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Quantifying and Engineering Protein Dynamics in Bacteria Quantifying and Engineering Protein Dynamics in Bacteria The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Potvin-Trottier, Laurent. 2017. Quantifying and Engineering Protein Dynamics in Bacteria. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:41140241 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 Quantifying and engineering protein dynamics in bacteria a dissertation presented by Laurent Potvin-Trottier to The Committee on Higher Degrees in Biophysics in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Biophysics Harvard University Cambridge, Massachusetts May 2017 ©2017 – Laurent Potvin-Trottier all rights reserved. Thesis advisor: Professor Johan Paulsson Laurent Potvin-Trottier Quantifying and engineering protein dynamics in bacteria Abstract Genetically identical cells can display heterogeneity in their level of proteins due to stochastic gene expression. Such noise has been found to be widespread across biology, and can have a tremen- dous impact by allowing cells to access different phenotypes. However, the timescales of these fluc- tuations matter: slow fluctuations are potentially much more potent whereas if fluctuations are corrected rapidly they are relatively innocuous. Many simple mechanisms in cells – such as gene cascades, time-averaging to suppress fluctuations and epigenetic modifications – can create slow fluctuations. Quantifying protein dynamics remains a technical challenge as it requires measuring gene expression of long time series under constant growth conditions, and thus the timescales of fluctuations in gene expression remain largely unknown. By using a newly developed microfluidic device – to follow hundreds of cells for hundreds of cell divisions under constant growth conditions – we quantify the timescale of fluctuations of ∼ 50 transcriptional reporters and ∼10 translational reporters in Escherichia coli. All reporters show a strikingly similar and surprising behavior: an expo- nential decorrelation with a half-life of one generation. We show that the discrepancy with previous studies can be explained by artifacts arising from calculating autocorrelation functions with short time series or non-uniform growth conditions. The general absence of slow fluctuations in cells opens up the question of how difficult it is to iii Thesis advisor: Professor Johan Paulsson Laurent Potvin-Trottier create epigenetic memory with fluctuations in protein copy number. To answer this question, we revisit a synthetic oscillator, the repressilator, to engineer oscillations correlated over hundreds of generations. Synthetic gene circuits typically have much lower accuracy than their natural counter- part, and our hypothesis for this difference is that they are usually designed without considering stochastic gene expression. We used principles from stochastic chemistry in single cells to reduce error propagation and information loss. By simply removing features from the circuit rather than adding feedback loops, we created highly regular and robust oscillations, with circuits keeping their 14-generation periods in a wide range of growth conditions. The phase was kept for hundreds of generations such that flasks of cells and bacterial colonies displayed synchronous oscillations, even without coupling between cells. Proteins cannot adapt to upstream changes on a scale faster than their lifetime. In order to re- spond to changes in their environment more quickly than their division time, cells must degrade their proteins. Fluctuations in the proteolytic machinery could therefore cause fluctuations in the half-lives of the degraded proteins. We develop and validate the first tool measuring instantaneous proteolysis rates in single bacterial cells. By measuring the saturation curve of ssrA-tagged proteins, we show that these substrates exhibit Michaelis-Menten kinetics, with very high affinity and a half- life of ∼45 seconds. We show that the SspB adapter protein increases the affinity of the ClpXP pro- tease to ssrA-tagged substrates, which was previously demonstrated in vitro but not in vivo. How- ever, depending on the substrate’s local structure, degradation can be faster without the adapter. By measuring changes in degradation rates over time, we discover a proteolytic response: at high con- centration of ssrA-tagged substrates, cells compensate by producing more proteases. We connect iv Thesis advisor: Professor Johan Paulsson Laurent Potvin-Trottier this phenomenon to the heat shock response and characterize the associated toxicity. We discuss the limitations of using fluorescent proteins as reporters for degradation – fluorescent proteins targeted with natural tags can be partially degraded such that there is no decay in fluorescence – and suggest potential alternative methods for measuring degradation in single cells. v Contents 1 Introduction 1 2 Fluctuation timescales in micro-organisms 5 2.1 Introduction ..................................... 6 2.2 Quantifying protein dynamics ............................ 9 2.3 Experimental results ................................. 13 2.4 Discussion and conclusion .............................. 18 3 Synchronous long-term oscillations in a synthetic gene circuit 21 3.1 Abstract ....................................... 23 3.2 Main text ....................................... 23 3.3 Box 1 | Relaxation oscillations of the repressilator . 31 3.4 Extended Data Figures ................................ 34 3.5 Supplementary results and discussion ........................ 52 3.6 Theory ........................................ 58 4 Protein degradation kinetics in single bacterial cells 73 4.1 Introduction ..................................... 75 4.2 Counter-intuitive effect of an adapter protein on the repressilator . 84 4.3 Measuring instantaneous proteolysis rates in vivo . 88 4.4 Comparison to pulse-decay method and limitations . 95 4.5 Interference with the cell ............................... 105 4.6 Discussion ...................................... 112 4.7 Alternative methods for measuring proteolysis in single cells . 118 4.8 Conclusion and future directions . 121 Appendix A Supplementary figures 126 A.1 Chapter 2: Fluctuation timescales in micro-organisms . 126 A.2 Chapter 4: Protein degradation kinetics in single bacterial cells . 128 Appendix B Materials and methods 137 B.1 Chapter 2: Fluctuation timescales in micro-organisms . 137 B.2 Chapter 3: Synchronous long-term oscillations in a synthetic gene circuit . 141 B.3 Chapter 4: Protein degradation kinetics in single bacterial cells . 157 B.4 Microfluidic master fabrication ............................ 165 vi References 184 vii ToKathy, mon amour, ma raison d’ê tre. viii Acknowledgments This work would not have been possible without the contribution and support of many people that I would like to thank here. I would like to thank Johan “the beacon of light” Paulsson for being such a fantastic advisor dur- ing these years. His endless energy and passion for science were contagious, and he created and main- tained a great open and collaborative work environment which enabled my research to blossom. Members of the lab were always isolated from all non-science external factors, such as ups and down of grants. I deeply appreciated his generous support during job applications, whether it was a typical career path or not. I enjoyed being bombarded by an endless stream of ideas, and last but not least, I come out enlightened with a new perspective, which is not simply about stochastic noise, but on how to approach problems differently and fundamentally. I would like to thank all members of the Paulsson lab, past and present, for keeping a fun envi- ronment and for always being generous with their time. Thank you Andreas Hilfinger, for “being Andy”, for so many coffee breaks, for working hard to keep the levels of coffee up, for your endless enthusiasm in fixing small problems, for showing how to keep your head up and for your moral support. Thank you Somenath Bakshi for your endless excitement about science and for all the in- teresting discussions; Silvia Cañas-Duarte for making the lab a little more normal; Charles Baker ix for holding up the fort in the morning and for many lunchtimes; Charlotte Strandqvist for being always so positive; Emanuele Leoncini for keeping the Italian touch in the lab; Shlomi Reuveni for thoughtful discussions and advices; Ruoshi Yuan for being the nicest cubicle-mate; Janelle Vultaggio for somehow enduring the lab and making it run smoothly. Thank you Stephan Uphoff for setting the standard for the perfect lab citizen, for your incredible generosity, positivity and enthusiasm for science, it was an exceptional opportunity to work with you. Thank you Rishi Jajoo for your amaz- ing patience, your endless enthusiasm for science, and for always being there to teach me biology; Martin Loose for insightful discussions and comments on the manuscript; Dirk Landgraf for being so generous with his time and somehow having made half of the strains that I needed; Nathan Lord for developing and sharing the experimental setup, his patience when I had so many questions at the wrong time
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