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Synaptic Specificity and Plasticity in Parvalbumin-Basket Cell Circuits

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Synaptic Specificity and Plasticity in Parvalbumin-Basket Cell Circuits Synaptic Specificity and Plasticity in Parvalbumin-Basket Cell Circuits The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Bogart, Luke Joseph. 2015. Synaptic Specificity and Plasticity in Parvalbumin-Basket Cell Circuits. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:23845470 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 Synaptic specificity and plasticity in parvalbumin-basket cell circuits A dissertation presented by Luke Joseph Bogart to The Division of Medical Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Neurobiology Harvard University Cambridge, Massachusetts August 2015 © 2015 Luke Joseph Bogart All rights reserved. Dissertation Advisor: Dr. Takao K. Hensch, Ph.D. Luke Joseph Bogart Synaptic specificity and plasticity in parvalbumin-basket cell circuits Abstract Inhibitory interneurons regulate experience-dependent plasticity across brain regions. Perisomatic inhibition by fast-spiking, parvalbumin-positive basket cells (PV-cells) is central to these processes, but which synapses are key remains unknown. Here we show using immunohistochemistry that PV-input to pyramidal cells in layer 5 of primary visual cortex (V1) differs on a cell type-specific basis, with subcortically-projecting pyramidal cells more highly-innervated by PV-cells than callosally-projecting pyramidal cells. Surprisingly, the density of PV-inputs to either pyramidal cell-type was not changed by dark- rearing mice to adulthood, a classical manipulation that delays V1 plasticity. Instead, dark- rearing selectively reduced PV-inputs onto other PV-cells, which normally form highly-recurrent networks. To investigate the circuit-level basis of this plasticity, PV-cells in both normal and dark-reared mice were labeled by Brainbow. In both conditions, individual innervations of PV- cell bodies by PV-axons were mediated on average by just 2 boutons/axon, revealing that loss of inputs to dark-reared PV-cells results from decreased convergence within the PV-network. On a molecular level, PV-cells sorted from dark-reared mice exhibited a reduction in GABAA receptor 1-subunit mRNA, a marker of functionally-mature inhibition. In whole-cell recordings of dark-reared PV-cells in vitro, spontaneous inhibitory postsynaptic currents (sIPSCs) were broader than in light-reared controls, an effect not seen in pyramidal cells in which 1-level was unchanged. Optogenetics experiments revealed that PV-cell-mediated synaptic events exhibited broader currents selectively in PV-cells. Also, consistent with their loss iii of PV-inputs, the frequency of sIPSCs received by dark-reared PV-cells was markedly lower than in light-reared controls, with sIPSC amplitude decreased as well. We modeled the loss of fast, PV-inhibition onto PV-cells through conditional gene deletion of the 1-subunit from PV-cells. This did not physically disconnect PV-PV connections, but rather decreased the amplitude and broadened the decay of individual sIPSCs. 1-deletion alone was sufficient to extend plasticity in light-reared adult V1, implicating recurrent PV- inhibition in critical period regulation. Together, our results suggest that reorganization of this recurrent PV-cell network by early experience or gene mutation may contribute to aberrant plasticity and associated cognitive disorders more broadly. iv TABLE OF CONTENTS Abstract ………………………………………………………………………………………… iii Table of Contents ………………………………………………………………………………... v Table of Figures ………………………………………………………………………………... vii Acknowledgments …………………………………………………………………………….. viii CHAPTER 1 - Introduction ……………………………………………………………………... 1 CHAPTER 2 - Methods ………………………………………………………………………... 20 CHAPTER 3 - Cell type-specificity of PV-cell afferent connectivity …………………………. 31 Abstract …………………………………………………………………………………….. 32 Introduction ……………………………………………………………………………..….. 33 Results ………………………………………………………………………………..…….. 37 Discussion ………………………………………………………………………………….. 44 CHAPTER 4 - Plasticity of PV-cell circuits following dark-rearing ………………………….. 51 Abstract …………………………………………………………………………………….. 52 Introduction ………………………………………………………………………………... 53 Results ……………………………………………………………………………………... 60 Discussion …………………………………………………………………………………. 73 CHAPTER 5 - Circuit-level impact of dark-rearing on PV-PV connections ………………….. 86 Abstract …………………………………………………………………………………….. 