Comparison of PCR Binary Typing (P-BIT), a New Approach to Epidemiological Subtyping of Campylobacter jejuni, with Serotyping, Pulsed-Field Gel Electrophoresis, and Multilocus Sequence Typing Methods

Abstract

To overcome some of the deficiencies with current molecular typing schema for Campylobacter spp., we developed a prototype PCR binary typing (P-BIT) approach. We investigated the distribution of 68 gene targets in 58 Campylobacter jejuni strains, one Campylobacter lari strain, and two Campylobacter coli strains for this purpose. Gene targets were selected on the basis of distribution in multiple genomes or plasmids, and known or putative status as an epidemicity factor. Strains were examined with Penner serotyping, pulsed-field gel electrophoresis (PFGE; using SmaI and KpnI enzymes), and multilocus sequence typing (MLST) approaches for comparison. P-BIT provided 100% typeability for strains and gave a diversity index of 98.5%, compared with 97.0% for SmaI PFGE, 99.4% for KpnI PFGE, 96.1% for MLST, and 92.8% for serotyping. Numerical analysis of the P-BIT data clearly distinguished strains of the three Campylobacter species examined and correlated somewhat with MLST clonal complex assignations and with previous classifications of “high” and “low” risk. We identified 18 gene targets that conferred the same level of discrimination as the 68 initially examined. We conclude that P-BIT is a useful approach for subtyping, offering advantages of speed, cost, and potential for strain risk ranking unavailable from current molecular typing schema for Campylobacter spp.

Campylobacter species, particularly C. jejuni subsp. jejuni (hereafter C. jejuni), represent the most commonly reported bacterial cause of gastroenteritis in humans in the developed world (47), with New Zealand having one of the highest rates of infection (55). The sheer scale of infection makes concerted epidemiological studies difficult, as does the extremely wide distribution of the organism, found in all major avian and mammalian food animals, their products, and indeed environments. Moreover, many Campylobacter spp. are susceptible to spontaneous genetic change through a variety of mechanisms that can result in conflicting data for genetic typing methods aiming to establish a molecular epidemiological link between strains (reviewed by On and colleagues [47]).

The poor discrimination of phenotypic typing methods led to intense developments in molecular epidemiological tools for more accurate data. Although a wide range of genotypic methods have been described (47), two methods are now more commonly used by laboratories worldwide. The availability of standardized protocols for macrorestriction profiling with pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST) have facilitated major contributions to our understanding of the epidemiology of these bacteria. Nonetheless, issues remain, notably relating to the speed, cost, and ease of data analysis from these methods. Furthermore, although MLST has proven useful in evaluating the original host of a given strain, no current methods provide information on the relative risk to human health from individual strains. Various studies, including those identifying stable clones found in humans and various animals as well as strain types only in a particular animal host (5, 13, 38, 48, 61), and whole-genome microarray-based comparisons revealing a correlation between genome content and stress survival (46) indicate that not all strains are of equal risk to humans.

In this study, we designed a range of specific PCR assays and investigated the distribution of 68 genes associated with epidemicity factors in C. jejuni, to establish the basis of a novel PCR binary typing (P-BIT) system that is inexpensive, rapid, and highly portable. We compared our data with MLST and PFGE (using restriction enzymes SmaI and KpnI) results for the same isolates of C. jejuni (n = 58), C. coli (n = 2), and C. lari (n = 1).

MATERIALS AND METHODS

Bacterial isolates.

Fifty-eight C. jejuni isolates, 2 C. coli isolates, and 1 C. lari isolate from four countries and a variety of sources were included in this study (Table 1). Isolates were grown on Columbia sheep blood agar plates for 48 h at 42°C under microaerobic conditions.

TABLE 1.

