Skip to main content
Microbial Genomics logoLink to Microbial Genomics
. 2025 Jul 10;11(7):001446. doi: 10.1099/mgen.0.001446

Genomic insights into the diversity, antimicrobial resistance and zoonotic potential of Campylobacter fetus across diverse hosts and geographies

Ellis Kobina Paintsil 1,2,*, Cynthia Kyerewaa Adu-Asiamah 3, Kennedy Gyau Boahen 4, Charity Wiafe Akenten 3, Alexander Kwarteng 5, Stefan Berg 2, Kwasi Obiri-Danso 6, Jürgen May 7,8,9, Denise Dekker 2, Linda Aurelia Ofori 6
PMCID: PMC12244366  PMID: 40638214

Abstract

Introduction. Campylobacter fetus causes reproductive diseases in livestock and can lead to zoonotic infections such as bacteraemia, particularly in immunocompromised individuals. Despite its significance, its genomic characteristics remain poorly understood. This study analysed 114 publicly available C. fetus genomes to provide global insights into genetic diversity, antimicrobial resistance (AMR) and zoonotic risk.

Results. A total of 32 distinct sequence types (STs) were identified across 111 of the 114 C. fetus genomes, spanning 6 continents and diverse hosts (cattle, humans, sheep and reptiles). The majority of strains from cattle (75.6%, n/N=34/45) were assigned to ST-4, which was the most prevalent overall (n=45), while human-associated genomes exhibited the highest diversity with 16 STs. C. fetus subsp. venerealis (Cfv) and its biovar intermedius (Cfvi) genomes clustered closely, forming distinct branches at the biovar level; however, six Cfv genomes were located within Cfvi clades, suggesting a shared ancestry. C. fetus subsp. testudinum (Cft), primarily isolated from humans (60.0%, n/N=18/30), exhibited a more diverse genetic profile, with 20 STs. Cfv from North America and Cfvi from South America formed distinct geographic clusters, while C. fetus subsp. fetus genomes showed no clear geographic patterns, indicating global spread. Pangenomic analysis revealed substantial variation in gene presence/absence in Cft. Five AMR genes were detected, with tet(O) (n=3) being the most common. A total of 220 plasmid contigs were identified across 47 genomes, predominantly in Cfvi (66.8%, n/N=147/220) and Cfv (29.1%, n/N=64/220). Horizontal gene transfer analysis identified 140 genomic islands across 41 genomes, and virulence factor analysis revealed cheY as the sole conserved virulence gene across 35 genomes.

Conclusion. These findings provide critical insights into the genomic diversity, zoonotic potential and global distribution of C. fetus, emphasizing the need for integrated genomic and epidemiological strategies to assess its impact on human and animal health.

Keywords: antimicrobial resistance (AMR), Campylobacter fetus, comparative genomics, horizontal gene transfer (HGT), zoonotic transmission


Impact Statement.

Campylobacter fetus is a globally significant zoonotic and veterinary pathogen, yet its genomic diversity, antimicrobial resistance (AMR) mechanisms and evolutionary dynamics remain poorly understood. This study provides a comprehensive genomic analysis of C. fetus, utilizing 114 publicly available genomes from diverse hosts across 6 continents. Our findings challenge current assumptions about AMR evolution in C. fetus, revealing horizontal gene transfer as a key driver of resistance. While AMR genes were identified, their limited presence – combined with the predominance of predicted plasmid contigs in C. fetus subsp. venerealis and C. fetus subsp. venerealis biovar intermedius – suggests that plasmids may play a significant role in the adaptation or pathogenicity of C. fetus. These insights highlight the urgent need for global genomic surveillance to track AMR emergence, improve diagnostics and inform One Health strategies for mitigating C. fetus infections in both humans and animals.

Data Summary

The authors confirm that all supporting data have been provided within the article or through supplementary data files.

Introduction

Campylobacter fetus is a bacterial species comprising three recognized subspecies: C. fetus subsp. fetus (Cff), C. fetus subsp. venerealis (Cfv) and C. fetus subsp. testudinum (Cft). These subspecies are primarily distinguished by host specificity, ecological niche, pathogenicity and genetic features [1]. Cff has a broad host range, including sheep, cattle and humans, where it causes systemic infections and reproductive disorders, such as ovine abortion and bacteraemia in immunocompromised individuals [2,4]. Cfv is host restricted to cattle, colonizing the reproductive tract and causing bovine genital campylobacteriosis, a major concern in livestock due to its impact on fertility and early embryonic loss ([5,6]. Cft, primarily associated with reptiles, is an emerging zoonotic pathogen occasionally implicated in human infections, especially in individuals with close reptile contact [7,8]. Although traditionally considered a zoonotic pathogen, recent genomic evidence suggests that some C. fetus lineages may have originated in humans before adapting to livestock during domestication [9]. The Cfv lineage includes a phenotypic variant, C. fetus subsp. venerealis biovar intermedius (Cfvi), which exhibits distinct biochemical traits but lacks sufficient genetic divergence to warrant classification as a separate subspecies [10]. Differentiation among these subspecies requires multilocus sequence typing (MLST), comparative genomic analysis and host-adaptive molecular markers, as standard biochemical tests alone are often insufficient for accurate classification [10].

Whole-genome sequencing has become a crucial tool for exploring the genetic diversity of Campylobacter spp., enabling the identification of sequence types (STs), antimicrobial resistance genes (ARGs), virulence factors and potential zoonotic transmission routes [11]. Previous studies have highlighted the presence of resistance to several antibiotics, including tetracycline, streptomycin and fluoroquinolones, and they have also begun to uncover the role of virulence factors in C. fetus pathogenicity [12,15]. However, the full extent of its genomic diversity, the mechanisms underlying antimicrobial resistance (AMR) and its zoonotic potential remain poorly understood, particularly in the context of global surveillance and cross-species transmission. Although C. fetus harbours several ARGs [16], comprehensive genomic studies are urgently needed to uncover the mechanisms driving AMR dissemination and the factors contributing to the persistence and spread of resistance genes. The role of mobile genetic elements (MGEs), such as plasmids and genomic islands (GIs), in C. fetus is an emerging area of interest, with preliminary findings indicating their potential significance in virulence, immune evasion and AMR [17].

