Comparison of Intestinal Bacterial Populations Between Two Dairy Cattle Herds Colonized or Not by Campylobacter jejuni

Introduction

C ampylobacter jejuni is a leading cause of bacterial gastroenteritis in industrialized countries. In Canada, C. jejuni is the third most frequently reported foodborne pathogen after norovirus and Clostridium perfringens (Thomas et al., 2013). C. jejuni is present in cattle manure, and its survival in the environment contributes to its propagation. One strategy to reduce foodborne diseases is to reduce on-farm reservoirs of inoculum. However, factors influencing cattle colonization and bacterial shedding of C. jejuni are poorly characterized (Inglis et al., 2005; Rapp et al., 2012). Comprehending on-farm reservoirs of C. jejuni, aided by detection or quantification methods, is critical for the development of pathogen reduction programs. For instance, feeding practices have been shown to be associated with bacterial presence and abundance (Guévremont et al., 2014).

Terminal restriction fragment length polymorphism (T-RFLP) is a 16S rRNA gene fingerprinting method successfully used in numerous studies to explore microbial diversity of the predominant populations in various habitats and to characterize the modifications of microbial communities (Schütte et al., 2008). A significant advantage of T-RFLP is the ability to resolve DNA fragments differing by only one base pair.

In order to identify whether the bovine microbiota is linked to the presence of C. jejuni, the fecal bacterial populations were compared for the first time, using T-RFLP, between a dairy cattle herd where C. jejuni was previously detected and one where it was not.

Materials and Methods

Fecal samples collected directly from the animals were used as described by Guévremont et al. (2014). Two herds were selected based on the highest and lowest rate of C. jejuni detected formerly by culture methods from lactating cows (n=20 per herd). The prevalence of C. jejuni was currently verified by polymerase chain reaction (PCR) (Yamazaki-Matsune et al., 2007) with DNA extracted using QIAamp DNA stool kit as recommended by the manufacturer (Qiagen, Mississauga, Ontario, Canada).

For a herd, four composite fecal samples (500 mg/composite sample) were each made from five lactating cows and DNA was extracted in triplicate with QIAamp DNA stool kit (Qiagen) according to the manufacturer's instructions for larger volumes. Based on culture method results, C. jejuni–-positive samples were evenly distributed among composite samples. The three replicate DNA elutions were then pooled back for each sample.

The bacterial 16S rRNA gene was amplified in 100 μL PCR mixtures as described by Costa et al. (2009) using the HotStarTaq Plus Master Mix kit (Qiagen) according to the manufacturer's instructions. The 8F (5′-AGAGTTTGATCCTGGCTCAG-3′) or 1492R (5′-GGTTACCTTGTTACGACTT-3′) primers were fluorescently labeled with Well Red D4 at the 5′ terminus (IDT, Coralville, IA). PCR reactions were performed in triplicate, purified using the QIAquick PCR Purification Kit (Qiagen) and pooled. DNA was quantified with the Agilent 2100 Bioanalyzer (Agilent Technologies Canada, Mississauga, Ontario, Canada).

PCR products from each pooled fecal sample (75 ng) were digested in triplicate with 3 U of restriction endonuclease HaeIII (Roche Applied Science Canada, Laval, Quebec, Canada) at 37°C for 4 h in the dark for a final volume of 25 μL. Reactions were stopped with 4 μL of a freshly prepared stop solution (1.5 M sodium acetate and 50 mM EDTA), 1 μL of glycogen (20 mg/mL), and 60 μL of 95% ethanol. After an overnight incubation at −20°C, the digested DNA was retrieved by centrifugation (14,000 rpm, 20 min at 4°C) and washed twice with cold 70% ethanol. After drying, DNA pellets were resuspended in 40 μL of SLS (Beckman Coulter, Fullerton, CA) and diluted 1:10 with sample loading solution (SLS) and DNA size standard marker 600 (Beckman Coulter). Terminal restriction fragments (T-RFs) were detected using a CEQ8000 genetic analysis system (Beckman Coulter) and compared to the size standard fragments. For each sample, the electropherograms were combined and analyzed with FPQuest software version 5 (Bio-Rad Laboratories, Mississauga, Ontario, Canada). Dendrograms were generated using the composite data sets with the “average from experiments” similarity option and unweighted-pair group method with arithmetic mean algorithm as clustering method.

Results and Discussion

After a confirmatory PCR, C. jejuni was not detected in farm number 29 (0/20) but in farm number 30, 55% of fecal samples (11/20) were contaminated by C. jejuni. In the current study, approximately 40 different T-RFs in total were detected for each sample (data not shown). After clustering the T-RFs profiles with a similarity cut-off fixed at 85%, two distinct clusters were observed with the labeled forward primer, one with a mix of samples from both farms and a second with two samples from farm number 29 (Fig. 1A). With the labeled reverse primer, the diversity was slightly higher and four clusters were formed (Fig. 1B). Each pool of feces from farm number 30 had at least two C. jejuni–positive samples within. The T-RFs profiles between both herds had a similarity level >79% when labeled reverse or forward primer was used (Fig. 1).

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FIG. 1.

Dendrograms based on the unweighted-pair group method with arithmetic mean algorithm and representing the bacterial community relatedness between pools of bovine feces collected from animals from two different farms: farm number 29 where Campylobacter jejuni was not detected in fecal samples, and farm number 30 where C. jejuni was predominant. (A) Terminal restriction fragments (T-RFs) profiles detected with the fluorescently labeled forward (F) polymerase chain reaction (PCR) primer. (B) T-RFs profiles detected with the fluorescently labeled reverse (R) PCR primer.

As fecal samples isolated from farm number 29 and number 30 were not distinctly grouped after cluster analysis, variations in the composition of the predominant intestinal bovine bacteria were minimal and not related to the presence of C. jejuni in fecal samples collected in farm number 30.

It is reported that dairy cattle intestinal microbiota can be influenced by diet and host (Frey et al., 2009). In the current study, animals from both farms were component-fed, which is predominantly composed of maize. The only difference was the distribution system, where rations were given manually in farm number 30 compared to the use of an automated conveyor in farm number 29. The presence of C. jejuni can result from a contamination of feeding by soiled tools. Variables such as herd size, season, age of the animals, isolation method, and husbandry practices have also been pinpointed as factors affecting carriage rates (Inglis et al., 2004). However, the influence of microbial populations of the intestine on foodborne pathogens' colonization is rarely addressed. Besides the feeding system, no other variations were associated with the samples analyzed (Guévremont et al., 2014). Holstein was the only breed present, and dairy cattle were the only type of production. The size of the herds was similar (30.5 animal in average). Even if the sampling occurred in summer, animals did not have access to pasture, and housing and manure storage were the same.

Analysis of intestinal populations in dairy cattle herds in relation to C. jejuni presence is reported here for the first time. Nevertheless, further characterization using more animals and focusing on other parameters is needed to better understand C. jejuni colonization in dairy cattle in order to reduce the pathogen dispersion.