Previous Article | Next Article ![]()
Journal of Clinical Microbiology, January 2007, p. 102-108, Vol. 45, No. 1
0095-1137/07/$08.00+0 doi:10.1128/JCM.01012-06
Copyright © 2007, American Society for Microbiology. All Rights Reserved.

Lance Mickan,4
Rosa Rios,5 and
the Australian Campylobacter Subtyping Study Group
Department of Primary Industries, Elizabeth Macarthur Agricultural Institute, Camden, New South Wales,1 OzFoodNet, Hunter New England Population Health, Wallsend, New South Wales, and the National Centre for Epidemiology and Population Health, Australian National University, Canberra,2 Flinders Medical Centre, Bedford Park, South Australia,3 Institute of Medical and Veterinary Science, Adelaide, South Australia,4 Microbiological Diagnostic Unit, Parkville, Victoria, Australia5
Received 15 May 2006/ Returned for modification 11 July 2006/ Accepted 7 October 2006
|
|
|---|
|
|
|---|
Efforts have recently focused on determining important risk factors for Campylobacter infection to guide interventions aimed at reducing disease burden. Such risk factors are commonly determined for other pathogens through investigations of outbreaks; however, despite the large number of Campylobacter notifications, outbreaks are rarely detected. Case control studies to determine risk factors for infection have identified consumption of chicken, exposure to animals, and consumption of contaminated water as significant (1, 4, 16). A meaningful typing system that could be applied to Campylobacter isolates as they arrive in the public health laboratory could aid outbreak detection and help identify common sources of infection.
Numerous typing strategies, including pulsed field gel electrophoresis (PFGE), PCR-restriction fragment length polymorphism analysis of flagellin genes (RFLP-fla), sequencing of the short variable region of the fla locus (SVR-fla), ribotyping, multilocus enzyme electrophoresis, multilocus sequence typing (MLST), randomly amplified polymorphic DNA, and amplified fragment length polymorphism have been employed to examine epidemiological relationships between isolates within the species Campylobacter (reference 35 and references therein). PFGE and amplified fragment length polymorphism are understood to provide excellent discrimination between isolates of Campylobacter, and PFGE is often regarded as the "gold standard" for tracing outbreaks (9, 10, 27). MLST catalogues sequence variation among genes encoding proteins that are essential for metabolic function (so-called "housekeeping genes"), defining each isolate in terms of its sequence type (ST), which then can be grouped into clonal complexes (CCs) that represent lineages presumed to derive from a common ancestor (6). MLST studies of Campylobacter jejuni demonstrate that the organism is genetically diverse, predominantly as a result of frequent intra- and interspecies recombination, within a weakly clonal population structure (7, 16, 31). It is increasingly evident that the CC, as defined by MLST, is an epidemiologically relevant unit for both long- and short-term investigations of C. jejuni epidemiology (16). Furthermore, MLST has been successfully used to compare populations of C. jejuni from veterinary, human, and environmental sources and has highlighted meat-producing practices as reservoirs for pathogenic campylobacters that infect humans as well as identification of species-restricted clones (6, 16). MLST is highly reproducible, portable, and easy to interpret, has an international nomenclature, and thus, is rapidly becoming the method of choice for large-scale molecular epidemiological studies of C. jejuni and other Campylobacter spp.
Despite its many advantages, MLST is expensive to perform, and the equipment and expertise required to interpret data are not readily available to many clinical and veterinary laboratories that would be required to type a large number of isolates on an on-going basis. To more frequently identify outbreaks, to clarify infection routes, and to refine case control study analyses, a practical yet meaningful alternative to MLST is required. Previous studies suggest that RFLPs within flaA gene sequences (flaA-RFLP) alone provide sufficient discrimination for its use as a subtyping method for C. jejuni and Campylobacter coli (10). Despite the observation that recombination rates in C. jejuni may potentially have an adverse impact on the reliable interpretation of flaA-RFLP data (13, 34), several studies show that the majority of C. jejuni and C. coli isolates are genetically stable over long periods (2, 10, 11, 23, 25) and that susceptibility to genome rearrangement may be strain specific. These observations are largely consistent with data from large-scale comparative genomic studies that were compared to the C. jejuni NCTC11168 genome sequence (26) showing that significant regions of the C. jejuni genome are stable (32) but that as much as 21% of genes in NCTC11168 may be dispensable (8). More recently, an interlaboratory evaluation of three flagellin PCR/RFLP methods for typing C. jejuni and C. coli showed that analysis of the full flaA gene using the restriction endonuclease DdeI was an appropriate method for standardization, as demonstrated by 100% interlaboratory reproducibility (12). Collectively, these studies suggest that RFLP-flaA is a discriminatory, reliable, and relatively inexpensive typing tool for Campylobacter. However, longer-term studies require typing systems that can be used by multiple laboratories to systematically examine the epidemiology of Campylobacter infection and detect outbreaks and common sources of sporadic infection.
