DNA target-dependent features dictate Cas12a antimicrobial activity against multidrug-resistant and hypervirulent strains of Klebsiella pneumoniae
Description
CRISPR-Cas systems can be utilized as programmable-spectrum antimicrobials to combat bacterial infections. However, how CRISPR nucleases perform as antimicrobials across target sites and strains remains poorly explored. Here, we address this challenge by systematically interrogating the use of CRISPR antimicrobials against multidrug-resistant and hypervirulent strains of Klebsiella pneumoniae. Comparing different Cas nucleases, we found that AsCas12a exhibited robust targeting across different strains. The elucidated modes of escape from this nuclease varied widely, restraining opportunities to enhance killing. We also encountered individual guide RNAs yielding different extents of clearance across strains. The differences were attributed to improper RNA folding, leading to inefficient DNA cleavage and subsequent repair via homologous recombination. To explore targeting features that could improve targeting across strains, we performed a genome-wide guide screen in different K. pneumoniae strains that yielded guide design rules and trained an algorithm for predicting guide efficiency. Finally, we showed that Cas12a antimicrobials can be exploited to kill K. pneumoniae when encoded in phagemids delivered by T7-like phages. Altogether, our results highlight the importance of evaluating antimicrobial activity in the desired strain and define critical parameters for efficient CRISPR-based targeting.
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Steps to reproduce
For screen analysis: 1. The sequence data of library screening is available with accession number GSE237136. After merging the pair-end reads, run "fastq2counts.py" to obtain the counts of crRNAs. The library sequences can be found in "KP_Cas12a_library.txt". The processed data is in "processed_data.zip". 2. To obtain the differential abundance (depletion score) of crRNAs, run "screen_analysis_differential_abundance.R" . The result file *"*_QLFTest.csv"* is included here. For applying machine learning: 1. To optimize machine learning model using automated machine learning tool auto-sklearn, run "machine_learning_model_optimization_autosklearn.py". 2. To evaluate and interprete the optimized model from auto-sklearn, run "machine_learning_model_interpretation_treeSHAP.py". For each Python script, "-h" shows the detailed description, options, and example to run the script.