Applied Science, Faculty of
Biomedical Engineering, School of
DSpace
UBCV
Baker, Samuel F.
Master of Applied Science - MASc
University of British Columbia
Hypermethylated in Cancer 1 (Hic1) is a gene that marks a population of cells known as mesenchymal progenitors (MPs) in humans as well as mice. MPs can be found in almost all adult tissues, where they support repair and regeneration processes after injury. Hic1 regulates MP quiescence, a state where cells are growth arrested, but are poised to enter the cell cycle following activation with various stimuli (i.e., injury). When Hic1 is deleted, MPs exit quiescence and exhibit a mildly activated state, which is associated with an accumulation of MPs and their progeny. These conditions also provide an environment conducive for the development of various cancers and fibrosis. Both diseases are significant contributors to global mortality and morbidity, and treatment options are limited. This project investigated mechanisms for modifying MP activity, in part, through inducing quiescence. Using Hic1 expression as a surrogate for quiescence, an assay was developed to measure Hic1 transcript abundance. The assay relies on the use of cells from a genetically engineered mouse model, in which a nuclear LacZ gene was knocked-in to the Hic1 locus. In this manner, Hic1 expression and by extension quiescence, can be followed by measuring LacZ activity. CPRG is a chromogenic substrate of LacZ and was used to measure the abundance of LacZ after treating Hic1ⁿᴸªᶜᶻ/⁺ mouse embryonic fibroblasts (within the MP umbrella) with a library of drugs that passed at least Phase I of FDA clinical trials. High-throughput techniques such as robotic cell seeding and 96-well parallel drug administration by pinning tool were used to efficiently work through the over ~ 4,000 drugs. The screen identified various retinoids as hits, which was expected due to the direct targeting of Hic1 by retinoic acid (RA) receptors (Rs), and no non-retinoid compounds resulted in significant increases in Hic1 expression. Screen results were further investigated through a high content X-gal and nucleic acid stain screen, as well as through RNA sequencing. Secondary screening indicated that hit compounds were acting through modulating RAR signaling. Collectively, these findings reinforce the concept that MP quiescence is predominantly regulated by RAR-mediated signaling.
i A HIGH-THROUGHPUT SCREEN TO IDENTIFY COMPOUNDS THAT MODULATE QUIESCENCE IN MESENCHYMAL PROGENITOR CELLS by Samuel F. Baker B.Eng., McGill University, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Biomedical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2023 © Samuel F. Baker, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: A High-Throughput Screen to Identify Compounds That Modulate Quiescence in Mesenchymal Progenitor Cells submitted by Samuel F. Baker in partial fulfilment of the requirements for the degree of Master of Applied Science in Biomedical Engineering Examining Committee: Dr. Michael Underhill, Professor, School of Biomedical Engineering, UBC Supervisor Dr. Kelly McNagny, Professor, School of Biomedical Engineering, UBC Supervisory Committee Member Dr. Carl de Boer, Assistant Professor, School of Biomedical Engineering, UBC Supervisory Committee Member iii Abstract Hypermethylated in Cancer 1 (Hic1) is a gene that marks a population of cells known as mesenchymal progenitors (MPs) in humans as well as mice. MPs can be found in almost all adult tissues, where they support repair and regeneration processes after injury. Hic1 regulates MP quiescence, a state where cells are growth arrested, but are poised to enter the cell cycle following activation with various stimuli (i.e., injury). When Hic1 is deleted, MPs exit quiescence and exhibit a mildly activated state, which is associated with an accumulation of MPs and their progeny. These conditions also provide an environment conducive for the development of various cancers and fibrosis. Both diseases are significant contributors to global mortality and morbidity, and treatment options are limited. This project investigated mechanisms for modifying MP activity, in part, through inducing quiescence. Using Hic1 expression as a surrogate for quiescence, an assay was developed to measure Hic1 transcript abundance. The assay relies on the use of cells from a genetically engineered mouse model, in which a nuclear LacZ gene was knocked-in to the Hic1 locus. In this manner, Hic1 expression and by extension quiescence, can be followed by measuring LacZ activity. CPRG is a chromogenic substrate of LacZ and was used to measure the abundance of LacZ after treating Hic1nLacZ/+ mouse embryonic fibroblasts (within the MP umbrella) with a library of drugs that passed at least Phase I of FDA clinical trials. High-throughput techniques such as robotic cell seeding and 96-well parallel drug administration by pinning tool were used to efficiently work through the over ~ 4,000 drugs. The screen identified various retinoids as hits, which was expected due to the direct targeting of Hic1 by retinoic acid (RA) receptors (Rs), and no non-retinoid compounds resulted in significant increases in Hic1 expression. Screen results were further investigated through a high content X-gal and nucleic acid stain screen, as well as through RNA sequencing. Secondary iv screening indicated that hit compounds were acting through modulating RAR signaling. Collectively, these findings reinforce the concept that MP quiescence is predominantly regulated by RAR-mediated signaling. v Lay Summary Many of our tissues contain cells known as mesenchymal progenitors (MPs) that help with healing after an injury. They do this by making cells to replace lost or damaged ones, or by influencing the activity of other cells important in tissue regeneration. When tissues are healthy, MPs are in a state known as quiescence, ready to activate when needed. Quiescence in MPs is controlled by a protein encoded by a gene termed Hypermethylated in Cancer 1 (Hic1). Hic1 malfunction appears to affect the development of various diseases including cancer and fibrosis, which is excessive tissue scarring and impairment of tissue function. This project tested over 4,000 drugs for their ability to change the abundance of Hic1 within MPs. The results showed that retinoids, molecules derived from Vitamin A or β-carotene, are effective at increasing Hic1 expression and promoting quiescence, and may represent therapeutic candidates for treating fibrosis and cancer. vi Preface Dr. Michael Underhill’s lab has identified Hic1 as a marker of MPs as well as a regulator of the quiescent phenotype of these cells. Hic1nLacZ/+ mice were generated, MEFs collected from embryos and cryopreserved by Underhill lab members prior to this project. Genotyping of MEFs was done by the BRC Genotyping Core. Dr. Michael Underhill and Dr. Lesley Hill provided guidance in study design throughout the project. The CPRG screen was developed based on previous work done by the Underhill Lab in collaboration with the Centre for Drug Research and Discovery (CDRD). RNA sequencing data that was used to validate the CPRG screen initially was generated by Dr. Lesley Hill. Drug plates were aliquoted from the libraries by Dr. Lesley Hill and by the UBC Biofactorial High-Throughput Biology Facility. Samuel Baker completed the literature review, cell culture work, assays, data collection, data analysis and writing of this report. This work was done under the supervision of Dr. Michael Underhill. Dr. Lesley Hill, Hanna Girndt and Bobo Davenport helped with cell culture maintenance. RNA sequencing and data processing were performed by the BRC Sequencing Core. Imaging and analysis of X-gal and nucleic acid staining was done with the support of the Biofactorial High-Throughput Biology Facility. Animal work was approved by the University of British Columbia Animal Care Committee, Certificate # A21-0279. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations ................................................................................................................. xiii Acknowledgements .................................................................................................................... xvi Dedication .................................................................................................................................. xvii Chapter 1: Introduction ................................................................................................................1 1.1 Hic1+ Mesenchymal Progenitors .................................................................................... 1 1.1.1 Mesenchymal Progenitors ........................................................................................... 1 1.1.2 Hypermethylated in Cancer 1 ..................................................................................... 2 1.1.3 Hic1+ Mesenchymal Progenitors in Disease ............................................................... 3 1.2 Quiescence ...................................................................................................................... 6 1.3 Retinoid Signaling .......................................................................................................... 7 1.3.1 Retinoid Metabolism ................................................................................................... 8 1.3.2 Retinoic Acid Receptor ............................................................................................. 11 1.4 Drugs Modulating Retinoid Signaling .......................................................................... 12 1.5 Screening Compounds for Biological Activity ............................................................. 14 1.5.1 CPRG Assays ............................................................................................................ 15 1.6 Summary & Objectives ................................................................................................. 16 viii Chapter 2: Materials & Methods ...............................................................................................19 2.1 Cell Culture ................................................................................................................... 19 2.2 Robotics ........................................................................................................................ 21 2.3 Drug Libraries ............................................................................................................... 21 2.4 CPRG Screen ................................................................................................................ 23 2.4.1 Z-buffer Formulation ................................................................................................ 25 2.5 In vitro X-gal Staining .................................................................................................. 25 2.5.1 Staining ..................................................................................................................... 26 2.5.2 X-gal Staining Solution Formulation ........................................................................ 26 2.5.3 Imaging & Analysis .................................................................................................. 27 2.6 RNA Sequencing .......................................................................................................... 29 Chapter 3: Results........................................................................................................................32 3.1 Assay Optimization ....................................................................................................... 32 3.1.1 Cell Seeding Density & CPRG Concentration ......................................................... 32 3.1.2 Assessment of Cell Passage Number on LacZ Activity ........................................... 35 3.1.3 Optimization of CPRG Concentration for CPRG Assay in Confluent Cultures ...... 37 3.2 Assay Validation ........................................................................................................... 38 3.3 CPRG Screening Validation ......................................................................................... 40 3.3.1 Robotic Plating.......................................................................................................... 40 3.3.2 Stem Cell Signaling Library Screen Validation........................................................ 42 3.3.3 Highly Selective Inhibitor Library Screen Validation .............................................. 44 3.4 FDA-Approved Library CPRG Screening .................................................................... 46 3.5 X-gal High Content Screening ...................................................................................... 50 ix 3.6 RNA Sequencing .......................................................................................................... 52 3.7 Novel Hit Investigation ................................................................................................. 55 Chapter 4: Discussion ..................................................................................................................56 4.1 Screening for Hic1 Expression-Modulating Drugs ....................................................... 56 4.1.1 CPRG High-Throughput Screening .......................................................................... 56 4.1.2 X-gal High Content Screening .................................................................................. 57 4.1.3 RNA Sequencing ...................................................................................................... 58 4.2 Retinoids Control Hic1 Expression .............................................................................. 59 4.3 Conclusion .................................................................................................................... 61 4.3.1 Review of Objectives ................................................................................................ 62 4.3.1.1 Optimize and validate a CPRG-based assay using robotics to allow for high-throughput screening of Hic1-modulating compounds. .................................................... 62 4.3.1.2 Screen a library of drugs that have been approved for safety in humans for the ability to modulate the expression of Hic1. ...................................................................... 62 4.3.1.3 Perform additional testing on hits to evaluate potential use as therapeutics and gain insight into the mechanisms involved in Hic1 regulation. ........................................ 63 4.3.2 Significance............................................................................................................... 63 4.3.3 Strengths and Limitations ......................................................................................... 64 4.3.4 Future Directions ...................................................................................................... 65 Bibliography .................................................................................................................................67 Appendices ....................................................................................................................................80 Appendix A. RNA Counts for All Significantly Differentially Expressed Genes ................... 