The aim of this study was to determine whether RFLP-flaA typing could be a useful and cost-effective alternative to MLST for potential use in routine surveillance of Campylobacter isolates. To best assess RFLP-flaA typing as an alternative to other more expensive and comparatively technically demanding methodologies, we used isolates that were collected sequentially over a 30-month period from a public laboratory that would reflect isolates from mandatory Campylobacter notifications. RFLP-flaA typing was compared to MLST for congruence and for the ability to predict MLST-derived clonal complexes. The time period studied also offered the opportunity to explore the stability of the flaA locus and determine whether this is a limitation of the method for longer term epidemiological studies.
|
|
|---|
Molecular and biochemical tests used to confirm species identity. Isolates from diarrheal stool cultures were stored and subsequently tested for species by hippurate hydrolysis and PCR as described previously (3, 29). Template DNA for PCR was prepared using Instagene matrix as outlined in the manufacturer's instructions (Bio-Rad, California). PCR amplifications were performed in a 50-µl volume containing 10 mM Tris-HCl (pH 8.3), 1.5 mM MgCl2, 50 mM KCl, 200 µM concentrations of each deoxynucleoside triphosphate (dNTP), 0.2 µM concentrations of each primer, 1.25 U of Taq DNA polymerase (Roche Diagnostics, Castle Hill, Australia), and 1 µl of Instagene prepared DNA. All PCR experiments were performed on a PC-960G gradient thermal cycler or PC-960 and FTS thermal cyclers (Corbett Research, Australia), and the amplification products were analyzed on 1% agarose gels. This enabled classification of the isolates as either C. jejuni or non-C. jejuni. Of the 171 isolates, 153 were found to be C. jejuni, and these isolates were included in this study.
Typing methods. MLST was performed according to the method of Dingle et al. (7), with modified amplification conditions (denaturation at 94°C [1 min], annealing at 50°C [1 min], and extension at 72°C [1 min]) as described by Mickan et al. (18). Sequences were assigned allele numbers and the isolates were assigned their sequence types by interrogation of the Campylobacter MLST database (http://campylobacter.mlst.net/).
RFLP-flaA was undertaken by digestion of the PCR product with a single enzyme (DdeI) (RFLP-flaA[s]) based on the method described previously (20) or with two restriction enzymes (PstI and EcoRI) (RFLP-flaA[d]) according to the method of Wassenaar and Newell (35). Typing by determination of the gene sequence of the flaA gene short variable region (SVR-fla, base positions 145 to 600) was performed according to the method described by Meinersmann et al. (17). Sequences were assigned a type by submission to the website http://outbreak.ceid.ox.ac.uk/campylobacter.
Riboprinting (automated ribotyping [RT]), pulsed field gel electrophoresis (PFGE), and Laboratory of Enteric Pathogens (LEP) serotyping were also applied to isolates, as described by O'Reilly et al. (24).
Data analysis. (i) Typing assignment. All experimental data were imported into a BioNumerics database. The gel images from RFLP-flaA(s) and RFLP-flaA(d) analyses were imported as raw TIFF images. The BioNumerics software was used to analyze banding patterns using the Dice band matching coefficient and unweighted-pair group method using average linkages with a position tolerance of 1% and an optimization of 1%. The RFLP-flaA(s) and RFLP-flaA(d) patterns were grouped into subtypes when the banding patterns were visually indistinguishable.
(ii) Comparison of methods.
Methods were compared for congruence. To compare MLST with RFLP-flaA(s), a predominant RFLP-flaA(s) type was assigned, defined as the RFLP-flaA(s) type representing
50% of the isolates within that CC (with >1 isolate) and/or where
50% of the ST with a CC were represented by a predominant RFLP-flaA(s) type; the latter definition was required where a CC comprised several ST containing only a single isolate. The proportion of correct (successful) predictions of the predominant CC was calculated for each of the major RFLP-flaA(s) types as (number of isolates of predominant CC for RFLP-flaA[s]x)/(total number of isolates of RFLP-flaA[s]x). Predominant SVR-fla types were similarly assigned.
