DOCSLIB.ORG

View Is Portrayed Schematically in Figure 7B

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
View Is Portrayed Schematically in Figure 7B BASIC RESEARCH www.jasn.org Recombination Signal Binding Protein for Ig-kJ Region Regulates Juxtaglomerular Cell Phenotype by Activating the Myo-Endocrine Program and Suppressing Ectopic Gene Expression † † ‡ Ruth M. Castellanos-Rivera,* Ellen S. Pentz,* Eugene Lin,* Kenneth W. Gross, † Silvia Medrano,* Jing Yu,§ Maria Luisa S. Sequeira-Lopez,* and R. Ariel Gomez* *Department of Pediatrics, School of Medicine, †Department of Biology, Graduate School of Arts and Sciences, and §Department of Cell Biology, University of Virginia, Charlottesville, Virginia; and ‡Department of Molecular and Cellular Biology, Roswell Park Cancer Institute, Buffalo, New York ABSTRACT Recombination signal binding protein for Ig-kJ region (RBP-J), the major downstream effector of Notch signaling, is necessary to maintain the number of renin-positive juxtaglomerular cells and the plasticity of arteriolar smooth muscle cells to re-express renin when homeostasis is threatened. We hypothesized that RBP-J controls a repertoire of genes that defines the phenotype of the renin cell. Mice bearing a bacterial artificial chromosome reporter with a mutated RBP-J binding site in the renin promoter had markedly reduced reporter expression at the basal state and in response to a homeostatic challenge. Mice with conditional deletion of RBP-J in renin cells had decreased expression of endocrine (renin and Akr1b7)and smooth muscle (Acta2, Myh11, Cnn1,andSmtn) genes and regulators of smooth muscle expression (miR- 145, SRF, Nfatc4, and Crip1). To determine whether RBP-J deletion decreased the endowment of renin cells, we traced the fate of these cells in RBP-J conditional deletion mice. Notably, the lineage staining patterns in mutant and control kidneys were identical, although mutant kidneys had fewer or no renin- expressing cells in the juxtaglomerular apparatus. Microarray analysis of mutant arterioles revealed upreg- ulation of genes usually expressed in hematopoietic cells. Thus, these results suggest that RBP-J maintains the identity of the renin cell by not only activating genes characteristic of the myo-endocrine phenotype but also, preventing ectopic gene expression and adoption of an aberrant phenotype, which could have severe consequences for the control of homeostasis. J Am Soc Nephrol 26: 67–80, 2015. doi: 10.1681/ASN.2013101045 Juxtaglomerular (JG) cells are crucial for the JG cells express a unique set of genes character- maintenance of BP and fluid/electrolyte homeosta- istic of both endocrine and SM cells.7 This dual sis. These cells act as sensors that constantly monitor endocrine–contractile phenotype is crucial for the the physiologic status of the animal and convey rapid regulation of BP and renal hemodynamics. information to adjacent cells within the JG appa- However, the factors controlling this dual phenotype ratus and along the renal arterioles. Under normal circumstances, renin secretion by JG cells suffices to maintain homeostasis. However, when there is a Received October 4, 2013. Accepted April 4, 2014. more profound physiologic challenge, to restore Published online ahead of print. Publication date available at homeostasis, preexistent smooth muscle (SM) cells www.jasn.org. along the arterioles, mesangial cells, and interstitial Correspondence: Dr. R. Ariel Gomez, University of Virginia, School pericytes1 gradually dedifferentiate into renin- of Medicine, 409 Lane Road, MR4 Building 2001, Charlottesville, producing cells in a pattern resembling the one VA 22908. Email: [email protected] – found in fetal life.2 6 Copyright © 2015 by the American Society of Nephrology J Am Soc Nephrol 26: 67–80, 2015 ISSN : 1046-6673/2601-67 67 BASIC RESEARCH www.jasn.org and the mechanisms that regulate the ability of SM cells along Mut-BAC than treated WT-BAC mice (Figure 1F). As expec- the afferent arterioles to reacquire the renin phenotype have ted, renin staining