It is also the 1 intestinal disease diagnosed in travelers returning to the United States. Skip directly to site content Skip directly to page options Skip directly to A-Z link. Campylobacter Campylobacteriosis. Section Navigation. Facebook Twitter LinkedIn Syndicate. On the Farm Control of Campylobacter contamination on the farm may reduce contamination of carcasses, poultry, and red meat products at the retail level At Processing Slaughter and processing provide opportunities for reducing C.
Kist M. The historical background of Campylobacter infection: new aspects. In: Pearson AD, editor. London: Public Health Laboratory Service; Tauxe RV. Epidemiology of Campylobacter jejuni infections in the United States and other industrial nations. Campylobacter jejuni : current and future trends. Washington: American Society for Microbiology; Campylobacter enteritis in the United States: a multicenter study.
Ann Intern Med. PubMed Google Scholar. Peterson MC. Clinical aspects of Campylobacter jejuni infections in adults.
West J Med. Allos BM. J Infect Dis. Rheumatic manifestations of Campylobacter jejuni and C. Scand J Rheumatol. Int Arch Allergy Immunol. Blaser MJ. Campylobacter species. In: Principles and practice of infectious diseases. New York: Churchill Livingstone, ; Ciprofloxacin- and azithromycin-resistant Campylobacter causing traveler's diarrhea in U.
Clin Infect Dis. Piddock LJV. Quinolone resistance and Campylobacter spp. Antimicrob Agents Chemother. DOI Google Scholar. The induction of quinolone resistance in Campylobacter bacteria in broilers by quinolone treatment.
In: Campylobacters, helicobacters, and related organisms. New York: Plenum Press; Atlanta: Centers for Disease Control and Prevention; The influence of immunity on raw milk-associated Campylobacter infection. Experimental Campylobacter jejuni infection in humans. Ketley JM. Pathogenesis of enteric infection by Campylobacter. Nachamkin I. Campylobacter and Arcobacter. In: Manual of clinical microbiology. Washington: ASM Press; Humphrey TJ. An appraisal of the efficacy of pre-enrichment for the isolation of Campylobacter jejuni from water and food.
J Appl Bacteriol. Serotyping of Campylobacter jejuni and Campylobacter coli on the basis of thermostable antigens. Serotyping of Campylobacter jejuni by slide agglutination based on heat-labile antigenic factors. J Clin Microbiol. RAPD analysis of environmental, food and clinical isolates of Campylobacter spp. Discrimination of Campylobacter jejuni isolates by fla gene sequencing. Evidence for recombination in the flagellin locus of Campylobacter jejuni : implications for the flagellin gene typing scheme.
Epidemiologic aspects of Campylobacter jejuni enteritis. Journal of Hygiene Cambridge. Handling raw chicken as a source for sporadic Campylobacter jejuni infections [letter]. Risk factors for sporadic Campylobacter infections: results of a case-control study in southeastern Norway. Epidemiologic investigations on Campylobacter jejuni in households with primary infection.
Endemic Campylobacter jejuni infection in Colorado: identified risk factors. Am J Public Health. Campylobacter enteritis at a university from eating chickens and from cats. Am J Epidemiol. A study of risk factors for Campylobacter infection in spring. Public Health. Distribution of serotypes of Campylobacter jejuni and C. Isolation of Campylobacter fetus subsp.
Occurrence of Campylobacter spp. Most cases of infection are thought to result from handling raw poultry or eating undercooked poultry meat. One common way for infection to be spread is when raw poultry is prepared on work surfaces including chopping boards that are used to prepare foods which will not be cooked e.
Contamination can then pass from the raw meat to ready-to-eat foods. Handling raw poultry and then handling a ready-to-eat food, without washing hands can result in contamination of the ready-to-eat food. Drinking contaminated untreated water or unpasteurised milk may also lead to campylobacteriosis. Person to person spread is unusual but has been reported.
Infection may also be spread from an infected dog or cat. Outbreaks of campylobacteriosis are rare and when they do occur they tend to be small family outbreaks associated with contaminated food. Strategies to control Campylobacter spp. At farm level, efforts should be aimed at reducing or eliminating colonisation of live animals with this organism through good agricultural practices and by employing strict biosecurity measures in the case of poultry flocks.
Retailers and caterers must ensure that opportunities for cross-contamination between raw poultry and ready-to-eat foods are eliminated. Retailers should ensure that poultry is sold in packaging that does not easily leak as a study by FSAI has found contamination on the surface of chicken packaging in Irish retail outlets.
