UCSF UC San Francisco Electronic Theses and Dissertations Title Deep phenotypic profiling of neuroactive drugs in larval zebrafish Permalink https://escholarship.org/uc/item/3vk1d0gr Author Gendelev, Leo Publication Date 2019 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Deep phenotypic profiling of neuro-active drugs in larval Zebrafish by Lev Gendelev DISSERTATION Submitted in partial satisfaction of the requirements for degree of DOCTOR OF PHILOSOPHY in Biophysics in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, SAN FRANCISCO Approved: ______________________________________________________________________________Michael Keiser Chair ______________________________________________________________________________DAVID KOKEL ______________________________________________________________________________Jim Wells ______________________________________________________________________________ ______________________________________________________________________________ Committee Members Copyright 2019 by Lev (Leo) Gendelev ii In Loving Memory of My Mother, Lucy Gendelev iii Acknowledgements First of all, I thank my advisor, Michael Keiser, for his unwavering support through the most difficult of times. I will above all else reminisce over our brainstorming sessions, where my half- wrought white-board scribbles were met with enthusiasm and sometimes even developed into exciting side-projects. My PhD experience felt less like work and more like a fantastical scientific journey. For this bottomless support and experience that will surely will never repeat again, I am forever grateful. I thank David Kokel, who was very much the enabler of this journey with rare scientific generosity and collaborative enthusiasm; for encouraging me to push the high-risk, high-reward Deepfish project forward, not just with words but with the impressive Zebrafish assays and talented personnel of his lab; for allowing me to fully indulge in my love affair for small molecules and neural networks. Thank you for this unique opportunity to work on a truly cutting-edge, exciting, and highly-impactful project. I thank Jim Wells for helping us navigate this deep-fishing ship safely to shore and for letting an Astrophysicist play with a pipette and even purify proteins as a rotation student. I thank Bill DeGrado for infusing my mortal brain with a glimpse of what it’s like to design proteins in one’s head and for getting me to unconditionally surrender a pairwise view of molecular interactions; Matt Jacobson for sharing his encyclopedic insights into computational chemistry both as a field and industry; Michael Grabe for his support and being a key mentor for me over the years. I thank members of the Kokel lab for all the exciting science and support over the years; Matt McCarroll for testing enough dual-target predictions to map out a sizable pairwise view of the Zebrafish pharmacological landscape, and for making sure the soft-tap project stayed on track, even when I couldn’t physically be there to do it myself; Douglas Turnbull for his passion and key iv insights and contributions to the Deepfish project and for countless troubleshooting sessions; Cole Helsell for his enthusiasm in discussing details of every aspect of Zebrafish science; Chris Ki for his round-the-clock tech support; Jack Taylor, for not missing a fish and enthusiastically running more plates than most would consider reasonable. I thank Steve Chen from the Small Molecule Discovery Center for his masterful and unwavering help with randomizing plate layouts. I thank all members of the Keiser Lab for contributing to a truly unique and special scientific environment; Ben Wong for all his cluster-support; Nick Mew and Elena Caceres for key scientific discussions and ideas; Kangway Chuang for his encyclopedic knowledge, insights, and discussions into machine learning approaches; Garrett Gaskins for being there with me from the very beginning. Here’s to all the new and talented additions to the Keiser Lab who will continue to push the frontiers of machine learning in the biosciences. I dedicate this thesis to my mother, who had unwavering faith in me not only as a scientist, but as a person, and in whose memory I will strive to continue my research in the biological sciences. To my father, thank you for everything. I’m sorry I didn’t turn out to be an Astrophysicist, but I hope this will do as a compromise. To my sister; thank you for your love and demonstrating to me that a PhD is attainable - with enough sweat and tears. I thank my friend, Ilan Chemmama, for making sure I made it in mostly one piece. To UCSF, for believing in and accepting an Astrophysicist to the Biophysics PhD program, and for giving us 11 more years with mom than we would have had otherwise. v The text of Chapter 1 is a reprint of the material as it appears in: McCarroll, M., Gendelev, L., et al, Leveraging Large-scale Behavioral Profiling in Zebrafish to Explore Neuroactive Polypharmacology, ACS Chem. Biol., (2016) It appears here with permission from the authors. The text of Chapter 2 is a reprint of the material as will appear in: McCarroll, M.*, Gendelev, L.*, et al, Large-scale behavior-based chemical screening identifies ligands and targets related to paradoxical excitation in zebrafish, Nature Communications (2019); accepted. *co-first authors It appears here with permission from the authors. The supplementary material from this paper has been included as Appendix A.1. vi Abstract Deep phenotypic profiling of neuro-active drugs in larval Zebrafish Lev (Leo) Gendelev In-vivo phenotypic screening in larval zebrafish has shown much promise for neuroactive drug discovery, but unleashing its full potential remains a challenge. How do we robustly quantify how drugs modulate zebrafish behavior, and in parallel, how do we unravel which targets or pathways they act by? We develop a phenotypic screening and computational pipeline to begin meeting these challenges. Starting with a motion index (MI) as a readout for phenotype, and correlation distance (CD) as a measure of phenotypic similarity, we extend the similarity ensemble approach to computationally predict targets for sets of phenotypic screening hits. Using this approach, we predict an “antipsychotic” target profile for previously uncharacterized hit compounds with MI’s matching those of known antipsychotic compounds. For a novel phenotype associated with sedation and paradoxical excitation caused by anesthetics such as etomidate and propofol, we predict not only the canonical GABAergic pathway, but a novel target entirely; the serotonin-6 receptor, which we validate with both in-vitro and in-vivo experiments. However, our initial attempts at extending this approach to other known drug classes such as stimulants and convulsants are met with unexpected challenges; we hypothesize that the MI signatures and the CD used to compare might not be robust enough for these more subtle phenotypes. And so the Deepfish project is born. We train Siamese neural networks (SNNs) on a highly replicated screen of 650 known neuroactive drugs to learn a custom distance metric for comparing MI. This new distance metric scores higher than CD at the task of separating same-drug replicate pairs versus different-drug pairs, all while generalizing to a quality control screen done vii months prior. In that arena, the new distance metric gets higher classification accuracy on average, but also strikingly outperforms CD for 3 drugs with more subtle phenotypes. Armed with a way of training robust distance metrics, we make progress with using unsupervised deep-learning approaches to find more robust representations of behavior. We discover that for computing similarities between these high-dimensional embedded fingerprints, training custom distance metrics is even more imperative. However, we see signs that overfitting is possible with the Siamese networks on our highly-replicated dataset - both with the raw MI and high-dimensional embedded representations - so we design and perform a version of the screen with fully randomized drug layouts, which we will use to benchmark our methods in the near future. viii Table of Contents INTRODUCTION ...................................................................................................................... 1 I. A platform for phenotypic screening in larval zebrafish ...................................................................... 2 II. A pipeline: from phenotype to target prediction ................................................................................. 4 III. Beyond motion index and correlation distance .................................................................................. 4 IV. Guide to the chapters ............................................................................................................................. 5 V. Birds-eye view ............................................................................................................................................ 6 V. Methods ...................................................................................................................................................... 6 IV. References ................................................................................................................................................ 8 PREAMBLE TO CHAPTER 1 ..................................................................................................... 10 I. References