Semester project: Exploiting CRISPRi-induced metabolic changes in A549 cancer cells to predict unknown drug-gene targets
Advances in gene editing technologies (e.g. CRISPRi) have allowed to systematically screen for promising gene targets for cancer therapy. However, the ability to develop drugs for these identified gene targets largely lacks behind, repurposing of already FDA approved drugs could close this shortage. However, it remains experimentally very tedious to identify drugs targeting specific enzymes or gene products. Multidimensional molecular profiling of treatment effects (e.g. metabolomics) can capture broader functional impacts of small molecules, allowing to derive drug-specific metabolic fingerprints. Such a multidimensional molecular profiling is not restricted to drug perturbations, but can also be performed on genetic perturbations, such as CRISPRi, allowing to derive CRISPRi-specific metabolic fingerprints. By simply comparing CRISPRi and drug-specific metabolic fingerprints to each other, a guilty by association principle can be used to identify drugs targeting promising genes. In this project, we will exploit a dataset of 2000 metabolite changes in 219 CRISPRi perturbation to derive CRISPRi-specific metabolic fingerprints, integrate them with 1520 drug metabolic fingerprints to derive predictions on drugs, targeting promising genes for cancer therapy. Based on these predictions experimentally testable hypothesis will be generated that will lay the groundwork to validate our approach. This work will potentially lay the foundation of a novel approach for drug repurposing. Overall, the student will become familiar with metabolome, proteome and growth data, as well as established analysis methods in the field (i.e. iterative Similarity). Additionally, the student will be trained and probed in critical thinking, hypothesis generation and self-independent work.
The proposed project is purely computational and requires no wet lab experiments. Programming experience from previous project/courses (i.e. Matlab, R or Python) is of advantage. However, basic skills are sufficient to be built on during the project.
Contact: Laurentz Schuhknecht ()