Novel Parent-Of-Origin-Specific Differentially Methylated Loci on Chromosome 16 Katharina V
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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. -
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Published OnlineFirst August 15, 2016; DOI: 10.1158/1078-0432.CCR-16-0290 Biology of Human Tumors Clinical Cancer Research Recurrent TRIO Fusion in Nontranslocation– Related Sarcomas Lucile Delespaul1,2, Tom Lesluyes1,2,Gaelle€ Perot 1,3,Celine Brulard1, Lydia Lartigue1,2, Jessica Baud1,2, Pauline Lagarde1, Sophie Le Guellec4,Agnes Neuville1,3, Philippe Terrier5, Dominique Vince-Ranchere 6, Susanne Schmidt7, Anne Debant7, Jean-Michel Coindre1,2,3, and Fred eric Chibon1,3 Abstract Purpose: Despite various differences, nontranslocation-related with various partners, was identified in 5.1% of cases. TRIO sarcomas (e.g., comprising undifferentiated pleomorphic sarcoma, translocations are either intrachromosomal with TERT or inter- leiomyosarcoma, myxofibrosarcoma) are unified by their complex chromosomal with LINC01504 or ZNF558. Our results suggest genetics. Extensive analysis of the tumor genome using molecular that all translocations led to a truncated TRIO protein either cytogenetic approaches showed many chromosomal gains, losses, directly or indirectly by alternative splicing. TRIO rearrangement and translocations per cell. Genomic quantitative alterations and is associated with a modified transcriptomic program to immu- expression variations have been extensively studied by adapted nity/inflammation, proliferation and migration, and an increase high-throughput approaches, yet translocations still remained in proliferation. unscreened. We therefore analyzed 117 nontranslocation-related Conclusions: TRIO fusions have been identified in four -
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 -
ACB Figure S1. Analysis of Lineage Negative Pbmcs. (A
A C Lin CD16 Before sort A - Before sort Lin FSC Sorted CD56hiNK cells Sorted Lin-CD45+CD56-CD127- cells Sorted Sorted CD56dimNK cells Lin-CD45+CD56-CD127+ Cells (ILCs) CD56 CD127 Sorted CD56-NK cells B A - Lin Sorted ILCs FSC CD117 CD56 CD127 CRTH2 CD56 CD127 Figure S1. Analysis of lineage negative PBMCs. (A) Sorting strategy for Lin–CD45+CD56–CD127– and Lin–CD45+CD56–CD127+cells. (B) Lin–CD45+CD56–PBMCs were detected with CD127, CD117 and CRTH2. (C) Sorting strategy for CD56–, CD56dim and CD56hiNK cells and ILCs. All data were generated using blood from healthy donors. Related to Figure 1. 3000 250 600 A 20000 IL7R KIT 800 IL4R IL1RL1 CCR7 200 600 15000 2000 400 150 10000 400 100 1000 200 5000 200 50 0 0 0 0 0 800 8000 150 GPR183 TCF7 MYC 600 6000 100 - Normalized counts Normalized CD56 NK 400 4000 CD56dimNK hi 50 CD56 NK 200 2000 ILC 0 0 0 B Areg C Gata3 D Rorc E Tbx21 *** ** 10000 *** 40000 20000 *** 8000 *** *** *** * * *** ** 8000 ** ns*** *** 30000 *** 15000 6000 6000 20000 10000 4000 4000 10000 5000 2000 2000 Normalized counts Normalized 0 0 0 0 1 2 p 3 s 1 2 p 3 s 1 2 p 3 s 1 2 p 3 s C C 2 C ll C C 2 C ll C C 2 C ll C C 2 C ll L L C L e L L C L e L L C L e L L C L e I I L I c I I L I c I I L I c I I L I c I K I K I K I K N N N N 800 F 15000 JAK1 400 IRF8 6000 DAP12 SYK 2000 LYN 300 600 1500 10000 4000 200 400 1000 5000 2000 100 200 500 - 0 0 0 0 CD56 NK 0 CD56dimNK hi 2500 FYN 8000 KLRD1 8000 KLRK1 1000 KLRC2 FCER1G CD56 NK 4000 ILC 2000 800 6000 6000 3000 Normalized counts Normalized 1500 600 4000 4000 2000 1000 400 2000 2000 -
Supplementary Material
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Neurol Neurosurg Psychiatry Page 1 / 45 SUPPLEMENTARY MATERIAL Appendix A1: Neuropsychological protocol. Appendix A2: Description of the four cases at the transitional stage. Table A1: Clinical status and center proportion in each batch. Table A2: Complete output from EdgeR. Table A3: List of the putative target genes. Table A4: Complete output from DIANA-miRPath v.3. Table A5: Comparison of studies investigating miRNAs from brain samples. Figure A1: Stratified nested cross-validation. Figure A2: Expression heatmap of miRNA signature. Figure A3: Bootstrapped ROC AUC scores. Figure A4: ROC AUC scores with 100 different fold splits. Figure A5: Presymptomatic