Hepadnaviruses are partially double-stranded DNA viruses that infect a variety of species. The prototypical virus in this family is the human hepatitis B virus, which chronically infects approximately 400 million people worldwide and is a risk factor for progressive liver disease and liver cancer. The first hepadnavirus isolated from carnivores was a domestic cat hepadnavirus (DCH), initially identified in Australia and subsequently detected in cats in Europe and Asia. As with all characterized hepadnaviruses so far, DCH infection has been associated with hepatic disease in its host. Prevalence of this infection in the United States has not been explored broadly. Thus, we utilized conventional and quantitative PCR to screen several populations of domestic cats to estimate DCH prevalence in the United States. We detected DCH DNA in 1 out of 496 animals (0.2%) in the U.S. cohort. In contrast, we detected circulating DCH DNA in 7 positive animals from a cohort of 67 domestic cats from Australia (10.4%), consistent with previous studies. The complete consensus genome of the U.S. DCH isolate was sequenced by Sanger sequencing with overlapping PCR products. An in-frame deletion of 157 bp was identified in the N-terminus of the core open reading frame. The deletion begins at the direct repeat 1 sequence (i.e., the 5′ end of the expected double-stranded linear DNA form), consistent with covalently closed circular DNA resultant from illegitimate recombination described in other hepadnaviruses. Comparative genome sequence analysis indicated that the closest described relatives of the U.S. DCH isolate are those previously isolated in Italy. Motif analysis supports DCH using NTCP as an entry receptor, similar to human HBV. Our work indicates that chronic DCH prevalence in the U.S. is likely low compared to other countries.

Identifying drivers of transmission prior to an epidemic—especially of an emerging pathogen—is a formidable challenge for proactive disease management efforts. To overcome this gap, we tested a novel approach hypothesizing that an apathogenic virus could elucidate drivers of transmission processes, and thereby predict transmission dynamics of an analogously transmitted virulent pathogen. We evaluated this hypothesis in a model system, the Florida panther (Puma concolor coryi), using apathogenic feline immunodeficiency virus (FIV) to predict transmission dynamics for another retrovirus, pathogenic feline leukemia virus (FeLV). We derived a transmission network using FIV whole genome sequences, and used exponential random graph models to determine drivers structuring this network. We used the identified drivers to predict transmission pathways among panthers; simulated FeLV transmission using these pathways and three alternate modeling approaches; and compared predictions against empirical data collected during a historical FeLV outbreak in panthers. FIV transmission was primarily driven by panther age class and distances between panther home range centroids. Prospective FIV-based predictions of FeLV transmission dynamics performed at least as well as simpler, often retrospective approaches, with evidence that FIV-based predictions could capture the spatial structuring of the observed FeLV outbreak. Our finding that an apathogenic agent can predict transmission of an analogously transmitted pathogen is an innovative approach that warrants testing in other host-pathogen systems to determine generalizability. Use of such apathogenic agents holds promise for improving predictions of pathogen transmission in novel host populations, and can thereby revolutionize proactive pathogen management in human and animal systems.Significance StatementPredicting infectious disease transmission dynamics is fraught with assumptions which limit our ability to proactively develop targeted control strategies. We show that transmission of non-disease causing (apathogenic) agents provides invaluable insight into drivers of transmission prior to outbreaks of more serious diseases. Integrating genomic and network approaches, we tested an apathogenic virus as a proxy for predicting transmission dynamics of a deadly virus in the Florida panther. We found that apathogenic virus-based predictions of pathogen transmission dynamics performed at least as well as simpler transmission models, and offered the advantage of prospectively identifying the underlying management-relevant drivers of transmission. Our innovative approach offers an opportunity to proactively design disease control strategies in at-risk animal and human populations.

Feline leukemia virus (FeLV) is a retrovirus that primarily affects domestic cats. Close interactions with domestic cats, including predation, can lead to the interspecific transmission of the virus to pumas, bobcats, or other feline species.

Identifying drivers of transmission—especially of emerging pathogens—is a formidable challenge for proactive disease management efforts. While close social interactions can be associated with microbial sharing between individuals, and thereby imply dynamics important for transmission, such associations can be obscured by the influences of factors such as shared diets or environments. Directly-transmitted viral agents, specifically those that are rapidly evolving such as many RNA viruses, can allow for high-resolution inference of transmission, and therefore hold promise for elucidating not only which individuals transmit to each other, but also drivers of those transmission events. Here, we tested a novel approach in the Florida panther, which is affected by several directly-transmitted feline retroviruses. We first inferred the transmission network for an apathogenic, directly-transmitted retrovirus, feline immunodeficiency virus (FIV), and then used exponential random graph models to determine drivers structuring this network. We then evaluated the utility of these drivers in predicting transmission of the analogously transmitted, pathogenic agent, feline leukemia virus (FeLV), and compared FIV-based predictions of outbreak dynamics against empirical FeLV outbreak data. FIV transmission was primarily driven by panther age class and distances between panther home range centroids. FIV-based modeling predicted FeLV dynamics similarly to common modeling approaches, but with evidence that FIV-based predictions captured the spatial structuring of the observed FeLV outbreak. While FIV-based predictions of FeLV transmission performed only marginally better than standard approaches, our results highlight the value of proactively identifying drivers of transmission—even based on analogously-transmitted, apathogenic agents—in order to predict transmission of emerging infectious agents. The identification of underlying drivers of transmission, such as through our workflow here, therefore holds promise for improving predictions of pathogen transmission in novel host populations, and could provide new strategies for proactive pathogen management in human and animal systems.

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