and JGA index for renin were similar in not been fully defined. both groups, indicating that introduction of the BAC trans- JG cells and adjacent cells along the arterioles express all the gene did not disrupt the expression of the endogenous renin components of the Notch signaling pathway,7 an ancestral cell– gene (Figure 1, E and F). Together, these data indicate that cell communication system involved in cell fate decisions. We RBP-J regulates the renin promoter directly (Figure 7A) and hypothesized that the Notch pathway through its major tran- is involved in the ability of SM cells along the arteriole to scriptional effector, recombination signal binding protein for reacquire the renin phenotype. Ig-kJ region (RBP-J), maintains the identity of the JG cell and the ability of cells along the arterioles to regain the renin phe- RBP-J Deletion Does Not Affect the Endowment of notype when homeostasis is threatened. In fact, mice with Cells from the Renin Lineage conditional deletion of RBP-J (conditional knockout [cKO]) To determine whether the marked diminution in the number in cells from the renin lineage have very few JG cells, and SM of JG cells resulted from a decreased population or a change in cells along the afferent arterioles cannot reacquire the renin the distribution of cells from the renin lineage, we performed phenotype on a physiologic challenge.8 However, it was lineage studies in cKO and control mice harboring the R26R10 unclear whether in vivo Notch/RBP-J regulated the renin reporter. After cre-mediated recombination in the control and promoter directly and/or the expression of genes known to cKO mice, cells of the renin lineage express b-galactosidase be characteristic of or responsible for the dual endocrine– (b-gal), effectively labeling renin cells and their descendants. contractile phenotype of the renin cell. Therefore, we We found that, as expected, these RBP-J cKO;R26R mice had designed a series of experiments to test the hypothesis reduced renin expression (Supplemental Table 1) as previ- that RBP-J regulates a gene network that controls the dual ously described in cKO mice lacking the reporter.8 Interest- endocrine–contractile identity of the JG cell and the ability ingly, the distribution of b-gal–positive cells was identical in of cells upstream from the glomerulus to reacquire the renin the control and cKO kidneys (Figure 2), and the b-gal JGA phenotype. indices were the same (Supplemental Figure 1). In contrast to controls, costaining for renin verified that cKO mice had few or no renin-expressing cells in the JGAs, but they were still b-gal– RESULTS positive (Figure 2F, Supplemental Table 1). Furthermore, assays for apoptosis (Supplemental Table 2) and cell proliferation (not RBP-J Activates the Renin Promoter shown) showed no difference between control and cKO mice. To determine whether RBP-J directly affects renin expression, These data indicate that the decrease in the number of renin- we used a bacterial artificial chromosome (BAC) system to expressing cells was not caused by an increase in the percentage generate control wild-type BAC (WT-BAC) transgenic mice, in of dead cells or a decrease in the number and/or location of the which the first exon of the Ren1c gene was replaced with an renin precursors and subsequent progeny of renin-derived enhanced green fluorescent protein (GFP), and mutant BAC cells. Therefore, former renin-expressing cells and their de- (Mut-BAC) mice, in which the four nucleotides in the RBP-J scendants are still present in the appropriate locations in cKO consensus sequence critical for its binding9 were substituted in mice, although they are no longer capable of expressing renin, the BAC construct (Figure 1A). suggesting the possibility that they have adopted a different We studied two independent transgenic lines each contain- phenotype. ing two copies of the WT-BAC or Mut-BAC transgene. In the basal state, Mut-BAC mice had 87% lower GFP mRNA RBP-J Deletion Affects the Myo-Endocrine Phenotype expression than WT-BAC mice (Figure 