Statistical analysis. We aimed to measure the degree of congruence of RFLP-flaA(s) and RFLP-flaA(d) using Cramer's V statistic calculated using SPSS version 12.0. This statistic assumes that the marginal differences in rows are similar to those for columns, an assumption that is unlikely to be met with these data but would result in a smaller V statistic. Furthermore, Cramer's V assumes that the change from one type to another is equally likely. Despite these limitations, Cramer's V determines an approximate measure of association between two methods with different levels of discrimination, which is likely to be conservative. The discrimination index (DI) was determined using the method of Hunter and Gaston (15).
|
|
|---|
|
View this table: [in a new window] |
TABLE 1. Comparison of the frequency and distribution of C. jejuni subtypes
|
95%) between predominant RFLP-flaA(s) types was found, particularly for CCs 48, 257, and 354, the most common CCs (Table 2). More than one RFLP-flaA(s) type was detected for STs 530, 354, 50, 161, 523, and 42, with the greatest variation for ST 50. RFLP-flaA(s) types 2, 4, 6, 8, 9, 10, 13, 16, 17, and 19 were detected among more than one CC (Table 2). |
View this table: [in a new window] |
TABLE 2. Correlation of multilocus sequence typing clonal complexes with RFLP-flaA(s) types
|
SVR-fla has been considered a candidate typing method with potential for public health application. A high degree of congruence was found for CCs 48, 354, and 257, and all CCs had a predominant SVR-fla type. More than one SVR-fla type was detected for eight STs (STs 531, 257, 354, 528, 50, 161, 523, and 42). Of the 10 newly described STs represented by more than one isolate, 6 possessed a single SVR-fla type (525, 530, 197, 532, 524, and 527).
Predicting MLST clonal complexes. The ability of the major RFLP-flaA(s) types to predict CCs is outlined in Table 3. There were nine RFLP-flaA(s) types which comprised at least five isolates [or nonassigned STs for 2 RFLP-flaA(s) types]. Of these, the proportion of correct predictions of the predominant CC ranged from 0.47 to 1.0. There were seven RFLP-flaA(s) types (78%) where the probability of predicting the predominant CC was >0.8. Two RFLP-flaA(s) types (types 14 and 9) predicted the same CC (CC 52).
|
View this table: [in a new window] |
TABLE 3. Ability of the majora RFLP-flaA(s) types to predict MLST CCs
|
|
View this table: [in a new window] |
TABLE 4. Ability of the majora SVR-fla types to predict MLST CCs
|
|
View this table: [in a new window] |
TABLE 5. Analysis of C. jejuni isolates with RFLP-flaA(s) types noncongruent with predominant RFLP-flaA(s) MLST pairsa
|
|
|
|---|
Among the 153 C. jejuni isolates used in our study, 40 ST were identified among 15 CC; a clonal complex could not be assigned to 19 isolates comprising 4 STs (18). Twelve of the 15 CC were represented by more than one isolate, and a predominant RFLP-flaA type was found for 83%. Seven of the 10 (70%) RFLP-flaA types successfully predicted the predominant CC >0.8 of the time. SVR-fla was examined as a potential predictor of CCs. A large proportion of isolates (33%) typed as SVR-fla type 1 and the finding that only 2 of 9 (22%) major SVR-fla types were able to predict CC with a probability of >0.8 suggested that RFLP-flaA is a more reliable predictor of CC than SVR-fla.
Data presented here suggest that the lower-cost RFLP-flaA(s) is an alternative typing methodology that is capable of reliably predicting MLST CC. However RFLP-flaA(s) has its limitations. Most notable is the need for standardization and subsequent interpretation of resultant electrophoretic patterns, similar to that required for PFGE. Since completing this study, we have applied RFLP-flaA(s) to the typing of more than 600 clinical isolates of C. jejuni collected from and typed in different regions of Australia. Standardization of the method appeared to have been achieved, as evidenced by detection of five of the six predominant RFLP-flaA(s) types described in this study in all five states of Australia with the typing conducted in four laboratories (L. E. Unicomb, L. C. O'Reilly, M. D. Kirk, R. J. Stafford, H. V. Smith, N. G. Becker, M. S. Patel, G. L. Gilbert, and the Australian Campylobacter Subtyping Study Group, unpublished results). Our observations are consistent with others who reported that RFLP-flaA(s) can be standardized with 100% interlaboratory reproducibility (12).
