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A Reference Pan-Genome Approach to Comparative Bacterial Genomics: Identification of Novel Epidemiological Markers in Pathogenic Campylobacter

  • Guillaume Méric,

    Affiliation Institute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom

  • Koji Yahara,

    Current address: The Biostatistics Center, Kurume University, Fukuoka, Japan

    Affiliations Institute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom, Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo, Japan

  • Leonardos Mageiros,

    Affiliation Institute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom

  • Ben Pascoe,

    Affiliation Institute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom

  • Martin C. J. Maiden,

    Affiliation Department of Zoology, University of Oxford, Oxford, United Kingdom

  • Keith A. Jolley,

    Affiliation Department of Zoology, University of Oxford, Oxford, United Kingdom

  • Samuel K. Sheppard

    Affiliation Institute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom


The increasing availability of hundreds of whole bacterial genomes provides opportunities for enhanced understanding of the genes and alleles responsible for clinically important phenotypes and how they evolved. However, it is a significant challenge to develop easy-to-use and scalable methods for characterizing these large and complex data and relating it to disease epidemiology. Existing approaches typically focus on either homologous sequence variation in genes that are shared by all isolates, or non-homologous sequence variation - focusing on genes that are differentially present in the population. Here we present a comparative genomics approach that simultaneously approximates core and accessory genome variation in pathogen populations and apply it to pathogenic species in the genus Campylobacter. A total of 7 published Campylobacter jejuni and Campylobacter coli genomes were selected to represent diversity across these species, and a list of all loci that were present at least once was compiled. After filtering duplicates a 7-isolate reference pan-genome, of 3,933 loci, was defined. A core genome of 1,035 genes was ubiquitous in the sample accounting for 59% of the genes in each isolate (average genome size of 1.68 Mb). The accessory genome contained 2,792 genes. A Campylobacter population sample of 192 genomes was screened for the presence of reference pan-genome loci with gene presence defined as a BLAST match of ≥70% identity over ≥50% of the locus length - aligned using MUSCLE on a gene-by-gene basis. A total of 21 genes were present only in C. coli and 27 only in C. jejuni, providing information about functional differences associated with species and novel epidemiological markers for population genomic analyses. Homologs of these genes were found in several of the genomes used to define the pan-genome and, therefore, would not have been identified using a single reference strain approach.


Periodic advances in DNA sequencing technology, such as wide-spread adoption of automated DNA sequencing in the 1990s, have revolutionized understanding of microbial processes, from single-cell physiology to population biology [1], [2]. The last decade saw the increased use of high-throughput or ‘next-generation’ sequencing methods that parallelize the DNA sequencing process beyond what was possible with standard dye-terminator methods. These technologies have underpinned important research in pathogen epidemiology and evolution [3], [4], [5], [6], [7], [8], but there are still major technical challenges for effectively archiving and analyzing hundreds or thousands of bacterial genomes [9].

A popular approach to describe the genetic variation among multiple bacterial genomes has been to map stretches of DNA sequences from multiple isolates to a reference bacterial genome to identify variable sites that display single nucleotide polymorphisms (SNPs). This is an effective way of condensing large genomes into panels of informative sites. This has provided detailed information on the genetic structure and transmission of pathogen species with relatively low sequence diversity, such as Mycobacterium tuberculosis [10] or Yersinia pestis [11], and for single lineages of more diverse species, for example E. coli O157:H7 [12]. However, this approach has potential limitations, particularly when applied to highly diverse species such as Campylobacter jejuni. First, because it requires careful separation of biologically informative SNPs from relatively common sequencing errors, and second because this approach typically treats dispersed and locally clustered SNPs equally even though the later are likely to be the consequence of horizontal genetic exchange.

An alternative to using a reference genome SNP-based approach is to use genes as the units of comparison. In this reference gene based approach [13], genetic variation within the sample is catalogued one gene at a time by comparison with reference gene sequences, and each new variant is assigned a unique arbitrary allele number in order of description. This whole-genome multilocus sequence typing (MLST) approach enables locus information to be defined in simultaneously in hundreds of genomes and has been implemented for genera, including Campylobacter, Staphylococcus, and Neisseria, using the web-based BIGSdb platform ( [14], [15]).

Both the SNP-based and gene-by-gene approaches rely on reference-based mapping and cannot be used to detect variation in genes that are not present in the reference isolate sequence or locus list. This is not important in analyses based on comparison of a core genome shared among all isolates, but may be less suitable for the discovery of novel genes and functions, and for the examination of the accessory genome composed of genes that vary in presence across isolates of the same population. Here we address this challenge by combining multiple reference genomes to create a single list of genes present in 7 reference genomes from which gene presence and variation can be examined in other bacterial genomes. This list of genes will be hereafter termed the ‘reference pan-genome’ - not to be confused with the true pan-genome as it is based on just 7 isolates. This technique is then applied to characterize the genetic variation in Campylobacter jejuni and Campylobacter coli.

