eDNA tech tracks lethal jellyfish with CRISPR precision

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY
Maslin Osathanunkul
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引用次数: 0

Abstract

Coastal public safety depends on detecting and monitoring dangerous box jellyfish, especially Chiropsoides buitendijki. CRISPR-Cas12a technology was used to create and validate a new eDNA-based detection technique for rapid, sensitive, and accurate identification of C. buitendijki eDNA in the Gulf of Thailand. After analysing 567 reactions across 63 sites, CRISPR-Cas12a successfully detected eDNA at 17 locations where earlier methods had been unsuccessful. Interestingly, a single water sample and one CRISPR-Cas12a replicate per site could achieve 95 % or higher detection rates, proving the method's efficacy. This method's accuracy was evident in both field sample collection and laboratory analysis stages, as it exhibited a low false positive rate and a high true positive rate. Although the CRISPR-Cas12a eDNA method does not require expensive scientific instruments, it cannot quantify eDNA in samples, simply reporting its presence or absence. Despite this limitation, the method achieved a detection limit (LOD) of 0.15 copies per reaction, with robust specificity validated through both in silico and in vitro testing. CRISPR-Cas12a's effectiveness, cost-efficiency, and ease to use make it particularly advantageous for developing countries. The method offers a powerful tool for early warning and preventive measures, enhancing beach safety protocols, and mitigating risks associated with jellyfish encounters. Its scalability and affordability support routine monitoring, promoting sustainable coastal management, and protecting tourism-dependent economies. The CRISPR-Cas12a eDNA system enhances marine biodiversity monitoring by integrating scientific rigour with practical utility, paving the way for advancements in public health and environmental management.

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来源期刊
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
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