Trajectories of DNA Methylation Associated with Clozapine Exposure and Clinical Outcomes
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
(12) United States Patent (10) Patent No.: US 8,603,824 B2 Ramseier Et Al
USOO8603824B2 (12) United States Patent (10) Patent No.: US 8,603,824 B2 Ramseier et al. (45) Date of Patent: Dec. 10, 2013 (54) PROCESS FOR IMPROVED PROTEIN 5,399,684 A 3, 1995 Davie et al. EXPRESSION BY STRAIN ENGINEERING 5,418, 155 A 5/1995 Cormier et al. 5,441,934 A 8/1995 Krapcho et al. (75) Inventors: Thomas M. Ramseier, Poway, CA 5,508,192 A * 4/1996 Georgiou et al. .......... 435/252.3 (US); Hongfan Jin, San Diego, CA 5,527,883 A 6/1996 Thompson et al. (US); Charles H. Squires, Poway, CA 5,558,862 A 9, 1996 Corbinet al. 5,559,015 A 9/1996 Capage et al. (US) 5,571,694 A 11/1996 Makoff et al. (73) Assignee: Pfenex, Inc., San Diego, CA (US) 5,595,898 A 1/1997 Robinson et al. 5,610,044 A 3, 1997 Lam et al. (*) Notice: Subject to any disclaimer, the term of this 5,621,074 A 4/1997 Bjorn et al. patent is extended or adjusted under 35 5,622,846 A 4/1997 Kiener et al. 5,641,671 A 6/1997 Bos et al. U.S.C. 154(b) by 471 days. 5,641,870 A 6/1997 Rinderknecht et al. 5,643,774 A 7/1997 Ligon et al. (21) Appl. No.: 11/189,375 5,662,898 A 9/1997 Ligon et al. (22) Filed: Jul. 26, 2005 5,677,127 A 10/1997 Hogan et al. 5,683,888 A 1 1/1997 Campbell (65) Prior Publication Data 5,686,282 A 11/1997 Lam et al. -
9, 2015 Glasgow, Scotland, United Kingdom Abstracts
Volume 23 Supplement 1 June 2015 www.nature.com/ejhg European Human Genetics Conference 2015 June 6 - 9, 2015 Glasgow, Scotland, United Kingdom Abstracts EJHG_OFC.indd 1 4/1/2006 10:58:05 AM ABSTRACTS European Human Genetics Conference joint with the British Society of Genetics Medicine June 6 - 9, 2015 Glasgow, Scotland, United Kingdom Abstracts ESHG 2015 | GLASGOW, SCOTLAND, UK | WWW.ESHG.ORG 1 ABSTRACTS Committees – Board - Organisation European Society of Human Genetics ESHG Office Executive Board 2014-2015 Scientific Programme Committee European Society President Chair of Human Genetics Helena Kääriäinen, FI Brunhilde Wirth, DE Andrea Robinson Vice-President Members Karin Knob Han Brunner, NL Tara Clancy, UK c/o Vienna Medical Academy Martina Cornel, NL Alser Strasse 4 President-Elect Yanick Crow, FR 1090 Vienna Feliciano Ramos, ES Paul de Bakker, NL Austria Secretary-General Helene Dollfus, FR T: 0043 1 405 13 83 20 or 35 Gunnar Houge, NO David FitzPatrick, UK F: 0043 1 407 82 74 Maurizio Genuardi, IT E: [email protected] Deputy-Secretary-General Daniel Grinberg, ES www.eshg.org Karin Writzl, SI Gunnar Houge, NO Treasurer Erik Iwarsson, SE Andrew Read, UK Xavier Jeunemaitre, FR Mark Longmuir, UK Executive Officer Jose C. Machado, PT Jerome del Picchia, AT Dominic McMullan, UK Giovanni Neri, IT William Newman, UK Minna Nyström, FI Pia Ostergaard, UK Francesc Palau, ES Anita Rauch, CH Samuli Ripatti, FI Peter N. Robinson, DE Kristel van Steen, BE Joris Veltman, NL Joris Vermeesch, BE Emma Woodward, UK Karin Writzl, SI Board Members Liaison Members Yasemin Alanay, TR Stan Lyonnet, FR Martina Cornel, NL Martijn Breuning, NL Julie McGaughran, AU Ulf Kristoffersson, SE Pascal Borry, BE Bela Melegh, HU Thomas Liehr, DE Nina Canki-Klain, HR Will Newman, UK Milan Macek Jr., CZ Ana Carrió, ES Markus Nöthen, DE Tayfun Ozcelik, TR Isabella Ceccherini, IT Markus Perola, FI Milena Paneque, PT Angus John Clarke, UK Dijana Plaseska-Karanfilska, MK Hans Scheffer, NL Koen Devriendt, BE Trine E. -
Role of Amylase in Ovarian Cancer Mai Mohamed University of South Florida, [email protected]
