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Downloadable Software, Databases, and Web-Based Platforms UC Berkeley UC Berkeley Electronic Theses and Dissertations Title Interventions to Encourage and Facilitate Greener Industrial Chemicals Selection Permalink https://escholarship.org/uc/item/4px4399n Author Faulkner, David Publication Date 2017 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Interventions to Encourage and Facilitate Greener Industrial Chemicals Selection by David Michael Faulkner A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Molecular Toxicology in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Christopher D. Vulpe, Co-Chair Associate Professor Jen-Chywan Wang, Co-Chair Associate Professor Daniel K. Nomura Professor John Arnold Fall 2017 Abstract Interventions to Encourage and Facilitate Greener Industrial Chemicals Selection by David Michael Faulkner Doctor of Philosophy in Molecular Toxicology University of California, Berkeley Professor Christopher D. Vulpe, Co-Chair Associate Professor Jen-Chywan Wang, Co-Chair Despite their ubiquity in modern life, industrial chemicals are poorly regulated in the United States. Statutory law defines industrial chemicals as chemicals that are not foods, drugs, cosmetics, nor pesticides, but may be used in consumer products, and this distinction places them under the purview of the Toxic Substances Control Act (TSCA), which received a substantial update when the US congress passed a revision of the act in 2016. The revised law, the Frank R. Lautenberg Chemical Safety for the 21st Century Act addresses many but not all of TSCA’s failings, and rightfully emphasizes the development and adoption of high throughput screens, in vitro, and alternative assays to improve the process for registering new chemicals and to address the tens of thousands of untested chemicals currently in the TSCA inventory. As the discipline of toxicology gradually shifts from its’ history as a reactive science (responding to problems after they’ve occurred) to a proactive science (attempting to predict and circumvent dangers to human and environmental health), two things become clear: 1.) traditional low throughput toxicological testing methodologies are inadequate to address both the volume of chemicals of interest and the pace of research; and 2.) the modern industrial chemical ecosystem is complex and no single testing solution will be appropriate for all the actors that populate that ecosystem. To address these challenges, three interventions are proposed, each of which targets a different population within the industrial chemical ecosystem. The first intervention is a suite of computational toxicology methods targeted towards chemists in the initial phases of chemical design and development. The second intervention is an alternative assay, the yeast functional toxicogenomic assay, permits industrial or government labs to rapidly investigate differences in cytotoxic mechanism between different chemicals – even if they are structurally very similar. The third intervention explored in this work is a method for enhancing the metabolic capacity of cell lines currently used by regulators for high throughput cytotoxicity testing. These interventions individually are not necessarily appropriate for all actors across the US industrial chemicals ecosystem, but as bespoke solutions they may be quite useful, and, it is hoped, support a larger exploration of where and how similar efforts may be spent most effectively to reduce industrial chemical hazard. 1 Dedication page I dedicate this dissertation to my mother and sister, whom I love most dearly. 1 Table of Contents Abstract 1 Preliminary Pages Dedication i Table of Contents ii List of Figures and tables iii Acknowledgements v Main Text Chapter 1: Introduction to Green and Industrial Chemistry 1 Chapter 2: Computational Tools For Green Molecular Design 9 To Reduce Toxicological Risk Chapter 3: Functional Toxicogenomics and Combinatorial Chemistry For Greener Design 32 Chapter 4: Using CRISPR To Enhance The Metabolic Capacity Of Cell Lines Commonly Used In High Throughput Screening 79 Appendices Appendix 1: Mutants with altered growth in 2,5-DMF 114 Appendix 2: Mutants with altered growth in 2,3-DMF 118 Appendix 3: Mutants with altered growth in 2-MF 148 Appendix 4: Mutants with altered growth in 2-EF 181 Appendix 5: Yeast Functional Toxicogenomic Assay Sequencing Design 187 2 List of Figures and Tables Chapter 2: Tools for Green Molecular Design to Reduce Toxicological Risk Figure 2.1: Derek Nexus Can Assess the Level of Confidence in a Prediction Figure 2.2: Derek Nexus Alert 351 Example Figure 2.3: Hydrogen Compounds Used in Case Study Figure 2.4: Compounds Generated From Second Iteration of Indoline Figure 2.5: Example Workflows for Chemists Chapter 3: Functional Toxicogenomics and Combinatorial Chemistry For Greener Design Table 3.1: 2,5-DMF 15G Sensitive Strains GO Biological Process Table 3.2: 2,5-DMF 15G Sensitive Strains GO Cellular Component Table 3.3: 2,5-DMF 15G Sensitive Strains MIPS Functional Classification