Campylobacter jejuni binary typing isolates

Isolate no.	Species	Country	Source	Isolation date	P-BIT coded	Penner serotype(s)	PFGE type		MLST type
							SmaI	KpnI	ST	CC
CMB06688/2	C. jejuni	New Zealand	Bovine	January 2007	005551	3	Sm029	Kp186	3607	1332
CMB06690	C. coli	New Zealand	Bovine	January 2007	250640		Sm131	Kp132	3072	828
CMB06690/1	C. lari	New Zealand	Bovine	January 2007	006000		Sm207	Kp185
CPH003647	C. jejuni	New Zealand	Bovine	August 2000	774601	10	Sm098	Kp260	520	21
CPH004049	C. jejuni	New Zealand	Bovine	September 2000	774601	10	Sm039	Kp060	520	21
CPH004158	C. jejuni	New Zealand	Human	September 2000	774601	10	Sm039	Kp060	520	21
CPH005816	C. jejuni	New Zealand	Duck	December 2001	000512	8, 17	Sm223	Kp018	995	Ub
CPH010117	C. jejuni	New Zealand	Duck	January 2001	200610	5	Sm147	Kp271	699	692
CPH010873	C. jejuni	New Zealand	Bovine	February 2001	770601	10	Sm016	Kp262	520	21
CPH0110608	C. jejuni	New Zealand	Water	October 2001	000675	2	Sm140	Kp098	977	1034
CPH0110747	C. jejuni	New Zealand	Water	October 2001	420000	5	Sm001	Kp087	3581	U
CPH0112578j	C. jejuni	New Zealand	Water	December 2001	424200	1	Sm218	Kp252	2381	U
CPH011377	C. jejuni	New Zealand	Bovine	March 2001	477600	1, 44	Sm098	Kp179	50	21
CPH012121	C. jejuni	New Zealand	Water	April 2001	600420	15	Sm235	Kp102	45	45
CPH013864	C. jejuni	New Zealand	Duck	June 2001	020550	NTa	Sm147	Kp129	1525	U
CPH014045	C. jejuni	New Zealand	Water	July 2001	200010	33	Sm100	Kp190	1457	U
CPH014202	C. jejuni	New Zealand	Water	July 2001	420000	27	Sm001	Kp290	3580	U
CPH0210258	C. jejuni	New Zealand	Human	January 2002	410450	52	Sm001	Kp184	418	45
CPH0214000-01	C. jejuni	New Zealand	Ovine	October 2002	074010	5	Sm053	Kp373	2392	52
CPH0214086-01	C. jejuni	New Zealand	Human	October 2002	270000	3	Sm033	Kp080	436	U
CPH0214128-01	C. jejuni	New Zealand	Human	October 2002	675610	8, 17	Sm036	Kp096	50	21
CPH0214244-01	C. jejuni	New Zealand	Ovine	October 2002	776601	10	Sm037	Kp019	520	21
CPH0214392-01	C. jejuni	New Zealand	Human	October 2002	020410	5	Sm090	Kp079	693	U
CPH0214443-01	C. jejuni	New Zealand	Human	October 2002	274540	15	Sm068	Kp088	3606	U
CPH0214447-01	C. jejuni	New Zealand	Human	October 2002	270010	3	Sm033	Kp081	436	U
CPH0214589-01	C. jejuni	New Zealand	Human	October 2002	400030	45	Sm001	Kp032	137	45
CPH0310726-01	C. jejuni	New Zealand	Ovine	February 2003	274000	5	Sm053	Kp373	2382	52
CPH0310881-01	C. jejuni	New Zealand	Human	February 2003	377630	1, 44	Sm098	Kp043	50	21
CPH0310892-01	C. jejuni	New Zealand	Human	February 2003	677610	1, 44	Sm098	Kp043	50	21
CPH0311081-01	C. jejuni	New Zealand	Human	March 2003	677600	2	Sm246	Kp119	53	21
CPH0311417-01	C. jejuni	New Zealand	Human	March 2003	005500	9	Sm152	Kp068	2374	U
CPH0311750	C. coli	New Zealand	Human	April 2003	250040		Sm142	Kp042	829	828
CPH0311897-01	C. jejuni	New Zealand	Human	April 2003	377610	2	Sm039	Kp038	53	21
CPH0312128-01	C. jejuni	New Zealand	Human	April 2003	274050	11	Sm021	Kp109	257	257
CPH0312133-01	C. jejuni	New Zealand	Human	April 2003	677600	1, 44	Sm038	Kp037	50	21
CPH0312157	C. jejuni	New Zealand	Water	April 2003	377630	2	Sm048	Kp021	53	21
CPH0314240	C. jejuni	New Zealand	Chicken	October 2003	274050	11	Sm021	Kp178	257	257
CPH0315217	C. jejuni	New Zealand	Water	December 2003	410030	45	Sm001	Kp032	137	45
CPH0410333	C. jejuni	New Zealand	Chicken	January 2004	677600	1	Sm038	Kp037	50	21
CPH0411548	C. jejuni	New Zealand	Chicken	April 2004	674150	58	Sm002	Kp004	1517	354
CPH0411624-04	C. jejuni	New Zealand	Water	May 2004	201611	5	Sm145	Kp243	991	692
CPH0512595C	C. jejuni	New Zealand	Chicken	September 2005	674150	NT	Sm047	Kp173	1517	354
NCTC11168	C. jejuni	U.K.	Human	1977	777630	2	Sm039	Kp243	43	21
NZRM 4128	C. jejuni	New Zealand	Human	2002	677600	1, 44	Sm013	Kp020	50	21
NZRM 4136	C. jejuni	New Zealand	Chicken	2001	410552	27	Sm004	Kp002	474	48
NZRM 4141	C. jejuni	New Zealand	Human	2003	411440	25	Sm001	Kp013	45	45
P110b	C. jejuni	New Zealand	Chicken	August 2005	374610	4, 13	Sm010	Kp066	474	48
P145a	C. jejuni	New Zealand	Chicken	September 2005	374650	4, 13	Sm006	Kp299	3609	48
RM1221	C. jejuni	USA	Chicken	2000	254150	53	Sm168	Kp301	354	354
RM1864 = 81-176	C. jejuni	USA	Human	1988	011013	23, 36	Sm248	Kp315	604	42
SVS1099	C. jejuni	Denmark	Chicken		777610	2	Sm037	Kp295	21	21
SVS1425	C. jejuni	Denmark	Chicken		677600	1, 44	Sm083	Kp060	50	21
SVS3141	C. jejuni	Denmark	Porcine		377630	2	Sm039	Kp039	53	21
SVS380-827	C. jejuni	Denmark	Chicken		410550	55	Sm001	Kp003	45	45
SVS4039	C. jejuni	Denmark	Bovine		410440	6, 7	Sm001	Kp291	45	45
SVS5001	C. jejuni	Denmark	Human	1996	377630	2	Sm016	Kp039	53	21
SVS5141	C. jejuni	Denmark	Human	1996	377630	2	Sm016	Kp039	53	21
SVS72-64077	C. jejuni	Denmark	Turkey		000555	17	Sm252	Kp364	48	48
SVS72-74172	C. jejuni	Denmark	Turkey		270030	NT	Sm009	Kp294	48	48
SVS835-770	C. jejuni	Denmark	Chicken		410010	42	NVc	NV	NV	NV
SVS992	C. jejuni	Denmark	Chicken		777630	2	Sm176	Kp314	21	21