1B). To investigate of Cells of the Renin Lineage whether Mut-BAC mice can increase the expression of GFP Given that cells of the renin lineage were still present in the under a physiologic threat, we treated Mut-BAC and WT-BAC appropriate locations in RBP-J cKO mice, suggesting that they mice with captopril and sodium depletion for 5 days, a ma- may have switched their phenotype, led us to investigate nipulation known to induce arteriolar SM cells upstream from whether repression of renin was accompanied by alterations the JG area (JGA) to reacquire the renin phenotype. Treated in the expression of other genes characteristic of renin cells.7 Mut-BAC mice had diminished capacity to increase GFP ex- Aldo-keto reductase 1b7 (Akr1b7), an independent endocrine pression (only 30% of that in treated WT-BAC mice) (Figure marker of JG cells, is coexpressed at almost the same level as 1C). In correspondence with the smaller increase in GFP ex- renin in JG cells throughout development and in response to pression in the treated Mut-BAC mice, immunohistochemis- physiologic manipulations.7 Akr1b7 immunostaining in cKO try for GFP showed that, in contrast to treated WT-BAC mice, mice was markedly diminished with respect to controls (Fig- treated Mut-BAC mice did not have GFP-positive cells up- ure 3A), and quantitation showed a significantly lower Akr1b7 stream of the arterioles distant from the glomeruli (Figure JGA index (Figure 3B). The decrease in the number of JGAs 1D). Quantification of the immunostaining results showed expressing Akr1b7 in the cKO mice was accompanied by a re- that the JGA index for GFP was significantly lower in treated duction in Akr1b7 mRNA expression to the same level as renin 68 Journal of the American Society of Nephrology J Am Soc Nephrol 26: 67–80, 2015 www.jasn.org BASIC RESEARCH Figure 1. RBP-J regulates the renin promoter in vivo. Mutation of the RBP-J site in the renin promoter diminishes GFP expression, a surrogate of renin expression.
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]
  • Activated Peripheral-Blood-Derived Mononuclear Cells
    Transcription factor expression in lipopolysaccharide- activated peripheral-blood-derived mononuclear cells Jared C. Roach*†, Kelly D. Smith*‡, Katie L. Strobe*, Stephanie M. Nissen*, Christian D. Haudenschild§, Daixing Zhou§, Thomas J. Vasicek¶, G. A. Heldʈ, Gustavo A. Stolovitzkyʈ, Leroy E. Hood*†, and Alan Aderem* *Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103; ‡Department of Pathology, University of Washington, Seattle, WA 98195; §Illumina, 25861 Industrial Boulevard, Hayward, CA 94545; ¶Medtronic, 710 Medtronic Parkway, Minneapolis, MN 55432; and ʈIBM Computational Biology Center, P.O. Box 218, Yorktown Heights, NY 10598 Contributed by Leroy E. Hood, August 21, 2007 (sent for review January 7, 2007) Transcription factors play a key role in integrating and modulating system. In this model system, we activated peripheral-blood-derived biological information. In this study, we comprehensively measured mononuclear cells, which can be loosely termed ‘‘macrophages,’’ the changing abundances of mRNAs over a time course of activation with lipopolysaccharide (LPS). We focused on the precise mea- of human peripheral-blood-derived mononuclear cells (‘‘macro- surement of mRNA concentrations. There is currently no high- phages’’) with lipopolysaccharide. Global and dynamic analysis of throughput technology that can precisely and sensitively measure all transcription factors in response to a physiological stimulus has yet to mRNAs in a system, although such technologies are likely to be be achieved in a human system, and our efforts significantly available in the near future. To demonstrate the potential utility of advanced this goal. We used multiple global high-throughput tech- such technologies, and to motivate their development and encour- nologies for measuring mRNA levels, including massively parallel age their use, we produced data from a combination of two distinct signature sequencing and GeneChip microarrays.