Despite the recent advancements in understanding C. fetus [14,18], key questions regarding its genomic diversity, AMR and zoonotic potential largely remain unresolved. Further, more comprehensive insights into the mechanisms driving horizontal gene transfer (HGT), the role of MGEs in AMR dissemination and the genomic features underlying host adaptation and pathogenicity are still lacking [13,17, 19]. This study leverages publicly available C. fetus genomes to conduct an in-depth comparative genomic analysis, providing a global perspective on its genetic diversity, AMR patterns and zoonotic risk across diverse hosts and geographies. By addressing these critical gaps, our findings will advance the understanding of C. fetus as a significant zoonotic and veterinary pathogen, offering valuable insights for global surveillance, public health strategies and animal disease management.

Methods

Strain selection

For a comprehensive genomic analysis of C. fetus, genomes were retrieved from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) server (https://www.bv-brc.org/, last accessed on 7 December 2024). The search was performed using the term ‘C. fetus’, and the filter was set to include both complete genomes and whole-genome shotgun sequences. To ensure high-quality data, only genomes flagged as good quality by the BV-BRC database were considered. Additionally, genomes were cross-referenced with NCBI taxonomy data to confirm accurate species identification (https://www.ncbi.nlm.nih.gov/datasets/genome/?taxon=196). A total of 114 C. fetus genomes, isolated from various hosts (cattle, humans, sheep and reptiles) and geographically distributed across 6 continents, met the inclusion criteria and were retrieved for comparative genomic analysis (Table S1).

Genome annotation and taxonomic identification

All C. fetus genomes were annotated using Prokka v1.14.6 [20], with default parameters. The genome annotations were outputted in GFF3 format for downstream analysis. Prokka was employed to predict genes, rRNA, tRNA and other genomic features, and the resulting files were used for subsequent analyses. Taxonomic classification of the genomes was performed using GTDB-Tk (v2.3.2) to assign objective taxonomic classifications based on the Genome Taxonomy Database (GTDB) [21]. Subspecies assignments of Cff, Cfv and Cfvi were further refined through phylogenetic analysis and the use of secondary data [18].

In silico MLST

In silico MLST was performed using the open-source tool MLST (https://github.com/tseemann/mlst), which queries the PubMLST database [22]. The tool identifies STs by aligning the genomic data to loci defined in the PubMLST database, using default parameters for allele and ST determination.

Phylogenetic analysis

Phylogenetic analysis was performed using the Newick file generated from the Roary output, which was visualized as a circular phylogenetic tree using iTOL v6.5.1 [23]. The tree was annotated with information on genomic features and geographical origin to investigate the evolutionary relationships between the genomes.

Pangenome analysis

Pangenome analysis was conducted using Roary v3.13.0 [24] with a minimum sequence identity threshold of 90% for blastp. Genes were classified into core (present in ≥99% of genomes), soft-core (95–99%), shell (15–95%) and cloud (0–15%) categories. The gene presence/absence matrix generated by Roary was visualized using the Phandango v1.3.1 web-based visualization tool (www.phandango.net) [25].

In silico analysis of AMR, virulence, plasmids and MGEs

AMR genes and virulence factors were identified using Abricate v1.0.1 (https://github.com/tseemann/abricate) with the ResFinder [26] and VFDB [27] databases for AMR and virulence gene prediction, respectively. Default parameters were used for gene identification, and only hits with ≥90% identity and ≥80% coverage were considered for downstream analysis. Additionally, AMRFinderPlus v4.0 (https://github.com/ncbi/amr/wiki), with database v2024-07-22.1, was used to identify Campylobacter-specific point mutations in the assemblies [28]. The genomes identified to be harbouring AMR genes were further visualized using Proksee (https://proksee.ca/) [29]. Putative HGT events were identified using Alien Hunter [30].

GIs were predicted using IslandPath-DIMOB v1.0.0 (https://github.com/brinkmanlab/islandpath), a tool designed based on dinucleotide biases and the presence of mobility genes [31]. The tool was run via the command line, with the default parameters and assembled genome sequences in FASTA format as input. GIs were identified based on their characteristic features, such as the presence of mobile elements and atypical dinucleotide frequencies.

Plasmid contigs were predicted using RFPlasmid v0.0.18 (https://klif.uu.nl/rfplasmid) with a plasmid vote score ≥0.6 [32]. The tool identifies chromosomal and plasmid replication genes using CheckM (https://ecogenomics.github.io/CheckM/) and DIAMOND (https://github.com/bbuchfink /diamond) blast and assesses pentamer frequencies and contig sizes. RFPlasmid shows high sensitivity (up to 99% accuracy) and low error rates (0.002–4.66%) for contigs >3 kb. The model was trained on plasmid and chromosomal sequences from 19 species, including Campylobacter, and validated with known chromosomal and plasmid contigs from various bacteria.