The validity of RFLP-flaA has been called into question due to significant levels of recombination that is a characteristic of the C. jejuni population structure. Despite this, RFLP-flaA has been shown to be a reliable typing tool for epidemiological investigations, and groupings determined using this approach correlate well with PFGE, ribotyping, and random amplified polymorphic DNA typing strategies (10, 21, 22, 30). We examined our data for evidence of recombination affecting the flaA locus. Assuming that isolates belonging to a particular ST are clonal, more than one RFLP-flaA(s) per ST was taken as an indication of possible genetic rearrangement, and this was identified for 12 of 121 isolates (10%). Although it is conceivable that point mutations may contribute to this variability, other typing methodologies (Table 5) suggested that recombination was not confined to the flaA locus and that RFLP-flaA is able to discriminate within a CC. In one MLST study of C. jejuni, sequence variation due to recombination was estimated to be 50 times greater than that contributed by mutation (28). Despite this, genetically stable RFLP-flaA(s) types were detected [RFLP-flaA(s) types 14 and 8] throughout the study period of 30 months from one geographic region. Moreover, a subsequent national study described above has detected 20 of the 26 RFLP-flaA(s) types described in this study in at least one of the five states included (Unicomb et al., unpublished results), demonstrating RFLP-flaA(s) stability over a period of up to 44 months, an observation consistent with other studies (10).
While MLST is likely to remain the method of choice for distinction of Campylobacter subtypes, until it can be undertaken a lower cost, using readily available equipment with minimal labor requirements, it is unlikely to be used for routine public health surveillance. RFLP-flaA(s) typing offers a medium- to high-throughput alternative to MLST for these purposes, but further subtyping would be required for some types, especially when outbreaks were suspected, and in these cases, methods such as MLST or PFGE could be considered. Finally, our data suggest that RFLP-flaA(s) not only reliably predicts MLST CC but is also capable of identifying isolates of C. jejuni that require further subtyping.
We thank Wendy Forbes for excellent technical assistance in performing RFLP-flaA(d). We are also grateful for the assistance from Michael Hornitzky for providing expertise and equipment for the routine culture of C. jejuni.
Published ahead of print on 8 November 2006. ![]()
Present address: Academic Unit of Molecular Vascular Medicine, The LIGHT Laboratories, Clarendon Way, University of Leeds, Leeds LS2 9JT, United Kingdom. ![]()
S.P.D., L.E.U., P.J.A., L.M., and R.R. are members of the Australian Campylobacter Subtyping Study Group. Other members (listed in alphabetical order) include Penny Adamson (Flinders Medical Centre, South Australia), Kellie Cheung (Institute of Clinical Pathology and Medical Research, Westmead, New South Wales), Barry Combs (Department of Human Services, Adelaide, South Australia), Craig Dalton (Hunter New England Population Health, Newcastle, New South Wales), Robyn Doyle (Institute of Medical and Veterinary Science, Adelaide, South Australia), John Ferguson (Hunter New England Health Service, Newcastle, New South Wales), Lyn Gilbert (Institute of Clinical Pathology and Medical Research, Westmead, New South Wales), Rod Givney (Department of Human Services, Adelaide, South Australia), David Gordon (Flinders Medical Centre, Bedford Park, South Australia), Joy Gregory (Department of Human Services, Melbourne, Victoria), Geoff Hogg (Microbiological Diagnostic Unit, University of Melbourne, Parkville, Victoria), Tim Inglis (Division of Microbiology & Infectious Diseases, PathWest, Nedlands, Western Australia), Peter Jelfs (Institute of Clinical Pathology and Medical Research, Westmead, New South Wales), Martyn Kirk (OzFoodNet, Canberra, Australian Capital Territory), Karin Lalor (Department of Human Services, Melbourne, Victoria), Jan Lanser (Institute of Clinical Pathology and Medical Research, Westmead, New South Wales), Lyn O'Reilly (Division of Microbiology & Infectious Diseases, PathWest, Nedlands, Western Australia), Minda Sarna (Department of Health, Perth, Western Australia), Hemant Sharma (Hunter New England Health Service, Newcastle New South Wales), Helen Smith (Queensland Health Scientific Services, Coopers Plains, Queensland), and Mary Valcanis (Microbiological Diagnostic Unit, University of Melbourne, Parkville, Victoria). ![]()
|
|
|---|
This article has been cited by other articles:
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Copyright © 2009 by the American Society for Microbiology. For an alternate route to Journals.ASM.org, visit: http://intl-journals.asm.org | More Info»