Back

Fine-tuning of a CRISPRi screen in the seventh pandemic Vibrio cholerae

Debatisse, K.; Niault, T.; Peeters, S.; Maire, A.; Darracq, B.; Baharoglu, Z.; Bikard, D.; Mazel, D.; Loot, C.

2024-07-03 genetics
10.1101/2024.07.03.601881 bioRxiv
Show abstract

Vibrio cholerae O1 El Tor, the etiological agent responsible for the last cholera pandemic, has become a well-established model organism for which some genetic tools exist. While CRISPRi has been applied in V. cholerae, improvements were necessary to upscale it and enable pooled screening by high-throughput sequencing in this bacterium. In this study, we introduce a pooled genome wide CRISPRi library construction specifically optimized for this V. cholerae strain, characterized by minimal cytotoxicity and streamlined experimental setup. This library allows the depletion of 3, 674 (98.9%) annotated genes from the V. cholerae genome. To confirm its effectiveness, we screened for essential genes during exponential growth in rich medium and identified 368 genes for which guides were significantly depleted from the library (log2FC < - 2). Remarkably, 82% of these genes had previously been described as hypothetical essential genes in V. cholerae or in a closely related bacterium, V. natriegens. We thus validated the robustness and accuracy of our CRISPRi-based approach for assessing gene fitness in a given condition. Our findings highlight the efficacy of the developed CRISPRi platform as a powerful tool for high-throughput functional genomics studies of V. cholerae. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/601881v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@bab54aorg.highwire.dtl.DTLVardef@1d412c6org.highwire.dtl.DTLVardef@1cba6borg.highwire.dtl.DTLVardef@12d11d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.