Zebrafish B Cell Acute Lymphoblastic Leukemia: New Findings in an Old Model
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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. -
WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, C12Q 1/68 (2018.01) A61P 31/18 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, C12Q 1/70 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2018/056167 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 16 October 2018 (16. 10.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/573,025 16 October 2017 (16. 10.2017) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, ΓΕ , IS, IT, LT, LU, LV, (71) Applicant: MASSACHUSETTS INSTITUTE OF MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TECHNOLOGY [US/US]; 77 Massachusetts Avenue, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, Cambridge, Massachusetts 02139 (US). -
Figure S1. Reverse Transcription‑Quantitative PCR Analysis of ETV5 Mrna Expression Levels in Parental and ETV5 Stable Transfectants
Figure S1. Reverse transcription‑quantitative PCR analysis of ETV5 mRNA expression levels in parental and ETV5 stable transfectants. (A) Hec1a and Hec1a‑ETV5 EC cell lines; (B) Ishikawa and Ishikawa‑ETV5 EC cell lines. **P<0.005, unpaired Student's t‑test. EC, endometrial cancer; ETV5, ETS variant transcription factor 5. Figure S2. Survival analysis of sample clusters 1‑4. Kaplan Meier graphs for (A) recurrence‑free and (B) overall survival. Survival curves were constructed using the Kaplan‑Meier method, and differences between sample cluster curves were analyzed by log‑rank test. Figure S3. ROC analysis of hub genes. For each gene, ROC curve (left) and mRNA expression levels (right) in control (n=35) and tumor (n=545) samples from The Cancer Genome Atlas Uterine Corpus Endometrioid Cancer cohort are shown. mRNA levels are expressed as Log2(x+1), where ‘x’ is the RSEM normalized expression value. ROC, receiver operating characteristic. Table SI. Clinicopathological characteristics of the GSE17025 dataset. Characteristic n % Atrophic endometrium 12 (postmenopausal) (Control group) Tumor stage I 91 100 Histology Endometrioid adenocarcinoma 79 86.81 Papillary serous 12 13.19 Histological grade Grade 1 30 32.97 Grade 2 36 39.56 Grade 3 25 27.47 Myometrial invasiona Superficial (<50%) 67 74.44 Deep (>50%) 23 25.56 aMyometrial invasion information was available for 90 of 91 tumor samples. Table SII. Clinicopathological characteristics of The Cancer Genome Atlas Uterine Corpus Endometrioid Cancer dataset. Characteristic n % Solid tissue normal 16 Tumor samples Stagea I 226 68.278 II 19 5.740 III 70 21.148 IV 16 4.834 Histology Endometrioid 271 81.381 Mixed 10 3.003 Serous 52 15.616 Histological grade Grade 1 78 23.423 Grade 2 91 27.327 Grade 3 164 49.249 Molecular subtypeb POLE 17 7.328 MSI 65 28.017 CN Low 90 38.793 CN High 60 25.862 CN, copy number; MSI, microsatellite instability; POLE, DNA polymerase ε. -
Genome Wide Association Study in 3,173 Outbred Rats Identifies Multiple Loci for Body Weight, Adiposity, and Fasting Glucose
bioRxiv preprint doi: https://doi.org/10.1101/422428; this version posted May 11, 2020. 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 a CC-BY-NC-ND 4.0 International license. Genome wide association study in 3,173 outbred rats identifies multiple loci for body weight, adiposity, and fasting glucose Apurva S. Chitre1, Oksana Polesskaya1, Katie Holl2, Jianjun Gao1, Riyan Cheng1, Hannah Bimschleger1, Angel Garcia Martinez3, Tony George6, Alexander F. Gileta1,10, Wenyan Han3, Aidan Horvath4, Alesa Hughson4, Keita Ishiwari6, Christopher P. King5, Alexander Lamparelli5, Cassandra L. Versaggi5, Connor Martin6, Celine L. St. Pierre11, Jordan A. Tripi5, Tengfei Wang 3, Hao Chen3, Shelly B. Flagel12, Paul Meyer5, Jerry Richards6, Terry E. Robinson7, Abraham A. Palmer1,8*, Leah C. Solberg Woods9* * These authors contributed equally to this study as senior authors 1University of California, San Diego, Department of Psychiatry, La Jolla, CA, 2Medical College of Wisconsin, Human and Molecular Genetic Center, Milwaukee, WI, 3University of Tennessee Health