DOCSLIB.ORG

UC Riverside UCR Honors Capstones 2020-2021

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
UC Riverside UCR Honors Capstones 2020-2021 UC Riverside UCR Honors Capstones 2020-2021 Title Transcriptomic Analysis of Molecular Mechanisms of Neuroprotection by Neuregulin-1 Following Ischemic Stroke Permalink https://escholarship.org/uc/item/1c89b11s Author Bennett, Kimberly R. Publication Date 2021-08-13 Data Availability The data associated with this publication are within the manuscript. eScholarship.org Powered by the California Digital Library University of California TRANSCRIPTOMIC ANALYSIS OF MOLECULAR MECHANISMS OF NEUROPROTECTION BY NERUEGULIN-1 FOLLOWING ISCHEMIC STROKE By Kimberly R. Bennett A capstone project submitted for graduation with University Honors May 06, 2021 University Honors University of California, Riverside APPROVED Dr. Victor G. J. Rodgers Department of Bioengineering Dr. Byron D. Ford Department of Biomedical Sciences Dr. Richard Cardullo, Howard H Hays Jr. Chair University Honors ABSTRACT Ischemic stroke is a global health problem that is characterized by early neuronal death, apoptosis, inflammation, and oxidative stress following an obstruction of the blood supply to the brain. Previous studies have shown that ischemic stroke causes a release of pro-inflammatory cytokines that produce changes in gene expression, primarily in inflammation and cell death. Neuregulin-1 (NRG-1) is growth factor that has been investigated for its neuroprotective properties and ability to delay neuronal death following ischemic stroke. While NRG-1 has shown significant promise in preventing brain damage and stimulating post-injury repair following stroke, the mechanisms behind its neuroprotective effects are unclear. The goal of this research was to investigate the effects of NRG-1 treatment on ischemia-induced gene expression profiles following a permanent middle cerebral artery occlusion (pMCAO) in rat models. Rats were sacrificed twelve hours following vehicle or NRG-1 treatment. From the total RNA extracted from the rat brains, mRNA from the peri-infarct cortex was used to generate microarrays, which were then hybridized to an Affymetrix Rat Genome 2.0st Gene Chip and analyzed using the Affymetrix Transcriptome Analysis Console (TAC) Software. Our results show that NRG-1 delivery increases the regulation of pro-survival genes. Most notably, NRG-1 treatment upregulates the CREB1 and FOXO1 transcription factors, which are involved in increasing anti-inflammatory and cell proliferation responses and decreasing apoptosis and oxidative stress responses, respectively. These findings provide novel insight into the molecular mechanisms involved in neuregulin-1 neuroprotection. Further region-specific research is necessary to elucidate the importance of these genes in stroke and the significance of their regulation by NRG-1. 2 ACKNOWLEDGEMENTS I would like to thank my principal investigator and my faculty mentor, Dr. Victor Rodgers, for his enthusiastic support and mentorship throughout my entire time in the B2K Group. I would also like to thank our collaborating mentor, Dr. Byron Ford, for his consistent dedication towards providing me with an engaging and enriching experience in the laboratory. I also express special thanks to two graduate students, Jennifer Yang and Catherine Augello, for being dependable mentors and being open to any questions I had. Having experiments of their own, as well as other commitments, their patience and willingness to explain various concepts, techniques, and assays was instrumental to my success. Lastly, I would express my appreciation for all members of the B2K Group and the Ford Lab. I would not have been able to conduct this project, or any research for that matter, without the supportive environment and collaborative team effort put forth by everyone. Additionally, I would like to recognize the Bourns College of Engineering, the UC Riverside School of Medicine, the MARC U-STAR program, and the University Honors Program for granting me the opportunity to accomplish scientific research and share my findings with the academic community, as well as nurturing my interest in the pursuit for novel discovery. Overall, I am positive the skills and experiences I have learned from my time in the B2K Group will serve me well in the near future, as I will attend the Harvard-MIT Health Sciences and Technology PhD Program. 3 TABLE OF CONTENTS Abstract…………………..………………………………………………………………………. 2 Acknowledgements………………………………………………………………………………. 3 Table of Contents………………………………………………………………………………… 4 Introduction………………………………………………………………………………………. 5 Methodology……………………………………………………………………………………... 7 Animals.……………………………………………………………………………….…. 7 Microarray Analysis.………………………………………………..………..…….……. 8 Multiplex Transcription Factor Array…………………………….…………………...… 9 Immunohistochemistry...……...……………………………………..………..…….…... 