DOCSLIB.ORG

24651 Story.Indd

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
24651 Story.Indd Aetiology of Depression: Insights from epidemiological and genetic research Olivera Story-Jovanova Acknowledgments: Financial support for the publication of this thesis by the Department of Epidemiology of the Erasmus MC, is gratefully acknowledged. ISBN: 978-94-6233-909-5 Cover: Concept by Olivera Story-Jovanova, design by Chris van Wolferen-Ketel, photography by Michal Macku. Layout: Gildeprint, Enschede. Printing: Gildeprint, Enschede. © Olivera Story-Jovanova, 2018 For all articles published, the copyright has been transferred to the respective publisher. No part of this thesis may be stored in a retrieval system, or transmitted in any form or by any means, without written permission from the author or, when appropriate, from the publisher. Aetiology of Depression: Insights from epidemiological and genetic research Etiologie van depressie: Inzichten vanuit de epidemiologisch en de genetisch onderzoek Proefschrift ter verkrijging van de graad van doctor aan de Erasmus Universiteit Rotterdam op gezag van de rector magnificus Prof. dr. H.A.P. Pols en volgens besluit van het College voor Promoties. De openbare verdediging zal plaatsvinden op 4 April 2018 om 15:30 uur door Olivera Story-Jovanova geboren te Skopje, Macedonia PROMOTIECOMMISSIE Promotor Prof. dr. H. Tiemeier Overige leden Prof. D. Boomsma Prof. K. Berger Prof. C. van Duijn Copromotor Dr. N. Amin Paranimfen Ayesha Sajjad Dina Atlas For my husband, children, sister, parents, grandparents, my parents in law and all you who believed in me. You are a gift of unconditional love, acceptance, joy and wisdom. I am thankful for that! ACKNOWLEDGEMENTS The research described in this thesis was performed within the frame work of the Rotterdam Study. The contribution of participants, the staff from the Rotterdam Study and the participating general practitioners, and pharmacists is gratefully acknowledged. The Rotterdam Study is supported by the Erasmus Medical Center and the Erasmus University, Rotterdam, the Netherlands; the Organization for the Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Ministry of Education, Culture and Science, the Ministry for Health, Welfare, and Sports; the European Commission (DG XII); and the Municipality Rotterdam. The research was supported by a Netherlands Organization for Scientific Research grant (NOW- ZonMw VIDI grant no. 017.106.370) awarded to prof. H. Tiemeier and by the European Commission – ERAWEB Grant (grant no. 2011-2586/001-001-EMA2) awarded to O. Jovanova, MD. CONTENTS Chapter 1 General introduction 9 Chapter 2 Biomarkers for depression 21 Chapter 2.1 Vitamin D serum levels and depression in the elderly 23 Chapter 2.2 Inflammatory markers and depression in the elderly 41 Chapter 3 The genetics of depression 63 Chapter 3.1 A rare Asn396Ser variant in the LIPG gene associated with depressive symptoms 65 Chapter 3.2 Nonsynonymous variation in NKPD1 increases depressive symptoms 85 Chapter 4 The epigenetics of depression 121 Chapter 4.1 DNA-methylation signatures and depressive symptoms 123 Chapter 5 Physical health factors for depression 203 Chapter 5.1 Multiple episodes of late-life depression and cognitive decline 205 Chapter 5.2 Myocardial infarction and the long-term risk for depression 223 Chapter 6 General discussion 255 Chapter 7 Summary 281 Chapter 8 Addendum 287 Chapter 8.1 PhD Portfolio 289 Chapter 8.2 Publications and manuscripts 293 Chapter 8.3 Word of thanks 297 Chapter 8.4 About the author 305 MANUSCRIPTS UPON WHICH THIS THESIS IS BASED Chapter 2.1: Jovanova O, Aarts N, Noordam R, Carola-Zillekens M, Hofman A, Tiemeier H. Vitamin D serum levels are cross-sectionally but not prospectively associated with late-life depression. ACTA Psychiatrica Scandinavica. 