FOODBORNE PATHOGENS AND DISEASE Volume 11, Number 5, 2014 ª Mary Ann Liebert, Inc. DOI: 10.1089/fpd.2013.1706
Detection and Characterization of Campylobacter spp. from 40 Dairy Cattle Herds in Quebec, Canada Evelyne Gue´vremont,1 Lisyanne Lamoureux,1 Catherine B. Loubier,1,2 Se´bastien Villeneuve,1 and Jocelyn Dubuc 2
Abstract
Dairy cattle are considered a Campylobacter reservoir in the epidemiology of campylobacteriosis. Currently, very little data on the prevalence of Campylobacter in dairy herds are available in the Province of Quebec, Canada. The objectives of this study were to evaluate the prevalence of Campylobacter associated with management practices in 40 dairy cattle herds as well as to characterize the bacterial genetic diversity. Fecal samples from 797 lactating cows of 40 dairy farms, water provided to animals, milk from bulk tank, and fecal matters from pens were analyzed for the presence of Campylobacter. Management information was collected using a short survey and the geographical positioning was mapped for each farm. Bacterial genetic characterization was performed by pulsed-field gel electrophoresis and flaA-typing. In total, 29 farms (72.5%) were found positive for Campylobacter spp. and 20 (50%) of them were positive for Campylobacter jejuni. In animals, 27.6% of the fecal samples were positive for Campylobacter spp. C. hyointestinalis was the most prevalent species (19.3%) in herds, followed by C. jejuni (6.5%). No Campylobacter were recovered from water or milk samples. Component-fed ration systems and the lack of biosecurity measures were associated with an increased prevalence of C. jejuni on the studied farms. Campylobacter-positive farms were scattered throughout the region, and bacterial genetic heterogeneity was observed between farms and inside the herds. This study is the first one to characterize C. jejuni isolates from dairy herds in the Province of Quebec. These observations may be useful in order to elaborate risk-mitigation strategies.
Introduction
C
ampylobacter spp., mostly Campylobacter jejuni, is a leading cause of bacterial gastroenteritis in industrialized countries. A recent study in Canada reported an incidence of around 145,000 cases within a population of 32.5 million inhabitants as of 2006, placing C. jejuni as the third most frequent foodborne pathogen after norovirus and Clostridium perfringens (Thomas et al., 2013). Most of the C. jejuni infections are sporadic, which makes it difficult to identify the source of contamination. Although handling and consumption of poultry meat products are recognized as a major source of contamination for human, other risk factors such as ingestion of contaminated food, consumption of raw milk or contaminated water, and contact with infected animals were reported (Rapp et al., 2012). The contamination routes are not always clearly identified, and reservoirs such as dairy cattle are considered in understanding the epidemiology of these bacteria. In fact, C. jejuni and Campylobacter
1 2
coli commonly colonize cattle and would be involved in the dispersal of the bacteria through the environment following manure spreading (Arsenault et al., 2012). A higher incidence of campylobacteriosis in young people was associated with ruminant density in Quebec, Canada (Arsenault et al., 2012) and in New Zealand (Spencer et al., 2012). Other Campylobacter species such as C. hyointestinalis and C. lanienae were also reported in cattle (Guevremont et al., 2008; Oporto and Hurtado, 2011). Nevertheless, further investigations need to be done in order to evaluate their role in human disease. The bacteria also have a high level of genetic diversity, and frequent recombination events, such as genomic rearrangements, intra- or intergenomic recombination, and natural transformation, are observed (Ragimbeau et al., 2008). Nevertheless, genotyping methods are widely used to characterize the various strains. Pulsed-field gel electrophoresis (PFGE) is a highly discriminatory method used for defining clones and lineages within Campylobacter population (On
Agriculture and Agri-Food Canada, Food Research and Development Centre, St-Hyacinthe, Quebec, Canada. Universite´ de Montre´al, Faculte´ de Me´decine Ve´te´rinaire, St-Hyacinthe, Quebec, Canada.
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CAMPYLOBACTER IN DAIRY CATTLE HERDS
et al., 1998; Fitzgerald et al., 2001). It is based on the digestion of intact genomic DNA with rare-cutting restriction enzymes followed by polymorphism analysis. Another genotyping technique is the flagellin A typing (flaA-typing) where the enzymatic digestion of the amplification product from the flaA gene of C. jejuni, associated with strain virulence, shows a great genetic polymorphism. The technique is easy to perform and inexpensive, as it relies on the digestion of a polymerase chain reaction (PCR) product. Because the technique targets one gene, it is found to be less discriminatory than the whole genome analysis. On the other hand, it makes it possible to see whether more isolates from a defined farm share some homologies. It is known that different farm characteristics and management practices, such as indoor housing or water supply, can affect bacterial abundance or distribution (Ellis-Iversen et al., 2009). Moreover, each farm is a unique combination of numerous environmental characteristics that could set baseline conditions for the presence of pathogenic bacteria (Strawn et al., 2013). In that perspective, geographic information systems (GIS) are powerful tools that can provide information at a spatial level by mapping parameters such as roads, rivers, soil types, or nearby farms. Identifying those attributes can contribute to the understanding of the distribution and persistence of bacterial pathogens. The objectives of this study were to evaluate the prevalence of Campylobacter at the herd level and its association with management practices in dairy cattle farms, and to characterize the bacterial genetic diversity among certain herds, given that few data are available on the occurrence of these bacteria in the Province of Quebec, Canada.
