1
Host and environmental factors modulate the exposure of free-ranging and farmed
2
red deer (Cervus elaphus) to Coxiella burnetii
3 4
Running title: Coxiella burnetii epidemiology in red deer
5 6
David González-Barrio1*, Ana Luisa Velasco Ávila1, Mariana Boadella2, Beatriz
7
Beltrán-Beck1, José Ángel Barasona1, João P.V. Santos1,3, João Queirós1,4,5, Ana L.
8
García-Pérez6, Marta Barral6, Francisco Ruiz-Fons1
9 10 11 12 13 14 15 16 17 18 19 20 21
1
Health & Biotechnology (SaBio) Group, Spanish Wildlife Research Institute IREC
(CSIC-UCLM), Ronda de Toledo s/n, 13005 Ciudad Real, Spain. 2
SABIOtec, Edificio Polivalente UCLM, local 1.22. Camino de Moledores s/n, 13005
Ciudad Real, Spain. 3
Departament of Biology & CESAM, University of Aveiro, Campus Universitário de
Santiago, 3810-193 Aveiro, Portugal. 4
CIBIO/InBio - Centro de Investigacão em Biodiversidade e Recursos Genéticos,
Universidade do Porto, Campus Agrário de Vairão, 4485-661 Vairão, Portugal. 5
Departamento de Biologia, Faculdade de Ciências da Universidade do Porto (FCUP),
Rua Campo Alegre s/n, 4169-007 Porto, Portugal. 6
Animal Health Department, Instituto Vasco de Investigación y Desarrollo Agrario
(Neiker), Derio, Spain.
22 23
*
Corresponding author: David González-Barrio, Spanish Wildlife Research Institute
24
IREC, Ronda de Toledo s/n, 13005 Ciudad Real, Spain. Phone: 0034926295450; Fax:
25
0034926295451; E-mail address:
[email protected]
1
26
Abstract
27
The control of multi-host pathogens such as Coxiella burnetii should rely in accurate
28
information on the roles played by main hosts. We aimed to determine the implication
29
of the red deer (Cervus elaphus) in the ecology of C. burnetii. We predicted that red
30
deer populations from wide geographic areas within a European context would be
31
exposed to C. burnetii and therefore we hypothesized that a series of factors would
32
modulate C. burnetii exposure in red deer. To test this hypothesis we designed a
33
retrospective survey over 47 Iberian red deer populations from which 1,751 sera and
34
489 spleen samples were collected. Sera were analyzed by ELISA to estimate exposure
35
to C. burnetii and spleen samples were analyzed by PCR to estimate the prevalence of
36
systemic infections. Thereafter, we gathered 23 variables - within environment, host and
37
management factors - potentially modulating the risk of exposure of deer to C. burnetii
38
and performed multivariate statistical analyses to identify main risk factors. Twenty-
39
three populations were seropositive (48.9%) and C. burnetii DNA in spleen was
40
detected in 50% of analyzed populations. Statistical analyses reflected the complexity of
41
C. burnetii ecology and suggest that although red deer may maintain C. burnetii
42
circulating without third species, the most frequent scenario probably includes other
43
wild and domestic host species. These findings together with previous evidence of C.
44
burnetii shedding by naturally infected red deer point at this wild ungulate as a true
45
reservoir for C. burnetii and as an important node in the life cycle of C. burnetii, at least
46
in the Iberian Peninsula.
47
Keywords: Red deer; Q fever; Risk analysis; Wildlife; Zoonoses
48 49 50
2
51
Introduction
52
Coxiella burnetii is a Gram-negative intracellular bacterium that causes Q fever, a
53
disease shared by humans and animals. Whereas the epidemiological status of C.
54
burnetii in European domestic ruminants is well known (1), information in wildlife is
55
mostly local and scattered (2, 3). Although the major part of human Q fever outbreaks
56
are linked to transmission of C. burnetii from domestic ruminants (4, 5), the ability of
57
C. burnetii to infect wild hosts (3, 6) and its high environmental resistance (1) make
58
wildlife species potential reservoirs of C. burnetii. Wildlife could - based on this
59
hypothesis - maintain C. burnetii and transmit it to wildlife (7), domestic animals (8) or
60
humans (9). It is, therefore, of paramount relevance to: i) identify those potential wild
61
reservoir species that - through direct and indirect interactions - could transmit C.
