Metabolic networks provide a vital framework for understanding the cellular metabolism in both physiological and pathophysiological states, which will ultimately facilitate network analysis-based drug discovery. In this thesis, we aim to employ a metabolic network analysis approach to study cancer metabolism (a pathophysiological state) and the metabolism of the bacterial pathogen, S. aureus (a physiological state), in order to understand, predict, and ultimately target cell metabolism for drug discovery.
Cancer cells have distinct metabolism that highly depend on glycolysis instead of mitochondrial oxidative phosphorylation alone, even in the presence of oxygen, also called aerobic glycolysis or the Warburg effect, which may offer novel therapeutic opportunities. However, the origin of the Warburg effect is only partially understood. To understand the origin of cancer metabolism, our theoretical collaborator, Prof. Alexei Vazquez, developed a reduced flux balance model of human cell metabolism incorporating the macromolecular crowding (MC) constraint and the maximum glucose uptake constraint. The simulations successfully captured the main characteristics of cancer metabolism (aerobic glycolysis), indicating that MC constraint may be a potential origin of the Warburg effect. Notably, when we experimentally tested the model with mammalian cells from low to high growth rates as a proxy of MC alteration, we find that, consistent with the model, faster growing cells indeed have increased aerobic glycolysis.
Moreover, the metabolic network analysis approach has also been shown to be capable of predicting the drug targets against pathogen metabolism when completely reconstructed metabolic networks are available. We deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and demonstrated experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability.
Our results indicate that the metabolic network analysis approach is able to facilitate the understanding of cellular metabolism by identifying potential constraints and predicting as well as ultimately targeting the metabolism of the organisms whose complete metabolic networks are available through the seamless integration of virtual screening with experimental validation.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-12072010-100902 |
| Date | 09 December 2010 |
| Creators | Liu, Jiangxia |
| Contributors | Marie C. DeFrances, Bennett Van Houten, Alexander Doemling, Rober Bowser, Zoltan N. Oltvai |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-12072010-100902/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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