Biology subjects

Borremans, B.

Publications and source records attributed to Borremans, B..

12 recordsLinked to original sources

bioRxiv2024

Integrating predictors of host condition into spatiotemporal multi-scale models of virus shedding

Understanding where and when pathogens occur in the environment has implications for reservoir population health and infection risk. In reservoir hosts, infection status and pathogen shedding are affected by processes interacting across different scales: from landscape features affecting host location and transmission to within-host processes affecting host immunity and infectiousness. While uncommonly done, simultaneously incorporating processes across multiple scales may improve pathogen shedding predictions. In Australia, the black flying fox (Pteropus alecto) is a natural host for the zoonotic Hendra virus, which is hypothesized to cause latent infections in bats. Re-activation and virus shedding may be triggered by poor host condition, leading to virus excretion through urine. Here, we developed a statistical modeling approach that combined data at multiple spatial and temporal scales to capture ecological and biological processes potentially affecting virus shedding. We parameterized these models using existing datasets and compared model performance to under-roost virus shedding data from 2011-2014 in 23 roosts across a 1200-km transect. Our approach enabled comparisons among multiple model structures to determine which variables at which scales are most influential for accurate predictions of virus shedding in space and time. We identified environmental predictors and temporal lags of these features that were important for determining where reservoirs are located and multiple independent proxies for reservoir condition. The best-performing multi-scale model delineated periods of low and high virus prevalence, reflecting observed shedding patterns from pooled under-roost samples. Incorporating regional indicators of food scarcity enhanced model accuracy while incorporating other stress indicators at local scales confounded this signal. This multiscale modeling approach enabled the combination of processes from different ecological scales and identified environmental variables influencing Hendra virus shedding, highlighting how integrating data across scales may improve risk forecasts for other pathogen systems.

ecology↗

bioRxiv2024

Antibodies against medically relevant arthropod-borne viruses in the ubiquitous African rodent Mastomy natalensis

Over the past decades, the number of arthropod-borne virus (arbovirus) outbreaks has increased worldwide. Knowledge regarding the sylvatic cycle (i.e., non-human hosts/environment) of arboviruses is limited, particularly in Africa, and the main hosts for virus maintenance are unknown. Previous studies have shown the presence of antibodies against certain arboviruses (i.e., chikungunya-, dengue- and zika virus) in African non-human primates and bats. We hypothesize that small mammals, specifically rodents, may function as amplifying hosts in anthropogenic environments. The detection of RNA of most arboviruses is complicated by the viruss short viremic period within their hosts. An alternative to determine arbovirus hosts is by detecting antibodies, which can persist several months. We developed a high-throughput multiplex immunoassay to detect antibodies against 15 medically relevant arboviruses. We used this assay to assess almost 1,300 blood samples of the multimammate mouse, Mastomys natalensis from Tanzania. In 24% of the samples, we detected antibodies against at least one of the tested arboviruses, with high seroprevalences of antibodies reacting against dengue virus serotype one (7.6%) and two (8.4%) and chikungunya virus (6%). Seroprevalence was higher in females and increased with age, which could be explained by inherent immunity and behavioral differences between sexes and the increased chance of exposure to an arbovirus with age. We evaluated whether antibodies against multiple arboviruses co-occur more often than randomly and found that this may be true for some members of the Flaviviridae and Togaviridae. In conclusion, the development of an assay against a wide diversity of medically relevant arboviruses enabled the analysis of a large sample collection of one of the most abundant African small mammals. Our findings suggest a role in the transmission of multiple arboviruses by this ubiquitous rodent and provide a solid foundation for future molecular screening to elucidate the role in the arbovirus transmission cycle. Author summaryOne of the main causes of zoonotic related human morbidity and mortality is the transmission of arthropod-borne viruses such as dengue virus, Yellow Fever virus, and chikungunya virus. These viruses cannot only infect humans but also livestock, pets, and wildlife, though our understanding of their non-human hosts remains limited. Rodents are thought to be an interesting host for these viruses because they can be abundant, often live near humans and some are already known to be viral hosts. However, research has focused on non-human primates, neglecting other potential hosts. To address this gap, we have developed a high-throughput antibody test to screen rodent blood against 15 different arboviruses. Our findings reveal that a proportion of Mastomys natalensis, a common African rodent species, carry antibodies that (cross-)react against these viruses. We hypothesize that immunologically naive juveniles may drive transmission, particularly during population outbreaks. These outbreaks coincide with environmental conditions that are favorable for mosquitoes, the vectors of these viruses. Thus, increasing the risk of spillover to humans, livestock, and wildlife. Understanding the role of rodents in arbovirus transmission dynamics is crucial for mitigating zoonotic disease risks.

