Biology subjects

Juman, M. M.

Publications and source records attributed to Juman, M. M..

3 recordsLinked to original sources

bioRxiv2026

Historic and contemporary museum specimens implicate Northern Red-backed Vole (Clethrionomys rutilus) as borealpox host as early as 1990s

Borealpox virus (BRPV; formerly Alaskapox) is an orthopoxvirus that has caused seven reported human infections in Alaska since 2015, including a fatal case in 2023. The natural reservoir of BRPV is unknown, although previous investigations have raised the possibility of wild small mammals transmitting the virus to humans, either through direct contact or via domestic cats and dogs. To understand which species may be involved in the maintenance and/or spillover of BRPV in Alaska, we trapped and sampled wild small mammals (including voles, shrews, and squirrels) in 2021 and 2024 near reported human case locations in Fairbanks and the Kenai Peninsula, respectively. We found evidence of previous exposure to orthopoxviruses in five species (including the House Mouse, Mus musculus) and detected BRPV DNA as well as viable virus in Northern Red-backed Voles (Clethrionomys rutilus). Further, screening of tissues from historical museum specimens revealed BRPV DNA in C. rutilus specimens collected in Denali National Park and Preserve in 1998 and 1999, 17 years before the first reported human case of BRPV. Phylogenomic analysis of all human and animal BRPV isolates strongly supports the hypothesis of local human infections through multiple spillover events. These findings suggest C. rutilus as a possible reservoir species for BRPV and indicate that BRPV has been present in Alaskan wild small-mammal populations for at least 25 years. Our study highlights the potential of museum collections to elucidate the temporal, spatial, and host ranges of emerging pathogens. Further museum- and field-based sampling will clarify the true geographic range of BRPV, which is closely related to Old World orthopoxviruses and may be circulating beyond North America.

ecology↗

bioRxiv2025

Biodiversity databases as underutilized resources for pathogen discovery: a quantitative synthesis of bat and rodent tissue collections in natural history museums

Zoonotic spillovers are becoming increasingly frequent, and the devastating effects of the SARS-CoV-2 pandemic demonstrate our continued inability to combat their consequences effectively. Natural history museums can enhance the study of zoonoses by serving as valuable resources for understanding the ecology and evolutionary history of pathogens and their wildlife hosts. Despite the growing interest in the role museums can play in pathobiology research and zoonotic risk assessment, there remains a lack of centralized resources for locating tissue samples that may be leveraged for pathogen discovery. Using the worlds most significant global aggregator of museum specimens, The Global Biodiversity Information Facility (GBIF), we examined how such tools could be adopted to identify specimens that might be sources of viral genetic material. Focusing on tissue samples from the mammalian orders Rodentia and Chiroptera, speciose taxa that host a high diversity of known zoonotic viruses, we examined temporal, spatial, and taxonomic gaps and patterns in the available tissue samples. Our analyses reveal a heavy bias toward tissue samples collected from the Americas (and consequently, taxonomic groups found in the Americas), with most collected samples housed in North American institutions. This limits the scope of future pathogen discovery efforts and presents a barrier to pandemic preparedness in the Global South. We also examine gaps in metadata quality (e.g., descriptions of preservation method and storage medium) and outline recommendations for GBIF to facilitate future biosurveillance projects and effectively incorporate natural history museums into One Health disease research. Author SummaryThe SARS-CoV-2 pandemic has driven a greater research focus on understanding wildlife reservoirs of zoonotic pathogens. As a complement to field-based sampling of wildlife, natural history museum collections house millions of specimens that could be used by researchers to study pathogens quickly, safely, and cost-effectively. Digital databases of museum specimens and their associated tissue samples were originally created for biodiversity research, but these could be adapted to guide pathogen discovery research. Using the largest of these databases, The Global Biodiversity Information Facility, we explored tissue samples from rodents and bats, which have been shown to carry a disproportionate number of zoonotic pathogens. We searched for keywords that would indicate the presence of relevant tissue samples and then mapped the results visually. This revealed disproportionate distributions of tissue samples across time, space, and taxonomy. Our study is the first attempt to assess how a biodiversity database can be used for this novel purpose. We suggest changes that would improve these databases for zoonotic disease research.

ecology↗

bioRxiv2025

Museum collections and machine learning guide discovery of novel coronaviruses and paramyxoviruses

Natural history museum collections are valuable but underutilized resources for viral discovery, offering opportunities to test hypotheses about viral occurrence across space, time, and taxonomic groups. We developed machine learning models of bat host suitability to guide coronavirus and paramyxovirus screening of 1330 and 491 tissues, respectively, in a museum collection. For the first time, we recovered coronavirus (n = 16) and paramyxovirus (n = 3) sequences from archived museum tissues, confirming three novel coronavirus host species and three novel paramyxovirus host species (3% and 33% prediction success rate, respectively). These sequences included a SARS-like coronavirus and an orthoparamyxovirus from Angolan Rhinolophus fumigatus specimens collected in June 2019, suggesting that viruses with epidemic potential may be more widespread in sub-Saharan Africa than previously believed. Our study demonstrates the value of combining predictive modeling and collections-based viral discovery, particularly for filling outstanding sampling gaps and investigating changes in host-virus associations over time.

ecology↗