Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Author: search.filters.author.Biggs PJSubject: search.filters.subject.Campylobacter

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    New Campylobacter Lineages in New Zealand Freshwater: Pathogenesis and Public Health Implications
    (John Wiley and Sons, 2024-12) Cookson AL; Burgess S; Midwinter AC; Marshall JC; Moinet M; Rogers L; Fayaz A; Biggs PJ; Brightwell G
    This study investigated the diversity of thermophilic Campylobacter species isolated from three New Zealand freshwater catchments affected by pastoral and urban activities. Utilising matrix-assisted laser desorption ionisation-time of flight and whole genome sequence analysis, the study identified Campylobacter jejuni (n = 46, 46.0%), C. coli (n = 39, 39%), C. lari (n = 4, 4.0%), and two novel Campylobacter species lineages (n = 11, 11%). Core genome sequence analysis provided evidence of prolonged persistence or continuous faecal shedding of closely related strains. The C. jejuni isolates displayed distinct sequence types (STs) associated with human, ruminant, and environmental sources, whereas the C. coli STs included waterborne ST3302 and ST7774. Recombination events affecting loci implicated in human pathogenesis and environmental persistence were observed, particularly in the cdtABC operon (encoding the cytolethal distending toxin) of non-human C. jejuni STs. A low diversity of antimicrobial resistance genes (aadE-Cc in C. coli), with genotype/phenotype concordance for tetracycline resistance (tetO) in three ST177 isolates, was noted. The data suggest the existence of two types of naturalised waterborne Campylobacter: environmentally persistent strains originating from waterbirds and new environmental species not linked to human campylobacteriosis. Identifying and understanding naturalised Campylobacter species is crucial for accurate waterborne public health risk assessments and the effective allocation of resources for water quality management.
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    Genomic diversity of Campylobacter jejuni and Campylobacter coli isolates recovered from human and poultry in Australia and New Zealand, 2017 to 2019.
    (Microbiology Society, 2024-11-05) Cribb DM; Biggs PJ; McLure AT; Wallace RL; French NP; Glass K; Kirk MD
    We used genomic and epidemiological data to assess and compare the population structure and origins of Campylobacter, a major foodborne pathogen, in two neighbouring countries with strong trade and cultural links, similar poultry production systems and frequent movement of people and food products. The most common sequence types (STs) differed between Australia and New Zealand, with many unique to each country. Over half of all STs were represented by a single isolate. Multidrug-resistant (MDR) genotypes were detected in 0.8% of all samples, with no MDR isolates detected in poultry. Quinolone and tetracycline resistant ST6964 was prevalent in New Zealand (10.6% of C. jejuni). Closely related isolates suggested some similar food sources or contacts. We have shown that there is little genetic overlap in human and poultry STs of Campylobacter between the countries, which highlights that this common foodborne pathogen has domestic origins in Australia and New Zealand.
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    Lost in the Forest: Encoding categorical variables and the absent levels problem
    (Springer Nature, 2024-04-10) Smith HL; Biggs PJ; French NP; Smith ANH; Marshall JC; Gama J
    Levels of a predictor variable that are absent when a classification tree is grown can not be subject to an explicit splitting rule. This is an issue if these absent levels are present in a new observation for prediction. To date, there remains no satisfactory solution for absent levels in random forest models. Unlike missing data, absent levels are fully observed and known. Ordinal encoding of predictors allows absent levels to be integrated and used for prediction. Using a case study on source attribution of Campylobacter species using whole genome sequencing (WGS) data as predictors, we examine how target-agnostic versus target-based encoding of predictor variables with absent levels affects the accuracy of random forest models. We show that a target-based encoding approach using class probabilities, with absent levels designated the highest rank, is systematically biased, and that this bias is resolved by encoding absent levels according to the a priori hypothesis of equal class probability. We present a novel method of ordinal encoding predictors via principal coordinates analysis (PCO) which capitalizes on the similarity between pairs of predictor levels. Absent levels are encoded according to their similarity to each of the other levels in the training data. We show that the PCO-encoding method performs at least as well as the target-based approach and is not biased.
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    Genomic adaptations of Campylobacter jejuni to long-term human colonization
    (BioMed Central Ltd, 2021-12-10) Bloomfield SJ; Midwinter AC; Biggs PJ; French NP; Marshall JC; Hayman DTS; Carter PE; Mather AE; Fayaz A; Thornley C; Kelly DJ; Benschop J
    BACKGROUND: Campylobacter is a genus of bacteria that has been isolated from the gastrointestinal tract of humans and animals, and the environments they inhabit around the world. Campylobacter adapt to new environments by changes in their gene content and expression, but little is known about how they adapt to long-term human colonization. In this study, the genomes of 31 isolates from a New Zealand patient and 22 isolates from a United Kingdom patient belonging to Campylobacter jejuni sequence type 45 (ST45) were compared with 209 ST45 genomes from other sources to identify the mechanisms by which Campylobacter adapts to long-term human colonization. In addition, the New Zealand patient had their microbiota investigated using 16S rRNA metabarcoding, and their level of inflammation and immunosuppression analyzed using biochemical tests, to determine how Campylobacter adapts to a changing gastrointestinal tract. RESULTS: There was some evidence that long-term colonization led to genome degradation, but more evidence that Campylobacter adapted through the accumulation of non-synonymous single nucleotide polymorphisms (SNPs) and frameshifts in genes involved in cell motility, signal transduction and the major outer membrane protein (MOMP). The New Zealand patient also displayed considerable variation in their microbiome, inflammation and immunosuppression over five months, and the Campylobacter collected from this patient could be divided into two subpopulations, the proportion of which correlated with the amount of gastrointestinal inflammation. CONCLUSIONS: This study demonstrates how genomics, phylogenetics, 16S rRNA metabarcoding and biochemical markers can provide insight into how Campylobacter adapts to changing environments within human hosts. This study also demonstrates that long-term human colonization selects for changes in Campylobacter genes involved in cell motility, signal transduction and the MOMP; and that genetically distinct subpopulations of Campylobacter evolve to adapt to the changing gastrointestinal environment.
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    Lost in the Forest
    (Cold Spring Harbor Laboratory, 2022) Smith HL; Biggs PJ; French NP; Smith ANH; Marshall JC
    To date, there remains no satisfactory solution for absent levels in random forest models. Absent levels are levels of a predictor variable encountered during prediction for which no explicit rule exists. Imposing an order on nominal predictors allows absent levels to be integrated and used for prediction. The ordering of predictors has traditionally been via class probabilities with absent levels designated the lowest order. Using a combination of simulated data and pathogen source-attribution models using whole-genome sequencing data, we examine how the method of ordering predictors with absent levels can (i) systematically bias a model, and (ii) affect the out-of-bag error rate. We show that the traditional approach is systematically biased and underestimates out-of-bag error rates, and that this bias is resolved by ordering absent levels according to the a priori hypothesis of equal class probability. We present a novel method of ordering predictors via principal coordinates analysis (PCO) which capitalizes on the similarity between pairs of predictor levels. Absent levels are designated an order according to their similarity to each of the other levels in the training data. We show that the PCO method performs at least as well as the traditional approach of ordering and is not biased.