^aNT, not typeable.

^bU, unassigned.

^cNV, nonviable.

^dTo generate the P-BIT code, the genes from the 18-target P-BIT scheme were divided into six groups of three genes in the order shown in Table 2. The PCR results were recorded as 0 or 1, and the result for the first gene in the group was multiplied by 1, the second by 2, and the third by 4. The results were then added together to give a number from 0 to 7 representing the results for the three genes. The six groups’ results were then concatenated.

DNA extraction.

Between 5 and 10 colonies were transferred to a 1.5-ml tube containing phosphate-buffered saline (PBS) (BR0014G; Oxoid, Basingstoke, England) and centrifuged at 6,000 × g. The supernatant was discarded and DNA was extracted from the pellet using DNeasy blood and tissue kits (Qiagen, Hilden, Germany). The DNA quantity and purity were established using the Nanodrop 1000 (Thermo Scientific, Waltham, MA), and the DNA was stored at −20°C until required. DNA was diluted in sterile Milli-Q water as required.

Risk-based binary typing of isolates.

Selection of gene targets for potential use in a PCR binary typing (P-BIT) system was based upon several criteria. We chose predominantly those genes implicated as markers of epidemicity that were overrepresented in genotypes associated with human illness (1, 4, 14, 16, 17, 20, 22, 26-31, 35-37, 43, 44, 46, 49, 50, 52, 53, 59, 60, 64, 65, 67, 69-71) or associated with virulence factors that were not components of the core genome (33, 34, 60), to evaluate a basis for subtyping that could also be related to the risk to human health from individual strains. These genes were generally involved in cell surface, mobility, and toxin production. Primers were designed using Primer3 (58) via Geneious Pro (10; available from http://www.geneious.com/) and the sequences of the identified genes and the identified strains as loaded in GenBank. Primers were designed to produce PCR products with sizes and melting temperatures that will allow for easy multiplexing in the future. The primers were checked for sequence homology with all known sequences using the Basic Local Alignment Search Tool (BLAST; www.ncbi.nlm.nih.gov/BLAST/) and synthesized by Invitrogen (Carlsbad, CA). In addition, one primer set (Cj0265) designed by Price and colleagues (56) was also included in the risk-based binary typing system. The target genes and primer sequences are listed in Table 2.

TABLE 2.

