Project description:BackgroundVibrio 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 are available. While CRISPRi technology has been applied to V. cholerae, improvements were necessary to upscale it and enable pooled screening by high-throughput sequencing in this bacterium.ResultsIn this study, we present a genome-wide CRISPR-dCas9 screen specifically optimized for the N16961 El Tor model strain of V. cholerae. This approach is characterized by a tight control of dCas9 expression and activity, as well as a streamlined experimental setup. Our library allows the depletion of 3,674 (98.9%) annotated genes from the V. cholerae genome. To confirm its effectiveness, we screened for genes that are essential during exponential growth in rich medium and identified 369 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.ConclusionWe 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.
Project description:Purpose: to characterize the regulatory targets of an AraC-like transcriptional regulator (VC0513) encoded on the Vibrio Seventh Pandemic Island -II (VSP-II) in V. cholerae O1 El Tor N16961 Methods: RNA was isolated from a wild-type N16961 carrying an IPTG-inducible copy of vc0513, vc0515, or an empty vector control Results: vc0513 induction significantly increased expression of other VSP-II encoded genes relative to the empty vector control Conclusions: our study represents the first analysis of a transcriptional regulator encoded on the VSP-II island
Project description:While the roles of (p)ppGpp on cytosolic proteins are well established, its effects on membrane remodeling remain elusive. The translocation of signal recognition particle (SRP)-dependent proteins can be regulated by binding (p)ppGpp to two key GTPase components: FtsY, which interacts with SecYEG, and Ffh, a homolog of SRP54. A (p)ppGpp-specific Broccoli RNA aptamer and the chemometer PyDPA were used to measure the (p)ppGpp levels in the ΔrelA and ΔrelA/ΔspoT strains of Acinetobacter baumannii, confirming a stepwise reduction in (p)ppGpp levels: wild type > ΔrelA > ΔrelA/ΔspoT. The overproduction of outer membrane vesicles (OMVs) in the ΔrelA strain with intermediate (p)ppGpp levels contrasts with reduced production in the ΔrelA/ΔspoT strain, highlighting an unexpected non-linear relationship between OMV production and (p)ppGpp levels. Scanning- and transmission- electron microscope (SEM and TEM) analyses revealed intriguing (p)ppGpp-dependent effects on cell envelope integrity, as the relA mutant showed outer membrane disruption leading to OMV formation, while the relA/spoT mutant maintained an intact outer membrane, suggesting a correlation between (p)ppGpp levels and membrane stability. Western blotting and proteomics analyses revealed significant OmpA accumulation in the inner membrane of the ΔrelA/ΔspoT strain and SRP-dependent inner membrane proteins such as NuoB, NuoL, and TolA in the ΔrelA strain, indicating that (p)ppGpp levels are crucial for regulating membrane protein incorporation in A. baumannii. The regulation of (p)ppGpp levels using the CRISPRi system revealed that outer membrane disruption and OMV formation were most significant at intermediate (p)ppGpp concentrations, highlighting the importance of fine-tuning (p)ppGpp for the regulation of bacterial phenotypes.
Project description:N-terminal coding sequences (NCS) are key regulatory elements for fine-tuning gene expression during translation initiation, the rate-limiting step of translation. However, due to complex combinatory effects of NCS biophysical factors and endogenous regulation, designing NCS remains challenging. Herein, we implemented multi-view learning strategy for model-driven generation of synthetic NCS for Saccharomyces cerevisiae and Bacillus subtilis, which are model microorganisms widely used in the laboratory and industry.