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Global and Single Cell Analyses to Connect P53 Dynamics with Gene Expression Global and Single Cell Analyses to Connect P53 Dynamics With Gene Expression The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Hafner, Antonina. 2017. Global and Single Cell Analyses to Connect P53 Dynamics With Gene Expression. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:41141869 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Global and single cell analyses to connect p53 dynamics with gene expression A dissertation presented by Antonina Hafner to The Committee on Higher Degrees in Systems Biology in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Systems Biology Harvard University Cambridge, Massachusetts April 2017 © 2017 Antonina Hafner All rights reserved. Dissertation Advisor: Professor Galit Lahav Antonina Hafner Global and single cell analyses to connect p53 dynamics with gene expression Abstract The dynamics of transcription factors have been shown to play important roles in a variety of biological systems. However, the mechanisms by which these dynamics are decoded to trigger different transcriptional responses are not well understood. Here we focus on the dynamics of tumor suppressor protein p53, whose dynamics have been shown to control cell fate decisions in response to DNA damage. p53 is known to regulate several hundred target genes but the selection of which genes to activate in response to a specific type of stress and in specific cellular background remain unsolved. In this work, we studied how p53 dynamics control gene expression at a global level (genome wide) and in single cells. In Chapter 2, we focused on a particular type of p53 dynamics: the pulses in p53 protein level that are induced in response to γ irradiation. To determine how p53 pulses are linked to gene expression genome wide, we performed time-course RNA-Seq and ChIP-Seq measurements. 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 iii Dissertation Advisor: Professor Galit Lahav Antonina Hafner measurements in response to a sustained p53 input, we concluded that p53 binds to and activates transcription of its target genes uniformly, while posttranscriptional mechanisms are responsible for the differences in gene expression dynamics. In Chapter 3, we developed a single cell system that allows us to follow the dynamics of the p53 protein together with the transcription and protein of one of its target genes, p21. We found that p21 transcription dynamics qualitatively track p53 protein levels in response to γ irradiation, as well for two additional treatments. In addition, we found that in response to p53 pulses, p21 transcription terminated prior to the decrease in p53 protein level. This suggests that transcriptionally active p53 represents a subset of total p53 level. We have constructed a mathematical model that capture this behavior and suggest experiments to test the hypothesis guided by the model. The combination of population level and single cell approaches allowed us to identify a general mechanism that enables differential expression between genes in response to p53 pulses as well as get a detailed picture of p21 regulation at the single cell level. Chapter 4 discusses the significance of our findings for p53 biology and future directions that need to be taken to gain a systematic understanding of p53 function under different cellular contexts and treatments. iv Contents CONTENTS V LIST OF FIGURES VI ACKNOWLEDGEMENTS VII CHAPTER 1: INTRODUCTION 1 1.1 Dynamic responses of transcription factors to stimuli 3 1.2 Upstream signaling components linked to specific transcription factor dynamics 8 1.3 Network properties of oscillatory systems 11 1.4 Transcription factor dynamics control gene expression 14 1.5 Gene regulation by p53 17 CHAPTER 2: GENOME WIDE ANALYSES REVEAL THAT P53 PULSES LEAD TO DISTINCT PATTERNS OF GENE EXPRESSION ALBEIT SIMILAR DNA BINDING. 23 2.1 Introduction 24 2.2 Results 26 2.3 Discussion 48 2.4 Methods 51 2.5 Manuscript information 56 CHAPTER 3: A SINGLE CELL SYSTEM TO CONNECT THE DYNAMICS OF P53 PROTEIN, P21 TRANSCRIPTION AND P21 PROTEIN IN LIVE CELLS 57 3.1 Introduction 57 3.2 Results 60 3.3 Discussion 73 3.4 Methods 75 3.5 Manuscript information 80 CHAPTER 4: CONCLUSION AND FUTURE DIRECTIONS 83 BIBLIOGRAPHY 93 v List of figures 1.1: Dynamics of transcription factors show specificity to the type of stimulus. 5 1.2: Averaging of single cell trajectories leads to damped population level p53 pulses. 7 1.3: Different signaling pathways are induced by different stimuli. 10 1.4: Multiple inputs and outputs of p53. 