DOCSLIB.ORG

Exploring Adomet-Dependent Methyltransferases in Yeast

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Exploring Adomet-Dependent Methyltransferases in Yeast EXPLORING ADOMET-DEPENDENT METHYLTRANSFERASES IN YEAST by Elena Lissina A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Molecular Genetics University of Toronto © Copyright by Elena Lissina, 2013 Exploring AdoMet-Dependent Methyltransferases in Yeast Elena Lissina Doctor of Philosophy Department of Molecular Genetics University of Toronto 2013 Abstract This work presents the investigation of fungal AdoMet-dependent methyltransferases. The first part of the dissertation focuses on two distinct methyltransferases with previously unknown functions in the budding yeast Saccharomyces cerevisiae and the human fungal pathogen Candida albicans. To characterize these enzymes I used a combinatorial approach that exploits contemporary high-throughput techniques available in yeast (chemical genetics, expression, lipid profiling and genetic interaction analysis) combined with rigorous biological follow-up. First, I showed that S. cerevisiae CRG1 (ScCRG1) is a small molecule methyltransferase that methylates cytotoxic drug cantharidin and is important for maintaining lipid homeostasis and actin cytoskeleton integrity in response to small-molecule cantharidin in the baker’s yeast. Similarly to ScCRG1, orf19.633 in the human fungal pathogen C. albicans (CaCRG1) methylates cantharidin and is important for GlcCer biosynthesis. I also demonstrated that CaCrg1 is a ceramide- and PIP-binding methyltransferase involved in Candida’s morphogenesis, membrane trafficking and fungal virulence. Together, the analysis of two genes in yeast illuminated the important roles of the novel small molecule methyltransferases in ii coupling drug response to lipid biosynthesis and fungal virulence. In the second part of my dissertation, I present the systematic characterization of the genetic architecture of the yeast methyltransferome by examining fitness of double-deletion methyltransferase mutants in standard and under environmental stress conditions. This analysis allowed me to describe specific properties of the methyltransferome network and to uncover functional relationships among methyltransferases inspiring multiple hypotheses and expanding the current knowledge of this family of enzymes. iii Acknowledgements This dissertation is the result of six years commitment to science and would not have been possible without the inspiration and support of my supervisors, colleagues, friends, and my family. First and foremost, I am truly grateful to my supervisors Corey Nislow and Guri Giaever for giving me the opportunity to satisfy my thirst for knowledge and for providing an incredible work environment that allowed me to grow as a scientist. Thank you for your never-ending enthusiasm, keeping your office door always open and believing in me! I am also grateful to the members of my thesis advisory committee Brent Derry and Brenda Andrews for their expert guidance over the years. I thank all members of HIPHOP lab (Andrew Smith, Kyle Tsui, Kahlin Cheung-Ong, Anthony Arnoldo, Elke Ericson, Zhun Yan, Ian Wallace, Kevin Song, Anna Lee, Nikko Torres, Ron Amar, Tanvi Shekhar, Simon Alfred, Anu Surendra, and Larry Heisler) for creating a perfect research environment to work in. Thanks to Malene Urbanus for her insightful discussions and help in the early years of my graduate career. Special thanks to Marinella Gebbia who gave me incredible technical support I needed to carry out my research and made my life so much easier and fun in many other aspects. I am also grateful to my friends-colleagues Anastasia Baryshnikova for her irreplaceable company throughout PhD years and to Marina Gorelik for being a reliable climbing partner. Finally, I am forever indebted to my mom, brother and dad for their understanding and unconditional love. Above all, I thank the Universe for allowing me to participate in this incredible learning adventure – science. iv Table of Contents Acknowledgements................................................................................................................iv Table of Contents..................................................................................................................... v List of Figures........................................................................................................................ viii List of Tables ............................................................................................................................ix List of Abbreviations .............................................................................................................. x List of Appendices ................................................................................................................. xv Chapter 1 Introduction.......................................................................................................... 1 1 Introduction ....................................................................................................................... 2 1.1 Introduction to AdoMet-dependent methyltransferases..........................................2 1.1.1 Mechanism of the methylation reaction.................................................................................3 1.1.2 Structural diversity of methyltransferases...........................................................................4 1.1.3 Substrate promiscuity....................................................................................................................5 1.1.4 Brief overview of biological functions ....................................................................................6 1.2 Importance of AdoMet-dependent methyltransferases............................................7 1.2.1 Disease associations .......................................................................................................................7 1.2.2 Drug interactions .............................................................................................................................8 1.2.3 Methylation of exogenous small molecules..........................................................................9 1.2.3.1 Arsenic methyltransferase (AS3MT)............................................................................................... 