Chen Li-Dunn, Carter Edward D, Urban Merrie P, Merolle Carmen T, Chen Devin M, Kouba Andrew J, Gray Matthew J, Miller Debra L, Kouba Carrie K
Department of Biochemistry, Molecular Biology, Entomology & Plant Pathology, Mississippi State University, Mississippi State, MS, USA.
Center for Wildlife Health, School of Natural Resources, University of Tennessee, Knoxville, TN, USA.
Commun Biol. 2025 Apr 17;8(1):625. doi: 10.1038/s42003-025-08025-8.
The emergence of Batrachochytrium salamandrivorans (Bsal) poses an imminent threat to caudate biodiversity worldwide, particularly through anthropogenic-mediated means such as the pet trade. Bsal is a fungal panzootic that has yet to reach the Americas, Africa, and Australia, presenting a significant biosecurity risk to naïve amphibian populations lacking the innate immune defenses necessary for combating invasive pathogens. We explored the capability of near-infrared spectroscopy (NIRS) coupled with predictive modeling as a rapid, non-invasive Bsal screening tool in live caudates. Using eastern newts (Notopthalmus viridescens) as a model species, NIR spectra were collected in tandem with dermal swabs used for confirmatory qPCR analysis. We identified that spectral profiles differed significantly by physical location (chin, cloaca, tail, and foot) as well as by Bsal pathogen status (control vs. exposed individuals; p < 0.05). The support vector machine algorithm achieved a mean classification accuracy of 80% and a sensitivity of 92% for discriminating Bsal-control (-) from Bsal-exposed (+) individuals. This approach offers a promising method for identifying Bsal-compromised populations, potentially aiding in early detection and mitigation efforts alongside existing techniques.
蛙壶菌(Batrachochytrium salamandrivorans,简称Bsal)的出现对全球有尾目动物的生物多样性构成了紧迫威胁,特别是通过宠物贸易等人为介导的方式。Bsal是一种真菌性大流行疾病,尚未传播到美洲、非洲和澳大利亚,这对缺乏对抗入侵病原体所需先天免疫防御的未受感染两栖动物种群构成了重大生物安全风险。我们探索了近红外光谱(NIRS)结合预测模型作为一种在活体有尾目动物中快速、非侵入性的Bsal筛查工具的能力。以东方蝾螈(Notopthalmus viridescens)作为模式物种,在采集用于确证性定量聚合酶链反应(qPCR)分析的皮肤拭子的同时收集近红外光谱。我们发现,光谱特征因物理位置(下巴、泄殖腔、尾巴和足部)以及Bsal病原体状态(对照与暴露个体;p < 0.05)而有显著差异。支持向量机算法在区分Bsal暴露(+)个体和Bsal对照(-)个体方面,平均分类准确率达到80%,灵敏度达到92%。这种方法为识别受Bsal影响的种群提供了一种有前景的方法,可能有助于与现有技术一起进行早期检测和缓解工作。