Results

Genome data

A total of 114 high-quality C. fetus genomes were retrieved from the BV-BRC database, including 23 complete genomes and 91 whole-genome shotgun sequences (draft genomes). Only genomes with high completeness (>95%) and low contamination (<5%) based on CheckM estimates were included (Table S1). Excluding one outlier (strain RUG14080), genome sizes ranged from 1.7 to 2.1 Mb, with GC content varying between 32.9 mol% and 34.4 mol%. The outlier genome, strain RUG14080, exhibited a genome size of 1.5 Mb and an unusually high GC content of 47.6 mol%, significantly higher than the other genomes. These genomes were isolated from various hosts, with host data available for 79 genomes: the majority originated from cows (55.7%, n=44), followed by humans (38.0%, n=30), sheep (5.1%, n=4) and reptiles (1.3%, n=1). Geographical data were available for 104 genomes, with the following distribution: Europe (30.8%, n=32), North America (24.0%, n=25), South America (15.4%, n=16), Asia (15.4%, n=16), Oceania (9.6%, n=10) and Africa (4.8%, n=5). Further details about these genomes are provided in Table S1.

Taxonomic identification and geographic/host distribution of MLST types

Taxonomic classification of the 114 C. fetus genomes was performed using GTDB-Tk (v2.3.2), which accurately classified all Cft genomes (n=35) and the remaining genomes as C. fetus (Table S2). Subspecies assignments for all C. fetus genomes were further refined using secondary data [18] and validated through phylogenetic analysis. However, subspecies identification for three genomes could not be confirmed with the current analysis and available data. Of these, two genomes (SRR5279288_bin.84_CONCOCT _v1.1_MAG and first) were confirmed as C. fetus using GTDB-Tk, while the third genome (RUG14080) was identified as a different Campylobacter species (Fig. S1).

A total of 32 distinct STs were identified across 111 of the 114 confirmed C. fetus genomes analysed, spanning 6 continents and multiple host species (Table S3). The distribution varied among the three subspecies. Although Cft is typically associated with reptiles, it was predominantly isolated from humans (51.4%, n/N=18/35) and exhibited the highest ST diversity (n=20). Overall, ST-4 was the most prevalent, representing 45 genomes, followed by ST-3, observed in 8 genomes. A substantial proportion (90.9%, n/N=40/44) of Cfv and its biovar intermedius (Cfvi) were assigned to ST-4, with 75.6% (n/N=34/45) originating from cattle. Europe exhibited the highest ST diversity (n=18), while South America and Africa each showed the lowest diversity (n=1), with only ST-4 detected in both regions. In North America, ST-6 and ST-15 were the second most frequently occurring STs, each accounting for 17.4% (n/N=4/23) of the genomes. Among genomes with host metadata, those associated with humans displayed the greatest diversity, encompassing 16 distinct STs.

Phylogenetic and pangenomic analyses

The phylogenetic tree, based on core genome SNPs (1,365 genes), illustrates the evolutionary relationships among C. fetus subspecies, revealing distinct clustering patterns that highlight genetic diversity and interrelationships (Fig. 1). All Cfv and Cfvi genomes clustered closely despite being isolated from five different continents. While Cfv and Cfvi genomes formed distinct branches at the biovar level, six Cfv genomes were located within Cfvi clades, suggesting a potential shared evolutionary lineage (Fig. S2). In contrast, Cft genomes, which exhibited 20 different STs, clustered together in closely associated clades, entirely distinct from the Cfv, Cfvi and Cff groups. Human-associated genomes were primarily distributed across the Cff and Cft clades, with 60.0% (n/N=18/30) clustering with Cft and the remaining 40.0% (n/N=12/30) with Cff. Geographical associations were observed for Cfv and Cfvi genomes. All Cfv genomes from North America clustered on a single branch, while most Cfvi genomes from South America formed a closely related cluster. In contrast, Cff genomes displayed no clear geographical patterns, as branches often contained strains from multiple continents, indicating a more dispersed global distribution.

Fig. 1. Phylogenetic tree illustrating the evolutionary relationships among the 114 C. fetus genomes obtained from the BV-BRC database. The tree was constructed using the Newick file generated by Roary, based on core genome SNP analysis, and visualized using the iTOL platform with metadata layers for enhanced interpretability. The inner ring represents the geographic distribution of isolates by continent, with the following colours: Africa (blue), Asia (orange), Europe (green), North America (brown), Oceania (pink) and South America (cyan). The second ring indicates host origins, represented by the following colours: human (red), cow (yellow-green), sheep (purple) and snake (grey), with blank spaces indicating genomes lacking host or geographic metadata. The third ring displays MLST data, and the outer ring depicts taxonomic classification at the species and subspecies levels (Cf, C. fetus), with individual strain names displayed in superscript.

Fig. 1.

The analysis of 114 C. fetus genomes identified a total of 9,409 genes, of which 849 were core genes (469 core and 380 soft-core genes) present in at least 95% of the genomes. The remaining 8,560 genes were classified as accessory genes, consisting of 1,866 shell genes (present in 15–95% of strains) and 6,694 cloud genes (present in fewer than 15% of strains). The gene presence/absence matrix revealed distinct patterns among the taxonomic groups. The most pronounced clustering was observed within Cft, where a subset of genes was either unique to Cft or broadly distributed across other subspecies but largely absent in Cft. This distinct gene presence/absence pattern in the pangenome matrix resulted in the segregation of genes exclusive to Cft, as well as those absent in Cft but present in other C. fetus subspecies, highlighting its genomic divergence (Fig. 2).

Fig. 2. Pangenome analysis of 114 C. fetus genomes. (a) Dendrogram showing the clustering of 114 genomes based on accessory gene distribution, with metadata layers colour coded to indicate host species, geographic origin, MLST and subspecies (Cf, C. fetus). (b) Roary matrix representing the complete genetic profile of each genome based on the presence/absence of core and accessory genes.

Fig. 2.

