Production and sensing of natural products in Saccharomyces cerevisiae
Abstract/Contents
- Abstract
- Natural products broadly refer to organic compounds formed in living organisms, but are usually taken to mean small molecule metabolites that are not required for growth and development. More than half of FDA-approved antibiotics and anticancer drugs are natural products (NPs) or their derivatives. In particular, fungi produce a wide range of bioactive compounds such as penicillin and lovastatin. Recent genomic DNA sequencing efforts yielded a surprise in NP research: Many NP gene clusters could be readily identified based on homology, but no associated NP could be identified as being produced by the organism, and often the genes were not even expressed. Even for the most well studied fungal strains, only 10% of the identified clusters are expressed under laboratory conditions. Tens of thousands of NP clusters that may encode a wealth of unknown natural products with attractive bioactivities have been identified but are yet to be explored. Difficulties in culturing and genetically manipulating the host organisms of natural products, and inducing the expression of NP genes, are the major obstacles in studying these gene clusters. To circumvent these problems, researchers have pioneered heterologously expressed them in various model organisms, among which S. cerevisiae has emerged as a powerful host. However, the limitations on synthetic biology technologies and our understanding of yeast as a host for heterologous natural products production have constraint the discovery of novel NPs. In this thesis project, I further developed yeast as a producer of fungal natural products by streamlining assembly and characterization process that enabled discovery of novel NPs. I then dissected how yeast responded to NPs through the highly conserved pleiotropic drug resistance (PDR) mechanism, identified multiple novel regulators of this process, and connected this drug response to cell cycle regulation. I further demonstrated coupled production and sensing of natural products in a single yeast cell, providing new tools to study and generate new NPs based on the heterologously expressed fungal NP clusters. My thesis work revolved around production and sensing of natural products in Saccharomyces cerevisiae, and took advantage of recent advances in DNA sequencing, synthesis and engineering. On the natural products front, this work provides an efficient alternative to traditional natural product chemistry that have increased the repertoire of currently available natural products, and may lead to further development of new compounds with interesting bioactivities. Towards the front of understanding yeast as a host, this work uncovers novel control mechanism of the yeast pleiotropic drug resistance, a vital process in the drug response, and its connection to cell cycle regulation. With more knowledge on both fronts, the utilization of yeast PDR transcription response as biosensor systems illustrates the potential of an untapped resource for future biological and biotechnological applications.
Description
Type of resource | text |
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Form | electronic; electronic resource; remote |
Extent | 1 online resource. |
Publication date | 2017 |
Issuance | monographic |
Language | English |
Creators/Contributors
Associated with | Li, Jian | |
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Associated with | Stanford University, Department of Biochemistry. | |
Primary advisor | Davis, Ronald W. (Ronald Wayne), 1941- | |
Thesis advisor | Davis, Ronald W. (Ronald Wayne), 1941- | |
Thesis advisor | Khosla, Chaitan, 1964- | |
Thesis advisor | Kim, Peter | |
Advisor | Khosla, Chaitan, 1964- | |
Advisor | Kim, Peter |
Subjects
Genre | Theses |
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Bibliographic information
Statement of responsibility | Jian Li. |
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Note | Submitted to the Department of Biochemistry. |
Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Jian Li
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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