C. jejuni and C. coli are common constituents of the commensal gut microbiota of various bird and mammal species. Human infection, typically associated with the consumption of contaminated meat or poultry [16], [17], results in symptoms of severe diarrhea and fever. Campylobacteriosis is currently the most common form of bacterial gastroenteritis in industrialized countries, accounting for an estimated 1 million cases in the UK each year [18], with an annual economic burden of £500 million [19]. In spite of its public health importance, aspects of the ecology and evolution of Campylobacter remain poorly understood, even though they could have a profound effect on transmission and human infection. For example, it is not fully explained how C. coli and C. jejuni, that have similar host niches and frequently exchange genetic material [20], [21], [22], differ in terms of their disease epidemiology. Furthermore, within C. jejuni there are lineages that are largely limited to one host and others that are frequently isolated from multiple hosts and are common in human disease [7], [23], [24]. This ecological variation will have an impact on transmission ecology in C. coli and C. jejuni and here we aim to define the genomic differences between species and lineages and identify informative epidemiological markers using a reference pan-genome approach.

Materials and Methods

Characterizing the reference pan genome

The reference pan-genome approach combines the genomes of several reference strains into a single list of genes for those isolates. This gene list was then used for genome comparisons with the larger sample collection (Figure 1). Different numbers of reference strains can be used depending on the genome size and diversity of the accessory genome, but it is important to note that the pan-genome size will influence computation time for downstream applications. For species where finished annotated genomes are not available the reference pan-genome list can be assembled from whole genome contiguous sequence files from several isolates with automatic annotation, for example using RAST [25]. The list of genes from the various reference genomes was then screened to remove genes that appeared more than once, to create the reference pan-genome. Homologous genes were defined using BLAST as those that had >70% sequence similarity over >50% of the sequence length to another gene in the list. In Campylobacter, the average core genome nucleotide sequence divergence between C. jejuni and C. coli is around 12.5% [6], corresponding to approximately 87.5% nucleotide sequence identity which is considerably higher the BLAST match criteria. Duplicate genes were then removed. The BLAST threshold can be altered depending on the bacterial species used and on the desired stringency.

Figure 1. The reference pan-genome approach.

Conceptual pipeline showing the approach to generate a list of unique genes from more than one reference strain. Step 1: Compiling a gene list from reference genomes reflecting strain diversity from public repositories such as NCBI, or after automatic annotation on assembled contiguous segments (for example using RAST). Step 2: Comparative list analysis to remove duplicate genes that show ≥70% sequence identity over ≥50% of the sequence of another gene in the list. Step 3: Creating a final reference pan-genome list.

To most effectively capture genetic variation within C. coli and C. jejuni, and therefore construct a representative reference pan-genome, lineages were selected to represent the known genetic diversity based on published genealogies (Table 1 [6], [7]). In Campylobacter, annotated published reference genomes were available for both C. coli and C. jejuni that reflected diversity. The resulting Campylobacter reference pan-genome was based upon 7 published genomes: C. jejuni subsp. jejuni strains NCTC11168 [26], 81–176 [27], 81116 [28] and M1 [29]; C. jejuni subsp. doylei strain 269.97 (Genbank: NC_009707.1); C. coli strains 76339 (Genbank: NC_022132.1) and CVM N2970 [30] (Table 1). These genomes included both C. coli and C. jejuni species, two C. jejuni subspecies (jejuni and doylei) and 6 clonal complexes defined as sharing 4 or more identical alleles at 7 MLST housekeeping gene loci.

Table 1. Publicly-available genomes used to produce a Campylobacter reference pan-genome.

Reference pan-genome analyses

Genetic variation at pan-genome loci was investigated in 192 C. jejuni and C. coli genomes from previously published studies (Table S1 [6], [7]). These isolate genomes were compared to the pan-genome locus list using BLAST. Variation within the population genomic sample was catalogued one gene at a time with gene presence defined as a match with >70% sequence identity to >50% of a locus. The result was a matrix recording the presence or absence of each gene by comparison with reference pan-genome gene sequences and each new gene sequence variant was assigned a unique arbitrary allele number in order of description. These analyses were implemented in the BIGSdb database platform [13], [14].