62
burnetii to target species (domestic animals and humans); and ii) determine which
63
environmental factors are main drivers of C. burnetii within most relevant wild
64
reservoirs. Efficient prevention of C. burnetii transmission at the wildlife-domestic
65
animal-human interface could only be approached once main reservoirs have been
66
identified and driving risk factors are known (10).
67
Several wild ruminant species are present and well distributed in Europe and, with the
68
premise of being susceptible to infection by C. burnetii, could constitute important wild
69
reservoirs of C. burnetii. However, among European wild ruminants, the red deer
70
(Cervus elaphus) could perhaps constitute a potential wild reservoir for C. burnetii due
71
to: i) geographic distribution; ii) demographic status; ii) game importance; and iv)
72
behavior. The red deer displays both global (11, 12) and European (13) wide geographic
73
distribution areas and displays increasing distribution and density trends (14, 15). The
74
red deer is currently one of the most important game species of European large
75
mammals (16). Many red deer populations in Europe are subjected to management for
3
76
hunting (17) and red deer farming has expanded in recent decades as a consequence of
77
the demand of venison and live individuals for population restocking programs (18).
78
Additionally, the gregarious behavior of the red deer (19, 20) promotes aggregation of
79
individuals. In domestic animals, host density and aggregation are important drivers of
80
C. burnetii transmission (21, 22) and some Iberian red deer populations reach densities
81
over 70 deer/Km2 (14). Increasing red deer densities, deer management (including
82
artificial feeding) and gregarious behavior constitute main factors favoring transmission
83
of circulating pathogens in red deer populations (23, 24).
84
Altogether - distribution, demography, management and behavior - point at red deer as
85
one of the most concerning reservoirs of shared pathogens among European wild
86
ruminants; e.g. 44% of red deer in Italy were found infected by piroplasms (25) and
87
over 60% of Slovakian red deer carried Anaplasma spp. (26). Therefore, we predicted
88
that C. burnetii will be circulating in red deer populations in Iberia and we hypothesized
89
that particular environment, management and host factors will contribute to the
90
exposure of red deer to C. burnetii. To test these hypotheses we designed a retrospective
91
epidemiological survey targeting Iberian - Spanish and Portuguese - red deer
92
populations within its geographic distribution range.
93 94
Materials and methods
95
Survey design
96
Red deer sera from forty-seven populations were collected from 2000 to 2012 in
97
mainland Spain and Portugal (Fig 1). Study populations were selected on the basis of: i)
98
management systems ˗ including naturally free-ranging unmanaged populations (game
99
reserves, and natural and national parks), free-ranging managed populations, and farms;
100
ii) geographic location ˗ location within the different bioregions established for wildlife
4
101
disease surveillance schemes in mainland Spain (27) and from different regions in
102
mainland Portugal; and iii) red deer geographic distribution range (Fig. 1), in order to
103
gain for spatial representativeness.
104
Serological analyses
105
The presence of specific antibodies against C. burnetii phase I and II antigens in deer
106
sera was analyzed with a commercial indirect ELISA test (LSIVet™ Ruminant Q Fever
107
Serum/Milk ELISA Kit, Life Technologies, USA) with an in-house modification in the
108
secondary antibody (Protein G−Horseradish peroxidase, Sigma-Aldrich, USA; 28) that
109
was previously validated for wild and domestic ungulates (29). Briefly, for validation
110
we employed positive (n=8) and negative (n=6) red and roe deer sera analyzed by
111
indirect immunofluorescence assay (IFA) and ELISA+/PCR+ and ELISA-/PCR- cattle
112
(n=14 and n=12, respectively) and sheep (n=16 and n=17, respectively) sera. For each
113
sample, the sample-to-positive control ratio (SP) was calculated according to the
114
formula: SP =
ODs - ODnc x100 ODpc - ODnc
115
where ‘ODs’ is the optical density of the sample at a dual wavelength 450-620 nm,
116
‘ODnc’ is the optical density of the negative control and ‘ODpc’ is the optical density of
117
the positive control. All SP values ≤ 40% were considered as negative, whereas SP
118
values > 40 were considered as positive.
119
PCR analyses
120
Spleen samples were collected from a subset of the studied populations during
121
necropsies performed over hunter-harvested or euthanized farmed deer. Spleen samples
122
from seropositive and seronegative deer were selected for PCR analyses. Total DNA
123
from spleen samples was purified with the DNeasy® Blood & Tissue kit (Qiagen,
124
Germany)
according
to
the
manufacturer’s
protocol 5
125
(http://mvz.berkeley.edu/egl/inserts/DNeasy_Blood_&_Tissue_Handbook.pdf).
