Host and environmental factors modulate the

0 downloads 0 Views 4MB Size Report
David González-Barrio1*, Ana Luisa Velasco Ávila1, Mariana Boadella2, Beatriz. 6 ...... go to José Antonio Ortiz from LO farm and to Christian Gortázar for whole ...
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

References

409

1. Angelakis E, Raoult D. 2010. Q fever. Vet Microbiol 140:297-309.

410

2. European Food Saftey Authority (EFSA). 2010. Panel on Animal Health and

411

Welfare (AHAW). Scientific opinion on Q Fever. EFSA J 8:1595.

412

3. Ruiz-Fons F. 2012. Coxiella burnetii Infection, p 409-412. In Gavier-Widén D, Duff

413

PJ, Meredith A (ed), Infectious Diseases of Wild Mammals and Birds in Europe, 1st ed.

414

Wiley-Blackwell, Chichester, UK.

415

4. Roest HIJ, Tilburg JJHC, Van der Hoek W, Vellema P, Van Zijderveld FG,

416

Klaassen CHW, Raoult D. 2011. The Q fever epidemic in The Netherlands: history,

417

onset, response and reflection. Epidemiol Infect 139:1-12.

418

5. Georgiev M, Afonso A, Neubauer H, Needham H, Thiéry R, Rodolakis A, Roest

419

HJ, Stärk KD, Stegeman JA, Vellema P, van der Hoek W, More SJ. 2013. Q fever

420

in humans and farm animals in four European countries, 1982 to 2010. Euro Surveill

421

18:20407.

422

6. Babudieri B. 1959. Q fever: a zoonosis. Adv Vet Sci 5:81.

17

423

7. González-Barrio D, Almería S, Caro MR, Salinas J, Ortíz JA, Gortázar C, Ruiz-

424

Fons J. 2013. Coxiella burnetii shedding by farmed red deer (Cervus elaphus).

425

Transboun Emerg Dis, in press.

426

8. Jado I, Carranza-Rodríguez C, Barandika JF, Toledo A, García-Amil C,

427

Serrano B, Bolaños M, Gil H, Escudero R, García-Pérez AL, Olmeda AS, Astobiza

428

I, Lobo B, Rodríguez-Vargas M, Pérez-Arellano JL, López-Gatius F, Pascual-

429

Velasco F, Cilla G, Rodríguez NF, Anda P. 2012. Molecular method for the

430

characterization of Coxiella burnetii from clinical and environmental samples:

431

variability of genotypes in Spain. BMC Microbiol 12:91.

432

9. Tozer SJ, Lambert SB, Strong CL, Field HE, Sloots TP, Nissen MD. 2014.

433

Potential animal and environmental sources of Q fever infection for humans in

434

Queensland. Zoonoses Pub Hlth 61:105-112.

435

10. Viana M, Mancy R, Biek R, Cleaveland S, Cross PC, Lloyd-Smith JO, Haydon

436

DT. 2014. Assembling evidence for identifying reservoirs of infection. Trends Ecol

437

Evolut 29:270-279.

438

11. Flueck WT, Smith-Flueck JM, Naumann CM. 2003. The current distribution of

439

red deer (Cervus elaphus) in southern Latin America. Eur J Wildl Res 49:112-119.

440

12. Ludt CJ, Schroeder W, Rottmann O, Kuehn R. 2004. Mitochondrial DNA

441

phylogeography of red deer (Cervus elaphus). Mol Phylogenet Evol 31:1064–1083.

442

13. Zachos FE, Hartl GB. 2011. Phylogeography, population genetics and

443

conservation of the European red deer Cervus elaphus. Mammal Rev 41:138-150.

444

14. Acevedo P, Ruiz-Fons F, Vicente J, Reyes-García AR, Alzaga V and Gortázar

445

C. 2008. Estimating red deer abundance in a wide range of management situations in

446

Mediterranean habitats. J Zool 276:37-47.

18

447

15. Apollonio M, Andersen R, Putman R. 2010. European Ungulates and their

448

management in the 21st Century. Cambrigde University Press, Cambridge, UK.

449

16. Milner JM, Nilsen EB, Andreassen HP. 2007. Demographic side effects of

450

selective hunting in ungulates and carnivores. Cons Biol 21:36-47.

451

17. Vicente J, Höfle U, Garrido JM, Fernández-De-Mera IG, Juste R, Barral M,

452

Gortazar C. 2006. Wild boar and red deer display high prevalences of tuberculosis-like

453

lesions in Spain. Vet Res 37:107-119.

454

18. Hoffman LC, Wiklund E. 2008. Game and venison – meat for de modern

455

consumer. Meat Sci 74:197-208.

456

19. Clutton-Brock TH, Guinness FE, Albon SP. 1982. Red deer: behaviour and

457

ecology of two sexes. University of Chicago Press, Chicago, MI.

458

20. Vander Wal E, Paquet PC, Messier F, McLoughlin PD. 2013. Effects of

459

phenology and sex on social proximity in a gregarious ungulate. Can J Zool 91:601-609.