80 x List of Tables Table 1. CellInsight CX5 preprocessing settings…………………………………………………28 Table 2. CellInsight CX5 data analysis reference levels………………………………………….29 Table 3. Signal-to-noise ratios for cell densities and CPRG concentrations……………………..35 Table 4. Z-factors for cell densities and CPRG concentrations…………………………………..35 Table 5. Signal-to-noise ration and Z-factor as a function of passage number……………………37 Table 6. Signal-to-noise ratios and Z-factors for confluent cells at varying CPRG concentrations……………………………………………………………………………………38 Table 7. Hits from the Stem Cell Signaling library CPRG screen……………………………….43 Table 8. Hits from the Highly Selective Inhibitor library CPRG Screen………………………….45 Table 9. Hits from the FDA-Approved & Passed Phase I library CPRG Screen……………….…47 Table 10. Transcript count matrix for select hit compounds with delta values……………….…80 Table 11. Transcript count matrix for EC23 and palovarotene at 100 nM and 1 nM……..……….88 xi List of Figures Figure 1. Retinoid metabolism and signaling……………………………………….……………..9 Figure 2. All-trans retinoic acid structure……………………………………….………………..13 Figure 3. Retinol structure………………………………………………………………………..13 Figure 4. EC23 structure………………………………………………………………………….13 Figure 5. AGN194310 structure…………...……………………………………………………..14 Figure 6. CPRG and β-gal reaction……………………………………………………………….16 Figure 7. Graphical abstract………………………………………………………………………17 Figure 8. Wildtype and LacZ knock-in alleles……………………………………………………20 Figure 9. Stem Cell Signaling Compound library composition…………………………………..22 Figure 10. Highly Selective Inhibitor library composition……………………………………….22 Figure 11. FDA-Approved & Passed Phase I library composition……………….……………….23 Figure 12. CPRG screen schematic………………………………………………………………25 Figure 13. Optimization of cell density and CPRG concentration for the CPRG assay…………34 Figure 14. Passage effect on CPRG assay………………………………………………………...36 Figure 15. Effect of CPRG concentration on confluent MEFs……………………………………38 Figure 16. Validation compound CPRG response at 10 μM……………………………………...39 Figure 17. Initial plating trends based on well…………………………………………………....40 Figure 18. Variation in plating based on plate number……………………………………………41 Figure 19. Plating trends based on well after optimization……………………………………….42 Figure 20. Stem Cell Signaling library CPRG screen results……………………………………..44 Figure 21. Highly Selective Inhibitor library CPRG screen results…………………….…………45 Figure 22. FDA-Approved & Passed Phase I library CPRG screen results……………………….48 xii Figure 23. Dose responses of selected hit compounds……………………………………………49 Figure 24. CellCyteX images of hit compounds………………………………………………….50 Figure 25. X-gal and Hoechst staining images……………………………………………………51 Figure 26. X-gal high content screening analysis………………………………………………...52 Figure 27. RNA sequencing differential expression heatmap of selected hit compounds….……..53 Figure 28. RNA sequencing differential expression heatmap of controls at 100 nM and 1 nM….54 Figure 29. Dose responses of screen vs standard drugs……………………………….…………..55 Figure 30. Allopurinol structure………………………………………………………………….60 Figure 31. Ursodiol structure……………………………………………………………………..60 xiii List of Abbreviations ADH – alcohol dehydrogenase ALDH – aldehyde dehydrogenase atRA – all-trans retinoic acid A574nm – absorbance of 574 nm light β-gal - β-galactosidase BMP – bone morphogenic protein BRC – UBC Biomedical Research Centre CBP – CREB-binding protein CCN – Cyclin CDKN – Cyclin dependent kinase inhibitor CDRD – Centre for Drug Research and Discovery CPRG - Chlorophenol red-β-D-galactopyranoside CRABP – cellular retinoic acid binding protein CRBP – cellular retinol binding protein CYP26 – a family of cytochrome P450 ΔA574nm – change in the absorbance of 574 nm light DHRS – dehydrogenase reductase DMEM – Dulbecco’s Modified Eagle Medium DMSO – dimethyl sulfoxide DNA - deoxyribonucleic acid ECM – extracellular matrix EDTA - ethylenediaminetetraacetic acid xiv FBS – fetal bovine serum FDA – U.S. Food and Drug Administration HDAC – histone deacetylase HIC1 – Hypermethylated in Cancer 1 HSC – hepatic stellate cells LRAT – lecithin:retinol acyltransferase MEF – mouse embryonic fibroblast MP – mesenchymal progenitor mRNA – messenger ribonucleic acid MTA – metastasis-associated protein NAD - nicotinamide adenine dinucleotide NADPH – reduced nicotinamide adenine dinucleotide phosphate NASH – non-alcoholic steatohepatitis NuRD – nucleosome remodeling deacetylase PBS – phosphate-buffered saline PDGFR – platelet-derived growth factor receptor RA – retinoic acid RALDH – retinaldehyde dehydrogenase RAR – retinoic acid receptor RARE – retinoic acid response element RBP – retinol binding protein RDH – retinol dehydrogenase REH – retinyl ester hydrolase xv RNA – ribonucleic acid RXR – retinoid X receptor SCA – stem cells antigen SIRT – sirtuin SMA – smooth muscle actin SNR – signal-to-noise ratio STRA – signaling receptor and transporter of retinol TDG – thymine-deoxyribonucleic acid glycosylase TGF – transforming growth factor TP53 – tumor protein p53 Z’ – Z-factor 4310 – AGN194310xvi Acknowledgements Thank you to Dr. Michael Underhill for providing me with the opportunity to experience research in your lab as well as your support, guidance, and encouragement throughout my studies. Thank you to my supervisory committee members Dr. Kelly McNagny and Dr. Carl de Boer for contributing their time and expertise in supporting this project, and for the insightful discussions at our meetings. Thank you to Dr. Lesley Hill for all your help with my project, time spent answering my questions, training me in new techniques and letting me use your robot. I would also like to thank my lab mates Dr. Martin Arostegui, Bobo Davenport, Hanna Girndt, and Dr. Wilder Scott for their help and friendship in the lab. Thank you to the BRC Core, BRC Transgenic Unit, BRC Sequencing Core, BRC Genotyping Core, UBC Flow and Biofactorial teams for all the work you do to make researchers’ lives easier. I would like to thank the Canadian Institute of Health Research (CIHR) and the Stem Cell Network for the grants that funded this work. Thank you to my parents Lisa & Royce Baker for their love, support, and encouragement over the past 25 years. And finally thank you to my partner Deniz Coskuner for believing in me and helping me through this experience. xvii Dedication To my family for their love, support, and encouragement. 1 Chapter 1: Introduction To illuminate the reasoning behind this research project and highlight the importance of this work, this chapter reviews relevant scientific literature surrounding mesenchymal progenitors (MPs), quiescence, retinoid signaling and its relevance to drugs, and finally chlorophenol red-β-D-galactopyranoside (CPRG) assays. The chapter concludes with a summary of the knowledge shared within and outlines the goals of the project. 1.1 Hic1+ Mesenchymal Progenitors 1.1.1 Mesenchymal Progenitors Fibroblasts are a largely heterogeneous category of cells that produce collagen1,2, and subpopulations have been shown to have progenitor properties, namely multilineage differentiation potential, and the ability to enter quiescence3–7. Nomenclature in this field is still debated with terms such as mesenchymal stem cells8,9, fibro-adipogenic progenitors4, and others being used to describe similar subpopulations, but in this report these cells will be referred to as MPs. MPs can be found in almost every tissue, and play vital roles in tissue renewal, repair and regeneration4,6,9–13. Similar to nomenclature in the field of MPs, defining markers also vary between studies7,9,13. It has been shown by the Underhill lab that the gene Hypermethylated in Cancer 1 (Hic1) serves as a marker for a population of MPs that are present from embryos and into adult animals6,10,11. This population has been identified by various lineage tracing methods in skeletal muscle, tendon, cartilage, epidermis, testis, and is present in embryonic tissue6,10,11,14–16. Ongoing research in the Underhill lab suggests that these cells are present in almost every adult tissue. 2 1.1.2 Hypermethylated in Cancer 1 HIC1 is a tumor suppressor gene that was initially identified by Wales et al. in 1995. It is expressed in healthy tissues but is silenced by hypermethylation in a variety of tumors17–19. Located on human chromosome 17, HIC1 resides in a rich CpG island19,20. The HIC1 protein is part of the zinc-finger N-terminal domain family and contains 5 Krüppel-type C2-H2 zinc-fingers that coordinate binding to cognate response elements within DNA19,20. HIC1 functions as a transcriptional repressor18,19,21, and interacts with MTA1 as part of the NuRD complex22. This transcriptional repressive activity influences a number of fundamental cellular processes including cell differentiation, cell survival, growth control and stress responses19,22,23. HIC1 expression has been shown to attenuate proliferation in various cancer cell lines14,19. HIC1 can form a repressor complex with sirtuin 1 (SIRT1) that autoregulates SIRT1 expression, as it targets SIRT1 and other genes, and can also form a complex which diverts it and β-catenin from Wnt signaling promoters18,23. The targets of HIC1 can differ depending on cell state, for example, it targets the genes cyclin D1 (Ccnd1) and cyclin dependent kinase inhibitor 1C (Cdkn1c) during quiescence, but not during cell growth22. Additionally, HIC1 has a murine homolog, Hic1, found on chromosome 11 in mice20. In the context of Hic1+ MPs, it has been shown that Hic1 expression regulates the quiescent phenotype6. These cells are present during embryonic development where they contribute to the genesis of the limb bud as well as other compartments and are maintained into adulthood, where they contribute to tissue renewal, repair and regeneration across tissues such as the heart, skeletal muscle and skin6,10,11,14,24. 3 Moreover, HIC1 expression is directly controlled by retinoic acid (RA) receptor (R) signaling25, and is also a target of TP5319. HIC1 activity and target-specificity are also impacted by post-translational modifications22. These include acetylation to reduce repressor activity and SUMOylation which increases HIC1’s repressor activity22. These modifications are toggled by a complex of SIRT1 and HDAC422. To summarize, HIC1 is a crucial tumor suppressor gene that is downregulated in tumors due to hypermethylation. Its encoded protein acts as a transcriptional repressor, participating in regulatory loops controlling various cellular processes. HIC1 expression and HIC1 function are regulated by interactions with other proteins and influenced by post-translational modifications. In Hic1+ MPs, Hic1 expression contributes to the maintenance of a quiescent phenotype and has a broad role in embryonic development, health, and disease. 1.1.3 Hic1+ Mesenchymal Progenitors in Disease The proper functioning of MPs is crucial for maintaining healthy tissue and preventing the development of diseases such as cancer and fibrosis. Hic1 dysregulation has widely been implicated in cancer, with its association with hypermethylation, TP53 mutations, increased Wnt signaling, and repression of other genes19,21,26–29. Furthermore, MP activity and their progeny, namely myofibroblasts, are key to the development of fibrosis4,30–32. The contribution of these cells to this disease is the formation of scar tissue and an excess of extracellular matrix protein deposition which negatively impacts tissue function33,34. Human osteosarcoma tumors have shown HIC1 hypermethylation is correlated to mutations in TP53, another tumor suppressor26,27. In addition, HIC1 disruption can also cause 4 cancer through chromosomal instability resulting from its interaction with MTA1 in the NuRD complex, independent of TP5328. Other carcinogenic effects of Hic1 silencing include increased Wnt signaling and the silencing of other genes27,29. Hypermethylation of CpG islands often leads to cancer27, and therapeutics that counter this effect operate through increased methylation35. Healthy tissues contain MPs that activate in response to injury and a subset return to quiescence after healing is complete6,14. One of the derivatives of MPs during regeneration is myofibroblasts, which are also lost after successful regeneration, but persist in repair and fibrosis and produce excessive amounts of extracellular matrix proteins, such as various collagens31,33. Additionally, MPs can differentiate into adipocytes, and fatty infiltration into skeletal muscle during repair when myogenic regeneration fails also can contribute to fibrosis4. Hic1 knock-out mice exhibit fibrofatty infiltration of cardiac tissue without requiring injury24. It has been shown that ablation of myofibroblasts reduces fibrosis progression in mice36,37. Fibrotic disease contributes to nearly 45% of deaths worldwide34, and has no cure currently, but treatments are available that slow the progression of disease38,39. In treating fibrosis, many current approaches focus on the activity of myofibroblasts and their interactions with extracellular matrix40. Though it has been posited that treating fibrotic disease at the level of mesenchymal progenitors, which are a source of the myofibroblasts, may prove more effective32,41,42. Fibrosis can occur in many organs such as the liver43–47, heart48–50, lung51–53, skin54,55, kidney36,56, pancreas57, and nerve tissues58. Fibrosis develops through a variety of pathways, but a key component is the inflammatory response after an injury45. Acute injuries can result in some amount of fibrogenesis, however fibrosis develops through chronic insult from the use of drugs 5 or alcohol, infection or disorders, that result in the accumulation of extracellular matrix (ECM) proteins over long periods of time45. One commonly investigated tissue that can develop fibrosis is the liver. Liver fibrosis develops predominantly from the hepatic stellate cells (HSCs), portal fibroblast and their myofibroblast progeny44,59. Both portal fibroblasts and HSCs are considered MPs of the liver. Variations within liver fibrosis exist, primarily distinguished based on the source of the disease, with viral infection leading to hepatitis B, hepatitis C, and steatosis resulting from drug use and obesity45. In conjunction with the increasing prevalence of obesity, research interest in non-alcoholic steatohepatitis (NASH) is growing45,60. NASH is defined as steatosis that occurs in the absence of alcohol abuse, often as a result of adipose tissue accumulation in the liver leading to metabolic disruptions60. Regardless of the source, fibrosis in the liver eventually progresses to a final stage known as cirrhosis, which is characterized by more scar presence and often leads to liver failure45. Changes in expression of many genes correlate to the development of fibrosis, such as PDGFRα overexpression61, SCA1 downregulation24, increase expression of TGFβ family members34,44, and α-SMA expression, which marks activated myofibroblasts52,57. Additionally, methylation of CpG islands can maintain an extended activation of HSCs in the liver, overproducing myofibroblasts and leading to fibrosis45. TGFβ has been shown to contribute to chronic fibrosis through prolonging the activation of myofibroblasts, and drugs that inhibit TGFβ resolve this issue30,31,62,63. The ECM stiffens during fibrosis due to increased collagen and proteoglycan deposition, ECM crosslinking, as well as myofibroblast contractility and remodeling, which provides additional activation signals thus 6 perpetuating myofibroblast activity64. Reversion of the fibrogenic myofibroblasts to less active cell states has been shown to occur in the regression of CCl4-induced liver fibrosis using lineage tracing in mouse models65. Cancer and fibrosis are diseases that can develop as a result of the loss of the quiescent phenotype in MPs45,65. Understanding the mechanisms at play in maintaining and returning to quiescence could lead to development of new therapies for these common diseases. 