Generation and Characterization of New Monoclonal Antibodies Targeting the PHF1 and AT8 Epitopes on Human Tau Kevin H
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Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
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. -
Activation of Naıve CD4 T Cells by Anti-CD3 Reveals an Important Role for Fyn in Lck-Mediated Signaling
Activation of naı¨ve CD4 T cells by anti-CD3 reveals an important role for Fyn in Lck-mediated signaling Katsuji Sugie*, Myung-Shin Jeon†, and Howard M. Grey*‡ Divisions of *Vaccine Discovery and †Cell Biology, La Jolla Institute for Allergy and Immunology, San Diego, CA 92121 Contributed by Howard M. Grey, August 23, 2004 Although there was no impairment in IL-2 secretion and prolifer- Fyn-deficient T cells by antigen-pulsed APCs led to a robust ation of Fyn-deficient naı¨veCD4 cells after stimulation with anti- proliferative and IL-2 response, whereas anti-CD3-mediated gen and antigen-presenting cells, stimulation of these cells with stimulation revealed a profound signaling defect in Fyn-deficient anti-CD3 and anti-CD28 revealed profound defects. Crosslinking of T cells. This defect could be corrected by the cocrosslinking of purified wild-type naı¨veCD4 cells with anti-CD3 activated Lck and CD3 with CD4. Furthermore, the signaling defect of Fyn- initiated the signaling cascade downstream of Lck, including phos- deficient T cells stimulated with anti-CD3 was restricted to naı¨ve phorylation of ZAP-70, LAT, and PLC-␥1; calcium flux; and dephos- T cells and was not observed when Fyn-deficient effector T cells phorylation and nuclear translocation of the nuclear factor of were analyzed. Biochemical experiments on anti-CD3 stimulated activated T cells (NFAT)p. All of these signaling events were cells strongly suggest that the presence of Fyn was necessary for diminished severely in Fyn-deficient naı¨ve cells activated by CD3 an effective recruitment and͞or activation of Lck in the region crosslinking. -
Supplementary Table S5. Differentially Expressed Gene Lists of PD-1High CD39+ CD8 Tils According to 4-1BB Expression Compared to PD-1+ CD39- CD8 Tils
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 Immunother Cancer Supplementary Table S5. Differentially expressed gene lists of PD-1high CD39+ CD8 TILs according to 4-1BB expression compared to PD-1+ CD39- CD8 TILs Up- or down- regulated genes in Up- or down- regulated genes Up- or down- regulated genes only PD-1high CD39+ CD8 TILs only in 4-1BBneg PD-1high CD39+ in 4-1BBpos PD-1high CD39+ CD8 compared to PD-1+ CD39- CD8 CD8 TILs compared to PD-1+ TILs compared to PD-1+ CD39- TILs CD39- CD8 TILs CD8 TILs IL7R KLRG1 TNFSF4 ENTPD1 DHRS3 LEF1 ITGA5 MKI67 PZP KLF3 RYR2 SIK1B ANK3 LYST PPP1R3B ETV1 ADAM28 H2AC13 CCR7 GFOD1 RASGRP2 ITGAX MAST4 RAD51AP1 MYO1E CLCF1 NEBL S1PR5 VCL MPP7 MS4A6A PHLDB1 GFPT2 TNF RPL3 SPRY4 VCAM1 B4GALT5 TIPARP TNS3 PDCD1 POLQ AKAP5 IL6ST LY9 PLXND1 PLEKHA1 NEU1 DGKH SPRY2 PLEKHG3 IKZF4 MTX3 PARK7 ATP8B4 SYT11 PTGER4 SORL1 RAB11FIP5 BRCA1 MAP4K3 NCR1 CCR4 S1PR1 PDE8A IFIT2 EPHA4 ARHGEF12 PAICS PELI2 LAT2 GPRASP1 TTN RPLP0 IL4I1 AUTS2 RPS3 CDCA3 NHS LONRF2 CDC42EP3 SLCO3A1 RRM2 ADAMTSL4 INPP5F ARHGAP31 ESCO2 ADRB2 CSF1 WDHD1 GOLIM4 CDK5RAP1 CD69 GLUL HJURP SHC4 GNLY TTC9 HELLS DPP4 IL23A PITPNC1 TOX ARHGEF9 EXO1 SLC4A4 CKAP4 CARMIL3 NHSL2 DZIP3 GINS1 FUT8 UBASH3B CDCA5 PDE7B SOGA1 CDC45 NR3C2 TRIB1 KIF14 TRAF5 LIMS1 PPP1R2C TNFRSF9 KLRC2 POLA1 CD80 ATP10D CDCA8 SETD7 IER2 PATL2 CCDC141 CD84 HSPA6 CYB561 MPHOSPH9 CLSPN KLRC1 PTMS SCML4 ZBTB10 CCL3 CA5B PIP5K1B WNT9A CCNH GEM IL18RAP GGH SARDH B3GNT7 C13orf46 SBF2 IKZF3 ZMAT1 TCF7 NECTIN1 H3C7 FOS PAG1 HECA SLC4A10 SLC35G2 PER1 P2RY1 NFKBIA WDR76 PLAUR KDM1A H1-5 TSHZ2 FAM102B HMMR GPR132 CCRL2 PARP8 A2M ST8SIA1 NUF2 IL5RA RBPMS UBE2T USP53 EEF1A1 PLAC8 LGR6 TMEM123 NEK2 SNAP47 PTGIS SH2B3 P2RY8 S100PBP PLEKHA7 CLNK CRIM1 MGAT5 YBX3 TP53INP1 DTL CFH FEZ1 MYB FRMD4B TSPAN5 STIL ITGA2 GOLGA6L10 MYBL2 AHI1 CAND2 GZMB RBPJ PELI1 HSPA1B KCNK5 GOLGA6L9 TICRR TPRG1 UBE2C AURKA Leem G, et al. -