Science Center, Department of Pharmacology, Memphis, TN, 4University of Michigan, Department of Psychiatry, Ann Arbor, MI, 5University at Buffalo, Department of Psychology, Buffalo, NY, 6University at Buffalo, Clinical and Research Institute on Addictions, Buffalo, NY, 7University of Michigan, Department of Psychology, Ann Arbor, MI, 8University of California San Diego, Institute for Genomic Medicine, La Jolla, CA 9Wake Forest School of Medicine, Department of Internal Medicine, Winston Salem, NC, 10 University of Chicago, Department of Human Genetics, Chicago, IL, 11 Department of Genetics, Washington University, St. -
Accelerated Discovery of Functional Genomic Variation in Pigs
Delft University of Technology Accelerated discovery of functional genomic variation in pigs Derks, Martijn F.L.; Groß, Christian ; Lopes, Marcos S. ; Reinders, Marcel .J.T.; Bosse, Mirte; Gjuvsland, Arne B. ; de Ridder, Dick; Megens, Hendrik-Jan; Groenen, Martien A.M. DOI 10.1016/j.ygeno.2021.05.017 Publication date 2021 Document Version Final published version Published in Genomics Citation (APA) Derks, M. F. L., Groß, C., Lopes, M. S., Reinders, M. J. T., Bosse, M., Gjuvsland, A. B., de Ridder, D., Megens, H-J., & Groenen, M. A. M. (2021). Accelerated discovery of functional genomic variation in pigs. Genomics, 113(4), 2229-2239. https://doi.org/10.1016/j.ygeno.2021.05.017 Important note To cite this publication, please use the final published version (if applicable). Please check the document version above. Copyright Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons. Takedown policy Please contact us and provide details if you believe this document breaches copyrights. We will remove access to the work immediately and investigate your claim. This work is downloaded from Delft University of Technology. For technical reasons the number of authors shown on this cover page is limited to a maximum of 10. Genomics 113 (2021) 2229–2239 Contents lists available at ScienceDirect Genomics journal homepage: www.elsevier.com/locate/ygeno Original Article Accelerated discovery of functional genomic variation in pigs Martijn F.L. -
Assessment of Network Module Identification Across Complex Diseases
ANALYSIS https://doi.org/10.1038/s41592-019-0509-5 Assessment of network module identification across complex diseases Sarvenaz Choobdar1,2,20, Mehmet E. Ahsen3,117, Jake Crawford4,117, Mattia Tomasoni 1,2, Tao Fang5, David Lamparter1,2,6, Junyuan Lin7, Benjamin Hescott8, Xiaozhe Hu7, Johnathan Mercer9,10, Ted Natoli11, Rajiv Narayan11, The DREAM Module Identification Challenge Consortium12, Aravind Subramanian11, Jitao D. Zhang 5, Gustavo Stolovitzky 3,13, Zoltán Kutalik2,14, Kasper Lage 9,10,15, Donna K. Slonim 4,16, Julio Saez-Rodriguez 17,18, Lenore J. Cowen4,7, Sven Bergmann 1,2,19,21* and Daniel Marbach 1,2,5,21* Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait- associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often com- prise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology. omplex diseases involve many genes and molecules that inter- assessed on in silico generated benchmark graphs11. -
Comprehensive Biological Information Analysis of PTEN Gene in Pan-Cancer
Comprehensive biological information analysis of PTEN gene in pan-cancer Hang Zhang Shanghai Medical University: Fudan University https://orcid.org/0000-0002-5853-7754 Wenhan Zhou Shanghai Medical University: Fudan University Xiaoyi Yang Shanghai Jiao Tong University School of Medicine Shuzhan Wen Shanghai Medical University: Fudan University Baicheng Zhao Shanghai Medical University: Fudan University Jiale Feng Shanghai Medical University: Fudan University Shuying Chen ( [email protected] ) https://orcid.org/0000-0002-9215-9777 Primary research Keywords: PTEN, correlated genes, TCGA, GEPIA, UALCAN, GTEx, expression, cancer Posted Date: April 12th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-388887/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/21 Abstract Background PTEN is a multifunctional tumor suppressor gene mutating at high frequency in a variety of cancers. However, its expression in