Abstract Emerging infectious diseases are increasingly recognized as a significant threat to global biodiversity conservation. Elucidating the relationship between pathogens and the host microbiome could lead to novel approaches for mitigating disease impacts. Pathogens can alter the host microbiome by inducing dysbiosis, an ecological state characterized by a reduction in bacterial alpha diversity, an increase in pathobionts, or a shift in beta diversity. We used the snake fungal disease (SFD; ophidiomycosis), system to examine how an emerging pathogen may induce dysbiosis across two experimental scales. We used quantitative polymerase chain reaction, bacterial amplicon sequencing, and a deep learning neural network to characterize the skin microbiome of free‐ranging snakes across a broad phylogenetic and spatial extent. Habitat suitability models were used to find variables associated with fungal presence on the landscape. We also conducted a laboratory study of northern watersnakes to examine temporal changes in the skin microbiome following inoculation withOphidiomyces ophidiicola. Patterns characteristic of dysbiosis were found at both scales, as were nonlinear changes in alpha and alterations in beta diversity, although structural‐level and dispersion changes differed between field and laboratory contexts. The neural network was far more accurate (99.8% positive predictive value [PPV]) in predicting disease state than other analytic techniques (36.4% PPV). The genusPseudomonaswas characteristic of disease‐negative microbiomes, whereas, positive snakes were characterized by the pathobiontsChryseobacterium,Paracoccus, andSphingobacterium. Geographic regions suitable forO. ophidiicolahad high pathogen loads (>0.66 maximum sensitivity + specificity). We found that pathogen‐induced dysbiosis of the microbiome followed predictable trends, that disease state could be classified with neural network analyses, and that habitat suitability models predicted habitat for the SFD pathogen.
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This content will become publicly available on October 22, 2026
Population genetics, trait mapping and fungal pathogen surveillance using untargeted sequencing in timber rattlesnakes ( Crotalus horridus)
Abstract Timber rattlesnakes (Crotalus horridus) face escalating threats in the Northeastern Appalachians, including habitat fragmentation, human encroachment, and the fungal pathogenOphidiomyces ophiodiicola. Using untargeted sequencing of DNA extracted from scale clips, we generated both host whole-genome and metagenomic data for 97 snakes from eight populations. Analysis of the snake genomes shows the populations surveyed exhibit relatively low levels of inbreeding and are genetically distinct, but that the degree of separation correlates only weakly with geographic distance. A genome-wide association analysis identified a locus associated with black-to-yellow color variation that contains an aldehyde dehydrogenase gene (ALDH4A1) related to genes involved in hair color differences in humans. Metagenomic analysis showed thatO. ophiodiicolaread counts were generally higher in snakes exhibiting clinical signs of Snake Fungal Disease, but some visually asymptomatic snakes had high pathogen loads. Together, these findings highlight the dual utility of untargeted sequencing for population genetics and pathogen surveillance, providing a foundation for future studies of adaptation, disease dynamics, and conservation in this declining species.
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- Award ID(s):
- 2022007
- PAR ID:
- 10655327
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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