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Table 1. Description of Management Variables and Their Prevalence in 40 Dairy Cattle Farms Enrolled in a Cross-Sectional Study in Quebec, Canada Management variable Presence of other animals on the farm
Description
Swine Poultry None Housing system Tiestall facility Freestall facility Feeding system Total mixed ration Component-fed ration Biosecurity measures No (boots and/or disinfectant) Yes Outside access for animals Yes No Pasture access for animals Yes No Manure storage handling Solid manure Semiliquid manure Source of water Public network Private well Water-cleaning frequency Never Occasionally Every week Presence of cats Yes on the farm No Presence of dogs Yes on the farm No
Prevalence N (%) 4 4 32 39 1 23
(10) (10) (80) (97.5) (2.5) (57.5)
17 (42.5) 26 14 14 26 17 23 25 15
(65) (35) (35) (65) (42.5) (57.5) (62.5) (37.5)
32 8 16 24 0 35 5 17 23
(80) (20) (40) (60) (0) (87.5) (12.5) (42.5) (57.5)
Materials and Methods Samples collection
A cross-sectional study was conducted between June and August 2011. Forty dairy herds were selected and visited once during the study period. Herd selection was based on convenience for being located within 30 km of the bovine ambulatory clinic of the Universite´ de Montre´al (StHyacinthe, Quebec, Canada) and for having a regular (monthly or more frequently) veterinary herd health visit. Animal use was approved by the ‘‘Comite´ d’e´thique sur l’utilisation des animaux’’ of the Faculte´ de Me´decine Ve´te´rinaire (Universite´ de Montre´al, Canada). The average number of lactating cows was 60.4 animals and Holstein was the only breed present. During farm visits, stool samples were collected from the rectum of 20 lactating cows selected all across the barn, and a questionnaire was completed by the farmer. The questionnaire focused on dairy farm management practices (listed in Table 1). Also, a pool of stools from replacement animal (calves and heifers) pens were collected from the floor with a maximum of five pools when there were more than five pens in the herd. Water samples (1 L) were collected from the milking shed tap (same piping for cattle troughs), and milk samples (1 L) were obtained from the bulk tank using the duct at the bottom. Bacterial isolation and identification
C. jejuni ATCC 33291 was used as control strain. Fecal samples were examined by an enrichment method followed by
plating on a selective media. Briefly, 1 g of stool was added to 9 mL of Bolton broth (CM983, supplement SR0183E, 5% laked horse blood, Oxoid) and incubated 48 h at 42C in a microaerophilic atmosphere. Water samples (250 mL) were filtered through a 0.22-lm membrane (Millipore) and the filters were incubated in 20 mL of Bolton broth the same way as for fecal samples. Milk samples (100 mL) were centrifuged at 16,000 · g for 20 min at 4C (Hunt et al., 2001). Fat and supernatant were discarded, and the pellet was suspended in 100 mL of Bolton broth and incubated as previously described. A loopful was then transferred onto Karmali media (CM0935, supplement SR0167E, Oxoid) and incubated 48 h at 42C in a microaerophilic atmosphere. Typical colonies were isolated and cultured on tryptic soy agar (TSA)–5% sheep blood. A herd was considered positive when one sample from either lactating cows or replacement animals was positive for Campylobacter. The negative herds referred to herein correspond to herds in which it was not possible to detect the bacteria using these techniques. Bacterial DNA was extracted from typical isolated colonies using a QIAamp DNA Mini extraction kit as recommended by the manufacturer (Qiagen). PCR amplifications were performed for species identification as described by YamazakiMatsune et al. (Yamazaki-Matsune et al., 2007) for C. jejuni, C. coli, C. lari, C. hyointestinalis, C. fetus, C. upsaliensis and as described by Inglis and Kalischuk for C. lanienae (Inglis and Kalischuk, 2003).
GUE´VREMONT ET AL.
390 PFGE characterization
PFGE was performed according to the PulseNet protocol for C. jejuni (Ribot et al., 2001) and the method described by Hunter et al. (Hunter et al., 2005). Six C. jejuni–positive farms with at least two positive isolates were randomly selected. Briefly, bacteria were suspended in 10 mL of phosphatebuffered saline and adjusted to an optical density (610 nm) of 1.0 using a spectrophotometer. Plugs were made using 400 lL of this suspension mixed with an equal volume of 1.0% wt/wt low-gelling-temperature agarose (Seakam Gold Agarose, Lonza) containing 1.0% sodium dodecyl sulfate (Gibco) and 25 lL of proteinase K. Plugs were placed in 5 mL of lysis buffer (50 mM Tris, 50 mM EDTA [pH 8.0], 1.0% N-lauroylsarcosine) with 25 lL of proteinase K (20 mg/mL, Qiagen) during 15 min at 54C in a shaker bath with constant agitation (60 rpm). The restriction enzymes used for Salmonella serotype Braenderup and Campylobacter were XbaI (50 U of enzyme at 37C for 4 h; Roche) and SmaI (50 U of enzyme at room temperature for 4 h; Roche), respectively. Gel patterns were analyzed with the FPQuest software version 5 (Bio-Rad) using Dice similarity coefficient with 0.5% optimization and 1.25% position tolerance. The strains were clustered by the unweighted-pair group method using average linkages. flaA-typing
Flagellin A gene typing was performed according to Hanel et al. (Hanel et al., 2007). The flaA gene PCR product (1.7 kb) was digested with 0.2 U/lL of DdeI (New England Biolabs) restriction enzyme and analyzed by electrophoresis on 2% agarose gels. The gel patterns were analyzed as for PFGE patterns. GIS analyses
A geodatabase was built by combining the farms database (addresses, longitude and latitude coordinates, management practices, and presence or absence of C. jejuni in herds) and layers of existing information (namely, road network and surface water). Maps were then created with ArcMapTM (ArcGIS desktop 10, ESRI, Redlands, CA) to observe geographic distribution of C. jejuni–positive farms among the studied farms. Visual observations were performed to identify farms proximity clusters or associations with the road network and surface water parameters. Statistical analyses
Statistical analyses were performed with SAS (version 9.3, SAS Institute Inc., Cary, NC). Descriptive statistics were calculated using the MEANS procedure in SAS. Univariable
linear regression models between herd management practices (predictors) and C. jejuni herd prevalence (dependent variable) were computed using the MIXED procedure in SAS. Predictors with p £ 0.15 were retained for multivariable modeling. A multivariable linear regression model was built with C. jejuni herd prevalence (dependent variable) and retained variables (predictors) using the MIXED procedure in SAS. A backward elimination procedure was used to build the multivariable model until all p £ 0.05. Pairwise comparison was performed using a Tukey–Kramer test. Least-square means of C. jejuni herd prevalence for every predictor remaining in the multivariable model were calculated. Results
A total of 29 herds (72.5%) were positive for Campylobacter spp., and 20 of them were positive for C. jejuni. The average herd prevalence of C. jejuni in lactating cows was 6.5 % (SD = 11.2; minimum = 0%; maximum = 55%). C. hyointestinalis was the most frequent bacterial species isolated (Table 2). No C. lari, C. fetus, C. upsaliensis, or C. lanienae were detected. Among herds with Campylobacter-negative lactating cows, feces from replacement animals were also found to be Campylobacter-negative 85% of the time. In the studied farms, the average number of lactating cows was 60.4 animals. Holstein cows were present in every herd. Management variables among the 40 farms are shown in Table 1. Univariable linear regression models were built to screen for potential predictors of C. jejuni herd prevalence. Univariable predictors offered to the multivariable model were feeding system ( p = 0.02) and presence of biosecurity measures (cleaning boots and/or washing or disinfecting the stalls; p = 0.03). In the end, the final multivariable model consisted of the predictors feeding systems ( p = 0.03) and presence of biosecurity measures ( p = 0.04). Least-square means of C. jejuni herd prevalence for feeding systems were 3.3% for total mixed rations and 10.9% for component-fed rations. Least-squares means of C. jejuni herd prevalence for biosecurity measures were 2.3% when present and 8.8% when absent. C. jejuni genetic diversity from six positive herds was analyzed by PFGE and flaA-typing. Among the 36 analyzed strains, 3 were untypeable by PFGE as well as 1 by flaAtyping. A total of 15 genetic patterns were observed by PFGE and 17 by flaA-typing. Results are presented in Table 3. A unique profile (one strain per profile) in both techniques was observed for 14% of the tested strains. The most frequent PFGE pattern (F) was distributed among three farms. Overall, the genetic diversity was frequent in each farm with different combinations of profiles observed, except for the F27 farm.