10 Results………………………………………………………………………………………...… 11 Microarray Analysis.………………………………………………..………..…….…... 11 Multiplex Transcription Factor Array…………………………….…………………......17 Immunohistochemistry...……...……………………………………..………..…….…... 18 Discussion……………..………………………………………………………………………... 21 Conclusion……………………………………………………………………………………… 23 References……………………………………………………………………………………… 24 Supplementary……………..…………………………………………………………………… 26 4 INTRODUCTION Stroke is the 5th leading cause of death worldwide, with about 87% of cases being ischemic stroke (1). Differing from hemorrhagic stroke, which occurs due to internal blood loss, ischemic stroke occurs when the brain blood supply is obstructed. Ischemic stroke is characterized by cell death, inflammation, and oxidative stress, that occur through molecular cascades and changes in regulatory gene expression (2,3). The only approved treatment for ischemic stroke is tPA, a limited and time-sensitive treatment aimed at restoring blood flow that only 3-5% of patients qualify for (4). Given the wide-spread and dire need for an effective ischemic stroke treatment that can be administered outside the three-hour therapeutic window of tPA, a better understanding of the pathophysiology associated with ischemic stroke is necessary to develop effective therapeutic treatments aimed at reducing the progression of the associated cell death. Previous studies have shown that ischemic stroke causes a release of pro-inflammatory cytokines that produce changes in gene expression, primarily in inflammation and cell death (5,6). The initial area of injury, known as the infarct core, develops within minutes of stroke onset and develops up to three hours after stroke. The infarct core is characterized by low cerebral blood flow, oxidative stress, and excitotoxicity, which is accompanied by an increased production of inflammatory molecules. The resulting inflammation begins to affect a larger area of brain tissue, known as the ischemic penumbra, where neurons can survive up to twenty-four hours after stroke onset, prolonging the therapeutic window for ischemic stroke treatment (7–9). Neuregulin-1 has been investigated as a potential therapeutic for ischemic stroke for its various roles in the nervous system. Neuregulin-1 is a small protein first discovered by several laboratories researching cancer biology mechanisms, cancer biology mechanisms, neuromuscular 5 junction function and Schwann cell proliferation. Multiple reports from our laboratory have demonstrated that the exogenous delivery of NRG-1 reduces neuronal damage in the penumbral region of rats that underwent middle cerebral artery occlusion in an extended therapeutic window (10–13). While NRG-1 has shown significant promise in preventing brain damage and stimulating post-injury repair following stroke, the regulatory mechanisms behind its neuroprotective effects are unclear. The investigation of ischemia-induced gene expression profiles following NRG-1 administration will provide valuable insight into the transcriptional regulation of these processes. In this study, we performed a computational transcriptomic analysis to identify gene expression profiles and their associated transcriptional mechanisms affected by NRG-1 treatment twelve hours following permanent MCAO using the Transcriptome Analysis Console (TAC) software. TAC software allows for the statistical analysis of differential gene expression. To investigate mechanisms involved in the inflammatory and cell death responses that occur in the penumbra, we identified genes that were significantly regulated following NRG-1 treatment at twelve hours following ischemic stroke and the transcription factors that regulated the expression of these genes. Additionally, we verified transcriptional regulation activity of these factors through a Multiplex Transcription Factor Array. Cellular processes were investigated through literature search and STRING database analysis, and the brain cell types involved in these processes were visually displayed using immunohistochemistry (which remains incomplete due to COVID-19). Understanding the mechanisms regulated by the NRG-1 phosphorylation cascade may explain the neuroprotective effects of NRG-1 on a molecular level. These findings could support the development of clinical studies using NRG-1 to treat patients with ischemic stroke. 6 METHODS Animals All animals were treated humanely with regard for the alleviation of pain and suffering. All surgical protocols involving animals were performed by aseptic techniques and were approved by the Institutional Animal Care and Use Committee at Morehouse School of Medicine prior to the experiment. Male adult Sprague-Dawley rattus norvegucis (250-300g; Charles River Laboratory International, Inc., USA) were housed in standard cages in a temperature-controlled
Load more

Recommended publications
  • Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis.