18 1.5: The p53 binding site. 19 2.1: Synchrony between cells during the first two pulses of p53 allows their detection at the population level and design of RNA-Seq experiment. 27 2.2: Time course mRNA-Seq reveals distinct clusters of gene expression dynamics in response to γ irradiation. 29 2.3: Functional analysis of clustered genes. 32 2.4: Time course p53 ChIP-Seq experiment shows pulsatile dynamics at the p21 gene. 33 2.5: Time course p53 ChIP-Seq experiment shows pulsatile dynamics genome wide. 35 2.6: p53 target genes with distinct expression dynamics show similar dynamics of p53 binding. 37 2.7: Genes from different clusters show pulsatile pre-mRNA dynamics. 39 2.8: Mathematical modeling recapitulates the different mRNA dynamics for induced genes. 41 2.9: Contribution of model parameters to the gene expression dynamics. 42 2.10: mRNA degradation rate can explain the differences in dynamics across gene expression clusters. 44 2.11: Experimental validation of the model under sustained p53 input dynamics. 46 3.1: Design and validation of the p21 endogenous transcription and protein tag. 62 3.2: Imaging and quantification of p21-MS2 transcriptional foci in live cells. 63 3.3: Addition of the p53 tag allows to follow p53, p21 transcription and protein dynamics in live cells. 65 3.4: Nutlin 3a and Cisplatin treatment reveal qualitatively similar dynamics to NCS 68 3.5: Time delays between p53 level and p21 transcription are recapitulated in a mathematical model supporting an Mdm2 sequestration hypothesis. 72 4.1: Relationship between p53 dynamics and target gene expression. 84 4.2: Comparison of p53 ChIP-Seq datasets across cell lines and conditions. 87 vi Acknowledgements These past, almost 6 years in Boston have represented a transition for me in many ways. I feel fortunate to have gotten the opportunity to be part of the incredibly rich and stimulating scientific community. On the path towards the PhD, I have met great scientists and friends and made lifelong connections. I would particularly like to thank: My thesis advisor Galit Lahav for being an amazing scientific mentor and teaching me to be a better scientist, for constant support and positivity that made the hard moments a lot easier. Past and present members of the Lahav lab for always being generous with their time and creating a motivating environment which was a pleasure to come back to every morning. Jacob, for being an outstanding postdoc, bay mate and friend. Martha Bulyk, for providing a second lab home and critical feedback throughout my PhD. Members of the Bulyk lab, for helping me get PBM data and teaching me how to do genomic analyses. The SysBio department for making everything run so smoothly, in particular Samantha Reed, Elizabeth Pomerantz, Maria Ferreira and Xheni Vaqari for always keeping their doors open and finding a solution to every problem. vii My G6 classmates and all SysBio PhD students for and mutual support. My DAC committee, Suzanne Gaudet, Timothy Mitchison and Aviv Regev for taking the time over the years to guide me on this work and my scientific career. Michael Springer and Allon Klein for agreeing to serve on my exam committee. The Boehringer Ingelheim Funds fellowship for financial support during my PhD, for mentorship in the form of courses and seminars and most importantly for welcoming me into the BIF family. My friends, Diane and Magali for always being there to listen and making Boston feel like home. My rowing teammates at Riverside Boat House, In particular Linda, Andi, Rachel, Emma, Erin, Beatrice and coach Nik for motivating me to get up early every morning and for all the incredible moments we shared on and off the water. My cycling teams, Harvard Cycling and Green Line Velo, for initiating me to cycling by sharing your passion, teaching me how to race and making me discover the Boston area and beyond. In particular, Senta, Portia, Crystal, Dana, from HUCA, Natasja, Gina, Julie, Meredith and all GLV women, for being such an inspiring group of women. Zoltan for motivating me to run. viii My Boston family, Maureen, Dave and Diana, your home will always be a special place to us. My parents Marina and Oleg, my sister Elena, my aunt Victoria and uncle Constantin, my lovely cousins Nicolai and Julia for invariable love and support and silly messages to make me laugh. Tanya, Yves and Dan for all the fun US holidays together and provisions in Swiss chocolate and cheese to fuel our science and sports. My longtime partner and friend Marc for always being by my side, sharing the passion for science, sports and adventures, helping me with code and figures or fixing my bike, making jams and cooking dinner, and all the other moments of constant patience, love and support.
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