immunology↗

bioRxiv2024

Navigating cross-reactivity and host species effects in a serological assay: A case study of the microscopic agglutination test for Leptospira serology

BackgroundSerology (the detection of antibodies formed by the host against an infecting pathogen) is frequently used to assess current infections and past exposure to specific pathogens. However, the presence of cross-reactivity among host antibodies in serological data makes it challenging to interpret the patterns and draw reliable conclusions about the infecting pathogen or strain. Methodology/Principal FindingsIn our study, we use microscopic agglutination test (MAT) serological data from three host species [California sea lion (Zalophus californianus), island fox (Urocyon littoralis), and island spotted skunk (Spilogale gracilis)] with confirmed infections to assess differences in cross-reactivity by host species and diagnostic laboratory. All host species are known to be infected with the same serovar of Leptospira interrogans. We find that absolute and relative antibody titer magnitudes vary systematically across host species and diagnostic laboratories. Despite being infected by the same Leptospira serovar, three host species exhibit different cross-reactivity profiles to a 5-serovar diagnostic panel. We also observe that the cross-reactive antibody titer against a non-infecting serovar can remain detectable after the antibody titer against the infecting serovar declines below detectable levels. Conclusions/SignificanceCross-reactivity in serological data makes interpretation difficult and can lead to common pitfalls. Our results show that the highest antibody titer is not a reliable indicator of infecting serovar and highlight an intriguing role of host species in shaping reactivity patterns. On the other side, seronegativity against a given serovar does not rule out that serovar as the cause of infection. We show that titer magnitudes can be influenced by both host species and diagnostic laboratory, indicating that efforts to interpret absolute titer levels (e.g., as indicators of recent infection) must be calibrated to the system under study. Thus, we implore scientists and health officials using serological data for surveillance to interpret the data with caution. AUTHOR SUMMARYSerology is frequently used for disease surveillance, especially in systems that are resource constrained or logistically challenging. Serological testing involves analyzing blood serum samples to detect antibodies with reactivity toward specific pathogens (or more generally, molecular antigens), with the goal of characterizing past exposure to those pathogens. However, these antibodies can be non-specific and may react against other related pathogens or strains - a phenomenon known as cross-reactivity. Interpretation of serological data exhibiting cross-reactivity is difficult and simplifying assumptions are often made (e.g., to interpret the strain that elicits the highest antibody titer level as the infecting pathogen strain). Our work shows that interpreting antibody data requires more nuance and more caution. Both absolute titer levels and relative reactivity against different strains can vary across host species and diagnostic laboratories, so it is essential to interpret these data in the appropriate context. These host species differences in antibody reactivity and cross-reactivity patterns make direct comparisons across species inadvisable.

ecology↗

bioRxiv2023

Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples

Pathogen transmission studies require sample collection over extended periods, which can be challenging and costly, especially in the case of wildlife. A useful strategy can be to collect pooled samples, but this presents challenges when the goal is to estimate prevalence. This is because pooling can introduce a dilution effect where pathogen concentration is lowered by the inclusion of negative or lower-concentration samples, while at the same time a pooled sample can test positive even when some of the contributing samples are negative. If these biases are taken into account, the concentration of a pooled sample can be leveraged to infer the most likely proportion of positive individuals, and thus improve overall prevalence reconstruction, but few methods exist that account for the sample mixing process. We present a Bayesian multilevel model that estimates prevalence dynamics over time using pooled and individual samples in a wildlife setting. The model explicitly accounts for the complete mixing process that determines pooled sample concentration, thus enabling accurate prevalence estimation even from pooled samples only. As it is challenging to link individual-level metrics such as age, sex, or immune markers to infection status when using pooled samples, the model also allows the incorporation of individual-level samples. Crucially, when individual samples can test false negative, a potentially strong bias is introduced that results in incorrect estimates of regression coefficients. The model, however, can account for this by leveraging the combination of pooled and individual samples. Last, the model en- ables estimation of extrinsic environmental effects on prevalence dynamics. Using a simulated dataset inspired by virus transmission in flying foxes, we show that the model is able to accurately estimate prevalence dynamics, false negative rate, and covariate effects. We test model performance for a range of realistic sampling scenarios and find that while it is generally robust, there are a number of factors that should be considered in order to maximize performance. The model presents an important advance in the use of pooled samples for estimating prevalence dynamics in a wildlife setting, can be used with any biomarker of infection (Ct values, antibody levels, other infection biomarkers) and can be applied to a wide range of host-pathogen systems.

