Hair Cells Use Active Zones with Different Voltage Dependence Of
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PLATFORM ABSTRACTS Abstract Abstract Numbers Numbers Tuesday, November 6 41
American Society of Human Genetics 62nd Annual Meeting November 6–10, 2012 San Francisco, California PLATFORM ABSTRACTS Abstract Abstract Numbers Numbers Tuesday, November 6 41. Genes Underlying Neurological Disease Room 134 #196–#204 2. 4:30–6:30pm: Plenary Abstract 42. Cancer Genetics III: Common Presentations Hall D #1–#6 Variants Ballroom 104 #205–#213 43. Genetics of Craniofacial and Wednesday, November 7 Musculoskeletal Disorders Room 124 #214–#222 10:30am–12:45 pm: Concurrent Platform Session A (11–19): 44. Tools for Phenotype Analysis Room 132 #223–#231 11. Genetics of Autism Spectrum 45. Therapy of Genetic Disorders Room 130 #232–#240 Disorders Hall D #7–#15 46. Pharmacogenetics: From Discovery 12. New Methods for Big Data Ballroom 103 #16–#24 to Implementation Room 123 #241–#249 13. Cancer Genetics I: Rare Variants Room 135 #25–#33 14. Quantitation and Measurement of Friday, November 9 Regulatory Oversight by the Cell Room 134 #34–#42 8:00am–10:15am: Concurrent Platform Session D (47–55): 15. New Loci for Obesity, Diabetes, and 47. Structural and Regulatory Genomic Related Traits Ballroom 104 #43–#51 Variation Hall D #250–#258 16. Neuromuscular Disease and 48. Neuropsychiatric Disorders Ballroom 103 #259–#267 Deafness Room 124 #52–#60 49. Common Variants, Rare Variants, 17. Chromosomes and Disease Room 132 #61–#69 and Everything in-Between Room 135 #268–#276 18. Prenatal and Perinatal Genetics Room 130 #70–#78 50. Population Genetics Genome-Wide Room 134 #277–#285 19. Vascular and Congenital Heart 51. Endless Forms Most Beautiful: Disease Room 123 #79–#87 Variant Discovery in Genomic Data Ballroom 104 #286–#294 52. -
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. -
Noninvasive Sleep Monitoring in Large-Scale Screening of Knock-Out Mice
bioRxiv preprint doi: https://doi.org/10.1101/517680; this version posted January 11, 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-ND 4.0 International license. Noninvasive sleep monitoring in large-scale screening of knock-out mice reveals novel sleep-related genes Shreyas S. Joshi1*, Mansi Sethi1*, Martin Striz1, Neil Cole2, James M. Denegre2, Jennifer Ryan2, Michael E. Lhamon3, Anuj Agarwal3, Steve Murray2, Robert E. Braun2, David W. Fardo4, Vivek Kumar2, Kevin D. Donohue3,5, Sridhar Sunderam6, Elissa J. Chesler2, Karen L. Svenson2, Bruce F. O'Hara1,3 1Dept. of Biology, University of Kentucky, Lexington, KY 40506, USA, 2The Jackson Laboratory, Bar Harbor, ME 04609, USA, 3Signal solutions, LLC, Lexington, KY 40503, USA, 4Dept. of Biostatistics, University of Kentucky, Lexington, KY 40536, USA, 5Dept. of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA. 6Dept. of Biomedical Engineering, University of Kentucky, Lexington, KY 40506, USA. *These authors contributed equally Address for correspondence and proofs: Shreyas S. Joshi, Ph.D. Dept. of Biology University of Kentucky 675 Rose Street 101 Morgan Building Lexington, KY 40506 U.S.A. Phone: (859) 257-2805 FAX: (859) 257-1717 Email: [email protected] Running title: Sleep changes in knockout mice bioRxiv preprint doi: https://doi.org/10.1101/517680; this version posted January 11, 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. -
Supplementary Figures 1-14 and Supplementary References
SUPPORTING INFORMATION Spatial Cross-Talk Between Oxidative Stress and DNA Replication in Human Fibroblasts Marko Radulovic,1,2 Noor O Baqader,1 Kai Stoeber,3† and Jasminka Godovac-Zimmermann1* 1Division of Medicine, University College London, Center for Nephrology, Royal Free Campus, Rowland Hill Street, London, NW3 2PF, UK. 2Insitute of Oncology and Radiology, Pasterova 14, 11000 Belgrade, Serbia 3Research Department of Pathology and UCL Cancer Institute, Rockefeller Building, University College London, University Street, London WC1E 6JJ, UK †Present Address: Shionogi Europe, 33 Kingsway, Holborn, London WC2B 6UF, UK TABLE OF CONTENTS 1. Supplementary Figures 1-14 and Supplementary References. Figure S-1. Network and joint spatial razor plot for 18 enzymes of glycolysis and the pentose phosphate shunt. Figure S-2. Correlation of SILAC ratios between OXS and OAC for proteins assigned to the SAME class. Figure S-3. Overlap