PURPOSE:In this project, we will use a combination of computational and experimental techniques to characterize p53 dynamics in healthy and cancerous cells in response to important cellular stresses. To measure the dynamics of circuit components, we will use long-term time-lapse fluorescence microscopy of living cells. 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 circuit functions, as well as new ways to combat cancers in which p53 dynamics are dysregulated.MATERIALS AND METHODS:1. Determining classes of p53 dynamics based on p53 stimuli:While there has been a great deal of interest in the p53 response to gamma radiation, it is only one of numerous stresses that can activate p53, including additional forms of DNA damage, ribosomal stress, and oncogenes. We have shown that p53 shows the strikingly distinct dynamical behavior of graded pulses when exposed to UV radiation. Our preliminary studies have also found that activation of p53 by certain chemicals can generate either undamped pulses or graded pulses. We will determine the p53 dynamical response to a broad range of stimuli and classify stresses based on p53 dynamics. We will use long-term time-lapse fluorescence microscopy to measure the dynamics of fluorescently-tagged p53 at high temporal resolution in single cells. Identifying the different dynamical classes and the stresses that cluster in each class will provide a foundation for understanding a previously unknown level of regulation of the p53 network, and will inform research into the function of p53 dynamics. 2. Using synthetic biology approaches to control p53 dynamics:New tools and approaches are increasingly becoming available to directly regulate the expression and activities of signaling molecules in live cells. We will use such approaches to perturb various characteristics of p53 dynamics (for example, p53 pulse amplitude, duration, and frequency), and determine the effect that such perturbations have on p53's downstream functions. This synthetic approach will complement the above approaches that rely on natural stresses to activate dynamical expression of p53.3. Identifying target gene expression patterns based on p53 dynamics:Our discovery of p53 dynamical classes suggests that p53 target genes may show complex dynamical expression patterns that impact cellular responses. For example, it may be that certain pro-apoptotic genes are expressed in a step-wise manner when p53 undergoes gamma-type dynamics, but are expressed with fast saturation kinetics when p53 undergoes UV-type dynamics. This could be translated into differential triggering of apoptosis, which is consistent with the fact that distinct cell fates occur in response to gamma or UV radiation.Since p53 regulates over 100 genes, we will initially take a qPCR approach to probe the function of p53 dynamics. Validation by more detailed studies of important target genes will be performed using single-cell level analysis with fluorescent transcriptional reporters. We will analyze gene expression profiles for stimuli within individual p53 dynamical classes and across distinct classes. We predict that genes with expression patterns that are dependent on p53 dynamics will cluster by the dynamical classes. To verify the dependence of gene expression on p53 dynamics, we will compare stress-response profiles to profiles for conditions in which we have perturbed p53 dynamics. In preliminary studies, we have used small molecules, RNAi of feedback regulators, and synthetic engineered feedbacks to perturb the dynamical response to gamma or UV. PROGRESS IN FY2012:The Systems Biology Section of the NCI Laboratory of Pathology was established in May 2011. In the past year, I have hired the full three members of my staff for this section. A staff biologist arrived at the beginning of FY2012, and has helped with the procurement of the equipment and reagents required for the proposed research. I have hired two postdoctoral research fellows, one a biophysicist with """"""""wet-lab"""""""" expertise, and the other an electrical engineer with experience in the computational modeling of biological regulatory circuits. they will be arriving in the lab at the start of the next fiscal year. I have also mentored two summer interns, and have started mentoring a Laboratory of Pathology clinical research fellow. Additionally, the major equipment for the research in the section, a long-term time-lapse fluorescence microscopy system, has been procured in this fiscal year, with a second system currently in the process of being procured. In the past year, I have been a contributing author on an article (Purvis et al, 2012, Science. 336: 1440-4) that showed that perturbation of p53 dynamics, at least in response to DNA double strand breaks, can affect cellular fate. By changing the natural oscillatory response of p53 to a response in which p53 remained constantly high, cells activated the senescence program at early times. We are currently following up with this research, developing synthetic biology tools using various protein fusions to identify ways to more precisely control p53 dynamics.We have also begun looking at a broad range of natural stimuli of p53, and have identified unique dynamical regulation of p53 in response to novel stresses. We are currently characterizing these new responses and identifying the regulatory mechanisms that generate them.

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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