Table 2. Prevalence of Campylobacter in 40 Dairy Cattle Farms in Quebec, Canada Enrolled in a Cross-Sectional Study Type of samples Stools from lactating cows Stools from replacement animals Tap water Bulk tank milk
Number of samples 797 138 40 40
Number of C. jejuni (%) 52 9 0 0
(6.5) (6.5) (0) (0)
Number of C. coli (%) 14 3 0 0
(1.8) (2.2) (0) (0)
Number of C. hyointestinalis (%) 154 17 0 0
(19.3) (12.3) (0) (0)
CAMPYLOBACTER IN DAIRY CATTLE HERDS
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Table 3. Distribution of SmaI Pulsed-Field Gel Electrophoresis (PFGE) and flaA-Typing Genetic Patterns of Campylobacter jejuni from Six Dairy Farms Enrolled in a Cross-Sectional Study Patterns Samplesa 2011-1 2011-4 2011-6A 2011-6B 2011-11 2011-27b 2011-486 2011-495 2011-496 2011-634 2011-637 2011-638 2011-641 2011-645b 2011-646b 2011-703 2011-708 2011-709 2011-711 2011-721 2011-722 2011-786 2011-788 2011-790 2011-792 2011-794 2011-795 2011-797 2011-798 2011-800 2011-801 2011-802 2011-1001 2011-1004 2011-1016 2011-1017
Farm
PFGE
flaA-Typing
F1 F1 F1 F1 F1 F1 F19 F19 F19 F24 F24 F24 F24 F24 F24 F27 F27 F27 F27 F27 F27 F30 F30 F30 F30 F30 F30 F30 F30 F30 F30 F30 F38 F38 F38 F38
A B C D B E F F B B G H F F F I I I I I K L M M L F Ud L L J O M P G U U
1 2 3 4 5 6 7 8 6 9 10 10 7 11 11 7 11 7 7 11 12 13 14 15 NDc 11 15 13 13 13 17 2 13 10 10 18
a All samples were constituted of stools from lactating cows except for b that came from replacement animals. c Not determined. d Untypeable.
There, PFGE profiles were homogeneous except for one strain. The number of different genetic profiles varies between farms and within farms are shown in Figure 1. Regarding the geographical distribution, C. jejuni–positive farms were scattered throughout the studied region, and no clusters based on proximity were observed (Fig. 2). No associations with the studied parameters (i.e., no river or road could be associated with positive farms) were found with GIS analyses. Discussion
This is the first study to assess the prevalence of Campylobacter in dairy cattle herds of Quebec, a province where milk production is a very important agricultural activity accounting for nearly 37% of Canada’s farm cash receipts from
milk production (Fe´de´ration des Producteurs de Lait du Que´bec; available at: http://www.lait.org/en/the-milk-economy/ profile-and-impact-of-milk-production.php, accessed October 4, 2013). The relationship between clinical isolates of C. jejuni and strains isolated from raw milk or dairy cattle samples was previously reported in Quebec (Levesque et al., 2008), but no analysis based solely on prevalence among herds and farm characteristics has been reported. Here, the herd prevalence of C. jejuni was 6.5%. Elsewhere, the prevalence varies depending on the sampling design (frequency, sampling type, or detection methods) or the size of the study. A previous study by Ellis-Iversen et al. reported 62.5% of the farms to be positive for Campylobacter (EllisIversen et al., 2009). Among those, the incidence in animal samples varies from 17% to 100%, but in most of them, the incidence was < 50%. In a study based in New Zealand, the incidence per farm varies from 44% to 100% in seven tested herds (Gilpin et al., 2008a). Nearby, a study conducted on cattle in the United States found a 7.5% incidence of C. jejuni in fecal samples, which was close to the observed results (Sanad et al., 2011). Variations in detecting Campylobacter might be associated with seasonality. A summer peak, probably linked to the grazing period, was observed in United Kingdom by Grove-White et al. (Grove-White et al., 2010). Stanley et al. (Stanley et al., 1998) observed similar results and concluded that seasonal peaks were not related to climate factors but to changes in diet. Another parameter to consider is variations in shedding by the animals. It was reported that cows excreted bacteria intermittently and that variation in the frequency and bacterial concentration were present between individual cows within the same herd (Rapp et al., 2012). In the current study, tested animal were housed through the whole sampling period and were sampled once, so a possible effect of seasonality or frequency of excretion was not possible to measure. Risk practices can influence the colonization of animals with Campylobacter by introducing the bacteria into the herd or maintaining the population already in place. The component-fed ration feeding system, often distributed by hand, was identified as a potential risk factor for the increased prevalence of C. jejuni compared to feeding total mixed rations. A possible explanation could be the dispersion of bacteria from the producers’ soiled boots or potentially contaminated tools, as they often walk in feeders when distributing the food. The mechanical cart used to distribute the total mixed ration is located in front of the cows, so there is no contact with feces, possibly reducing the risk of contamination. Also, the cleaning and/or disinfection of stalls were associated with a lower prevalence of C. jejuni in herds, which is consistent with the effects of biosecurity measures in reducing bacterial contamination. Elsewhere, reported risk factors such as indoor housing, private water supply, presence of horses, and feeding hay were associated with the presence of Campylobacter in farms (EllisIversen et al., 2009). It was not possible to measure any other associations between C. jejuni status among herds and the tested variables. Factors such as the presence of other production animals or the source of water were the same among the majority of farms. In the same way, few differences were observed in farm facilities and practices such as housing and manure storage. No environmental samples (milk or water trough) were found to be positive for Campylobacter.