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↗

bioRxiv2023

The Symptomatology and Diagnosis of Domoic Acid Toxicosis in Stranded California Sea Lions (Zalophus californianus): A Review and Evaluation of 20 Years of Cases to Guide Prognosis

Domoic acid (DA) is a glutaminergic excitatory neurotoxin that causes morbidity and mortality of California sea lions (Zalophus californianus; CSL) and other marine mammals due to a suite of effects mostly on the nervous and cardiac systems. Between 1998 and 2019, 11,737 live-stranded CSL were admitted to The Marine Mammal Center (TMMC; Sausalito, CA, USA), over 2,000 of which were intoxicated by DA. A plethora of clinical research has been performed over the past 20 years to characterize the range of toxic effects of DA exposure on CSLs, generating the largest dataset on the effects of natural exposure to this toxin in wildlife. Here we review published methods for diagnosing DA intoxication, clinical presentation and treatment of DA-intoxicated CSL, and present a practical, reproducible scoring system called the neuroscore (NS) to help assess whether a DA-affected CSL is fit for release to the wild following rehabilitation. The largest proportion of DA-intoxicated CSL were adult females (58.6%). The proportions of acute and chronic cases were 63.5% and 36.5%, respectively, with 44% of affected CSL released and 56% either dying naturally or euthanized. The average time in rehabilitation was 15.9 days (range 0-169) for all outcomes. Logistic regression models were used to assess the relationships between outcome (released vs. euthanized or died) and multiple variables to predict the outcome for a subset of 92 stranded CSLs. The best performing model (85% prediction accuracy; area under the curve = 0.90) consisted of four variables: final NS, change in NS over time, whether the animal began eating in rehabilitation, and the state of nutrition on admission. These results suggest that a behavioral scoring system is a useful tool to assess the fitness for release of stranded DA-intoxicated CSL.

zoology↗

bioRxiv2023

Pathogenic Leptospira are widespread in the urban wildlife of southern California

Leptospirosis is the most widespread zoonotic disease in the world, yet it is broadly understudied in multi-host wildlife systems. Knowledge gaps regarding Leptospira circulation in wildlife, particularly in densely populated areas, contribute to frequent misdiagnoses in humans and domestic animals. We assessed Leptospira prevalence levels and risk factors in five target wildlife species across the greater Los Angeles region: striped skunks (Mephitis mephitis), Northern raccoons (Procyon lotor), coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), and fox squirrels (Sciurus niger). We sampled more than 960 individual animals, including over 700 from target species in the greater Los Angeles region, and an additional 260 sampled opportunistically from other regions and species. In the five target species, seroprevalences ranged from 5-60% and active infection prevalences ranged from 0.8-15.2% in all except fox squirrels (0%). Patterns of serologic reactivity suggest that mainland terrestrial wildlife, particularly mesocarnivores, could be the source of repeated observed introductions of Leptospira into local marine and island ecosystems. Overall, we found evidence of widespread Leptospira exposure in wildlife across Los Angeles and surrounding regions. This indicates exposure risk for humans and domestic animals and highlights that this pathogen can circulate endemically in many wildlife species even in densely populated urban areas.

ecology↗

bioRxiv2022

Inferring time of infection from field data using dynamic models of antibody decay

Studies of infectious disease ecology often rely heavily on knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody level data being one of the most promising sources of information. The use of antibody levels to back-calculate infection time requires the development of a host-pathogen system-specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data. Here, we present a way to do this in a Bayesian framework that facilitates the incorporation of all available information about potential infection times. We apply the model to estimate infection times of Channel Island foxes infected with Leptospira interrogans, leading to reductions of 51-92% in the window of possible infection times. Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements of infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. The method substantially simplifies the challenge of modeling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology, and can even be applied to cross-sectional data once the model is trained.

