Philip J. Hepworth

Genomic variations define divergence of water/wildlife-associated Campylobacter jejuni niche specialists from common clonal complexes.

Summary Although the major food-borne pathogen Campylobacter jejuni has been isolated from diverse animal, human and environmental sources, our knowledge of genomic diversity in C. jejuni is based exclusively on human or human food-chain-associated isolates. Studies employing multilocus sequence typing have indicated that some clonal complexes are more commonly associated with particular sources. Using comparative genomic hybridization on a collection of 80 isolates representing diverse sources and clonal complexes, we identified a separate clade comprising a group of water/wildlife isolates of C. jejuni with multilocus sequence types uncharacteristic of human food-chain-associated isolates. By genome sequencing one representative of this diverse group (C. jejuni 1336), and a representative of the bank-vole niche specialist ST-3704 (C. jejuni 414), we identified deletions of genomic regions normally carried by human food-chain-associated C. jejuni. Several of the deleted regions included genes implicated in chicken colonization or in virulence. Novel genomic insertions contributing to the accessory genomes of strains 1336 and 414 were identified. Comparative analysis using PCR assays indicated that novel regions were common but not ubiquitous among the water/wildlife group of isolates, indicating further genomic diversity among this group, whereas all ST-3704 isolates carried the same novel accessory regions. While strain 1336 was able to colonize chicks, strain 414 was not, suggesting that regions specifically absent from the genome of strain 414 may play an important role in this common route of Campylobacter infection of humans. We suggest that the genomic divergence observed constitutes evidence of adaptation leading to niche specialization.

Early warning indicators for hock burn in broiler flocks

Hock burn is a common disease of broiler chickens affecting flock welfare and farmer income. Here we use hierarchical logistic regression (HLR) models to identify risk factors for hock burn using data from 5895 flocks, collected over 3.5 years by a large UK broiler company. The results suggest that at 2 weeks of age, weight and weight density may be useful predictors of flocks at risk of a high incidence of hock burn. In contrast, stocking density at placement is not. The use of these and other variables in disease prevention add value to routinely collected management data and can assist in improving broiler welfare and farm income.

Use of suppression subtractive hybridisation to extend our knowledge of genome diversity in Campylobacter jejuni

BackgroundPrevious studies have sought to identify a link between the distribution of variable genes amongst isolates of Campylobacter jejuni and particular host preferences. The genomic sequence data available currently was obtained using only isolates from human or chicken hosts. In order to identify variable genes present in isolates from alternative host species, five subtractions between C. jejuni isolates from different sources (rabbit, cattle, wild bird) were carried out, designed to assess genomic variability within and between common multilocus sequence type (MLST) clonal complexes (ST-21, ST-42, ST-45 and ST-61).ResultsThe vast majority (97%) of the 195 subtracted sequences identified had a best BLASTX match with a Campylobacter protein. However, there was considerable variation within and between the four clonal complexes included in the subtractions. The distributions of eight variable sequences, including four with putative roles in the use of alternative terminal electron acceptors, amongst a panel of C. jejuni isolates representing diverse sources and STs, were determined.ConclusionThere was a clear correlation between clonal complex and the distribution of the metabolic genes. In contrast, there was no evidence to support the hypothesis that the distribution of such genes may be related to host preference. The other variable genes studied were also generally distributed according to MLST type. Thus, we found little evidence for widespread horizontal gene transfer between clonal complexes involving these genes.

Using open-access taxonomic and spatial information to create a comprehensive database for the study of Mammalian and avian livestock and pet infections ☆