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FIG. 1. Dendrogram showing the distribution of pulsed-field gel electrophoresis profiles for SmaI restricted dairy cattle Campylobacter jejuni isolates from six dairy farms. The EGnumbers are the strain identification and the Fnumbers are the farm identification. Levels of similarity were calculated with Dice coefficient, and the unweighted-pair group method using average linkages was used for cluster analysis.
The geographical localization of the studied farms was mapped in order to visualize trends on the occurrence of C. jejuni. Farms were selected based on the veterinarian visits during the sampling period, and it was not possible to visualize a concentration of positive herds on the studied territory.
Besides, further investigations on different parameters such as soil type surrounding animal productions (such as poultry), manure spreading, or milk-collecting network using GIS indepth analysis could provide more information associated with the C. jejuni farm status. It was demonstrated that the
FIG. 2. Geospatial distribution of the dairy herds (dark circle: Campylobacter jejuni–positive farms; X-marked circle: negative farm). Note: Farm 25 (positive) and farm 32 (negative) are shown as one negative spot on the figure as they were located < 50 m apart.
CAMPYLOBACTER IN DAIRY CATTLE HERDS
proximity to a large poultry slaughterhouse as well as high ruminant density were associated with a higher incidence of campylobacteriosis (Arsenault et al., 2012). The genetic profiles recovered in the tested isolates showed a great diversity in C. jejuni populations. Similar observations were reported in cattle in other countries (Gilpin et al., 2008b; Sanad et al., 2013). In Finland, a study conducted on three dairy cattle herds showed similar results: one to four different PFGE profiles were observed depending on the number of C. jejuni–positive samples recovered (Hakkinen and Hanninen, 2009). By using a combination of two restriction enzymes, Gilpin et al. (Gilpin et al., 2008b) identified 53 different profiles from the 89 isolates of C. jejuni from bovine sources. This genetic diversity highlights the fact that dairy cattle can act as a reservoir of genetically diverse C. jejuni strains that can be transmitted to humans directly or spread through the environment. Conclusion
Even if the herd prevalence was low, it was possible to demonstrate and characterize for the first time C. jejuni isolates recovered from dairy herds in the Province of Quebec. Among the evaluated management practices, using the component-fed system as well as the lack of biosecurity measures can influence the prevalence of C. jejuni in herds. The genetic profiles of bacteria within specific herds are not homogeneous and there is variability between herds. These observations from dairy cattle reservoirs may be useful for understanding the ecology of Campylobacter in order to elaborate risk mitigation strategies. Acknowledgments
The authors would like to thank the participating farmers for their collaboration as well as Mr. Pierre Ward for his review of this article. The authors are grateful to Agriculture and Agri-Food Canada and the Growing Forward Initiative for funding this study. Disclosure Statement
No competing financial interests exist. References
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Gilpin BJ, Thorrold B, Scholes P, et al. Comparison of Campylobacter jejuni genotypes from dairy cattle and human sources from the Matamata-Piako District of New Zealand. J Appl Microbiol 2008b;105:1354–1360. Grove-White DH, Leatherbarrow AJ, Cripps PJ, Diggle PJ, French NP. Temporal and farm-management-associated variation in the faecal-pat prevalence of Campylobacter jejuni in ruminants. Epidemiol Infect 2010;138:549–558. Guevremont E, Normand V, Lamoureux L, Cote C. Genetic detection of Campylobacter lanienae in fecal matter and stored manure from swine and dairy cattle. Foodborne Pathog Dis 2008;5:361–364. Hakkinen M, Hanninen ML. Shedding of Campylobacter spp. in Finnish cattle on dairy farms. J Appl Microbiol 2009; 107:898–905. Hanel I, Borrmann E, Muller J, Alter T. Relationships between bacterial genotypes and in vitro virulence properties of Campylobacter jejuni and Campylobacter coli isolated from turkeys. J Appl Microbiol 2007;102:433–441. Hunt JM, Abeyta C, Tran T. Bacteriological Analytical Manual: Campylobacter. U.S. Food and Drug Administration. 2001. http://www.fda.gov/Food/FoodScienceResearch/Laboratory Methods/ucm072616.htm, accessed February 28, 2014. Hunter SB, Vauterin P, Lambert-Fair MA, et al. Establishment of a universal size standard strain for use with the PulseNet standardized pulsed-field gel electrophoresis protocols: Converting the national databases to the new size standard. J Clin Microbiol 2005;43:1045–1050. Inglis GD, Kalischuk LD. Use of PCR for direct detection of Campylobacter species in bovine feces. Appl Environ Microbiol 2003;69:3435–3447. Levesque S, Frost E, Arbeit RD, Michaud S. Multilocus sequence typing of Campylobacter jejuni isolates from humans, chickens, raw milk, and environmental water in Quebec, Canada. J Clin Microbiol 2008;46:3404–3411. On SL, Nielsen EM, Engberg J, Madsen M. Validity of SmaIdefined genotypes of Campylobacter jejuni examined by SalI, KpnI, and BamHI polymorphisms: Evidence of identical clones infecting humans, poultry, and cattle. Epidemiol Infect 1998;120:231–237. Oporto B, Hurtado A. Emerging thermotolerant Campylobacter species in healthy ruminants and swine. Foodborne Pathog Dis 2011;8:807–813. Ragimbeau C, Schneider F, Losch S, Even J, Mossong J. Multilocus sequence typing, pulsed-field gel electrophoresis, and fla short variable region typing of clonal complexes of Campylobacter jejuni strains of human, bovine, and poultry origins in Luxembourg. Appl Environ Microbiol 2008;74: 7715–7722. Rapp D, Ross CM, Pleydell EJ, Muirhead RW. Differences in the fecal concentrations and genetic diversities of Campylobacter jejuni populations among individual cows in two dairy herds. Appl Environ Microbiol 2012;78:7564–7571. Ribot EM, Fitzgerald C, Kubota K, Swaminathan B, Barrett TJ. Rapid pulsed-field gel electrophoresis protocol for subtyping of Campylobacter jejuni. J Clin Microbiol 2001;39:1889–1894. Sanad YM, Closs G, Kumar A, LeJeune JT, Rajashekara G. Molecular epidemiology and public health relevance of Campylobacter isolated from dairy cattle and European starlings in Ohio, USA. Foodborne Pathog Dis 2013;10:229–236. Sanad YM, Kassem II, Abley M, Gebreyes W, LeJeune JT, Rajashekara G. Genotypic and phenotypic properties of cattle-associated Campylobacter and their implications to public health in the USA. PLoS ONE 2011;6:e25778.
