An optimized binary typing panel improves the typing capability for Campylobacter jejuni

Automatic identification of optimal marker genes for phenotypic and taxonomic groups of microorganisms

Finding optimal markers for microorganisms important in the medical, agricultural, environmental or ecological fields is of great importance. Thousands of complete microbial genomes now available allow us, for the first time, to exhaustively identify marker proteins for groups of microbial organisms. In this work, we model the biological task as the well-known mathematical “hitting set” problem, solving it based on both greedy and randomized approximation algorithms. We identify unique markers for 17 phenotypic and taxonomic microbial groups, including proteins related to the nitrite reductase enzyme as markers for the non-anammox nitrifying bacteria group, and two transcription regulation proteins, nusG and yhiF, as markers for the Archaea and Escherichia/Shigella taxonomic groups, respectively. Additionally, we identify marker proteins for three subtypes of pathogenic E. coli, which previously had no known optimal markers. Practically, depending on the completeness of the database this algorithm can be used for identification of marker genes for any microbial group, these marker genes may be prime candidates for the understanding of the genetic basis of the group's phenotype or to help discover novel functions which are uniquely shared among a group of microbes. We show that our method is both theoretically and practically efficient, while establishing an upper bound on its time complexity and approximation ratio; thus, it promises to remain efficient and permit the identification of marker proteins that are specific to phenotypic or taxonomic groups, even as more and more bacterial genomes are being sequenced.

Orthogonal typing methods identify genetic diversity among Belgian Campylobacter jejuni strains isolated over a decade from poultry and cases of sporadic human illness

Campylobacter jejuni is a zoonotic pathogen commonly associated with human gastroenteritis. Retail poultry meat is a major food-related transmission source of C. jejuni to humans. The present study investigated the genetic diversity, clonal relationship, and strain risk-analysis of 403 representative C. jejuni isolates from chicken broilers (n = 204) and sporadic cases of human diarrhea (n = 199) over a decade (2006-2015) in Belgium, using multilocus sequence typing (MLST), PCR binary typing (P-BIT), and identification of lipooligosaccharide (LOS) biosynthesis locus classes. A total of 123 distinct sequence types (STs), clustered in 28 clonal complexes (CCs) were assigned, including ten novel sequence types that were not previously documented in the international database. Sequence types ST-48, ST-21, ST-50, ST-45, ST-464, ST-2274, ST-572, ST-19, ST-257 and ST-42 were the most prevalent. Clonal complex 21 was the main clonal complex in isolates from humans and chickens. Among observed STs, a total of 35 STs that represent 72.2% (291/403) of the isolates were identified in both chicken and human isolates confirming considerable epidemiological relatedness; these 35 STs also clustered together in the most prevalent CCs. A majority of the isolates harbored sialylated LOS loci associated with potential neuropathic outcomes in humans. Although the concordance between MLST and P-BIT, determined by the adjusted Rand and Wallace coefficients, showed low congruence between both typing methods. The discriminatory power of P-BIT and MLST was similar, with Simpson's diversity indexes of 0.978 and 0.975, respectively. Furthermore, P-BIT could provide additional epidemiological information that would provide further insights regarding the potential association to human health from each strain. In addition, certain clones could be linked to specific clinical symptoms. Indeed, LOS class E was associated with less severe infections. Moreover, ST-572 was significantly associated with clinical infections occurring after travelling abroad. Ultimately, the data generated from this study will help to better understand the molecular epidemiology of C. jejuni infection.