pan-cancer, correlated genes, survival prognosis, and regulatory pathways are not completely described. Here, we aimed to conduct a comprehensive analysis from the above perspectives in order to provide reference for clinical application. Methods we studied the expression levels in cancers by using data from TCGA and GTEx database. Obtain expression box plot from UALCAN database. Perform mutation analysis on the cBioportal website. Obtain correlation genes on the GEPIA website. Construct protein network and perform KEGG and GO enrichment analysis on the STRING database. Perform prognostic analysis on the Kaplan-Meier Plotter website. We also performed transcription factor prediction on the PROMO database and performed RNA-RNA association and RNA-protein interaction on the RNAup Web server and RPISEq. -
Dissecting the Genetics of Human Communication
DISSECTING THE GENETICS OF HUMAN COMMUNICATION: INSIGHTS INTO SPEECH, LANGUAGE, AND READING by HEATHER ASHLEY VOSS-HOYNES Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Epidemiology and Biostatistics CASE WESTERN RESERVE UNIVERSITY January 2017 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We herby approve the dissertation of Heather Ashely Voss-Hoynes Candidate for the degree of Doctor of Philosophy*. Committee Chair Sudha K. Iyengar Committee Member William Bush Committee Member Barbara Lewis Committee Member Catherine Stein Date of Defense July 13, 2016 *We also certify that written approval has been obtained for any proprietary material contained therein Table of Contents List of Tables 3 List of Figures 5 Acknowledgements 7 List of Abbreviations 9 Abstract 10 CHAPTER 1: Introduction and Specific Aims 12 CHAPTER 2: Review of speech sound disorders: epidemiology, quantitative components, and genetics 15 1. Basic Epidemiology 15 2. Endophenotypes of Speech Sound Disorders 17 3. Evidence for Genetic Basis Of Speech Sound Disorders 22 4. Genetic Studies of Speech Sound Disorders 23 5. Limitations of Previous Studies 32 CHAPTER 3: Methods 33 1. Phenotype Data 33 2. Tests For Quantitative Traits 36 4. Analytical Methods 42 CHAPTER 4: Aim I- Genome Wide Association Study 49 1. Introduction 49 2. Methods 49 3. Sample 50 5. Statistical Procedures 53 6. Results 53 8. Discussion 71 CHAPTER 5: Accounting for comorbid conditions 84 1. Introduction 84 2. Methods 86 3. Results 87 4. Discussion 105 CHAPTER 6: Hypothesis driven pathway analysis 111 1. Introduction 111 2. Methods 112 3. Results 116 4. -
Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ...................................................... -
An Evaluation of Cancer Subtypes and Glioma Stem Cell Characterisation Unifying Tumour Transcriptomic Features with Cell Line Expression and Chromatin Accessibility
An evaluation of cancer subtypes and glioma stem cell characterisation Unifying tumour transcriptomic features with cell line expression and chromatin accessibility Ewan Roderick Johnstone EMBL-EBI, Darwin College University of Cambridge This dissertation is submitted for the degree of Doctor of Philosophy Darwin College December 2016 Dedicated to Klaudyna. Declaration • I hereby declare that except where specific reference is made to the work of others, the contents of this dissertation are original and have not been submitted in whole or in part for consideration for any other degree or qualification in this, or any other university. • This dissertation is my own work and contains nothing which is the outcome of work done in collaboration with others, except as specified in the text and Acknowledge- ments. • This dissertation is typeset in LATEX using one-and-a-half spacing, contains fewer than 60,000 words including appendices, footnotes, tables and equations and has fewer than 150 figures. Ewan Roderick Johnstone December 2016 Acknowledgements This work was funded by the Biotechnology and Biological Sciences Research Council (BBSRC, Ref:1112564) and supported by the European Molecular Biology Laboratory (EMBL) and its outstation, the European Bioinformatics Institute (EBI). I have many people to thank for assistance in preparing this thesis. First and foremost I must thank my supervisor, Paul Bertone for his support and willingness to take me on as a student. My thanks are also extended to present and past members of the Bertone group, particularly Pär Engström and Remco Loos who have provided a great deal of guidance over the course of my studentship. -