9 1.2.3.2 Thiopurine S-methyltransferase (TPMT) ...................................................................................10 1.2.3.3 Glycine N-methyltransferase (GNMT)..........................................................................................11 1.2.4 Methylation of endogenous small molecules....................................................................13 1.2.4.1 Catechol O-methyltransferase (COMT)........................................................................................14 1.2.4.2 Yeast Trans-Aconitate Methyltransferase (TMT1) .................................................................15 1.3 Non-canonical roles of methyltransferases................................................................ 16 1.4 Methods to explore methyltransferases...................................................................... 18 1.4.1 In silico computational approaches.......................................................................................18 1.4.2 In vitro approaches.......................................................................................................................19 1.4.2.1 Methylation activity assays...............................................................................................................20 1.4.2.2 Biochemical identification of methylated substrates ............................................................20 1.4.2.3 Peptide and protein microarrays ...................................................................................................21 1.4.3 In vivo functional genomic methods .....................................................................................23 1.4.4 Chemical genetics..........................................................................................................................25 1.5 Thesis rationale ................................................................................................................... 27 Chapter 2 Characterizing a putative Saccharomyces cerevisiae methyltransferase in response to chemical stress.....................................................29 2 Characterizing a putative Saccharomyces cerevisiae methyltransferase in response to chemical stress...............................................................................................30 2.1 Introduction .......................................................................................................................... 30 2.2 Results..................................................................................................................................... 32 2.2.1 CRG1 is required for resistance to cantharidin ................................................................32 2.2.2 A functional Crg1 methyltransferase domain is required for resistance to cantharidin ......................................................................................................................................................34 v 2.2.3 Analysis of CRG1 transcript in response to cantharidin...............................................36 2.2.4 Analysis of yeast transcriptome upon exposure to cantharidin...............................38 2.2.5 CRG1 is a sequence homologue of small molecule methyltransferase
Load more

Recommended publications
  • Molecular Mechanisms Underlying Noncoding Risk Variations in Psychiatric Genetic Studies
    OPEN Molecular Psychiatry (2017) 22, 497–511 www.nature.com/mp REVIEW Molecular mechanisms underlying noncoding risk variations in psychiatric genetic studies X Xiao1,2, H Chang1,2 and M Li1 Recent large-scale genetic approaches such as genome-wide association studies have allowed the identification of common genetic variations that contribute to risk architectures of psychiatric disorders. However, most of these susceptibility variants are located in noncoding genomic regions that usually span multiple genes. As a result, pinpointing the precise variant(s) and biological mechanisms accounting for the risk remains challenging. By reviewing recent progresses in genetics, functional genomics and neurobiology of psychiatric disorders, as well as gene expression analyses of brain tissues, here we propose a roadmap to characterize the roles of noncoding risk loci in the pathogenesis of psychiatric illnesses (that is, identifying the underlying molecular mechanisms explaining the genetic risk conferred by those genomic loci, and recognizing putative functional causative variants). This roadmap involves integration of transcriptomic data, epidemiological and bioinformatic methods, as well as in vitro and in vivo experimental approaches. These tools will promote the translation of genetic discoveries to physiological mechanisms, and ultimately guide the development of preventive, therapeutic and prognostic measures for psychiatric disorders. Molecular Psychiatry (2017) 22, 497–511; doi:10.1038/mp.2016.241; published online 3 January 2017 RECENT GENETIC ANALYSES OF NEUROPSYCHIATRIC neurodevelopment and brain function. For example, GRM3, DISORDERS GRIN2A, SRR and GRIA1 were known to involve in the neuro- Schizophrenia, bipolar disorder, major depressive disorder and transmission mediated by glutamate signaling and synaptic autism are highly prevalent complex neuropsychiatric diseases plasticity.