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Yamazaki-Matsune W, Taguchi M, Seto K, et al. Development of a multiplex PCR assay for identification of Campylobacter coli, Campylobacter fetus, Campylobacter hyointestinalis subsp. hyointestinalis, Campylobacter jejuni, Campylobacter lari and Campylobacter upsaliensis. J Med Microbiol 2007;56:1467–1473.
Address correspondence to: Evelyne Gue´vremont, PhD Agriculture and Agri-Food Canada Food Research and Development Centre 3600 Casavant Boulevard West St-Hyacinthe, Quebec, J2S 8E3, Canada E-mail: [email protected]
Detection and Characterization of Campylobacter spp. from 40 Dairy Cattle Herds in Quebec, Canada Evelyne Gue´vremont,1 Lisyanne Lamoureux,1 Catherine B. Loubier,1,2 Se´bastien Villeneuve,1 and Jocelyn Dubuc 2
Abstract
Dairy cattle are considered a Campylobacter reservoir in the epidemiology of campylobacteriosis. Currently, very little data on the prevalence of Campylobacter in dairy herds are available in the Province of Quebec, Canada. The objectives of this study were to evaluate the prevalence of Campylobacter associated with management practices in 40 dairy cattle herds as well as to characterize the bacterial genetic diversity. Fecal samples from 797 lactating cows of 40 dairy farms, water provided to animals, milk from bulk tank, and fecal matters from pens were analyzed for the presence of Campylobacter. Management information was collected using a short survey and the geographical positioning was mapped for each farm. Bacterial genetic characterization was performed by pulsed-field gel electrophoresis and flaA-typing. In total, 29 farms (72.5%) were found positive for Campylobacter spp. and 20 (50%) of them were positive for Campylobacter jejuni. In animals, 27.6% of the fecal samples were positive for Campylobacter spp. C. hyointestinalis was the most prevalent species (19.3%) in herds, followed by C. jejuni (6.5%). No Campylobacter were recovered from water or milk samples. Component-fed ration systems and the lack of biosecurity measures were associated with an increased prevalence of C. jejuni on the studied farms. Campylobacter-positive farms were scattered throughout the region, and bacterial genetic heterogeneity was observed between farms and inside the herds. This study is the first one to characterize C. jejuni isolates from dairy herds in the Province of Quebec. These observations may be useful in order to elaborate risk-mitigation strategies.
Introduction
C
ampylobacter spp., mostly Campylobacter jejuni, is a leading cause of bacterial gastroenteritis in industrialized countries. A recent study in Canada reported an incidence of around 145,000 cases within a population of 32.5 million inhabitants as of 2006, placing C. jejuni as the third most frequent foodborne pathogen after norovirus and Clostridium perfringens (Thomas et al., 2013). Most of the C. jejuni infections are sporadic, which makes it difficult to identify the source of contamination. Although handling and consumption of poultry meat products are recognized as a major source of contamination for human, other risk factors such as ingestion of contaminated food, consumption of raw milk or contaminated water, and contact with infected animals were reported (Rapp et al., 2012). The contamination routes are not always clearly identified, and reservoirs such as dairy cattle are considered in understanding the epidemiology of these bacteria. In fact, C. jejuni and Campylobacter
1 2
coli commonly colonize cattle and would be involved in the dispersal of the bacteria through the environment following manure spreading (Arsenault et al., 2012). A higher incidence of campylobacteriosis in young people was associated with ruminant density in Quebec, Canada (Arsenault et al., 2012) and in New Zealand (Spencer et al., 2012). Other Campylobacter species such as C. hyointestinalis and C. lanienae were also reported in cattle (Guevremont et al., 2008; Oporto and Hurtado, 2011). Nevertheless, further investigations need to be done in order to evaluate their role in human disease. The bacteria also have a high level of genetic diversity, and frequent recombination events, such as genomic rearrangements, intra- or intergenomic recombination, and natural transformation, are observed (Ragimbeau et al., 2008). Nevertheless, genotyping methods are widely used to characterize the various strains. Pulsed-field gel electrophoresis (PFGE) is a highly discriminatory method used for defining clones and lineages within Campylobacter population (On
Agriculture and Agri-Food Canada, Food Research and Development Centre, St-Hyacinthe, Quebec, Canada. Universite´ de Montre´al, Faculte´ de Me´decine Ve´te´rinaire, St-Hyacinthe, Quebec, Canada.