Rarefaction and accumulation curves

The rarefaction and accumulation curves for core and pan-genome size estimations were created using a R software script (File S1), and were inferred from a matrix of presence/absence of loci from the reference pan-genome list in all 192 genomes. The input was a matrix of gene presence and absence of the 7-isolate reference pan-genome. These genes were identified in the 192 sample genomes by BLAST comparsion (as described above). The number of core genes shared by all isolates, and the cumulative number of different genes was calculated as the number of genomes sampled increased. Randomized calculations were carried out with 100 repeats, randomizing the order of the genomes each time to obtain mean core and pan genome size estimates and standard errors.

Phylogenetic analyses

Gene homologs were aligned on a gene-by-gene basis using MUSCLE [31] and then concatenated into contiguous sequence for each isolate genome including gaps for missing nucleotides (or entire genes) (File S2). A phylogeny of core genome alignments was reconstructed using FastTree v2.1.7 [32] with an approximation of the maximum-likelihood algorithm (Figure 2). The tree was created using 61,844 variable sites for a total of 378,003 shared sites from 1,035 loci.

Figure 2. Phylogenetic tree of 192 Campylobacter genomes and novel epidemiological markers.

Maximum-likelihood tree of 130 C. jejuni and 62 C. coli genomes. Isolates belonging to C. jejuni are shown in blue, and those belonging to C. coli clade 1 are indicated in red, clade 2 in yellow, and clade 3 in green. The scale bar indicates the estimated number of substitutions per site. Example genomes from C. coli clades 1-3 and C. jejuni ST-21, ST-45, ST-353 and ST-61 clonal complexes were used to define the 7 isolate reference pan-genome gene list. The number of epidemiological markers from this list is indicated for each lineage. The asterisk indicates that markers were not found to be absolutely specific to that lineage, but were also present at low frequency in other lineages. Details about the markers are shown in Table 2 and Table 3.

Results and Discussion

Core and accessory genome variation

The 7 reference genomes used to assemble the reference pan-genome list contained 12,178 genes with a total length of more than 11 Mbp. The resulting pan-genome list, after removal of duplicate genes present in more than one reference genome, consisted of 3,933 genes, with a total coding sequence length of 3.72 Mbp (Table 2). There were 8,245 duplicate genes. Core and pan-genome sizes were estimated using rarefaction and accumulation curves (Figure 3). As expected, the number of genes shared by all isolates (Figure 3A) decreased as the number of sample genomes in the dataset increased, with 660 genes present in all 192 genomes. The estimated core genome of C. coli was 1,042 genes in 62 genomes. The estimated core genome of C. jejuni was 947 genes in 130 genomes (Figure 3A). Our estimates are consistent with previous studies where core genome size of C. jejuni was estimated to range from 847 genes [33] and 1,001 genes [34] to a maximum of 1,295 genes [29]. However, it is interesting to note that the core genome size does not reach a clear plateau, even when about 200 genomes are sampled, which indicates that if more diverse samples were added to this analysis, even fewer genes would be shared, something that has also been shown for Escherichia coli [35].

Figure 3. Rarefaction and accumulation curve estimates of C. jejuni and C. coli core and pan-genomes.

The number of shared genes (A), and the total number of genes (B and C), were determined as genome sampling increased. Comparisons were made based on matrices of gene presence/absence, derived from the reference pan-genome list, for C. coli (62 genomes), C. jejuni (130 genomes) and the two species combined (192 genomes). Randomized genome sampling was carried out 100 times to obtain the average number of genes for each sample comparison number (plain lines) and standard deviations (dotted lines). Pan-genome size estimates were calculated using the reference pan-genome (B) or the NCTC11168 annotation (C).

Table 2. Functional categories of genes present in the reference pan-genome and in the reference genome of C. jejuni NCTC11168.

The pan-genome size was characterized for C. jejuni and C. coli by quantifying the number of reference pan genome genes as the number of sample genomes increased. The total pan-genome for 130 C. jejuni genomes contained 3,648 genes (Figure 3B). Ninety-two percent of this total was identified from comparison of only 7 genomes (3,388±31 genes), and 99% of the pan-genome size estimate was reached after comparing 75 genomes (Figure 3B). The pan-genome was smaller in C. coli with an estimated 3,520 genes identified in 62 sample genomes. A very similar proportion of the pan-genome genes were detected by comparison of 7 genomes (93%, 3267±50 genes), and comparison of 40 genomes captured 99% of the total reference pan-genome (Figure 3B). When a single reference genome comparison was used, rather than the 7 isolate reference, the pan genome was greatly underestimated (Figure 3C). Almost all genes present in 11168 were found to be present in just 5 sample genomes of C. jejuni and C. coli (Figure 1C).