Multitrait Genome Association Analysis Identifies New Susceptibility Genes
Complex traits J Med Genet: first published as 10.1136/jmedgenet-2018-105437 on 30 August 2018. Downloaded from ORIGINAL ARTICLE Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort Iván Galván-Femenía,1 Mireia Obón-Santacana,1,2 David Piñeyro,3 Marta Guindo-Martinez,4 Xavier Duran,1 Anna Carreras,1 Raquel Pluvinet,3 Juan Velasco,1 Laia Ramos,3 Susanna Aussó,3 J M Mercader,5,6 Lluis Puig,7 Manuel Perucho,8 David Torrents,4,9 Victor Moreno,2,10 Lauro Sumoy,3 Rafael de Cid1 ► Additional material is ABSTRact INTRODUCTION published online only. To view, Background Heritability estimates have revealed an Common disorders cause 85% of deaths in the please visit the journal online 1 (http:// dx. doi. org/ 10. 1136/ important contribution of SNP variants for most common European Union (EU). The increasing incidence jmedgenet- 2018- 105437). traits; however, SNP analysis by single-trait genome- and prevalence of cancer, cardiovascular diseases, wide association studies (GWAS) has failed to uncover chronic respiratory diseases, diabetes and mental For numbered affiliations see their impact. In this study, we applied a multitrait GWAS illness represent a challenge that leads to extra costs end of article. approach to discover additional factor of the missing for the healthcare system. Moreover, as European population is getting older, this scenario will be Correspondence to heritability of human anthropometric variation. Dr Rafael de Cid, GCAT lab Methods We analysed 205 traits, including diseases heightened in the next few years. Like complex Group, Program of Predictive identified at baseline in the GCAT cohort (Genomes For traits, many common diseases are complex inher- and Personalized Medicine of Life- Cohort study of the Genomes of Catalonia) (n=4988), ited conditions with genetic and environmental Cancer (PMPPC), Germans Trias determinants. -
From Sequence to Phenotype: the Impact of Deleterious Variation in Livestock
From Sequence to Phenotype: From Sequence to Phenotype: The Impact of Deleterious Variation in Livestock Martijn F.L. Derks The Impact of Deleterious Variation in Livestock From sequence to phenotype: the impact of deleterious variation in livestock Martijn F.L. Derks Thesis committee Promotor Prof. Dr M.A.M. Groenen Professor of Animal Breeding and Genomics Wageningen University & Research Co-promotor Dr H.-J.W.C. Megens Assistant professor, Animal Breeding and Genomics Wageningen University & Research Other members Dr C. Charlier, University of Liège, Belgium Dr P. Knap, Genus-PIC, Rabenkirchen-Fauluck, Germany Prof. Dr B.J. Zwaan, Wageningen University & Research, Netherlands Prof. Dr Cord Drögemüller, University of Bern, Switzerland This research was conducted under the auspices of the Graduate School of Wageningen Institute of Animal Sciences (WIAS). From sequence to phenotype: the impact of deleterious variation in livestock Martijn F.L. Derks Thesis submitted in fulfillment of the requirements for the degree of doctor at Wageningen University by the authority of the Rector Magnificus, Prof. Dr A.P.J. Mol, in the presence of the Thesis Committee appointed by the Academic Board to be defended in public on Wednesday 2 September, 2020 at 4. p.m. in the Aula. Martijn F.L. Derks From sequence to phenotype: the impact of deleterious variation in livestock PhD thesis, Wageningen University, the Netherlands (2020) With references, with summary in English ISBN 978-94-6395-320-7 DOI: https://doi.org/10.18174/515207 Abstract Derks, MFL. (2020). From sequence to phenotype: the impact of deleterious variation in livestock. PhD thesis, Wageningen University, the Netherlands The genome provides a blueprint of life containing the instruction, together with the environment, that determine the phenotype.