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et al., 1998; Fitzgerald et al., 2001). It is based on the digestion of intact genomic DNA with rare-cutting restriction enzymes followed by polymorphism analysis. Another genotyping technique is the flagellin A typing (flaA-typing) where the enzymatic digestion of the amplification product from the flaA gene of C. jejuni, associated with strain virulence, shows a great genetic polymorphism. The technique is easy to perform and inexpensive, as it relies on the digestion of a polymerase chain reaction (PCR) product. Because the technique targets one gene, it is found to be less discriminatory than the whole genome analysis. On the other hand, it makes it possible to see whether more isolates from a defined farm share some homologies. It is known that different farm characteristics and management practices, such as indoor housing or water supply, can affect bacterial abundance or distribution (Ellis-Iversen et al., 2009). Moreover, each farm is a unique combination of numerous environmental characteristics that could set baseline conditions for the presence of pathogenic bacteria (Strawn et al., 2013). In that perspective, geographic information systems (GIS) are powerful tools that can provide information at a spatial level by mapping parameters such as roads, rivers, soil types, or nearby farms. Identifying those attributes can contribute to the understanding of the distribution and persistence of bacterial pathogens. The objectives of this study were to evaluate the prevalence of Campylobacter at the herd level and its association with management practices in dairy cattle farms, and to characterize the bacterial genetic diversity among certain herds, given that few data are available on the occurrence of these bacteria in the Province of Quebec, Canada.
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Table 1. Description of Management Variables and Their Prevalence in 40 Dairy Cattle Farms Enrolled in a Cross-Sectional Study in Quebec, Canada Management variable Presence of other animals on the farm
Description
Swine Poultry None Housing system Tiestall facility Freestall facility Feeding system Total mixed ration Component-fed ration Biosecurity measures No (boots and/or disinfectant) Yes Outside access for animals Yes No Pasture access for animals Yes No Manure storage handling Solid manure Semiliquid manure Source of water Public network Private well Water-cleaning frequency Never Occasionally Every week Presence of cats Yes on the farm No Presence of dogs Yes on the farm No
Prevalence N (%) 4 4 32 39 1 23
(10) (10) (80) (97.5) (2.5) (57.5)
17 (42.5) 26 14 14 26 17 23 25 15
(65) (35) (35) (65) (42.5) (57.5) (62.5) (37.5)
32 8 16 24 0 35 5 17 23
(80) (20) (40) (60) (0) (87.5) (12.5) (42.5) (57.5)
Materials and Methods Samples collection
A cross-sectional study was conducted between June and August 2011. Forty dairy herds were selected and visited once during the study period. Herd selection was based on convenience for being located within 30 km of the bovine ambulatory clinic of the Universite´ de Montre´al (StHyacinthe, Quebec, Canada) and for having a regular (monthly or more frequently) veterinary herd health visit. Animal use was approved by the ‘‘Comite´ d’e´thique sur l’utilisation des animaux’’ of the Faculte´ de Me´decine Ve´te´rinaire (Universite´ de Montre´al, Canada). The average number of lactating cows was 60.4 animals and Holstein was the only breed present. During farm visits, stool samples were collected from the rectum of 20 lactating cows selected all across the barn, and a questionnaire was completed by the farmer. The questionnaire focused on dairy farm management practices (listed in Table 1). Also, a pool of stools from replacement animal (calves and heifers) pens were collected from the floor with a maximum of five pools when there were more than five pens in the herd. Water samples (1 L) were collected from the milking shed tap (same piping for cattle troughs), and milk samples (1 L) were obtained from the bulk tank using the duct at the bottom. Bacterial isolation and identification
C. jejuni ATCC 33291 was used as control strain. Fecal samples were examined by an enrichment method followed by
plating on a selective media. Briefly, 1 g of stool was added to 9 mL of Bolton broth (CM983, supplement SR0183E, 5% laked horse blood, Oxoid) and incubated 48 h at 42C in a microaerophilic atmosphere. Water samples (250 mL) were filtered through a 0.22-lm membrane (Millipore) and the filters were incubated in 20 mL of Bolton broth the same way as for fecal samples. Milk samples (100 mL) were centrifuged at 16,000 · g for 20 min at 4C (Hunt et al., 2001). Fat and supernatant were discarded, and the pellet was suspended in 100 mL of Bolton broth and incubated as previously described. A loopful was then transferred onto Karmali media (CM0935, supplement SR0167E, Oxoid) and incubated 48 h at 42C in a microaerophilic atmosphere. Typical colonies were isolated and cultured on tryptic soy agar (TSA)–5% sheep blood. A herd was considered positive when one sample from either lactating cows or replacement animals was positive for Campylobacter. The negative herds referred to herein correspond to herds in which it was not possible to detect the bacteria using these techniques. Bacterial DNA was extracted from typical isolated colonies using a QIAamp DNA Mini extraction kit as recommended by the manufacturer (Qiagen). PCR amplifications were performed for species identification as described by YamazakiMatsune et al. (Yamazaki-Matsune et al., 2007) for C. jejuni, C. coli, C. lari, C. hyointestinalis, C. fetus, C. upsaliensis and as described by Inglis and Kalischuk for C. lanienae (Inglis and Kalischuk, 2003).