1.2 Quiescence A key defining factor of stem and progenitor cells is their ability to maintain a quiescent state, which is a reversible form of growth arrest66. While quiescent cells are not proliferating they are still very active, and have the ability to efficiently re-enter the cell cycle following stimulation66,67. Quiescent cells exist in almost every adult tissue and maintaining a balance between proliferation and quiescence is essential for health66,68. When the quiescent phenotype is lost resident stem and progenitor cell populations can become rapidly depleted, exhausting the proliferative capacity of the tissue66. Quiescent cells exhibit decreased metabolic rates compared to their proliferative states, though this difference can be small depending on the cell type69. Part of this decrease may come from decreased nucleotide synthesis, however, they do not show reduced protein synthesis69. Quiescent cells vary in their transcriptome based on the resident tissue and the conditions leading to quiescence70. Despite this, common transcriptome qualities can be observed such as the downregulation of CCN and other cell cycle-related genes that are required to enter mitosis, and the upregulation of genes associated with stem cell regulation and cell cycle suppression67,70. 7 These similarities increase as the cells maintain quiescence for longer periods of time70. Epigenetic regulation of transcription has been identified in many of these quiescence associated genes, enabling rapid transition to an active state67,71,72. Quiescent cells can be activated in response to disruptions in their niche such as support cell failure73, or aberrant ECM composition74,75. An important regulator of cell cycle entry is TP53, which has been has been shown to regulate quiescence, and loss of TP53 function can cause cancer as it is a tumor suppressor67. Wnt signaling can also contribute to the maintenance of the quiescent phenotype74,76. In Hic1+ MPs, Hic1 regulates quiescence, and deletion of Hic1 results in a partially activated phenotype6. Notably, Hic1 is a direct target of retinoid signaling further supporting its involvement in MP quiescence25. A better understanding of the interaction between retinoid signaling, Hic1 and quiescence will improve efforts to find therapies for quiescence related diseases. 1.3 Retinoid Signaling Retinoids are a class of signaling molecules comprised of Vitamin A and its metabolites77. All-trans RA (atRA) is the primary active signaling molecule in this family of compounds78. Retinoid signaling controls many biological processes across a variety of organs and tissues including development79–84, regeneration85–89, MP maintenance89, and differentiation90,91. 8 1.3.1 Retinoid Metabolism Retinol is not synthesized in mammalian cells, and must be acquired through the diet78. RA levels are tightly regulated by a metabolic network, but the most direct control is balanced by the activity of two competing enzymes, a family of cytochrome P450 proteins (CYP26s) and retinaldehyde dehydrogenases (RALDHs), which metabolize and synthesize RA, respectively92. These genes are regulated by RA concentration and distribution, providing this metabolic pathway with feedback loops to maintain RA concentrations within a range93. 9 Figure 1. Retinoid metabolism and signaling. The above schematic depicts the metabolic processes involved in managing RA levels. CYP26 enzymes metabolize RA, converting it to less active forms that are generally more polar to limit RA abundance and RAR engagement81,83,94,95. CYP26b1 has been identified as critical in limiting retinoid signaling during development94. RA upregulates Cyp26 expression, providing negative feedback to control RA abundance96,97. 10 RALDH enzymes are the rate limiting step in synthesizing active RA from inactive forms when RA is needed83,92,98,99. Aldehyde dehydrogenase 1a2 (ALDH1A2) is a RALDH that is essential for limb development92, and contributes to cardiac regeneration in zebrafish that is not seen in mammals, which have less pronounced ALDH1A2 expression post-injury100. Aldh1a2 is a target of bone morphogenic protein signaling (specifically BMP4), which represses ALDH1A2 activity, and thus retinoid signaling, to promote chondrogenesis91. Retinol binding protein 4 (RBP4) escorts retinol to the cell via the bloodstream where it is internalized via signaling receptor and transporter of retinol 6 (STRA6) action101,102. Cellular retinol binding protein (CRBP) binds to retinol once it has entered the cytoplasm and assists in the storage of retinoids101,103. Retinol is converted into RA via a two-step process. First alcohol dehydrogenase (ADH) or retinol dehydrogenase 10 (RDH10) using NAD+ as a cofactor, converts retinol into retinaldehyde104,105. Then retinaldehyde is converted into RA by RALDHs83,92,98,99. Cellular retinoic acid binding protein (CRABP) serves an intracellular transporter of RA, and interacts with RARs or retinoid X receptors (RXRs) to deliver ligand to the receptor complex106, and is also involved in degradation by escorting RA to CYP26107. CRABP-I exclusively transports RA for degradation, whereas CRABP-II is involved in both the RAR/RXR signaling and the CYP26 degradation pathways108. Retinaldehyde can also be converted back into retinol when necessary, by dehydrogenase reductase (DHRS) or RDH12 activity using cofactor NADPH104,109. DHRS3 forms a complex with RDH10 that converts retinol into retinaldehyde and vice versa109. Metabolites of RA produced by CYP26 have lower activity with RARs and RXRs than RA, and can affect other signaling pathways110. In addition to different signaling functions, 11 these metabolites are also further degraded for metabolic energy production and waste elimination78. LRAT (lecithin:retinol acyltransferase) is the primary enzyme responsible for conversion of retinol into retinyl esters for storage in mammalian tissues96,111,112. The liver is the primary storage site for retinyl esters, with the majority of retinol ingested being converted and stored within HSCs112. Retinyl ester hydrolase (REH) converts retinyl esters back into retinol for transport via serum RBP4 when it is needed113. 1.3.2 Retinoic Acid Receptor RARs are the nuclear receptors for RA that form heterodimers with RXRs, and act as ligand-inducible transcription factors that target retinoic acid response elements (RAREs)82,114. Direct RAR target genes harbor RAREs, but the abundance of transcription factors that contain RAREs in their promoter regions leads to many genes without RAREs being indirect targets of retinoid signaling115. Both RARs and RXRs exist in three varieties α, β, and γ77,116. Differences in spatial expression patterns, sensitivity and target genes exist between the different RARs116. RAR (β but not α) transcription is responsive to the concentration of RA116. RARα is expressed more highly in hematopoietic stem cells than other tissues116. RAR signaling has been shown to reduce adipo- and fibrogenesis in muscle repair86,89. RARs can also regulate target activity through epigenetic changes80. Retinol depletion from liver stores has been shown to improve cardiac regeneration in mice, demonstrative of the importance of retinoid signaling in coordination of tissue repair117. 12 As discussed earlier, Hic1 has been shown to be a direct target of retinoid signaling25. It targets many other genes including those that encode the proteins involved in the metabolism and storage of retinoids. RA stimulates CRABP expression through RAR-RXR heterodimer binding114. 1.4 Drugs Modulating Retinoid Signaling Due to the involvement of RA in many critical processes, retinoids have demonstrated utility in treating a variety of indications such as cancer118, fibrosis57,119,120, emphysema87, and to improve recovery after spinal cord injury121. RA treatment of obese mice reduces collagen deposition in the heart, preventing the development of fibrosis122. AtRA has also been shown to prevent the development of pulmonary fibrosis after radiation treatment in the lungs, and was also effective in preventing bleomycin-induced pulmonary fibrosis in mice123,124. As mentioned previously, retinoid treatment can be used to modulate Hic1 expression25 and MP regeneration14. EC23 is a synthetic retinoid that increases Hic1 expression in mouse embryonic fibroblasts (MEFs), and AGN194310 (4310) is a pan-RAR antagonist that decreases Hic1 expression25,125,126. RA activity causes RARα and RXRα to heterodimerize, and then form a complex with TDG and CBP to bind RAREs that are found both up- and down-stream of Hic1, increasing transcription25. 13 Figure 2. All-trans retinoic acid structure. The above schematic depicts the chemical structure of all-trans retinoic acid. Figure 3. Retinol structure. The above schematic depicts the chemical structure of retinol. EC23 is a synthetic retinoid that was developed to improve the stability of retinoids in solution compared to atRA which is photosensitive and relatively labile125. EC23 acts as a ligand for RAR-RXR heterodimers, initiating biological processes similar to atRA127. EC23 is able to bind to RARα, β, and γ ligand-binding domains similarly to atRA127. Figure 4. EC23 structure. The above schematic depicts the chemical structure of EC23, a synthetic retinoid. 14 4310 is a pan-RAR antagonist, meaning that it inhibits activity across α, β, and γ RARs126. This drug was developed as a means of reducing toxicity experienced by patients treated with RAR agonists126. Figure 5. AGN194310 structure. The above schematic depicts the chemical structure of 4310, a pan-RAR antagonist. 1.5 Screening Compounds for Biological Activity To discover new compounds with biological activity, or identify specific biological activity from known compounds, screens are a commonly used strategy. Various screen through puts can be employed, such as high-throughput screens that typically test thousands of agents in well standardized and robust assay. In this manner, drug collections that are commercially available are accessible sources of large numbers of cataloged compounds that can be used in drug screening experiments. Screens can be carried with annotated or non-annotated collections, with the former providing the advantage of being able to decipher which pathways and/or programs are contributing to the test compound activity. 15 These experiments measure an isolated dependant variable to evaluate the effect of every treatment being tested. Due to the large number of experiments, having a single number readout for each condition enables simple analysis, often comparing to a negative control that has been shown to be highly replicable. Additionally, effective quality control measures are crucial during experimentation as artifacts in the results may not be easy to identify. One method for high-throughput screening is measuring changes in the absorbance of light using chromogenic assays in microplate reader devices. One chromogenic substrate that has been employed successfully in the past is CPRG. 1.5.1 CPRG Assays CPRG is a chromogenic substrate of LacZ that has been used to effectively measure the concentration of β-galactosidase (β-gal) in bulk cell culture samples by measuring the absorbance of light passing through the CPRG solution128. The hydrolyzing activity of the β-gal enzyme cleaves CPRG, releasing chlorophenol red into the solution, changing the colour to red. As the original CPRG solution is yellow, the absorbance of 574 nm light (A574nm) provides a measure of the amount of β-gal and in this case, by proxy the expression of Hic1. 16 Figure 6. CPRG and β-gal reaction. The above schematic depicts the reaction between CPRG and the β-galactosidase enzyme resulting in a change of solution colour from yellow to red due to the spectral properties of chlorophenol red. 1.6 Summary & Objectives MPs contribute to the maintenance of healthy tissues and their dysregulation leads to a spectrum of diseases. Hic1 serves as a marker for a subpopulation of these cells and regulates the quiescent state of these cells. Retinoid signaling directly regulates the transcription of Hic1 among many other genes. Drugs that target retinoid signaling such as synthetic retinoids or RAR antagonists are used to treat various diseases including cancer, fibrosis, emphysema and to improve regeneration after spinal cord injury57,87,118–121. CPRG assays are an effective and cost-efficient method for measuring the abundance of a target gene that has been modified to incorporate the LacZ gene. 17 Figure 7. Graphical abstract. The goal of this project is to find new therapeutics that can modulate Hic1 expression to intervene in cases where the gene is dysregulated by returning persistently activated MPs to their quiescent state. Created using BioRender.com The project outlined in this thesis involves a CPRG assay utilizing automation techniques to enable high-throughput screening of a drug library to identify small molecules that impact MP quiescence. These drugs have all at least passed Phase I of FDA clinical trials, expediting potential translational efforts that come from this work. This will be done to investigate the effects of these compounds on the expression of Hic1. To achieve this goal genetically modified MEFs that have heterozygous LacZ incorporation at the Hic1 locus will be used. After identifying compounds that influence Hic1 expression, selected hit compounds will undergo further testing through high-content screens, as well as RNA sequencing to learn more about the biological mechanisms at work in this system. The ultimate goal of this research is to discover new uses for drugs as therapies for Hic1-related disease. While searching for these it is also 18 expected that new insights into MP quiescence will be found that will improve future work in treating diseases such as cancer and fibrosis. To summarize, this project has the following objectives: A. Optimize and validate a CPRG-based assay using robotics to allow for high-throughput screening of Hic1-modulating compounds. B. Screen a library of drugs that have been approved for safety in humans for the ability to modulate the expression of Hic1. C. Perform additional testing on hits to: a. Evaluate potential use as therapeutics, b. Gain insight into the mechanisms involved in Hic1 regulation. 19 Chapter 2: Materials & Methods This chapter describes the experimental methods used for this research project, as well as the materials and devices that were employed in the process. Hic1nLacZ/+ MEFs and their culture conditions are outlined. Followed by the robotics used to increase the efficiency and throughput of the screen. The composition of the drug libraries used during validation and the project itself are then described. Assay procedure for the screen is then explained. Finally, the methods are given for the secondary experiments; X-gal staining and RNA sequencing. 