2017 Mar; 135(3):185-194. Chapter 2.2: Zalli A, Jovanova O, Hoogendijk WJ, Tiemeier H, Carvalho LA. Low-grade inflammation predicts persistence of depressive symptoms. Psychopharmacology. 2016 May; 233(9):1669-78. Chapter 3.1: Amin N, Jovanova O, Adams HH, Dehghan A, Kavousi M, Vernooji MW, Peeters RP, de Vrij FM, van der Lee SJ, van Rooij JG, van Leeuwen EM, Chaker L, Demirkan A, Hofman A, Brouwer RW, Kraaij R, Willems van Dijk K, Hankemeier T, van Ijcken WF, Uitterlinden AG, Niessen WJ, Franco OH, Kushner SA, Ikram MA, Tiemeier H, van Duijn CM. Exome- sequencing in a large population-based study reveals a rare Asn396Ser variant in the LIPG gene associated with depressive symptoms. Molecular Psychiatry. 2017 Apr; 22(4):537- 543. Chapter 3.2: Amin N, Belonogova NM, Jovanova O, Brouwer RW, van Rooij JG, van den Hout MC, Svishcheva GR, Kraaij R, Zorkoltseva IV, Kirichenko AV, Hofman A, Uitterlinden AG, van IJcken WF, Tiemeier H, Axenovich TI, van Duijn CM. Nonsynonymous Variation in NKPD1 Increases Depressive Symptoms in the European Populations. Biological Psychiatry. 2017 Apr; 81(8):702-707. Chapter 4.1: Jovanova O, Nedeljkovic I, Lemaitre R, Brody J, Swenson B, Liu Ch, Hongsheng, Lahti J, Kunze S, Kuhnel B, Luciano M, Deary IJ, Marioni R, Walker RM, Evans KL, Wang Z, Gondalia R, Levy D, Seshadri S, Karl-Heintz L, Waldenberg M, McRae AF, Starr JM, Wray N, Bressler J, Mosley TH, Guo X, Sotoodehnia N, Mendelson MM, Peters A, Russ TC, McIntosh AM, Porteous DJ, Fornage M, Whitsel AE, Tiemeier H, Amin N. DNA methylation signatures of depressive symptoms identified in a large multi-ethnic meta-analysis of epigenome-wide studies. Under review. Chapter 5.1: Jovanova O, Wolters FJ, Ikram MA, Tiemeier H, Schmitz N. DNA methylation signatures of depressive symptoms identified in a large multi-ethnic meta-analysis of epigenome-wide studies. Under review. Chapter 5.2: Jovanova O, Luik AI, Leening MJG, Noordam R, Aarts N, Hofman A, Franco OH, Dehghan A, Tiemeier H. The long-term risk of recognized and unrecognized myocardial infarction for depression in older men. Psychological Medicine. 2016 Mar; 46(9):1951-60. CHAPTER 1 General Introduction INTRODUCTION Have you ever felt depressed? The answer to this question is usually “YES” and this is not unreasonable since most of us have felt depressed once or more in our life-time. Typically, “feeling depressed” or “feeling BLUE” is not more than the well-accepted low mood or just being temporarily unhappy. On the contrary, clinical depression is a serious neuropsychiatric mood disorder that has a leading role in the global burden of diseases causing much of the disability world-wide.1,2 Individuals suffering from depression have feelings of guilt, helplessness, hopelessness, are occupationally impaired, often have desire for social withdrawal, suffer from sleep and concentration disturbances, have a loss of interest in life-pleasures and almost all other activities, and suffer from a loss of appetite and sex drive.3 This extensive list of depressive symptoms can also include daily experience of suicidal thoughts that can result in a suicidal act.3 As a matter of fact, depression is a complete contrast to the beauty of the colour BLUE; a colour that awakens feelings of tranquillity, stability and inspiration (Yves Klein dedicated a life-time of work to the colour blue: “Blue Revolution”). ®“Blue Monochrome (anthropometry)” - Yves Klein 1928-1962 R1 In the last 15 years a number of successful campaigns took place around the world to raise the R2 awareness for depression.4,5 One of the main goals of such campaigns was to reduce