matrix (r = 1) for groups of CORUM complexes containing 19 proteins of the 49-set. Figure S-4. Joint spatial razor plots for the Nop56p complex and FIB-associated complex involved in ribosome biogenesis. Figure S-5. Analysis of the response of emerin nuclear envelope complexes to OXS and OAC. Figure S-6. Joint spatial razor plots for the CCT protein folding complex, ATP synthase and V-Type ATPase. Figure S-7. Joint spatial razor plots showing changes in subcellular abundance and compartmental distribution for proteins annotated by GO to nucleocytoplasmic transport (GO:0006913). Figure S-8. Joint spatial razor plots showing changes in subcellular abundance and compartmental distribution for proteins annotated to endocytosis (GO:0006897). Figure S-9. Joint spatial razor plots for 401-set proteins annotated by GO to small GTPase mediated signal transduction (GO:0007264) and/or GTPase activity (GO:0003924). -
Review of Hair Cell Synapse Defects in Sensorineural Hearing Impairment
Otology & Neurotology 34:995Y1004 Ó 2013, Otology & Neurotology, Inc. Review of Hair Cell Synapse Defects in Sensorineural Hearing Impairment *†‡Tobias Moser, *Friederike Predoehl, and §Arnold Starr *InnerEarLab, Department of Otolaryngology, University of Go¨ttingen Medical School; ÞSensory Research Center SFB 889, þBernstein Center for Computational Neuroscience, University of Go¨ttingen, Go¨ttingen, Germany; and §Department of Neurology, University of California, Irvine, California, U.S.A. Objective: To review new insights into the pathophysiology of are similar to those accompanying auditory neuropathy, a group sensorineural hearing impairment. Specifically, we address defects of genetic and acquired disorders of spiral ganglion neurons. of the ribbon synapses between inner hair cells and spiral ganglion Genetic auditory synaptopathies include alterations of glutamate neurons that cause auditory synaptopathy. loading of synaptic vesicles, synaptic Ca2+ influx or synaptic Data Sources and Study Selection: Here, we review original vesicle turnover. Acquired synaptopathies include noise-induced publications on the genetics, animal models, and molecular hearing loss because of excitotoxic synaptic damage and subse- mechanisms of hair cell ribbon synapses and their dysfunction. quent gradual neural degeneration. Alterations of ribbon synapses Conclusion: Hair cell ribbon synapses are highly specialized to likely also contribute to age-related hearing loss. Key Words: enable indefatigable sound encoding with utmost temporal precision. GeneticsVIon -
APPL1 Associates with Trka and GIPC1 and Is Required for Nerve Growth Factor-Mediated Signal Transduction
University of Montana ScholarWorks at University of Montana Biomedical and Pharmaceutical Sciences Faculty Publications Biomedical and Pharmaceutical Sciences 12-2006 APPL1 Associates with TrkA and GIPC1 and is Required for Nerve Growth Factor-Mediated Signal Transduction D. C. Lin C. Quevedo N. E. Brewer A. Bell J. R. Testa See next page for additional authors Follow this and additional works at: https://scholarworks.umt.edu/biopharm_pubs Part of the Medical Sciences Commons, and the Pharmacy and Pharmaceutical Sciences Commons Let us know how access to this document benefits ou.y Recommended Citation Lin, D. C.; Quevedo, C.; Brewer, N. E.; Bell, A.; Testa, J. R.; Grimes, Mark L.; Miller, F. D.; and Kaplan, D. R., "APPL1 Associates with TrkA and GIPC1 and is Required for Nerve Growth Factor-Mediated Signal Transduction" (2006). Biomedical and Pharmaceutical Sciences Faculty Publications. 13. https://scholarworks.umt.edu/biopharm_pubs/13 This Article is brought to you for free and open access by the Biomedical and Pharmaceutical Sciences at ScholarWorks at University of Montana. It has been accepted for inclusion in Biomedical and Pharmaceutical Sciences Faculty Publications by an authorized administrator of ScholarWorks at University of Montana. For more information, please contact [email protected]. Authors D. C. Lin, C. Quevedo, N. E. Brewer, A. Bell, J. R. Testa, Mark L. Grimes, F. D. Miller, and D. R. Kaplan This article is available at ScholarWorks at University of Montana: https://scholarworks.umt.edu/biopharm_pubs/13 MOLECULAR AND CELLULAR BIOLOGY, Dec. 2006, p. 8928–8941 Vol. 26, No. 23 0270-7306/06/$08.00ϩ0 doi:10.1128/MCB.00228-06 Copyright © 2006, American Society for Microbiology. -
