No evidence for enzootic plague within black-tailed prairie dog (Cynomys ludovicianus) populations

Development and evaluation of a multi-target droplet digital PCR assay for highly sensitive and specific detection of Yersinia pestis

BACKGROUND

Plague, caused by the bacterium Yersinia pestis, is a zoonotic disease that poses considerable threats to human health. Nucleic acid tests are crucial for plague surveillance and the rapid detection of Y. pestis. However, inhibitors in complex samples such as soil and animal tissues often hamper nucleic acid detection, leading to a reduced rate of identifying low concentrations of Y. pestis. To address this challenge, we developed a sensitive and specific droplet digital polymerase chain reaction (ddPCR) assay for detecting Y. pestis DNA from soil and animal tissue samples.

METHODS

Three genes (ypo2088, caf1, and pla) from Y. pestis were used to develop a multi-target ddPCR assay. The limits of detection (LoD), reproducibility, and specificity were assessed for bacterial genomic DNA samples. The ability of the assay to detect low concentrations of Y. pestis DNA from simulated soil and mouse liver tissue samples was respectively evaluated and compared with that of quantitative real-time PCR (qPCR).

RESULTS

The results showed that the ddPCR LoDs ranged from 6.2 to 15.4 copies/reaction for the target genes, with good reproducibility and high specificity for Y. pestis. By testing 130 soil and mouse liver tissue samples spiked with Y. pestis, the ddPCR assay exhibited a better sensitivity than that of the qPCR assay used in the study, with LoDs of 102 colony forming units (CFU)/100 mg soil and 103 CFU/20 mg liver. Moreover, the assay presented good quantitative linearity (R2 = 0.99) for Y. pestis at 103-106 CFU/sample for soil and liver samples.

CONCLUSION

The ddPCR assay presented good performance for detecting Y. pestis DNA from soil and mouse tissue samples, showing great potential for improving the detection rate of low concentrations of Y. pestis in plague surveillance and facilitating the early diagnosis of plague cases.

Immunogenetics, sylvatic plague and its vectors: insights from the pathogen reservoir Mastomys natalensis in Tanzania

Yersinia pestis is a historically important vector-borne pathogen causing plague in humans and other mammals. Contemporary zoonotic infections with Y. pestis still occur in sub-Saharan Africa, including Tanzania and Madagascar, but receive relatively little attention. Thus, the role of wildlife reservoirs in maintaining sylvatic plague and spillover risks to humans is largely unknown. The multimammate rodent Mastomys natalensis is the most abundant and widespread rodent in peri-domestic areas in Tanzania, where it plays a major role as a Y. pestis reservoir in endemic foci. Yet, how M. natalensis' immunogenetics contributes to the maintenance of plague has not been investigated to date. Here, we surveyed wild M. natalensis for Y. pestis vectors, i.e., fleas, and tested for the presence of antibodies against Y. pestis using enzyme-linked immunosorbent assays (ELISA) in areas known to be endemic or without previous records of Y. pestis in Tanzania. We characterized the allelic and functional (i.e., supertype) diversity of the major histocompatibility complex (MHC class II) of M. natalensis and investigated links to Y. pestis vectors and infections. We detected antibodies against Y. pestis in rodents inhabiting both endemic areas and areas considered non-endemic. Of the 111 nucleotide MHC alleles, only DRB*016 was associated with an increased infestation with the flea Xenopsylla. Surprisingly, we found no link between MHC alleles or supertypes and antibodies of Y. pestis. Our findings hint, however, at local adaptations towards Y. pestis vectors, an observation that more exhaustive sampling could unwind in the future.

A big data-model integration approach for predicting epizootics and population recovery in a keystone species

Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data-model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.

Reevaluation of the Role of Blocked Oropsylla hirsuta Prairie Dog Fleas (Siphonaptera: Ceratophyllidae) in Yersinia pestis (Enterobacterales: Enterobacteriaceae) Transmission

Prairie dogs in the western United States experience periodic epizootics of plague, caused by the flea-borne bacterial pathogen Yersinia pestis. An early study indicated that Oropsylla hirsuta (Baker), often the most abundant prairie dog flea vector of plague, seldom transmits Y. pestis by the classic blocked flea mechanism. More recently, an alternative early-phase mode of transmission has been proposed as the driving force behind prairie dog epizootics. In this study, using the same flea infection protocol used previously to evaluate early-phase transmission, we assessed the vector competence of O. hirsuta for both modes of transmission. Proventricular blockage was evident during the first two weeks after infection and transmission during this time was at least as efficient as early-phase transmission 2 d after infection. Thus, both modes of transmission likely contribute to plague epizootics in prairie dogs.

Plague risk in the western United States over seven decades of environmental change

After several pandemics over the last two millennia, the wildlife reservoirs of plague (Yersinia pestis) now persist around the world, including in the western United States. Routine surveillance in this region has generated comprehensive records of human cases and animal seroprevalence, creating a unique opportunity to test how plague reservoirs are responding to environmental change. Here, we test whether animal and human data suggest that plague reservoirs and spillover risk have shifted since 1950. To do so, we develop a new method for detecting the impact of climate change on infectious disease distributions, capable of disentangling long-term trends (signal) and interannual variation in both weather and sampling (noise). We find that plague foci are associated with high-elevation rodent communities, and soil biochemistry may play a key role in the geography of long-term persistence. In addition, we find that human cases are concentrated only in a small subset of endemic areas, and that spillover events are driven by higher rodent species richness (the amplification hypothesis) and climatic anomalies (the trophic cascade hypothesis). Using our detection model, we find that due to the changing climate, rodent communities at high elevations have become more conducive to the establishment of plague reservoirs-with suitability increasing up to 40% in some places-and that spillover risk to humans at mid-elevations has increased as well, although more gradually. These results highlight opportunities for deeper investigation of plague ecology, the value of integrative surveillance for infectious disease geography, and the need for further research into ongoing climate change impacts.