Development and Application of Lysate Microarray Technology for Quantitative Analysis of Human Disease
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CitationYe, Albert Shanbuo. 2013. Development and Application of Lysate Microarray Technology for Quantitative Analysis of Human Disease. Doctoral dissertation, Harvard University.
AbstractReductionist biology has yielded tremendous insight into the basis of biochemistry and genetic disease. However, the remarkable failure of reductionist biology to explain complex problems, especially cancer, has led to the development of systems biology. The vast complexity of biological systems remains the most difficult problem in biology today. In order to understand this complexity, we need tools to massively multiplex measurements of a signaling network. Therefore, we developed lysate microarray technology to fill this need. In this work, we discuss three ways in which lysate microarrays were applied to human disease. In the first work, we discuss a key stage in malaria development. The liver-stage malaria parasite represents a promising target for intervention, and we present the first use of lysate microarray technology as a screening tool for host-parasite interactions in an infectious disease. We identified three cancer-related pathways that are modified in malaria infection, and studied the p53 pathway in depth. Our finding that the parasite downregulates p53 and that treatment with Nutlin-3 strongly decreases parasite load may lead to the development of a prophylactic malaria vaccine. In the second work, we began by screening drug combinations and varying dosing schedule in triple-negative breast cancers (TNBCs). We systematically explored stimulation space and collected a large lysate microarray dataset, which was used for statistical analysis. We identified a sensitization effect when a growth factor signaling inhibitor was presented before a genotoxic agent. This sensitization was generalizable among a subset of TNBCs and may generally be important for cancers driven by growth factor signaling, as we found the effect extends to nonTNBC cancers. We hope this data will be useful in guiding cancer treatment strategies in patients. In the third work, we study the changing role of the DNA Damage Response (DDR) as a cell line evolves towards cancer. We used the MCF10A progression series and studied how these cell lines respond to genotoxic agents. We identified differences in cell fates after treatment, and collected a large lysate microarray dataset for statistical analysis. Early analysis of the data indicates gross rewiring within the DDR between the MCF10A cell lines.
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