2.1 Cell Culture MEFs were previously collected from C57Bl/6 mouse embryos with a Hic1f/+ genetic background crossed with a germline Cre-deleter mouse to induce deletion of Hic1 at the modified locus – this is associated with expression of nuclear LacZ. Heterozygous Hic1nLacZ/+ cells were used for CPRG and X-gal experiments, wildtype Hic1+/+ cells were used for RNA sequencing experiments. Cells were stored in cryopreservation media submerged in liquid nitrogen. Cryopreservation media is composed of 50% FBS (Sigma, cat # 1051-500mL), 40% DMEM (Gibco, cat # 11965-092), and 10% DMSO (Sigma, cat # D8418-250ML). After thawing, cultures were maintained in an incubator at 37 °C, 5% CO2 and 5% O2, in T75 flasks (Corning, cat # 430641U), and MEF media (see below). MEFs were sub-cultured every 3 days. 20 Figure 8. Wildtype and LacZ knock-in alleles. The above diagrams depict the genetic constructs that were included in the cell lines used for this project. Cells were passaged 1:6 after they reached ~80% confluency. This was done using Trypsin-EDTA (0.25%) phenol red (Gibco, cat # 25200-072). Trypsin was neutralized by the addition of media containing serum and cells pelleted by centrifugation at 500 rpm for 5 minutes. The supernatant was removed before resuspending the MEFs gently in MEF media. Cells were plated at the third passage for experimentation. When plating cells, a hemacytometer (Bright-Line) was used to more accurately determine cell density to support appropriate dilution calculations and plating of a consistent number of MEFs per well. MEF media is made by mixing DMEM (Gibco, cat # 11965-092) with 100 μL/mL (10%) of FBS 192 (Sigma, cat # 1051-500mL), 10 μL/mL each of penicillin/streptomycin (Gibco, cat # 15140-122), L-glutamine (Gibco, cat # 25030-081), and non-essential amino acids (Gibco, cat # 11140-050), and finally 2 μL/L of β-mercaptoethanol (Sigma, cat # M3148-25ML). Once produced, MEF media was stored at 4 °C and used within 1 month. 21 2.2 Robotics Cells were plated using a MultiFlo FX (BioTek) liquid handling robot in conjunction with a BioStack4 (BioTek) plate stacker. The peristaltic pump module was used to reduce the amount of shear stress experienced by cells during the plating process. Between plates, the lines were purged and re-primed to prevent uneven dispensation that may have been caused by cells settling in the tubing while the plate stacker changed plates. The cell suspension was stirred at a rate of 50 rpm using a suspended magnetic stir bar (Fisher Scientific, cat # 1451199A) to maintain an even distribution of cells during the plating process. 2.3 Drug Libraries Drugs from the Stem Cell Signaling Compound (Selleck Chemical, cat # L2100), Highly Selective Inhibitor (Selleck Chemical, cat # L3500), and FDA-Approved & Passed Phase I (Selleck Chemical, cat # L3800) libraries were purchased and assayed. The Stem Cell and Inhibitor libraries had been used in previous RNA sequencing experiments, providing reference points to validate the assay with small molecules previously shown to impact Hic1 expression. 22 Figure 9. Stem Cell Signaling Compound library composition. This chart categorizes the compounds contained in the Stem Cell Signaling Compound library. Figure 10. Highly Selective Inhibitor library composition. This chart categorizes the compounds contained in the Highly Selective Inhibitor library. 23 Figure 11. FDA-Approved & Passed Phase I library composition. This chart categorizes the compounds contained in the FDA-Approved & Passed Phase I library. The FDA-Approved & Passed Phase I library contains drugs that have at least passed Phase I clinical trials, meaning they are considered safe for human use. This is important for this project because it facilitates the ultimate goal of translation from discovery to potential therapeutic use. 2.4 CPRG Screen At the third passage Hic1nLacZ/+ MEFs were plated in 96-well plates (Costar, cat # 3596) for screening using a CPRG assay. Cells were plated at a density of ~5,000 cells/well and maintained in a hypoxic incubator at 37°C, 5% CO2 and 5% O2. Before treating the cells, images were captured using a CellCyteX (Cytena) with a 10X objective to take two images of each well with an overlapping edge. 24 A V&P Replicator (V&P Scientific, cat # VP 409) 96-well pinning tool was used to add 100 nL of each treatment (stock concentration 10 mM or 2 mM) to their respective wells. In proliferating experiments, treatments were administered 24 hours after plating, and in confluent experiments treatments were administered 72 hours after full confluency was observed. After 24 hours of incubation with the treatment, plates were imaged again using the same settings on the CellCyteX. Wells were then rinsed with cold PBS and lysed using 50 μL of 1:100 10% Triton in Z-buffer. Ten percent solution of triton was prepared by the addition of 10% Triton X-100 (EMD, cat # 9410) to distilled water. One hundred μL of CPRG (BioShop, cat # CPR524) in Z-buffer at a concentration of 1.2 mg/mL and 0.3 mg/mL was added 5 minutes later, for proliferating and confluent experiments respectively. Reagents were added to Z-buffer just before addition to the plate, CPRG solution containers were wrapped in aluminum foil to prevent excess exposure to light. Plates were left to incubate for 24 hours at 37 °C, then A574nm measurements were taken using a Synergy HTX (BioTek) multi-mode reader. Plates were measured with no lid, and a 5 second shake was applied by the device before readings were taken. An empty plate was measured first to account for the absorbance of the plastic when making calculations. 25 Figure 12. CPRG screen schematic. This graphic demonstrates the CPRG assay with representative colorimetric readouts corresponding to different effects on Hic1 expression. Created with BioRender.com 2.4.1 Z-buffer Formulation Z-buffer consists of distilled water with 60 mM sodium phosphate, dibasic (EM Science, cat # B10249-34), 40 mM sodium phosphate, monobasic, monohydrate (EM Science, cat # SX0710-1), 0.75 g/L potassium chloride (Sigma, cat # P9541-500G), and 0.245 g/L magnesium sulfate heptahydrate (Sigma, cat # 63138-250G), with a pH of 7.0. Z-buffer is stored at room temperature. 2.5 In vitro X-gal Staining The X-gal staining protocol outlined below was adapted from the protocol published by Scott & Underhill129. 26 2.5.1 Staining Plates containing confluent cells were incubated for 24 hrs with test molecules, after which wells were rinsed with 100 μL PBS for 10 minutes three times, then gently fixed with 100 μL of 0.2% glutaraldehyde in PBS for 15 minutes. Glutaraldehyde fixation solution was prepared from a 25% glutaraldehyde stock (Sigma, cat # G6257-100ML). Wells were subsequently washed 3x for 10-minutes with PBS. Cells were then stained with 50 μL X-gal staining solution and wells were covered with Parafilm MTM (BEMIS, cat # PM-996), then the lid was added, and aluminum foil was wrapped around the entire plate. Cells were incubated in the dark for 24 hours at 37 °C with gentle agitation at 70 rpm, to label nuclei expressing LacZ chromogenically. The wells were then washed 3x with PBS for 10 minutes. Hoechst (Sigma, cat # B2261-100MG) was then applied as a counterstain to label all nuclei fluorescently by diluting 1:500 of 10 mg/mL stock in PBS and adding 50 μL to each well for 5 minutes while protected from light. Again, the wells were then washed 3x with PBS for 10 minutes. Post-fixing was performed using 100 μL formalin for 24 hours at room temperature. A 10% formalin solution was prepared from a 37% formaldehyde stock (Avantor, cat # 2106-01) in PBS. 2.5.2 X-gal Staining Solution Formulation X-gal staining solution was composed of 20 mM magnesium chloride hexahydrate (Sigma, cat # M2393-500G), 0.02% Nonidet P-40 (BioBasic Inc., cat # NDB0385), 0.01% sodium deoxycholate (Sigma-Aldrich, cat # D6750-25G), 10 mM potassium ferricyanide (Sigma-Aldrich, cat # P8131-100G), 10 mM potassium ferrocyanide (Sigma-Aldrich, cat # P3289-100G), and 2.5% UltraPureTM X-gal (Invitrogen, cat # 15520-018) in PBS. The solution 27 was mixed from 10X stocks and then warmed to 37 °C, with X-gal being added just before addition to plates. 2.5.3 Imaging & Analysis X-gal- and Hoechst-stained plates were imaged and analyzed using a CellInsight CX5 High Content Screening Platform (Thermo Fisher Scientific, cat # CX51110) to identify X-gal+ nuclei and fluorescent nuclei. A brightfield image and a fluorescence image were taken across three fields in each well. Fluorescence excitation and emission wavelengths were 386 nm and 440 nm, respectively. The percentage of fluorescent nuclei that were also X-gal+ was calculated by the HCS Studio Cell Analysis Software included with the CellInsight CX5 platform. Wells for calculations were selected by visual inspection ensuring that cell monolayer folding did not occur, as this leads to images that are not processed properly. The included software was used for analysis of the images captured by the CellInsight CX5. The first step to calculate the percentage of X-gal-stained nuclei is to identify the primary objects and validate them using the settings outlined in Table 1, and then object clean-up was applied. It should be noted that primary object selection and filtering from the fluorescence image are not completed as these functions would remove data that is required for the analysis step. 28 Function Method Value/Range Smoothing Uniform 1 Thresholding Isodata 0.2 Segmentation Intensity 150 Validation Object Area 50.003-9999.998 μm2 Table 1. CellInsight CX5 preprocessing settings. The settings used to preprocess the images taken on the CX5 before data analysis are listed in this table. Data analysis was conducted using the thresholds listed in Table 2. These were set using an iterative approach, visually inspecting how adjusting each value changed the interpretation of the images of control wells. 29 Object Quality Low High Object Quality Low High Area Ch1 582.484 μm2 998.426 μm2 Circle Ring Average Intensity Ratio Ch2 2 7.5 Size Ch1 25.788 μm 35.455 μm Ring Spot Count Ch2 50 95 Shape Perimeter:Area Ch1 1.1 1.45 Ring Spot Total Area Ch2 249.608 μm2 399.45 μm2 Shape Length:Width Ch1 1.2 1.7 Ring Spot Average Area Ch2 2.063 μm2 7.627 μm2 Total Intensity Ch1 4030590 8061173 Ring Spot Total Intensity Ch2 33586 114199 Average Intensity Ch1 2400 4800 Ring Spot Average Intensity Ch2 40 200 Variability of Intensity Ch1 0 32767 Circle Spot Count Ch2 6 25 Ring Total Intensity Ch2 53739 134352 Circle Spot Total Area Ch2 0.1 μm2 15.002 μm2 Ring Average Intensity Ch2 20 56 Circle Spot Average Area Ch2 0.1 μm2 0.998 μm2 Circle Total Intensity Ch2 0 470237 Circle Spot Total Intensity Ch2 1 551497 Circle Average Intensity Ch2 100 2400 Circle Spot Average Intensity Ch2 0 500 Circle Ring Average Intensity Difference Ch2 60 200 Circle Average Intensity Ratio Ch2:Ch1 0.03 0.05 Table 2. CellInsight CX5 data analysis reference levels. The settings used to analyze the images taken using the CX5. 2.6 RNA Sequencing Wildtype MEFs were thawed and expanded before seeding of 12-well plates (Costar, cat # 3513) at the third passage at ~50,000 cells/well. Drugs selected from the CPRG screen of the FDA-Approved library that were identified as hits were administered along with EC23, 30 palovarotene and 4310 as controls, at concentrations of 10 μM and left to incubate overnight. Then QIAshredderTM Plus Micro Kits (QIAGEN, cat # 74034) were used to extract RNA from culture lysate before being sent to the BRC Sequencing Core for bio-analysis and sequencing. Sample quality control was completed first using an Agilent 4200 Tapestation. Samples that passed quality control (i.e., RIN values > 9.0) were prepared using standard protocols for Illumina Stranded mRNA prep (Illumina). Sequencing was then performed using the Illumina NextSeq2000 with Paired End 59bp × 59bp reads. Sequencing data was demultiplexed using Illumina’s BCL Convert. De-multiplexed read sequences were then aligned to the Mus Musculus (mm10) reference sequence using the DRAGEN RNA app on Basespace Sequence Hub (https://support-docs.illumina.com/SW/DRAGEN_v41/Content/SW/DRAGEN/TPipelineIntro_ fDG.htm). Heatmaps were generated from estimated count matrices using the package DESeq2130 in R. Genes with less than 50 counts across treatments were filtered out before running DESeq2 results. Once the results were generated, genes with adjusted p-values of more than 0.05 were removed, as well as genes that had a base mean of less than 50, or a log2 fold change of less than 0.5. The threshold for log2 fold change was selected to include Hic1 in the analysis. Next the count matrix was generated from the remaining genes, and any gene with more than 20,000 counts for any treatment was removed to improve legibility of the heatmaps. The final filter that was applied was comparing the difference in expression between EC23 and 4310 as RAR agonists and antagonists. This was compared to the expression level of the vehicle-treated negative control, the resulting value is included in the Appendix in Table 10 under the delta column. From this value, genes that were above 5 and genes that were below -2, 31 indicating an increase or a reduction in expression by RAR signaling, were chosen. Hic1 was cherry-picked to be included as it is the focus of this study. It returned a value of 3.91 for this metric. This resulted in a list of 19 genes that were used to generate the heatmaps in Figures 27 and 28. The gene list was generated from the selected hit RNA sequencing data and the same genes were used to analyse the results of the dose response experiment for EC23 and palovarotene. 32 Chapter 3: Results The first phase of this project aimed to optimize the CPRG assay based on previous work carried out in the Underhill lab in collaboration with the CDRD. Initial experiments focused on cell seeding density and the concentration of CPRG to prevent saturation. The effect of cell passaging on the assay results was also analyzed. Compounds (i.e., RAR agonists) with known effects on Hic1 expression were then used to validate the assay. The robotic cell seeding process was optimized to ensure consistent cell density across wells and across groups of plates. Pilot screens using the optimized assay were then initiated on the smaller Stem Cell and Inhibitor libraries to evaluate the ability to identify hits in libraries that had previously been studied in the Underhill lab. Once the screen was optimized and validated, the FDA-Approved library was screened and hit compounds were identified. Some of these compounds were selected to undergo additional experimentation consisting of high-content screening using a stain for nuclear LacZ and RNA sequencing. These experiments were performed to gain a better understanding of what mechanism these hit drugs were using to influence the expression of Hic1. 3.1 Assay Optimization 3.1.1 Cell Seeding Density & CPRG Concentration The original protocol developed in consultation with the CDRD seeded wells with 5,000 cells, followed by measuring LacZ activity with 1.2 mg/mL of CPRG. To optimize the assay for screening in this project, different cell densities and various concentrations of CPRG were tested 33 to maximize the dynamic range of the assay, while limiting the possibility of signal saturation, which occurs if an absorbance reading exceeds an A574nm of 4. Using the protocols described in Section 2.1, Hic1nLacZ/+ MEFs were plated by hand at densities of 2,500, 5,000, 10,000, 12,500, 15,000, and 20,000 cells/well. Cells were then treated with 50 nM EC23 (RAR agonist), 50 nM 4310 (RAR antagonist), or DMSO (vehicle) for 24 hrs, and then assayed for LacZ activity with staining solution containing 1.2 mg/mL CPRG. Signal saturation was observed for EC23-treated wells at 10,000 cells/well and higher densities. This process was repeated using the three lowest concentrations, with dilutions of CPRG being tested as well at concentrations of 1.2, 0.6, 0.3 and 0.15 mg/mL. The net absorbance readings for these treatments are plotted in Figure 13 below. 