University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School July 2017 Role of Amylase in Ovarian Cancer Mai Mohamed University of South Florida, [email protected] Follow this and additional works at: http://scholarcommons.usf.edu/etd Part of the Pathology Commons Scholar Commons Citation Mohamed, Mai, "Role of Amylase in Ovarian Cancer" (2017). Graduate Theses and Dissertations. http://scholarcommons.usf.edu/etd/6907 This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Role of Amylase in Ovarian Cancer by Mai Mohamed A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Pathology and Cell Biology Morsani College of Medicine University of South Florida Major Professor: Patricia Kruk, Ph.D. Paula C. Bickford, Ph.D. Meera Nanjundan, Ph.D. Marzenna Wiranowska, Ph.D. Lauri Wright, Ph.D. Date of Approval: June 29, 2017 Keywords: ovarian cancer, amylase, computational analyses, glycocalyx, cellular invasion Copyright © 2017, Mai Mohamed Dedication This dissertation is dedicated to my parents, Ahmed and Fatma, who have always stressed the importance of education, and, throughout my education, have been my strongest source of encouragement and support. They always believed in me and I am eternally grateful to them. I would also like to thank my brothers, Mohamed and Hussien, and my sister, Mariam. I would also like to thank my husband, Ahmed. -
Variation in Protein Coding Genes Identifies Information
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
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 -
Placenta-Derived Exosomes Continuously Increase in Maternal
Sarker et al. Journal of Translational Medicine 2014, 12:204 http://www.translational-medicine.com/content/12/1/204 RESEARCH Open Access Placenta-derived exosomes continuously increase in maternal circulation over the first trimester of pregnancy Suchismita Sarker1, Katherin Scholz-Romero1, Alejandra Perez2, Sebastian E Illanes1,2,3, Murray D Mitchell1, Gregory E Rice1,2 and Carlos Salomon1,2* Abstract Background: Human placenta releases specific nanovesicles (i.e. exosomes) into the maternal circulation during pregnancy, however, the presence of placenta-derived exosomes in maternal blood during early pregnancy remains to be established. The aim of this study was to characterise gestational age related changes in the concentration of placenta-derived exosomes during the first trimester of pregnancy (i.e. from 6 to 12 weeks) in plasma from women with normal pregnancies. Methods: A time-series experimental design was used to establish pregnancy-associated changes in maternal plasma exosome concentrations during the first trimester. A series of plasma were collected from normal healthy women (10 patients) at 6, 7, 8, 9, 10, 11 and 12 weeks of gestation (n = 70). We measured the stability of these vesicles by quantifying and observing their protein and miRNA contents after the freeze/thawing processes. Exosomes were isolated by differential and buoyant density centrifugation using a sucrose continuous gradient and characterised by their size distribution and morphology using the nanoparticles tracking analysis (NTA; Nanosight™) and electron microscopy (EM), respectively. The total number of exosomes and placenta-derived exosomes were determined by quantifying the immunoreactive exosomal marker, CD63 and a placenta-specific marker (Placental Alkaline Phosphatase PLAP). -
Appendix 2. Significantly Differentially Regulated Genes in Term Compared with Second Trimester Amniotic Fluid Supernatant