Supplementary Table S1. Correlation Between the Mutant P53-Interacting Partners and PTTG3P, PTTG1 and PTTG2, Based on Data from Starbase V3.0 Database
Supplementary Table S1. Correlation between the mutant p53-interacting partners and PTTG3P, PTTG1 and PTTG2, based on data from StarBase v3.0 database. PTTG3P PTTG1 PTTG2 Gene ID Coefficient-R p-value Coefficient-R p-value Coefficient-R p-value NF-YA ENSG00000001167 −0.077 8.59e-2 −0.210 2.09e-6 −0.122 6.23e-3 NF-YB ENSG00000120837 0.176 7.12e-5 0.227 2.82e-7 0.094 3.59e-2 NF-YC ENSG00000066136 0.124 5.45e-3 0.124 5.40e-3 0.051 2.51e-1 Sp1 ENSG00000185591 −0.014 7.50e-1 −0.201 5.82e-6 −0.072 1.07e-1 Ets-1 ENSG00000134954 −0.096 3.14e-2 −0.257 4.83e-9 0.034 4.46e-1 VDR ENSG00000111424 −0.091 4.10e-2 −0.216 1.03e-6 0.014 7.48e-1 SREBP-2 ENSG00000198911 −0.064 1.53e-1 −0.147 9.27e-4 −0.073 1.01e-1 TopBP1 ENSG00000163781 0.067 1.36e-1 0.051 2.57e-1 −0.020 6.57e-1 Pin1 ENSG00000127445 0.250 1.40e-8 0.571 9.56e-45 0.187 2.52e-5 MRE11 ENSG00000020922 0.063 1.56e-1 −0.007 8.81e-1 −0.024 5.93e-1 PML ENSG00000140464 0.072 1.05e-1 0.217 9.36e-7 0.166 1.85e-4 p63 ENSG00000073282 −0.120 7.04e-3 −0.283 1.08e-10 −0.198 7.71e-6 p73 ENSG00000078900 0.104 2.03e-2 0.258 4.67e-9 0.097 3.02e-2 Supplementary Table S2. -
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 -
Supplementary Material Contents
Supplementary Material Contents Immune modulating proteins identified from exosomal samples.....................................................................2 Figure S1: Overlap between exosomal and soluble proteomes.................................................................................... 4 Bacterial strains:..............................................................................................................................................4 Figure S2: Variability between subjects of effects of exosomes on BL21-lux growth.................................................... 5 Figure S3: Early effects of exosomes on growth of BL21 E. coli .................................................................................... 5 Figure S4: Exosomal Lysis............................................................................................................................................ 6 Figure S5: Effect of pH on exosomal action.................................................................................................................. 7 Figure S6: Effect of exosomes on growth of UPEC (pH = 6.5) suspended in exosome-depleted urine supernatant ....... 8 Effective exosomal concentration....................................................................................................................8 Figure S7: Sample constitution for luminometry experiments..................................................................................... 8 Figure S8: Determining effective concentration ......................................................................................................... -
The Proteasomal Deubiquitinating Enzyme PSMD14 Regulates Macroautophagy by Controlling Golgi-To-ER Retrograde Transport
Supplementary Materials The proteasomal deubiquitinating enzyme PSMD14 regulates macroautophagy by controlling Golgi-to-ER retrograde transport Bustamante HA., et al. Figure S1. siRNA sequences directed against human PSMD14 used for Validation Stage. Figure S2. Primer pairs sequences used for RT-qPCR. Figure S3. The PSMD14 DUB inhibitor CZM increases the Golgi apparatus area. Immunofluorescence microscopy analysis of the Golgi area in parental H4 cells treated for 4 h either with the vehicle (DMSO; Control) or CZM. The Golgi marker GM130 was used to determine the region of interest in each condition. Statistical significance was determined by Student's t-test. Bars represent the mean ± SEM (n =43 cells). ***P <0.001. Figure S4. CZM causes the accumulation of KDELR1-GFP at the Golgi apparatus. HeLa cells expressing KDELR1-GFP were either left untreated or treated with CZM