ARG, virulence factors and MGE

ARG profiling

ARG analysis across the 114 C. fetus genomes was conducted using Abricate, revealing the presence of 5 distinct ARGs. The most commonly identified gene was tet(O) (n=3), detected exclusively in human isolates, which confers resistance to tetracyclines. The distribution of these ARGs was geographically diverse, with genes found in genomes from Asia (n=3) and North America (n=2) (Table 1). The two North American strains each harboured two distinct AMR genes. A comprehensive summary of the ARGs, their genomic locations and corresponding accession numbers is provided in Table S4. Additionally, using AMRFinderPlus, a point mutation (rpsL_K43R) associated with streptomycin resistance in the rpsL gene was detected in strain CFF09A980.

Table 1. Distribution and frequency of ARGs in C. fetus genomes.

Gene Antibiotic resistance conferred Host Region Frequency
tet(O) Doxycycline, tetracycline, minocycline Human Asia, North America 3
tet(44) Doxycycline, tetracycline, minocycline Cow North America 1
ant(6)-Ib Streptomycin Cow North America 1
aph(3′)-III Amikacin na North America 1
lnu(C) Lincomycin na Asia 1

na, not available.

HGT and GIs

An in-depth analysis of the five genomes harbouring AMR genes using Proksee revealed several HGT regions. The CARD RGI [Comprehensive Antibiotic Resistance Database (CARD) Resistance Gene Identifier (RGI)] further elucidated the resistance mechanisms associated with these ARGs, highlighting a diverse array of resistance pathways (Fig. 3). A total of 140 GIs were identified across 41 genomes, emphasizing the widespread presence of HGT elements. The distribution of GIs varied across subspecies: Cff genomes contained the highest proportion (46.3%, n=19), while Cft genomes exhibited the lowest number (12.2%, n=5) (Table S5).

Fig. 3. Circular genome representation of the reference genome CFF00A031 compared with five genomes harbouring AMR genes. The circular visualization highlights key genomic features across multiple rings. Starting from the innermost ring: (1) predicted resistance mechanisms (pink), (2) GC skew (purple and green), (3) GC content (grey), (4 and 5) coding sequences (CDS) (two blue rings) and (6) genome backbone (solid grey). Followed by the blastn comparison results of the five AMR-harbouring genomes in the following order: SRR5279288 (coral), 772 (brown), wqj7 (greenish-yellow), D5375 (light blue-green) and CFF09A980 (black). HGT events are indicated in the next ring marked in cyan, with the outermost ring representing the predicted AMR genes highlighted in pink.

Fig. 3.

Virulence factors

A virulence factor analysis of all 114 C. fetus genomes identified cheY, a key gene in the chemotaxis signalling pathway, as the sole virulence factor present. This gene was found in 35 strains across various hosts and regions (Table S6).

Plasmid prediction

RFPlasmid analysis predicted a total of 220 plasmid contigs across 47 of the 114 (41.2%) analysed C. fetus genomes. The majority of these genomes were Cfvi (51.1%, n/N=24/47) and Cfv (31.9%, n/N=15/47), and accordingly, the majority of predicted plasmid contigs were found in Cfvi (66.8%, n/N=147/220) and Cfv (29.1%, n/N=64/220). Plasmid contig lengths ranged from 0.99 to 91.4 kb, with an average of nine plasmid marker genes per contig. Most predicted plasmids had a high proportion of plasmid marker genes (median 0.89) and low proportions of chromosomal marker genes (median 0.36), supporting their classification as plasmids. Interestingly, all of the predicted plasmid contigs showed no significant hits against the PlasmidFinder database, with only four (1.8%, n/N=4/220) contigs showing weak similarity, with their identity percentages ranging from 31.2% to 38.8% (Table S7).

Discussion

In this study, we analysed 114 publicly available C. fetus genomes and observed pronounced geographic and host-associated variation in ST distribution. Europe exhibited the greatest ST diversity, while South America and Africa were dominated by ST-4, primarily among cattle-associated Cfv and Cfvi isolates. These genomes originated from three BioProjects, which may reflect limited regional surveillance or localized outbreaks rather than a true lack of diversity [2,33,35]. In contrast, the broad ST diversity among human-associated Cft, spanning multiple continents, highlights its ecological plasticity and zoonotic potential [36]. These patterns suggest that the global distribution of C. fetus is shaped by both host specificity and anthropogenic factors such as livestock trade [37], as well as by the species’ intrinsic capacity for homologous recombination and interspecies transmission [38]. While Cfv and Cfvi exhibited clonal population structures and regional clustering – likely reflecting host restriction and vertical transmission – Cff and Cft displayed broader dispersal patterns and greater ST diversity, consistent with wider host ranges and increased recombination rates [7]. These dynamics resemble the population structure of Helicobacter pylori, where regional lineages are shaped by recombination and human migration [39]; however, the zoonotic and environmentally resilient nature of C. fetus indicates that its global spread could be more influenced by cross-species transmission and agricultural networks than by human ancestry alone.

Our phylogenomic analysis revealed subspecies-specific clustering patterns that offer insight into the evolutionary and ecological dynamics of C. fetus. The tight clustering of Cfv and Cfvi genomes across geographically diverse regions suggests limited genetic exchange and strong host adaptation, likely driven by conserved genomic features linked to immune evasion or metabolic specialization [18,40]. However, the placement of six Cfv genomes within Cfvi clades raises questions regarding the influence of HGT, recombination events or potential biovar misclassification [17,18]. This genomic overlap challenges traditional subspecies boundaries and suggests that current classification frameworks may not fully capture the complexity of genetic relationships between Cfv and Cfvi, potentially due to shared evolutionary origins and ecological adaptations [41,42]. In contrast, Cff genomes were more phylogenetically dispersed, consistent with reduced host specificity and greater environmental resilience [43,44]. Pangenome analysis revealed distinct gene presence/absence profiles within Cft, including unique gene sets absent from other subspecies, further supporting its genomic distinctiveness. The high genomic and ecological plasticity of Cft is also evident in its capacity to colonize diverse hosts, supported by its pronounced gene content variability and ST diversity [45,47]. The distribution of human-associated strains across both Cff and Cft clades highlights their zoonotic potential and suggests independent evolutionary pathways for interspecies transmission [38]. Together, these findings underscore the complex interplay of evolutionary, ecological and anthropogenic forces shaping C. fetus populations and emphasize the need for integrative genomic and epidemiological surveillance to refine taxonomy and inform public health strategies.