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390 PFGE characterization
PFGE was performed according to the PulseNet protocol for C. jejuni (Ribot et al., 2001) and the method described by Hunter et al. (Hunter et al., 2005). Six C. jejuni–positive farms with at least two positive isolates were randomly selected. Briefly, bacteria were suspended in 10 mL of phosphatebuffered saline and adjusted to an optical density (610 nm) of 1.0 using a spectrophotometer. Plugs were made using 400 lL of this suspension mixed with an equal volume of 1.0% wt/wt low-gelling-temperature agarose (Seakam Gold Agarose, Lonza) containing 1.0% sodium dodecyl sulfate (Gibco) and 25 lL of proteinase K. Plugs were placed in 5 mL of lysis buffer (50 mM Tris, 50 mM EDTA [pH 8.0], 1.0% N-lauroylsarcosine) with 25 lL of proteinase K (20 mg/mL, Qiagen) during 15 min at 54C in a shaker bath with constant agitation (60 rpm). The restriction enzymes used for Salmonella serotype Braenderup and Campylobacter were XbaI (50 U of enzyme at 37C for 4 h; Roche) and SmaI (50 U of enzyme at room temperature for 4 h; Roche), respectively. Gel patterns were analyzed with the FPQuest software version 5 (Bio-Rad) using Dice similarity coefficient with 0.5% optimization and 1.25% position tolerance. The strains were clustered by the unweighted-pair group method using average linkages. flaA-typing
Flagellin A gene typing was performed according to Hanel et al. (Hanel et al., 2007). The flaA gene PCR product (1.7 kb) was digested with 0.2 U/lL of DdeI (New England Biolabs) restriction enzyme and analyzed by electrophoresis on 2% agarose gels. The gel patterns were analyzed as for PFGE patterns. GIS analyses
A geodatabase was built by combining the farms database (addresses, longitude and latitude coordinates, management practices, and presence or absence of C. jejuni in herds) and layers of existing information (namely, road network and surface water). Maps were then created with ArcMapTM (ArcGIS desktop 10, ESRI, Redlands, CA) to observe geographic distribution of C. jejuni–positive farms among the studied farms. Visual observations were performed to identify farms proximity clusters or associations with the road network and surface water parameters. Statistical analyses
Statistical analyses were performed with SAS (version 9.3, SAS Institute Inc., Cary, NC). Descriptive statistics were calculated using the MEANS procedure in SAS. Univariable
linear regression models between herd management practices (predictors) and C. jejuni herd prevalence (dependent variable) were computed using the MIXED procedure in SAS. Predictors with p £ 0.15 were retained for multivariable modeling. A multivariable linear regression model was built with C. jejuni herd prevalence (dependent variable) and retained variables (predictors) using the MIXED procedure in SAS. A backward elimination procedure was used to build the multivariable model until all p £ 0.05. Pairwise comparison was performed using a Tukey–Kramer test. Least-square means of C. jejuni herd prevalence for every predictor remaining in the multivariable model were calculated. Results
A total of 29 herds (72.5%) were positive for Campylobacter spp., and 20 of them were positive for C. jejuni. The average herd prevalence of C. jejuni in lactating cows was 6.5 % (SD = 11.2; minimum = 0%; maximum = 55%). C. hyointestinalis was the most frequent bacterial species isolated (Table 2). No C. lari, C. fetus, C. upsaliensis, or C. lanienae were detected. Among herds with Campylobacter-negative lactating cows, feces from replacement animals were also found to be Campylobacter-negative 85% of the time. In the studied farms, the average number of lactating cows was 60.4 animals. Holstein cows were present in every herd. Management variables among the 40 farms are shown in Table 1. Univariable linear regression models were built to screen for potential predictors of C. jejuni herd prevalence. Univariable predictors offered to the multivariable model were feeding system ( p = 0.02) and presence of biosecurity measures (cleaning boots and/or washing or disinfecting the stalls; p = 0.03). In the end, the final multivariable model consisted of the predictors feeding systems ( p = 0.03) and presence of biosecurity measures ( p = 0.04). Least-square means of C. jejuni herd prevalence for feeding systems were 3.3% for total mixed rations and 10.9% for component-fed rations. Least-squares means of C. jejuni herd prevalence for biosecurity measures were 2.3% when present and 8.8% when absent. C. jejuni genetic diversity from six positive herds was analyzed by PFGE and flaA-typing. Among the 36 analyzed strains, 3 were untypeable by PFGE as well as 1 by flaAtyping. A total of 15 genetic patterns were observed by PFGE and 17 by flaA-typing. Results are presented in Table 3. A unique profile (one strain per profile) in both techniques was observed for 14% of the tested strains. The most frequent PFGE pattern (F) was distributed among three farms. Overall, the genetic diversity was frequent in each farm with different combinations of profiles observed, except for the F27 farm.
Table 2. Prevalence of Campylobacter in 40 Dairy Cattle Farms in Quebec, Canada Enrolled in a Cross-Sectional Study Type of samples Stools from lactating cows Stools from replacement animals Tap water Bulk tank milk
Number of samples 797 138 40 40
Number of C. jejuni (%) 52 9 0 0
(6.5) (6.5) (0) (0)
Number of C. coli (%) 14 3 0 0
(1.8) (2.2) (0) (0)
Number of C. hyointestinalis (%) 154 17 0 0
(19.3) (12.3) (0) (0)
CAMPYLOBACTER IN DAIRY CATTLE HERDS
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Table 3. Distribution of SmaI Pulsed-Field Gel Electrophoresis (PFGE) and flaA-Typing Genetic Patterns of Campylobacter jejuni from Six Dairy Farms Enrolled in a Cross-Sectional Study Patterns Samplesa 2011-1 2011-4 2011-6A 2011-6B 2011-11 2011-27b 2011-486 2011-495 2011-496 2011-634 2011-637 2011-638 2011-641 2011-645b 2011-646b 2011-703 2011-708 2011-709 2011-711 2011-721 2011-722 2011-786 2011-788 2011-790 2011-792 2011-794 2011-795 2011-797 2011-798 2011-800 2011-801 2011-802 2011-1001 2011-1004 2011-1016 2011-1017
Farm
PFGE
flaA-Typing
F1 F1 F1 F1 F1 F1 F19 F19 F19 F24 F24 F24 F24 F24 F24 F27 F27 F27 F27 F27 F27 F30 F30 F30 F30 F30 F30 F30 F30 F30 F30 F30 F38 F38 F38 F38
A B C D B E F F B B G H F F F I I I I I K L M M L F Ud L L J O M P G U U
1 2 3 4 5 6 7 8 6 9 10 10 7 11 11 7 11 7 7 11 12 13 14 15 NDc 11 15 13 13 13 17 2 13 10 10 18
a All samples were constituted of stools from lactating cows except for b that came from replacement animals. c Not determined. d Untypeable.
