HAM-ART: An optimised culture-free Hi-C metagenomics pipeline for tracking antimicrobial resistance genes in complex microbial communities.

Shotgun metagenomics is a powerful tool to identify antimicrobial resistance (AMR) genes in microbiomes but has the limitation that extrachromosomal DNA, such as plasmids, cannot be linked with the host bacterial chromosome. Here we present a comprehensive laboratory and bioinformatics pipeline HAM-ART (Hi-C Assisted Metagenomics for Antimicrobial Resistance Tracking) optimised for the generation of metagenome-assembled genomes including both chromosomal and extrachromosomal AMR genes. We demonstrate the performance of the pipeline in a study comparing 100 pig faecal microbiomes from low- and high-antimicrobial use pig farms (organic and conventional farms). We found significant differences in the distribution of AMR genes between low- and high-antimicrobial use farms including a plasmid-borne lincosamide resistance gene exclusive to high-antimicrobial use farms in three species of Lactobacilli. The bioinformatics pipeline code is available at https://github.com/lkalmar/HAM-ART.

Spatial dynamics of pathogen transmission in communally roosting species: Impacts of changing habitats on bat-virus dynamics.

The spatial organization of populations determines their pathogen dynamics. This is particularly important for communally roosting species, whose aggregations are often driven by the spatial structure of their environment. We develop a spatially explicit model for virus transmission within roosts of Australian tree-dwelling bats (Pteropus spp.), parameterized to reflect Hendra virus. The spatial structure of roosts mirrors three study sites, and viral transmission between groups of bats in trees was modelled as a function of distance between roost trees. Using three levels of tree density to reflect anthropogenic changes in bat habitats, we investigate the potential effects of recent ecological shifts in Australia on the dynamics of zoonotic viruses in reservoir hosts. We show that simulated infection dynamics in spatially structured roosts differ from that of mean-field models for equivalently sized populations, highlighting the importance of spatial structure in disease models of gregarious taxa. Under contrasting scenarios of flying-fox roosting structures, sparse stand structures (with fewer trees but more bats per tree) generate higher probabilities of successful outbreaks, larger and faster epidemics, and shorter virus extinction times, compared to intermediate and dense stand structures with more trees but fewer bats per tree. These observations are consistent with the greater force of infection generated by structured populations with less numerous but larger infected groups, and may flag an increased risk of pathogen spillover from these increasingly abundant roost types. Outputs from our models contribute insights into the spread of viruses in structured animal populations, like communally roosting species, as well as specific insights into Hendra virus infection dynamics and spillover risk in a situation of changing host ecology. These insights will be relevant for modelling other zoonotic viruses in wildlife reservoir hosts in response to habitat modification and changing populations, including coronaviruses like SARS-CoV-2.

Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

An efficient moments-based inference method for within-host bacterial infection dynamics.

Over the last ten years, isogenic tagging (IT) has revolutionised the study of bacterial infection dynamics in laboratory animal models. However, quantitative analysis of IT data has been hindered by the piecemeal development of relevant statistical models. The most promising approach relies on stochastic Markovian models of bacterial population dynamics within and among organs. Here we present an efficient numerical method to fit such stochastic dynamic models to in vivo experimental IT data. A common approach to statistical inference with stochastic dynamic models relies on producing large numbers of simulations, but this remains a slow and inefficient method for all but simple problems, especially when tracking bacteria in multiple locations simultaneously. Instead, we derive and solve the systems of ordinary differential equations for the two lower-order moments of the stochastic variables (mean, variance and covariance). For any given model structure, and assuming linear dynamic rates, we demonstrate how the model parameters can be efficiently and accurately estimated by divergence minimisation. We then apply our method to an experimental dataset and compare the estimates and goodness-of-fit to those obtained by maximum likelihood estimation. While both sets of parameter estimates had overlapping confidence regions, the new method produced lower values for the division and death rates of bacteria: these improved the goodness-of-fit at the second time point at the expense of that of the first time point. This flexible framework can easily be applied to a range of experimental systems. Its computational efficiency paves the way for model comparison and optimal experimental design.