The ARG profiling across the 114 C. fetus genomes in this study revealed a relatively limited but geographically diverse presence of ARGs, highlighting the potential for AMR spread within this species. The presence of ARGs in C. fetus strains isolated from both human and animal hosts emphasizes their ability to acquire and maintain resistance traits across different host species [16,48]. Given the limited number of strains harbouring these genes, further surveillance of C. fetus in diverse geographic regions and host populations is crucial to better understand the dynamics of AMR dissemination and its potential public health impact. Several HGT regions were identified in the analysed C. fetus genomes, which provides further insight into the mechanisms underlying ARG acquisition and dissemination [19]. In line with the current findings, an earlier study suggested that C. fetus may acquire resistance genes through genetic exchange with other micro-organisms [49]. The widespread distribution of GIs underscores the importance of HGT in shaping the genetic landscape of C. fetus, suggesting that species may be more prone to acquiring foreign genetic material, potentially enhancing its adaptability and survival in different ecological niches [50]. Predicted plasmid contigs were identified in nearly half of the C. fetus genomes, with a notable predominance in Cfvi and Cfv. This pattern suggests a potential role for plasmids in the adaptation or pathogenicity of venerealis-associated subspecies [13], possibly due to reduced barriers to exogenous DNA uptake in Cfv/Cfvi [51]. The absence of significant matches in the PlasmidFinder database points to a diverse and largely uncharacterized C. fetus plasmidome, underscoring the need for expanded reference datasets. Further investigation into the role of plasmid and other MGEs in the evolution of AMR in C. fetus is essential to unravel the full scope of resistance mechanisms in this pathogen.

Virulence factor analysis revealed that C. fetus strains were only harbouring cheY, a key gene involved in the chemotaxis signalling pathway, which plays a crucial role in bacterial motility and host colonization [52]. This gene was present in 35 strains across a range of hosts and geographic regions, suggesting its potential role in facilitating C. fetus adaptation to diverse environments and hosts. The widespread distribution of cheY in both human and animal-associated strains highlights its importance in the pathogenicity of C. fetus and its ability to colonize different host species [53,54]. However, the limited number of virulence factors identified in this study suggests that C. fetus may rely on other yet-to-be-identified factors for its pathogenicity, and further studies are warranted to explore the full repertoire of virulence determinants in this species. The presence of virulence factors in C. fetus strains from both human and animal hosts also reinforces the zoonotic potential of this pathogen, suggesting that cross-species transmission may be facilitated by the presence of conserved virulence traits [55,56]. Additionally, the identification of a single virulence factor in this study contrasts with the more complex virulence profiles observed in other Campylobacter spp., highlighting the unique pathogenic strategies employed by C. fetus. Understanding the role of cheY and other potential virulence factors in C. fetus pathogenicity will be essential for developing targeted interventions to mitigate the impact of this pathogen on both human and animal health.

While this study provides valuable insights into the genomic diversity, plasmid content, AMR and virulence potential of C. fetus, several limitations should be acknowledged. First, the relatively small sample size of 114 genomes, although geographically and host-diverse, may not fully capture the species’ genetic variability across different environments or over time. While RFPlasmid offers high sensitivity in plasmid prediction, its accuracy decreases for short contigs and when reference data updates are not performed regularly. Predictions should be regarded as potential plasmids requiring experimental validation. Additionally, reliance on publicly available genomic data introduces potential biases, as these genomes may not represent the entire C. fetus population, especially in under-sampled regions or host species. The absence of detailed phenotypic data, such as antimicrobial susceptibility or virulence assays, limits the ability to correlate ARGs and virulence factors with pathogenicity. Finally, while subspecies classification was based on GTDB-Tk, secondary data and phylogenetic analysis, confirmation using more robust methods like Kraken2 would have ensured more precise subspecies identification. Future studies incorporating larger, more diverse datasets, phenotypic data and longitudinal sampling are essential for a comprehensive understanding of C. fetus evolution, host adaptation and AMR mechanisms.

Conclusion

This study provides a comprehensive genomic analysis of C. fetus, revealing significant genetic diversity, AMR profiles and zoonotic potential across diverse geographic regions and host species. Our findings highlight the complexity of C. fetus subspecies classification, with evidence of HGT and possible subsp. misclassification, suggesting the need for more robust in silico methods for subspecies identification. While ARGs were identified, their limited presence, combined with the predominance of predicted plasmid contigs in Cfvi and Cfv, suggests that plasmids could play a significant role in the adaptation or pathogenicity of C. fetus. Despite relying on publicly available genomes and the absence of phenotypic data, our findings emphasize the importance of integrating genomic, epidemiological and phenotypic approaches to better understand C. fetus evolution, host adaptation and AMR mechanisms, informing strategies to mitigate its impact on human and veterinary health.

Supplementary material

Uncited Supplementary Material 1.
mgen-11-01446-s001.pdf (325.2KB, pdf)
DOI: 10.1099/mgen.0.001446
Supplementary Material 2.
mgen-11-01446-s002.xlsx (120.4KB, xlsx)
DOI: 10.1099/mgen.0.001446

Acknowledgements

We thank the teams and developers responsible for maintaining public genomic databases and bioinformatic tools, whose efforts were integral to this study.

