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CRISPR-based diagnostics detects invasive insect pests

Rapid identification of organisms is essential across many biological and medical disciplines, from understanding basic ecosystem processes and how organisms respond to environmental change, to disease diagnosis and detection of invasive pests. CRISPR-based diagnostics offers a novel and rapid alter...

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Autores principales: Shashank, Pathour R., Parker, Brandon M., Rananaware, Santosh R., Plotkin, David, Couch, Christian, Yang, Lilia G., Nguyen, Long T., Prasannakumar, N. R., Braswell, W. Evan, Jain, Piyush K., Kawahara, Akito Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245733/
https://www.ncbi.nlm.nih.gov/pubmed/37292907
http://dx.doi.org/10.1101/2023.05.16.541004
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author Shashank, Pathour R.
Parker, Brandon M.
Rananaware, Santosh R.
Plotkin, David
Couch, Christian
Yang, Lilia G.
Nguyen, Long T.
Prasannakumar, N. R.
Braswell, W. Evan
Jain, Piyush K.
Kawahara, Akito Y.
author_facet Shashank, Pathour R.
Parker, Brandon M.
Rananaware, Santosh R.
Plotkin, David
Couch, Christian
Yang, Lilia G.
Nguyen, Long T.
Prasannakumar, N. R.
Braswell, W. Evan
Jain, Piyush K.
Kawahara, Akito Y.
author_sort Shashank, Pathour R.
collection PubMed
description Rapid identification of organisms is essential across many biological and medical disciplines, from understanding basic ecosystem processes and how organisms respond to environmental change, to disease diagnosis and detection of invasive pests. CRISPR-based diagnostics offers a novel and rapid alternative to other identification methods and can revolutionize our ability to detect organisms with high accuracy. Here we describe a CRISPR-based diagnostic developed with the universal cytochrome-oxidase 1 gene (CO1). The CO1 gene is the most sequenced gene among Animalia, and therefore our approach can be adopted to detect nearly any animal. We tested the approach on three difficult-to-identify moth species (Keiferia lycopersicella, Phthorimaea absoluta, and Scrobipalpa atriplicella) that are major invasive pests globally. We designed an assay that combines recombinase polymerase amplification (RPA) with CRISPR for signal generation. Our approach has a much higher sensitivity than other real time-PCR assays and achieved 100% accuracy for identification of all three species, with a detection limit of up to 120 fM for P. absoluta and 400 fM for the other two species. Our approach does not require a lab setting, reduces the risk of cross-contamination, and can be completed in less than one hour. This work serves as a proof of concept that has the potential to revolutionize animal detection and monitoring.
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spelling pubmed-102457332023-06-08 CRISPR-based diagnostics detects invasive insect pests Shashank, Pathour R. Parker, Brandon M. Rananaware, Santosh R. Plotkin, David Couch, Christian Yang, Lilia G. Nguyen, Long T. Prasannakumar, N. R. Braswell, W. Evan Jain, Piyush K. Kawahara, Akito Y. bioRxiv Article Rapid identification of organisms is essential across many biological and medical disciplines, from understanding basic ecosystem processes and how organisms respond to environmental change, to disease diagnosis and detection of invasive pests. CRISPR-based diagnostics offers a novel and rapid alternative to other identification methods and can revolutionize our ability to detect organisms with high accuracy. Here we describe a CRISPR-based diagnostic developed with the universal cytochrome-oxidase 1 gene (CO1). The CO1 gene is the most sequenced gene among Animalia, and therefore our approach can be adopted to detect nearly any animal. We tested the approach on three difficult-to-identify moth species (Keiferia lycopersicella, Phthorimaea absoluta, and Scrobipalpa atriplicella) that are major invasive pests globally. We designed an assay that combines recombinase polymerase amplification (RPA) with CRISPR for signal generation. Our approach has a much higher sensitivity than other real time-PCR assays and achieved 100% accuracy for identification of all three species, with a detection limit of up to 120 fM for P. absoluta and 400 fM for the other two species. Our approach does not require a lab setting, reduces the risk of cross-contamination, and can be completed in less than one hour. This work serves as a proof of concept that has the potential to revolutionize animal detection and monitoring. Cold Spring Harbor Laboratory 2023-05-18 /pmc/articles/PMC10245733/ /pubmed/37292907 http://dx.doi.org/10.1101/2023.05.16.541004 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Shashank, Pathour R.
Parker, Brandon M.
Rananaware, Santosh R.
Plotkin, David
Couch, Christian
Yang, Lilia G.
Nguyen, Long T.
Prasannakumar, N. R.
Braswell, W. Evan
Jain, Piyush K.
Kawahara, Akito Y.
CRISPR-based diagnostics detects invasive insect pests
title CRISPR-based diagnostics detects invasive insect pests
title_full CRISPR-based diagnostics detects invasive insect pests
title_fullStr CRISPR-based diagnostics detects invasive insect pests
title_full_unstemmed CRISPR-based diagnostics detects invasive insect pests
title_short CRISPR-based diagnostics detects invasive insect pests
title_sort crispr-based diagnostics detects invasive insect pests
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245733/
https://www.ncbi.nlm.nih.gov/pubmed/37292907
http://dx.doi.org/10.1101/2023.05.16.541004
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