    [Show full text]
  • DNA Methylation Regulates Discrimination of Enhancers From
    Sharifi-Zarchi et al. BMC Genomics (2017) 18:964 DOI 10.1186/s12864-017-4353-7 RESEARCHARTICLE Open Access DNA methylation regulates discrimination of enhancers from promoters through a H3K4me1-H3K4me3 seesaw mechanism Ali Sharifi-Zarchi1,2,3,4†, Daniela Gerovska5†, Kenjiro Adachi6, Mehdi Totonchi3, Hamid Pezeshk7,8, Ryan J. Taft9, Hans R. Schöler6,10, Hamidreza Chitsaz2, Mehdi Sadeghi8,11, Hossein Baharvand3,12* and Marcos J. Araúzo-Bravo5,13,14* Abstract Background: DNA methylation at promoters is largely correlated with inhibition of gene expression. However, the role of DNA methylation at enhancers is not fully understood, although a crosstalk with chromatin marks is expected. Actually, there exist contradictory reports about positive and negative correlations between DNA methylation and H3K4me1, a chromatin hallmark of enhancers. Results: We investigated the relationship between DNA methylation and active chromatin marks through genome- wide correlations, and found anti-correlation between H3K4me1 and H3K4me3 enrichment at low and intermediate DNA methylation loci. We hypothesized “seesaw” dynamics between H3K4me1 and H3K4me3 in the low and intermediate DNA methylation range, in which DNA methylation discriminates between enhancers and promoters, marked by H3K4me1 and H3K4me3, respectively. Low methylated regions are H3K4me3 enriched, while those with intermediate DNA methylation levels are progressively H3K4me1 enriched. Additionally, the enrichment of H3K27ac, distinguishing active from primed enhancers, follows a plateau in the lower range of the intermediate DNA methylation level, corresponding to active enhancers, and decreases linearly in the higher range of the intermediate DNA methylation. Thus, the decrease of the DNA methylation switches smoothly the state of the enhancers from a primed to an active state.
    [Show full text]
  • Understanding Chronic Kidney Disease: Genetic and Epigenetic Approaches
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2017 Understanding Chronic Kidney Disease: Genetic And Epigenetic Approaches Yi-An Ko Ko University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, Genetics Commons, and the Systems Biology Commons Recommended Citation Ko, Yi-An Ko, "Understanding Chronic Kidney Disease: Genetic And Epigenetic Approaches" (2017). Publicly Accessible Penn Dissertations. 2404. https://repository.upenn.edu/edissertations/2404 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2404 For more information, please contact [email protected]. Understanding Chronic Kidney Disease: Genetic And Epigenetic Approaches Abstract The work described in this dissertation aimed to better understand the genetic and epigenetic factors influencing chronic kidney disease (CKD) development. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) significantly associated with chronic kidney disease. However, these studies have not effectively identified target genes for the CKD variants. Most of the identified variants are localized to non-coding genomic regions, and how they associate with CKD development is not well-understood. As GWAS studies only explain a small fraction of heritability, we hypothesized that epigenetic changes could explain part of this missing heritability. To identify potential gene targets of the genetic variants, we performed expression quantitative loci (eQTL) analysis, using genotyping arrays and RNA sequencing from human kidney samples. To identify the target genes of CKD-associated SNPs, we integrated the GWAS-identified SNPs with the eQTL results using a Bayesian colocalization method, coloc. This resulted in a short list of target genes, including PGAP3 and CASP9, two genes that have been shown to present with kidney phenotypes in knockout mice.
    [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]
  • 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.
    [Show full text]
  • 1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor
    xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A
    [Show full text]
  • A Systems Chemoproteomic Analysis of Acyl-Coa/Protein Interaction Networks
    bioRxiv preprint doi: https://doi.org/10.1101/665281; this version posted July 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A systems chemoproteomic analysis of acyl-CoA/protein interaction networks Michaella J. Levy,1# David C. Montgomery,2# Mihaela E. Sardiu,1 Sarah E. Bergholtz,2 Kellie D. Nance, 2 Jose Montano,2 Abigail L. Thorpe,2 Stephen D. Fox,3 Qishan Lin,4 Thorkell Andresson,3 Laurence Florens,1 Michael P. Washburn,1,5 and Jordan L. Meier2* 1. Stowers Institute for Medical Research, Kansas City, MO, 64110, USA 2. Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA. 3. Laboratory of Proteomics and Analytical Technologies, Leidos, Inc, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, USA 4. RNA Epitranscriptomics & Proteomics Resource, University of Albany, 1400 Washington Ave, Albany, NY 12222 5. Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, 66160, USA # M.J.L. and D.C.M. contributed equally to this work. * Lead contact to whom correspondence should be addressed. Email: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/665281; this version posted July 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Summary 2 Acyl-CoA/protein interactions are required for many functions essential to life including membrane synthesis, 3 oxidative metabolism, and macromolecular acetylation.