Abbreviations

AMR

antimicrobial resistance

ARG

antimicrobial resistance gene

BV-BRC

Bacterial and Viral Bioinformatics Resource Center

Cff

Campylobacter fetus subsp. fetus

Cft

Campylobacter fetus subsp. testudinum

Cfv

Campylobacter fetus subsp. venerealis

Cfvi

Campylobacter fetus subsp. venerealis biovar intermedius

GIs

genomic islands

HGT

horizontal gene transfer

MGE

mobile genetic element

MLST

multilocus sequence typing

ST

sequence type

Footnotes

Funding: The authors received no specific grant from any funding agency.

Consent for publication: Not applicable

Author contributions: Conceptualization: E.K.P., L.A.O., K.O.-D. and D.D. Methodology: E.K.P., C.K.A.-A., K.G.B., C.W.A., A.K. and S.B. Formal analysis: E.K.P. Data curation: C.K.A.-A., C.W.A., K.G.B. and S.B. Writing – original draft: E.K.P. Writing – review and editing: E.K.P., L.A.O., K.O.-D., D.D., A.K. and S.B. Supervision: D.D., L.A.O., K.O.-D., S.B. and A.K. All authors read and approved the final manuscript.

Contributor Information

Ellis Kobina Paintsil, Email: ellis.paintsil@kcl.ac.uk.

Cynthia Kyerewaa Adu-Asiamah, Email: c.aduasiamah@kccr.de.

Kennedy Gyau Boahen, Email: gyaukennedy@yahoo.com.

Charity Wiafe Akenten, Email: danquah@kccr.de.

Alexander Kwarteng, Email: senkwarteng@yahoo.co.uk.

Stefan Berg, Email: stefan.berg@bnitm.de.

Kwasi Obiri-Danso, Email: obirid@knust.edu.gh.

Jürgen May, Email: may@bni-hamburg.de.

Denise Dekker, Email: dekker@bnitm.de.

Linda Aurelia Ofori, Email: laandoh.cos@knust.edu.gh.