DNA
126
concentration in aliquots was quantified (NanoDrop 2000c/2000, Thermo Scientific,
127
USA) and aliquots were frozen at -20ºC until the PCR was performed. Sample cross-
128
contamination during DNA extraction was discarded by including negative controls
129
(Nuclease free water; Promega, USA) that were also tested by PCR.
130
DNA samples were analyzed by a real time PCR (qPCR) targeting a transposon-like
131
repetitive region of C. burnetii as previously described (Table 1, 30). SsoAdvanced™
132
Universal Probes Supermix (BioRad, USA) was used in qPCR, according to the
133
specifications of the manufacturers. DNA extraction and PCR were performed in
134
separate laboratories under biosafety level II conditions (BIO II A Cabinet, TELSTAR,
135
Spain) to avoid cross-contamination. We used as positive control in this real time PCR a
136
DNA extract of Coxiella burnetii from the vaccine COXEVAC (CEVA Santé Animale,
137
France). We considered that a sample was positive at threshold cycle (Ct) value below
138
40 (30).
139
Risk predictor variables
140
In order to identify factors modulating the risk of exposure of individual red deer to C.
141
burnetii infection, a set of abiotic and biotic variables within three main factors ‐
142
environment, management and host (see Table 2) ‐ were gathered on the basis of their
143
potential impact in C. burnetii ecology.
144
Environmental factors. - Both spatial and metereology-related variables were considered
145
for risk factor modeling. Longitude (X) and latitude (Y) were considered as spatial
146
factors to control for any potential spatial autocorrelation of data. Coordinates were
147
recorded at the sampling site level with portable GPS devices (Garmin Ltd., Cayman
148
Islands), so all deer from a sampling site were assigned the same X and Y values.
149
Average spring temperature (AvSpT) and the season (Se) in which deer were surveyed
6
150
(4 categorical classes: spring (Sp) ‐ April-June; summer (Su) ‐ July-September; autumn
151
(Au)‐ October-December; and winter (Wi) ‐ January-March) were considered as
152
metereology-related variables. Average spring temperature (AvSpT) was considered as
153
a potential proxy for C. burnetii environmental survival and as a potential driver of air-
154
borne transmission of C. burnetii - probably dependent on air moisture which is highly
155
correlated with temperature (31) - in the expected shedding season. C. burnetii shedding
156
prevalence is expected to be higher in spring, when calving takes place, as a recent
157
study suggests (32). Season was considered as a proxy of potential year-round changing
158
variation in infection risk because of the expected predominance of C. burnetii shedding
159
in spring.
160
Management factors.- Human interference in deer ecology and behavior was considered
161
on the basis of deer population management systems: a) unmanaged free-ranging deer
162
populations (Um); b) free-ranging deer populations managed for hunting purposes (Mg;
163
high-wire fencing restriction and year-round supplementary feeding); and c) farmed
164
deer populations (Fd; extensively produced red deer in 6-10 Has. enclosures).
165
Host factors.- Different host population and host individual variables were considered
166
since C. burnetii is a multi-host pathogen (33):
167
a) Density of domestic ruminants and domestic ruminant farms in the municipality to
168
which individual deer belong. Domestic ruminant density (cattle (Cd), sheep (Sd), goat
169
(Gd) and combinations of them; Rud) and farm density (CFd, SFd, GFd, SrFd and
170
RuFd, respectively) values at the municipality level were calculated on the basis of
171
livestock census data gathered by the Spanish and Portuguese National Statistics
172
Institutes (http://www.ine.es and http://www.ine.pt, respectively) in 2009.
173
b) Environmental favorability index (ranging from 0 ‐ minimum favorability ‐ to 1 ‐
174
maximum favorability) for red deer (RdFi), roe deer (RoFi) and wild boar (WbFi) at 7
175
UTM 10x10 Km resolution squares, calculated for peninsular Spain (34). This index is a
176
measure of the suitability of a land surface for a particular species and it is well
177
correlated with the real abundance of the species (35). Environmental favorability
178
indices of wild ungulates have not been estimated for Portugal. Therefore, Portuguese
179
populations that were close to the Spanish border (n=6; Fig. 1) were linked to
180
favorability indices of the closest Spanish UTM 10x10 Km square. The only population
181
surveyed in central Portugal couldn’t be associated with any wild ungulate favorability
182
index and was not considered for risk factor analyses. Red deer, roe deer and wild boar
183
have been found infected by C. burnetii previously (36, 37, 38). No favourability
184
indices for any other potential wild host of C. burnetii are available for the study area.
