Mr Benjamín Durán-Vinet1, Dr Jo-Ann L. Stanton1, Dr Gert-Jan Jeunen2, Dr Ulla von Ammon3, Mr Jackson Treece1, Miss Michelle Scriver3,4,5, Dr Xavier Pochon3,4, Dr Anastasija Zaiko5, Dr Neil Gemmell1
1Department of Anatomy, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand, 2Department of Marine Sciences, University of Otago, Dunedin, New Zealand, 3Cawthron Institute, Nelson, New Zealand, 4Institute of Marine Science, University of Auckland, Auckland, New Zealand, 5Sequench, Nelson, New Zealand
Biography:
Ben is a dedicated PhD candidate in Genetics at the University of Otago, New Zealand. With building expertise in CRISPR-Cas-based diagnostics, Ben is at the forefront of developing innovative solutions for environmental biosecurity, supervised and mentored by Distinguished Professor Neil Gemmell, Dr Jo-Ann Staton, Dr Gert-Jan Jeunen, Dr Anastasija Zaiko and Dr Xavier Pochon.
Ben's research focuses on harnessing the precision and sensitivity of CRISPR-Cas technology to detect and monitor environmental threats, ensuring the protection and sustainability of ecosystems and the services they provide.
In addition to his work with CRISPR-Cas, Ben integrates an experimental deep-learning pipeline to enhance the accuracy and efficiency of these diagnostic tools. This interdisciplinary approach not only advances the field of genetics but also sets new standards for environmental monitoring and biosecurity measures.
Ben’s academic journey is marked by a commitment to scientific excellence and innovation. His work has significant implications for the early detection and management of invasive species, pathogens, and other environmental hazards. By combining genetic diagnostics with machine learning, Ben is pioneering methods that offer faster deployment and ease of use for end-users.
Abstract:
Environmental biosecurity challenges are steadily increasing in aquatic ecosystems due to climate change and human activities, broadly impacting ecosystem sustainability and related economic activities. This pressing issue demands innovative, reliable, and robust solutions to manage and mitigate biosecurity risks. Our framework proposes an integrated approach combining CRISPR-Cas technology (Clustered Regularly Interspaced Short Palindromic Repeats and associated CRISPR protein) with artificial intelligence (AI) to enhance biosecurity surveillance using environmental DNA (eDNA). Our study focuses on designing CRISPR RNA (crRNAs) sequences leveraging a trained convolutional neural network termed ADAPT (Activity-informed Design with All-inclusive Patrolling of Targets) that identifies optimal diagnostic crRNA configurations. Thereby, this process maximises target specificity and minimises off-target detection, delivering highly active and precise diagnostic assays.
We tested this pipeline on Sabella spallanzanii (Mediterranean fanworm) and Undaria pinnatifida (Asian seaweed). Our in vitro settings demonstrate the efficacy of AI-assisted crRNA designs in detecting invasive species, showcasing a significant fold-change in the attomolar range (10-18 mol/L) when using synthetic samples, genomic DNA (down to 0.018 pg/µL), and eDNA samples (˃10-fold change); all within one hour of reaction commencement. Our results underscore the potential of combining cutting-edge diagnostic CRISPR-Cas technology with AI to address complex biosecurity challenges, including quick deployment, early detection and portability, paving the way for streamlined deployment of environmental biosecurity assays. We termed this approach CRISPR-based EnvirOnmental BiosecuRity ASsisted via Artificial Intelligence crRNAs (CORSAIR), which will support sustainable and resilient ecosystem management, advancing our ability to monitor and protect aquatic environments effectively.
Funding acknowledgement: CAWX1904—A Toolbox to Underpin and Enable Tomorrow's Marine Biosecurity System.