The effects of vaccination and immunity on bacterial infection dynamics in vivo.

Salmonella enterica infections are a significant global health issue, and development of vaccines against these bacteria requires an improved understanding of how vaccination affects the growth and spread of the bacteria within the host. We have combined in vivo tracking of molecularly tagged bacterial subpopulations with mathematical modelling to gain a novel insight into how different classes of vaccines and branches of the immune response protect against secondary Salmonella enterica infections of the mouse. We have found that a live Salmonella vaccine significantly reduced bacteraemia during a secondary challenge and restrained inter-organ spread of the bacteria in the systemic organs. Further, fitting mechanistic models to the data indicated that live vaccine immunisation enhanced both the bacterial killing in the very early stages of the infection and bacteriostatic control over the first day post-challenge. T-cell immunity induced by this vaccine is not necessary for the enhanced bacteriostasis but is required for subsequent bactericidal clearance of Salmonella in the blood and tissues. Conversely, a non-living vaccine while able to enhance initial blood clearance and killing of virulent secondary challenge bacteria, was unable to alter the subsequent bacterial growth rate in the systemic organs, did not prevent the resurgence of extensive bacteraemia and failed to control the spread of the bacteria in the body.

Ecological approaches in veterinary epidemiology: mapping the risk of bat-borne rabies using vegetation indices and night-time light satellite imagery.

Rabies remains a disease of significant public health concern. In the Americas, bats are an important source of rabies for pets, livestock, and humans. For effective rabies control and prevention, identifying potential areas for disease occurrence is critical to guide future research, inform public health policies, and design interventions. To anticipate zoonotic infectious diseases distribution at coarse scale, veterinary epidemiology needs to advance via exploring current geographic ecology tools and data using a biological approach. We analyzed bat-borne rabies reports in Chile from 2002 to 2012 to establish associations between rabies occurrence and environmental factors to generate an ecological niche model (ENM). The main rabies reservoir in Chile is the bat species Tadarida brasiliensis; we mapped 726 occurrences of rabies virus variant AgV4 in this bat species and integrated them with contemporary Normalized Difference Vegetation Index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The correct prediction of areas with rabies in bats and the reliable anticipation of human rabies in our study illustrate the usefulness of ENM for mapping rabies and other zoonotic pathogens. Additionally, we highlight critical issues with selection of environmental variables, methods for model validation, and consideration of sampling bias. Indeed, models with weak or incorrect validation approaches should be interpreted with caution. In conclusion, ecological niche modeling applications for mapping disease risk at coarse geographic scales have a promising future, especially with refinement and enrichment of models with additional information, such as night-time light data, which increased substantially the model's ability to anticipate human rabies.

Spread and transmission of bacterial pathogens in experimental populations of the nematode Caenorhabditis elegans.

Caenorhabditis elegans is frequently used as a model species for the study of bacterial virulence and innate immunity. In recent years, diverse mechanisms contributing to the nematode's immune response to bacterial infection have been discovered. Yet despite growing interest in the biochemical and molecular basis of nematode-bacterium associations, many questions remain about their ecology. Although recent studies have demonstrated that free-living nematodes could act as vectors of opportunistic pathogens in soil, the extent to which worms may contribute to the persistence and spread of these bacteria has not been quantified. We conducted a series of experiments to test whether colonization of and transmission between C. elegans nematodes could enable two opportunistic pathogens (Salmonella enterica and Pseudomonas aeruginosa) to spread on agar plates occupied by Escherichia coli. We monitored the transmission of S. enterica and P. aeruginosa from single infected nematodes to their progeny and measured bacterial loads both within worms and on the plates. In particular, we analyzed three factors affecting the dynamics of bacteria: (i) initial source of the bacteria, (ii) bacterial species, and (iii) feeding behavior of the host. Results demonstrate that worms increased the spread of bacteria through shedding and transmission. Furthermore, we found that despite P. aeruginosa's relatively high transmission rate among worms, its pathogenic effects reduced the overall number of worms colonized. This study opens new avenues to understand the role of nematodes in the epidemiology and evolution of pathogenic bacteria in the environment.

