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

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.

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

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

BACKGROUND: Previous 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). RESULTS: The 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. CONCLUSION: There 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.

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.

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.

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.