185
c) Density of humans in the municipality (HUd). Updated human demographic data
186
were obtained from Spanish and Portuguese National Statistics Institutes in 2011 and
187
2010, respectively.
188
d) Straight-line Euclidean distance to the nearest human settlement (HsDi; town or city)
189
was
190
http://www.qgis.org/es/site/). Human and their pets may be hosts for C. burnetii and
191
potentially modulate the risk of exposure of deer (33). For this reason, HUd and HsDi
192
were considered for modeling analyses.
193
e) Host sex, Sx; Male (M) vs. female (F). In farmed deer, the number of reared stags
194
was significantly lower than the number of reared females and therefore there was a sex
195
bias in the sample.
196
f) Host age (Ag). For free-ranging deer, tooth eruption patterns (39) were used to
197
estimate the age of animals < 2 years old, whereas for animals > 2 years, age was
198
determined by the number of cementum annuli of the incisor 1 root (40). Farm keepers
199
provided the year of birth for farmed deer. For analytical purposes, four age classes
measured
with
Geographic
Information
Systems
(Quantum
GIS;
8
200
were established: calf (Cf; 0-1 years old), yearling (Yr; 1-2 years old), sub-adult (Sa; 2-
201
3 years old) and adult (Ad; >3 years old). In free-ranging populations, a conscious
202
negative bias to calves existed according to reported age-related C. burnetii
203
seroprevalence patterns (32, 41).
204
The year (Sy) in which each individual deer was sampled was additionally considered
205
as a survey-associated factor modulating the risk of exposure of deer to C. burnetii (22).
206
Statistical analyses
207
Four different datasets were employed to test for the main hypothesis of this study – the
208
modulating effect of risk factors over the risk of exposure of deer to C. burnetii – in
209
order to seek for major driving factors, including and not including management system
210
(an expected major epidemiological driver according to existing literature on wild
211
ungulate pathogen dynamics). Datasets included: i) overall studied deer populations; ii)
212
unmanaged free-roaming deer populations; iii) managed free-roaming deer populations;
213
and iv) deer farms. Deer management system was included when modeling the dataset
214
that included all deer to test for the effect of management on the risk of exposure of deer
215
to C. burnetii. Within each dataset and with the aim of reducing the interference of
216
multi-collinearity among predictor variables in modeling output, a correlation matrix ‐
217
Spearman´s rank tests ‐ of continuous variables was built. Therefore, only uncorrelated
218
variables (Spearman rho2; 43). The statistical uncertainty associated to the
230
estimation of individual prevalence values was assessed by calculating the associated
231
Clopper-Pearson exact 95% confidence interval (95%CI).
232
Results
233
One thousand seven hundred and fifty-one serum samples were analyzed; 822 (46.9%)
234
from unmanaged populations (n=27), 329 (18.8%) from managed populations (n=14)
235
and 600 (34.3%) from farmed populations (n=6). Out of the 1,629 samples in which sex
236
could be recorded, 1,147 (70.4%) were females and 482 (29.6%) were males. In 1,593
237
samples age could be recorded; 100 (6.3%) belonged to calves, 240 (15.1%) belonged
238
to yearlings, 251 (15.7%) belonged to sub-adults and 1,002 (62.9%) belonged to adults.
239
Age and sex could be recorded from 1,560 individuals at the time. Average individual
240
seroprevalence by bioregion and deer management system are shown in Table 3.
241
All IFA+ red and roe deer sera presented SP values >100 whereas IFA- sera had SP
242
values 40). ELISA+/PCR+ cattle and sheep sera displayed SP
243
values >70 and >100, respectively, whereas ELISA-/PCR- cattle and sheep sera had SP
244
values 40.
246
Twenty-three of the 47 deer populations surveyed (48.9%) had at least one seropositive
247
sample; Four out of six (66.7%) deer farms and 55.6% (15/27) of unmanaged free-
248
ranging populations had seropositive animals in contrast to the 21.4% (3/14) of
249
managed free-ranging deer populations. Seven of the 47 (14.9%) red deer populations
10
250
had average individual seroprevalences over 10%. Average seroprevalence values by
251
sex and age are shown in Table 4.