for 30, 60 or 90 min. Cells were fixed and representative confocal images were acquired. Figure S5. Effect of CZM on proteasome activity. Parental H4 cells were treated either with the vehicle (DMSO; Control), CZM or MG132, for 90 min. Protein extracts were used to measure in vitro the Chymotrypsin-like peptidase activity of the proteasome. The enzymatic activity was quantified according to the cleavage of the fluorogenic substrate Suc-LLVY-AMC to AMC, and normalized to that of control cells. The statistical significance was determined by One-Way ANOVA, followed by Tukey’s test. Bars represent the mean ± SD of biological replicates (n=3). **P <0.01; n.s., not significant. Figure S6. Effect of CZM and MG132 on basal macroautophagy. (A) Immunofluorescence microscopy analysis of the subcellular localization of LC3 in parental H4 cells treated with either with the vehicle (DMSO; Control), CZM for 4 h or MG132 for 6 h. -
Transcriptional Regulation by Polycomb Group Proteins
RE V IE W Transcriptional regulation by Polycomb group proteins Luciano Di Croce1,2 & Kristian Helin3–5 Polycomb group (PcG) proteins are epigenetic regulators of transcription that have key roles in stem-cell identity, differentiation and disease. Mechanistically, they function within multiprotein complexes, called Polycomb repressive complexes (PRCs), which modify histones (and other proteins) and silence target genes. The dynamics of PRC1 and PRC2 components has been the focus of recent research. Here we discuss our current knowledge of the PRC complexes, how they are targeted to chromatin and how the high diversity of the PcG proteins allows these complexes to influence cell identity. The DNA of eukaryotic cells is organized in chromatin fibers, with PcG and TrxG appear to be required for propagation of the repressed the nucleosome forming the basic repeating unit. Each nucleo- or activated transcriptional state during the cell-division cycle4,5. some comprises 145–147 bp of DNA wrapped in 1.8 helical turns TrxG and PcG proteins are involved in maintaining cellular identity around an octamer of four highly evolutionarily conserved histone and are required for normal differentiation6,7. Moreover, in accord- proteins—H2A, H2B, H3 and H4. Histone H1 binds the linker DNA ance with the notion that cancer is a disease of stem cells and dif- between two adjacent nucleosomes, causing further compaction of ferentiation, TrxG and PcG genes are frequently found to be mutated the chromatin fibers into higher-order structures, often referred to as and/or deregulated in cancer8–10. In this Review, we will discuss recent solenoids. Analysis of the crystal structure of the nucleosome revealed advances in understanding of the role of PcG proteins in gene regula- that N-terminal histone tails are flexible and protrude outward tion, specifically in controlling self-renewal of embryonic stem cells from the nucleosome core1. -
Immune Presentation and Recognition of Class I MHC Phosphopeptide Antigens
Immune presentation and recognition of class I MHC phosphopeptide antigens by Daniel Henry Stones A thesis submitted to the University of Birmingham for the degree of DOCTOR OF PHILOSOPHY Supervisor: Professor Benjamin E. Willcox University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. Abstract Alterations to metabolic pathways, in particular post-translational modification, are a recognised hallmark of diseases such as autoimmunity, inflammation and cancer, and potentially provide a source of altered self antigens that can stimulate immune responses. Most notably, phosphorylated peptides have emerged as a group of tumour associated antigens which can be presented by MHC molecules and recognised by T-cells, and therefore represent promising candidates for future cancer immunotherapy strategies. However, how antigen phosphorylation impacts upon antigen presentation and recognition remains unclear. During this study I demonstrated that the phosphate moiety of phosphopeptides bearing the canonical P4 phosphorylation -