What are all the species of pathogen that affect our livestock? As 6 out of every 10 human pathogens came from animals, with a good number from livestock and pets, it seems likely that the majority that emerge in the future, and which could threaten or devastate human health, will come from animals. Only 10 years ago, the first comprehensive pathogen list was compiled for humans; we still have no equivalent for animals. Here we describe the creation of a novel pathogen database, and present outputs from the database that demonstrate its value. The ENHanCEd Infectious Diseases database (EID2) is open-access and evidence-based, and it describes the pathogens of humans and animals, their host and vector species, and also their global occurrence. The EID2 systematically collates information on pathogens into a single resource using evidence from the NCBI Taxonomy database, the NCBI Nucleotide database, the NCBI MeSH (Medical Subject Headings) library and PubMed. Information about pathogens is assigned using data-mining of meta-data and semi-automated literature searches. Here we focus on 47 mammalian and avian hosts, including humans and animals commonly used in Europe as food or kept as pets. Currently, the EID2 evidence suggests that: • Within these host species, 793 (30.5%) pathogens were bacteria species, 395 (15.2%) fungi, 705 (27.1%) helminths, 372 (14.3%) protozoa and 332 (12.8%) viruses. • The odds of pathogens being emerging compared to not emerging differed by taxonomic division, and increased when pathogens had greater numbers of host species associated with them, and were zoonotic rather than non-zoonotic. • The odds of pathogens being zoonotic compared to non-zoonotic differed by taxonomic division and also increased when associated with greater host numbers. • The pathogens affecting the greatest number of hosts included: Escherichia coli, Giardia intestinalis, Toxoplasma gondii, Anaplasma phagocytophilum, Cryptosporidium parvum, Rabies virus, Staphylococcus aureus, Neospora caninum and Echinococcus granulosus. • The pathogens of humans and domestic animal hosts are characterised by 4223 interactions between pathogen and host species, with the greatest number found in: humans, sheep/goats, cattle, small mammals, pigs, dogs and equids. • The number of pathogen species varied by European country. The odds of a pathogen being found in Europe compared to the rest of the world differed by taxonomic division, and increased if they were emerging compared to not emerging, or had a larger number of host species associated with them.

A Quantitative Prioritisation of Human and Domestic Animal Pathogens in Europe

Disease or pathogen risk prioritisations aid understanding of infectious agent impact within surveillance or mitigation and biosecurity work, but take significant development. Previous work has shown the H-(Hirsch-)index as an alternative proxy. We present a weighted risk analysis describing infectious pathogen impact for human health (human pathogens) and well-being (domestic animal pathogens) using an objective, evidence-based, repeatable approach; the H-index. This study established the highest H-index European pathogens. Commonalities amongst pathogens not included in previous surveillance or risk analyses were examined. Differences between host types (humans/animals/zoonotic) in pathogen H-indices were explored as a One Health impact indicator. Finally, the acceptability of the H-index proxy for animal pathogen impact was examined by comparison with other measures. 57 pathogens appeared solely in the top 100 highest H-indices (1) human or (2) animal pathogens list, and 43 occurred in both. Of human pathogens, 66 were zoonotic and 67 were emerging, compared to 67 and 57 for animals. There were statistically significant differences between H-indices for host types (humans, animal, zoonotic), and there was limited evidence that H-indices are a reasonable proxy for animal pathogen impact. This work addresses measures outlined by the European Commission to strengthen climate change resilience and biosecurity for infectious diseases. The results include a quantitative evaluation of infectious pathogen impact, and suggest greater impacts of human-only compared to zoonotic pathogens or scientific under-representation of zoonoses. The outputs separate high and low impact pathogens, and should be combined with other risk assessment methods relying on expert opinion or qualitative data for priority setting, or could be used to prioritise diseases for which formal risk assessments are not possible because of data gaps.

Hock burn: an indicator of broiler flock health

This study aimed to identify risk factors for the prevalence of hock burn, a common disease of broiler chickens that influences the welfare and profitability of affected flocks, using hierarchical logistic regression analysis of routine flock management data. The study identified an association between hock burn and other diseases detected at slaughter and found that the management of flocks around the slaughter period was of particular importance for the control of hock burn, providing a rational basis for intervention.

Systematic Assessment of the Climate Sensitivity of Important Human and Domestic Animals Pathogens in Europe

Climate change is expected to threaten human health and well-being via its effects on climate-sensitive infectious diseases, potentially changing their spatial distributions, affecting annual/seasonal cycles, or altering disease incidence and severity. Climate sensitivity of pathogens is a key indicator that diseases might respond to climate change, but the proportion of pathogens that is climate-sensitive, and their characteristics, are not known. The climate sensitivity of European human and domestic animal infectious pathogens, and the characteristics associated with sensitivity, were assessed systematically in terms of selection of pathogens and choice of literature reviewed. Sixty-three percent (N = 157) of pathogens were climate sensitive; 82% to primary drivers such as rainfall and temperature. Protozoa and helminths, vector-borne, foodborne, soilborne and waterborne transmission routes were associated with larger numbers of climate drivers. Zoonotic pathogens were more climate sensitive than human- or animal-only pathogens. Thirty-seven percent of disability-adjusted-life-years arise from human infectious diseases that are sensitive to primary climate drivers. These results help prioritize surveillance for pathogens that may respond to climate change. Although this study identifies a high degree of climate sensitivity among important pathogens, their response to climate change will be dependent on the nature of their association with climate drivers and impacts of other drivers.

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

Author Correction: Systematic Assessment of the Climate Sensitivity of Important Human and Domestic Animals Pathogens in Europe

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