Target genes for Campylobacter jejuni binary typing

Gene/ORFc	Primera	Product size (bp)	Reference(s)
Cj0008	For: AGTCCTGAAGCGTTTGCTGT	245	8
	Rev: CATTTTTCATTTACCCATTGCTT
cdtB (Cj0078c)	For: AGCCACAGAAAGCAAATGGA	550	59
	Rev: TGCTTGAGTTGCGCTAGTTG
Cj0122	For: TCCTTTGCAAACACTTACCAA	250	46
	Rev: CCAACGAGGGTGTTTAGCAT
exbD1 (Cj0180)	For: AGAGTTCGCAGCAAGCAGAG	248	46, 70
	Rev: TGAAAGAGCTATTTTTCCATAGCC
tonB1 (Cj0181)	For: TTTTAAACAAGGAGCGGAATTT	547	46, 70
	Rev: TTTTGGCAATGGAGAAGCTC
Cj0265b	For: AAGCGAAAATAACAGGGTTTTGC	175	56
	Rev: GCTTACCTTATCCCATTTGCCA
cheW (Cj0283c)	For: TGCAAAAACAGCAAACTCAGA	398	31, 69
	Rev: AAGTTTCAGGTGGTGGATCG
panB (Cj0298c)	For: TTTCTCGCGGAGCTAAAAAG	150	8
	Rev: CGATTTCTATACCCCCTTCCA
modC (Cj0300)	For: GCCTAAATTGGGGCGTATAGA	299	46
	Rev: GCAAGATTTTTGGTTCTCTTGC
fdxA (Cj0333c)	For: TGCTTGTGGTTCTTGTATTGATG	245	69
	Rev: CTGGAGTGTCCCCACTTCTC
cmeB (Cj0366c)	For: CAACCCGCACCAGGTTATAC	546	35, 36, 50
	Rev: TCCACCAATAAGCCCTGTTC
Cj0423	For: GCAAGTGGCGGGTCTATTT	148	46
	Rev: CAAGCCAAACAATAATAGTCCAA
Cj0484	For: AAATGGGAGTTCCAATTGCTT	445	46
	Rev: ATTGGCAAGGCGATAAAAAC
cft (Cj0612c)	For: TAAACAAAGAAATGTATGCAGCAA	445	27, 70
	Rev: GCAATATTTTTGATGTATTGATCAGC
Cj0659	For: TGYCTTTGTGCTTTGTTTTTAGC	101	46
	Rev: TCATTTAATGCAAGGTAATTATGTTCT
Cj0727	For: TTATGCCTTARGYGGGGATG	345	46
	Rev: ATTGGCCTTGCATAACCTTTT
CJE0837	For: ATGCTGTACCACGAGGAGGT	148	12
	Rev: TTTACACTTTCCCCCTGAGC
cfrA (Cj0755)	For: AGCAGGGATAAGCCCTCTTG	203	46, 49, 69, 70
	Rev: AGCGATCTATTTGCCAYTCG
tpx (Cj0779)	For: GCAGCGGGTAAAACTCAAAT	353	49
	Rev: TTTGCAATATCTGGCATTTCTG
flgS (Cj0793)	For: CTTGCAAGCATGGGTAGTGT	404	71
	Rev: GGCTTTTCACATTCGCTTTC
serA (Cj0891c)	For: GGGGTGGGCGTAGATAATGT	450	49
	Rev: TTTTGCAATTTCTTGCTTTCC
peb1A (Cj0921c)	For: TTGATCAAGCAACAGGTGAAA	503	14, 27, 28, 69
	Rev: TGGTTCAAAACTATCTGGCAAA
cheB (Cj0924c)	For: GGATCTTCAACAGGTGGTCCT	354	31
	Rev: TCCCATGCCCGTTAAAATAA
jlpA (Cj0983)	For: AGCTTGCGGAAATTCCATAG	147	14
	Rev: TCACCATCTGCATTGCATTTA
csrA (Cj1103)	For: GTCGTTCAAACAGGGAAAGG	152	50, 51
	Rev: TTTTTGCTTAAGTCATCAAGTTTAATA
pglB-s/wlaF (Cj1126c)	For: CAAAGCAATGTGGATTTGTTTC	247	65
	Rev: CGCTTAAAACCACTCCGTTG
pglB-g/wlaF (Cj1126c)	For: GCCCGCTAGAATGTCTTTGA	153	65
	Rev: AAAACCACTCCGTTGCTAAGAT
Cj1135	For: TTAGAATTTGCAAAACGCAAAG	303	8, 9
	Rev: ATCTTCCCTACCCCAGCCTA
Cj1136	For: TGGTTTGACCATGCTTGTATTT	252	8, 9, 46
	Rev: CAAAACCATGCCAAAATGAA
wlaN (Cj1139)	For: AGCATTAGAAAGTTGCATTAACCA	252	8, 9, 30, 43, 52
	Rev: CATTCTTCGCAAGCATTAAGTTC
neuA1 (Cj1143)	For: GAATGTGGTTTTATCGATGGAG	446	8, 9
	Rev: GGCCTATCATGCAAAAGAGC
CJE1500	For: ACGATTTCGTTTTCCCTAAACTC	545	12
	Rev: AGGACTAAAATCCCCATAAGGATAA
neuB3 (Cj1317)	For: CAGGTGCTGATGCGATAAAA	349	4, 22, 69
	Rev: GTAGCAATGCCCGTTGAAA