Mass Spectrometry–Based Identification of MHC-Bound
PROTOCOL https://doi.org/10.1038/s41596-019-0133-y Mass spectrometry–based identification of MHC-bound peptides for immunopeptidomics Anthony W. Purcell 1*, Sri H. Ramarathinam 1 and Nicola Ternette2* Peptide antigens bound to molecules encoded by the major histocompatibility complex (MHC) and presented on the cell surface form the targets of T lymphocytes. This critical arm of the adaptive immune system facilitates the eradication of pathogen-infected and cancerous cells, as well as the production of antibodies. Methods to identify these peptide antigens are critical to the development of new vaccines, for which the goal is the generation of effective adaptive immune responses and long-lasting immune memory. Here, we describe a robust protocol for the identification of MHC-bound peptides from cell lines and tissues, using nano-ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (nUPLC–MS/MS) and recent improvements in methods for isolation and characterization of these peptides. The protocol starts with the immunoaffinity capture of naturally processed MHC-peptide complexes. The peptides dissociate from the class I human leukocyte antigens (HLAs) upon acid denaturation. This peptide cargo is then extracted and separated into fractions by HPLC, and the peptides in these fractions are identified using nUPLC–MS/MS. With this protocol, several thousand peptides can be identified from a wide variety of cell types, including cancerous and infected cells and those from tissues, with a turnaround time of 2–3d. 1234567890():,; 1234567890():,; Introduction The recognition of foreign antigens by T lymphocytes is the cornerstone of adaptive immunity. Specifically, intracellular antigens are degraded in the cytosol and antigenic precursor peptides are transported to the endoplasmic reticulum, where they are further trimmed to short peptides (8–11 amino acids (aa)) that can assemble with class I HLAs. -
Analysis of Gene Expression in Wild Type and Notch1 Mutant Retinal Cells by Single Cell Profiling Karolina Mizeracka1, Jeffrey M
Research Article Developmental Dynamics DOI 10.1002/dvdy.24006 Analysis of gene expression in wild type and Notch1 mutant retinal cells by single cell profiling Karolina Mizeracka1, Jeffrey M. Trimarchi1,2, Michael B. Stadler3, Constance L. Cepko1,4 1 Department of Genetics, Department of Ophthalmology, Harvard Medical School, Boston, MA 02115 2 Current Address: Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50014 3 Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland 4 Howard Hughes Medical Institute, Department of Genetics, Department of Ophthalmology, Harvard Medical School, Boston, MA 02115 Correspondence: Constance Cepko 77 Avenue Louis Pasteur, Boston, MA 02115 Phone: (617) 432-7618 Fax: (617) 432-7595 Running title: Single cell profiling of Notch1 mutant retinal cells Key words: retina, progenitor, microarray, cell fate Summary: • Profiling of individual Notch1 deficient and wild type postnatal retinal cells on microarrays reveals changes in gene expression obscured by whole tissue analysis • Notch1 deficient cells downregulate progenitor and cell cycle markers with a concomitant upregulation in early rod photoreceptor markers • Based on classification, single Notch1 deficient and wild type cells represent Developmental Dynamics transition from progenitor to postmitotic cell • Individual wild type retinal cells express cell type markers of both photoreceptors and interneurons Grant sponsor and number: National Institutes of Health Grant R01EY09676 Accepted Articles are accepted, unedited articles for future issues, temporarily published online in advance of the final edited version. © 2013 Wiley Periodicals, Inc. Received: Mar 04, 2013; Revised: May 02, 2013; Accepted: May 13, 2013 Developmental Dynamics Page 2 of 66 Abstract Background: The vertebrate retina comprises sensory neurons, the photoreceptors, as well as many other types of neurons and one type of glial cell.