Reference

  • 1.Bergen MAP, Putten JPM, Dingle KE, Blaser MJ, Wagenaar JA. In Campylobacter. John Wiley & Sons, Ltd; 2008. Isolation, Identification, Subspecies Differentiation, and Typing of Campylobacter fetus; pp. 213–225. [DOI] [Google Scholar]
  • 2.Kaakoush NO, Castaño-Rodríguez N, Mitchell HM, Man SM. Global epidemiology of Campylobacter infection. Clin Microbiol Rev. 2015;28:687–720. doi: 10.1128/CMR.00006-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Dumic I, Sengodan M, Franson JJ, Zea D, Ramanan P. Early onset prosthetic joint infection and bacteremia due to campylobacter fetus subspecies fetus. Case Rep Infect Dis. 2017;2017:5892846. doi: 10.1155/2017/5892846. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Fujihara N, Takakura S, Saito T, Iinuma Y, Ichiyama S. A case of perinatal sepsis by Campylobacter fetus subsp. fetus infection successfully treated with carbapenem--case report and literature review. J Infect. 2006;53:e199–202. doi: 10.1016/j.jinf.2006.01.009. [DOI] [PubMed] [Google Scholar]
  • 5.Sahin O, Yaeger M, Wu Z, Zhang Q. Campylobacter-associated diseases in animals. Annu Rev Anim Biosci. 2017;5:21–42. doi: 10.1146/annurev-animal-022516-022826. [DOI] [PubMed] [Google Scholar]
  • 6.Zan Bar T, Yehuda R, Hacham T, Krupnik S, Bartoov B. Influence of Campylobacter fetus subsp. fetus on ram sperm cell quality. J Med Microbiol. 2008;57:1405–1410. doi: 10.1099/jmm.0.2008/001057-0. [DOI] [PubMed] [Google Scholar]
  • 7.Gilbert MJ, Duim B, van der Graaf-van Bloois L, Wagenaar JA, Zomer AL. Homologous recombination between genetically divergent Campylobacter fetus lineages supports host-associated speciation. Genome Biol Evol. 2018;10:716–722. doi: 10.1093/gbe/evy048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Giacomelli M, Piccirillo A. Pet reptiles as potential reservoir of Campylobacter species with zoonotic potential. Vet Rec. 2014;174:479. doi: 10.1136/vr.102243. [DOI] [PubMed] [Google Scholar]
  • 9.Iraola G, Forster SC, Kumar N, Lehours P, Bekal S, et al. Distinct Campylobacter fetus lineages adapted as livestock pathogens and human pathobionts in the intestinal microbiota. Nat Commun. 2017;8:1367. doi: 10.1038/s41467-017-01449-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Iraola G, Pérez R, Naya H, Paolicchi F, Harris D, et al. Complete genome sequence of Campylobacter fetus subsp. venerealis biovar intermedius, isolated from the prepuce of a bull. Genome Announc. 2013;1:e00526-13. doi: 10.1128/genomeA.00526-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Oniciuc EA, Likotrafiti E, Alvarez-Molina A, Prieto M, Santos JA, et al. The present and future of whole genome sequencing (WGS) and whole metagenome sequencing (WMS) for surveillance of antimicrobial resistant microorganisms and antimicrobial resistance genes across the food chain. Genes (Basel) 2018;9:268. doi: 10.3390/genes9050268. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Abril C, Brodard I, Perreten V. Two novel antibiotic resistance genes, tet(44) and ant(6)-Ib, are located within a transferable pathogenicity island in Campylobacter fetus subsp. fetus. Antimicrob Agents Chemother . 2010;54:3052–3055. doi: 10.1128/AAC.00304-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Pena-Fernández N, van der Graaf-van Bloois L, Duim B, Zomer A, Wagenaar JA, et al. Campylobacter fetus plasmid diversity: comparative analysis of fully sequenced plasmids and proposed classification scheme. Genome Biol Evol. 2024;16:evae203. doi: 10.1093/gbe/evae203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Silva MF, Pereira AL, Fraqueza MJ, Pereira G, Mateus L, et al. Genomic and phenotypic characterization of Campylobacter fetus subsp. venerealis Strains. Microorganisms . 2021;9:340. doi: 10.3390/microorganisms9020340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Taylor DE, Chau AS. Cloning and nucleotide sequence of the gyrA gene from Campylobacter fetus subsp. fetus ATCC 27374 and characterization of ciprofloxacin-resistant laboratory and clinical isolates. Antimicrob Agents Chemother. 1997;41:665–671. doi: 10.1128/AAC.41.3.665. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.van der Graaf-van Bloois L, Duim B, Looft T, Veldman KT, Zomer AL, et al. Antimicrobial resistance in Campylobacter fetus: emergence and genomic evolution. Microb Genom. 2023;9:000934. doi: 10.1099/mgen.0.000934. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Kienesberger S, Sprenger H, Wolfgruber S, Halwachs B, Thallinger GG, et al. Comparative genome analysis of Campylobacter fetus subspecies revealed horizontally acquired genetic elements important for virulence and niche specificity. PLoS One. 2014;9:e85491. doi: 10.1371/journal.pone.0085491. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Pena-Fernández N, Ocejo M, van der Graaf-van Bloois L, Lavín JL, Kortabarria N, et al. Comparative pangenomic analysis of Campylobacter fetus isolated from Spanish bulls and other mammalian species. Sci Rep. 2024;14:4347. doi: 10.1038/s41598-024-54750-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Golz JC, Stingl K. In: Fighting Campylobacter Infections: Towards a One Health Approach. Backert S, editor. Springer International Publishing; 2021. Natural Competence and Horizontal Gene Transfer in Campylobacter; pp. 265–292. [DOI] [PubMed] [Google Scholar]
  • 20.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–2069. doi: 10.1093/bioinformatics/btu153. [DOI] [PubMed] [Google Scholar]
  • 21.Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, et al. KBase: The United States department of energy systems biology knowledgebase. Nat Biotechnol. 2018;36:566–569. doi: 10.1038/nbt.4163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Jolley KA, Bray JE, Maiden MCJ. Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications. Wellcome Open Res. 2018;3:124. doi: 10.12688/wellcomeopenres.14826.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Letunic I, Bork P. Interactive tree of life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res. 2024;52:W78–W82. doi: 10.1093/nar/gkae268. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31:3691–3693. doi: 10.1093/bioinformatics/btv421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Hadfield J, Croucher NJ, Goater RJ, Abudahab K, Aanensen DM, et al. Phandango: an interactive viewer for bacterial population genomics. Bioinformatics. 2018;34:292–293. doi: 10.1093/bioinformatics/btx610. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, et al. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother. 2012;67:2640–2644. doi: 10.1093/jac/dks261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Chen L, Yang J, Yu J, Yao Z, Sun L, et al. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res. 2005;33:D325–8. doi: 10.1093/nar/gki008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Feldgarden M, Brover V, Gonzalez-Escalona N, Frye JG, Haendiges J, et al. AMRFinderPlus and the reference gene catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep. 2021;11:12728. doi: 10.1038/s41598-021-91456-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Grant JR, Enns E, Marinier E, Mandal A, Herman EK, et al. Proksee: in-depth characterization and visualization of bacterial genomes. Nucleic Acids Res. 2023;51:W484–W492. doi: 10.1093/nar/gkad326. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Vernikos GS, Parkhill J. Interpolated variable order motifs for identification of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands. Bioinformatics. 2006;22:2196–2203. doi: 10.1093/bioinformatics/btl369. [DOI] [PubMed] [Google Scholar]
