Low Soo Jen, O'Neill Matthew, Kerry William J, Wild Natasha, Krysiak Marcelina, Nong Yi, Azzato Francesca, Hor Eileen, Williams Lewis, Taiaroa George, Steinig Eike, Pasricha Shivani, Williamson Deborah A
Department of Infectious Diseases, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.
Victorian Infectious Diseases Reference Laboratory, The Royal Melbourne Hospital at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.
Commun Biol. 2025 Jan 30;8(1):147. doi: 10.1038/s42003-025-07591-1.
Critical to the success of CRISPR-based diagnostic assays is the selection of a diagnostic target highly specific to the organism of interest, a process often requiring iterative cycles of manual selection, optimisation, and redesign. Here we present PathoGD, a bioinformatic pipeline for rapid and high-throughput design of RPA primers and gRNAs for CRISPR-Cas12a-based pathogen detection. PathoGD is fully automated, leverages publicly available sequences and is scalable to large datasets, allowing rapid continuous monitoring and validation of primer/gRNA sets to ensure ongoing assay relevance. We designed primers and gRNAs for five clinically relevant bacterial pathogens, and experimentally validated a subset of the designs for detecting Streptococcus pyogenes and/or Neisseria gonorrhoeae in assays with and without pre-amplification. We demonstrated high specificity of primers and gRNAs designed, with minimal off-target signal observed for all combinations. We anticipate PathoGD will be an important resource for assay design for current and emerging pathogens. PathoGD is available on GitHub at https://github.com/sjlow23/pathogd .
基于CRISPR的诊断检测成功的关键在于选择对目标生物体具有高度特异性的诊断靶点,这一过程通常需要经过手动选择、优化和重新设计的反复循环。在此,我们展示了PathoGD,这是一种生物信息学流程,用于基于CRISPR-Cas12a的病原体检测的RPA引物和gRNA的快速高通量设计。PathoGD是完全自动化的,利用公开可用的序列,并且可扩展到大型数据集,能够对引物/gRNA集进行快速持续监测和验证,以确保检测的持续相关性。我们为五种临床相关的细菌病原体设计了引物和gRNA,并通过实验验证了其中一部分设计在有和没有预扩增的检测中用于检测化脓性链球菌和/或淋病奈瑟菌的情况。我们证明了所设计的引物和gRNA具有高度特异性,所有组合的脱靶信号均极低。我们预计PathoGD将成为当前和新出现病原体检测设计的重要资源。PathoGD可在GitHub上获取,网址为https://github.com/sjlow23/pathogd 。