    [Show full text]
  • Modeling the Human Segmentation Clock with Pluripotent Stem Cells 2 3 Mitsuhiro Matsuda1,10, Yoshihiro Yamanaka2,10, Maya Uemura2,3, Mitsujiro Osawa4, 4 Megumu K
    bioRxiv preprint doi: https://doi.org/10.1101/562447; this version posted February 27, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Modeling the Human Segmentation Clock with Pluripotent Stem Cells 2 3 Mitsuhiro Matsuda1,10, Yoshihiro Yamanaka2,10, Maya Uemura2,3, Mitsujiro Osawa4, 4 Megumu K. Saito4, Ayako Nagahashi4, Megumi Nishio3, Long Guo5, Shiro Ikegawa5, 5 Satoko Sakurai6, Shunsuke Kihara7, Michiko Nakamura6, Tomoko Matsumoto6, Hiroyuki 6 Yoshitomi2,3, Makoto Ikeya6, Takuya Yamamoto6,8, Knut Woltjen6,9, Miki Ebisuya1*, 7 Junya Toguchida2,3, Cantas Alev2* 8 9 1 Laboratory for Reconstitutive Developmental Biology, RIKEN Center for Biosystems 10 Dynamics Research (RIKEN BDR), Kobe 650-0047, Japan. 11 2 Department of Cell Growth and Differentiation, Center for iPS Cell Research and 12 Application (CiRA), Kyoto University, Kyoto 606-8507, Japan. 13 3 Department of Regeneration Science and Engineering, Institute for Frontier Life and 14 Medical Sciences, Kyoto University, Kyoto 606-8507, Japan. 15 4 Department of Clinical Application, Center for iPS Cell Research and Application 16 (CiRA), Kyoto University, Kyoto 606-8507, Japan. 17 5 Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical 18 Sciences (RIKEN IMS), Tokyo 108-8639, Japan. 19 6 Department of Life Science Frontiers, Center for iPS Cell Research and Application 20 (CiRA), Kyoto University, 606-8507, Kyoto 108-8639, Japan. 21 7 Department of Fundamental Cell Technology, Center for iPS Cell Research and 22 Application (CiRA), Kyoto University, Kyoto 606-8507, Japan. 23 8 AMED-CREST, AMED 1-7-1 Otemachi, Chiyodaku, Tokyo 100-004, Japan.
    [Show full text]
  • Identification of Jade1, a Gene Encoding a PHD Zinc Finger Protein, in a Gene Trap Mutagenesis Screen for Genes Involved in Anteroposterior Axis Development
    Edinburgh Research Explorer Identification of Jade1, a gene encoding a PHD zinc finger protein, in a gene trap mutagenesis screen for genes involved in anteroposterior axis development Citation for published version: Tzouanacou, E, Tweedie, S & Wilson, V 2003, 'Identification of Jade1, a gene encoding a PHD zinc finger protein, in a gene trap mutagenesis screen for genes involved in anteroposterior axis development', Molecular and Cellular Biology, vol. 23, no. 23, pp. 8553-2. https://doi.org/10.1128/MCB.23.23.8553- 8562.2003 Digital Object Identifier (DOI): 10.1128/MCB.23.23.8553-8562.2003 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: Molecular and Cellular Biology Publisher Rights Statement: RoMEO blue General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 02. Oct. 2021 MOLECULAR AND CELLULAR BIOLOGY, Dec. 2003, p. 8553–8562 Vol. 23, No. 23 0270-7306/03/$08.00ϩ0 DOI: 10.1128/MCB.23.23.8553–8562.2003 Copyright © 2003, American Society for Microbiology.
    [Show full text]
  • Table SII. Significantly Differentially Expressed Mrnas of GSE23558 Data Series with the Criteria of Adjusted P<0.05 And
    Table SII. Significantly differentially expressed mRNAs of GSE23558 data series with the criteria of adjusted P<0.05 and logFC>1.5. Probe ID Adjusted P-value logFC Gene symbol Gene title A_23_P157793 1.52x10-5 6.91 CA9 carbonic anhydrase 9 A_23_P161698 1.14x10-4 5.86 MMP3 matrix metallopeptidase 3 A_23_P25150 1.49x10-9 5.67 HOXC9 homeobox C9 A_23_P13094 3.26x10-4 5.56 MMP10 matrix metallopeptidase 10 A_23_P48570 2.36x10-5 5.48 DHRS2 dehydrogenase A_23_P125278 3.03x10-3 5.40 CXCL11 C-X-C motif chemokine ligand 11 A_23_P321501 1.63x10-5 5.38 DHRS2 dehydrogenase A_23_P431388 2.27x10-6 5.33 SPOCD1 SPOC domain containing 1 A_24_P20607 5.13x10-4 5.32 