Comparison of cone-beam and fan-beam computed tomography and low-field magnetic resonance imaging for detection of proximal phalanx dorsoproximal osteochondral defects.

BACKGROUND: Dorsoproximal osteochondral defects commonly affect the proximal phalanx, but information about diagnosis on computed tomography (CT) and magnetic resonance imaging (MRI) is limited. OBJECTIVES: To assess CT and MRI diagnoses of osteochondral defects, describe the lesions and compare sensitivity and specificity of the modalities using macroscopic pathology as gold standard. STUDY DESIGN: Cross-sectional study. METHODS: Thirty-five equine cadaver limbs underwent standing cone-beam CT (CBCT), fan-beam CT (FBCT), low-field MRI and pathological examination. CT and MR images were examined for proximal phalanx dorsomedial and dorsolateral eminence osteochondral defects. Defect dimensions were measured. Imaging diagnoses and measurements were compared with macroscopic examination. RESULTS: Fifty-six defects were seen over 70 potential locations. On CBCT and FBCT, osteochondral defects appeared as subchondral irregularity/saucer-shaped defects. On MRI, osteochondral defects were a combination of articular cartilage defect on dorsal images and subchondral flattening/irregularity on sagittal images. Subchondral thickening and osseous short tau inversion recovery hyperintensity were found concurrent with osteochondral defects. Compared with pathological examination, the sensitivity and specificity of diagnosis were 86% (95% confidence interval [95% CI] 75%-93%) and 64% (95% CI 38%-85%) for FBCT; 64% (95% CI 51%-76%) and 71% (95% CI 46%-90%) for CBCT; and 52% (95% CI 39%-65%) and 71% (95% CI 46%-90%) for MRI. Sensitivity of all modalities increased with defect size. Macroscopic defect dimensions were strongly correlated with CBCT (r = 0.76, p < 0.001) and moderately correlated with FBCT and MRI (r = 0.65, p < 0.001). Macroscopic measurements were significantly greater than all imaging modality dimensions (p < 0.001), potentially because macroscopy included articular cartilage pathology. MAIN LIMITATIONS: Influence of motion artefact could not be assessed. CONCLUSIONS: Osteochondral defects could be visualised using both CT and MRI with sensitivity increasing with defect size. Diagnostic performance was best using FBCT, followed by CBCT then MRI, but CBCT-measured defect size best correlated with macroscopic examination. MRI provided useful information on fluid signal associated with defects, which could represent active pathology.

Evidence-based control of canine rabies: a critical review of population density reduction.

Control measures for canine rabies include vaccination and reducing population density through culling or sterilization. Despite the evidence that culling fails to control canine rabies, efforts to reduce canine population density continue in many parts of the world. The rationale for reducing population density is that rabies transmission is density-dependent, with disease incidence increasing directly with host density. This may be based, in part, on an incomplete interpretation of historical field data for wildlife, with important implications for disease control in dog populations. Here, we examine historical and more recent field data, in the context of host ecology and epidemic theory, to understand better the role of density in rabies transmission and the reasons why culling fails to control rabies. We conclude that the relationship between host density, disease incidence and other factors is complex and may differ between species. This highlights the difficulties of interpreting field data and the constraints of extrapolations between species, particularly in terms of control policies. We also propose that the complex interactions between dogs and people may render culling of free-roaming dogs ineffective irrespective of the relationship between host density and disease incidence. We conclude that vaccination is the most effective means to control rabies in all species.

Cryptic susceptibility to penicillin/ß-lactamase inhibitor combinations in emerging multidrug-resistant, hospital-adapted Staphylococcus epidermidis lineages.

