Jacob Stewart-Ornstein

A comprehensive strategy enabling high-resolution functional analysis of the yeast genome

Functional genomic studies in Saccharomyces cerevisiae have contributed enormously to our understanding of cellular processes. Their full potential, however, has been hampered by the limited availability of reagents to systematically study essential genes and the inability to quantify the small effects of most gene deletions on growth. Here we describe the construction of a library of hypomorphic alleles of essential genes and a high-throughput growth competition assay to measure fitness with unprecedented sensitivity. These tools dramatically increase the breadth and precision with which quantitative genetic analysis can be performed in yeast. We illustrate the value of these approaches by using genetic interactions to reveal new relationships between chromatin-modifying factors and to create a functional map of the proteasome. Finally, by measuring the fitness of strains in the yeast deletion library, we addressed an enigma regarding the apparent prevalence of gene dispensability and found that most genes do contribute to growth.

A Ribosome-Bound Quality Control Complex Triggers Degradation of Nascent Peptides and Signals Translation Stress

The conserved transcriptional regulator heat shock factor 1 (Hsf1) is a key sensor of proteotoxic and other stress in the eukaryotic cytosol. We surveyed Hsf1 activity in a genome-wide loss-of-function library in Saccaromyces cerevisiae as well as ~78,000 double mutants and found Hsf1 activity to be modulated by highly diverse stresses. These included disruption of a ribosome-bound complex we named the Ribosome Quality Control Complex (RQC) comprising the Ltn1 E3 ubiquitin ligase, two highly conserved but poorly characterized proteins (Tae2 and Rqc1), and Cdc48 and its cofactors. Electron microscopy and biochemical analyses revealed that the RQC forms a stable complex with 60S ribosomal subunits containing stalled polypeptides and triggers their degradation. A negative feedback loop regulates the RQC, and Hsf1 senses an RQC-mediated translation-stress signal distinctly from other stresses. Our work reveals the range of stresses Hsf1 monitors and elucidates a conserved cotranslational protein quality control mechanism.

In silico feedback for in vivo regulation of a gene expression circuit

We show that difficulties in regulating cellular behavior with synthetic biological circuits may be circumvented using in silico feedback control. By tracking a circuits output in Saccharomyces cerevisiae in real time, we precisely control its behavior using an in silico feedback algorithm to compute regulatory inputs implemented through a genetically encoded light-responsive module. Moving control functions outside the cell should enable more sophisticated manipulation of cellular processes whenever real-time measurements of cellular variables are possible.

Dynamics of CDKN1A in Single Cells Defined by an Endogenous Fluorescent Tagging Toolkit

Observing the endogenous abundance, localization, and dynamics of proteins in mammalian cells is crucial to understanding their function and behavior. Currently, there is no systematic approach for the fluorescent tagging of endogenous loci. Here, we used Cas9-catalyzed DNA breaks, short homology arms, and a family of donor plasmids to establish endogenous Fluorescent tagging (eFlut): a low-cost and efficient approach to generating endogenous proteins with fluorescent labels. We validated this protocol on multiple proteins in several cell lines and species and applied our tools to study the cell-cycle inhibitor CDKN1A in single cells. We uncover heterogeneity in the timing and rate of CDKN1A induction post-DNA damage and show that this variability is post-transcriptionally regulated, depends on cell-cycle position, and has long-term consequences for cellular proliferation. The tools developed in this study should support widespread study of the dynamics and localization of diverse proteins in mammalian cells.

Building Robust Functionality in Synthetic Circuits Using Engineered Feedback Regulation

The ability to engineer novel functionality within cells, to quantitatively control cellular circuits, and to manipulate the behaviors of populations, has many important applications in biotechnology and biomedicine. These applications are only beginning to be explored. In this review, we advocate the use of feedback control as an essential strategy for the engineering of robust homeostatic control of biological circuits and cellular populations. We also describe recent works where feedback control, implemented in silico or with biological components, was successfully employed for this purpose.

p53 dynamics in response to DNA damage vary across cell lines and are shaped by efficiency of DNA repair and activity of the kinase ATM

