Biology subjects

Restif, O.

Publications and source records attributed to Restif, O..

7 recordsLinked to original sources

bioRxiv2025

Antimicrobial Resistance Cartography: A Generalisable Framework for Studying Multivariate Drug Resistance

Beta-lactam resistance in Streptococcus pneumoniae is typically analysed one antibiotic at a time, often using resistant/susceptible classifications. This obscures the fact that minimum inhibitory concentrations (MICs) in this species are continuous, vary across beta-lactam subclasses in correlated but non-identical ways, and reflect complex genetic variation in underlying penicillin-binding proteins (PBPs). To address this, we develop a framework for analysing multivariate resistance phenotypes, which we term Antimicrobial Resistance Cartography, and apply it to 3,628 invasive pneumococcal isolates from the U.S. Active Bacterial Core surveillance programme with MICs measured for six beta-lactams. We demonstrate how this approach simplifies visualisation of resistance data across multiple drugs, resolves common issues such as missing or censored values, and identifies genetic mutations driving correlated resistance changes across multiple beta-lactam antibiotics. We characterise penicillin-binding protein (PBP) substitutions associated with increases in minimum inhibitory concentration to beta-lactam subclasses, revealing subtle differences in their correlated phenotypic effects, and identifying potential constraints on resistance evolution across genetic backgrounds. Moreover, we find pneumococcal lineages harbour distinct combinations of PBP substitutions which give rise to lineage-specific multivariate resistance profiles. Together, these results establish a multivariate view of pneumococcal beta-lactam resistance, revealing how combinations of PBP substitutions work together to generate distinct resistance phenotypes across related antibiotics.

genomics↗

bioRxiv2025

Paramyxoviruses in Old World fruit bats (Pteropodidae): an open database and synthesis of sampling effort, viral positivity, and coevolution

Over the past 25 years, research interest in the links between Old World fruit bats (family Pteropodidae) and paramyxoviruses has driven a rapid proliferation of surveillance studies in this system. We performed a systematic review and data synthesis of all published paramyxovirus studies of wild pteropodids. Here, we present an open, static, PRISMA-compliant database called pteroparamyxo, which includes 1,482 records of prevalence or seroprevalence from 119 studies published between 1971 and 2023. Using this dataset, we examined biases in sampling effort, predictors of viral positivity, tissue tropism, and pteropodid-paramyxovirus coevolution. We found significant spatial and taxonomic bias in sampling effort, largely driven by overrepresentation of Pteropus-henipavirus systems; in particular, African bats were undersampled for paramyxoviruses relative to Asian and Oceanian taxa. On the viral side, henipaviruses (specifically Hendra, Nipah, and Cedar viruses) were overrepresented relative to pararubulaviruses. We also identified substantial non-random variability in paramyxovirus prevalence and seroprevalence. Spleens and pooled fecal samples from pteropodids yielded the highest PCR positivity, while samples from Oceania tended to have slightly higher seroprevalence than those from Asia and Africa. Collection year was not a significant predictor of positivity, suggesting limited overall change in paramyxovirus PCR prevalence or seroprevalence over the last several decades. Finally, we found weak evidence of pteropodid-paramyxovirus coevolution, supporting relatively frequent host-switching within this system. Our study highlights critical sampling gaps to address in future surveillance studies and provides preliminary evidence for sample and tissue types to prioritize in field- and museum-based sampling. Author SummaryParamyxoviruses are a family of viruses that include the human measles and mumps viruses as well as emerging zoonoses like Hendra and Nipah henipaviruses. These henipaviruses spill over from wild fruit bats to domestic animals and humans in Australia and Asia, causing high fatality rates with no known human treatments or vaccines. The occurrence and prevalence of paramyxoviruses across the entire fruit bat family (Pteropodidae) has never been formally investigated, as published detection attempts have not yet been consolidated into a database. We conducted a systematic review of the existing literature on pteropodid-paramyxovirus sampling efforts and compiled an open database for researchers to reference and use. We then leveraged this novel database for a suite of analyses and demonstrated that Australian and Asian fruit bats are disproportionately sampled for paramyxoviruses while African species are understudied, despite the fact that African samples have slightly higher rates of paramyxovirus detection. We identified sample types that may be particularly likely to yield viral detections, a finding which can help guide future survey efforts using field studies as well as museum collections. We also demonstrate that paramyxoviruses appear liable to switch host species within Pteropodidae, challenging earlier hypotheses that these viruses are host specific.