CXCL11 C-X-C motif chemokine ligand 11 A_24_P11061 3.70x10-3 5.30 CSAG1 chondrosarcoma associated gene 1 A_23_P87700 1.03x10-4 5.25 MFAP5 microfibrillar associated protein 5 A_23_P150979 1.81x10-2 5.25 MUCL1 mucin like 1 A_23_P1691 2.71x10-8 5.12 MMP1 matrix metallopeptidase 1 A_23_P350005 2.53x10-4 5.12 TRIML2 tripartite motif family like 2 A_24_P303091 1.23x10-3 4.99 CXCL10 C-X-C motif chemokine ligand 10 A_24_P923612 1.60x10-5 4.95 PTHLH parathyroid hormone like hormone A_23_P7313 6.03x10-5 4.94 SPP1 secreted phosphoprotein 1 A_23_P122924 2.45x10-8 4.93 INHBA inhibin A subunit A_32_P155460 6.56x10-3 4.91 PICSAR P38 inhibited cutaneous squamous cell carcinoma associated lincRNA A_24_P686965 8.75x10-7 4.82 SH2D5 SH2 domain containing 5 A_23_P105475 7.74x10-3 4.70 SLCO1B3 solute carrier organic anion transporter family member 1B3 A_24_P85099 4.82x10-5 4.67 HMGA2 high mobility group AT-hook 2 A_24_P101651
    [Show full text]
  • RNA Splicing in the Transition from B Cells
    RNA Splicing in the Transition from B Cells to Antibody-Secreting Cells: The Influences of ELL2, Small Nuclear RNA, and Endoplasmic Reticulum Stress This information is current as of September 24, 2021. Ashley M. Nelson, Nolan T. Carew, Sage M. Smith and Christine Milcarek J Immunol 2018; 201:3073-3083; Prepublished online 8 October 2018; doi: 10.4049/jimmunol.1800557 Downloaded from http://www.jimmunol.org/content/201/10/3073 Supplementary http://www.jimmunol.org/content/suppl/2018/10/05/jimmunol.180055 http://www.jimmunol.org/ Material 7.DCSupplemental References This article cites 44 articles, 16 of which you can access for free at: http://www.jimmunol.org/content/201/10/3073.full#ref-list-1 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision by guest on September 24, 2021 • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2018 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology RNA Splicing in the Transition from B Cells to Antibody-Secreting Cells: The Influences of ELL2, Small Nuclear RNA, and Endoplasmic Reticulum Stress Ashley M.
    [Show full text]
  • Genome-Wide Association Study and Functional Validation Implicates JADE1 in Tauopathy
    bioRxiv preprint doi: https://doi.org/10.1101/2021.06.30.450599; this version posted July 1, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Title: Genome-wide association study and functional validation implicates JADE1 in tauopathy Authors: Kurt Farrell1,2, SoongHo Kim1,2, Natalia Han1,2, Megan A. Iida1,2, Elias Gonzalez3, Marcos Otero-Garcia4, Jamie Walker5, Tim Richardson5, Alan E. Renton2,6, Shea J. Andrews2,6, Brian Fulton-Howard2,6, Jack Humphrey2,6, Ricardo A. Vialle2,6, Kathryn R. Bowles2,6, Kristen Whitney1,2, Diana K. Dangoor1,2, Edoardo Marcora2,6, Marco M. Hefti7, Alicia Casella1,2, Cheick Sissoko1,2, Manav Kapoor 2,6, Gloriia Novikova2,6, Evan Udine2,6, Garrett Wong2,6,Weijing Tang30, Tushar Bhangale8, Julie Hunkapiller8, Gai Ayalon9, Rob Graham10, Jonathan D. Cherry11, Etty Cortes1,2, Valeriy Borukov1,2, Ann C. McKee11, Thor D. Stein11, Jean-Paul Vonsattel12, Andy F. Teich12, Marla Gearing13, Jonathan Glass13, Juan C. Troncoso14, Matthew P. Frosch15, Bradley T. Hyman15, Dennis W. Dickson16, Melissa E. Murray16, Johannes Attems17, Margaret E. Flanagan18, Qinwen Mao18, M-Marsel Mesulam18, Sandra Weintraub18, Randy Woltjer19, Thao Pham19, Julia Kofler20, Julie A. Schneider21, Lei Yu21, Dushyant P. Purohit1,22, Vahram Haroutunian22, Patrick R. Hof2, Sam Gandy22,38, Mary Sano22, Thomas G. Beach23, Wayne Poon24, Claudia Kawas39, María Corrada24, Robert A. Rissman25, Jeff Metcalf25, Sara Shuldberg25, Bahar Salehi25, Peter T. Nelson26, John Q. Trojanowski27, Edward B. Lee27, David A. Wolk27, Corey T. McMillan28, Dirk C. Keene29, Thomas J. Montine29,30, Gabor G.
    [Show full text]