PURPOSE: In this project, we will use a combination of computational and experimental techniques to identify the role of p53 dynamics in the regulation of important cellular stress stresses. To measure the dynamics of downstream pathways, we will make use of a suite of complementary approaches described below to assay p53 activity in both cancerous and non-transformed cellular systems. We will use chemical and genetic perturbations to alter p53 dynamics and determine the effect on p53 target gene expression and cell fate. Using computational modeling, we will integrate these data with measurements of cellular outcomes to predict pathway behavior in response to specific perturbations. By allowing us to study emergent properties that are not evident at the level of smaller-scale interactions, this type of approach will provide novel strategies for manipulating biological circuit functions, as well as generate new ways to combat cancers in which p53 dynamics are dysregulated. MATERIALS AND METHODS: 1. Using optogenetic and small molecule perturbation approaches to control p53 dynamics: We will use optogenetic and small molecule perturbation approaches to alter characteristics of p53 dynamics (for example, p53 pulse amplitude, duration, or frequency), and determine the effect that such perturbations have on p53's transcriptional activity and cell fate. 2. Identifying p53 target expression patterns based on p53 dynamics: We will probe the function of p53 dynamics in the regulation of the over 100 p53 targets at the level of single cells and in cell populations. Validation by more detailed studies of important targets will be performed using single-cell level analysis with fluorescent reporters, as well as immunofluorescent imaging and flow cytometry of fixed cells at key time points. 3. We are generating methods to create synthetic systems with desired expression patterns of novel targets driven by the endogenous p53 oscillator. Using this system, we are restoring cell death pathways in cancer cells that are mutated for proper apoptotic regulation. 4. p53 dyanmics for mutated p53 and in patient samples: WE are determining how common p53 mutations alter p53 dynamics, and how that impacts downstream cell fate regulation. We are using cancer cell lines and plan to use patient-derived tumor samples. PROGRESS IN FY2018: 1. We have developed a novel synthetic method to control p53 localization, and as a consequence specific features of p53 dynamics. Using this system, we observed differential regulation of specific p53 target genes. We have characterizing the impact of p53 dynamics on several important gene regulatory features, including the noise in gene expression. 2. We extended the results from our previous work published in Cell Systems last year, moving from the analysis of p53 targets at the transcript level to analysis of protein expression dynamics of p53 targets. From these studies, we have identified and characterized expression dynamics for key mediators of several cell fate pathways regulated by p53. 3. We developed systems to regulate distinct expression patterns and cell fate outcomes through modulation of synthetic targets engineered to be activated by p53 oscillations. 4. We have developed cell lines harboring p53 mutations and are analyzing their dynamics and the effect on the transcriptome.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Investigator-Initiated Intramural Research Projects (ZIA)
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Batchelor, Eric; Loewer, Alexander (2017) Recent progress and open challenges in modeling p53 dynamics in single cells. Curr Opin Syst Biol 3:54-59
Porter, Joshua R; Telford, William G; Batchelor, Eric (2017) Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards. J Vis Exp :
Harton, Marie D; Batchelor, Eric (2017) Determining the Limitations and Benefits of Noise in Gene Regulation and Signal Transduction through Single Cell, Microscopy-Based Analysis. J Mol Biol 429:1143-1154
Porter, Joshua R; Fisher, Brian E; Batchelor, Eric (2016) p53 Pulses Diversify Target Gene Expression Dynamics in an mRNA Half-Life-Dependent Manner and Delineate Co-regulated Target Gene Subnetworks. Cell Syst 2:272-82
Porter, Joshua R; Batchelor, Eric (2015) Using computational modeling and experimental synthetic perturbations to probe biological circuits. Methods Mol Biol 1244:259-76
Batchelor, Eric; Kann, Maricel G; Przytycka, Teresa M et al. (2013) Modeling cell heterogeneity: from single-cell variations to mixed cells. Pac Symp Biocomput :445-50
Moody, Amie D; Batchelor, Eric (2013) Promoter decoding of transcription factor dynamics. Mol Syst Biol 9:703
Purvis, Jeremy E; Karhohs, Kyle W; Mock, Caroline et al. (2012) p53 dynamics control cell fate. Science 336:1440-4
Batchelor, Eric; Loewer, Alexander; Mock, Caroline et al. (2011) Stimulus-dependent dynamics of p53 in single cells. Mol Syst Biol 7:488
Geva-Zatorsky, Naama; Dekel, Erez; Batchelor, Eric et al. (2010) Fourier analysis and systems identification of the p53 feedback loop. Proc Natl Acad Sci U S A 107:13550-5

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