Comparative genomics approaches based on the alignment to a single genome will ignore genetic variation that is not present in this reference genome. For example, the C. jejuni strain NCTC11168 - which has a well annotated genome [26] - is commonly used in comparative genome studies [6], [7]. This strain belongs to the ST-21 clonal complex, and while 93% (1,521/1,623) of its genes are present in all ST-21 clonal complex isolates, this number drops to 88% (1,424/1,623) for ST-45 clonal complex isolates and 69% (1,121/1,623) for C. coli Clade 1 isolates. Core genome analyses may not be affected by this issue, as only the shared genes between all strains are examined. However, 263 accessory genes, identified using the reference pan-genome approach in this study, are absent in C. jejuni 11168 but present in the ST-45 complex. Amongst this accessory genome there could be genes associated with important adaptive traits such as virulence or colonization factors linked to metabolism or host-association [7], [36].

Variation in functional gene categories

By investigating variation in functional categories of genes in the reference pan-genome, some inference can be made about putative phenotype differences between species and lineages. To maximize the available genome annotation information beyond that which is available for the C. jejuni NCTC11168 isolate [26], all the genes comprising the pan-genome were concatenated and submitted to the RAST automatic annotation server, to attribute putative function (Table 2). From a total of 1,623 genes, 1,431 (88%) were assigned to functional categories. Among these, the categories with most genes were associated with protein metabolism, the cell wall and capsule, cofactors and vitamins and amino-acids and derivatives (Table 2). The proportions of the various functional categories attributed by RAST to the reference pan-genome list were different from those of C. jejuni NCTC11168 (χ2 = 40.09, d.f. = 25; p = 0.0286). Higher proportions for genes involved in cell wall and capsule and virulence factors were found in the pan-genome compared to C. jejuni NCTC11168, indicating that these genes functions are better represented in the pan-genome gene list.

Lineage specific genes in C. jejuni and C. coli

Comparison of patterns of gene presence/absence in the pan-genome genes of 192 C. jejuni and C. coli genomes was performed to identify genes that segregated by species or lineage. Segregation was either: complete, with genes present in one group and absent in the other; or frequency dependent, where genes were significantly more frequent in one lineage. Consistent with the aim of discovering informative epidemiological markers, we focused on accessory genes that were the most specific to each of the 7 examined lineages of C. coli and C. jejuni. Of the 3,933 genes of the pan-genome, there were 20 genes specific to each of the 7 lineages (Figure 2).

Forty-eight genes were found to be differentially present in the species C. jejuni and C. coli (Table 3). It is interesting to note that the genes present in C. coli (62/62, 100%) but not C. jejuni (0/130, 0%) are all present in the C. coli 76339 reference genome used to compile the pan-genome, and none were present in the C. jejuni reference strain NCTC 11168. This highlights the fact that if a typical single strain reference approach, based on a NCTC 11168, had been used to identify genetic markers specific to C. coli, all of these genes would have been missed. Twenty-seven genes were found to be present in all C. jejuni (130/130, 100%) and absent in all C. coli (0/62, 0%).

Table 3. Prevalence of C. coli and C. jejuni associated genes from a comparison of 192 genomes.

There were several genes that segregated according to 3-clade structure in C. coli [37] (Figure 2, Table 4). One gene (CcCVMN29710_G157_03450), annotated as encoding a reductase involved in fatty acid biosynthesis, was present in all C. coli clade 1 isolates (47/47) but was absent from isolates in the other C. coli clades (0/9) and from C. jejuni (0/130). Similarly, one gene (Cc76339_10830), encoding a hypothetical protein was present in C. coli clade 2 (4/4) but absent elsewhere (0/188). Three genes were present in all 5 genomes of C. coli clade 3 and absent in all other C. coli (0/57) and most C. jejuni (2/130) except for 2 environmental isolates. These genes putatively encoded a biotin sulfoxide reductase, a secreted serine protease and a cytochrome C-type periplasmic protein,

Table 4. Lineage associated genes in C. coli and C. jejuni from a comparison of 192 genomes.

Within C. jejuni, accessory gene specificity for particular lineages was not complete with every gene present in high frequency in one of the major lineages also being present in minor lineages. This is not surprising as the genetic distance between clonal complexes is less than between species or the C. coli clades leading to increased gene flow because of the homology dependence of recombination [38]. There were, however, genes that could be associated with the ST-21 and ST-45 clonal complexes which are frequently isolated from multiple hosts [39], and the ST-353 and ST-61 complexes that are more host restricted.