Cj1321	For: TTTATACATCCTCAATCTTTTGTTTCA	249	3
	Rev: GCAAGGCAACAGGCTAACTC
neuC2/ptmD (Cj1328)	For: GCCCTGAGTTTGGACTCACT	253	37, 53
	Rev: CCGCCACATAAATGCACTAAA
neuA3/ptmB (Cj1331)	For: CGCAAGTGTAGCCCAAAAAT	447	20, 22, 37, 53, 64, 65
	Rev: TCCATTACAAAAAGCCCTGTG
maf5/pseE (Cj1337)	For: TTGGGGCTAGACGATTATGG	146	22, 26
	Rev: TGAGAATGTGTTTGCCCATC
pldA (Cj1351)	For: GCTTTGGCTAATTATCTTGGTGA	455	70
	Rev: CAAAGGCGTGGAACAAATAAA
Cj1357	For: TGGTGGAATGGAACTCAGAA	552	46
	Rev: TCCATCGCTTTGGAATTTTT
Cj1365	For: CTGATACAGCTGGGAAACAGC	150	3
	Rev: GAAACACCCCAGCTCCATAA
gmhA2 (Cj1424)	For: TAGCAGGAAATGGTGGCAGT	450	9, 46
	Rev: GCTTGCAAGAAAAACCTTTACC
fcl (Cj1428)	For: CGCAGGTACAGCATTGGTAG	546	9
	Rev: CCCTCGCTTAACAATTTTGC
Cj1435	For: TCCTTATCCTCGTTATGAATGGTT	154	9
	Rev: TAACGATTGTGTGGCCTTCA
glf (Cj1439)	For: TTGGTTCTGGATTGTTTGGT	555	9
	Rev: CGCCTTTAGGGATACCTTGAT
cadF (Cj1439)	For: ATGGTTTAGCAGGTGGAGGA	348	1, 14, 28, 50, 51, 59, 69
	Rev: CAAAACCAAAATGACCTTCCA
Cj1549	For: AACCCAATACGGCTTTGATG	153	8, 9
	Rev: CTTTCAATTGGCGTTCCTGT
Cj1551	For: GGTAGTGTTTTTGGCTCGGTA	145	8, 9
	Rev: GGGTTTTGTTTTGCTCGTGT
iamA (Cj1647)	For: TTAGTGGCGGTATGCAAAAA	254	43, 59
	Rev: ATAAGTGCCGCAAAATCCAA
p19 (Cj1659)	For: CGGCGATCCAAAAGAACTTA	252	49, 70
	Rev: GCCACCATAGGCATCAAAGT
flgE2 (Cj1729c)	For: TAACGCGAAAGGACAAGGAC	555	22, 29, 43
	Rev: GCTTGAGGAGGTTGTGGTGT
cst-II	For: ATTGCTGGAAATGGACCAAG	546	1, 16, 17
	Rev: TCCGATATAGTGTGAATTATCATTTTT
CJE1733	For: GATGTATCCGCCTTTGGCTA	447	12
	Rev: GGGTTCTTGTTATAAAGCCCATT
cgtA	For: CCTTTGAATCCTTGGGCTTT	555	1, 16, 17, 44
	Rev: TGAAGAAAAACTTTGTTTTGTTGCT
cgtB	For: GCAAGGGCAATAGAAAGCTG	550	1, 16, 17, 30, 44
	Rev: TTTGAGCTTGACTTAACATTGGA
virB8/comB1 (Cjp1)	For: AATGGATATGAGAGCCGATGA	345	33
	Rev: AGCTCCATTGGTATAAAATCAAAAA
virB9/comB2 (Cjp2)	For: AACACAGGCGAAAGCATTTT	349	33
	Rev: CCATTGCAAAGCGTGTTCT
virB10 (Cjp3)	For: TTAGCTGATCGTGGCAACAC	452	33
	Rev: GCGTAAGGCATTCCGTATCT
virB11 (Cjp5)	For: GAAATGGAAGCAAACGAGCTT	452	1, 30, 33
	Rev: TTTGGATCAATTTCACCCATT
virD4 (Cjp6)	For: TCAGAGCTAAAATGCCCCTAAG	351	33
	Rev: TCGCACTTGTAACCCAATGA
virB4/comB4 (Cjp53)	For: ACAGGAGCATTTGCACCAC	345	33
	Rev: GTTCGCCCCAATCATTTTT
tetO	For: TTTCCCGTTTATCACGGAAG	448	60, 67
	Rev: CAAGCAATATTTCCCGCTGT
HS1.07	For: TGAACTTGCTTGGGAAATAAATC	455	24, 25
	Rev: CAATCATCGGAGCATTAGCA
HS19.09	For: ACCTTAGAGAAATTTTTACCCTTGG	345	24, 25
	Rev: ATCAGAATTTTCCAAATCGATAGC
HS23/36.17	For: TCTTCTTTTTGGTTTGGATGG	355	24, 25
	Rev: GCAAATCTTGATGGGTTTGTG
HS41.06	For: TTACTTGGTGGAAGCAATTCG	249	24, 25
	Rev: TTCTTTTTCCCAAGTGTGTGC
fhuA	For: GCCGGTGATCTGTTCTTCAT	547	15, 69, 70
	Rev: TGTTACCGAGGAAGGTGTCC
aphA3	For: ACCGCTGCGTAAAAGATACG	154	67
	Rev: TCCAGCCATAGCATCATGTC
CAT	For: AAAGCCTAATCCTCCGGAAA	148	67
	Rev: GTATTTTCCGCCCTCCTCAT