the stigma R3 of depression and to clarify the differences between being sad and suffering from depressive R4 disorders. These goals were mostly achieved and the knowledge over depressive illness among R5 the public has increased. More persons have some understanding of the physical and mental R6 exhaustion caused by this medical illness. However, the reality is: “We still do not completely R7 understand all aspects of depression”! Even though depression may not be an enigma anymore, R8 there are many core questions asked by lay people, medical doctors, and academic experts that R9 remain unclear. One such question is “What actually causes depression?”! Briefly, this thesis is an R10 attempt to unravel some aspects of the aetiology of depression. R11 R12 IS DEPRESSION the DIABETES OF the BRAIN or a DEMONIC POSSESSION? A brief historical R13 aspect R14 R15 I would first like to quote the famous Chinese philosopher Confucius who said “Study the past R16 if you would define the future”. Indeed, if we try to obtain new insights into the aetiology of R17 depression and draw conclusions on this illness; and finally define some future perspectives for a R18 better understanding of depression, we should certainly understand the history of this disorder. R19 Throughout history, depression transformed from the old concept of melancholia to the current R20 concept of depression which is mainly viewed as a multifactorial behavioural mental disorder. This R21 transition did not occur all of a sudden, but throughout the centuries, many philosophers and R22 scientists struggled explaining depression while facing a lot of controversies. This explains why R23 studying depression and its aetiology is a great challenge even today.6 R24 R25 It was in the ancient times that the first theories were postulated to define and explain depression: R26 Melancholia, a condition characterized by fear, loss-of appetite, and sleeplessness. The humoral R27 theory proposed by Hippocrates (370-460 B.C), the father of medicine; explained melancholia.7 R28 According to the humoral concept melancholia was a condition caused by disequilibrium between R29 the four humors and more specifically by an increase in the black bile.7 The core of this theory R30 was slightly modified and edited during the centuries to come. It took almost 2,500 years to R31 move from Hippocrates melancholia to Emil Kraepelin’s concept of depression (1856 –1926). He R32 was the first scientist to propose the use of the term depression, but what made it so difficult to R33 progress to defining depression as a disorder?8 R34 R35 In order to define this medical condition as a disorder, depression must be associated with specific R36 symptoms and signs that are caused by external factors and internal dysfunctions.
Load more

Recommended publications
  • Gentaur Products List
    Chapter 2 : Gentaur Products List • Rabbit Anti LAMR1 Polyclonal Antibody Cy5 Conjugated Conjugated • Rabbit Anti Podoplanin gp36 Polyclonal Antibody Cy5 • Rabbit Anti LAMR1 CT Polyclonal Antibody Cy5 • Rabbit Anti phospho NFKB p65 Ser536 Polyclonal Conjugated Conjugated Antibody Cy5 Conjugated • Rabbit Anti CHRNA7 Polyclonal Antibody Cy5 Conjugated • Rat Anti IAA Monoclonal Antibody Cy5 Conjugated • Rabbit Anti EV71 VP1 CT Polyclonal Antibody Cy5 • Rabbit Anti Connexin 40 Polyclonal Antibody Cy5 • Rabbit Anti IAA Indole 3 Acetic Acid Polyclonal Antibody Conjugated Conjugated Cy5 Conjugated • Rabbit Anti LHR CGR Polyclonal Antibody Cy5 Conjugated • Rabbit Anti Integrin beta 7 Polyclonal Antibody Cy5 • Rabbit Anti Natrexone Polyclonal Antibody Cy5 Conjugated • Rabbit Anti MMP 20 Polyclonal Antibody Cy5 Conjugated Conjugated • Rabbit Anti Melamine Polyclonal