在危的加勒比海鹿角珊瑚中使用机器学习识别假定珊瑚病原体
Jason D Selwyn1,2, Brecia A Despard1,2, Miles V Vollmer1,2
1Marine Science Center, Northeastern University, Nahant, Massachusetts, USA.
Environmental microbiology
|September 18, 2024
概括
识别珊瑚病病原体是一项挑战. 这项研究使用机器学习确定了Acropora珊瑚中白带病的两个潜在细菌罪祸首,Cysteiniphilum litorale和Vibrio sp.
科学领域:
- 海洋生物学 海洋生物学
- 微生物学 微生物学
- 生态生态学 生态生态学
背景情况:
- 珊瑚礁由于疾病而面临快速衰退.
- 使用传统的16S rRNA基因调查,很难识别珊瑚中的细菌病原体.
- 白带病 (WBD) 已经摧毁了加勒比Acropora珊瑚,但病原体仍然未知.
研究的目的:
- 为了确定特定的细菌病原体,负责白带病 (WBD) 在Acropora珊瑚.
- 使用机器学习开发珊瑚病的预测模型.
- 通过实验传播来验证潜在的病原体.
主要方法:
- 在健康和WBD感染的Acropora cervicornis上进行了多年,多地点的16S rRNA基因测序.
- 采用机器学习模型来预测疾病的结果,并识别关键的细菌安普利康序列变异 (ASV).
- 进行了基于坦克的传播实验,以测试已识别的ASVs的致病性.
主要成果:
- 机器学习模型以>97%的准确度准确预测疾病.
- 确定了19个与疾病相关的ASV和5个与健康相关的ASV.
- 两个候选病原体, *Cysteiniphilum litorale* (ASV25) 和 *Vibrio* sp. 这两种病原体. (ASV8),通过传播实验来确定.
结论:
- 机器学习有效地识别了与疾病相关的细菌,并预测了珊瑚病.
- *Cysteiniphilum litorale* 和 *Vibrio* sp. 这两种植物的种类. 是WBD病原体的有希望的候选者.
- 需要进一步的研究来确认病原性和了解疾病机制.
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