^aFor, forward; Rev, reverse.

^bPrimers designed by Price and colleagues (56).

^cGenes selected for the 18-target P-BIT system are shown in boldface.

DNeasy-extracted DNA from each isolate was tested using each gene target in PCRs containing 2.5 mM MgCl₂, 1× PCR buffer II (50 mM KCl, 10 mM Tris, pH 8.3; Applied Biosystems, Foster City, CA), 0.2 mg/ml bovine serum albumin (Sigma-Aldrich, St. Louis, MO), 250 μM each deoxynucleoside triphosphate (dNTP), 12.5 pmol each primer, 1.25 U AmpliTaq DNA polymerase (Applied Biosystems, Foster City, CA), and 0.5 ng or 5 ng of extracted DNA. PCRs were run in ABI 9700 (Applied Biosystems, Foster City, CA) thermal cyclers using the following cycling conditions: 5 min at 95°C followed by 40 cycles of 1 min at 95°C, 1 min at 59°C, and 1 min at 74°C and then a final extension at 74°C for 8 min.

PCR products were run in 2% agarose gels with 1× Tris-borate-EDTA (TBE) for 70 min at 110 V and visualized using ethidium bromide. The presence or absence of target PCR products was loaded into a BioNumerics version 5.10 (Applied Maths, Ghent, Belgium) database. Interstrain relationships were assessed by numerical analysis of the P-BIT data using the simple matching coefficient and Ward's clustering.

Typing of isolates.

The Penner serotyping system was used to determine the heat-stable serotypes of the isolates using the passive hemagglutination technique and antisera produced in house according to the method of Penner and Hennessy (54).

All isolates except SVS835-770, which had become nonviable, were analyzed by PFGE using the standardized PulseNet protocol (57), and Salmonella enterica serovar Braenderup H9812 restricted with XbaI as a size standard (21). Samples were digested using SmaI and KpnI. Gels were prepared with 1% (wt/vol) SeaKem Gold agarose (Lonza, Rockland, ME) and electrophoresed for 18 h using respective initial and final switch times of 6.8 s and 38.4 s for SmaI and 5.2 s and 42.3 s for KpnI. PFGE profiles were analyzed and compared using BioNumerics version 5.10 and submitted to the PulseNet Aotearoa (New Zealand) Campylobacter database, where SmaI and KpnI pattern designations were assigned.

DNeasy-extracted DNA from each isolate, except SVS835-770, which had become nonviable, was analyzed using multilocus sequence typing (MLST) as described by Dingle and colleagues (7). Amplification was performed in a 25-μl reaction mixture using AmpliTaq Gold master mixture (Applied Biosystems, Foster City, CA) and 5 pmol of each primer. Products were sequenced with an ABI Genetic Analyser 3130XL (Applied Biosystems, Foster City, CA). The sequence information was collated and alleles were assigned using the Campylobacter PubMLST database (http://pubmlst.org/campylobacter/) (23). Novel alleles and sequence types (ST) were submitted for allele and ST and clonal complex (CC) designations when appropriate.

RESULTS

Risk-based binary typing.

Sixty-eight PCRs were designed for 67 prospective risk-based binary typing genes. Two primer sets for pglB were designed using the sequence from positions 81 to 176 (accession no. AF108897) (66). Both sequences aligned only with C. jejuni, but one primer set contained mismatches with some strains (pglB-s) while the other showed 100% alignment for both (pglB-g). Five targets produced negative results for all 61 isolates tested, fhuA, aphA-3, CAT, HS41.06, and HS19.09, of which the latter two were serotype-specific targets (24) subsequently shown to produce positive results for isolates of the target serotype (data not shown). Four targets (cheW, cft, pglB-g, and p19) were positive for all C. jejuni and C. coli isolates tested and negative for C. lari. One target (csrA) was positive for all C. jejuni isolates, negative for C. lari, and variable for C. coli isolates. Eleven targets (fdxA, cmeB, Cj0659, flgS, serA, peb1A, cheB, pglB-s, pldA, Cj1357, and iamA) were positive for all C. jejuni isolates and negative for C. coli and C. lari isolates. The remaining 49 targets were detected in at least 1, and at most 57, of the 58 C. jejuni isolates studied. The risk-based binary typing system produced 47 types from 61 isolates with a diversity index (62) of 98.5%.

A subset of 18 targets was selected to yield the same discriminatory potential as the entire scheme. Their selection was based upon several criteria, including discriminatory potential among the strain set examined, position in the genome, and known or predicted function.

Numerical analysis of P-BIT data.

A dendrogram of the cluster analysis (Fig. 1) revealed nine clusters at the 67% similarity level (S-level). Cluster 1 comprised the two C. coli isolates and one C. lari isolate studied, distinguished at the species level at the 68% S-level. Cluster 2 contained 13 C. jejuni isolates, comprised of several highly related clusters assigned to CC 48, 257, 354, and 52, in addition to three sequence types yet to be assigned to a CC. Cluster 3 contained 13 isolates assigned to CC 45 and 692 (in which each of these types clustered closely with isolates unassigned to a CC) and one isolate (SVS835-770) for which MLST data was not available. Cluster 4 contained six isolates, three of which were unassigned to a CC and one each of CC 692, 1034, and 1332. Cluster 5 was represented by a single isolate of CC 42. Cluster 6 comprised three isolates unassigned to any CC. Clusters 7, 8, and 9 contained a total of 22 isolates all assigned to CC 21, but representing ST 50 (cluster 7), ST 520 (cluster 8), and ST 21, ST 43, and ST 53 (cluster 9), respectively. A cluster analysis based on the suggested 18-target P-BIT set yielded a similar topology (Fig. 2).

FIG. 1.

Cluster analysis of P-BIT data based on 68 gene targets using the simple matching coefficient and Ward's clustering.

FIG. 2.

