Antimicrobial resistance is a major global health threat, with disproportionate impact in regions with limited diagnostic infrastructure. To address this challenge, we developed BADLOCK (Bacterial and AMR Detection by SHERLOCK), a rapid, low-cost molecular diagnostic platform for direct detection of bacterial pathogens and resistance genes from clinical samples. BADLOCK operates as a one-pot CRISPR-Cas13a reaction capable of detecting nine bacterial species and four major resistance genes directly from positive blood culture. It requires only a heat block and supports both fluorescence and paper-based lateral flow readouts. We validated BADLOCK on a prospectively collected clinical cohort of 194 blood culture specimens, supplemented with 69 mock samples generated from banked isolates enriched for targeted resistance genes. Across all cohorts, we conducted 2,224 individual reactions, achieving 97.6% accuracy (2,171/2,224) at the reaction level. At the assay level, 89.5% (274/306) showed perfect or partial concordance with gold-standard species and resistance gene detection, including 255 assays with perfect concordance and 19 with partial concordance (correct detection of at least one pathogen). This included an evaluation of BADLOCK as a potential culture-free diagnostic for urinary tract infections (UTIs), achieving 98.0% reaction-level accuracy. At the assay level, 90.7% (41/43) were perfectly concordant with gold-standard detection of both species and resistance genes, with 2 additional assays showing partial concordance. To our knowledge, this represents the first demonstration of the CRISPR-Cas13a diagnostic platform on clinical bloodstream infections to date and supports BADLOCK’s potential as a practical and scalable solution for rapid pathogen and resistance gene detection in resource-constrained settings.
Competing Interest StatementThe authors have declared no competing interest.
Funding StatementThis work was supported in part by the Broad Institute NextGen Award to RPB. DJR was supported in part by the National Institute of Health T32 Training Grant (T32AI007061-44) and in part by an Antibacterial Resistance Leadership Group fellowship (National Institute of Allergy and Infectious Diseases UM1AI104681). SB was supported by the Harvard College Herchel Smith Undergraduate Science Research Program and the Harvard College Research Program. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the Broad Institute, or Harvard College.
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Study protocol was reviewed and approved by the Institutional Review Board (IRB) under Mass General Brigham IRB protocol 2015P002215.
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Data AvailabilityAll data produced in the present study are available upon reasonable request to the authors
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