  • 31.Bertelli C, Brinkman FSL. Improved genomic island predictions with IslandPath-DIMOB. Bioinformatics. 2018;34:2161–2167. doi: 10.1093/bioinformatics/bty095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.van der Graaf-van Bloois L, Wagenaar JA, Zomer AL. RFPlasmid: predicting plasmid sequences from short-read assembly data using machine learning. Microb Genom. 2021;7:000683. doi: 10.1099/mgen.0.000683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Iskandar K, Molinier L, Hallit S, Sartelli M, Hardcastle TC, et al. Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture. Antimicrob Resist Infect Control . 2021;10:63. doi: 10.1186/s13756-021-00931-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Platts-Mills JA, Kosek M. Update on the burden of Campylobacter in developing countries. Curr Opin Infect Dis. 2014;27:444–450. doi: 10.1097/QCO.0000000000000091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Sher AA, Ashraf MA, Mustafa BE, Raza MM. Epidemiological trends of foodborne Campylobacter outbreaks in the United States of America, 1998-2016. Food Microbiol. 2021;97:103751. doi: 10.1016/j.fm.2021.103751. [DOI] [PubMed] [Google Scholar]
  • 36.Costa D, Iraola G. Pathogenomics of emerging Campylobacter species. Clin Microbiol Rev. 2019;32:10. doi: 10.1128/CMR.00072-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Rohr JR, Barrett CB, Civitello DJ, Craft ME, Delius B, et al. Emerging human infectious diseases and the links to global food production. Nat Sustain. 2019;2:445–456. doi: 10.1038/s41893-019-0293-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Mourkas E, Yahara K, Bayliss SC, Calland JK, Johansson H, et al. Host ecology regulates interspecies recombination in bacteria of the genus Campylobacter. Elife. 2022;11:e73552. doi: 10.7554/eLife.73552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Hofer U. The complex mosaic of H. pylori evolution. Nat Rev Microbiol. 2020;18:604. doi: 10.1038/s41579-020-00453-z. [DOI] [PubMed] [Google Scholar]
  • 40.Toft C, Andersson SGE. Evolutionary microbial genomics: insights into bacterial host adaptation. Nat Rev Genet. 2010;11:465–475. doi: 10.1038/nrg2798. [DOI] [PubMed] [Google Scholar]
  • 41.Emele MF, Karg M, Hotzel H, Bloois LG, Groß U, et al. Differentiation of Campylobacter fetus Subspecies by Proteotyping. Eur J Microbiol Immunol. 2019;9:62–71. doi: 10.1556/1886.2019.00006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.van der Graaf-van Bloois L, Miller WG, Yee E, Rijnsburger M, Wagenaar JA, et al. Inconsistency of phenotypic and genomic characteristics of Campylobacter fetus subspecies requires reevaluation of current diagnostics. J Clin Microbiol. 2014;52:4183–4188. doi: 10.1128/JCM.01837-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Liu Y-H, Yamazaki W, Huang Y-T, Liao C-H, Sheng W-H, et al. Clinical and microbiological characteristics of patients with bacteremia caused by Campylobacter species with an emphasis on the subspecies of C. fetus. J Microbiol, Immunol Infect. 2019;52:122–131. doi: 10.1016/j.jmii.2017.07.009. [DOI] [PubMed] [Google Scholar]
  • 44.Mshelia GD, Singh J, Amin JD, Woldehiwet Z, Egwu GO, et al. Bovine venereal campylobacteriosis: an overview. CABI Reviews . 2008:14. doi: 10.1079/PAVSNNR20072080. [DOI] [Google Scholar]
  • 45.Fitzgerald C, Tu ZC, Patrick M, Stiles T, Lawson AJ, et al. Campylobacter fetus subsp. Testudinum subsp. nov., isolated from humans and reptiles. Int J Syst Evol Microbiol. 2014;64:2944–2948. doi: 10.1099/ijs.0.057778-0. [DOI] [PubMed] [Google Scholar]
  • 46.Gilbert MJ, Miller WG, Yee E, Zomer AL, van der Graaf-van Bloois L, et al. Comparative genomics of Campylobacter fetus from reptiles and mammals reveals divergent evolution in host-associated lineages. Genome Biol Evol. 2016;8:2006–2019. doi: 10.1093/gbe/evw146. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Patrick ME, Gilbert MJ, Blaser MJ, Tauxe RV, Wagenaar JA, et al. Human infections with new subspecies of Campylobacter fetus. Emerg Infect Dis. 2013;19:1678–1680. doi: 10.3201/eid1910.130883. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Lynch CT, Buttimer C, Epping L, O’Connor J, Walsh N, et al. Phenotypic and genetic analyses of two Campylobacter fetus isolates from a patient with relapsed prosthetic valve endocarditis. Pathog Dis. 2022;79:ftab055. doi: 10.1093/femspd/ftab055. [DOI] [PubMed] [Google Scholar]
  • 49.Tshipamba ME, Lubanza N, Mwanza M. Genome Analysis of antimicrobial resistance genes and virulence factors in multidrug-resistant Campylobacter fetus subspecies isolated from sheath wash. WVJ . 2020;10:465–480. doi: 10.54203/scil.2020.wvj57. [DOI] [Google Scholar]
  • 50.Gorkiewicz G, Kienesberger S, Schober C, Scheicher SR, Gülly C, et al. A genomic island defines subspecies-specific virulence features of the host-adapted pathogen Campylobacter fetus subsp. venerealis. J Bacteriol . 2010;192:502–517. doi: 10.1128/JB.00803-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Nadin-Davis SA, Chmara J, Carrillo CD, Amoako K, Goji N, et al. A comparison of fourteen fully characterized mammalian-associated Campylobacter fetus isolates suggests that loss of defense mechanisms contribute to high genomic plasticity and subspecies evolution. PeerJ. 2021;9:e10586. doi: 10.7717/peerj.10586. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Foynes S, Dorrell N, Ward SJ, Stabler RA, McColm AA, et al. Helicobacter pylori possesses two CheY response regulators and a histidine kinase sensor, CheA, which are essential for chemotaxis and colonization of the gastric mucosa. Infect Immun. 2000;68:2016–2023. doi: 10.1128/IAI.68.4.2016-2023.2000. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Bolton DJ. Campylobacter virulence and survival factors. Food Microbiol. 2015;48:99–108. doi: 10.1016/j.fm.2014.11.017. [DOI] [PubMed] [Google Scholar]
  • 54.LaGier MJ, Bilokopytov I, Cockerill B, Threadgill DS. Identification and characterization of a putative chemotaxis protein, CheY, from the oral pathogen Campylobacter rectus. Internet J Microbiol. 2014;12:21300. doi: 10.5580/IJMB.21300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Majumdar S, Pal S. Cross- species communication in bacterial world. J Cell Commun Signal. 2017;11:187–190. doi: 10.1007/s12079-017-0383-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Miller RS, Sweeney SJ, Slootmaker C, Grear DA, Di Salvo PA, et al. Cross-species transmission potential between wild pigs, livestock, poultry, wildlife, and humans: implications for disease risk management in North America. Sci Rep. 2017;7:7821. doi: 10.1038/s41598-017-07336-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Uncited Supplementary Material 1.
mgen-11-01446-s001.pdf (325.2KB, pdf)
DOI: 10.1099/mgen.0.001446
Supplementary Material 2.
mgen-11-01446-s002.xlsx (120.4KB, xlsx)
DOI: 10.1099/mgen.0.001446

Articles from Microbial Genomics are provided here courtesy of Microbiology Society

RESOURCES