045, SE=0.008), requiring the fewest steps to access every other horse. As such, horse E10 is influential for the spread of the virus during the outbreak. Additionally, five other horses were critical for transmission to a subtree of the network (i.e., articulation points). For example, horse E07 was critical for transmission to E14, E09, E15 and E10 and horse L25 was critical for the transmission to L39, L44 and L40.Given the central role of some horses in the spread of EIV during the outbreak, we looked for evidence of superspreaders. Accordingly, the geometric distribu- tion was the best fit to the data, very closely followed by the negative binomial; this provides limited evidence of potential superspreaders either as a result of the mode of transmission of EIV or the relatively small sample size of the study (Table 2 and Figure 3C) [25]. We also looked for individual factors associated with increased transmission. Accordingly, there was no significant relationship between the number of horses transmitted to and the age of the horse, time since last vaccination, the number of vaccine doses in the horses’ lifetime and shedding load (n=15 after removal of missing data).
Number of ties that actors have, their so-called degrees. The distribution of actors is often highly skewed with small number of actors have unusually high numbers of ties. This will have important implications for the way pathogens are transmitted through a network. Nonrandom social phenomena at work in the shaping of the network. if a comparison between a network and the equivalent random model reveals substantial disagreement, it strongly suggests that there are significant social forces at work in the network.By fitting exponential random graph models to the directed network, we compared the graph to a random network as well as a network including the yard as an exogenous covariate. The latter model was a better fit to the data (AIC: 820.74, BIC: 826.46 versus AIC: 797.07, BIC: 808.52 with yard), suggesting that the yard a horse belonged to might have played a role in the transmission dynamics during the outbreak.
1) There is not a significant correlation between the number of shared mutations and the distance between yards or the number of days separating the dates of infection in the yards 2) There is no significant difference in the number of shared mutations within yards compared to between yards 3) The spread is not local to yards as most transmission events indicated by the distance to the nearest potential (or actually identified) parent, the median of which is just 2.3 km (and mean 2 km including Newmarket yards only), indicating that most transmissions are between yards. Additionally, our data shows that EIV does not necessarily spread between horses sharing the same yard. Indeed, EIV was likely transmitted between horses that were kept in yards that were 2.7 km apart (yard E to L). Social networks may better explain part of the transmission dynamics especially as horses from different yards establish direct contact during their daily routines, and which may also explain the fluid dynamics seen in the transmission of human influenza virus [36,37]. However, we have limited information on the contact network between horses and yards, which constrains further integrated social network analyses
Our analysis of the determinants of phylogenetic clustering based on a distribution of Bayesian trees revealed that significant clustering by yard in the data set as a whole under both the AI and PS statistics (p < 0.00; Supplementary Table 1 x). However, this significant result was largely due to a single yard – yard L (p = 0.001 under the MC statistic) – which has the largest number of horses sampled (n = 10) and is one of those furthest away from the gallops; additionally, all horses within this yard had the A230 mutation. No other yards were observed to contain EIV sequences more closely clustered than expected by chance alone (although yard Q was of borderline significance; Supplementary Table 1), indicating that viruses were able to move relatively freely among themBayesian-tip association significance testing
4) The use of the training yard for geo-positioning of the horses for spatial analysis was justified because horses spend considerable time in the yard, which constitutes a highly probable site of transmission. Nevertheless, transmission might also occur at other places and this fact certainly may have had some impact on our data, which would be hard to account for. 5) During training in Newmarket, there is frequently close contact between ‘strings’ of racehorses from different yards passing each other as they walk to and from training gallops along dedicated walkways in the town.
We can use the dates of positive ELISA and the number of cases per week and fit a logistic curve to get an R0Similar to estimates of an outbreak in AustraliaThese models do not take into account the individual variation in transmission and the changes in reproductive rate as a result of control strategy.The outbreak investigation, sampling protocols and the methodused for establishing a positive diagnosis of equine influenza virus were previously published [14]. R0 was estimated directly from the infectious histories of 899 horses tested over the course of the outbreak. The date of infection was determined based on the first positive ELISA or the date when the viral copy numbers was above 150 copies per microliter (due to the limits of false positive detection in qPCR), whichever occurred first. The intervals between infections were determined based on the time period between positive nasal swabs determined by qPCR from different experiments including [2]. These experiments were based on natural transmission of H3N8 in naı¨ve and vaccinated horses (heterologous or homologous vaccination) (Dataset S4). We combined the data from these studies as the intervals between infections did not vary significantly between studies (F3,19=3.27, P=0.8). We fitted Poisson, geometric and gamma distributions to these intervals using maximum likelihood. A gamma distribution (mean=3.3 days, variance=1.3) provided the best fit and had the lowest AIC, thus was used for further calculations.We estimated the initial growth rate of the epidemic (l)byfitting an exponential curve to incidence data using a generalized linear model with Poisson errors. We explored a range of intervals for fitting, up to and including peak incidence; the short time-series limited the fitting procedures for the shorter intervals but by peak incidence, the rate of epidemic growth had potentially been curtailed. We converted the estimated growth rate to measure R0 (initial Rt) using the serial interval from the transmission experiments.
Using an epidemic tree, we can generate the effective reproductive rate over the course of the outbreakHere, as we have several possible sources for a number of the horses, we sampled randomly from the possible ancestors and generated a weekly averageThis suggests that whichever control strategy they had put in place, it started working immediately as the reproductive rate started decreasing although it did not reach one until the 15th of May.While these estimates are broadly similar, the network approach may initially overestimate Rt because of under-sampling individ- uals early in the outbreak (Figure 4C), whereas the epidemic inference is relatively robust to sampling but it is subject to some uncertainty as the time-series is very short (FigureThe network approach allows further dissection of transmission pathways including those that result in mixed infections (red and black lines in Figure 4C), which would not be apparent from incidence data alone.Reference: Haydon D et al. Proc. R. Soc. 2003;270:121-127To calculate Rt over the course of the epidemic as in [60], weused a resampling approach from the network to select a single donor from possible lists of candidates with equal probability to generate 100 epidemic trees. The number of secondary cases per infected horse inferred from each tree was calculated and averaged across the possible epidemic trees to provide a time varying
In Summary., I have shown youA high level of within host diversityA need to understand what makes a host more infectious and predict which will be more infectious.
