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241115s2024 xx |||||o 00| ||eng c |
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|a 10.1111/cobi.14411
|2 doi
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|a pubmed24n1601.xml
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|a (DE-627)NLM380170035
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|a (NLM)39530499
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Romer, Alexander S
|e verfasserin
|4 aut
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| 245 |
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|a Effects of snake fungal disease (ophidiomycosis) on the skin microbiome across two major experimental scales
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|c 2024
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 12.11.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a © 2024 The Author(s). Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.
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|a 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 with Ophidiomyces 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 genus Pseudomonas was characteristic of disease-negative microbiomes, whereas, positive snakes were characterized by the pathobionts Chryseobacterium, Paracoccus, and Sphingobacterium. Geographic regions suitable for O. ophidiicola had 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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|a Journal Article
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|a deep learning neural network
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|a disbiosis
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|a dysbiosis
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| 650 |
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4 |
|a enfermedad fúngica en serpientes
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| 650 |
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4 |
|a enfermedades de la fauna
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| 650 |
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4 |
|a microbioma dérmico
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| 650 |
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4 |
|a red neural de aprendizaje profundo
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| 650 |
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|a skin microbiome
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| 650 |
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|a snake fungal disease
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| 650 |
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|a wildlife diseases
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| 650 |
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|a 关键词: 皮肤微生物组
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| 650 |
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|a 深度学习神经网络
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| 650 |
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|a 菌群失调
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| 650 |
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|a 蛇真菌病
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| 650 |
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|a 野生动物疾病
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| 700 |
1 |
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|a Grisnik, Matthew
|e verfasserin
|4 aut
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1 |
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|a Dallas, Jason W
|e verfasserin
|4 aut
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|a Sutton, William
|e verfasserin
|4 aut
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|a Murray, Christopher M
|e verfasserin
|4 aut
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|a Hardman, Rebecca H
|e verfasserin
|4 aut
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|a Blanchard, Tom
|e verfasserin
|4 aut
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|a Hanscom, Ryan J
|e verfasserin
|4 aut
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|a Clark, Rulon W
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|a Godwin, Cody
|e verfasserin
|4 aut
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|a Alexander, N Reed
|e verfasserin
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|a Moe, Kylie C
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|a Cobb, Vincent A
|e verfasserin
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|a Eaker, Jesse
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|a Colvin, Rob
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|a Thames, Dustin
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|a Ogle, Chris
|e verfasserin
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|a Campbell, Josh
|e verfasserin
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|a Frost, Carlin
|e verfasserin
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|a Brubaker, Rachel L
|e verfasserin
|4 aut
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|a Snyder, Shawn D
|e verfasserin
|4 aut
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|a Rurik, Alexander J
|e verfasserin
|4 aut
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|a Cummins, Chloe E
|e verfasserin
|4 aut
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|a Ludwig, David W
|e verfasserin
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|a Phillips, Joshua L
|e verfasserin
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|a Walker, Donald M
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Conservation biology : the journal of the Society for Conservation Biology
|d 1999
|g (2024) vom: 12. Nov., Seite e14411
|w (DE-627)NLM098176803
|x 1523-1739
|7 nnns
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|g year:2024
|g day:12
|g month:11
|g pages:e14411
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|u http://dx.doi.org/10.1111/cobi.14411
|3 Volltext
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