    [Show full text]
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
    [Show full text]
  • +3 Oxidation State) Methyltransferase (AS3MT
    Environmental and Molecular Mutagenesis 58:411^422 (2017) Research Article Associations between Arsenic (13OxidationState) Methyltransferase (AS3MT) and N-6 Adenine-specific DNA Methyltransferase1 (N6AMT1) Polymorphisms, Arsenic Metabolism, and Cancer Risk in a Chilean Population Rosemarie de la Rosa,1 Craig Steinmaus,1 Nicholas K. Akers,1 Lucia Conde,1 Catterina Ferreccio,2 David Kalman,3 Kevin R. Zhang,1 Christine F.Skibola,1 Allan H. Smith,1 Luoping Zhang,1 and Martyn T.Smith1* 1Superfund Research Program, Divisions of Environmental Health Sciences and Epidemiology, School of Public Health, University of California, Berkeley, California 2Departamento de Salud Publica, Facultad de Medicina, Pontificia Universidad Catolica de Chile, Advanced Center for Chronic Diseases, ACCDiS, Santiago, Chile 3Department of Environmental & Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, DC Inter-individual differences in arsenic metabolism have gene. We found several AS3MT polymorphisms asso- been linked to arsenic-related disease risks. Arsenic ciated with both urinary arsenic metabolite profiles (13) methyltransferase (AS3MT) is the primary and cancer risk. For example, compared to wildtypes, enzyme involved in arsenic metabolism, and we previ- individuals carrying minor alleles in AS3MT ously demonstrated in vitro that N-6 adenine-specific rs3740393 had lower %MMA (mean differ- DNA methyltransferase 1 (N6AMT1) also methylates ence 521.9%, 95% CI: 23.3, 20.4), higher the toxic inorganic arsenic (iAs) metabolite, monome- %DMA (mean difference 5 4.0%, 95% CI: 1.5, 6.5), thylarsonous acid (MMA), to the less toxic dimethylar- and lower odds ratios for bladder (OR 5 0.3; 95% sonic acid (DMA). Here, we evaluated whether CI: 0.1–0.6) and lung cancer (OR 5 0.6; 95% CI: AS3MT and N6AMT1 gene polymorphisms alter 0.2–1.1).
    [Show full text]
  • Arsenic-Associated Diabetes: Mechanisms and the Role of Arsenic Metabolism
    ARSENIC-ASSOCIATED DIABETES: MECHANISMS AND THE ROLE OF ARSENIC METABOLISM Madelyn C. Huang A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum of Toxicology within the School of Medicine. Chapel Hill 2018 Approved by: Miroslav Stýblo Rebecca Fry Ilona Jaspers Eric Klett Zuzana Drobná © 2018 Madelyn C. Huang ALL RIGHTS RESERVED ii ABSTRACT Madelyn C. Huang: Arsenic-associated diabetes: mechanisms and the role of arsenic metabolism (Under the direction of Miroslav Stýblo) Inorganic arsenic (iAs) is a ubiquitous drinking water and food contaminant. Chronic exposure to iAs has been linked to diabetes mellitus in many populations. Experimental studies support these associations and suggest mechanisms, but the precise pathogenesis of iAs- associated diabetes is unclear. Efficient metabolism of iAs through methylation, catalyzed by arsenic (+3) methyltransferase (AS3MT), influences the risk of iAs-associated disease. Additionally, higher intake of methyl donor nutrients such as folate and methylcobalamin (B12) has been linked to increased efficiency of iAs metabolism and lowered disease risk. However, while metabolism of iAs facilitates its excretion from the body, iAs metabolites are more toxic than iAs itself. The role of iAs metabolism in iAs toxicity is understudied, especially in cardiometabolic disease. Understanding how iAs metabolism affects iAs-associated diabetes will help design appropriate intervention strategies and improve risk assessment. This project investigated how iAs metabolism influences iAs-associated diabetes. First, we compared metabolite profiles of C57BL6 (WT) with As3mt-KO (KO) mice, which cannot methylate iAs, with and without exposure to iAs.