252
Four hundred and eighty-nine spleen samples were analyzed by qPCR (Fig. 1); 305, 155
253
and 29 spleen samples came from unmanaged, managed and farmed deer populations,
254
respectively. The 5.7% (28/489; 95%CI: 3.8-8.2) of spleen samples were qPCR positive
255
(Cycle threshold range of positive samples: 32.1-39.9). Prevalences of C. burnetii DNA
256
in spleen were 6.2% (19/305; 95%CI: 3.8-9.6), 5.2% (8/155; 95%CI: 2.3-9.9) and 3.5%
257
(1/29; 95%CI: 0.1-17.8) in unmanaged, managed and farmed deer populations,
258
respectively. Ten of 140 male (7.1%; 95%CI: 3.5-12.6) and 18 of 234 females (7.7%;
259
95%CI: 4.6-11.9) were qPCR positive. One of 10 analyzed calves (10.0%; 95%CI: 0.3-
260
44.5), 5 of 41 juveniles (12.2%; 95%CI: 4.1-26.2), 2 of 19 subadults (10.5%; 95%CI:
261
1.3-33.1) and 20 of 302 adults (6.6%; 95%CI: 4.1-10.1) were positive to C. burnetii
262
DNA in spleen by qPCR.
263
Twenty-six deer populations were studied for C. burnetii DNA prevalence in spleen
264
samples. Of those, 12 were seronegative and 14 had at least a seropositive individual.
265
Thirteen of those 26 deer populations (50.0%) had at least one positive spleen sample; 8
266
were seropositive and 5 were seronegative.
267
The best-fitted risk factor general model for exposure of deer to C. burnetii (see Table
268
5) retained variables within the host and environment factors. Human density and spring
269
temperature were positively related to increasing risk of exposure to C. burnetii (Fig. 2)
270
and, in contrast to domestic ruminant density – that showed a negative relationship, this
271
relationship was statistically significant. The statistically significant negative effect of
272
season was linked to the higher risk of exposure to C. burnetii in spring (Fig. 2).
273
According to outputs from partial models (for unmanaged, managed and farmed deer
274
datasets; Table 5), host and environmental factors were also evidenced as relevant
11
275
drivers of exposure to C. burnetii. However, main drivers varied within each particular
276
management system. Whereas human density, season and red deer environmental
277
favorability index were retained by the best-fitted risk factor model for unmanaged deer,
278
average spring temperature was retained by the best-fitted model for managed, and
279
season and domestic ruminant density appeared as main drivers of the risk of exposure
280
to C. burnetii in red deer farms (Table 5; Fig. 2).
281
Discussion
282
This study constitutes a transnational-scale survey of C. burnetii in European wild
283
ruminants and a first approach to identify the factors that drive the ecology of C.
284
burnetii in red deer. We found that C. burnetii is present in approximately the 50% of
285
free-ranging and farmed Iberian red deer populations, and that systemic infections occur
286
in the 50% of them. These facts support the implication of the red deer in the ecology of
287
C. burnetii. Indeed, a first hint to support that a concrete host species is acting as a true
288
reservoir for a specific pathogen, provided the species is well distributed and abundant
289
(44), is identifying that: i) the pathogen is widely distributed in populations of that host
290
within a relatively large territory; ii) the pathogen is able to cause systemic infections
291
(33, 45); and iii) the host is able to shed the pathogen. We herein confirm the first two
292
requisites; the third requisite was confirmed previously, too (7). Hereby, the red deer
293
may be confirmed as a true C. burnetii reservoir.
294
Methodological considerations
295
True C. burnetii seroprevalence in free-ranging deer populations may have been
296
underestimated since most deer sera (1,017 of 1,151) were collected from early autumn
297
to mid-winter because this is the main big game hunting season in Iberia. Recent data
298
from LO farm (Fig. 1) suggest that annual individual seroprevalence fluctuates
299
according to the red deer calving season, with lowest values in winter and maximum
12
300
values in late spring (32). Seroprevalence levels are higher in late spring- early summer,
301
coinciding with the time of calving and supposedly, with the main Coxiella burnetii
302
excretion season.