    [Show full text]
  • Atlas Antibodies in Breast Cancer Research Table of Contents
    ATLAS ANTIBODIES IN BREAST CANCER RESEARCH TABLE OF CONTENTS The Human Protein Atlas, Triple A Polyclonals and PrecisA Monoclonals (4-5) Clinical markers (6) Antibodies used in breast cancer research (7-13) Antibodies against MammaPrint and other gene expression test proteins (14-16) Antibodies identified in the Human Protein Atlas (17-14) Finding cancer biomarkers, as exemplified by RBM3, granulin and anillin (19-22) Co-Development program (23) Contact (24) Page 2 (24) Page 3 (24) The Human Protein Atlas: a map of the Human Proteome The Human Protein Atlas (HPA) is a The Human Protein Atlas consortium cell types. All the IHC images for Swedish-based program initiated in is mainly funded by the Knut and Alice the normal tissue have undergone 2003 with the aim to map all the human Wallenberg Foundation. pathology-based annotation of proteins in cells, tissues and organs expression levels. using integration of various omics The Human Protein Atlas consists of technologies, including antibody- six separate parts, each focusing on References based imaging, mass spectrometry- a particular aspect of the genome- 1. Sjöstedt E, et al. (2020) An atlas of the based proteomics, transcriptomics wide analysis of the human proteins: protein-coding genes in the human, pig, and and systems biology. mouse brain. Science 367(6482) 2. Thul PJ, et al. (2017) A subcellular map of • The Tissue Atlas shows the the human proteome. Science. 356(6340): All the data in the knowledge resource distribution of proteins across all eaal3321 is open access to allow scientists both major tissues and organs in the 3.
    [Show full text]
  • 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.
    [Show full text]
  • Predictive Toxicogenomics for the Identification of Chemical Carcinogens : Application to Human Hepatic Cell Lines
    Predictive toxicogenomics for the identification of chemical carcinogens : application to human hepatic cell lines Citation for published version (APA): Christina Magkoufopoulou, C. (2011). Predictive toxicogenomics for the identification of chemical carcinogens : application to human hepatic cell lines. Datawyse / Universitaire Pers Maastricht. https://doi.org/10.26481/dis.20111208cc Document status and date: Published: 01/01/2011 DOI: 10.26481/dis.20111208cc Document Version: Publisher's PDF, also known as Version of record Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.
    [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]
  • 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]
  • Page 1 Supplemental Table I Upin WTVG Upin Humlowfb FAR1