    [Show full text]
  • Supp Table 6.Pdf
    Supplementary Table 6. Processes associated to the 2037 SCL candidate target genes ID Symbol Entrez Gene Name Process NM_178114 AMIGO2 adhesion molecule with Ig-like domain 2 adhesion NM_033474 ARVCF armadillo repeat gene deletes in velocardiofacial syndrome adhesion NM_027060 BTBD9 BTB (POZ) domain containing 9 adhesion NM_001039149 CD226 CD226 molecule adhesion NM_010581 CD47 CD47 molecule adhesion NM_023370 CDH23 cadherin-like 23 adhesion NM_207298 CERCAM cerebral endothelial cell adhesion molecule adhesion NM_021719 CLDN15 claudin 15 adhesion NM_009902 CLDN3 claudin 3 adhesion NM_008779 CNTN3 contactin 3 (plasmacytoma associated) adhesion NM_015734 COL5A1 collagen, type V, alpha 1 adhesion NM_007803 CTTN cortactin adhesion NM_009142 CX3CL1 chemokine (C-X3-C motif) ligand 1 adhesion NM_031174 DSCAM Down syndrome cell adhesion molecule adhesion NM_145158 EMILIN2 elastin microfibril interfacer 2 adhesion NM_001081286 FAT1 FAT tumor suppressor homolog 1 (Drosophila) adhesion NM_001080814 FAT3 FAT tumor suppressor homolog 3 (Drosophila) adhesion NM_153795 FERMT3 fermitin family homolog 3 (Drosophila) adhesion NM_010494 ICAM2 intercellular adhesion molecule 2 adhesion NM_023892 ICAM4 (includes EG:3386) intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)adhesion NM_001001979 MEGF10 multiple EGF-like-domains 10 adhesion NM_172522 MEGF11 multiple EGF-like-domains 11 adhesion NM_010739 MUC13 mucin 13, cell surface associated adhesion NM_013610 NINJ1 ninjurin 1 adhesion NM_016718 NINJ2 ninjurin 2 adhesion NM_172932 NLGN3 neuroligin
    [Show full text]
  • Methyltransferase (AS3MT) and N-6 Adenine-Specific DNA Methyltransferase 1 (N6AMT1) Polymorphisms, Arsenic Metabolism, and Cancer Risk in a Chilean Population
    HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Environ Manuscript Author Mol Mutagen. Author Manuscript Author manuscript; available in PMC 2018 July 01. Published in final edited form as: Environ Mol Mutagen. 2017 July ; 58(6): 411–422. doi:10.1002/em.22104. Associations between arsenic (+3 oxidation state) methyltransferase (AS3MT) and N-6 adenine-specific DNA methyltransferase 1 (N6AMT1) polymorphisms, arsenic metabolism, and cancer risk in a Chilean population Rosemarie de la Rosa1, Craig Steinmaus1, Nicholas K Akers1, Lucia Conde1, Catterina Ferreccio2, David Kalman3, Kevin R Zhang1, Christine F Skibola1, Allan H Smith1, Luoping Zhang1, and Martyn T Smith1 1Superfund Research Program, School of Public Health, University of California, Berkeley, CA 2Pontificia Universidad Católica de Chile, Santiago, Chile, Advanced Center for Chronic Diseases, ACCDiS 3School of Public Health, University of Washington, Seattle, WA Abstract Inter-individual differences in arsenic metabolism have been linked to arsenic-related disease risks. Arsenic (+3) methyltransferase (AS3MT) is the primary enzyme involved in arsenic metabolism, and we previously demonstrated in vitro that N-6 adenine-specific DNA methyltransferase 1 (N6AMT1) also methylates the toxic iAs metabolite, monomethylarsonous acid (MMA), to the less toxic dimethylarsonic acid (DMA). Here, we evaluated whether AS3MT and N6AMT1 gene polymorphisms alter arsenic methylation and impact iAs-related cancer risks. We assessed AS3MT and N6AMT1 polymorphisms and urinary arsenic metabolites (%iAs, %MMA, %DMA) in 722 subjects from an arsenic-cancer case-control study in a uniquely exposed area in northern Chile. Polymorphisms were genotyped using a custom designed multiplex, ligation-dependent probe amplification (MLPA) assay for 6 AS3MT SNPs and 14 tag SNPs in the N6AMT1 gene.
