Synthetic epigenetics in yeast

Epigenetics is the study of heritable biological variation not related to changes in DNA sequence. Epigenetic processes are responsible for establishing and maintaining transcriptional programs that define cell identity. Defects to epigenetic processes have been linked to a host of disorders, including mental retardation, aging, cancer and neurodegenerative diseases. The ability to control and engineer epigenetic systems would be valuable both for the basic study of these critical cellular processes as well as for synthetic biology. Indeed, while synthetic biology has made progress using bottom-up approaches to engineer transcriptional and signaling circuitry, epigenetic systems have remained largely underutilized. The predictive engineering of epigenetic systems could enable new functions to be implemented in synthetic organisms, including programmed phenotypic diversity, memory, reversibility, inheritance, and hysteresis. This thesis broadly focuses on the development of foundational tools and intellectual frameworks for applying synthetic biology to epigenetic regulation in the model eukaryote, Saccharomyces cerevisiae. Epigenetic regulation is mediated by diverse molecular mechanisms: e.g. self-sustaining feedback loops, protein structural templating, modifications to chromatin, and RNA silencing. Here we develop synthetic tools and circuits for controlling epigenetic states through (1) modifications to chromatin and (2) self-templating protein conformations. On the former, the synthetic tools we develop make it possible to study and direct how chromatin regulators operate to produce distinct gene expression programs. On the latter, we focus our studies on yeast prions, which are self-templating protein conformations that act as elements of inheritance, developing synthetic tools for detecting and controlling prion states in yeast cells. This thesis explores the application of synthetic biology to these epigenetic systems through four aims: Aim 1. Development of inducible expression systems for precise temporal expression of epigenetic regulators Aim 2. Construction of a library of chromatin regulators to study and program chromatin-based epigenetic regulation. Aim 3. Development of a genetic tool for quantifying protein aggregation and prion states in high-throughput Aim 4. Dynamics and control of prion switching Our tools and studies enable a deeper functional understanding of epigenetic regulation in cells, and the repurposing of these systems for synthetic biology toward addressing industrial and medical applications.2019-10-08T00:00:00

Modeling the impact of drug interactions on therapeutic selectivity

While drugs can interact in both target and off-target cell types, more favorable interaction in the target cell may nevertheless allow for a therapeutic window. Here, the authors show, using two yeast species as a model, that differential drug interactions indeed adjust the selective window

Drug interaction screen in two yeast species

Raw cell growth measurements for drug interaction assays of 76 pairwise combinations in two yeast species. Each file contains a 64 column matrix of numbers, which corresponds to OD595 readings for one drug‐drug interaction. Rows correspond to different time points with 15 minutes intervals. Columns correspond to the 8 x 8 matrix of drug concentration combinations. Also included are (i) dose-response measurements for 12 drugs in two yeast species and (ii) interaction and selectivity scores for each drug pair

A Genetic Tool to Track Protein Aggregates and Control Prion Inheritance

Protein aggregation is a hallmark of many diseases but also underlies a wide range of positive cellular functions. This phenomenon has been difficult to study because of a lack of quantitative and high-throughput cellular tools. Here, we develop a synthetic genetic tool to sense and control protein aggregation. We apply the technology to yeast prions, developing sensors to track their aggregation states and employing prion fusions to encode synthetic memories in yeast cells. Utilizing high-throughput screens, we identify prion-curing mutants and engineer “anti-prion drives” that reverse the non-Mendelian inheritance pattern of prions and eliminate them from yeast populations. We extend our technology to yeast RNA-binding proteins (RBPs) by tracking their propensity to aggregate, searching for co-occurring aggregates, and uncovering a group of coalescing RBPs through screens enabled by our platform. Our work establishes a quantitative, high-throughput, and generalizable technology to study and control diverse protein aggregation processes in cells