Table S1. Identified Proteins with Exclusive Expression in Cerebellum of Rats of Control, 10Mg F/L and 50Mg F/L Groups
Table S1. Identified proteins with exclusive expression in cerebellum of rats of control, 10mg F/L and 50mg F/L groups. Accession PLGS Protein Name Group IDa Score Q3TXS7 26S proteasome non-ATPase regulatory subunit 1 435 Control Q9CQX8 28S ribosomal protein S36_ mitochondrial 197 Control P52760 2-iminobutanoate/2-iminopropanoate deaminase 315 Control Q60597 2-oxoglutarate dehydrogenase_ mitochondrial 67 Control P24815 3 beta-hydroxysteroid dehydrogenase/Delta 5-->4-isomerase type 1 84 Control Q99L13 3-hydroxyisobutyrate dehydrogenase_ mitochondrial 114 Control P61922 4-aminobutyrate aminotransferase_ mitochondrial 470 Control P10852 4F2 cell-surface antigen heavy chain 220 Control Q8K010 5-oxoprolinase 197 Control P47955 60S acidic ribosomal protein P1 190 Control P70266 6-phosphofructo-2-kinase/fructose-2_6-bisphosphatase 1 113 Control Q8QZT1 Acetyl-CoA acetyltransferase_ mitochondrial 402 Control Q9R0Y5 Adenylate kinase isoenzyme 1 623 Control Q80TS3 Adhesion G protein-coupled receptor L3 59 Control B7ZCC9 Adhesion G-protein coupled receptor G4 139 Control Q6P5E6 ADP-ribosylation factor-binding protein GGA2 45 Control E9Q394 A-kinase anchor protein 13 60 Control Q80Y20 Alkylated DNA repair protein alkB homolog 8 111 Control P07758 Alpha-1-antitrypsin 1-1 78 Control P22599 Alpha-1-antitrypsin 1-2 78 Control Q00896 Alpha-1-antitrypsin 1-3 78 Control Q00897 Alpha-1-antitrypsin 1-4 78 Control P57780 Alpha-actinin-4 58 Control Q9QYC0 Alpha-adducin 270 Control Q9DB05 Alpha-soluble NSF attachment protein 156 Control Q6PAM1 Alpha-taxilin 161 -
Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A. -
Confirmation of Pathogenic Mechanisms by SARS-Cov-2–Host
Messina et al. Cell Death and Disease (2021) 12:788 https://doi.org/10.1038/s41419-021-03881-8 Cell Death & Disease ARTICLE Open Access Looking for pathways related to COVID-19: confirmation of pathogenic mechanisms by SARS-CoV-2–host interactome Francesco Messina 1, Emanuela Giombini1, Chiara Montaldo1, Ashish Arunkumar Sharma2, Antonio Zoccoli3, Rafick-Pierre Sekaly2, Franco Locatelli4, Alimuddin Zumla5, Markus Maeurer6,7, Maria R. Capobianchi1, Francesco Nicola Lauria1 and Giuseppe Ippolito 1 Abstract In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level, but the mechanisms of interaction between host and SARS-CoV-2, determining the grade of COVID-19 severity, are still unknown. We provide a network analysis on protein–protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus interactome was inferred, applying an explorative algorithm (Random Walk with Restart, RWR) triggered by 28 proteins of SARS-CoV-2. The analysis of PPI allowed to estimate the distribution of SARS-CoV-2 proteins in the host cell. Interactome built around one single viral protein allowed to define a different response, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, the network-based approach highlighted a possible direct action of ORF3a and NS7b to enhancing Bradykinin Storm. This network-based representation of SARS-CoV-2 infection could be a framework for pathogenic evaluation of specific 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; clinical outcomes. -
And Pancreatic Cancer: from the Role of Evs to the Interference with EV-Mediated Reciprocal Communication
biomedicines Review Extracellular Vesicles (EVs) and Pancreatic Cancer: From the Role of EVs to the Interference with EV-Mediated Reciprocal Communication 1, 1, 1 1 1 Sokviseth Moeng y, Seung Wan Son y, Jong Sun Lee , Han Yeoung Lee , Tae Hee Kim , Soo Young Choi 1, Hyo Jeong Kuh 2 and Jong Kook Park 1,* 1 Department of Biomedical Science and Research Institute for Bioscience & Biotechnology, Hallym University, Chunchon 24252, Korea; [email protected] (S.M.); [email protected] (S.W.S.); [email protected] (J.S.L.); [email protected] (H.Y.L.); [email protected] (T.H.K.); [email protected] (S.Y.C.) 2 Department of Medical Life Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-33-248-2114 These authors contributed equally. y Received: 29 June 2020; Accepted: 1 August 2020; Published: 3 August 2020 Abstract: Pancreatic cancer is malignant and the seventh leading cause of cancer-related deaths worldwide. However, chemotherapy and radiotherapy are—at most—moderately effective, indicating the need for new and different kinds of therapies to manage this disease. It has been proposed that the biologic properties of pancreatic cancer cells are finely tuned by the dynamic microenvironment, which includes extracellular matrix, cancer-associated cells, and diverse immune cells. Accumulating evidence has demonstrated that extracellular vesicles (EVs) play an essential role in communication between heterogeneous subpopulations of cells by transmitting multiplex biomolecules. EV-mediated cell–cell communication ultimately contributes to several aspects of pancreatic cancer, such as growth, angiogenesis, metastasis and therapeutic resistance. -