34 Figure 13. Optimization of cell density and CPRG concentration for the CPRG assay. Absorbance of 574 nm light as a function of cell density and CPRG concentration for the control treatments. Signal-to-noise ratios (SNR) and Z-factor (Z’) scores were calculated for each cell density and CPRG concentration using the formulae below, where μ represents the mean and σ represents the standard deviation. Subscript p indicates the positive control and subscript n indicates the negative control. 𝑆𝑁𝑅 = 𝜇𝜎 𝑍′ = 1 −3(𝜎𝑝 + 𝜎𝑛)|𝜇𝑝 − 𝜇𝑛| 35 These results are tabulated in Tables 3 and 4. Based on these values it was determined that the original protocol using 5,000 cells per well and 1.2 mg/mL of CPRG maximized the dynamic range while limiting the possibility of saturation. CPRG Concentration (mg/mL) 2,500 cells/well 5,000 cells/well 10,000 cells/well EC23 Vehicle 4310 EC23 Vehicle 4310 EC23 Vehicle 4310 1.2 41.7 5.8 5.2 37.1 32.3 20.5 25.2 20.8 54.3 0.6 20.4 40.0 26.7 23.4 24.9 40.4 27.7 39.0 38.9 0.3 31.7 25.4 23.9 17.6 26.7 31.3 21.9 39.9 35.9 0.15 13.5 15.9 2.0 32.5 4.4 12.8 14.7 19.9 19.8 Table 3. Signal-to-noise ratios for cell densities and CPRG concentrations. The signal-to-noise ratios for EC23, 4310 and vehicle treatment as functions of cell density and CPRG concentration. CPRG Concentration (mg/mL) 2,500 cells/well 5,000 cells/well 10,000 cells/well EC23 4310 EC23 4310 EC23 4310 1.2 0.315 -6.801 0.786 -5.216 0.712 -1.597 0.6 0.513 -13.184 0.660 -6.599 0.763 -1.838 0.3 0.411 -17.635 0.508 -5.814 0.679 -7.748 0.15 -0.364 -2.378 -0.099 -18.399 0.470 -10.664 Table 4. Z-factors for cell densities and CPRG concentrations. The Z-factors for each CPRG concentration and cell seeding density were calculated using EC23 or 4310 as positive controls and vehicle as the negative control. 3.1.2 Assessment of Cell Passage Number on LacZ Activity After optimizing the cell seeding density and CPRG concentration, the effect of the number of passages on the assay was investigated. As MEFs are repeatedly passaged, their 36 proliferation rate begins to slow and differences in culture behaviour emerge which could influence LacZ activity. To investigate the effect of passage number on the CPRG assay, 10 compounds were selected based on prior work done in the lab that had known effects on the expression of Hic1. These were aliquoted in a 96-well plate format with each column containing 8 replicates of the same drug. The 11th column contained vehicle in media to determine whether the diluent for these drugs would impact the assay, and the 12th column contained media alone to serve as a negative control for comparison. The tested compounds include ABT-737, EC23, venetoclax, palovarotene, omipalisib, DAPT, FIIN 1 HCl, nintedanib, nitazoxanide, and 4310. CPRG activity was assessed in MEFs plated at passages 3, 5, and 7. Similar trends were seen across drug treatments, only control values are shown in Figure 14 for simplicity. Figure 14. Passage effect on CPRG assay. This graph plots the absorbance of 574 nm light for the control treatments across cells that were seeded at passages 3, 5, and 7. 37 Passage Number SNR Z’ 3 28.6 0.787 5 14.1 0.328 7 13.7 0.310 Table 5. Signal-to-noise ration and Z-factor as a function of passage number. SNR and Z’ for EC23 positive control treatments on proliferating cells are calculated for varying passage number. Based on the consistent decrease in LacZ abundance observed as the passage number increased, experiments were conducted using MEFs plated at the third passage. This passage number also allowed sufficient cell expansion to enable seeding of 30 plates (maximum number that can be loaded into the BioStack at one time). These low-passage conditions also yielded higher CPRG activity. 3.1.3 Optimization of CPRG Concentration for CPRG Assay in Confluent Cultures Cells that reach full confluency will begin to enter growth arrest, this affects a number of fundamental biological processes. Based on prior reports, Hic1 expression was expected to increase following growth arrest, as cells enter a quiescent state. This combined with an increased number of cells was expected to result in higher A574nm values for confluent experiments. For this reason, a similar calibration experiment as outlined above was run on growth arrested cells to determine an ideal CPRG concentration for these experiments. Based on the data in Table 6, 0.3 mg/mL of CPRG was selected as the concentration for confluent experiments as it provided the highest Z’ and SNR while remaining unsaturated for the positive control EC23. Concentrations above 0.3 mg/mL resulted in saturated absorbance readings. 38 Figure 15. Effect of CPRG concentration on confluent MEFs. This graph plots the absorbance of 574 nm light for untreated and EC23 treated confluent cells based on the concentration of CPRG in solution. CPRG Concentration (mg/mL) SNR Z’ 0.3 21.2 0.555 0.2 17.2 0.412 Table 6. Signal-to-noise ratios and Z-factors for confluent MEFs at varying CPRG concentrations. Assay metrics for EC23 positive control treatments are presented for confluent MEFs at varying concentrations of CPRG. 3.2 Assay Validation After optimizing the assay, validation was required to demonstrate that functional results could be gleaned from its use on drugs other than EC23 and 4310. To validate the assay, 19 compounds of interest were administered at 10 μM to both proliferating and confluent cells. The compounds of interest were selected for their Hic1 expression-modifying activity, as previously determined in the laboratory. Figure 16 plots the activity of the compounds that were used for 39 validation. Dose response curves were also generated, and these compounds were aliquoted into the 96-well plate format for validation of the V&P Replicator’s consistency when used for this assay. Figure 16. Validation compound CPRG response at 10 μM. This scatter plot shows the response of the CPRG assay to 10 μM treatments of the 19 compounds that were used to validate the screen. After demonstrating consistency with expected findings, the assay now needed to be evaluated under high-throughput screen conditions to ensure that a higher throughput format did not affect the integrity of the experiment, consistency and LacZ measurements. 40 3.3 CPRG Screening Validation With the optimized parameters set and the assay validated, the next stage in the project investigated the consistency of the screen, incorporating robotic plating and treating the cells using different drugs from the Stem Cell Signaling and Highly Selective Inhibitor libraries. 3.3.1 Robotic Plating Prior to the screening of compound libraries, untreated plates were assayed to ensure consistent cell density across wells when being seeded using the MultiFlo. Trends in position on the plate, as well as average A574nm based on plate number were investigated. Plate averages were relatively consistent throughout the process, though consistent trends based on position were observed. Figure 17. Initial plating trends based on well. This heatmap displays the percent difference compared to the plate average for each well using the MultiFlo to seed cells. 41 Figure 18. Variation in plating based on plate number. This graph depicts changes in the variation of each plate compared to the average of all plates when using the MultiFlo to seed cells. The consistent trends in column averages in plates were assumed to be a result of cell settling in the MultiFlo peri-pump lines during BioStack plate exchange. To rectify this issue the MultiFlo cell plating protocol was adjusted to purge the line after each plate and prime the lines once a new plate had been delivered. The trends that are seen in the rows were resolved by re-calibrating the peri-pump as outlined in the BioTek user manual. 42 Figure 19. Plating trends based on well after optimization. This heatmap displays the percent difference compared to the plate average for each well using the MultiFlo to seed cells after re-calibrating the peristaltic pump and adjusting the protocol. Another validation experiment was conducted with control compounds to measure the consistency including the treatment step. The Z’ for EC23 was 0.854 in this setting, and for 4310 was -9.914. With robotic plating optimized for consistency, the Stem Cell Signaling and Highly Selective Inhibitor libraries were used to validate high-throughput screening using this assay. 3.3.2 Stem Cell Signaling Library Screen Validation The first library of small molecules that were screened for validation purposes was the Stem Cell Signaling library. The results shown in Table 7 and Figure 20 were consistent with data from prior work in the Underhill Lab. Two compounds resulted in saturated absorbance readings and are shown as a 300% increase in Table 7 and Figure 20, because that is the approximate minimum increase that would result in a saturated reading. 43 Drug ΔA574 (%) Standard Deviation Drug ΔA574 (%) Standard Deviation Isotretinoin 300* N/A Stattic -58.79 5.29 Tretinoin 300* N/A Bisindolylmaleimide IX -58.91 1.99 TTNPB 281.25 22.91 PI-103 -61.37 1.53 AM580 277.23 15.14 Omipalisib -62.64 3.19 Bexarotene 141.41 6.74 HS-173 -62.83 2.56 Vorinostat -50.62 1.22 AT7867 -63.79 4.30 MTX-211 -51.65 1.43 BIX 01294 -65.06 3.12 SF2523 -52.00 1.42 Halofuginone -69.43 4.26 BMS-833923 -52.68 7.79 Fedratinib -70.99 3.36 Brefeldin A -52.79 4.09 SH-4-54 -71.13 6.12 Osimertinib -52.89 8.95 Homoharringtonine -71.52 2.17 Tanespimycin -53.13 4.80 Napabucasin -72.17 6.69 Buparlisib -53.51 1.08 TG101209 -72.58 1.74 Adavivint -54.53 2.05 Pluripotin -73.04 2.72 CUDC-907 -55.92 2.91 CA3 -76.29 1.66 AZ 960 -57.12 3.40 SF1670 -76.48 4.72 Pyrvinium -57.72 2.60 BGT226 -78.89 2.65 CC-90003 -58.42 6.14 Table 7. Hits from the Stem Cell Signaling library CPRG screen. The drug names, change in absorbance of 574 nm light, and standard deviation are listed for every compound that caused a change of greater than 50% in the absorbance reading. * - 300% was used to represent saturated readings as this is the approximate minimum value that would result in saturation. 44 Figure 20. Stem Cell Signaling library CPRG screen results. This graph plots the change in absorbance of 574 nm light caused by each drug in the Stem Cell Signaling library in order of descending effect. Two values are reported at ΔA574nm = 300%, these treatments saturated the absorbance signal, indicating that there was at least a 300% increase in absorbance. 3.3.3 Highly Selective Inhibitor Library Screen Validation The Highly Selective Inhibitor library of small molecules was screened. The results of this screen are shown in Figure 21 and the compounds that resulted in a greater than 50% decrease are listed in Table 8 below. There were no compounds that increased A574nm by more than 50% in this library. 45 Drug ΔA574 (%) Standard Deviation Drug ΔA574 (%) Standard Deviation CC-223 -51.05 8.43 Aurora A Inhibitor I -56.90 2.12 WAY-600 -54.88 2.17 Stattic -59.02 5.46 B-Raf IN 1 -55.29 11.44 BI-78D3 -59.08 8.39 Ro3280 -55.55 4.64 Cobimetinib -62.03 10.56 BDA-366 -56.31 1.78 NH125 -70.25 4.66 Table 8. Hits from the Highly Selective Inhibitor library CPRG Screen. The drug names, change in absorbance of 574 nm light, and standard deviation are listed for every compound that caused a change of greater than 50% in the absorbance reading. Figure 21. Highly Selective Inhibitor library CPRG screen results. This graph plots the change in absorbance of 574 nm light caused by each drug in the Highly Selective Inhibitor library in order of descending effect. These results were also consistent with what was expected based on unpublished data from the Underhill lab. With two successful smaller-scale screens providing results that were congruent with other experiments, the screen was considered ready to be applied on the largest library, FDA-Approved & Passed Phase I. 46 3.4 FDA-Approved Library CPRG Screening With the screen optimized and validated, the FDA-Approved & Passed Phase I library was investigated to identify Hic1 expression-modulating compounds. Over multiple rounds, all 45 plates of drugs were assayed in triplicate, and hits were identified as any treatment that caused an increase or decrease in absorbance greater than 50%. These hit compounds are listed in Table 9, and all of the results are shown in Figure 22. 47 Drug ΔA574 (%) Standard Deviation Drug ΔA574 (%) Standard Deviation Isotretinoin 312.42 17.66 Cediranib Maleate -55.05 10.51 Tretinoin 293.16 10.21 Harringtonine -55.33 1.21 Tazarotene 260.65 8.68 Astemizole -56.62 1.36 Acitretin 257.73 22.34 Homoharringtonine -56.79 0.63 Tamibarotene 212.54 8.62 ENMD-2076 L-(+)-Tartaric acid -56.9 3.37 Allopurinol* 189.31 1.19 Dabrafenib -56.97 1.37 Ursodiol* 147.4 4.2 Volasertib -57.07 0.58 Adapalene 115.87 9.15 Fedratinib -57.35 5.7 Fenretinide 104.8 2.69 Fingolimod -57.69 9.44 Bexarotene 86.18 12.20 Halofuginone -59.54 0.88 Vitamin A 69.81 8.00 Dioscin -60.17 10.04 Trichlormethiazide 68.81 6.17 Triciribine -60.53 0.92 Etretinate 53.43 10.07 UM171 -61.16 17.37 Refametinib -50.02 0.5 Malachite Green -61.34 2.59 PD0325901 -50.03 3.29 Ceritinib Dihydrochloride -62.43 14.83 Flavopiridol HCl -50.53 12.3 Ponatinib -65.85 1.45 Crenolanib -52.04 0.72 VLX1570 -66.02 2.19 Buparlisib -52.11 2.42 Omaveloxolone -66.56 2.07 Visomitin -52.18 5.82 GZD824 Dimesylate -68.71 5.14 Pirarubicin -52.66 2.86 Zinc Pyrithione -69.55 1.17 Crizotinib -53.56 3.99 Napabucasin -70.01 2.95 Staurosporin -53.64 9.2 Sanguinarine -70.57 2.63 Puromycin 2HCl -53.72 6.46 Sanguinarine chloride -71.19 3.84 Metoprolol -53.91 6.46 Quinacrine 2HCl -73.53 0.78 Ceritinib -53.93 10.95 Amcasertib -74.25 1.96 ENMD-2076 -54.69 2.85 Auranofin -74.53 1.13 Tegaserod Maleate -54.71 30.19 Beta-Lapachone -75.69 1.37 WP1066 -54.88 2.68 Melittin -78.68 1.17 Table 9. Hits from the FDA-Approved & Passed Phase I library CPRG Screen. The drug names, change in absorbance of 574 nm light, and standard deviation are listed for every compound that caused a change of greater than 50% in the absorbance reading. * - result invalidated by later experiments. 48 Figure 22. FDA-Approved & Passed Phase I library CPRG screen results. This graph plots the change in absorbance of 574 nm light caused by each drug in the FDA-Approved & Passed Phase I library in order of descending effect. After identifying hits, additional experiments were used to generate a deeper understanding of what mechanisms may have been operating to influence LacZ activity. These include a high-content screen using X-gal staining for nuclear LacZ, and RNA sequencing with wildtype MEFs. Dose responses were also investigated for selected compounds, 4 hits were chosen from both the compounds that increase Hic1 expression and that decrease it. These were tested alongside the controls, including both palovarotene (RARγ agonist) and EC23 as positive controls. Allopurinol and ursodiol were chosen as they were the only non-retinoids to surpass a 50% increase in Hic1 expression. 