    [Show full text]
  • The Human Protein Methyltransferases
    The human protein methyltransferases Methyltransferases are enzymes that facilitate the transfer of a methyl (–CH3) group to of methyl ‘marks’ as regulators of gene expression. Human protein methyltransferases specific nucleophilic sites on proteins, nucleic acids or other biomolecules. They share (PMTs) fall into two major families—protein lysine methyltransferases (PKMTs) and a reaction mechanism in which the nucleophilic acceptor site attacks the electrophilic protein arginine methyltransferases (PRMTs)—that are distinguishable by the amino carbon of S-adenosyl-L-methionine (SAM) in an SN2 displacement reaction that pro- acid that accepts the methyl group and by the conserved sequences of their respective duces a methylated biomolecule and S-adenosyl-L-homocysteine (SAH) as a byprod- catalytic domains. Given their involvement in many cellular processes, PMTs have at- Supplement to Nature Publishing Group Journals uct. Methylation reactions are essential transformations in small-molecule metabolism, tracted attention as potential drug targets, spurring the search for small-molecule PMT and methylation is a common modification of DNA and RNA. The recent discovery of inhibitors. Several classes of inhibitors have been identified, but new specific chemi- dynamic and reversible methylation of amino acid side chains of chromatin proteins, cal probes that are active in cells will be required to elucidate the biological roles of particularly within the N-terminal tail of histone proteins, has revealed the importance PMTs and serve as potent leads for PMT-focused drug development. Protein arginine methyltransferases (PRMTs) Protein lysine methyltransferases (PKMTs) H CH3 CH3 H3C H C CH3 H The phylogenetic tree shows 51 genes predicted to encode PKMTs, which are positioned H H H H H 3 The human PRMT phylogenetic tree comprises 45 predicted enzymes including the PKMT H N H N H N H H3C N H H3C N H 1 N H N CH3 N CH3 CH3 1 N in the tree on the basis of the similarities of their amino acid sequences .
    [Show full text]
  • Sex Differences in Hepatic One-Carbon Metabolism Farrah Sadre-Marandi1, Thabat Dahdoul2, Michael C
    Sadre-Marandi et al. BMC Systems Biology (2018) 12:89 https://doi.org/10.1186/s12918-018-0621-7 RESEARCH ARTICLE Open Access Sex differences in hepatic one-carbon metabolism Farrah Sadre-Marandi1, Thabat Dahdoul2, Michael C. Reed3* and H. Frederik Nijhout4 Abstract Background: There are large differences between men and women of child-bearing age in the expression level of 5 key enzymes in one-carbon metabolism almost certainly caused by the sex hormones. These male-female differences in one-carbon metabolism are greatly accentuated during pregnancy. Thus, understanding the origin and consequences of sex differences in one-carbon metabolism is important for precision medicine. Results: We have created a mathematical model of hepatic one-carbon metabolism based on the underlying physiology and biochemistry. We use the model to investigate the consequences of sex differences in gene expression. We give a mechanistic understanding of observed concentration differences in one-carbon metabolism and explain why women have lower S-andenosylmethionine, lower homocysteine, and higher choline and betaine. We give a new explanation of the well known phenomenon that folate supplementation lowers homocysteine and we show how to use the model to investigate the effects of vitamin deficiencies, gene polymorphisms, and nutrient input changes. Conclusions: Our model of hepatic one-carbon metabolism is a useful platform for investigating the mechanistic reasons that underlie known associations between metabolites. In particular, we explain how gene expression differences lead to metabolic differences between males and females. Keywords: One-carbon metabolism, Mathematical model, Sex differences, Regulation Background within loops (see Fig. 1 below). Furthermore, many sub- There are significant sex differences in one-carbon strates in the network influence, through allosteric bind- metabolism (OCM) and these differences are ing, the activity level of enzymes at distant locations in accentuated in pregnancy [1–3].