Cluster analysis of P-BIT data based on the suggested minimal set of 18 gene targets using the simple matching coefficient and Ward's clustering. “68 p-bit” refers to the clusters illustrated in Fig. 1.

A minimum spanning tree was prepared for the 18-target P-BIT subset using the binary coefficient, maximum neighbor distance of 2 changes, and minimum size of 2 types. The tree is displayed in Fig. 3 with ST labels. Six clusters were revealed. Cluster 1 comprised isolates assigned to CC 48 and 21, and cluster 2 contained the ST 520 isolates from CC 21. Cluster 3 comprised isolates of CC 48, 52, 257, 354, and 828 (C. coli) and isolates that have yet to be assigned a CC. Cluster 4 contained isolates of CC 692 and one unassigned isolate. Cluster 5 contained isolates assigned to CC 45 and one isolate for which MLST was unavailable (SVS835-770). Clusters 6 and 7 contained only isolates that had not been assigned a CC.

FIG. 3.

Minimum spanning tree based on 18 P-BIT targets. The tree was created using BioNumerics 5.1 software, using the binary coefficient. The MST is presented with logarithmic branch lengths; complexes are shaded based on a maximum neighbor distance of 2 changes and minimum of 2 types. Thick lines between nodes indicate one change, thinner lines two changes, dark dotted lines three to four changes, and gray dotted lines five or more changes. Mix 1 contains ST 3580 and 3581, and mix 2 contains ST 21 and 43. nv, nonviable; lari, C. lari isolate.

The wide range of sources and wide variation in detection rates between target genes in this study make investigation of source bias problematic. However chi-square analysis of the P-BIT data revealed that, at the 95% confidence level, CJE1500 was detected more frequently in poultry and wild bird isolates and less frequently in animal, human, and water samples than would be expected if isolates from all sources had an equal probability of carrying this gene (data not shown). However, we do not know how many of the human isolates were derived from diarrheal cases that arose from consumption of contaminated poultry but assume the proportion is not insignificant (47).

Typing.

One of the isolates (SVS835-770) became nonviable before PFGE and MLST could be undertaken. In our laboratory, Penner serotyping is only available for C. jejuni and MLST is only available for C. jejuni and C. coli isolates. Three isolates were untypeable by Penner serotyping, and the remaining 55 isolates had 22 Penner serotypes and a diversity index of 92.8%. The 60 isolates analyzed by PFGE produced 38 SmaI and 52 KpnI types with diversity indices of 97.0% and 99.4%, respectively. Combining SmaI and KpnI produced 54 types and a diversity index of 99.7%. The 59 isolates analyzed by MLST produced 32 sequence types and a diversity index of 96.1%.

DISCUSSION

Typing systems for microorganisms may be assessed by a number of criteria to establish their fitness for purpose (68; R. J. Meinersmann, presented at Campylobacter, Helicobacter and Related Organisms, Cape Town, South Africa, 1997). Some C. jejuni strains resist digestion with certain restriction enzymes, making characterization by PFGE typing problematic (13, 18), while MLST target genes may not always provide data appropriate for the scheme (40). P-BIT relies on a code derived from positive or negative results of PCR analyses for a wide range of genes widely distributed in C. jejuni genomes and extrachromosomal elements and thus offers complete typeability. In addition, our results indicate the P-BIT approach to be more discriminatory than MLST and SmaI-based PFGE typing, another important feature. We found KpnI-based PFGE typing to be the most discriminatory of the methods used in our study, notwithstanding that not all strains prove typeable with this enzyme (18).

Although the more widespread use of standardized methodologies for PFGE-based typing of bacterial pathogens has proven valuable for epidemiological studies (2, 11, 18, 19, 32), determining and using normalization parameters to enable a meaningful comparison of PFGE macrorestriction profiles can be challenging. Furthermore, the PFGE apparatus is of moderate cost. The increased availability of DNA sequencing facilities has made MLST a popular option and offers excellent portability, enabling researchers to readily compare their data with results obtained worldwide. The MLST approach also offers phylogenetically meaningful data that have proven invaluable for population genetic (6, 41) and source attribution (13, 42) studies. However, while technological advances continue to reduce the cost of performing the analysis, MLST still requires substantive capital outlay for implementation and running costs can be prohibitive for small routine laboratories. Our P-BIT approach requires only the most basic molecular biology laboratory equipment for implementation (approximately one-tenth the cost of PFGE equipment, for example) and no more than e-mail for the exchange of data between laboratories. The PCRs used have been designed to enable multiplexing, to further reduce running costs. We have identified a core set of 18 gene targets that conferred the same discriminatory potential in this study as the complete range of 68 targets employed. We believe these features, together with its high discriminatory power and complete typeability, render P-BIT a worthy tool for laboratories engaged in epidemiological studies of C. jejuni. The distinctiveness of profiles obtained from the few C. coli and C. lari strains included suggest P-BIT data may also provide a basis for species identification between closely related Campylobacter species (itself a challenging task [45]), but further work is required to substantiate this.