49 Figure 23. Dose responses of selected hit compounds. This graph plots dose responses of selected positive and negative hit compounds from the FDA-Approved & Passed Phase I Clinical Trials library. Several compounds were found to decrease CPRG activity, including, ENMD-2076, crenolanib, fedratinib, and amcasertib. These were identified as likely causing cytotoxicity based on images acquired before and after treatment as shown in Figure 24. Herein an abundance of supposedly dead (i.e., rounded up and/or detached) cells could be visualized shortly after compound treatment. In addition to dose response experiments, high content X-gal screening and RNA sequencing were conducted to further investigate actives. 50 Figure 24. CellCyteX images of hit compounds. These 10X magnification images demonstrate the cytotoxic effect of fedratinib, one of the selected hits that decreased A574nm, compared to untreated cells and cells treated with 4310. 3.5 X-gal High Content Screening The CPRG screen provided a single-value readout for each compound that measured the change in LacZ abundance after treatment. Imaging of X-gal-stained cultures was performed to assess the nature of the LacZ signal more carefully and distinguish between compounds that induced broad low-level expression versus induction of more intense staining in a smaller number of cells. To better understand how these compounds are affecting the cells, imaging-based analyses were carried out using X-gal staining for nuclear LacZ with enumeration and demarcation of nuclei by counterstaining with Hoechst. Stained culture plates were imaged and analyzed using the CellInsight CX5. Images representative of the results are shown in Figure 25, and data from the quantitative analysis are plotted in Figure 26. 51 Figure 25. X-gal and Hoechst staining images. These images were captured by the CellInsight CX5 at 10X magnification and processed for analysis. X-gal staining is shown in monochrome brightfield images, and Hoechst staining is shown by fluorescence at 386/440nm excitation/emission. 52 Figure 26. X-gal high content screening analysis. This graph plots the mean percentage of X-gal and Hoechst positive nuclei compared to the total population of imaged nuclei determined by Hoechst staining. Statistical significance was determined using a two-tailed t-test. ** = p < 0.005, *** = p < 10-6, **** = p < 10-7. The Z-factor for this screen can also be calculated, and the results for EC23 as the positive control is -0.061, and 4310 as an RAR antagonist is -46.479. Quantitatively this experiment has more noise than the CPRG assay, which is why a lower Z’ score is expected. The value of this experiment comes from the qualitative insight that can be derived from the images. 3.6 RNA Sequencing RNA sequencing of treated wildtype MEFs was then conducted to investigate differences in expression patterns that may result from the identified actives. Retinoids and RAR antagonists were included to investigate the differences among this family of compounds, and allopurinol and ursodiol were included as they are not retinoids and had positive activity in the screen. 53 Figure 27. RNA sequencing differential expression heatmap of selected hit compounds. This heat map shows expression levels of differentially expressed genes according to treatment with selected hit compounds or controls. The positive hits shown in Figure 27 have consistent expression patterns that are expected of retinoids. Most notably the increase in Cyp26b1, Dhrs3, Hic1, Crabp2, Rbp1, combined with the decrease in Aldh1a3. This pattern holds at lower levels for allopurinol and ursodiol. Duplicates for these two drugs were not run as the results shown in Figure 29 had 54 indicated that these drugs were cross contaminated with a retinoid before the replicates were sent for sequencing. Figure 28 shows similar patterns, with the I50 doses of 1 nM for EC23 and palovarotene included alongside the maximum effect that was seen at 100 nM. Figure 28. RNA sequencing differential expression heatmap of controls at 100 nM and 1 nM. This heat map shows the expression levels of the genes that were used in Figure 27 for treatments with EC23 and palovarotene at concentrations of 100 nM and 1 nM. 55 3.7 Novel Hit Investigation With the previously described results indicating allopurinol and ursodiol exhibited positive CPRG-induction activity, fresh powders were ordered to further evaluate the activity of these drugs. Allopurinol (Cedarlane, cat # S1630-25MG) and ursodiol (Cedarlane, cat # S1643-5MG) were evaluated in a dose response experiment which included their counterparts that were used in the screen. Controls of EC23 and 4310 were also included. Solutions prepared from fresh powders of both compounds failed to elicit the response observed from the screening plate. It should also be noted that on the screening plates, both allopurinol and ursodiol are located in close proximity to retinoid compounds. Collectively, these data suggest that these two compounds represent false positives. Figure 29. Dose responses of screen vs standard drugs. This graph plots dose responses of library-sourced drug that was used in the screen as well as separately ordered powdered drug for allopurinol and ursodiol alongside EC23 and 4310 controls. 56 Chapter 4: Discussion In this study, a CPRG-based “high-throughput screen” was developed for the detection of molecules that modulate the expression of the quiescence-associated gene Hic1. The screen was optimized and validated using control compounds such as the synthetic retinoid EC23 and the pan-RAR antagonist 4310. Subsequently, the screen was deployed to identify hits from a large library of drugs in a high-throughput manner. Identified hits were chosen for further experimentation which included dose response studies, high content screening using X-gal staining, and bulk RNA sequencing. This chapter discusses the significance of the results shown in Chapter 3 and concludes with comments on the findings and future studies. 4.1 Screening for Hic1 Expression-Modulating Drugs The central goal of this project was to develop a CPRG assay and optimize its use in a high-throughput manner to efficiently screen a library containing more than 3,000 compounds for their effect on Hic1 expression. In this case, high-throughput is being used to connotate an assay that can be applied potentially to drug collections in excess of 100,000 compounds, while only smaller screens were performed in this work. A subset of the hits that were identified were then further investigated to learn more about the biological mechanism that were underlying their activity on Hic1 expression. 4.1.1 CPRG High-Throughput Screening The CPRG screen proved to be an effective method for identifying compounds that influenced the expression of Hic1. It was shown that the cell seeding density of 5,000 cells per 57 well in the 96-well format worked well with 1.2 mg/mL CPRG. The initial Z’ of 0.786 surpassed the threshold of excellence for assays which is considered to be 0.5. As the automation tools were incorporated and the workflow optimized, the Z’ continued to increase from the initial value to 0.854 demonstrating the decreased variability of the assay using these automation methods. However, the Z’ using the RAR antagonist control 4310 consistently returned negative values, indicating a poor assay. This is understandable as the proliferating MEFs have largely reduced Hic1 expression, which leaves a much smaller dynamic range to demonstrate downregulation. Repeating the screen using cells that have expanded to full confluency would result in increased quiescence, and therefore Hic1 expression. In this regard, for efficient detection of negative regulators of Hic1, the assay could be further optimized to improve the dynamic range for detection of such agents. The identification of drugs that reduce Hic1 expression using this assay is further confounded by cytotoxic effects. Drugs that reduce the cell population will also reduce the total expression of Hic1 in the well by virtue of there being less cells. Secondary experiments can also aid identification of the mechanisms operating in these instances. 4.1.2 X-gal High Content Screening The quantitative results of the X-gal screen presented in Figure 26 show similar results to what was found using the CPRG assay. In this case, the Z’ using EC23 returned a negative value indicating that the quantitative results from this analysis may reflect poor assay conditions, and thus not be reliable. This is acceptable as the analysis of this high content screen includes other 58 data, and qualitative observations can be made using the images, such as those shown in Figure 25. The conditions used here all appear to be functioning under the same RAR signaling pathway, and thus do not highlight the differences that may arise due to different biological mechanisms. Morphological changes due to differentiation, a subpopulation of cells showing a more significant increase in expression, and a general increase in cell number are all potential causes for an increase in expression that can be identified using this technique. 4.1.3 RNA Sequencing The differential expression profiles that were gained from RNA sequencing show in Figures 27 and 28 that the drugs causing an increase in Hic1 expression are using similar biological mechanisms to achieve this result. EC23, palovarotene, tamibarotene and tazarotene are all retinoids, and RAR signaling directly regulates Hic1 expression25. This idea is further supported by the increase in Cyp26b1, Dhrs3, Crabp2, and Rbp1 expression and decrease in Aldh1a3 expression observed from analysis of the transcriptomic data. Cyp26b1 is involved in metabolizing RA, and Crabp2 is involved in the intracellular transport of RA. Similarly, Dhrs3 and Rbp1 reduce RA synthesis through conversion of retinaldehyde into retinol, and the export of retinol, respectively. Aldh1a3 is involved in the synthesis of RA, the transcripts encoding RA synthesizing enzymes are often downregulated in response to treatment with retinoids93. The opposite trends to these expression patterns are observed with treatment of the pan-RAR antagonist 4310, providing further support to our contention that the target genes are being 59 regulated by RARs. These drugs also caused an increase in Hic1 expression which is consistent with the identification of these drugs as hits through the CPRG screen. The 1 nM dose of EC23 and palovarotene as shown in Figure 28 were consistent with the differences in dose response of these two drugs shown in Figure 23. Palovarotene has a lower effect on Hic1 expression at 1 nM, and from the RNA analysis it appears that other RA-responsive genes are similarly less responsive to low doses of palovarotene compared to EC23 treatment at the same dose. This could be partly result from the fact that at 1 nM, palovarotene is acting as an RARγ-selective agonist, whereas EC23 is a pan-RAR agonist. There are also difference in Kd of the different molecules which would impact their activity. 4.2 Retinoids Control Hic1 Expression Interestingly, initial assay results indicated that allopurinol and ursodiol also appeared to be operating in similar fashion to retinoids. This is most clearly demonstrated by the transcriptomic analysis, where the expression patterns mimic EC23 and palovarotene, albeit with less difference compared to the control. This was surprising as these molecules are dissimilar to retinoids in their chemical structures as is shown below in Figures 30 and 31. Further investigation of these compounds with fresh powder preparations suggested that these drugs had been cross contaminated with a retinoid. 60 Figure 30. Allopurinol structure. The above schematic depicts the chemical structure of allopurinol. Figure 31. Ursodiol structure. The above schematic depicts the chemical structure of ursodiol. In addition to the chemical differences seen in these compounds, both were located on the same plate adjacent to the same well containing a retinoid in this library. Furthermore, the dose responses shown by these two compounds in Figure 23 was nearly identical and could be viewed as a 1:100 dilution of a retinoid. This led us to investigate whether these compounds in the screen had been cross contaminated with some form of RAR agonist. The dose responses of separately ordered drugs clearly show that there is no effect on the expression of Hic1 as seen in Figure 29. This result indicates that the two wells containing allopurinol and ursodiol were cross contaminated in the library plates. These two compounds should therefore be removed from the list of hits in Table 9. 61 The lack of effect shown by allopurinol and ursodiol remove the only two non-retinoid compounds from the hit list identified by this screen, including from the Stem Cell Signaling library and the Highly Selective Inhibitor library, which found no positive regulators. This result establishes that no new positive regulators of Hic1 expression were discovered through this study. However, the apparent lack of any other signaling pathway controlling the expression of Hic1 is significant in that it provides a target that is highly tunable in treating diseases that arise from Hic1+ MPs. If there are no other compensatory mechanism involved in the regulation of Hic1, therapies that aim to correct abnormal Hic1 expression will more precisely be able to intervene as the auto-regulated RA metabolic pathway will be the primary system interacting with the drug. However, RAR signaling is involved in many other biological processes and potential side effects need to be considered. Further studies need to be conducted to determine whether other mechanisms exist to provide redundancy to Hic1 regulation. 4.3 Conclusion The study described herein involved development of a robust high-throughput screen for drugs that modulate quiescence through Hic1 expression. Multiple libraries were screened, and all valid hits were identified as retinoids. This finding was expected as Hic1 has previously been identified as a direct target of RAR signaling25. The lack of any other compounds influencing Hic1 expression indicates that retinoid signaling may be the sole regulator of this MP marking gene. Further studies need to be conducted to determine whether there are other signaling axes that can influence this gene. 62 4.3.1 Review of Objectives Reflection on the objectives that were set at the outset of this study is important in assessing the success of the research project. This section will review the objectives of the project and discuss the level success in achieving each of them. 4.3.1.1 Optimize and validate a CPRG-based assay using robotics to allow for high-throughput screening of Hic1-modulating compounds. The CPRG assay was optimized and successfully incorporated robotics. This enabled efficient screening of multiple drug libraries and an improvement in assay performance as determined by Z’ score. This assay was effective for testing upregulation of Hic1 but ineffective at screening for decreases in expression. 4.3.1.2 Screen a library of drugs that have been approved for safety in humans for the ability to modulate the expression of Hic1. The FDA-Approved and Passed Phase I library was screened for drugs that cause a change in Hic1 expression. False positives were found among the hit compounds, and secondary experimentation is recommended to corroborate the results found from the screen in future studies. After removing invalid hits, it was observed that the only family of compounds to influence Hic1 expression were retinoids. 