subjects probability scores. Figure A6: Heatmap of the level of enrichment in KEGG pathways. Kmetzsch V, et al. J Neurol Neurosurg Psychiatry 2021; 92:485–493. doi: 10.1136/jnnp-2020-324647 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Neurol Neurosurg Psychiatry Appendix A1. Neuropsychological protocol The PREV-DEMALS cognitive evaluation included standardized neuropsychological tests to investigate all cognitive domains, and in particular frontal lobe functions. The scores were provided previously (Bertrand et al., 2018). Briefly, global cognitive efficiency was evaluated by means of Mini-Mental State Examination (MMSE) and Mattis Dementia Rating Scale (MDRS). Frontal executive functions were assessed with Frontal Assessment Battery (FAB), forward and backward digit spans, Trail Making Test part A and B (TMT-A and TMT-B), Wisconsin Card Sorting Test (WCST), and Symbol-Digit Modalities test. -
Mechanisms Underlying Phenotypic Heterogeneity in Simplex Autism Spectrum Disorders
Mechanisms Underlying Phenotypic Heterogeneity in Simplex Autism Spectrum Disorders Andrew H. Chiang Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2021 © 2021 Andrew H. Chiang All Rights Reserved Abstract Mechanisms Underlying Phenotypic Heterogeneity in Simplex Autism Spectrum Disorders Andrew H. Chiang Autism spectrum disorders (ASD) are a group of related neurodevelopmental diseases displaying significant genetic and phenotypic heterogeneity. Despite recent progress in ASD genetics, the nature of phenotypic heterogeneity across probands is not well understood. Notably, likely gene- disrupting (LGD) de novo mutations affecting the same gene often result in substantially different ASD phenotypes. We find that truncating mutations in a gene can result in a range of relatively mild decreases (15-30%) in gene expression due to nonsense-mediated decay (NMD), and show that more severe autism phenotypes are associated with greater decreases in expression. We also find that each gene with recurrent ASD mutations can be described by a parameter, phenotype dosage sensitivity (PDS), which characteriZes the relationship between changes in a gene’s dosage and changes in a given phenotype. Using simple linear models, we show that changes in gene dosage account for a substantial fraction of phenotypic variability in ASD. We further observe that LGD mutations affecting the same exon frequently lead to strikingly similar phenotypes in unrelated ASD probands. These patterns are observed for two independent proband cohorts and multiple important ASD-associated phenotypes. The observed phenotypic similarities are likely mediated by similar changes in gene dosage and similar perturbations to the relative expression of splicing isoforms. -
Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure
Journal of Personalized Medicine Article Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure Ismael Henarejos-Castillo 1,2 , Alejandro Aleman 1, Begoña Martinez-Montoro 3, Francisco Javier Gracia-Aznárez 4, Patricia Sebastian-Leon 1,3, Monica Romeu 5, Jose Remohi 2,6, Ana Patiño-Garcia 4,7 , Pedro Royo 3, Gorka Alkorta-Aranburu 4 and Patricia Diaz-Gimeno 1,3,* 1 IVI Foundation-Instituto de Investigación Sanitaria La Fe, Av. Fernando Abril Martorell 106, Torre A, Planta 1ª, 46026 Valencia, Spain; [email protected] (I.H.-C.); [email protected] (A.A.); [email protected] (P.S.-L.) 2 Department of Paediatrics, Obstetrics and Gynaecology, University of Valencia, Av. Blasco Ibáñez 15, 46010 Valencia, Spain; [email protected] 3 IVI-RMA Pamplona, Reproductive Medicine, C/Sangüesa, Número 15-Planta Baja, 31003 Pamplona, Spain; [email protected] (B.M.-M.); [email protected] (P.R.) 4 CIMA Lab Diagnostics, University of Navarra, IdiSNA, Avda Pio XII, 55, 31008 Pamplona, Spain; [email protected] (F.J.G.-A.); [email protected] (A.P.-G.); [email protected] (G.A.-A.) 5 Hospital Universitario y Politécnico La Fe, Av. Fernando Abril Martorell 106, 46026 Valencia, Spain; [email protected] 6 IVI-RMA Valencia, Reproductive Medicine, Plaça de la Policia Local, 3, 46015 Valencia, Spain 7 Laboratorio de Pediatría-Unidad de Genética Clínica, Clínica Universidad de Navarra, Avda Pio XII, 55, Citation: Henarejos-Castillo, I.; 31008 Pamplona, Spain Aleman, A.; Martinez-Montoro, B.; * Correspondence: [email protected] Gracia-Aznárez, F.J.; Sebastian-Leon, P.; Romeu, M.; Remohi, J.; Patiño- Abstract: Ovarian failure (OF) is a common cause of infertility usually diagnosed as idiopathic, Garcia, A.; Royo, P.; Alkorta-Aranburu, with genetic causes accounting for 10–25% of cases. -
