Probing the essential genome of Burkholderia cenocepacia K56-2 with CRISPRi to identify bioactive compounds with novel mechanisms of action(s)
| dc.contributor.author | Rahman, A S M Zisanur | |
| dc.contributor.examiningcommittee | Oresnik, Ivan (Microbiology) | |
| dc.contributor.examiningcommittee | Davis, Rebecca (Chemistry) | |
| dc.contributor.examiningcommittee | Hu, Pingzhao (Biochemistry and Medical Genetics) | |
| dc.contributor.examiningcommittee | Tahlan, Kapil (Memorial University of Newfoundland) | |
| dc.contributor.supervisor | Cardona, Silvia | |
| dc.date.accessioned | 2024-08-22T17:40:38Z | |
| dc.date.available | 2024-08-22T17:40:38Z | |
| dc.date.issued | 2024-08-16 | |
| dc.date.submitted | 2024-08-16T17:59:15Z | en_US |
| dc.degree.discipline | Microbiology | |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | |
| dc.description.abstract | A significant bottleneck in the discovery of novel antibiotics is the poor understanding of the mechanism of action as it impedes compound prioritization for further development. Chemical-genetics, which links bioactive compounds with their cellular targets, offers a streamlined approach to antibiotic discovery. Integrating empirical investigations with machine learning (ML) algorithms enables rapid exploration of large compound libraries in silico to identify promising antimicrobial compounds. The objective of this thesis was to develop an approach that increases the probability of early antimicrobials discovery together with the determination of their mechanism of action. I began by validating that a ML model could effectively learn from large training data and rapidly screen compound libraries to predict bioactivity. Therefore, a machine learning model trained with a previous high throughput screening (HTS) dataset was employed for bioactivity prediction on large compound libraries. The ML-approach yielded a 30-fold increase in hit rate compared to the conventional HTS approach and led to the identification of PHAR(A), a growth inhibitory compound with novel chemical scaffold. Because antibiotics usually target essential gene products, my project focused on probing bacterial essential genome to explore a broader antibiotic target space. I constructed and characterized an arrayed essential gene mutant library in Burkholderia cenocepacia K56-2 using CRISPR interference (CRISPRi). By analyzing the clonal growth parameters of the individual mutants and their depletion from a pool, an optimized CRISPRi-mediated pooled library of essential genes (CIMPLE) was created. Upon exposure of CIMPLE to a bacterial growth inhibitor or a novel class, PHAR(A), changes in fitness of the individual mutants were detected by CRISPRi-Seq. We identified peptidyl-tRNA hydrolase as the putative cellular target of PHAR(A). Overall, this thesis demonstrates the sensitivity and specificity of our strategy in uncovering the mechanisms of action of novel antimicrobials against targets that otherwise would have eluded a conventional drug discovery pipeline. It underscores how combining in silico approaches with experimental studies can accelerate the discovery of novel antimicrobials. This work can be extended to generate large chemical-genetic interaction profiles to train a ML platform to predict the bioactivity and mechanism of action of active compounds. | |
| dc.description.note | October 2024 | |
| dc.identifier.uri | http://hdl.handle.net/1993/38408 | |
| dc.language.iso | eng | |
| dc.subject | Essential Genome | |
| dc.subject | CRISPRi | |
| dc.subject | CRISPRi-Seq | |
| dc.subject | Chemical-genetics | |
| dc.subject | Machine learning | |
| dc.subject | Burkholderia cenocepacia K56-2 | |
| dc.subject | Antibiotic discovery | |
| dc.title | Probing the essential genome of Burkholderia cenocepacia K56-2 with CRISPRi to identify bioactive compounds with novel mechanisms of action(s) | |
| local.subject.manitoba | no | |
| oaire.awardTitle | Identification of novel antibiotic molecules by chemogenetic analysis and machine learning | |
| oaire.awardURI | https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=410533&lang=en | |
| project.funder.identifier | https://doi.org/10.13039/501100000024 | |
| project.funder.name | Canadian Institutes of Health Research |