Antibody Cy5 Conjugated • Rabbit Anti BCHE NT Polyclonal Antibody Cy5 Conjugated • Rabbit Anti NAP1 NAP1L1 Polyclonal Antibody Cy5 • Rabbit Anti Acetyl p53 K382 Polyclonal Antibody Cy5 • Rabbit Anti BCHE CT Polyclonal Antibody Cy5 Conjugated Conjugated Conjugated • Rabbit Anti HPV16 E6 Polyclonal Antibody Cy5 Conjugated • Rabbit Anti CCP Polyclonal Antibody Cy5 Conjugated • Rabbit Anti JAK2 Polyclonal Antibody Cy5 Conjugated • Rabbit Anti HPV18 E6 Polyclonal Antibody Cy5 Conjugated • Rabbit Anti HDC Polyclonal Antibody Cy5 Conjugated • Rabbit Anti Microsporidia protien Polyclonal Antibody Cy5 • Rabbit Anti HPV16 E7 Polyclonal Antibody Cy5 Conjugated • Rabbit Anti Neurocan Polyclonal
    [Show full text]
  • Mutant IDH, (R)-2-Hydroxyglutarate, and Cancer
    Downloaded from genesdev.cshlp.org on October 1, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW What a difference a hydroxyl makes: mutant IDH, (R)-2-hydroxyglutarate, and cancer Julie-Aurore Losman1 and William G. Kaelin Jr.1,2,3 1Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02215, USA; 2Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA Mutations in metabolic enzymes, including isocitrate whether altered cellular metabolism is a cause of cancer dehydrogenase 1 (IDH1) and IDH2, in cancer strongly or merely an adaptive response of cancer cells in the face implicate altered metabolism in tumorigenesis. IDH1 of accelerated cell proliferation is still a topic of some and IDH2 catalyze the interconversion of isocitrate and debate. 2-oxoglutarate (2OG). 2OG is a TCA cycle intermediate The recent identification of cancer-associated muta- and an essential cofactor for many enzymes, including tions in three metabolic enzymes suggests that altered JmjC domain-containing histone demethylases, TET cellular metabolism can indeed be a cause of some 5-methylcytosine hydroxylases, and EglN prolyl-4-hydrox- cancers (Pollard et al. 2003; King et al. 2006; Raimundo ylases. Cancer-associated IDH mutations alter the enzymes et al. 2011). Two of these enzymes, fumarate hydratase such that they reduce 2OG to the structurally similar (FH) and succinate dehydrogenase (SDH), are bone fide metabolite (R)-2-hydroxyglutarate [(R)-2HG]. Here we tumor suppressors, and loss-of-function mutations in FH review what is known about the molecular mechanisms and SDH have been identified in various cancers, in- of transformation by mutant IDH and discuss their im- cluding renal cell carcinomas and paragangliomas.
    [Show full text]
  • Gene PMID WBS Locus ABR 26603386 AASDH 26603386
    Supplementary material J Med Genet Gene PMID WBS Locus ABR 26603386 AASDH 26603386 ABCA1 21304579 ABCA13 26603386 ABCA3 25501393 ABCA7 25501393 ABCC1 25501393 ABCC3 25501393 ABCG1 25501393 ABHD10 21304579 ABHD11 25501393 yes ABHD2 25501393 ABHD5 21304579 ABLIM1 21304579;26603386 ACOT12 25501393 ACSF2,CHAD 26603386 ACSL4 21304579 ACSM3 26603386 ACTA2 25501393 ACTN1 26603386 ACTN3 26603386;25501393;25501393 ACTN4 21304579 ACTR1B 21304579 ACVR2A 21304579 ACY3 19897463 ACYP1 21304579 ADA 25501393 ADAM12 21304579 ADAM19 25501393 ADAM32 26603386 ADAMTS1 25501393 ADAMTS10 25501393 ADAMTS12 26603386 ADAMTS17 26603386 ADAMTS6 21304579 ADAMTS7 25501393 ADAMTSL1 21304579 ADAMTSL4 25501393 ADAMTSL5 25501393 ADCY3 25501393 ADK 21304579 ADRBK2 25501393 AEBP1 25501393 AES 25501393 AFAP1,LOC84740 26603386 AFAP1L2 26603386 AFG3L1 21304579 AGAP1 26603386 AGAP9 21304579 Codina-Sola M, et al. J Med Genet 2019; 56:801–808. doi: 10.1136/jmedgenet-2019-106080 Supplementary material J Med Genet AGBL5 21304579 AGPAT3 19897463;25501393 AGRN 25501393 AGXT2L2 25501393 AHCY 25501393 AHDC1 26603386 AHNAK 26603386 AHRR 26603386 AIDA 25501393 AIFM2 21304579 AIG1 21304579 AIP 21304579 AK5 21304579 AKAP1 25501393 AKAP6 21304579 AKNA 21304579 AKR1E2 26603386 AKR7A2 21304579 AKR7A3 26603386 AKR7L 26603386 AKT3 21304579 ALDH18A1 25501393;25501393 ALDH1A3 21304579 ALDH1B1 21304579 ALDH6A1 21304579 ALDOC 21304579 ALG10B 26603386 ALG13 21304579 ALKBH7 25501393 ALPK2 21304579 AMPH 21304579 ANG 21304579 ANGPTL2,RALGPS1 26603386 ANGPTL6 26603386 ANK2 21304579 ANKMY1 26603386 ANKMY2
