The reprogramming of gene expression in response to changes in the external environment is critical for cellular adaptation and survival. These hard-coded responses evolve over geological time-scales and are fine- tuned to the requirements of the native habitat. However, organisms may encounter novel or extreme environments for which their gene regulatory network is inadequate for appropriate reprogramming of gene expression. Our laboratory has recently discovered a powerful new epigenetic mechanism in Saccharomyces cerevisiae that allows cells to adapt to novel extreme environments without the benefit of hardwired regulatory networks. We call this mechanism ?stochastic tuning,? by which inherent transcriptional noise and fitness- driven feedback enable cells to optimize appropriate gene expression states without the need to sense the external environment directly.
The aim of this study is to characterize the stochastic tuning dynamics in individual cells and to discover the underlying effectors and regulators involved. Given the inherently stochastic nature of the phenomenon, characterizing the single-cell trajectory of tuning at the molecular level is an essential step. The characterization of gene expression can be achieved at a global scale using high- throughput methods or at high resolution using microscopy to study mRNA temporal dynamics. Single-cell transcriptomics in mammalian cells is a rapidly advancing field. However, these methods are not effective in yeast due the presence of a thick cell wall. I have therefore started to develop a promising alternative technology for efficient and low-cost transcriptome profiling in yeast cells. I will also use live cell imaging of MS2/ PP7 tagged mRNAs to study transcriptional dynamics at high temporal and spatial resolution. In addition, I aim to systematically discover all the factors involved in stochastic tuning. I will use Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology to activate or repress all essential and non-essential genes in order to systematically discover loci that affect stochastic tuning. Our preliminary studies in yeast demonstrate that stochastic tuning operates locally at the level of each individual gene and that chromatin modification and remodeling machinery modulates the efficacy of this process. I will study the dynamics of local histone modifications using Chromatin Immunoprecipitation coupled with quantitative PCR along the different stages of stochastic tuning. In addition, I will study the local chromatin state using DNase-I- Hypersensitivity assays. These studies will enable us to monitor the local chromatin state during the process of stochastic tuning and match it to transcriptional output and cellular fitness. The proposed studies will reveal the molecular details of a powerful new adaptation mechanism at the single-cell level and within the local chromatin context. Our work will also reveal the key effectors and regulators of tuning, a critical step in achieving a full mechanistic understanding of this powerful new phenomenon of cellular adaptation.!

Public Health Relevance

Organisms need to navigate many transcriptional states and survive in a variety of microenvironments inconsistent with their evolved regulatory networks; for example cancer cells can become resistant to chemotherapy drugs. Our laboratory has recently discovered a powerful new epigenetic mechanism in Saccharomyces cerevisiae, which we call ?stochastic tuning?. This proposal aims to characterize the molecular details of stochastic tuning, providing fundamental insights that can change our understanding of resistance to chemotherapy in a variety of cancer types. !

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Postdoctoral Individual National Research Service Award (F32)
Project #
1F32GM125170-01A1
Application #
9537804
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Maas, Stefan
Project Start
2018-07-01
Project End
2019-06-30
Budget Start
2018-07-01
Budget End
2019-06-30
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
Biology
Type
Schools of Medicine
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032