Global spread of multidrug-resistant, hospital-adapted Staphylococcus epidermidis lineages underscores the need for new therapeutic strategies. Here we show that many S. epidermidis isolates belonging to these lineages display cryptic susceptibility to penicillin/ß-lactamase inhibitor combinations under in vitro conditions, despite carrying the methicillin resistance gene mecA. Using a mouse thigh model of S. epidermidis infection, we demonstrate that single-dose treatment with amoxicillin/clavulanic acid significantly reduces methicillin-resistant S. epidermidis loads without leading to detectable resistance development. On the other hand, we also show that methicillin-resistant S. epidermidis is capable of developing increased resistance to amoxicillin/clavulanic acid during long-term in vitro exposure to these drugs. These findings suggest that penicillin/ß-lactamase inhibitor combinations could be a promising therapeutic candidate for treatment of a high proportion of methicillin-resistant S. epidermidis infections, although the in vivo risk of resistance development needs to be further addressed before they can be incorporated into clinical trials.

Model-guided fieldwork: practical guidelines for multidisciplinary research on wildlife ecological and epidemiological dynamics.

Infectious disease ecology has recently raised its public profile beyond the scientific community due to the major threats that wildlife infections pose to biological conservation, animal welfare, human health and food security. As we start unravelling the full extent of emerging infectious diseases, there is an urgent need to facilitate multidisciplinary research in this area. Even though research in ecology has always had a strong theoretical component, cultural and technical hurdles often hamper direct collaboration between theoreticians and empiricists. Building upon our collective experience of multidisciplinary research and teaching in this area, we propose practical guidelines to help with effective integration among mathematical modelling, fieldwork and laboratory work. Modelling tools can be used at all steps of a field-based research programme, from the formulation of working hypotheses to field study design and data analysis. We illustrate our model-guided fieldwork framework with two case studies we have been conducting on wildlife infectious diseases: plague transmission in prairie dogs and lyssavirus dynamics in American and African bats. These demonstrate that mechanistic models, if properly integrated in research programmes, can provide a framework for holistic approaches to complex biological systems.

Optimal Bayesian design for model discrimination via classification.

Performing optimal Bayesian design for discriminating between competing models is computationally intensive as it involves estimating posterior model probabilities for thousands of simulated data sets. This issue is compounded further when the likelihood functions for the rival models are computationally expensive. A new approach using supervised classification methods is developed to perform Bayesian optimal model discrimination design. This approach requires considerably fewer simulations from the candidate models than previous approaches using approximate Bayesian computation. Further, it is easy to assess the performance of the optimal design through the misclassification error rate. The approach is particularly useful in the presence of models with intractable likelihoods but can also provide computational advantages when the likelihoods are manageable. Supplementary Information: The online version contains supplementary material available at 10.1007/s11222-022-10078-2.

Eimeria stiedae causes most of the white-spotted liver lesions in wild European rabbits in Cambridgeshire, United Kingdom.

In rabbits, a white-spotted liver can be indicative of one of several disease processes, frequently caused by parasites. To date, the prevalence of white-spotted liver in wild rabbits, Oryctolagus cuniculus, in the United Kingdom is undetermined. We evaluated the prevalence and main parasitic etiologies of this entity in a U.K. population of wild rabbits. Wild rabbits (n = 87) were shot in Cambridgeshire for population control, and cadavers were donated for research. Postmortem examination was undertaken, including gross and histologic hepatic examination. Macroscopic lesions consistent with white-spotted liver were found in 46 of 87 (53%) rabbits examined; most of these lesions were considered to be mild. For 28 of 46 (59%) rabbits with gross hepatic lesions, an etiologic agent was apparent histologically. Eimeria stiedae was detected in 21 of 87 (24%) rabbits, and Calodium hepaticum (syn. Capillaria hepatica) was detected in 7 of 87 (8%). In the subset of rabbits killed in the summer, there was a significant association between white-spotted liver and juvenile age class. There was also an association between white-spotted liver caused by E. stiedae and juvenile age class. When restricting analysis to rabbits with white-spotted liver caused by E. stiedae and submitted in the summer, both juvenile age class and female had significant effects. E. stiedae and C. hepaticum can be transmitted to pet lagomorphs via contaminated vegetation, and to humans in the case of the latter, which demonstrates the importance of monitoring the prevalence of these parasitic diseases in wild rabbits.

Ecology, evolution and spillover of coronaviruses from bats.