There, PFGE profiles were homogeneous except for one strain. The number of different genetic profiles varies between farms and within farms are shown in Figure 1. Regarding the geographical distribution, C. jejuni–positive farms were scattered throughout the studied region, and no clusters based on proximity were observed (Fig. 2). No associations with the studied parameters (i.e., no river or road could be associated with positive farms) were found with GIS analyses. Discussion
This is the first study to assess the prevalence of Campylobacter in dairy cattle herds of Quebec, a province where milk production is a very important agricultural activity accounting for nearly 37% of Canada’s farm cash receipts from
milk production (Fe´de´ration des Producteurs de Lait du Que´bec; available at: http://www.lait.org/en/the-milk-economy/ profile-and-impact-of-milk-production.php, accessed October 4, 2013). The relationship between clinical isolates of C. jejuni and strains isolated from raw milk or dairy cattle samples was previously reported in Quebec (Levesque et al., 2008), but no analysis based solely on prevalence among herds and farm characteristics has been reported. Here, the herd prevalence of C. jejuni was 6.5%. Elsewhere, the prevalence varies depending on the sampling design (frequency, sampling type, or detection methods) or the size of the study. A previous study by Ellis-Iversen et al. reported 62.5% of the farms to be positive for Campylobacter (EllisIversen et al., 2009). Among those, the incidence in animal samples varies from 17% to 100%, but in most of them, the incidence was < 50%. In a study based in New Zealand, the incidence per farm varies from 44% to 100% in seven tested herds (Gilpin et al., 2008a). Nearby, a study conducted on cattle in the United States found a 7.5% incidence of C. jejuni in fecal samples, which was close to the observed results (Sanad et al., 2011). Variations in detecting Campylobacter might be associated with seasonality. A summer peak, probably linked to the grazing period, was observed in United Kingdom by Grove-White et al. (Grove-White et al., 2010). Stanley et al. (Stanley et al., 1998) observed similar results and concluded that seasonal peaks were not related to climate factors but to changes in diet. Another parameter to consider is variations in shedding by the animals. It was reported that cows excreted bacteria intermittently and that variation in the frequency and bacterial concentration were present between individual cows within the same herd (Rapp et al., 2012). In the current study, tested animal were housed through the whole sampling period and were sampled once, so a possible effect of seasonality or frequency of excretion was not possible to measure. Risk practices can influence the colonization of animals with Campylobacter by introducing the bacteria into the herd or maintaining the population already in place. The component-fed ration feeding system, often distributed by hand, was identified as a potential risk factor for the increased prevalence of C. jejuni compared to feeding total mixed rations. A possible explanation could be the dispersion of bacteria from the producers’ soiled boots or potentially contaminated tools, as they often walk in feeders when distributing the food. The mechanical cart used to distribute the total mixed ration is located in front of the cows, so there is no contact with feces, possibly reducing the risk of contamination. Also, the cleaning and/or disinfection of stalls were associated with a lower prevalence of C. jejuni in herds, which is consistent with the effects of biosecurity measures in reducing bacterial contamination. Elsewhere, reported risk factors such as indoor housing, private water supply, presence of horses, and feeding hay were associated with the presence of Campylobacter in farms (EllisIversen et al., 2009). It was not possible to measure any other associations between C. jejuni status among herds and the tested variables. Factors such as the presence of other production animals or the source of water were the same among the majority of farms. In the same way, few differences were observed in farm facilities and practices such as housing and manure storage. No environmental samples (milk or water trough) were found to be positive for Campylobacter.
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FIG. 1. Dendrogram showing the distribution of pulsed-field gel electrophoresis profiles for SmaI restricted dairy cattle Campylobacter jejuni isolates from six dairy farms. The EGnumbers are the strain identification and the Fnumbers are the farm identification. Levels of similarity were calculated with Dice coefficient, and the unweighted-pair group method using average linkages was used for cluster analysis.
The geographical localization of the studied farms was mapped in order to visualize trends on the occurrence of C. jejuni. Farms were selected based on the veterinarian visits during the sampling period, and it was not possible to visualize a concentration of positive herds on the studied territory.
Besides, further investigations on different parameters such as soil type surrounding animal productions (such as poultry), manure spreading, or milk-collecting network using GIS indepth analysis could provide more information associated with the C. jejuni farm status. It was demonstrated that the
FIG. 2. Geospatial distribution of the dairy herds (dark circle: Campylobacter jejuni–positive farms; X-marked circle: negative farm). Note: Farm 25 (positive) and farm 32 (negative) are shown as one negative spot on the figure as they were located < 50 m apart.
CAMPYLOBACTER IN DAIRY CATTLE HERDS
proximity to a large poultry slaughterhouse as well as high ruminant density were associated with a higher incidence of campylobacteriosis (Arsenault et al., 2012). The genetic profiles recovered in the tested isolates showed a great diversity in C. jejuni populations. Similar observations were reported in cattle in other countries (Gilpin et al., 2008b; Sanad et al., 2013). In Finland, a study conducted on three dairy cattle herds showed similar results: one to four different PFGE profiles were observed depending on the number of C. jejuni–positive samples recovered (Hakkinen and Hanninen, 2009). By using a combination of two restriction enzymes, Gilpin et al. (Gilpin et al., 2008b) identified 53 different profiles from the 89 isolates of C. jejuni from bovine sources. This genetic diversity highlights the fact that dairy cattle can act as a reservoir of genetically diverse C. jejuni strains that can be transmitted to humans directly or spread through the environment. Conclusion
Even if the herd prevalence was low, it was possible to demonstrate and characterize for the first time C. jejuni isolates recovered from dairy herds in the Province of Quebec. Among the evaluated management practices, using the component-fed system as well as the lack of biosecurity measures can influence the prevalence of C. jejuni in herds. The genetic profiles of bacteria within specific herds are not homogeneous and there is variability between herds. These observations from dairy cattle reservoirs may be useful for understanding the ecology of Campylobacter in order to elaborate risk mitigation strategies. Acknowledgments
The authors would like to thank the participating farmers for their collaboration as well as Mr. Pierre Ward for his review of this article. The authors are grateful to Agriculture and Agri-Food Canada and the Growing Forward Initiative for funding this study. Disclosure Statement
No competing financial interests exist. References
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Yamazaki-Matsune W, Taguchi M, Seto K, et al. Development of a multiplex PCR assay for identification of Campylobacter coli, Campylobacter fetus, Campylobacter hyointestinalis subsp. hyointestinalis, Campylobacter jejuni, Campylobacter lari and Campylobacter upsaliensis. J Med Microbiol 2007;56:1467–1473.
Address correspondence to: Evelyne Gue´vremont, PhD Agriculture and Agri-Food Canada Food Research and Development Centre 3600 Casavant Boulevard West St-Hyacinthe, Quebec, J2S 8E3, Canada E-mail: [email protected]
