Abstract
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.
Highlights
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]
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)
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
Summary
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]. 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] This approach has potential limitations, when applied to highly diverse species such as Campylobacter jejuni. 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 even though the later are likely to be the consequence of horizontal genetic exchange
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