In the past two decades, three coronaviruses with ancestral origins in bats have emerged and caused widespread outbreaks in humans, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first SARS epidemic in 2002-2003, the appreciation of bats as key hosts of zoonotic coronaviruses has advanced rapidly. More than 4,000 coronavirus sequences from 14 bat families have been identified, yet the true diversity of bat coronaviruses is probably much greater. Given that bats are the likely evolutionary source for several human coronaviruses, including strains that cause mild upper respiratory tract disease, their role in historic and future pandemics requires ongoing investigation. We review and integrate information on bat-coronavirus interactions at the molecular, tissue, host and population levels. We identify critical gaps in knowledge of bat coronaviruses, which relate to spillover and pandemic risk, including the pathways to zoonotic spillover, the infection dynamics within bat reservoir hosts, the role of prior adaptation in intermediate hosts for zoonotic transmission and the viral genotypes or traits that predict zoonotic capacity and pandemic potential. Filling these knowledge gaps may help prevent the next pandemic.

Human IgG isotypes and activating Fcγ receptors in the interaction of Salmonella enterica serovar Typhimurium with phagocytic cells.

Several classes and multiple subclasses of immunoglobulins are produced towards protein and polysaccharide antigens in response to Salmonella infection and play a key role in protection against systemic disease. The targeting of Salmonella to Fc receptors (FcR) on phagocytes is a key step in the antibody-mediated antibacterial functions of host cells. We wished to compare the relative efficiency of different human IgG subclasses, which targeted the Salmonella enterica OmpA surface protein in modulating the interaction of bacteria with human phagocytes. To this end, we developed a novel system by tagging OmpA with a foreign CD52 mimotope (TSSPSAD) and opsonizing the bacteria with a panel of humanized CD52 antibodies that share the same antigen-binding V-region, but have constant regions of different subclasses. Our data revealed that opsonization with all the IgG subclasses increases Salmonella uptake by human phagocytes. IgG3 resulted in the highest level of bacterial uptake and the highest average bacterial load per infected cell, which was closely followed by IgG1, then IgG4 and lastly IgG2. Phagocytosis mediated by IgG1, IgG3 and IgG4 had a higher dependency on FcγRI than FcγRIIA, whereas IgG2-mediated phagocytosis required FcγRIIA more than FcγRI. The results show that IgG binding to OmpA increases the uptake of Salmonella by human phagocytic cells and that the efficiency of this process depends both on the subclass of the IgG and the type of FcR that is available for antibody binding.

Modelling within-host spatiotemporal dynamics of invasive bacterial disease.

Mechanistic determinants of bacterial growth, death, and spread within mammalian hosts cannot be fully resolved studying a single bacterial population. They are also currently poorly understood. Here, we report on the application of sophisticated experimental approaches to map spatiotemporal population dynamics of bacteria during an infection. We analyzed heterogeneous traits of simultaneous infections with tagged Salmonella enterica populations (wild-type isogenic tagged strains [WITS]) in wild-type and gene-targeted mice. WITS are phenotypically identical but can be distinguished and enumerated by quantitative PCR, making it possible, using probabilistic models, to estimate bacterial death rate based on the disappearance of strains through time. This multidisciplinary approach allowed us to establish the timing, relative occurrence, and immune control of key infection parameters in a true host-pathogen combination. Our analyses support a model in which shortly after infection, concomitant death and rapid bacterial replication lead to the establishment of independent bacterial subpopulations in different organs, a process controlled by host antimicrobial mechanisms. Later, decreased microbial mortality leads to an exponential increase in the number of bacteria that spread locally, with subsequent mixing of bacteria between organs via bacteraemia and further stochastic selection. This approach provides us with an unprecedented outlook on the pathogenesis of S. enterica infections, illustrating the complex spatial and stochastic effects that drive an infectious disease. The application of the novel method that we present in appropriate and diverse host-pathogen combinations, together with modelling of the data that result, will facilitate a comprehensive view of the spatial and stochastic nature of within-host dynamics.

Challenges in modelling the dynamics of infectious diseases at the wildlife-human interface.