87 Introduction ………………………………………………………………………………… 88 Results ……………………………………………………………………………………… 95 Discussion ………………………………………………………………………………… 105 v CHAPTER 6 - PV-PV connectivity regulates cortical plasticity ……………………………...117 Abstract ……………………………………………………………………………………118 Introduction ………………………………………………………………………………. 119 Results ……………………………………………………………………………………..123 Discussion …………………………………………………………………………………137 CHAPTER 7 - Discussion …………………………………………………………………….144 References ……………………………………………………………………………………..161 vi TABLE OF FIGURES Figure 1.1 ………………………………………………………………………………………8 Figure 1.2 ………………………………………………………………………………………11 Figure 3.1 ………………………………………………………………………………………38 Figure 3.2 ………………………………………………………………………………………41 Figure 3.3 ………………………………………………………………………………………42 Figure 3.4 ………………………………………………………………………………………43 Figure 4.1 ………………………………………………………………………………………61 Figure 4.2 ………………………………………………………………………………………64 Figure 4.3 ………………………………………………………………………………………66 Figure 4.4 ………………………………………………………………………………………69 Figure 4.5 ………………………………………………………………………………………70 Figure 4.6 ………………………………………………………………………………………81 Figure 5.1 ………………………………………………………………………………………97 Figure 5.2 ………………………………………………………………………………………100 Figure 5.3 ………………………………………………………………………………………103 Figure 5.4 ………………………………………………………………………………………108 Figure 6.1 ………………………………………………………………………………………124 Figure 6.2 ………………………………………………………………………………………128 Figure 6.3 ………………………………………………………………………………………130 Figure 6.4 ………………………………………………………………………………………134 Figure 7.1 ………………………………………………………………………………………157 vii Acknowledgments There are many people I would like to thank for the help and support they gave me while I completed my PhD. Thank you to all of my extraordinary colleagues in the labs of Takao Hensch & Michela Fagiolini their expertise and assistance have been invaluable in shaping both my learning and the body of work that I have produced. Thank you also to my class- & program-mates in the Program in Neuroscience, for all of our discussions at seminars & beer hours, and for late-summer memories at the Woods Hole retreats. I would like to thank my dissertation advisory committee and qualifying exam committee members Venki Murthy, Dick Masland, Jeff Lichtman, Aravi Samuel, & Roz Segal for patiently guiding me throughout this process. I also thank my dissertation exam committee members Venki Murthy, Gina Turrigiano, Paola Arlotta, & Uwe Rudolph for reading and offering feedback on this thesis, and for pushing my thinking in new directions. I must also thank the leadership of the Program in Neuroscience Roz Segal, Rick Born, Rachel Wilson, & Karen Harmin for their valued advice and support, from beginning to end. I would also like to thank many of my past advisers & teachers Ania Majewska & Dan Feldman, for showing me what real science is and for inspiring my passion for neuroanatomy; and, Melissa Webster-Shandroff, Doron Markus, & David Holtzman, for first sparking my interest in biology and in neuroscience specifically. I also want to give a special thank you to Jeff Lichtman, for his eager willingness to discuss the finer details of Brainbow analyses and imaging best practices, and for his help in asking the right questions. I also need to thank my unendingly supportive family Janet, Chris, & Lindsay Bogart and the rest of my family and friends both near and far. I could not have come this far without their constant encouragement and reassurance. I also thank my late grandmother, Adele Bogart, viii who passed away shortly after this thesis was completed and who always took an interest in my studies. This work is dedicated to her memory. Finally, I would like to thank Takao Hensch for being an inspiring scientist and mentor, who always urged me to “dig deep” in search of the most rewarding questions. This thesis and all of the work and ideas it contains is a testament to his patient guidance and steadfast pursuit of discovery. ix CHAPTER 1 Introduction 1 Critical periods of development Development of the nervous system occurs along a protracted timeline that extends into early postnatal life (Hensch, 2004). As the nervous system undergoes periods of growth and maturation, an organism gains sensitivity to new stimuli in the environment as well as the ability to process these stimuli in novel ways. Interestingly, this enhanced sensitivity is the result of environmental influence on the nervous system’s own development, which adapts to respond optimally to the environmental features that are present. For example, neurons in the visual cortex of kittens are sensitive to the direction of motion of visual stimuli, a response property that matures over the first few weeks of life. In cats reared under a strobe light, however, this sensitivity fails to develop as motion stimuli are absent from the environment (Cynader & Chernenko, 1976). Strikingly,
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