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
    [Show full text]
  • The Role of EZH2 in the Induction and Maintenance of Acute Myeloid Leukaemia
    The Role of EZH2 in the Induction and Maintenance of Acute Myeloid Leukaemia Faisal Basheer Downing College University of Cambridge This dissertation is submitted for the degree of Doctor of Philosophy September 2017 Declaration The work presented in this dissertation was carried out under the supervision of Professor Brian Huntly from November 2013 to November 2016 in the Department of Haematology, University of Cambridge. This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. It does not exceed the prescribed word limit set by the Clinical Medicine and Clinical Veterinary Medicine Degree Committee. Faisal Basheer September 2017 i Acknowledgements First and foremost, I would like to thank my supervisor, Professor Brian Huntly, for providing me with the opportunity to work towards this thesis - for his help, encouragement and support over the last four years of my PhD. His dedicated high-level supervision, meticulous attention to detail and excellent direction throughout this time has undoubtedly helped me to maximise my potential in the lab throughout a highly interesting and rewarding project. I would also like to thank all of my colleagues and friends in the Huntly laboratory over the past four years - working together with all of you as a team has been an unforgettable and rewarding experience.
    [Show full text]
  • High Density Genome Wide Genotyping-By-Sequencing and Association
    www.nature.com/scientificreports OPEN High density genome wide genotyping-by-sequencing and association identifies common and Received: 24 March 2016 Accepted: 14 July 2016 low frequency SNPs, and novel Published: 10 August 2016 candidate genes influencing cow milk traits Eveline M. Ibeagha-Awemu1, Sunday O. Peters2, Kingsley A. Akwanji3, Ikhide G. Imumorin4 & Xin Zhao3 High-throughput sequencing technologies have increased the ability to detect sequence variations for complex trait improvement. A high throughput genome wide genotyping-by-sequencing (GBS) method was used to generate 515,787 single nucleotide polymorphisms (SNPs), from which 76,355 SNPs with call rates >85% and minor allele frequency ≥1.5% were used in genome wide association study (GWAS) of 44 milk traits in 1,246 Canadian Holstein cows. GWAS was accomplished with a mixed linear model procedure implementing the additive and dominant models. A strong signal within the centromeric region of bovine chromosome 14 was associated with test day fat percentage. Several SNPs were associated with eicosapentaenoic acid, docosapentaenoic acid, arachidonic acid, CLA:9c11t and gamma linolenic acid. Most of the significant SNPs for 44 traits studied are novel and located in intergenic regions or introns of genes. Novel potential candidate genes for milk traits or mammary gland functions include ERCC6, TONSL, NPAS2, ACER3, ITGB4, GGT6, ACOX3, MECR, ADAM12, ACHE, LRRC14, FUK, NPRL3, EVL, SLCO3A1, PSMA4, FTO, ADCK5, PP1R16A and TEP1. Our study further demonstrates the utility of the GBS approach for identifying population-specific SNPs for use in improvement of complex dairy traits. Milk is an important source of nutrients in human nutrition all over the world.
