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Rodents are the largest and most diverse group of mammals. Covering a wide range of structural and functional adaptations, rodents successfully occupy virtually every terrestrial habitat, and they are often found in close association with humans, domestic animals, and wildlife. Although a significant amount of research has focused on rodents’ prominence as known reservoirs of zoonotic viruses, there has been less emphasis on the viral ecology of rodents in general. Here, we utilized a viral metagenomics approach to investigate polyomaviruses in wild rodents from the Baja California peninsula, Mexico, using fecal samples. We identified a novel polyomavirus in fecal samples from two rodent species, a spiny pocket mouse (Chaetodipus spinatus) and a Dulzura kangaroo rat (Dipodomys simulans). These two polyomaviruses represent a new species in the genus Betapolyomavirus. Sequences of this polyomavirus cluster phylogenetically with those of other rodent polyomaviruses and two other non-rodent polyomaviruses (WU and KI) that have been identified in the human respiratory tract. Through our continued work on seven species of rodents, we endeavor to explore the viral diversity associated with wild rodents on the Baja California peninsula and expand on current knowledge of rodent viral ecology and evolution. 

ABSTRACT South polar skuas migrate from subtropical regions to breed along coastal Antarctica. In a fecal sample collected on Ross Island, Antarctica, we identified 20 diverse microviruses ( Microviridae ) that share low levels of similarity to currently known microviruses; 6 appear to use a Mycoplasma/Spiroplasma codon translation table. 

Feline foamy virus (FFV) is a contact-dependent retrovirus forming chronic, largely apathogenic, infections in domestic and wild felid populations worldwide. Given there is no current ‘gold standard’ diagnostic test for FFV, efforts to elucidate the ecology and epidemiology of the virus may be complicated by unknown sensitivity and specificity of diagnostic tests. Using Bayesian Latent Class Analysis, we estimated the sensitivity and specificity of the only two FFV diagnostic tests available—ELISA and qPCR—as well as the prevalence of FFV in a large cohort of pumas from Colorado. We evaluated the diagnostic agreement of ELISA and qPCR, and whether differences in their diagnostic accuracy impacted risk factor analyses for FFV infection. Our results suggest ELISA and qPCR did not have strong diagnostic agreement, despite FFV causing a persistent infection. While both tests had similar sensitivity, ELISA had higher specificity. ELISA, but not qPCR, identified age to be a significant risk factor, whereas neither qPCR nor ELISA identified sex to be a risk factor. This suggests FFV transmission in pumas may primarily be via non-antagonistic, social interactions between adult conspecifics. Our study highlights that combined use of qPCR and ELISA for FFV may enhance estimates of the true prevalence of FFV and epidemiological inferences.

 

Urban expansion can fundamentally alter wildlife movement and gene flow, but how urbanization alters pathogen spread is poorly understood. Here, we combine high resolution host and viral genomic data with landscape variables to examine the context of viral spread in puma (Puma concolor) from two contrasting regions: one bounded by the wildland urban interface (WUI) and one unbounded with minimal anthropogenic development (UB). We found landscape variables and host gene flow explained significant amounts of variation of feline immunodeficiency virus (FIV) spread in the WUI, but not in the unbounded region. The most important predictors of viral spread also differed; host spatial proximity, host relatedness, and mountain ranges played a role in FIV spread in the WUI, whereas roads might have facilitated viral spread in the unbounded region. Our research demonstrates how anthropogenic landscapes can alter pathogen spread, providing a more nuanced understanding of host-pathogen relationships to inform disease ecology in free-ranging species.

 

ABSTRACT Termites have a unique ability to effectively digest lignocellulose with the help of mutualistic symbionts. While gut bacteria and protozoa have been relatively well characterized in termites, the virome remains largely unexplored. Here, we report two genomes of microviruses (termite-associated microvirus-1 [TaMV-1] and termite-associated microvirus-2 [TaMV-2]) associated with the gut of Coptotermes formosanus . 

We introduce a new R package “MrIML” (“Mister iml”; Multi‐response Interpretable Machine Learning). MrIML provides a powerful and interpretable framework that enables users to harness recent advances in machine learning to quantify multilocus genomic relationships, to identify loci of interest for future landscape genetics studies, and to gain new insights into adaptation across environmental gradients. Relationships between genetic variation and environment are often nonlinear and interactive; these characteristics have been challenging to address using traditional landscape genetic approaches. Our package helps capture this complexity and offers functions that fit and interpret a wide range of highly flexible models that are routinely used for single‐locus landscape genetics studies but are rarely extended to estimate response functions for multiple loci. To demonstrate the package's broad functionality, we test its ability to recover landscape relationships from simulated genomic data. We also apply the package to two empirical case studies. In the first, we model genetic variation of North American balsam poplar (Populus balsamifera, Salicaceae) populations across environmental gradients. In the second case study, we recover the landscape and host drivers of feline immunodeficiency virus genetic variation in bobcats (Lynx rufus). The ability to model thousands of loci collectively and compare models from linear regression to extreme gradient boosting, within the same analytical framework, has the potential to be transformative. The MrIML framework is also extendable and not limited to modelling genetic variation; for example, it can quantify the environmental drivers of microbiomes and coinfection dynamics.