ecology↗

bioRxiv2023

Periodic shifts in viral load increase risk of spillover from bats

Prediction and management of zoonotic pathogen spillover requires an understanding of infection dynamics within reservoir host populations. Transmission risk is often assessed using prevalence of infected hosts, with infection status based on the presence of genomic material. However, detection of viral genomic material alone does not necessarily indicate the presence of infectious virus, which could decouple prevalence from transmission risk. We undertook a multi-faceted investigation of Hendra virus shedding in Pteropus bats, combining insights from virus isolation, viral load proxies, viral prevalence, and longitudinal patterns of shedding, from 6,151 samples. In addition to seasonal and interannual fluctuation in prevalence, we found evidence for periodic shifts in the distribution of viral loads. The proportion of bats shedding high viral loads was higher during peak prevalence periods during which spillover events were observed, and lower during non-peak periods when there were no spillovers. We suggest that prolonged periods of low viral load and low prevalence reflect prolonged shedding of non-infectious RNA, or viral loads that are insufficient or unlikely to overcome dose barriers to spillover infection. These findings show that incorporating viral load (or proxies of viral load) into longitudinal studies of virus excretion will better inform predictions of spillover risk than prevalence alone. Significance statementWe present a comprehensive analysis of a high-profile bat-virus system (Hendra virus in Australian flying-foxes) to demonstrate that both prevalence and viral loads can shift systematically over time, resulting in concentrated periods of increased spillover risk when prevalence and viral loads are high. We further suggest that prolonged periods of low-prevalence, low-load shedding may not reflect excretion of infectious virus, resolving the outstanding puzzle of why spillovers have not been observed during periods of low off-season prevalence in subtropical Australia, or more frequently in tropical Australia despite consistent low-prevalence shedding. The consideration of viral loads (or proxies of viral load) along with prevalence may improve risk inference from longitudinal surveys of zoonotic viruses across wildlife reservoir hosts.

ecology↗

bioRxiv2019

Relation of seasonal birth pulses and maternal immunity with viral invasion and persistence: A case study of Hendra virus infection in a population of black flying foxes (Pteropus alecto)

Increasing outbreaks of emerging infectious diseases, originating from wildlife, has intensified interest in understanding the dynamics of these diseases in their wildlife reservoir hosts. Until recently, the effect of seasonal birth pulses and subsequent waning of maternally derived antibodies on epidemics in a wild mammal population has received little attention and has remained obscure. In this study, we explore how population structure, influenced by seasonal breeding and maternally derived immunity, affects viral invasion and persistence, using a hypothetical system loosely based on Hendra virus infection in black flying foxes (Pteropus alecto). We used deterministic epidemic models to simulate transient epidemics, following viral introduction into an infection-free population, with a variety of timings within a year and different levels of pre-existing herd immunity. Moreover, we applied different levels of birth synchrony and different modelling methods of waning maternal immunity to examine the effect of birth pulses and maternally derived immunity, both individually and in combination. The presence of waning maternal immunity dispersed the supply time of susceptible individuals in seasonally breeding populations, hence diminishing the effect of birth pulse. Dampened epidemics, caused by waning maternal immunity, made viral invasion and persistence easier. This study enhanced our understanding of viral invasion, persistence, and timing of epidemics in wildlife populations.

