Chromosomal characterization of three species of the genus Mastomys in Senegal

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.

The 2019 emergence of SARS-CoV-2 is a grim reminder of the threat animal-borne pathogens pose to human health. Even prior to SARS-CoV-2, the spillover of pathogens from animal reservoirs was a persistent problem, with pathogens such as Ebola, Nipah, and Lassa regularly but unpredictably causing outbreaks. Machine-learning models that anticipate when and where pathogen transmission from animals to humans is likely to occur would help guide surveillance efforts and preemptive countermeasures like information campaigns or vaccination programs. We develop a novel machine learning framework that uses datasets describing the distribution of a virus within its host and the range of its animal host, along with data on spatial patterns of human immunity, to infer rates of animal-to-human transmission across a region. By training the model on data from the animal host alone, our framework allows rigorous validation of spillover predictions using human data. We apply our framework to Lassa fever, a viral disease of West Africa that is spread to humans by rodents, and use the predictions to update estimates of Lassa virus infections in humans. Our results suggest that Nigeria is most at risk for the emergence of Lassa virus, and should be prioritized for outbreak-surveillance.

Hematology and Clinical Chemistry Reference Ranges for Laboratory-Bred Natal Multimammate Mice (Mastomys natalensis)

Laboratory-controlled physiological data for the multimammate rat (Mastomys natalensis) are scarce, despite this species being a known reservoir and vector for zoonotic viruses, including the highly pathogenic Lassa virus, as well as other arenaviruses and many species of bacteria. For this reason, M. natalensis is an important rodent for the study of host-virus interactions within laboratory settings. Herein, we provide basic blood parameters for age- and sex-distributed animals in regards to blood counts, cell phenotypes and serum chemistry of a specific-pathogen-monitored M.natalensis breeding colony, to facilitate scientific insight into this important and widespread rodent species.

Genetic distinction between contiguous urban and rural multimammate mice in Tanzania despite gene flow

Special conditions are required for genetic differentiation to arise at a local geographical scale in the face of gene flow. The Natal multimammate mouse, Mastomys natalensis, is the most widely distributed and abundant rodent in sub-Saharan Africa. A notorious agricultural pest and a natural host for many zoonotic diseases, it can live in close proximity to humans and appears to compete with other rodents for the synanthropic niche. We surveyed its population genetic structure across a 180-km transect in central Tanzania along which the landscape varied between agricultural land in a rural setting and natural woody vegetation, rivers, roads and a city (Morogoro). We sampled M. natalensis across 10 localities and genotyped 15 microsatellite loci from 515 individuals. Hierarchical STRUCTURE analyses show a K-invariant pattern distinguishing Morogoro suburbs (located in the centre of the transect) from nine surrounding rural localities. Landscape connectivity analyses in Circuitscape and comparison of rainfall patterns suggest that neither geographical isolation nor natural breeding asynchrony could explain the genetic differentiation of the urban population. Using the isolation-with-migration model implemented in IMa2, we inferred that a split between suburban and rural populations would have occurred recently (<150 years ago) with higher urban effective population density consistent with an urban source to rural sink of effective migration. The observed genetic differentiation of urban multimammate mice is striking given the uninterrupted distribution of the animal throughout the landscape and the high estimates of effective migration (2N_e M = 3.0 and 29.7), suggesting a strong selection gradient across the urban boundary.

Population genetic structure of two ecologically distinct multimammate rats: the commensal Mastomys natalensis and the wild Mastomys erythroleucus in southeastern Senegal

Using the same set of microsatellite markers, we compared the population genetic structure of two Mastomys species, one being exclusively commensal in southeastern Senegal, and the other being continuously distributed outside villages in this region. Both species were sampled in the same landscape context and at the same spatial scale. According to the expectations based on the degree of habitat patchiness (which is higher for commensal populations in this rural area), genetic diversity was lower and genetic differentiation was higher in commensal populations of Mastomys natalensis than in wild populations of Mastomys erythroleucus. Contrasting estimates of effective dispersal and current migration rates corroborates previous data on differences in social structure between the two species. Isolation-by-distance analyses showed that human-mediated dispersal is not a major factor explaining the pattern of genetic differentiation for M. natalensis, and that gene flow is high and random between M. erythroleucus populations at the spatial scale considered.

Cytochrome b-based phylogeny of the Praomys group (Rodentia, Murinae): a new African radiation?

Complete cytochrome b gene sequences allows, for the first time, establishing a nearly complete phylogeny among the Praomys group sensu lato. The genera Praomys, Mastomys and Stenocephalemys appear paraphyletic. Myomys is polyphyletic and this genus name probably needs to be restricted to its type species, M. verreauxii. The genera Zelotomys and Colomys appear as sister groups. Mastomys pernanus and Malacomys verschureni nest within the Praomys group, but their generic assignation must be further clarified. The genus Heimyscus appears closest to Praomys than to Hylomyscus. The different lineages probably result from an adaptive radiation at the end of the Miocene.