303
Geographic distribution of C. burnetii in red deer populations
304
It is noteworthy mentioning the wide geographical distribution of C. burnetii in Iberian
305
red deer populations. Up to date, and to the best of our knowledge, no exhaustive
306
national extensive study of C. burnetii in wild ruminants has been performed in Europe.
307
C. burnetii DNA was found in tissues of 23% of the roe deer analyzed from 9 of the 12
308
Dutch provinces during the massive human Q fever epidemic affecting The Netherlands
309
from 2007 to 2010 (36); however, the number of samples analyzed was low (n=79).
310
Comparisons with results from previous regional studies in Spanish red deer are
311
difficult because of differing geographic scales and pathogen exposure diagnostic
312
techniques: indirect immunofluorescence test with 9.5% wild red deer and 40.0%
313
farmed red deer reacting seropositive (46) and molecular analyses (35), where none of
314
the 22 red deer analyzed was positive. In general terms, and with the premise of a
315
possible under-estimation of real seroprevalence values, we may conclude that C.
316
burnetii circulates widely in Iberian red deer populations.
317
Factors modulating exposure of red deer to C. burnetii
318
Modeling output of overall deer dataset partly confirmed our second hypothesis since
319
environment and host factors were found to be significant drivers of C. burnetii
320
transmission in red deer. However, no effect of management system was observed in the
321
risk of exposure to C. burnetii despite main drivers in partial datasets slightly varied
322
(Table 5). The three management categories of red deer considered in this study are
323
related to deer abundance and aggregation (14, 47). Therefore, and according to the
324
observed effect of cattle density over the risk of exposure to C. burnetii (21, 22), we
13
325
expected a clear management effect. One would expect that in deer farms, and even in
326
some intensively managed free-ranging deer populations, horizontal C. burnetii
327
transmission would be enhanced due to the high animal-to-animal contact rate. This
328
seems not to occur in general terms, perhaps due to the complexity of C. burnetii
329
ecology and the existence of multiple reservoir hosts (3, 45). The low percentage of
330
variance explained by the best-fitted model for unmanaged deer populations (Table 5)
331
may be reflecting the existence of a complex scenario in environments with higher
332
biological diversity. This would suggest that endemic cycles of C. burnetii implicating
333
different wild (and domestic) host species might be established in Iberia.
334
Climatic conditions during the C. burnetii shedding season modulate the risk of
335
exposure of deer to this pathogen. Nonetheless, partial models revealed that average
336
spring temperature is relevant only in free-ranging managed populations; this variable
337
itself explained 16.5% of model variance. Whether this observation is related to a direct
338
effect of temperature on C. burnetii survival or transmission, or to indirect non-
339
considered effects - e.g. by an effect over transmission - cannot be ruled out with our
340
findings and with existing literature. Therefore, this finding should be the object of
341
further studies aiming to deepen in C. burnetii ecology.
342
Although a general effect of the season was evidenced, the risk of exposure to C.
343
burnetii was higher in spring for unmanaged deer and similar in spring and winter in
344
farmed deer. This observation in farmed deer may be caused by a seasonal bias in
345
farmed deer sampling in this study, with only deer from the high seropositive LO farm
346
surveyed in winter. The observation in unmanaged deer agrees with the expected higher
347
shedding of C. burnetii at the time of deer calving by mid-spring as mentioned above.
348
Finally, host effects were revealed by the general model and by models for unmanaged
349
and farmed deer populations. The influence of human density in the general model may
14
350
be slightly modulated by an apparently outstanding result (see Fig. 2) from a highly
351
seroprevalent red deer population. However, this factor also modulated exposure to C.
352
burnetii of unmanaged deer, therefore showing the influence of human activities on the
353
risk of exposure of deer to this pathogen. Density of coexisting domestic ruminants
354
seems to dilute the risk of exposure of deer to C. burnetii. This result is shocking for a
355
pathogen that is endemic in domestic ruminants in Iberia and whose transmission has
356
been proven to be linked to host density (21, 22). This contrasting finding again
357
suggests that the ecology of C. burnetii in wildlife should be complex and different wild
358
and domestic species should be involved in its maintenance, independently on the
359
ability of the red deer to act as a true reservoir host. Indeed, modeling output suggests
360
that although an independent cycle of C. burnetii in red deer is possible without the
361
intervention of third species (susceptibility, systemic infection and shedding
362
demonstrated), other hosts may be implicated in the circulation of C. burnetii in wild
363
foci.