    Supplemental Table I UPinWTvG UPinHuMlowFB overlap FAR1 CD209 PLEKHO2 B2M IL27 GCLC CALR HMGN2P46 ME1 XPO1 CLEC1A NEK6 PDIA3 HSD17B14 TMEM106A SERPINH1 NSUN7 KCNJ15 PLEKHO2 SEMA6B SH3PXD2B HSPD1 BAALC RHOU SYVN1 CEACAM4 TGFBI DNAJB9 KATNAL2 CTSZ SLC25A19 CCDC175 SULF2 MHCII CARD14 P2RY6 MDN1 TDO2 CD74 GCLC FCAR HLA-DQB1 PTPN2 GLDN CD14 CHORDC1 MMP7 CSF2RB LOX CLEC5A MRC1 STIP1 ZMYND15 ITGAX BPIFB1 ITLN1 SLAMF7 ME1 DKK2 CD84 PDIA6 FAM124A FCGR2A HYOU1 F3 CLEC10A NLRC5 DZIP1L IFI30 NEK6 CECR6 CLEC4A SLC39A14 NDP CLEC7A TMEM106AFCGR1A TFEC KCNJ15 CCL7 C1QC SH3PXD2B CRABP2 C1QA RHOU PIPOX FOLR2 LRP2 CCL2 CH25H MVD VSIG4 C1QB TGFBI LINC01010 SIGLEC1 NFKB2 DPRXP4 CTSS C5 FAM20A CCR1 HSP90B1 ANKRD29 SLAMF8 ALDH18A1 OCSTAMP MS4A7 EDEM1 TGM2 HK3 CTSZ TM4SF19 CXCL10 C6 TRPV4 CTSK AACS CCL8 MSR1 PPA1 CCL1 STEAP4 PIK3R5 KCNE1 CXCL9 RASAL3 SLC9A7P1 MS4A6A DOCK11 CHI3L1 TIMP1 BHLHE40 LOC731424 CD209 HCLS1 DCSTAMP CCL7 FASN MSR1 PDCD1LG2 PDIA4 IL31RA CCL2 ITK CXCL3 CXCL2 CRELD2 TREM2 C15orf48 SFTPD MGST1 GPR84 BCL3 METTL7B CXCL5 SULF2 TMEM86A OCSTAMP CYP51A1 A2M SERPINA1 CREB3L1 AQP9 MMP12 DUSP2 NUPR1 CCL8 ADAM8 FHAD1 CCL24 P2RY6 YPEL4 FBP1 KCNAB2 FBP1 NA NFKBIE LOC100506585 NA FSCN1 CXCL16 NA MANF RAB13 NA SLC5A3 LOC391322 NA CTSC IL8 NA COTL1 MS4A4A NA HSPA5 SERPING1 NA MUC5B PLA2G4C NA CD74 CA12 NA HLA-DQB1 GBP1P1 NA SLC7A2 C11orf45 NA FABP5 ACVRL1 NA CIITA SPP1 NA RAB3IL1 TLN2 NA HSPE1 NDRG2 NA SCD C15orf48 NA ITIH4 KCNJ15 NA SERPINA3 MEIS3P1 NA LAG3 IL1RN NA FOXM1 HNMT NA CD14 CYP27B1 NA RRM2 CDCP1 NA ABCD2 FOLR2 NA FCRL2 ECM1 NA PDE3B ADAMDEC1
    [Show full text]
  • (12) Patent Application Publication (10) Pub. No.: US 2014/0304845 A1 Loboda Et Al
    US 201403.04845A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2014/0304845 A1 Loboda et al. (43) Pub. Date: Oct. 9, 2014 54) ALZHEMIERS DISEASE SIGNATURE Publicationublication ClassificatiClassification MARKERS AND METHODS OF USE (51) Int. Cl. (71) Applicant: MERCKSHARP & DOHME CORP, CI2O I/68 (2006.01) Rahway, NJ (US) AOIK 67/027 (2006.01) (52) U.S. Cl. (72) Inventors: SIES yet.sS); CPC .......... CI2O I/6883 (2013.01); A0IK 67/0275 Icnaei NepoZnyn, Yeadon, (2013.01) le.East,Italia; David J. Stone, Wyncote, USPC .............. 800/12:536/23.5:536/23.2:506/17 (US); Keith Tanis, Quakertown, PA (US); William J. Ray, Juniper, FL (US) (57) ABSTRACT (21) Appl. No.: 14/354,622 Methods, biomarkers, and expression signatures are dis 1-1. closed for assessing the disease progression of Alzheimer's (22) PCT Filed: Oct. 26, 2012 disease (AD). In one embodiment, BioAge (biological age), NdStress (neurodegenerative stress), Alz (Alzheimer), and (86). PCT No.: PCT/US12A62218 Inflame (inflammation) are used as biomarkers of AD pro S371 (c)(1), gression. In another aspect, the invention comprises a gene (2), (4) Date: Apr. 28, 2014 signature for evaluating disease progression. In still another Related U.S. Application Data abiliticises in (60) Provisional application No. 61/553,400, filed on Oct. used to identify animal models for use in the development and 31, 2011. evaluation of therapeutics for the treatment of AD. Patent Application Publication Oct. 9, 2014 Sheet 1 of 16 US 2014/0304845 A1 Y : : O O O O O O O O D O v v CN On cy Patent Application Publication Oct.
