Transcriptomics is the large-scale study of RNA molecules produced by the genome, in single cells or population of cells using high-throughput methods. With the advances in transcriptomic analysis, the monitoring of genome-wide gene expression provides a powerful approach for determining the action of drugs. In this study, we analyzed the transcriptional responses of cells treated with drugs either alone or in combinations to explore their effects in two different applications: breast cancer therapy and cell conversion.
In the first part of this thesis, we aim at modeling the relationship between single and multidrug breast therapy at the transcriptome level. We monitored the effects of three drugs, and their combinations in human breast cancer MCF-7 cells using the cap analysis of gene expression method. We are the first to explore the impact of single and combinatorial treatment on promoter and enhancer expression on a genome-wide scale. After applying and customizing a broad spectrum of regression algorithms, we showed that the transcriptional response to combinatorial drug treatment at both promoters and enhancers is accurately described by a linear combination of the responses to the individual drugs. Our analysis is promising for eliciting the transcriptional reaction to multidrug
therapies in an unbiased genome-wide way, which may minimize the need for exhaustive combinatorial screens.
Following the drug combination analysis, we explored the possibility to systematically identify drugs that either alone or in combinations facilitate cell conversion. To date, no computational approach prioritizes or suggests chemical compounds promoting cell reprogramming. Using transcriptomic data of human primary cells and drug response expression profiles, we developed a computational framework which accurately predicts single drugs or drug cocktails driving any source cell type towards the desired lineage. Experimental and in-silico validation on human pluripotent stem cells confirms the ability of the top predicted drugs to enhance reprogramming. The introduced method has countless applications in regenerative medicine and can significantly speed up the research in this field.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/656212 |
| Date | 07 1900 |
| Creators | Rapakoulia, Trisevgeni |
| Contributors | Gao, Xin, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Gao, Xin, Hoehndorf, Robert, Arold, Stefan T., Chen,Wei |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
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