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The pathogen Ophidiomyces ophidiicola (O.o.), widely known as the primary cause of snake fungal disease (SFD) has been detected in Texas’s naïve snakes. Our team set out to begin to characterize O. ophidiicola’s spread in east Texas. From July 2019 until October 2021, we sampled 176 snakes across east Texas and detected 27 positives cases (qPCR confirmed 27/176). From a ribbon snake with clear clinical display, we isolated and cultured what we believe to be the Texas isolate of O. ophidiicola. With over 1/10 snakes that may be infected in East Texas, gives credence to the onset of SFD in Texas.
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Effects of snake fungal disease (ophidiomycosis) on the skin microbiome across two major experimental scales
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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- PAR ID:
- 10645389
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Conservation Biology
- Volume:
- 39
- Issue:
- 2
- ISSN:
- 0888-8892
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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