The P-BIT approach to typing yields a simple binary code (albeit one based on whole-genomic polymorphism) that is not suited for analysis with algorithms aimed at predicting phylogenetic relationships. The bases for determining strain relatedness with MLST and P-BIT are also very different, with the former evaluating change between housekeeping gene sequences and the latter determining, by PCR, the presence of diverse, widely distributed genes that may be subject to selective pressure. Nevertheless, some congruence between these methods was seen when comparing the interstrain relationships inferred. In particular, isolates assigned to CC 21, 45, 52, 257, and 354 formed or dominated well-defined clusters in the P-BIT analyses (Fig. 1 to 3). With the population genetic structure of C. jejuni believed to be significantly shaped through recombination events and horizontal gene transfer (7, 63), the correlation of P-BIT and MLST is perhaps not surprising, with P-BIT potentially offering additional insight into the genomic evolution of strains that may be useful for relatively uncommon MLST types or those that cannot be assigned to a clonal complex.

Subtyping using PFGE depends on the distribution of restriction sites within the organismal cellular DNA. As with P-BIT, the data are not aimed at determining strain phylogeny unless banding patterns are indistinguishable, in which case strains are assumed to be related. It has become common practice to confirm strain relationships inferred with one restriction enzyme with the use of a second (47). Despite these caveats, we compared the clustering of C. jejuni strains in a dendrogram based on SmaI and KpnI PFGE profiles (data not shown) with the P-BIT and MLST results, given the agreement between the latter two methods. The PFGE analysis agreed with some of the P-BIT and MLST results, with homogeneous clusters of ST 257, 436, and 2392 each formed at S-levels of 80%; other clusters were dominated (but not exclusive to other ST) by strains assigned to ST 137, 50, and 520 at S-levels between 70 and 92%. Five of 6 ST 53 strains also clustered together (S-level of 75%), and two highly related ST 45 strains (NZRM 4141 and SVS 4039) gave PFGE profiles determined as 90% similar. However, although all CC 21 strains formed a distinct cluster in the PFGE analysis, the infraspecific relationships between the constituent ST differed from that of the P-BIT and MLST analyses and also included the three CC 48 strains examined. Strains assigned to CC 45 and 354 were divergently distributed in the PFGE analysis. Although the discriminatory potential of PFGE typing can exceed that of both P-BIT and MLST, our results support the need for caution when using PFGE data to infer clonal relationships, for reasons described in more detail elsewhere (47).

One feature we had in mind when developing this approach was the possibility of quantifying the risk to human health from individual strains through generation of a simple binary code relating to epidemicity factors. There have been various observations of “stable clones” present in human clinical specimens and various host animals (39, 47, 48, 61) as well as whole-genomic comparative studies indicating that host-specific types are less well adapted for stress survival than those found more widely (46). The selection of initial marker genes in this study was on a metagenomic basis but focused on genes that conferred, or were implicated in, some aspect of strain virulence. Many aspects of pathogenicity in Campylobacter are poorly understood, but examination of some of the strains classified as “high risk” and “low risk” by On and colleagues (46) showed them to cluster distinctly (Fig. 1). Notably, all “high-risk” strains (NCTC11168, SVS992, SVS1099, SVS1425, SVS3141, and SVS5001) were found to belong to CC 21, a globally common type, with four of the five “low-risk” strains (SVS380-827, SVS4039, SVS72-64077, and SVS835-770) appearing in related clusters 3 and 4 (Fig. 1). We recognize that our P-BIT system uses PCR primers that are designed to amplify specific sequence orientations of the target genes and that genes may be present in particular strains with different flanking regions that would therefore not be detected. Thus, P-BIT data provides indicative but not absolute data regarding the presence or absence of a gene. Nevertheless, for the establishment of a simple, inexpensive, and effective typing method, we feel this is an acceptable compromise. For genes carried on plasmids, there is a risk in long-term studies that strains will be cured of such extrachromosomal DNA and thereby lose a specific marker. This appears to be the case with one strain examined here, 81-176 (RM1864), in which the plasmid-borne tetO gene conferring tetracycline resistance was not detected. Our example of this strain was a kind gift from colleagues, and we have no information on the number of passages it may have experienced before it was received. However, the isolate's first description was in 1988 in another laboratory, and it is likely to have been subcultured many times before we received it, allowing ample opportunity for the plasmid to be cured. We examined a heated lysate of 81-176 to ascertain if the result may have been due to the DNA preparation method used being suited for genomic DNA extraction, but still obtained no tetO-derived amplicon (data not shown). In the context of the P-BIT system, we do not consider the inclusion of tetO inappropriate since it represents an important virulence marker (found in two of the strains examined here: see Fig. 2) and potentially valuable for short-term epidemiological studies such as outbreaks, where plasmid loss is unlikely to occur. Further studies of the relationship between P-BIT profile, stress survival, and strain type frequency in human populations are also warranted.

We conclude that P-BIT is a useful approach for subtyping, offering advantages of speed, cost, and potential for strain risk ranking unavailable elsewhere. We hope the wider scientific community will be encouraged to investigate further its usage in national and global surveillance studies and outbreak investigations.