Hands and utensils should always be washed after handling raw poultry, and poultry should be cooked thoroughly until there is no pink meat and the juices run clear. Chicken and cattle are the principal sources of C. Our results imply that the primary transmission route is through the food chain, and suggest that incidence could be dramatically reduced by enhanced on-farm biosecurity or preventing food-borne transmission. In humans, it is responsible for causing more gastro-enteritis than any other identified bacterial species.
Humans may contract campylobacter from a variety of sources. Eating raw or undercooked meat or poultry, and poor food hygiene that leads to cross-contamination of uncooked food, can cause human disease. However, humans may be exposed to the feces of infected wild animals, and campylobacter can survive in water.
Contamination of drinking water can lead to outbreaks, and previous genetic studies have suggested that livestock are not the principal source of human infection. We extracted campylobacter DNA from patients and compared it to campylobacter DNA found in livestock, wild animals, and the environment.
We developed a new evolutionary model to identify the most probable source populations. Very few cases were attributable to campylobacter found in wild animals or the environment. Our results imply that the primary transmission route is the food chain and also add new impetus to measures that reduce infection in livestock and prevent food-borne transmission. Campylobacter is the most commonly identified cause of bacterial gastro-enteritis in the developed world [1] , [2] , [3].
Both species are zoonotic pathogens with wide host ranges including farm animals cattle, sheep, poultry, pigs and wild animals birds and mammals [1] , [6] , [7]. The bacterium thrives at 37—42C in the mammalian and avian gut, but survives longest ex vivo in cold, dark, moist environments.
Campylobacter is routinely isolated from fresh and marine water sources, and sewage [8]. Epidemiological studies have demonstrated a link with exposure to contaminated food. Handling and eating raw and undercooked poultry have consistently been shown to be important risk factors. Case-control studies show that red meat and seafood are risk factors, as are eating at restaurants and barbecues, and drinking raw milk [9] , [10].
However, food is not the only danger, and some studies have shown that regularly eating poultry and red meat in the home actually has a protective effect [10]. Water, particularly when untreated, can present a threat. Incidence of campylobacteriosis is typically sporadic, but outbreaks do occur that can often be traced to contamination of the water supply [11] — [13].
Some authors have suggested that the strong seasonal variation in sporadic disease, which rises sharply in spring and peaks in summer, bears the hallmark of water-borne diseases such as cryptosporidiosis [8] , [9] , [14]. DNA-based methods of typing C. However, a model-based approach that includes disparate sources is needed. Although C. Phylogenetic approaches to tracing the source of infection have suggested that human isolates are more closely related to C. But recombination is frequent in C.
Here we report a systematic study of 1, cases of C. We infer the source of infection of each patient by comparison to 1, animal and environmental C. We treat the animal and environmental reservoirs of C. Within these populations the bacteria evolve through de novo mutation and horizontal gene transfer recombination. We estimate the amount of mutation, migration and recombination, and use these estimates to assign probabilistically each human case to one of the source populations.
From these population assignments we estimate the total amount of human disease attributable to each source. We observed distinct genotypes or sequence types, STs in the 1, human isolates. The two most frequent genotypes STs 21 and made up a quarter of cases alone, while genotypes were observed once only.
There were distinct STs in the 1, animal and environmental isolates, and overlap with the human genotypes was extensive. Six STs featured in both the human and non-human lists of ten most common genotypes STs 19, 21, 45, 50, 53, However, nearly a quarter of human cases exhibited genotypes exclusive to humans STs , most of those at low frequency. The most abundant human-specific genotypes were ST 14 cases and ST 19 cases.
Over a third of non-human isolates possessed genotypes absent from our human sample STs. Certain genotypes common in non-human isolates were host-restricted to varying degrees. ST 61 is common in ruminants cattle and sheep but rare in other groups, while ST 45 was frequent in all the non-human reservoirs except pig and sand. At the level of individual loci, many alleles were frequently observed in a range of animal and environmental sources.
Because of the large overlap in genetic variation between C. By pooling samples of C. See Table S1 for details. However, by combining samples this way, we implicitly assumed that within each group chicken, cattle, sheep, pig, bird, rabbit, sand and water gene frequencies are consistent across sources and across studies.