    [Show full text]
  • What Your Genome Doesn't Tell
    UC San Diego UC San Diego Electronic Theses and Dissertations Title Multi-layered epigenetic control of T cell fate decisions Permalink https://escholarship.org/uc/item/8rs7c7b3 Author Yu, Bingfei Publication Date 2018 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA SAN DIEGO Multi-layered epigenetic control of T cell fate decisions A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Biology by Bingfei Yu Committee in charge: Professor Ananda Goldrath, Chair Professor John Chang Professor Stephen Hedrick Professor Cornelis Murre Professor Wei Wang 2018 Copyright Bingfei Yu, 2018 All rights reserved. The dissertation of Bingfei Yu is approved, and it is ac- ceptable in quality and form for publication on microfilm and electronically: Chair University of California San Diego 2018 iii DEDICATION To my parents who have been giving me countless love, trust and support to make me who I am. iv EPIGRAPH Stay hungary. Stay foolish. | Steve Jobs quoted from the back cover of the 1974 edition of the Whole Earth Catalog v TABLE OF CONTENTS Signature Page.................................. iii Dedication..................................... iv Epigraph.....................................v Table of Contents................................. vi List of Figures.................................. ix Acknowledgements................................x Vita........................................ xii Abstract of
    [Show full text]
  • Adipogenesis at a Glance
    Cell Science at a Glance 2681 Adipogenesis at a Stephens, 2010). At the same time attention has This Cell Science at a Glance article reviews also shifted to many other aspects of adipocyte the transition of precursor stem cells into mature glance development, including efforts to identify, lipid-laden adipocytes, and the numerous isolate and manipulate relevant precursor stem molecules, pathways and signals required to Christopher E. Lowe, Stephen cells. Recent studies have revealed new accomplish this. O’Rahilly and Justin J. Rochford* intracellular pathways, processes and secreted University of Cambridge Metabolic Research factors that can influence the decision of these Adipocyte stem cells Laboratories, Institute of Metabolic Science, cells to become adipocytes. Pluripotent mesenchymal stem cells (MSCs) Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK Understanding the intricacies of adipogenesis can be isolated from several tissues, including *Author for correspondence ([email protected]) is of major relevance to human disease, as adipose tissue. Adipose-derived MSCs have the Journal of Cell Science 124, 2681-2686 adipocyte dysfunction makes an important capacity to differentiate into a variety of cell © 2011. Published by The Company of Biologists Ltd doi:10.1242/jcs.079699 contribution to metabolic disease in obesity types, including adipocytes, osteoblasts, (Unger et al., 2010). Thus, improving adipocyte chondrocytes and myocytes. Until recently, The formation of adipocytes from precursor function and the complementation or stem cells in the adipose tissue stromal vascular stem cells involves a complex and highly replacement of poorly functioning adipocytes fraction (SVF) have been typically isolated in orchestrated programme of gene expression. could be beneficial in common metabolic pools that contain a mixture of cell types, and the Our understanding of the basic network of disease.
    [Show full text]
  • Gene Networks Activated by Specific Patterns of Action Potentials in Dorsal Root Ganglia Neurons Received: 10 August 2016 Philip R
    www.nature.com/scientificreports OPEN Gene networks activated by specific patterns of action potentials in dorsal root ganglia neurons Received: 10 August 2016 Philip R. Lee1,*, Jonathan E. Cohen1,*, Dumitru A. Iacobas2,3, Sanda Iacobas2 & Accepted: 23 January 2017 R. Douglas Fields1 Published: 03 March 2017 Gene regulatory networks underlie the long-term changes in cell specification, growth of synaptic connections, and adaptation that occur throughout neonatal and postnatal life. Here we show that the transcriptional response in neurons is exquisitely sensitive to the temporal nature of action potential firing patterns. Neurons were electrically stimulated with the same number of action potentials, but with different inter-burst intervals. We found that these subtle alterations in the timing of action potential firing differentially regulates hundreds of genes, across many functional categories, through the activation or repression of distinct transcriptional networks. Our results demonstrate that the transcriptional response in neurons to environmental stimuli, coded in the pattern of action potential firing, can be very sensitive to the temporal nature of action potential delivery rather than the intensity of stimulation or the total number of action potentials delivered. These data identify temporal kinetics of action potential firing as critical components regulating intracellular signalling pathways and gene expression in neurons to extracellular cues during early development and throughout life. Adaptation in the nervous system in response to external stimuli requires synthesis of new gene products in order to elicit long lasting changes in processes such as development, response to injury, learning, and memory1. Information in the environment is coded in the pattern of action-potential firing, therefore gene transcription must be regulated by the pattern of neuronal firing.
    [Show full text]