Single-cell imaging shows that the p53 response to DNA damage is dynamic and cell line–specific. Variations in p53 dynamics A cell’s response to changes in its extracellular or intracellular environment (the path between signal and output) involves complex molecular networks. These networks are temporally dynamic, requiring investigators to consider multiple time points when analyzing pathway activity. Using single-cell imaging, Stewart-Ornstein and Lahav showed not only that DNA damage dynamically induced the tumor suppressor protein p53, which regulates cell cycle arrest and apoptosis, but also that those dynamics varied between cell lines, even of the same tissue type. The findings provide insight into the p53-mediated DNA damage response in multiple cell types. Because many other signaling pathways likely demonstrate this high degree of variability, the study also reveals that making generalized assumptions about cell signaling behavior based on a single cell line, or even one line for each tissue, is prone to error. Cellular systems show a wide range of signaling dynamics. Many of these dynamics are highly stereotyped, such as oscillations at a fixed frequency. However, most studies looking at the role of signaling dynamics focus on one or a few cell lines, leaving the diversity of dynamics across tissues or cell lines a largely unexplored question. We focused on the dynamics of the tumor suppressor protein p53, which regulates cell cycle arrest and apoptosis in response to DNA damage. We established live-cell reporters for 12 cancer cell lines expressing wild-type p53 and quantified p53 dynamics in response to double-strand break–inducing DNA damage. In many of the tested cell lines, we found that p53 abundance oscillated in response to ionizing radiation or the DNA-damaging chemotherapeutic neocarzinostatin and that the periodicity of the oscillations was fixed. In other cell lines, p53 abundance dynamically changed in different ways, such as a single broad pulse or a continuous induction. By combining single-cell assays of p53 signaling dynamics, small-molecule screening in live cells, and mathematical modeling, we identified molecules that perturbed p53 dynamics and determined that cell-specific variation in the efficiency of DNA repair and the activity of the kinase ATM (ataxia-telangiectasia mutated) controlled the signaling landscape of p53 dynamics. Because the dynamics of wild-type p53 varied substantially between cell lines, our study highlights the limitation of using one line as a model system and emphasizes the importance of studying the dynamics of other signaling pathways across different cell lines and genetic backgrounds.

Single cell analysis of circadian dynamics in tissue explants

Studying signaling dynamics in single cells in vivo is critical to understanding how cells act and interact in 3D environments. Experimental and computational tools to quantify a circadian reporter in single cells in intact tissues for >1 wk are used to analyze the period, amplitude, and synchrony of circadian rhythms in vivo.

Using dynamic noise propagation to infer causal regulatory relationships in biochemical networks.

Cellular decision making is accomplished by complex networks, the structure of which has traditionally been inferred from mean gene expression data. In addition to mean data, quantitative measures of distributions across a population can be obtained using techniques such as flow cytometry that measure expression in single cells. The resulting distributions, which reflect a populations variability or noise, constitute a potentially rich source of information for network reconstruction. A significant portion of molecular noise in a biological process is propagated from the upstream regulators. This propagated component provides additional information about causal network connections. Here, we devise a procedure in which we exploit equations for dynamic noise propagation in a network under nonsteady state conditions to distinguish between alternate gene regulatory relationships. We test our approach in silico using data obtained from stochastic simulations as well as in vivo using experimental data collected from synthetic circuits constructed in yeast.

p53 pulses lead to distinct patterns of gene expression albeit similar DNA-binding dynamics

The dynamics of transcription factors play important roles in a variety of biological systems. However, the mechanisms by which these dynamics are decoded into different transcriptional responses are not well understood. Here we focus on the dynamics of the tumor-suppressor protein p53, which exhibits a series of pulses in response to DNA damage. We performed time course RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) measurements to determine how p53 oscillations are linked with gene expression genome wide. We discovered multiple distinct patterns of gene expression in response to p53 pulses. Surprisingly, p53-binding dynamics were uniform across all genomic loci, even for genes that exhibited distinct mRNA dynamics. Using a mathematical model, supported by additional experimental measurements in response to sustained p53 input, we determined that p53 binds to and activates transcription of its target genes uniformly, whereas post-transcriptional mechanisms are responsible for the differences in gene expression dynamics.

Model-guided optogenetic study of PKA signaling in budding yeast

Optogenetic activation of the adenylate cyclase enzyme in Saccharomyces cerevisiae, paired with computational modeling, enables study of the dynamic quantitative properties of the cAMP/PKA signaling network. The ability to deliver such precise perturbation reveals fundamental dynamical features of PKA signaling, including the time scales of feedback.