To test this assumption, and to quantify genetic differentiation between groups, we used analyses of molecular variance AMOVA [29]. Except for the sand group, there was significant heterogeneity within the groups that comprised more than one source type or study. Genetic differentiation between sub-groups ranged from 2.
This suggests that gene frequencies vary significantly between similar sources and between different studies of the same source. In order to assign human cases to source populations with any degree of accuracy, there must be genetic differentiation between the groups, over and above within-group heterogeneity. Table 1 shows the results. However, there were some major groups that were not significantly differentiated. The preliminary analyses of the animal and environmental C. There was significant variation in gene frequencies within groups, probably caused by the heterogeneous nature of the studies from which the non-human isolates were drawn, and the inherently stochastic nature of the epidemic process.
This could distort the gene frequency information upon which source assignment relies, and cause higher than expected linkage disequilibrium between loci. AMOVA also showed that genetic differentiation between groups was weak in some cases. Within-group heterogeneity could therefore obscure or potentially distort the signal of differentiation between groups.
Another concern was that the large differences in sample size between non-human groups, which reflect a tendency among researchers to preferentially sample certain hosts, could bias the source assignment. To investigate the sensitivity of our method to these effects, and to test its robustness to violating the assumption of homogeneous mixing within groups, we performed empirical cross-validation.
Traditional methods that can assign large numbers of individuals to populations based on their genotype tend to assume that loci provide independent sources of information [30] , [31].
In other words, they assume that gene frequencies between loci are uncorrelated in the source populations. While this simplifying assumption is computationally convenient, it may not be appropriate for C.
Therefore we developed two models, one in which loci were assumed to be unlinked i. We used empirical cross-validation to scrutinize both. In each of simulations, we removed the source information from half the non-human isolates, chosen at random.
These we termed the pseudo-human cases. We used our unlinked and linked models to assign the source of the pseudo-human cases using the other non-human isolates. Table 2 shows that the two models differed considerably in performance. We used a number of performance indicators to measure the ability of each model to correctly estimate the total proportion of pseudo-human cases attributable to a given source see Table 2.
The parameter estimates obtained by using the linked model generally exhibited lower bias and smaller variance measured by root mean squared error, RMSE than those obtained using the unlinked model. For seven out of eight groups, the linked model obtained the target coverage of 95 or above. Coverage was 93 for chicken; the small negative bias suggests this may have been caused by slightly under-estimating the proportion of pseudo-human cases attributable to chicken. The unlinked and linked models are defined in the Methods.
The predicted proportion of isolates correctly assigned assumes that isolates are assigned to their most probable source a posteriori. Bias, RMSE root mean squared error and coverage are reported for the proportion of isolates estimated to originate from each source. In the empirical cross-validation the linked model performs well despite the potential concerns due to heterogeneity within the animal and environmental groups, and differences in sample size.
Most importantly, it is well-calibrated in assigning isolates to source populations, and estimating the overall proportion of cases attributable to each source. In contrast, the unlinked model assigns fewer isolates to source populations correctly, and is very poorly calibrated. This underlines the importance of adequately modeling recombination in the study of pathogen evolution.
Clearly the computational efficiency gains made by assuming independent inheritance among loci in the unlinked model are out-weighed by its poor performance. Therefore we use the linked model for our analysis proper. We applied our novel method to the 1, newly-sequenced human isolates from Lancashire, England.
For every case, the assignment probability was calculated for each source population chicken, cattle, sheep, pig, bird, rabbit, sand, water , and the total proportion of cases attributable to each source was estimated.
We found that the vast majority We estimated that chicken is the source of infection in the majority We found that pig is unlikely to be the source of C. Of the two groups of wild animals we studied, bird and rabbits, there was somewhat more support for a wild bird origin of human C.
There was very little support for an environmental origin of human infections. Even so, the results suggested that infection with C. Overall, the analysis reported that with The posterior probability of source of infection was estimated for each patient in our study; Figure 2 illustrates the results. The source populations are color-coded as in Figure 1. Cases are arranged horizontally, and the vertical column space occupied by each color represents the posterior probability of infection from that source.
The dominant color in any column indicates the most likely source for a particular case. The principal distinction in human cases is between those attributed to chicken versus ruminants cattle and sheep. Most cases lie on a continuum between assignment to ruminants and to chicken. The existence of this continuum, as opposed to a clear separation, emphasizes the overlap in genotypes between these source populations, and the advantage of a probabilistic approach to assignment.
Some common genotypes were strongly assigned to ruminants e.
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