    [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]
  • The Metabolic Serine Hydrolases and Their Functions in Mammalian Physiology and Disease Jonathan Z
    REVIEW pubs.acs.org/CR The Metabolic Serine Hydrolases and Their Functions in Mammalian Physiology and Disease Jonathan Z. Long* and Benjamin F. Cravatt* The Skaggs Institute for Chemical Biology and Department of Chemical Physiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States CONTENTS 2.4. Other Phospholipases 6034 1. Introduction 6023 2.4.1. LIPG (Endothelial Lipase) 6034 2. Small-Molecule Hydrolases 6023 2.4.2. PLA1A (Phosphatidylserine-Specific 2.1. Intracellular Neutral Lipases 6023 PLA1) 6035 2.1.1. LIPE (Hormone-Sensitive Lipase) 6024 2.4.3. LIPH and LIPI (Phosphatidic Acid-Specific 2.1.2. PNPLA2 (Adipose Triglyceride Lipase) 6024 PLA1R and β) 6035 2.1.3. MGLL (Monoacylglycerol Lipase) 6025 2.4.4. PLB1 (Phospholipase B) 6035 2.1.4. DAGLA and DAGLB (Diacylglycerol Lipase 2.4.5. DDHD1 and DDHD2 (DDHD Domain R and β) 6026 Containing 1 and 2) 6035 2.1.5. CES3 (Carboxylesterase 3) 6026 2.4.6. ABHD4 (Alpha/Beta Hydrolase Domain 2.1.6. AADACL1 (Arylacetamide Deacetylase-like 1) 6026 Containing 4) 6036 2.1.7. ABHD6 (Alpha/Beta Hydrolase Domain 2.5. Small-Molecule Amidases 6036 Containing 6) 6027 2.5.1. FAAH and FAAH2 (Fatty Acid Amide 2.1.8. ABHD12 (Alpha/Beta Hydrolase Domain Hydrolase and FAAH2) 6036 Containing 12) 6027 2.5.2. AFMID (Arylformamidase) 6037 2.2. Extracellular Neutral Lipases 6027 2.6. Acyl-CoA Hydrolases 6037 2.2.1. PNLIP (Pancreatic Lipase) 6028 2.6.1. FASN (Fatty Acid Synthase) 6037 2.2.2. PNLIPRP1 and PNLIPR2 (Pancreatic 2.6.2.
    [Show full text]
  • Genome Sequencing Unveils a Regulatory Landscape of Platelet Reactivity
    ARTICLE https://doi.org/10.1038/s41467-021-23470-9 OPEN Genome sequencing unveils a regulatory landscape of platelet reactivity Ali R. Keramati 1,2,124, Ming-Huei Chen3,4,124, Benjamin A. T. Rodriguez3,4,5,124, Lisa R. Yanek 2,6, Arunoday Bhan7, Brady J. Gaynor 8,9, Kathleen Ryan8,9, Jennifer A. Brody 10, Xue Zhong11, Qiang Wei12, NHLBI Trans-Omics for Precision (TOPMed) Consortium*, Kai Kammers13, Kanika Kanchan14, Kruthika Iyer14, Madeline H. Kowalski15, Achilleas N. Pitsillides4,16, L. Adrienne Cupples 4,16, Bingshan Li 12, Thorsten M. Schlaeger7, Alan R. Shuldiner9, Jeffrey R. O’Connell8,9, Ingo Ruczinski17, Braxton D. Mitchell 8,9, ✉ Nauder Faraday2,18, Margaret A. Taub17, Lewis C. Becker1,2, Joshua P. Lewis 8,9,125 , 2,14,125✉ 3,4,125✉ 1234567890():,; Rasika A. Mathias & Andrew D. Johnson Platelet aggregation at the site of atherosclerotic vascular injury is the underlying patho- physiology of myocardial infarction and stroke. To build upon prior GWAS, here we report on 16 loci identified through a whole genome sequencing (WGS) approach in 3,855 NHLBI Trans-Omics for Precision Medicine (TOPMed) participants deeply phenotyped for platelet aggregation. We identify the RGS18 locus, which encodes a myeloerythroid lineage-specific regulator of G-protein signaling that co-localizes with expression quantitative trait loci (eQTL) signatures for RGS18 expression in platelets. Gene-based approaches implicate the SVEP1 gene, a known contributor of coronary artery disease risk. Sentinel variants at RGS18 and PEAR1 are associated with thrombosis risk and increased gastrointestinal bleeding risk, respectively. Our WGS findings add to previously identified GWAS loci, provide insights regarding the mechanism(s) by which genetics may influence cardiovascular disease risk, and underscore the importance of rare variant and regulatory approaches to identifying loci contributing to complex phenotypes.