Integrative Bulk and Single-Cell Profiling of Premanufacture T-Cell Populations Reveals Factors Mediating Long-Term Persistence of CAR T-Cell Therapy
Published OnlineFirst April 5, 2021; DOI: 10.1158/2159-8290.CD-20-1677 RESEARCH ARTICLE Integrative Bulk and Single-Cell Profiling of Premanufacture T-cell Populations Reveals Factors Mediating Long-Term Persistence of CAR T-cell Therapy Gregory M. Chen1, Changya Chen2,3, Rajat K. Das2, Peng Gao2, Chia-Hui Chen2, Shovik Bandyopadhyay4, Yang-Yang Ding2,5, Yasin Uzun2,3, Wenbao Yu2, Qin Zhu1, Regina M. Myers2, Stephan A. Grupp2,5, David M. Barrett2,5, and Kai Tan2,3,5 Downloaded from cancerdiscovery.aacrjournals.org on October 1, 2021. © 2021 American Association for Cancer Research. Published OnlineFirst April 5, 2021; DOI: 10.1158/2159-8290.CD-20-1677 ABSTRACT The adoptive transfer of chimeric antigen receptor (CAR) T cells represents a breakthrough in clinical oncology, yet both between- and within-patient differences in autologously derived T cells are a major contributor to therapy failure. To interrogate the molecular determinants of clinical CAR T-cell persistence, we extensively characterized the premanufacture T cells of 71 patients with B-cell malignancies on trial to receive anti-CD19 CAR T-cell therapy. We performed RNA-sequencing analysis on sorted T-cell subsets from all 71 patients, followed by paired Cellular Indexing of Transcriptomes and Epitopes (CITE) sequencing and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) on T cells from six of these patients. We found that chronic IFN signaling regulated by IRF7 was associated with poor CAR T-cell persistence across T-cell subsets, and that the TCF7 regulon not only associates with the favorable naïve T-cell state, but is maintained in effector T cells among patients with long-term CAR T-cell persistence. -
1 Supplemental Table 1. Demographics, Clinicopathological
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) Gut 1 Supplemental Table 1. Demographics, clinicopathological, and operative characteristics of archived iCCA specimens included in immunohistochemical survival correlative analyses. Patients with perioperative mortality (within 30 days) were excluded from survival analysis. Supplemental Table 2. Demographics, clinicopathological, and treatment characteristics of patients with unresectable CCA treated at the University of Rochester Medical Center with complete blood counts included for peripheral monocyte and neutrophil to lymphocyte ratio analysis. Univariate and Multivariate Models constructed using the Cox Proportional Hazard method [risk ratio (95% Confidence interval)]. Supplemental Table 3. Antibodies used for IHC, IHF, and flow cytometry analysis. IHC, immunohistochemistry; IHF, immunofluorescence; FC, flow cytometry. Supplemental Table 4 Enrollment characteristics of spontaneous tumour bearing mice enrolled into therapeutic trial. P value determined by Mann-Whitney U or χ2 test. Supplemental Table 5 Differentially expressed protein coding genes (DEGs) from RNA-sequencing analysis of bone marrow derived macrophages educated with tumour supernatant. DEGs compare anti-GM-CSF and IgG control treated conditions.Genes filtered to include DEGs with Log2(Fold Change) < -1.0 or >1.0 and p< 0.05. Supplemental Table 6 Pathway enrichment analysis of downregulated Gene Ontology Biological Processes from RNA- sequencing analysis of bone marrow derived macrophages educated with tumour supernatant. Pathways compare anti-GM-CSF and IgG control treated conditions. Gene sets were filtered based on p value <0.05 and Log2(Fold Change) >1.5. Supplemental Table 7 Differentially expressed protein coding genes (DEGs) from RNA-sequencing analysis of URCCA4.3 treated tumours.