Eight genes were present in 41/41 ST-21 clonal complex isolates, absent in C. jejuni ST-45, ST-61 and ST-353 complexes (0/89), but present in C. coli clade 1 (up to 43/47 isolates). This is consistent with previously reported gene flow between C. coli and C. jejuni [6], [21]. These genes were also present in less frequent lineages of our dataset, notably ST-257 (in all 3 isolates) and ST-354 (in all 3 isolates) clonal complexes. One gene (cj81116_1569), encoding a putative periplasmic protein, was present at high frequency in the ST-45 clonal complex (28/28 isolates) and was absent from all other C. jejuni C. and C. coli isolates. The genes that are differentially present in ST-21 and ST-45 clonal complexes, provide support to the idea that while these lineages occupy the same hosts, they may have characteristics that differentiate them.

There were fewer lineage-specific genes in the chicken and cattle host associated ST-353 and ST-61 clonal complexes. Three genes were found to be present in all ST-353 clonal complex isolates (7/7, 100%) and not in the other most common C. jejuni clonal complexes in our dataset (Table 3). They were, however, present in 11(32%) of C. coli Clade 1 isolates and in the ST-257, ST-354, ST-508 and ST-573 clonal complexes. An interesting observation is that when these three genes were present in C. jejuni, it was mostly in isolates from chicken (17/21, 81%). As the ST-353 clonal complex is a chicken associated lineage [20], it can be expected that genes associated with this clonal complex may also be present in other chicken-associated lineages. Three other genes were found to be associated with the ST-61 clonal complex (Table 4), also without absolute specificity as the genes were commonly found in C. coli and some other C. jejuni clonal complexes.

Functional grouping of discriminating genes

Campylobacter uses short-chained fatty acids as nutrients, which are typical by-products of acetate and lactate metabolism by many gastrointestinal bacteria [40]. As in other bacteria [41], [42], one of the β-oxidation by-products of fatty acids chains is metabolized via the methylcitrate cycle. Interestingly, 4 genes specifically found in C. coli and not C. jejuni were involved in the methylcitrate cycle (Table 3), which could highlight a preference or enhanced ability of C. coli strains to grow on odd-chained fatty acids compared to C. jejuni. With more focused development, this observation could potentially lead to the development of specific fatty-acid-rich media designed to discriminate more efficiently between C. jejuni and C. coli, or to improve isolation frequency of C. coli in the laboratory.

Another functional characteristic that differed between the two species was described by genes involved in copper and iron acquisition and homeostasis, which absolutely segregated between C. jejuni and C. coli in our dataset (Table 3). This could indicate that while these functions are important for both species, the genes involved in them are divergent, maybe indicative of convergent evolution, or compensatory systems. Additionally, we observed that the genes preferentially found in the ST-21 complex of C. jejuni were involved in the metabolism of various compounds (Table 3) such as L-rhamnose, L-fucose or aromatic compounds, as previously suggested [43]. The metabolism of L-fucose has been shown to be associated with gastrointestinal fitness in C. jejuni [36], but also enriched in ST-21 clonal complex isolates [43] and in introgressed Clade 1 C. coli [6]. This could potentially indicate that isolates from the ST-21 complex could have a higher metabolic plasticity compared to others.


The reference pan-genome approach, in this case based on 7 diverse C. jejuni and C. coli isolate genomes, was useful for investigating patterns of species- and lineage-specific genetic variation. Enhanced estimates of the core and accessory genome size were possible and several genes that were differentially present in the species and lineages were identified. The genetic segregation varied among lineages and was more pronounced for the 3 C. coli clades than within C. jejuni, where absolute segregation was rarely observed because of frequent genetic exchange. However, it was possible to identify genes that may provide information about some of the putative differences between species, clades and clonal complexes. As well as informing studies based on gene function, these genes can potentially act as epidemiological markers for differentiating strains.

Supporting Information

Table S1.

List of 192 genomes used in this study.


File S1.

Scripts to calculate core genome rarefaction and pan-genome accumulation. The file contains R scripts and an example input file.


File S2.

Core genome alignment (FASTA format) for the 192 genomes used in this study. Core genes shared by all 192 isolates were aligned in a gene-by-gene manner (see methods) and concatenated.



We acknowledge Dr. Caroline P.A. de Haan, Prof. Marja-Liisa Hänninen and Dr. Mirko Rossi (University of Helsinki) for their kind help and access to unpublished genomes.

Author Contributions

Conceived and designed the experiments: GM SKS. Performed the experiments: GM KY LM. Analyzed the data: GM. Contributed reagents/materials/analysis tools: BP MCJM KAJ. Wrote the paper: GM SKS.


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