460

21. Álvarez J, Pérez A, Mardones FO, Pérez-Sancho M, García-Seco T, Pagés E,

461

Mirat F, Díaz R, Carpintero J and Domínguez L. 2012. Epidemiological factors

462

associated with the exposure of cattle to Coxiella burnetii in the Madrid region of Spain.

463

Vet J 194:102-107.

464

22. Piñero A, Ruiz-Fons F, Hurtado A, Barandika JF, Atxaerandio R, García-

465

Pérez AL. 2014. Changes in the dynamics of Coxiella burnetii infection in dairy cattle:

466

An approach to match field data with the epidemiological cycle of C. burnetii in

467

endemic herds. J Dairy Sci 97:2718-2730.

468

23. Ruiz-Fons F, Reyes-García AR, Alcaide V, Gortázar C. 2008. Spatial and

469

temporal evolution of Bluetongue virus in wild ruminants, Spain. Emerg Infect Dis

470

14:951-953.

19

471

24. Boadella M, Carta T, Oleaga A, Pajares G, Muñoz M, Gortázar C. 2010.

472

Serosurvey for selected pathogens in Iberian roe deer. BMC Vet Res 6:51.

473

25. Zanet S, Trisciuoglio A, Bottero E, Fernandez De Mera IG, Gortázar C,

474

Carpignano MG, Ferroglio E. 2014. Piroplasmosis in wildlife: Babesia and Theileria

475

affecting free-ranging ungulates and carnivores in the Italian Alps. Parasite Vector 7:70.

476

26. Víchová B, Majláthová V, Nováková M, Stanko M, Hviščová I, Pangrácová L,

477

Chrudimský T, Čurlík J, Peťko B. 2014. Anaplasma infections in ticks and reservoir

478

host from Slovakia. Infect Genet Evol 22:265-272.

479

27. Muñoz PM, Boadella M, Arnal M, de Miguel MJ, Revilla M, Martínez D,

480

Vicente J, Acevedo P, Oleaga T, Ruiz-Fons F, Marín CM, Prieto JM, de la

481

Fuente J, Barral M, Barberán M, de Luco DF, Blasco JM, Gortázar C. 2010.

482

Spatial distribution and risk factors of Brucellosis in Iberian wild ungulates. BMC Infect

483

Dis 10:46

484

28. Stöbel K, Schönberg A, Staak C. 2002. A new non-species dependent ELISA for

485

detection of antibodies to Borrelia burgdorferi s.l. in zoo animals. Int J Med Microbiol

486

291:88-89.

487

29. Ruiz-Fons F, Astobiza I, Barral M, Barandika JF, García-Pérez AL. 2010.

488

Modification of a commercial ELISA to detect antibodies against Coxiella burnetii in

489

wild ungulates: application to population surveillance. Abstr 9th Biennial Conference.

490

European

491

https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxld2

492

Rhd2Vic2l0ZXxneDo2ZDIyZmFjNmFjZjgxOWY1

493

30. Tilburg JJHC, Melchers WJ, Petterson AM, Rossen JM, Hermans MH, van

494

Hannen EJ, Nabuurs-Franssen MH, de Vries MC, Horrevorts AM, Klaassen

495

CHW. 2010. Interlaboratory evaluation of different extraction and real-time PCR

Wildlife

Disease

Association,

abstr

6.

Available

on-line

at:

20

496

methods for detection of Coxiella burnetii DNA in serum. J Clin Microbiol 48:3923-

497

3927.

498

31. Ruiz-Fons F and Gilbert L. 2010. The role of deer as vehicles to move ticks,

499

Ixodes ricinus, between contrasting hábitats. Int J Parasitol 40:1013-1020

500

32. González-Barrio D, Queirós J, Fernández-de-Mera IG, Ruiz-Fons F. 2014.

501

Dynamics of individual exposure to Coxiella burnetii infection in a Q fever endemic red

502

deer (Cervus elaphus) farm. Abstr 12th Society For Tropical And Veterinary Medicine

503

& 8th Ticks And Tick-Borne Pathogens Conference., abstr 29. Available on-line at:

504

http://www.savetcon.co.za/TTP8/files/TTP%20STVM%20Poster%20abstracts.pdf

505

33. Maurin M, Raoult D. 1999. Q fever. Clin Microbiol Rev 12:518-553.

506

34. Acevedo P, Ruiz-Fons F, Estrada R, Márquez AL, Miranda MA, Gortázar C,

507

Lucientes L. 2010. A broad assessment of factors determining Culicoides imicola

508

abundance: modelling the present and forecasting its future in climate change scenarios.

509

PLoS One 6:e14236.

510

35. Real R, Barbosa AM, Rodríguez R, García FJ, Vargas JM. 2009. Conservation

511

biogeography of ecologically interacting species: the case of the Iberian lynx and the

512

European Rabbit. Divers Distrib 5:390-400.