    [Show full text]
  • Urinary Arsenic Methylation Species, Arsenic Methylation Efficiency During Pregnancy and Birth Outcomes
    Urinary Arsenic Methylation Species, Arsenic Methylation Efficiency during Pregnancy and Birth Outcomes The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:37945565 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Urinary Arsenic Methylation Species, Arsenic Methylation Efficiency during Pregnancy and Birth Outcomes Shangzhi Gao A Dissertation Submitted to the Faculty of The Harvard T.H. Chan School of Public Health in Partial Fulfillment of the Requirements for the Degree of Doctor of Science in the Department of Environmental Health Harvard University Boston, Massachusetts. May, 2018 Dissertation Advisor: Dr. David C. Christiani Shangzhi Gao Urinary Arsenic Methylation Species, Arsenic Methylation Efficiency during Pregnancy and Birth Outcomes Abstract Arsenic exposure from drinking water has been associated with symptoms during pregnancy, as well as negative birth outcomes. The individual’s capability to methylate arsenic modifies the risk of arsenic-related health outcomes. Bangladesh is facing the world’s most severe arsenic crisis, as well as a high prevalence of low birth weight. However, little is known about determinants of arsenic metabolism during pregnancy and its relationship to birth outcomes. Thus, we conducted this study in Bangladesh to identify determinants of arsenic methylation capability during pregnancy, and its relationship with birth outcomes. The study was based on a Bangladesh prospective reproductive cohort (Project Jeebon) of 1,613 pregnant women recruited between 2008 and 2011.
    [Show full text]
  • Summary of Literature and Clinical Impact May 2019
    (Formally known as the Genecept Assay®) PERSONAL. PROVEN. PRECISE. Summary of Literature and Clinical Impact May 2019 The following is a summary of the key published literature relevant to a variety of genetic variations. The purpose of this document is to summarize the information available. Prescribing health care professionals must use their independent medical judgment and are solely responsible for determining the most appropriate medication for their patients. The clinican must consider other relevant clinical factors in determining which is the most appropriate medication. The understanding of the relationship between genetics, pharmacokinetics and pharmacodynamics changes periodically. The information in this summary is current at the time of publication. CONTACT US: 877-895-8658 [email protected] 2200 Renaissance Boulevard, Suite 100 | King of Prussia, PA 19406 | www.genomind.com Introduction Genomind, a unique personalized medicine platform that brings innovation to healthcare around the world, is pleased to present this summary of the genes behind Genomind Professional PGx® (formerly The Genecept Assay), a genetic test that analyzes both pharmacokinetic and pharmacodynamic genes. The current test includes the analysis of 15 pharmacodynamic genes and 9 pharmacokinetic genes. Genomind Professional PGx® is used to assist clinical decision-making when prescribing medication for psychiatric conditions. It is a simple, non-invasive buccal test (cheek swab) that can be administered quickly in a clinician’s office. The comprehensive results report provides clear data that can inform clinical decisions. A complimentary consultation with experts in the field of psychopharmacogenetics is available to the clinician along with each patient report. Background on Genomind Professional PGx Psychiatric practice is uniquely challenging because of the variability in treatment response.