Table 3.4: 2,3-DMF 5G Resistant Strains MIPS Functional Classification Table 3.5: 2,3-DMF 10G Resistant Strains GO Molecular Function Table 3.6: 2,3-DMF 15G Resistant Strains GO Biological Process Table 3.7: 2,3-DMF 15G Resistant Strains GO Cellular Component Table 3.8: 2,3-DMF 15G Resistant Strains MIPS Functional Classification Table 3.9: 2,3-DMF 5G Sensitive Strains MIPS Functional Classification Table 3.10: 2,3-DMF 5G Sensitive Strains GO Biological Process Table 3.11: 2,3-DMF 10G Sensitive Strains GO Biological Process Table 3.12: 2,3-DMF 15G Sensitive Strains GO Biological Process Table 3.13: 2,3-DMF Sensitive Strains Across All Time Points GO Biological Process Table 3.14: 2,3-DMF Sensitive Strains Across All Time Points GO Cellular Component Table 3.15: 2-MF 5G Resistant Strains GO Biological Process Table 3.16: 2-MF 5G Resistant Strains GO Cellular Component Table 3.17: 2-MF 5G Resistant Strains MIPS Functional Classification Table 3.18: 2-MF 10G Resistant GO Biological Process Table 3.19: 2-MF 10G Resistant GO Cellular Component Table 3.20: 2-MF 10G Resistant MIPS Functional Classification Table 3.21: 2-MF 15G Resistant GO Biological Process Table 3.22: 2-MF 15G Resistant GO Cellular Component Table 3.23: 2-MF 15G Resistant MIPS Functional Classification Table 3.24: 2-MF Resistant Across All Time Points GO Biological Process Table 3.25: 2-MF Resistant Across All Time Points GO Cellular Component Table 3.26: 2-MF Resistant across all time points MIPS Functional Classification Table 3.27: 2-MF 5G Sensitive Strains GO Biological Process Table 3.28: 2-MF 5G Sensitive Strains GO Cellular Component Table 3.29: 2-MF 5G Sensitive Strains MIPS Functional Classification Table 3.30: 2-MF 10G Sensitive Strains GO Biological Process Table 3.31: 2-MF 10G Sensitive Strains GO Cellular Component Table 3.32: 2-MF 10G Sensitive Strains GO MIPS Functional Classification Table 3.33: 2-MF 15G Sensitive GO Biological Process Table 3.34: 2-MF 15G Sensitive GO Cellular Component Table 3.35: 2-MF 15G Sensitive MIPS Functional Classification 3 Table 3.36; 2-MF Sensitive across all time points GO Biological Process Table 3.37: 2-MF Sensitive across all time points GO Cellular Component Table 3.38: 2-MF Sensitive across all time points MIPS Functional Classification Table 3.39: 2-EF 5G Resistant Strains MIPS Functional Classification Table 3.40: 2-EF 5G Sensitive Strains GO Biological Process Table 3.41: 2-EF 5G Sensitive Strains GO Cellular Component Table 3.42: 2-EF 5G Sensitive Strains MIPS Functional Classification Table 3.43: 2-EF 10G Sensitive Strains GO Biological Process Table 3.44: 2-EF 10G Sensitive Strains GO Cellular Component Table 3.45: 2-EF 10G Sensitive Strains MIPS Functional Classification Table 3.46: 2-EF 15G Sensitive Strains GO Biological Process Table 3.47: 2-EF 15G Sensitive Strains GO Cellular Component Table 3.48: 2-EF 15G Sensitive Strains MIPS Functional Classification Table 3.49: 2-EF Sensitive Strains Across All Time Points GO Molecular Function Table 3.50: 2-EF Sensitive Strains Across All Time Points GO Biological Process Table 3.51: 2-EF Sensitive Strains Across All Time Points MIPS Functional Classification Chapter 4 Using CRISPR To Enhance The Metabolic Capacity Of Cell Lines Commonly Used In High Throughput Screening Figure 4.1: CRISPRa Tests in HepG2 Cells Figure 4.2: Guides Test for CYP1A2, CYP2E1, CYP3A4 and CYP2B6 in HEK293T Cells Figure 4.3: Guides Test for UGT1A6 in HEK293T Cells Figure 4.4: HEK293T Cells Transfected With Multiple CYPs Versus CYP3A4 Alone Figure 4.5: Generation of HEK-SAM, a Cell Line Stably Expressing dCas9 and MS2-P65-HSF1 Figure 4.6: Expression of Proteins in the CYP3A4 Regulatory Pathway in the HEK293T Cell Line Figure 4.7: Expression of Proteins in the CYP3A4 Regulatory Pathway in the HEK-SAM Cell Line Figure 4.8: HNF4Ain the CYP3A4 Regulatory Pathway in the HEK293T Cell Line Figure 4.9: DMSO treatment Decreases CYP Expression in CRISPRa-Transfected HEK293T Cells Figure 4.10: CYP Expression in DMSO-Treated CRISPRa-Transfected HEK293T Cells is Much Lower Than In Primary Liver Cells Figure 4.11: AZA Treatment Enchances CYP3A4 expression in HEK293T cells Figure 4.12: Cytotoxicity Testing with Cyclophosphamide and Doxorubican Hydrochloride Figure 4.13: Cytotoxicity Testing with Benzo[a]Pyrene and 2-naphthlamine Figure 4.14: Cytotoxicity Testing with Acrylamide and Aflatoxin B1 Figure 4.15: Cytotoxicity Testing with 8-Methoxypsoran and 6-aminochrysene Figure 4.16: Cytotoxicity Testing with 4-nitrophenol Figure 4.17: CYP3A4 Activation with CRISPRa Increases sensitivity of HEK293T Cells to 8- Methoxypsoran and Aflatoxin B1 Figure 4.18: CYP3A4 Activation with CRISPRa Increases sensitivity of HEK293T Cells to 4- nitrophenol 4 Acknowledgements I cannot properly express my gratitude to all of the patient, generous, and loving people in my life who made this dissertation possible, to do so would necessitate at least the space of another one hundred and eighty or so pages, and even then I’m sure I’d be forgetting someone.
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