63 4.3.1.3 Perform additional testing on hits to evaluate potential use as therapeutics and gain insight into the mechanisms involved in Hic1 regulation. An X-gal high content screen and RNA sequencing were employed to gain further insight into selected hit compounds. The X-gal screen provided consistent quantitative results with the CRPG screen. The observational analysis of this experiment demonstrated consistent responses to treatment accounting for differences in the magnitude of response based on the drug administered. RNA sequencing indicated that RAR signaling pathways were being activated by the hit compounds. This raised questions regarding a potential cross contamination of the non-retinoid hit compounds that was supported by the results of experiments using fresh powder preparations. 4.3.2 Significance The techniques that were used for this project were designed on a foundation of screening strategies that are widely used. The synchronized implementation of CPRG screening, robotic-assisted cell culture and high-throughput drug delivery to investigate Hic1 regulation is what make this work unique. These protocols will continue to be iterated upon within the lab. The CPRG screen developed in this study can be re-tooled to investigate other biological systems of interest with relative ease. Other projects in the Underhill lab are adopting the automated cell culture processes that were optimized to increase throughput during this work. Additionally, retinoids were shown to be powerful regulators of Hic1 expression. Though other biological mechanisms that lead to Hic1 regulation were not identified, future work in this 64 area will help to identify new potential avenues to targeting quiescence through this gene. Should no other pathways lead to Hic1 regulation, this gene would prove to be a target for highly tunable therapies harnessing MP quiescence to treat related diseases. The utility of retinoids as inducers of Hic1 expression, and thus quiescence, in MPs is speculated to be key in novel treatments for fibrosis and cancer. Both these diseases have proven to be difficult to treat, and both have been linked to loss of quiescence in MPs. It is hypothesized that returning MPs to their quiescent state can halt progression of fibrosis and MP-associated cancer. 4.3.3 Strengths and Limitations This study produced an efficient and robust screen for high-throughput testing of Hic1 expression inducing drugs. Assay performance parameters such as SNR and Z’ demonstrated replicability and stratification of results. Retinoids, the class of compounds that were expected to have an effect, showed a consistently significant effect during the screen. The work that was done is limited in the scope of drugs that it could identify as a hit due to the low baseline expression of Hic1 in proliferating MEFs. Drugs that downregulate expression require an adjustment to the assay to be properly screened. Repeating the screen using the confluent conditions would potentially improve results for drugs that reduce Hic1 expression. Using a library of drugs that have already been identified as safe for human use by the FDA strengthens translational efforts that arise from the findings of this study. This falls outside of the scope of this project, but future work that builds off the results of this research will be expedited. 65 While cell culture enabled the high-throughput screen by providing many experimental conditions that could be worked on in parallel, the surrounding biological systems have been ignored. This screen provided insight into methods for controlling Hic1 expression, but in vivo there are other factors at play that may disrupt the effects that were observed in this in vitro work. Simplifying the system is necessary to achieve efficient high-throughput results. To account for this, future work should be done in vivo to investigate whether hit drugs continue to regulate Hic1 expression when administered to an organism. Another limitation to this study is the lack of quality control imaging used during the screening process. The CellCyteX was delivered after the screen had been performed, and the previous plate imager ceased to function during the validation phase of the project. Images taken pre- and post-treatment would benefit the identification of cytotoxic compounds that decrease the CPRG response by means of cell death and reduce the list of hit compounds identified that downregulate Hic1, such as the ones shown in Figure 24 that were taken during secondary experiments. They would also help in ensuring that outlier wells which are seeded with disproportionately small or large numbers of cells are removed from the screen before being treated. However, the ineffectiveness of the assay in identifying drugs that cause a decrease in Hic1 expression buffers the disruption that not having this quality control step had. 4.3.4 Future Directions To more efficiently and robustly identify small molecules that down-regulate Hic1 expression under confluent conditions will require further assay optimization. In this experiment including a cytotoxicity assay in parallel would be prudent to identify and remove cytotoxic 66 compounds from the negative regulator hit list. RAR antagonists are likely candidates for decreasing Hic1 expression, and any hits that interact with other pathways would establish a novel mechanism for regulation of this gene. Additional testing of the retinoid hit compounds in disease models would continue towards the translational aspirations of this project. These experiments could be carried out in vitro or in vivo. Fibrotic and other disease models can be treated with the hit compounds, with the goal of resolving the disease state. In addition to these efficacy-focused experiments, optimization of dosing schedules can be investigated. Improving the screen through the addition of fluorescent cells was discussed during this project, however mouse lines could not be generated in time for inclusion in this writing. The use of fluorescence allows for robust identification of cell density on a per well basis and can allow for correction in readout based on the number of cells in each well. This method would need to be modeled to enable the use of this data to account for cell density. If this approach is successful, it is expected to further improve the Z’ of the assay through a reduction in variability across wells. The CPRG assay was a robust method for screening compounds for their ability to upregulate Hic1 expression. 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The delta value for these genes is also included as calculated for the selected hits RNA sequencing experiment. Table 10 shows the values for the selected hits experiment and Table 11 contains the I50 dose response experiment with EC23 and palovarotene. Gene Taza-rote-ne 1 Taza-rote-ne 2 Tami-baro-tene 1 Tami-baro-tene 2 Allop-urinol Urso-diol EC23 1 EC23 2 Palo-varo-tene 1 Palo-varo-tene 2 4310 1 4310 2 Vehi-cle 1 Vehi-cle 2 Delta Abca1 964.3 1068.9 919.9 1044 715.8 601.7 640 734 474.5 551.1 288.5 330.4 528.7 584.4 0.7 Acp5 502.8 475 513.5 518 326.7 293.6 475.2 429.6 441.3 497.6 39.1 68.7 93.4 95.1 4.3 Adamts9 1890.1 2168 1977.9 1985.7 1394.6 1215.7 1799.4 1790.8 1804.8 2052.8 638.7 667.9 916.4 972.2 1.2 Adra1d 610 594.8 609.6 610.5 653.4 677.6 619 574.3 622.7 644.7 376 415.6 541.2 603.6 0.4 Adra2a 347.8 342.2 294.3 298.3 250.6 287.5 247.2 253.7 240.5 249.7 102 90 180.1 187 0.9 Afap1l2 1698.4 1783.5 1820.7 1838.5 914.6 725.9 1448.7 1561.9 1593.3 1662.2 242.1 227.4 364.4 416.7 3.5 Agtr1a 428.6 449.1 438.4 432.5 376.4 316.2 460.9 414.5 424.1 468.2 107.1 123.2 187.6 168.8 1.7 Ahrr 138.2 125 89.1 115.3 135.3 128.3 89.1 74.5 136.4 144.5 313.2 333.9 179.3 176.3 -1.3 Aldh1a3 16 11.2 18 9.9 40.2 31.8 12.5 11.9 14 8 1188.9 1308.5 441.1 391 -2.8 81 Gene Taza-rote-ne 1 Taza-rote-ne 2 Tami-baro-tene 1 Tami-baro-tene 2 Allop-urinol Urso-diol EC23 1 EC23 2 Palo-varo-tene 1 Palo-varo-tene 2 4310 1 4310 2 Vehi-cle 1 Vehi-cle 2 Delta Angpt2 386.3 354.3 356.3 341.1 213.6 169.4 278.8 255.8 280.2 336.2 46.4 41.4 109.2 107.9 2 Angptl4 119.4 111.2 99.1 150.1 84.6 70.8 102.5 116.6 204 180.1 187.5 210.8 85.9 109 -1 Angptl7 498.1 546.5 378.4 512.1 351 267 390 428.5 377.9 489.6 53.6 68.7 202.6 191.2 1.7 Aoc3 477.5 449.1 377.4 429.5 499.1 373.7 294.2 318.4 366.1 362 225.6 239.2 316.9 362.2 0.2 Aqp5 464.3 434.5 395.4 388.8 394.4 403.5 304.7 277.4 484.2 530.6 76.2 79.3 181.8 213.7 1.2 Arhgef10l 1257.6 1168.9 1257.2 1187.2 948.4 970.3 1177.6 1135.6 1200.3 1243.1 534.7 509.2 741.3 879.2 0.9 Arsi 1188.9 1119.8 1034 1034.1 1045.7 1010.3 1032.9 995.2 999.5 1079 534.7 502.1 841.3 928.4 0.6 C1qtnf1 550.8 597.4 536.5 592.6 360.5 348.1 439.8 477.1 563.7 594.8 178.2 157.5 273.5 312 1.1 C1qtnf5 741.6 706.9 684.7 685.1 737 759.8 562.4 579.7 580.8 599.2 576.9 547.1 752.1 920.9 0 Car6 90.2 86.2 110.1 118.3 114.2 173.5 143.7 116.6 110.6 110.6 228.7 278.3 144.3 150.6 -0.9 Ccbe1 838.4 842.2 828.8 749.7 636.5 590.4 706.2 776.1 616.3 615.3 126.7 130.3 191.8 195.5 3.2 Ccn3 5720.1 5626.4 5193.9 4648.5 7403.4 5874 3998.4 3371.1 6489 5270.1 3770.6 3172.4 6344.7 5663.3 0 Cdsn 169.2 194 186.2 191.9 234.7 298.8 182 206.2 238.3 240.8 2860.9 3226.9 1049 1161.3 -2.7 Cebpd 437 364.6 357.3 365.9 456.8 485.6 342.1 318.4 369.3 356.7 420.3 364.7 612.9 553.4 -0.1 Cemip 9501.2 9461.6 9155.7 9478.9 7945.8 7620.4 8106.9 8756.4 9199.9 9858 3808.7 4026.2 5460 5653.6 0.8 Cfh 1001.9 1005.1 1026 1030.1 633.3 408.6 826.9 753.4 1102.6 946.1 74.2 69.9 112.6 117.5 6.4 Chst11 2458.7 2529.2 2525.4 2699.6 1390.4 1305 2444.2 2517.3 2217 2228.4 461.5 444.1 632.9 697.6 3.2 Chst15 526.3 486.2 503.5 515.1 393.3 302.9 440.8 498.7 411.2 428.9 160.7 130.3 215.1 222.2 1.5 82 Gene Taza-rote-ne 1 Taza-rote-ne 2 Tami-baro-tene 1 Tami-baro-tene 2 Allop-urinol Urso-diol EC23 1 EC23 2 Palo-varo-tene 1 Palo-varo-tene 2 4310 1 4310 2 Vehi-cle 1 Vehi-cle 2 Delta Cisd3 208.7 199.1 220.2 199.9 170.2 202.3 224.2 215.9 196.5 214.9 94.8 111.3 193.5 157 0.6 Cmtm3 2266 2209.4 2379.3 2254.1 2222.5 2092.5 1808 1801.6 2053.8 2012.6 1301.2 1325.1 2032.1 2010.6 0.2 Col10a1 9124.3 9144.4 9332.9 8651.6 9335.2 7257 8157.7 7111.3 7112.8 5775.7 1363 1372.5 2557.4 2415.5 2.5 Col15a1 161.7 142.2 143.1 150.1 190.3 224.9 138 175.9 126.7 136.4 1582.4 1667.3 926.4 950.8 -1.6 Col18a1 840.2 850.8 842.8 720.9 923 1020.6 807.7 765.3 850.3 842.7 3706.7 3763.3 2317.2 2549.1 -1.3 Cpxm2 1242.5 1227.5 1121.1 1113.6 1282.5 1154.1 948.6 935.9 1039.3 1153.9 614 689.2 966.4 915.6 0.3 Crabp2 3920.2 4191.2 4495.3 4188.1 2232 1912.8 4392.2 4410.6 4479.2 4622.7 269.9 261.7 637.1 596.1 6.5 Crispld2 7928.8 8171.1 8362 9023.5 5075.2 4229.1 7983.3 8041.8 7048.4 7370.1 642.9 650.1 1222.4 1230.7 6 Cul9 268.8 275.8 275.3 284.4 254.8 223.8 277.9 248.3 282.4 329.9 137 169.3 222.6 261.7 0.5 Cxcl12 12975 12691.6 12946.3 12441 11850.6 10536.4 10922.9 10409 12645.2 11981.2 3782.9 3512.3 7021.8 6833.1 1 Cygb 614.7 662 584.6 597.6 512.8 466.1 428.3 445.8 627 679.5 118.5 104.2 227.6 258.5 1.4 Cyp26b1 10763.4 11341.7 11166.6 11148.4 3555.8 2388.2 9894.8 10103.5 9558.5 10146.9 133.9 118.4 364.4 334.4 27.1 Dhrs3 7115.8 7076.4 7461.1 7055.7 4454.5 3351.3 6805.8 6496.1 6591 6931.4 533.6 452.4 848.8 951.9 7.3 Dmpk 1360 1436.1 1232.2 1376.1 1494 1153 1273.4 1176.6 1170.3 1252.9 461.5 477.2 818 919.8 0.9 Dusp1 1951.2 1860.3 1892.8 1845.5 1574.4 1421 1977.6 1868.5 2176.2 2110.7 538.8 542.4 879.7 818.3 1.6 Ecrg4 256.6 231.9 254.2 278.4 295 231 199.3 168.4 215.8 208.7 152.5 161 303.5 251.1 0.1 Errfi1 2342.2 2452.5 2042.9 2442.1 1845 1751.6 2005.4 2128.7 1863.8 1977.8 807.7 821.8 1162.4 1317.3 1.1 Fabp4 683.3 716.3 660.6 625.4 709.5 743.4 538.5 514.9 586.2 580.5 247.3 195.4 368.6 297 0.8 83 Gene Taza-rote-ne 1 Taza-rote-ne 2 Tami-baro-tene 1 Tami-baro-tene 2 Allop-urinol Urso-diol EC23 1 EC23 2 Palo-varo-tene 1 Palo-varo-tene 2 4310 1 4310 2 Vehi-cle 1 Vehi-cle 2 Delta Fam43a 1073.3 886.2 947.9 886.9 835.3 805 967.7 824.7 993.1 975.5 426.5 445.3 681.2 658.1 0.7 Fap 1378.8 1375.8 1670.6 1488.5 823.7 576 1332.8 1163.6 1365.7 1301.9 61.8 47.4 161.8 75.9 7.4 Fgf18 258.5 280.2 271.3 227.7 137.5 123.2 234.7 196.5 286.7 244.3 45.3 30.8 85.9 101.5 2.1 Foxp2 675.8 739.6 737.7 680.1 709.5 658.1 658.3 596.9 756.9 633.1 300.8 306.7 459.4 412.4 0.7 Gdf10 507.5 467.2 450.4 458.4 248.5 112.9 305.7 297.9 460.6 462.8 26.8 27.2 55 67.3 5 Gfra1 495.3 521.5 492.5 477.3 420.8 436.4 451.3 493.3 455.2 436.1 227.7 212 316.9 338.7 0.8 Gpr39 487.8 525.8 535.5 521 363.7 283.4 522.2 584 540 571.6 41.2 46.2 91.7 95.1 5.6 Gprc5a 1094 1062.9 1097 1189.2 726.4 687.9 1134.5 1213.3 1038.2 1051.3 293.6 348.1 431.9 506.4 2 Heg1 9506.8 10017.6 9621.2 9859.7 5971.8 4750.7 8714.4 9287.5 9568.2 9762.6 1506.2 1686.3 2695.8 2736 2.7 Hexb 2553.6 2675.7 2821.7 2697.6 2173.9 2013.4 2514.2 2217.2 2902 2814.3 966.3 991.2 1512.6 1511.7 0.9 Hic1 3691.8 3269.7 3560.4 3607.4 1832.4 1715.7 3592.1 3077.5 3209.1 3384.1 536.7 506.8 719.6 806.6 3.9 Hs3st1 551.7 501.7 608.6 603.6 133.2 67.8 470.5 441.5 462.7 493.1 4.1 7.1 18.3 18.2 24.6 Htra3 1038.6 1022.4 986.9 1056 911.4 809.1 821.1 852.8 1018.9 1125.4 490.4 541.2 707.1 787.4 0.5 Igfbp3 42172.2 42660.8 41313.3 40219.5 21793.7 12674 36185.6 32179.2 40777.4 38804.3 1310.4 1178.3 3220.3 3260.6 10.2 Il1rn 1258.5 1414.6 1301.2 1338.4 1340.7 1298.8 1030 931.6 1689.9 1590.8 536.7 688 1144 1214.7 0.3 Itga8 2075.2 2168 2038.9 1812.7 2024.8 1644.8 1665.3 1555.5 1757.5 1551.6 753.1 641.8 1183.2 1020.3 0.8 Itgbl1 597.8 597.4 530.5 442.5 649.2 681.8 436.9 365.9 529.3 432.5 519.2 464.2 720.4 694.4 -0.1 Itih2 2348.7 2155.9 2442.3 2304.8 1487.7 1181.8 2210.5 2218.2 2562.7 2586 150.4 137.4 321.9 346.1 6.4 84 Gene Taza-rote-ne 1 Taza-rote-ne 2 Tami-baro-tene 1 Tami-baro-tene 2 Allop-urinol Urso-diol EC23 1 EC23 2 Palo-varo-tene 1 Palo-varo-tene 2 4310 1 4310 2 Vehi-cle 1 Vehi-cle 2 Delta Larp1b 675.8 531.9 613.6 555.8 579.4 545.2 588.3 513.8 578.7 539.5 242.1 242.8 404.4 362.2 0.8 Lbp 485 469.8 483.5 510.1 374.3 317.3 381.3 350.8 532.5 568.9 242.1 238 367.7 382.5 0.3 Lgr6 1736.9 1866.3 1759.7 1954.8 750.7 620.1 1832 2008.8 1684.5 1697 27.8 28.4 48.4 57.7 39.1 Lipg 235.9 262.1 223.2 244.6 315.1 344 261.6 225.6 179.3 195.3 920 953.3 642.1 657 -1.1 Lmcd1 1065.8 978.4 1015 1013.2 922 852.2 855.6 829 924.4 964.8 375 406.2 602 641 0.8 Lrrc15 1595.9 1706.8 1352.3 1440.8 1652.6 1518.5 1389.3 1583.5 1038.2 1085.2 814.9 914.2 1294.1 1436.9 0.5 Lrrc32 15189.3 15187.2 15002.3 16350.7 11813.6 11484.1 13830.9 14781.8 14306.1 15352.8 2797 3153.5 6265.5 7196.3 1.8 Ltbp3 8353.6 8074.6 8499.1 8606.9 7124.3 6983.9 8687.6 8847.1 8609.4 9367.6 3381.2 3604.6 4941.3 5348.1 1.1 Lum 485.9 383.6 448.4 364.9 547.7 479.5 270.2 272 389.7 311.2 760.3 585 1224.1 986.1 -0.3 Lvrn 692.7 751.7 680.6 803.4 510.7 445.6 572 632.6 756.9 722.3 80.4 87.6 153.4 120.7 3.4 Medag 4430.6 4448.1 4138.9 4257.7 3483.9 2926.2 4613.5 4400.9 4871 4757.4 895.3 819.5 1555.9 1447.6 2.3 Megf6 466.2 512 475.5 476.3 370.1 338.8 489.6 501.9 448.8 554.7 1141.5 1185.4 772.1 837.6 -0.9 Mest 269.7 326.7 293.3 249.6 597.4 604.7 223.2 213.7 298.5 260.4 4538.1 4276.1 2917.6 2720 -1.4 Mfap2 628.8 600 611.6 610.5 571 503.1 529.9 479.3 561.5 568.9 321.4 311.4 507.8 498.9 0.4 Mgp 3831.9 3249 2815.7 2767.2 2834.7 2414.9 2077.3 1696.9 2350.2 2225.7 949.9 976.9 1671 1654.9 0.6 Mme 890.1 941.3 862.8 811.4 909.3 755.7 699.5 671.4 1038.2 947.9 539.8 542.4 903.9 806.6 0.2 Mmp13 307.3 295.7 324.3 347 225.2 185.8 212.7 192.1 328.5 280 8.2 5.9 49.2 26.7 4 