Revealing the Acute Asthma Ignorome: Characterization and Validation of Uninvestigated Gene Networks
www.nature.com/scientificreports OPEN Revealing the acute asthma ignorome: characterization and validation of uninvestigated gene Received: 07 December 2015 Accepted: 01 April 2016 networks Published: 21 April 2016 Michela Riba1,*, Jose Manuel Garcia Manteiga1,*, Berislav Bošnjak2,*, Davide Cittaro1, Pavol Mikolka2,†, Connie Le2,‡, Michelle M. Epstein2,# & Elia Stupka1,# Systems biology provides opportunities to fully understand the genes and pathways in disease pathogenesis. We used literature knowledge and unbiased multiple data meta-analysis paradigms to analyze microarray datasets across different mouse strains and acute allergic asthma models. Our combined gene-driven and pathway-driven strategies generated a stringent signature list totaling 933 genes with 41% (440) asthma-annotated genes and 59% (493) ignorome genes, not previously associated with asthma. Within the list, we identified inflammation, circadian rhythm, lung-specific insult response, stem cell proliferation domains, hubs, peripheral genes, and super-connectors that link the biological domains (Il6, Il1ß, Cd4, Cd44, Stat1, Traf6, Rela, Cadm1, Nr3c1, Prkcd, Vwf, Erbb2). In conclusion, this novel bioinformatics approach will be a powerful strategy for clinical and across species data analysis that allows for the validation of experimental models and might lead to the discovery of novel mechanistic insights in asthma. Allergen exposure causes a complex interaction of cellular and molecular networks leading to allergic asthma in susceptible individuals. Experimental mouse models of allergic asthma are widely used to understand disease pathogenesis and elucidate mechanisms underlying the initiation of allergic asthma1. For example, gene profiling of lung tissue from experimental mice during the initiation of allergic asthma in experiments with different proto- cols2–7 validated well-known genes and identified new genes with roles in disease pathogenesis such as C53, Arg18, Adam89, and Pon17, and dissected pathways activated by Il1310 and Stat611. -
Novel Parent-Of-Origin-Specific Differentially Methylated Loci on Chromosome 16
CORE Metadata, citation and similar papers at core.ac.uk Provided by Digital Commons@Becker Washington University School of Medicine Digital Commons@Becker Open Access Publications 4-8-2019 Novel parent-of-origin-specific differentially methylated loci on chromosome 16 Katharina V Schulze Przemyslaw Szafranski Harry Lesmana Robert J Hopkin Aaron Hamvas See next page for additional authors Follow this and additional works at: https://digitalcommons.wustl.edu/open_access_pubs Authors Katharina V Schulze, Przemyslaw Szafranski, Harry Lesmana, Robert J Hopkin, Aaron Hamvas, Jennifer A Wambach, Marwan Shinawi, Gladys Zapata, Claudia M B Carvalho, Qian Liu, Justyna A Karolak, James R Lupski, Neil A Hanchard, and Paweł Stankiewicz Schulze et al. Clinical Epigenetics (2019) 11:60 https://doi.org/10.1186/s13148-019-0655-8 RESEARCH Open Access Novel parent-of-origin-specific differentially methylated loci on chromosome 16 Katharina V. Schulze1†, Przemyslaw Szafranski1†, Harry Lesmana2, Robert J. Hopkin2, Aaron Hamvas3, Jennifer A. Wambach4, Marwan Shinawi5, Gladys Zapata6, Claudia M. B. Carvalho1, Qian Liu1, Justyna A. Karolak1, James R. Lupski1,6,7, Neil A. Hanchard1,8*† and Paweł Stankiewicz1*† Abstract Background: Congenital malformations associated with maternal uniparental disomy of chromosome 16, upd(16)mat, resemble those observed in newborns with the lethal developmental lung disease, alveolar capillary dysplasia with misalignment of pulmonary veins (ACDMPV). Interestingly, ACDMPV-causative deletions, involving FOXF1 or its lung- specific upstream enhancer at 16q24.1, arise almost exclusively on the maternally inherited chromosome 16. Given the phenotypic similarities between upd(16)mat and ACDMPV, together with parental allelic bias in ACDMPV, we hypothesized that there may be unknown imprinted loci mapping to chromosome 16 that become functionally unmasked by chromosomal structural variants. -