Appendix 2. Significantly Differentially Regulated Genes in Term Compared With Second Trimester Amniotic Fluid Supernatant Fold Change in term vs second trimester Amniotic Affymetrix Duplicate Fluid Probe ID probes Symbol Entrez Gene Name 1019.9 217059_at D MUC7 mucin 7, secreted 424.5 211735_x_at D SFTPC surfactant protein C 416.2 206835_at STATH statherin 363.4 214387_x_at D SFTPC surfactant protein C 295.5 205982_x_at D SFTPC surfactant protein C 288.7 1553454_at RPTN repetin solute carrier family 34 (sodium 251.3 204124_at SLC34A2 phosphate), member 2 238.9 206786_at HTN3 histatin 3 161.5 220191_at GKN1 gastrokine 1 152.7 223678_s_at D SFTPA2 surfactant protein A2 130.9 207430_s_at D MSMB microseminoprotein, beta- 99.0 214199_at SFTPD surfactant protein D major histocompatibility complex, class II, 96.5 210982_s_at D HLA-DRA DR alpha 96.5 221133_s_at D CLDN18 claudin 18 94.4 238222_at GKN2 gastrokine 2 93.7 1557961_s_at D LOC100127983 uncharacterized LOC100127983 93.1 229584_at LRRK2 leucine-rich repeat kinase 2 HOXD cluster antisense RNA 1 (non- 88.6 242042_s_at D HOXD-AS1 protein coding) 86.0 205569_at LAMP3 lysosomal-associated membrane protein 3 85.4 232698_at BPIFB2 BPI fold containing family B, member 2 84.4 205979_at SCGB2A1 secretoglobin, family 2A, member 1 84.3 230469_at RTKN2 rhotekin 2 82.2 204130_at HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2 81.9 222242_s_at KLK5 kallikrein-related peptidase 5 77.0 237281_at AKAP14 A kinase (PRKA) anchor protein 14 76.7 1553602_at MUCL1 mucin-like 1 76.3 216359_at D MUC7 mucin 7, -
(12) Patent Application Publication (10) Pub. No.: US 2006/0110747 A1 Ramseier Et Al
US 200601 10747A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2006/0110747 A1 Ramseier et al. (43) Pub. Date: May 25, 2006 (54) PROCESS FOR IMPROVED PROTEIN (60) Provisional application No. 60/591489, filed on Jul. EXPRESSION BY STRAIN ENGINEERING 26, 2004. (75) Inventors: Thomas M. Ramseier, Poway, CA Publication Classification (US); Hongfan Jin, San Diego, CA (51) Int. Cl. (US); Charles H. Squires, Poway, CA CI2O I/68 (2006.01) (US) GOIN 33/53 (2006.01) CI2N 15/74 (2006.01) Correspondence Address: (52) U.S. Cl. ................................ 435/6: 435/7.1; 435/471 KING & SPALDING LLP 118O PEACHTREE STREET (57) ABSTRACT ATLANTA, GA 30309 (US) This invention is a process for improving the production levels of recombinant proteins or peptides or improving the (73) Assignee: Dow Global Technologies Inc., Midland, level of active recombinant proteins or peptides expressed in MI (US) host cells. The invention is a process of comparing two genetic profiles of a cell that expresses a recombinant (21) Appl. No.: 11/189,375 protein and modifying the cell to change the expression of a gene product that is upregulated in response to the recom (22) Filed: Jul. 26, 2005 binant protein expression. The process can improve protein production or can improve protein quality, for example, by Related U.S. Application Data increasing solubility of a recombinant protein. Patent Application Publication May 25, 2006 Sheet 1 of 15 US 2006/0110747 A1 Figure 1 09 010909070£020\,0 10°0 Patent Application Publication May 25, 2006 Sheet 2 of 15 US 2006/0110747 A1 Figure 2 Ester sers Custer || || || || || HH-I-H 1 H4 s a cisiers TT closers | | | | | | Ya S T RXFO 1961. -
Bioinformatics Analysis for the Identification of Differentially Expressed Genes and Related Signaling Pathways in H
Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells Dingyu Chen*, Chao Li, Yan Zhao, Jianjiang Zhou, Qinrong Wang and Yuan Xie* Key Laboratory of Endemic and Ethnic Diseases , Ministry of Education, Guizhou Medical University, Guiyang, China * These authors contributed equally to this work. ABSTRACT Aim. Helicobacter pylori cytotoxin-associated protein A (CagA) is an important vir- ulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA- induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods. AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, | logFC |> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Submitted 20 August 2020 Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the Accepted 11 March 2021 key genes derived from the PPI network. Further analysis of the key gene expressions Published 15 April 2021 in gastric cancer and normal tissues were performed based on The Cancer Genome Corresponding author Atlas (TCGA) database and RT-qPCR verification. -