    [Show full text]
  • BY Aaron J. Gottschalk
    BIOCHEMICAL AND DEVELOPMENTAL CHARACTERIZATION OF A SNF2-LIKE ATPASE AMPLIFIED IN LIVER CANCER 1 (ALC1) BY Aaron J. Gottschalk Submitted to the graduate degree program in Biochemistry and Molecular Biology and the Graduate faculty of the University of Kansas Medical Center in partial fulfillment for the degree of Doctor of Philosophy. _______________________ Chairperson Committee members* _______________________* _______________________* _______________________* _______________________* Date defended: ________________ The Dissertation Committee for Aaron J. Gottschalk certifies that this is the approved version of the following dissertation: BIOCHEMICAL AND DEVELOPMENTAL CHARACTERIZATION OF A SNF2-LIKE ATPASE AMPLIFIED IN LIVER CANCER 1 (ALC1) Committee: _______________________ Chairperson* _______________________ _______________________ _______________________ _______________________ Date defended: ________________ 2 ACKNOWLEDGEMENTS First and foremost, I would like to express my deepest thanks and gratitude to my mentors Joan and Ron Conaway. During my tenure in their lab as a graduate student I have been afforded an amazing opportunity to learn and grow as a scientist while independently developing this project. It is their extreme kindness, steadfast encouragement, guidance, and humor that have made even the toughest of problems both scientific and in life seem very surmountable, and for that I thank them profoundly. Next, I would like to thank my committee members Drs. Glen Andrews, Jennifer Gerton, Leslie Heckert, and Ken Peterson for all their useful advice and suggestions throughout the course of my graduate career. Not only did my committee members provide great scientific direction but they also made bi-annual meetings fun and engaging. I would like to thank all of the members of the Conaway Lab including: Charles Banks, Margaret Banks, Yong Cai, Jingji Jin, Stephanie Kong, Nawel Mahrour, Ana Pedraza, Chieri Sato, Shigeo Sato, Henrietta Szutorisz, Hide Takahashi, Tingting Yao, Dotan Sela, Lu Chen, and Merry McClaird.
    [Show full text]
  • (12) Patent Application Publication (10) Pub. No.: US 2016/0186168 A1 Konieczka Et Al
    US 2016O1861 68A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2016/0186168 A1 Konieczka et al. (43) Pub. Date: Jun. 30, 2016 (54) PROCESSES AND HOST CELLS FOR Related U.S. Application Data GENOME, PATHWAY. AND BIOMOLECULAR (60) Provisional application No. 61/938,933, filed on Feb. ENGINEERING 12, 2014, provisional application No. 61/935,265, - - - filed on Feb. 3, 2014, provisional application No. (71) Applicant: ENEVOLV, INC., Cambridge, MA (US) 61/883,131, filed on Sep. 26, 2013, provisional appli (72) Inventors: Jay H. Konieczka, Cambridge, MA cation No. 61/861,805, filed on Aug. 2, 2013. (US); James E. Spoonamore, Publication Classification Cambridge, MA (US); Ilan N. Wapinski, Cambridge, MA (US); (51) Int. Cl. Farren J. Isaacs, Cambridge, MA (US); CI2N 5/10 (2006.01) Gregory B. Foley, Cambridge, MA (US) CI2N 15/70 (2006.01) CI2N 5/8 (2006.01) (21) Appl. No.: 14/909, 184 (52) U.S. Cl. 1-1. CPC ............ CI2N 15/1082 (2013.01); C12N 15/81 (22) PCT Filed: Aug. 4, 2014 (2013.01); C12N 15/70 (2013.01) (86). PCT No.: PCT/US1.4/49649 (57) ABSTRACT S371 (c)(1), The present disclosure provides compositions and methods (2) Date: Feb. 1, 2016 for genomic engineering. Patent Application Publication Jun. 30, 2016 Sheet 1 of 4 US 2016/O186168 A1 Patent Application Publication Jun. 30, 2016 Sheet 2 of 4 US 2016/O186168 A1 &&&&3&&3&&**??*,º**)..,.: ××××××××××××××××××××-************************** Patent Application Publication Jun. 30, 2016 Sheet 3 of 4 US 2016/O186168 A1 No.vaegwzºkgwaewaeg Patent Application Publication Jun. 30, 2016 Sheet 4 of 4 US 2016/O186168 A1 US 2016/01 86168 A1 Jun.
    [Show full text]