    [Show full text]
  • Experimental Eye Research 129 (2014) 93E106
    Experimental Eye Research 129 (2014) 93e106 Contents lists available at ScienceDirect Experimental Eye Research journal homepage: www.elsevier.com/locate/yexer Transcriptomic analysis across nasal, temporal, and macular regions of human neural retina and RPE/choroid by RNA-Seq S. Scott Whitmore a, b, Alex H. Wagner a, c, Adam P. DeLuca a, b, Arlene V. Drack a, b, Edwin M. Stone a, b, Budd A. Tucker a, b, Shemin Zeng a, b, Terry A. Braun a, b, c, * Robert F. Mullins a, b, Todd E. Scheetz a, b, c, a Stephen A. Wynn Institute for Vision Research, The University of Iowa, Iowa City, IA, USA b Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA c Department of Biomedical Engineering, College of Engineering, The University of Iowa, Iowa City, IA, USA article info abstract Article history: Proper spatial differentiation of retinal cell types is necessary for normal human vision. Many retinal Received 14 September 2014 diseases, such as Best disease and male germ cell associated kinase (MAK)-associated retinitis pigmen- Received in revised form tosa, preferentially affect distinct topographic regions of the retina. While much is known about the 31 October 2014 distribution of cell types in the retina, the distribution of molecular components across the posterior pole Accepted in revised form 4 November 2014 of the eye has not been well-studied. To investigate regional difference in molecular composition of Available online 5 November 2014 ocular tissues, we assessed differential gene expression across the temporal, macular, and nasal retina and retinal pigment epithelium (RPE)/choroid of human eyes using RNA-Seq.
    [Show full text]
  • 1 Endothelial Lipase Is Synthesized by Hepatic and Aorta Endothelial
    Endothelial Lipase Is Synthesized by Hepatic and Aorta Endothelial Cells and Its Expression Is Altered in apoE Deficient Mice Kenneth C-W. Yu†, Christopher David¶, Sujata Kadambi¶, Andreas Stahl†¶, Ken-Ichi Hirata†§, Tatsuro Ishida†§, Thomas Quertermous†, Allen D Cooper†¶ and Sungshin Y. Choi¶* Palo Alto Medical Foundation-Research Institute¶, Palo Alto, CA, School of Medicine, Stanford University†, Palo Alto, CA. *Corresponding author: Sungshin Y. Choi, Ph.D. Research Institute, Palo Alto Medical Foundation 795 El Camino Real – Ames Bldg. Palo Alto, CA 94301 650-853-2866 email address: [email protected] §: Current Address: Division of Cardiovascular and Respiratory Medicine, Kobe University Graduate School of Medicine, Kobe, Japan Short title: Tissue specific expression of endothelial lipase Abbreviations: EL = endothelial lipase, EC = endothelial cells, HL = hepatic lipase, LPL = lipoprotein lipase, EKO = apoE knockout, WT = wild-type, CA = cholic acid, HL = high fat, NC = normal chow, RT-PCR = real-time PCR, vWF = von Willbrand Factor, EC = endothelial cells. 1 ABSTRACT Both LPL and HL are synthesized in parenchymal cells, secreted and bind to endothelial cells. To learn where endothelial lipase (EL) is synthesized in the adult animals the localization of EL in mouse and rat liver was studied by immunohistochemical analysis. Further, to test if EL could play a role in atherogenesis, expression of EL in the aorta and liver of apoE knockout mice was determined. EL, in both mouse and rat liver was colocalized with the vascular endothelial cells and not hepatocytes. In contrast, hepatic lipase (HL) was present in both hepatocytes and endothelial cells. By in situ hybridization EL mRNA was present only in endothelial cells in liver sections.
    [Show full text]