The Covid-19 pandemic is of zoonotic origin, and many other emerging infections of humans have their origin in an animal host population. We review the challenges involved in modelling the dynamics of wildlife-human interfaces governing infectious disease emergence and spread. We argue that we need a better understanding of the dynamic nature of such interfaces, the underpinning diversity of pathogens and host-pathogen association networks, and the scales and frequencies at which environmental conditions enable spillover and host shifting from animals to humans to occur. The major drivers of the emergence of zoonoses are anthropogenic, including the global change in climate and land use. These, and other ecological processes pose challenges that must be overcome to counterbalance pandemic risk. The development of more detailed and nuanced models will provide better tools for analysing and understanding infectious disease emergence and spread.

Expression of Phosphorylated Signal Transducer and Activator of Transcription 3 and its Prognostic Significance in Canine Anal Sac Adenocarcinoma.

Prognostication in canine anal sac adenocarcinomas (ASACs) is difficult due to conflicting evidence regarding metastatic rates and median survival times (MSTs). The transcription factor signal transducer and activator of transcription 3 (STAT3) is a prognostic predictor in several human cancers. The aim of this retrospective study was to assess STAT3 expression in ASACs and to explore its association with clinical presentation and outcome. We hypothesized that STAT3 expression would distinguish tumours with early versus late metastasis. Records from The Queen's Veterinary School Hospital, Cambridge, UK, were searched for dogs diagnosed with ASAC from 2008 to 2019. Immunohistochemical expression of phosphorylated STAT3 (pSTAT3) was assessed in primary tumours (n = 57) and metastatic lymph nodes (n = 30) and MSTs were calculated for cases with low and high pSTAT3 expression. Of the 57 cases assessed, 27 presented with primary tumours but no metastasis and 30 with both primary and local metastatic disease. Most cases (50/57) expressed nuclear pSTAT3 within neoplastic cells in both primary tumour and metastatic lymph nodes. pSTAT3 expression was predominantly observed in neoplastic cells at the edges of neoplastic lobules, suggesting a potential role in invasion. There was no significant difference in pSTAT3 expression between cases metastatic at presentation and those that did not have detectable metastasis at presentation. There was no significant difference between the MSTs in cases with high and low pSTAT3 expression. Cases that presented with metastatic disease had shorter MSTs (395 days) than those with primary tumours alone (623 days). Although pSTAT3 is variably expressed in primary and metastatic ASAC cells, pSTAT3 did not provide prognostic information for canine ASAC.

Optimizing noninvasive sampling of a zoonotic bat virus.

Outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to bat-borne zoonoses, which has motivated increased ecological and epidemiological studies on bat populations. Field sampling methods often collect pooled samples of bat excreta from plastic sheets placed under-roosts. However, positive bias is introduced because multiple individuals may contribute to pooled samples, making studies of viral dynamics difficult. Here, we explore the general issue of bias in spatial sample pooling using Hendra virus in Australian bats as a case study. We assessed the accuracy of different under-roost sampling designs using generalized additive models and field data from individually captured bats and pooled urine samples. We then used theoretical simulation models of bat density and under-roost sampling to understand the mechanistic drivers of bias. The most commonly used sampling design estimated viral prevalence 3.2 times higher than individual-level data, with positive bias 5-7 times higher than other designs due to spatial autocorrelation among sampling sheets and clustering of bats in roosts. Simulation results indicate using a stratified random design to collect 30-40 pooled urine samples from 80 to 100 sheets, each with an area of 0.75-1 m2, and would allow estimation of true prevalence with minimum sampling bias and false negatives. These results show that widely used under-roost sampling techniques are highly sensitive to viral presence, but lack specificity, providing limited information regarding viral dynamics. Improved estimation of true prevalence can be attained with minor changes to existing designs such as reducing sheet size, increasing sheet number, and spreading sheets out within the roost area. Our findings provide insight into how spatial sample pooling is vulnerable to bias for a wide range of systems in disease ecology, where optimal sampling design is influenced by pathogen prevalence, host population density, and patterns of aggregation.