In This Table Protein Name, Uniprot Code, Gene Name P-Value
Supplementary Table S1: In this table protein name, uniprot code, gene name p-value and Fold change (FC) for each comparison are shown, for 299 of the 301 significantly regulated proteins found in both comparisons (p-value<0.01, fold change (FC) >+/-0.37) ALS versus control and FTLD-U versus control. Two uncharacterized proteins have been excluded from this list Protein name Uniprot Gene name p value FC FTLD-U p value FC ALS FTLD-U ALS Cytochrome b-c1 complex P14927 UQCRB 1.534E-03 -1.591E+00 6.005E-04 -1.639E+00 subunit 7 NADH dehydrogenase O95182 NDUFA7 4.127E-04 -9.471E-01 3.467E-05 -1.643E+00 [ubiquinone] 1 alpha subcomplex subunit 7 NADH dehydrogenase O43678 NDUFA2 3.230E-04 -9.145E-01 2.113E-04 -1.450E+00 [ubiquinone] 1 alpha subcomplex subunit 2 NADH dehydrogenase O43920 NDUFS5 1.769E-04 -8.829E-01 3.235E-05 -1.007E+00 [ubiquinone] iron-sulfur protein 5 ARF GTPase-activating A0A0C4DGN6 GIT1 1.306E-03 -8.810E-01 1.115E-03 -7.228E-01 protein GIT1 Methylglutaconyl-CoA Q13825 AUH 6.097E-04 -7.666E-01 5.619E-06 -1.178E+00 hydratase, mitochondrial ADP/ATP translocase 1 P12235 SLC25A4 6.068E-03 -6.095E-01 3.595E-04 -1.011E+00 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 Protein kinase C and casein Q9BY11 PACSIN1 3.837E-03 -5.863E-01 3.680E-06 -1.824E+00 kinase substrate in neurons protein 1 Tubulin polymerization- O94811 TPPP 6.466E-03 -5.755E-01 6.943E-06 -1.169E+00 promoting protein MIC C9JRZ6 CHCHD3 2.912E-02 -6.187E-01 2.195E-03 -9.781E-01 Mitochondrial 2- -
Genetic Risk Prediction and Neurobiological Understanding of Alcoholism
OPEN Citation: Transl Psychiatry (2014) 4, e391; doi:10.1038/tp.2014.29 © 2014 Macmillan Publishers Limited All rights reserved 2158-3188/14 www.nature.com/tp ORIGINAL ARTICLE Genetic risk prediction and neurobiological understanding of alcoholism DF Levey1, H Le-Niculescu1, J Frank2, M Ayalew1, N Jain1, B Kirlin1, R Learman1, E Winiger1, Z Rodd1, A Shekhar1, N Schork3, F Kiefe4, N Wodarz5, B Müller-Myhsok6, N Dahmen7, GESGA Consortium, M Nöthen8, R Sherva9, L Farrer9, AH Smith10, HR Kranzler11, M Rietschel2, J Gelernter10 and AB Niculescu1,12 We have used a translational Convergent Functional Genomics (CFG) approach to discover genes involved in alcoholism, by gene-level integration of genome-wide association study (GWAS) data from a German alcohol dependence cohort with other genetic and gene expression data, from human and animal model studies, similar to our previous work in bipolar disorder and schizophrenia. A panel of all the nominally significant P-value single-nucleotide length polymorphisms (SNPs) in the top candidate genes discovered by CFG (n = 135 genes, 713 SNPs) was used to generate a genetic risk prediction score (GRPS), which showed a trend towards significance (P = 0.053) in separating alcohol dependent individuals from controls in an independent German test cohort. We then validated and prioritized our top findings from this discovery work, and subsequently tested them in three independent cohorts, from two continents. In order to validate and prioritize the key genes that drive behavior without some of the pleiotropic environmental confounds present in humans, we used a stress-reactive animal model of alcoholism developed by our group, the D-box binding protein (DBP) knockout mouse, consistent with the surfeit of stress theory of addiction proposed by Koob and colleagues.