513

36. Astobiza I, Barral M, Ruiz-Fons F, Barandika JF, Gerrikagoitia X, Hurtado A,

514

García-Pérez AL. 2011. Molecular investigation of the occurrence of Coxiella burnetii

515

in wildlife and ticks in an endemic area. Vet Microbiol 147:190-194.

516

37. Rijks JM, Roest HIJ, van Tulden PW, Kik MJL, Ijzer J, Gröne A. 2011.

517

Coxiella burnetii infection in roe deer during Q fever epidemic, The Netherlands.

518

Emerg Infect Dis 17:2369-2371.

519

38. Ejercito CL, Cai L, Htwe KK, Taki M, Inoshima Y, Kondo T, Kano C, Abe S,

520

Shirota K, Sugimoto T, Yamaguchi T, Fukushi H, Minamoto N, Kinjo T, Isogai E,

21

521

Hirai K. 1993. Serological evidence of Coxiella burnetii infection in wild animals in

522

Japan. J Wildl Dis 29:481–484

523

39. Sáenz de Buruaga M, Lucio AJ, Purroy J. 1991. Reconocimiento de Sexo y Edad

524

en Especies Cinegéticas. Diputación foral de Álava, Vitoria.

525

40. Hamlin KL, Pac DF, Sime CA, Desimone RM, Dusek GL. 2000. Evaluating the

526

accuracy of ages obtained by two methods for Montana ungulates. J Widl Manage

527

64:441-449.

528

41. Ruiz-Fons F, Astobiza I, Barandika J, Hurtado A, Atxaerandio R, Juste R,

529

García-Pérez AL. 2010. Seroepidemiological study of Q fever in domestic ruminants

530

in semi-extensive grazing systems. BMC Vet Res 6:3.

531

42. McCulloch CE, Searle SR, Neuhaus JM. 2008. Generalized, Linear, and Mixed

532

Models, 2nd ed. Wiley, New Jersey, NJ.

533

43. Burnham KP, Anderson DR. 2002. Model Selection and Multi-Model Inference.

534

Springer, New York, NY.

535

44. Wobeser GA. 1994. Investigation and Management of Disease in Wild Animals.

536

Plenum, New York, NY.

537

45. González-Barrio D, Maio E, Vieira-Pinto M, Ruiz-Fons. 2015. European rabbits

538

as reservoir for Coxiella burnetii. Emerg Infect Dis 21: 1055-1058.

539

46. Ruiz-Fons F, Rodríguez O, Torina A, Naranjo V, Gortázar C, de la Fuente J.

540

2008. Prevalence of Coxiella burnetii infection in wild and farmed ungulates. Vet

541

Microbiol 126:282-286.

542

47. Gortázar C, Acevedo A, Ruiz-Fons F, Vicente J. 2006. Disease risk and

543

overabundance of game species. Eur J Wildl Res 52:81-87.

22

544

48. Haydon DT, Cleaveland S, Taylor LH, Laurenson MK. 2002. Identifying

545

reservoirs of infection: a conceptual and practical challenge. Emerg Infect Dis 8:1468-

546

1473.

547

49. Barandika J, Hurtado A, García-Esteban C, Gil H, Escudero R, Barral M,

548

Jado I, Juste R, Anda P, García-Perez AL. 2007. Tick-borne zoonotic bacteria in

549

wild and domestic small mammals in Northern Spain. Appl Environ Microbiol 73:6166-

550

6171.

551

50. Thompson M, Mykytczuk N, Gooderham K, Schulte-Hostedde. 2012.

552

Prevalence of the bacterium Coxiella burnetii in wild rodents from a Canadian natural

553

environment park. Zoonoses Publ Hlth 59:553-560.

554

51. Astobiza I, Ruiz-Fons F, Piñero A, Barandika JF, Hurtado A, García-Pérez

555

AL. 2012. Estimation of Coxiella burnetii prevalence in dairy cattle in intensive

556

systems by serological and molecular analyses of bulk-tank milk samples. J Dairy Sci

557

95:1632-1638.

558

52. Kukielka E, Barasona JA, Cowie CE, Drewe JA, Gortazar C, Cotarelo I,

559

Vicente J. 2013. Spatial and temporal interactions between livestock and wildlife in

560

South Central Spain assessed by camera traps. Prev Vet Med 112:213-221.

561

53. Whitney EAS, Massung RF, Candee AJ, Ailes EC, Myers LM, Patterson NE,

562

Berkelman RL. 2009. Seroepidemiologic and occupational risk survey for Coxiella

563

burnetii antibodies among US veterinarians. Clin Infect Dis 48:550-557.

564

54. Salazar DC. 2009. Masters Thesis. Distribuição e estatuto do veado e corço em

565

Portugal. University of Aveiro, Aveiro, Portugal.

566

55. Palomo LJ, Gisbert J, Blanco JC. 2007. Atlas y Libro Rojo de los Mamíferos

567

Terrestres de España. Dirección General para la Biodiversidad-SECEM-SECEMU,

568

Madrid.

23

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; *