364
Implications for animal and human health
365
Haydon et al. (48) re-defined the reservoir concept for multi-host pathogens and stated
366
that a specific pathogen of relevance for a target host of our interest may be maintained
367
through a high number of combinations of host populations or environments that keep
368
the pathogen circulating. Defining therefore the risk of transmission of C. burnetii from
369
red deer to target hosts (livestock and humans) is difficult and prevents from concluding
370
if the red deer plays or not a major role in C. burnetii maintenance in Iberia. We believe
371
that red deer populations constitute a high relevant node in the life cycle of C. burnetii,
372
but particular scenarios of interaction with third species need to be further investigated.
373
Wild lagomorphs and small mammals infected by C. burnetii, among others, may
15
374
excrete infectious bacteria (45, 49, 50) and constitute therefore relevant pieces of the C.
375
burnetii maintenance and transmission puzzle.
376
The risk of C. burnetii transmission from red deer to humans could be comparable to
377
that from livestock if deer-human and livestock-human indirect interaction rates were
378
similar. This is supported by the fact that both individual and population
379
seroprevalences in red deer are similar to those found in domestic ruminants (21, 40,
380
51). Most effective livestock-human C. burnetii transmission events occur indirectly
381
through aerosols (33). We may expect that most deer-livestock and deer-human
382
transmission events would occur indirectly (52). Therefore the risk of transmission from
383
deer to livestock and humans relies on the exposure rate to deer, which suggests that
384
extensively produced domestic ruminants and humans linked to hunting and wild
385
ungulate management and conservation are those at a higher risk (9, 53).
386
Our results point to free-ranging deer, perhaps in connection with other wild and
387
domestic hosts, and deer farms as the main hot spots of circulation of C. burnetii in red
388
deer in Iberia, and maybe beyond in Europe. Further clarification of particular red deer-
389
livestock or red deer-human interaction rates at different geographic scales should
390
improve the chances of preventing C. burnetii transmission events.
391
Acknowledgements
392
We are grateful to game estate owners, gamekeepers, natural and national park
393
managers and farm managers for their collaboration in sample collection. Special thanks
394
go to José Antonio Ortiz from LO farm and to Christian Gortázar for whole support. We
395
also would like to acknowledge the great effort of colleagues from SaBio group at IREC
396
for sample collection (Joaquín Vicente, Pelayo Acevedo, Isabel G. Fernández-de-Mera,
397
Ursula Höfle, Paqui Talavera, Óscar Rodríguez, Álvaro Oleaga, Diego Villanúa, Vanesa
398
Alzaga, Elisa Pérez, Raquel Jaroso, Raquel Sobrino, Encarnación Delgado, Jesús
16
399
Carrasco, Ricardo Carrasco, Rafael Reyes García, Pablo Rodríguez, Mauricio Durán
400
Martínez, Valeria Gutiérrez and many others). This work was funded by EU FP7 Grant
401
ANTIGONE (278976) and CDTI (Centro para el Desarrollo Tecnológico Industrial,
402
Spanish Ministry for the Economy and Competitiveness - MINECO). J.A. Barasona
403
holds an FPU pre-doctoral scholarship from MECD. J.P.V. Santos was supported by a
404
PhD grant (SFRH/BD/65880/2009) from the Portuguese Science and Technology
405
Foundation (FCT). J. Queirós is supported by a PhD grant (SFRH/BD/73732/2010)
406
from the Portuguese Science and Technology Foundation (FCT). F. Ruiz-Fons is
407
supported by a ‘Ramón y Cajal’ contract from MINECO.
408
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569
Table headings
570
Table 1. Primers and probe used in the qPCR.
571
Table 2. Set of variables gathered for risk factor modeling of deer individual exposure
572
to Coxiella burnetii. Variables included in the statistical modeling process are marked
573
with an asterisk.
574
Table 3. Average individual seroprevalence (in percentage), number of positive samples
575
(Pos) over sampling size (N) and associated 95% confidence interval (95%CI)
576
throughout sampling bioregion (27) and deer management system.
577
Table 4. Average seroprevalence values (in percentage), number of positive samples
578
(Pos) over sampling size (N) and associated exact 95% confidence interval (95%CI)
579
through deer sex and age.
580
Table 5. Best fitted model output throughout deer dataset. The statistic (Z), the
581
coefficient (β), its associated standard error (SE), the significance value (p), model
582
Akaike information criterion (AIC), AIC increment (ΔAIC) and explained deviance
583
(ED) are shown. Abbreviations of variables are drawn according to Table 1.