Mylip 748.1 687 837.8 717.9 696.8 655.1 655.4 589.4 700 657.2 437.8 377.8 616.2 564.1 0.3 85 Gene Taza-rote-ne 1 Taza-rote-ne 2 Tami-baro-tene 1 Tami-baro-tene 2 Allop-urinol Urso-diol EC23 1 EC23 2 Palo-varo-tene 1 Palo-varo-tene 2 4310 1 4310 2 Vehi-cle 1 Vehi-cle 2 Delta Ndnf 350.6 373.3 361.3 322.2 680.9 560.6 329.6 321.7 410.1 399.5 523.3 587.4 818.8 893.1 -0.3 Ndrg1 229.3 209.5 196.2 213.8 468.4 529.8 242.4 255.8 263 331.7 1300.1 1489.7 915.6 959.4 -1.3 Npr3 5820.6 5875.6 6292 5853.6 4104.6 3358.5 6297 6487.4 5616.1 5206.8 920 851.4 1509.3 1332.2 3.6 Nqo1 495.3 411.2 519.5 534 467.3 528.8 451.3 438.3 675.3 626.9 970.5 819.5 593.7 605.7 -0.8 Nt5e 781 686.2 804.8 784.5 488.5 470.2 790.5 792.3 717.2 740.1 152.5 217.9 279.3 264.9 2.2 Ntn4 857.2 838.8 874.8 848.2 563.6 438.4 855.6 739.4 938.4 881 171 176.4 296 291.7 2.1 Ogn 14335 14480.3 15856.1 14168.2 11477.3 8231.4 13510.9 12243 17026.7 12069.5 1306.3 968.7 2833.4 2209.3 4.1 Olr1 398.5 435.3 434.4 434.5 285.5 218.7 408.2 418.8 420.9 353.1 33 17.8 46.7 56.6 8.3 Pcdhgc4 187 392.2 222.2 141.2 712.6 475.4 59.4 76.6 253.4 189.9 419.3 189.5 70 89.7 -3.4 Pde8b 779.2 766.3 770.7 807.4 544.5 438.4 610.3 617.4 718.3 774 200.9 170.5 326.9 305.5 1.3 Pdgfra 7402.5 7610 7499.1 7410.7 4946.2 4147 6811.5 6933.2 7850.4 7931.9 1705 1723 2719.2 2599.3 1.9 Pdgfrl 3550.8 3486 3632.5 3503 2813.6 2219.8 3253.9 3031.1 3072.7 3217.3 873.6 949.7 1398.4 1361.1 1.6 Pitpnc1 1688 1771.5 1947.9 1868.3 807.8 629.4 1806.1 1830.7 1910 1910.1 137 117.2 231 298.1 7.3 Plac8 252.8 230.2 218.2 258.5 252.7 180.7 237.6 215.9 269.5 255 67 71.1 135.9 116.4 1.2 Plcd3 449.3 480.1 458.4 517 351 356.3 494.4 444.7 472.4 523.4 149.4 178.8 221 277.8 1.4 Plekha6 274.4 260.3 227.2 293.3 161.8 121.2 232.8 256.9 255.5 287.1 15.5 13 53.4 44.9 4.3 Plscr2 267.9 333.6 294.3 362.9 213.6 166.3 349.7 315.2 289.9 339.7 22.7 30.8 63.4 60.9 4.8 Prg4 294.2 310.3 266.3 260.5 429.3 384 284.6 296.8 319.9 370.1 1879.1 1900.6 1177.4 1324.7 -1.4 Ptx3 1850.6 1867.1 1878.8 1776.9 3332.7 3383.1 2007.3 1867.4 2245 2282.8 10247.5 9938.8 7327.8 6470.9 -1.1 Rasl11a 410.7 403.4 369.4 357 334.1 298.8 378.5 327.1 479.9 564.5 96.8 105.4 173.4 176.3 1.5 86 Gene Taza-rote-ne 1 Taza-rote-ne 2 Tami-baro-tene 1 Tami-baro-tene 2 Allop-urinol Urso-diol EC23 1 EC23 2 Palo-varo-tene 1 Palo-varo-tene 2 4310 1 4310 2 Vehi-cle 1 Vehi-cle 2 Delta Rassf2 1084.6 1195.6 1092 1127.6 678.8 559.6 967.7 1024.4 1166 1298.4 358.5 403.8 542 590.8 1.1 Rbp1 2563 2363.7 2771.6 2583.3 1173.6 959 2365.7 2246.3 2083.9 2080.4 275.1 299.6 396.1 431.6 5.1 Sema3f 2528.3 2451.6 2370.3 2416.2 2423.4 2481.6 2441.4 2285.2 2498.3 2871.4 1358.8 1395 2017.1 2206.1 0.5 Serpina3i 47 37.9 42 36.8 86.7 129.4 36.4 44.3 41.9 41 629.5 695.1 386.1 395.3 -1.6 Serping1 1400.4 1418 1413.3 1560.1 1134.5 880.9 1077 1144.2 1477.3 1485.6 268.9 291.3 532.8 543.8 1.6 Sfrp1 5022.7 4811.8 4658.4 4648.5 4101.4 3022.7 3630.4 3352.7 5272.6 5139 1823.5 1702.9 3796.5 3411.2 0.5 Sfrp4 199.3 191.4 185.2 260.5 119.5 122.2 145.6 143.6 156.7 143.6 33 39.1 103.4 84.4 1.1 Slco4a1 340.2 348.3 351.3 360.9 328.8 274.1 363.1 396.2 318.9 403.1 70.1 95.9 140.9 144.2 2.1 Sned1 1114.7 1269.8 1197.1 1396 1214.9 1186.9 985 1187.4 1440.8 1874.4 1209.5 1418.6 1779.4 2169.8 -0.1 Snta1 1543.3 1408.6 1449.4 1509.4 1350.2 1443.6 1462.1 1363.3 1510.6 1553.4 517.2 511.6 803 800.2 1.1 Socs3 471.8 494.8 458.4 559.8 508.6 495.9 506.9 473.9 511 504.7 489.4 448.8 657.9 708.3 0 Synpo 5620.5 5748.9 5285 5335.6 4590.9 4330.8 4941.2 5147.8 5331.6 5643.7 2676.5 2791.1 3739.8 4237 0.6 Syt13 282 357.7 249.2 250.6 419.8 425.1 257.7 258 362.9 445 454.3 511.6 715.4 744.6 -0.3 Tgm2 7774.6 8267.7 8314.9 8659.6 3191 2421 7421.9 7847.5 8350.7 9170.5 352.3 390.8 702.9 728.6 10.3 Thbd 5948.5 5651.4 5798.5 5758.1 4299.1 3842 5355.1 5039.9 5493.8 5355.7 2484.9 2375.5 3520.5 3512.7 0.8 Thbs2 40196.6 41294.5 39921 40619.2 29434 26144.8 36380.1 37389.6 42190.3 42742.2 12139 12452.8 18231.9 18544.2 1.3 Tinagl1 7459.8 7090.2 7592.2 7635.4 4947.2 4603.9 6647.7 6640.7 6710.2 6918 754.1 825.4 1520.9 1731.8 3.8 87 Gene Taza-rote-ne 1 Taza-rote-ne 2 Tami-baro-tene 1 Tami-baro-tene 2 Allop-urinol Urso-diol EC23 1 EC23 2 Palo-varo-tene 1 Palo-varo-tene 2 4310 1 4310 2 Vehi-cle 1 Vehi-cle 2 Delta Tmem119 4815.9 4915.3 4941.7 5114.8 3368.7 3142.8 4630.8 4548.8 4750.8 4764.5 1436.1 1313.3 1996.2 2244.6 1.6 Tmem150a 756.6 669.8 761.7 744.8 763.4 710.5 613.2 641.2 727.9 755.3 395.6 373 544.5 602.5 0.4 Tmem86a 1367.5 1277.5 1378.3 1389.1 645 619.1 1299.3 1138.8 1471.9 1488.3 149.4 156.3 263.5 292.7 4 Tnfrsf1b 1458.7 1460.3 1607.5 1664.5 961.1 870.7 1363.5 1506.9 1552.5 1534.7 276.1 309.1 415.3 470.1 2.8 Uchl1 408.8 386.2 431.4 345 371.1 349.1 404.3 394 398.3 387.9 620.2 677.4 437.8 438 -0.6 Wnt10b 218.1 217.2 274.3 255.5 122.7 88.3 311.4 292.5 260.9 247 11.3 10.7 27.5 35.3 10.6 Zfp469 6708.8 7243.6 6276 7314.3 4087.6 4546.4 6227 8204.8 6106.8 7505.7 1888.4 2093.6 2815.9 2950.7 1.9 Zfp827 1572.4 1608.5 1637.6 1775.9 1062.6 1058.6 1602 1758.4 1624.4 1685.4 575.9 614.6 812.2 966.8 1.3 Table 10. Transcript count matrix for select hit compounds with delta values. This table contains estimated RNA transcript counts for every gene that had an adjusted p-value of less than 0.05 in the selected hits RNA sequencing experiment. It also has the calculated delta values that were used to determine which genes were included in heatmaps alongside Hic1. 88 Gene 100 nM EC23 1 100 nM EC23 2 1 nM EC23 1 1 nM EC23 2 100 nM Palovaro-tene 1 100 nM Palovaro-tene 2 1 nM Palovaro-tene 1 1 nM Palovaro-tene 2 Vehicle 1 Vehicle 2 Abca1 902.0 870.1 891.0 790.9 858.7 870.5 920.2 881.5 798.2 854.7 Acp5 410.8 411.8 363.9 329.2 374.9 360.0 177.9 157.6 134.1 140.1 Adamts9 2522.5 2375.3 2210.2 1857.1 2241.7 2160.0 1714.3 1680.7 1590.9 1515.5 Adra1d 618.2 652.0 793.0 723.6 718.2 754.4 731.1 686.3 646.4 584.7 Adra2a 115.1 157.7 121.3 131.5 143.4 123.9 78.3 65.1 63.3 56.8 Afap1l2 2509.6 2298.0 1804.3 1772.2 2120.0 2067.5 836.8 756.5 476.9 438.5 Agtr1a 248.1 261.5 221.1 224.6 215.7 230.2 160.6 196.2 128.5 132.0 Ahrr 96.3 101.6 111.0 97.3 97.0 99.3 134.2 122.0 131.3 170.5 Aldh1a3 8.9 9.5 29.9 24.8 22.8 28.5 53.9 35.6 96.9 85.3 Angpt2 205.4 192.7 147.4 143.9 149.4 172.1 100.7 82.4 79.2 56.8 Angptl4 179.6 174.7 174.5 140.8 174.1 144.6 140.3 117.9 134.1 144.1 Angptl7 539.8 511.3 593.4 619.0 522.3 476.1 700.5 641.6 497.4 487.2 Aoc3 241.1 221.2 245.4 249.5 206.8 204.6 192.2 189.1 185.4 176.6 Aqp5 229.2 283.7 292.0 227.7 232.5 265.6 187.1 172.8 111.8 113.7 Arhgef10l 927.8 947.4 909.6 841.6 849.8 914.8 745.3 729.0 584.9 527.9 Arsi 752.2 785.4 914.3 838.5 848.8 780.0 684.3 706.6 597.0 515.7 C1qtnf1 702.6 671.1 573.8 567.3 692.5 634.4 407.7 406.7 380.9 318.7 C1qtnf5 434.6 372.6 509.4 454.4 386.8 420.0 488.0 500.2 541.2 495.4 Car6 25.8 20.1 32.7 30.0 30.7 25.6 47.8 37.6 65.2 66.0 Ccbe1 518.0 558.9 408.6 385.1 436.3 521.3 214.5 220.6 85.7 111.7 Ccn3 2262.5 2530.9 2401.4 2624.2 2569.2 2785.6 3163.1 3197.6 2867.8 3409.7 Cdsn 142.9 121.7 157.7 149.1 138.5 134.8 256.2 260.3 444.3 445.6 Cebpd 522.0 556.8 602.7 539.3 488.7 605.9 719.9 636.5 701.4 692.3 Cemip 7812.5 7211.7 8172.7 6972.0 7445.4 7175.4 7323.7 7355.1 7919.8 6972.7 Cfh 850.4 928.3 563.5 542.4 688.5 789.8 221.7 249.1 87.6 79.2 Chst11 3148.6 2951.2 2207.4 2256.7 2653.3 2729.5 1305.5 1278.0 878.3 839.5 Chst15 425.7 453.0 373.2 312.6 378.9 330.5 254.2 231.8 225.4 230.4 89 Gene 100 nM EC23 1 100 nM EC23 2 1 nM EC23 1 1 nM EC23 2 100 nM Palovaro-tene 1 100 nM Palovaro-tene 2 1 nM Palovaro-tene 1 1 nM Palovaro-tene 2 Vehicle 1 Vehicle 2 Cisd3 265.9 236.0 231.4 280.5 244.4 255.7 212.5 195.2 215.2 190.8 Cmtm3 1574.8 1539.1 1577.6 1598.3 1617.5 1590.5 1810.9 1752.9 1694.2 1620.1 Col10a1 3135.7 3545.0 2561.9 2763.9 2835.3 2986.2 1721.4 1727.4 1634.6 1806.9 Col15a1 138.9 148.2 153.9 128.4 119.7 133.8 188.1 226.7 254.3 234.5 Col18a1 772.0 698.6 913.4 852.0 724.2 784.9 1145.9 1051.3 1256.5 1106.5 Cpxm2 671.8 675.3 742.6 658.4 670.7 664.9 588.7 563.3 642.7 554.2 Crabp2 6114.6 5180.4 4013.6 3760.8 5102.7 5101.0 1683.8 1766.1 678.1 585.7 Crispld2 6644.5 6685.6 4733.8 4497.9 5638.9 5342.0 1995.9 2142.3 1242.5 1175.5 Cul9 418.8 383.2 470.2 379.9 439.2 419.0 427.0 452.4 422.9 433.4 Cxcl12 13084.6 14007.4 12688.3 13431.5 13555.2 14146.2 10725.8 11199.4 7212.9 7593.0 Cygb 249.1 289.0 292.9 321.9 332.4 334.4 250.1 235.9 145.3 137.0 Cyp26b1 9236.4 8744.4 4745.0 4793.9 6343.3 5865.2 806.3 1003.5 308.3 254.8 Dhrs3 7069.2 6894.1 5912.2 6060.0 6283.9 6290.2 2528.7 2636.4 1138.2 1059.8 Dmpk 729.4 681.7 697.9 647.0 680.6 662.0 533.8 508.4 440.6 420.3 Dusp1 2191.0 2162.6 2042.3 2158.4 2173.5 2270.2 1699.0 1648.1 1493.1 1351.1 Ecrg4 169.7 217.0 200.6 194.6 228.5 214.4 181.0 157.6 223.5 189.8 Errfi1 2448.0 2280.0 2020.8 2133.5 2360.4 2344.9 1734.6 1675.6 1258.3 1292.2 Fabp4 327.5 340.8 330.3 303.3 268.1 313.8 207.4 281.6 278.5 236.5 Fam43a 1011.2 1080.7 933.0 1024.8 977.4 985.6 930.3 892.7 720.0 699.4 Fap 823.6 888.1 473.0 401.7 618.3 747.5 202.3 215.5 96.9 101.5 Fgf18 306.6 310.1 308.8 223.6 335.4 299.0 108.8 128.1 71.7 80.2 Foxp2 564.6 691.2 548.6 661.5 547.1 587.2 463.6 479.9 416.3 475.1 Gdf10 263.0 243.5 308.8 314.7 261.2 333.4 128.1 146.4 53.1 48.7 Gfra1 482.3 409.6 404.0 403.7 384.8 453.4 363.0 357.9 329.7 313.7 Gpr39 799.8 767.4 597.1 569.4 654.9 630.5 374.2 370.1 233.8 213.2 Gprc5a 1207.6 1150.6 1101.8 1103.5 1181.2 1112.5 895.8 801.2 599.8 660.8 Heg1 11826.4 11226.6 10036.8 8636.5 10709.0 9906.9 6220.6 5665.3 3879.3 3737.6 90 Gene 100 nM EC23 1 100 nM EC23 2 1 nM EC23 1 1 nM EC23 2 100 nM Palovaro-tene 1 100 nM Palovaro-tene 2 1 nM Palovaro-tene 1 1 nM Palovaro-tene 2 Vehicle 1 Vehicle 2 Hexb 2840.0 2814.6 2556.3 2927.5 2878.8 2945.9 2380.2 2461.5 1885.2 1900.3 Hic1 3130.7 3065.5 2338.0 2274.3 2778.9 2697.0 1097.1 984.2 648.3 547.1 Hs3st1 463.4 442.5 222.0 213.2 327.5 390.5 41.7 35.6 24.2 15.2 Htra3 854.4 711.3 827.5 856.1 860.7 902.0 899.8 654.8 651.1 562.4 Igfbp3 23671.6 22755.0 13592.3 13891.1 17799.2 18413.1 4850.0 4873.2 2458.9 2560.1 Il1rn 811.7 760.0 717.4 915.1 825.1 804.6 705.6 767.6 503.9 625.3 Itga8 1158.0 1163.3 1002.9 948.2 962.6 1069.2 793.1 877.4 814.1 871.0 Itgbl1 224.3 258.3 280.8 305.4 245.3 279.3 368.1 414.8 368.8 380.7 Itih2 1684.9 1465.0 1170.9 1083.8 1427.5 1260.0 598.9 625.3 363.3 356.3 Larp1b 522.9 562.1 551.4 518.6 558.9 517.4 403.7 374.2 341.8 377.6 Lbp 694.6 620.3 680.1 632.5 679.6 714.1 618.2 567.3 522.5 493.3 Lgr6 2956.1 2915.2 2068.4 2009.3 2457.4 2351.8 754.4 773.7 177.9 188.8 Lipg 232.2 246.6 316.3 342.6 297.8 294.1 508.4 466.7 702.3 659.8 Lmcd1 782.9 700.7 766.9 701.9 769.7 706.2 541.9 503.3 465.7 450.7 Lrrc15 514.0 457.3 469.3 457.6 457.1 445.6 409.8 425.0 380.0 419.2 Lrrc32 14808.3 14266.7 13063.3 12452.2 13577.0 13187.2 8642.5 8330.1 5692.8 5449.1 Ltbp3 12222.3 11733.7 12177.0 11399.5 12025.8 11439.3 9903.3 9259.4 7903.1 7559.5 Lum 345.3 336.6 338.7 434.8 366.0 474.1 631.4 585.6 462.9 718.7 Lvrn 396.9 409.6 353.6 320.9 383.8 389.5 158.6 150.5 66.1 82.2 Medag 4216.3 4103.9 3629.2 3354.0 3809.7 3891.1 1924.7 2008.1 993.8 1007.0 Megf6 571.6 504.9 613.9 551.8 521.4 475.1 569.4 510.4 843.9 829.3 Mest 365.2 369.4 537.4 627.3 377.9 468.2 1205.9 1207.9 2296.9 2324.6 Mfap2 326.5 276.3 347.1 356.1 319.5 286.2 340.6 320.3 258.9 299.5 Mgp 1963.8 2109.6 2423.8 2350.9 2195.2 2403.9 1780.4 1470.2 1125.2 1171.4 Mme 844.5 953.7 888.2 868.5 881.5 925.6 936.4 1088.9 847.6 1077.0 Mmp13 150.8 177.8 113.8 147.0 170.2 159.3 68.1 68.1 31.7 33.5 Mylip 916.9 973.8 871.4 891.3 901.2 912.8 772.7 787.0 811.3 727.8 91 Gene 100 nM EC23 1 100 nM EC23 2 1 nM EC23 1 1 nM EC23 2 100 nM Palovaro-tene 1 100 nM Palovaro-tene 2 1 nM Palovaro-tene 1 1 nM Palovaro-tene 2 Vehicle 1 Vehicle 2 Ndnf 389.0 384.2 418.0 369.6 415.5 385.6 547.0 518.5 561.6 599.9 Ndrg1 299.7 259.3 313.5 333.3 325.5 346.2 592.8 577.5 693.0 677.1 Npr3 6188.1 6683.5 5284.3 5106.6 5653.8 5819.0 3583.1 3875.8 2664.8 2754.0 Nqo1 937.7 911.4 906.8 925.5 886.4 903.9 1026.9 980.1 1060.0 1051.6 Nt5e 632.1 610.8 423.6 432.7 557.0 592.1 296.9 278.6 193.7 201.0 Ntn4 1492.4 1306.2 1201.7 1198.7 1389.9 1386.9 715.8 734.1 433.1 432.4 Ogn 10827.1 13708.9 5692.0 9514.4 9470.4 11030.2 4073.1 5418.2 1958.8 2922.5 Olr1 261.0 348.3 189.4 185.3 237.4 266.6 77.3 80.3 44.7 52.8 Pcdhgc4 313.6 346.1 134.3 919.2 395.7 588.2 120.0 79.3 91.3 72.1 Pde8b 576.5 584.3 504.7 415.1 468.9 556.7 366.0 347.7 272.0 272.0 Pdgfra 7106.0 6802.0 5974.7 5623.1 6358.1 6263.6 3462.1 3689.7 2855.7 2835.2 Pdgfrl 2982.9 2916.2 2572.2 2550.7 2676.0 2665.6 1835.3 1717.3 1440.9 1388.7 Pitpnc1 2367.7 2400.7 1756.8 1796.0 2229.9 2212.1 889.7 966.9 429.4 483.2 Plac8 183.6 187.4 130.6 150.1 129.6 140.7 103.7 130.1 108.0 74.1 Plcd3 593.4 566.3 570.0 546.6 516.4 573.4 386.4 432.1 309.2 330.9 Plekha6 170.7 169.4 165.1 156.3 188.0 206.6 139.3 122.0 36.3 58.9 Plscr2 394.9 357.8 212.7 254.7 297.8 307.9 137.3 162.7 95.9 86.3 Prg4 541.8 456.2 521.5 505.2 555.0 596.1 836.8 845.9 1109.3 990.7 Ptx3 4009.9 4449.0 5316.0 5336.4 4409.2 4742.0 7508.8 8014.9 10317.3 10358.1 Rasl11a 583.5 586.4 613.0 591.1 668.8 696.4 469.7 448.4 352.1 343.1 Rassf2 1316.8 1113.6 1241.8 1034.1 1309.8 1242.3 940.5 859.1 714.4 722.8 Rbp1 1735.6 1598.4 1193.3 1001.0 1462.2 1264.9 491.1 418.9 429.4 295.4 Sema3f 3491.0 3248.6 3909.1 3806.4 3552.5 3564.6 3691.9 3601.3 3411.8 3164.1 Serpina3i 46.6 47.6 70.9 54.9 56.4 42.3 95.6 144.4 201.2 212.2 Serping1 943.7 838.3 681.1 858.2 825.1 790.8 582.6 546.0 448.9 462.9 Sfrp1 3760.9 3730.2 3436.1 3300.2 3594.1 3574.4 3227.2 3347.1 2955.4 2961.1 Sfrp4 161.7 121.7 148.3 138.7 187.0 178.0 126.1 134.2 53.1 79.2 92 Gene 100 nM EC23 1 100 nM EC23 2 1 nM EC23 1 1 nM EC23 2 100 nM Palovaro-tene 1 100 nM Palovaro-tene 2 1 nM Palovaro-tene 1 1 nM Palovaro-tene 2 Vehicle 1 Vehicle 2 Slco4a1 381.0 310.1 321.9 325.0 311.6 282.3 240.0 222.7 169.5 151.3 Sned1 2746.7 2481.2 3549.9 2892.3 3153.8 2987.2 4386.3 3997.8 2513.0 2893.0 Snta1 1533.1 1487.2 1494.6 1445.1 1514.6 1386.9 1040.1 1057.4 837.3 895.3 Socs3 818.7 767.4 705.3 711.2 709.3 761.3 841.9 737.1 723.7 686.2 Synpo 8737.3 8293.5 8653.2 7829.1 8998.5 8305.6 6541.8 6027.2 5032.4 4533.5 Syt13 588.4 606.5 696.9 798.1 654.9 685.6 1086.9 929.3 1019.9 1037.4 Tgm2 8400.9 7376.8 5309.5 4959.6 6777.6 6559.7 2220.6 1909.4 823.4 875.0 Thbd 4488.2 4815.2 3958.6 4290.8 4422.1 4774.4 3626.8 3735.5 3272.1 3552.9 Thbs2 27453.3 26118.9 23839.0 21962.5 25206.0 24183.9 17851.3 17593.6 15569.6 14425.6 Tinagl1 9476.6 8837.6 6902.0 6745.3 7888.6 7825.6 3657.3 3437.6 2288.5 2044.4 Tmem119 3900.8 3694.2 3043.3 3135.6 3295.3 3220.3 2022.3 1821.0 1712.9 1539.9 Tmem150a 580.5 597.0 645.6 605.6 642.0 655.1 662.9 591.7 527.2 524.8 Tmem86a 1518.2 1489.3 1018.8 1190.5 1415.7 1357.4 568.4 488.0 347.4 340.1 Tnfrsf1b 2009.4 1838.6 1841.7 1907.8 1996.4 1872.8 1233.3 1186.5 704.2 769.4 Uchl1 473.3 437.2 494.5 448.2 464.0 485.9 480.9 494.1 584.0 493.3 Wnt10b 250.1 313.3 189.4 189.4 257.2 242.0 75.2 73.2 23.3 44.7 Zfp469 6127.5 6011.3 5346.8 4004.1 5667.6 4728.2 3348.2 3230.2 2628.5 2302.3 Zfp827 1588.7 1531.7 1341.6 1158.4 1403.8 1372.1 982.2 931.3 786.1 746.1 Table 11. Transcript count matrix for EC23 and palovarotene at 100 nM and 1 nM. This table contains estimated RNA transcript counts for cultures treated with EC23 and palovarotene at 100 nM and 1 nM for every gene that had an adjusted p-value of less than 0.05 in the data used to generate Table 10.
Thesis/Dissertation
eng
Biomedical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Underhill, T M
A high-throughput screen to identify compounds that modulate quiescence in mesenchymal progenitor cells
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