Meta-Analysis of Gene Expression in Individuals with Autism Spectrum Disorders
Meta-analysis of Gene Expression in Individuals with Autism Spectrum Disorders by Carolyn Lin Wei Ch’ng BSc., University of Michigan Ann Arbor, 2011 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Science in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Bioinformatics) The University of British Columbia (Vancouver) August 2013 c Carolyn Lin Wei Ch’ng, 2013 Abstract Autism spectrum disorders (ASD) are clinically heterogeneous and biologically complex. State of the art genetics research has unveiled a large number of variants linked to ASD. But in general it remains unclear, what biological factors lead to changes in the brains of autistic individuals. We build on the premise that these heterogeneous genetic or genomic aberra- tions will converge towards a common impact downstream, which might be reflected in the transcriptomes of individuals with ASD. Similarly, a considerable number of transcriptome analyses have been performed in attempts to address this question, but their findings lack a clear consensus. As a result, each of these individual studies has not led to any significant advance in understanding the autistic phenotype as a whole. The goal of this research is to comprehensively re-evaluate these expression profiling studies by conducting a systematic meta-analysis. Here, we report a meta-analysis of over 1000 microarrays across twelve independent studies on expression changes in ASD compared to unaffected individuals, in blood and brain. We identified a number of genes that are consistently differentially expressed across studies of the brain, suggestive of effects on mitochondrial function. In blood, consistent changes were more difficult to identify, despite individual studies tending to exhibit larger effects than the brain studies. -
Epigenetic Changes in Human Model KMT2A Leukemias Highlight Early Events During Leukemogenesis
Epigenetic changes in human model KMT2A leukemias highlight early events during leukemogenesis Thomas Milan1, Magalie Celton1, Karine Lagacé1, Élodie Roques1, Safia Safa-Tahar-Henni1, Eva Bresson2,3,4, Anne Bergeron2,3,4, Josée Hebert5,6, Soheil Meshinchi7, Sonia Cellot8, Frédéric Barabé2,3,4, 1,6 Brian T Wilhelm Author contributions: BTW and TM designed the study, analyzed data and drafted the manuscript. TM and KL performed ChIP-seq and ATAC-seq, analysed the data and generated figures. MC performed Methyl-seq experiments, analysed data, generated figures and drafted the manuscript. SS-T-H assisted with the ATAC-seq analysis and generation of figures. KL, ER, EB, and AB helped with molecular biology work, cloning and cell culture. EB, AB and FB generated the model on mice from CD34+ cells. SM provided expression and clinical data for patient samples and JH and SC collected, characterized, and banked the local patient samples used in this study. Affiliations: 1Laboratory for High Throughput Biology, Institute for Research in Immunology and Cancer, Montréal, QC, Canada 2Centre de recherche en infectiologie du CHUL, Centre de recherche du CHU de Québec – Université Laval, Québec City, QC, Canada, 3CHU de Québec – Université Laval – Hôpital Enfant-Jésus; Québec City, QC, Canada; 4Department of Medicine, Université Laval, Quebec City, QC, Canada 5Division of Hematology-Oncology and Leukemia Cell Bank of Quebec, Maisonneuve-Rosemont Hospital, Montréal, QC, Canada, 6Department of Medicine, Université de Montréal, Montréal, QC, Canada, 7Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA 8Department of pediatrics, division of Hematology, Ste-Justine Hospital, Montréal, QC, Canada Running head: Early epigenetic changes in KM3-AML *Correspondence to: Brian T Wilhelm Institute for Research in Immunology and Cancer Université de Montréal P.O. -
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 ......................................................