584 585 586 587 588 589 590 591 592 593
24
594
Figure captions
595
Figure 1. Spatial distribution of Coxiella burnetii seroprevalence in Iberian red deer and
596
presence of C. burnetii DNA in spleen samples. Each dot represents a surveyed red deer
597
population. Current geographic distribution of the red deer in the Iberian Peninsula is
598
shown in pale orange (54, 55). The number of sera analyzed per population is displayed
599
in numbers. A red asterisk within the sampling size indicates red deer farms. The map
600
of Spain has been split according to bioregions established in the current Spanish
601
wildlife disease surveillance program (27).
602 603
Figure 2. Relationships between population C. burnetii seroprevalence and explanative
604
factors identified through risk factor modeling for each of the modeled datasets (overall
605
deer populations (OD), unmanaged populations (UD), managed populations (MD) and
606
farmed populations (FD)).
FD
25
Table 3. Average individual seroprevalence (in percentage), number of positive samples (Pos) over sampling size (N) and associated 95% confidence interval (95%CI) throughout sampling bioregion (27) and deer management system.
Bioregion Seroprevalence (Pos/N; 95%CI) 1 2 3 4 5 Portugal Total
4.3% (7/161; 1.8-8.8) 5.7% (10/174; 2.8-10.3) 2.7% (18/675; 1.6-4.2) 1.7% (2/116; 0.2-6.1) 34.6% (175/506; 30.4-38.9) 1.7% (2/119; 0.2-5.9) 12.2% (214/1751; 10.7-13.9)
Unmanaged deer seroprevalence (Pos/N; 95%CI) 4.3% (7/161; 1.8-8.8) 14.3% (8/56; 6.4-26.2) 3.8% (14/372; 2.1-6.2) 1.3% (1/79; 0.0-6.7) 14.3% (5/35; 4.8-30.2) 1.7% (2/119; 0.2-5.9) 4.5% (37/822; 3.2-6.2)
Managed deer seroprevalence (Pos/N; 95%CI) NA 0.0% (0/59; 0.0-6.1) 1.5% (4/264; 0.4-3.8) 0.0% (0/6; 0.0-45.9) NA NA 1.2% (4/329) 0.00-0.03
Farmed deer seroprevalence (Pos/N; 95%CI) NA 3.4% (2/59; 0.4-11.7) 0.0% (0/39; 0.0-9.0) 3.2% (1/31; 0.1-16.7) 36.1% (170/471; 31.7-40.6) NA 28.8% (173/600; 25.2-32.6)
Table 4. Average seroprevalence values (in percentage), number of positive samples (Pos) over sampling size (N) and associated exact 95% confidence interval (95%CI) through deer sex and age.
Sex Male
Subtotal male Female
Subtotal female
Age class Calf Yearling Sub-adult Adult Calf Yearling Sub-adult Adult
Seroprevalence (Pos/N; 95%CI) 2.7% (1/37; 0.1-14.2) 1.6% (1/61; 0.0-8.8) 3.1% (1/32; 0.1-16.2) 3.9% (13/336; 2.1-6.5) 3.5% (17/482; 2.1-5.6) 2.6% (1/38; 0.1-13.8) 9.0% (16/177; 5.3-14.3) 33.0% (72/218; 26.8-39.7) 15.4% (102/661; 12.8-18.4) 16.9% (194/1,147; 14.8-19.2)
Table 5. Best fitted model output throughout deer dataset. The statistic (Z), the coefficient (β), its associated standard error (SE), the significance value (p), model Akaike information criterion (AIC), AIC increment (ΔAIC) and explained deviance (ED) are shown. Abbreviations of variables are drawn according to Table 1. Dataset
Variable Z β Intercept -2.561 -3.745 2.707 0.019 Unmanaged + HUd -2.476 -0.690 Managed + Se 2.008 0.204 Farmed AvSpT -1.573 -0.017 Rud -1.331 -1932 Intercept 1.788 0.053 HUd Unmanaged -1.587 -0.546 Se -0.862 -1.193 RdFi Managed -2.429 -20.233 Intercept 2.042 1.134 AvSpT -2.926 -2.119 Intercept Farmed 3.705 1.983 Se -3.499 -0.186 RuD ns: p>0.05; *