ecology↗

bioRxiv2019

Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface

AO_SCPCAPBSTRACTC_SCPCAPManaging pathogen spillover at the wildlife-livestock interface is a key step toward improving global animal health, food security, and wildlife conservation. However, predicting the effectiveness of management actions across host-pathogen systems with different life histories is an on-going challenge since data on intervention effectiveness are expensive to collect and results are system-specific. We developed a simulation model to explore how the efficacies of different management strategies vary according to host movement patterns and epidemic growth rates. The model suggested that fast-growing, fast-moving epidemics like avian influenza were best-managed with actions like biosecurity or containment, which limited and localized overall spillover risk. For fast-growing, slower-moving diseases like foot-and-mouth disease, depopulation or prophylactic vaccination were competitive management options. Many actions performed competitively when epidemics grew slowly and host movements were limited, and how management efficacy related to epidemic growth rate or host movement propensity depended on what objective was used to evaluate management performance. This framework may be a useful step in advancing how we classify and prioritise responses to novel pathogen spillover threats, and evaluate current management actions for pathogens emerging at the wildlife-livestock interface.

epidemiology↗

bioRxiv2017

Comparing arenavirus infection patterns in experimentally and naturally infected rodent hosts

Infectious diseases of wildlife are typically studied using data on antibody and pathogen presence/level. In order to interpret these data, it is necessary to know the course of antibodies and pathogen presence/levels after infection. Such data are typically collected using experimental infection studies in which host individuals are inoculated in the laboratory and sampled over an extended period, but because laboratory conditions are controlled and much less variable than natural conditions, the immune response and pathogen dynamics may differ. Here, we compared Morogoro arenavirus infection patterns between naturally and experimentally infected multimammate mice (Mastomys natalensis). Longitudinal samples were collected during three months of bi-weekly trapping in Morogoro, Tanzania, and antibody titer and viral RNA presence were determined to assess whether the natural temporal patterns are similar to those previously observed in the laboratory. A good match with laboratory data was found for 52% of naturally infected individuals, while most of the mismatches can be explained by the presence of chronically infected individuals (35%), maternal antibodies (10%) and an antibody detection limit (25%). These results suggest that while laboratory data are useful for interpreting field samples, there can still be differences due to conditions that were not tested in the laboratory.\n\nImportant noticeThis is a pre-print version of the manuscript, made available through bioRxiv.org. Note that this manuscript has not yet been peer-reviewed, and has been submitted to a peer-reviewed journal.

immunology↗

bioRxiv2017

No evidence for avoidance of black rat scent by the presumably less competitive Natal multimammate mouse in a choice experiment

In Africa, indigenous multimammate mice (Mastomys natalensis) only appear to live commensally in houses when invasive black rats (Rattus rattus) are absent, yet little is known about the underlying mechanism. Avoidance through smell may cause the absence of M. natalensis from areas occupied by R. rattus, but this hypothesis has not yet been tested. We conducted a Y-maze choice experiment where 15 M. natalensis were offered a choice between corridors containing conspecific scent, R. rattus scent, and a control scent. Residence time in the R. rattus corridor was greater than that in the control corridor but equal to that in the M. natalensis corridor, suggesting that multimammate mice do not actively avoid the scent of their invasive competitor.

zoology↗

bioRxiv2017

The shape of the contact-density function matters when modelling disease transmission in fluctuating populations

Models of disease transmission in a population with changing densities must assume a relation between infectious contacts and density. Typically, a choice is made between a constant (frequency-dependence) and a linear (density-dependence) contact-density function, but it is becoming increasingly clear that intermediate, nonlinear functions intermediate are more realistic. It is currently not clear however what the exact consequences would be of different contact-density functions in fluctuating populations. By combining field data on rodent host (Mastomys natalensis) demography, experimentally-derived contact-density data, and laboratory and field data Morogoro virus infection dynamics, we explored the effects of different contact-density function shapes on transmission dynamics and invasion/persistence. While invasion and persistence were clearly affected by the shape of the function, the effects on outbreak characteristics such as infection prevalence and seroprevalence were less obvious. This means that it may be difficult to distinguish between the different shapes based on how well models fit to real data. The shape of the transmission-density function should therefore be chosen with care, and is ideally based on existing information such as a previously quantified contact- or transmission-density relationship or the underlying biology of the host species in relation to the infectious agent.

epidemiology↗