epidemiology↗

bioRxiv2018

Optimizing non-invasive sampling of an infectious bat virus

Notable outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to the ecological origins of bat-borne zoonoses, resulting in an increase in ecological and epidemiological studies on bat populations in Africa, Asia, and Australia. The aim of many of these studies is to identify new viral agents with field sampling methods that collect pooled urine samples from large plastic sheets placed under a bat roost. The efficiency of under-roost sampling also makes it an attractive method for gathering roost-level prevalence data. However, the method allows multiple individuals to contribute to a pooled sample, potentially introducing positive bias. To assess the ability of under-roost sampling to accurately estimate viral prevalence, we constructed a probabilistic model to explore the relationship between four sampling designs (quadrant, uniform, stratified, and random) and estimation bias. We modeled bat density and movement with a Poisson cluster process and spatial kernels, and simulated the four underroost sheet sampling designs by manipulating a spatial grid of hexagonal tiles. We performed global sensitivity analyses to identify major sources of estimation bias and provide recommendations for field studies that wish to estimate roost-level prevalence. We found that the quadrant-based design had a positive bias 5-7 times higher than other designs due to spatial auto-correlation among sampling sheets and clustering of bats in the roost. The sampling technique is therefore highly sensitive to viral presence; but lacks specificity, providing poor information regarding dynamics in viral prevalence. Given population sizes of 5000-14000, our simulation results indicate that using a stratified random design to collect 30-40 urine samples from 80-100 sheets, each with an area of 0.75-1m2, would provide sufficient estimation of true prevalence with minimum sampling bias and false negatives. However, acknowledging the general problem of data aggregation, we emphasize that robust inference of true prevalence from field data require information of underpinning roost sizes. Our findings refine our understanding of the underroost sampling technique with the aim of increasing its specificity, and suggest that the method be further developed as an efficient non-invasive sampling technique that provides roost-level estimates of viral prevalence within a bat population.

ecology↗

bioRxiv2017

Inferring Within-Host Bottleneck Size: A Bayesian Approach

A number of approaches exist for bottleneck-size estimation with respect to within-host bacterial infections; however, some are more appropriate than others under certain circumstances. A Bayesian comparison of several approaches is made in terms of the availability of isogenic multitype bacteria (e.g., WITS), knowledge of post-bottleneck dynamics, and the suitability of dilution with monotype bacteria. The results are summarised by a guiding flowchart.\n\nA sampling approach to bottleneck-size estimation is also introduced.

microbiology↗

bioRxiv2017

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.\n\nAuthor SummaryRecent advancements in technology have meant that microbiologists are producing vast amounts of experimental data. However, statistical methods by which we can analyse that data, draw informative inference, and test relevant hypotheses, are much needed. Here, we present a new, efficient inference tool for estimating parameters of stochastic models, with a particular focus on models of within-host bacterial dynamics. The method relies on matching the two lower-order moments of the experimental data (i.e., mean, variance and covariance), to the moments from the mathematical model. The method is verified, and particular choices justified, through a number of simulation studies. We then use this method to estimate models that have been previously estimated using a \"gold-standard\" maximum likelihood procedure.\n\nList of symbolsO_LIA: number of animals\nC_LIO_LIT: number of tagged strains\nC_LIO_LIn: number of organs\nC_LIO_LINi: number of bacteria in organ i\nC_LIO_LImij: migration rate from organ i to organ j\nC_LIO_LIki: killing rate in organ i\nC_LIO_LIri: replication rate in organ i\nC_LIO_LI{tau}i: observation time i\nC_LIO_LIA, B, C: matrices\nC_LIO_LI{lambda}: vector of transition rates\nC_LIO_LIB: Number of bootstrap samples\nC_LIO_LI{theta}*: MDE parameter estimate\nC_LI\n\nAbbreviationsO_LIABC: approximate Bayesian computation\nC_LIO_LIIT: isogenic tagging\nC_LIO_LILV: live vaccine\nC_LIO_LIMARE: mean absolute relative error\nC_LIO_LIMDE: minimum divergence estimate\nC_LIO_LIMLE: maximum likelihood estimate\nC_LIO_LIqPCR: quantitative polymerase chain reaction\nC_LIO_LIWITS: wildtype isogenic tagged strain\nC_LI

microbiology↗