Fibrogenic Signals Persist in DAA-Treated HCV Patients After Sustained Virological Response

Total Page:16

File Type:pdf, Size:1020Kb

Fibrogenic Signals Persist in DAA-Treated HCV Patients After Sustained Virological Response Journal Pre-proof Fibrogenic signals persist in DAA-treated HCV patients after sustained virological response Claudia Montaldo, Michela Terri, Veronica Riccioni, Cecilia Battistelli, Veronica Bordoni, Gianpiero D’Offizi, Maria Giulia Prado, Flavia Trionfetti, Tiziana Vescovo, Eleonora Tartaglia, Raffaele Strippoli, Chiara Agrati, Marco Tripodi PII: S0168-8278(21)01901-2 DOI: https://doi.org/10.1016/j.jhep.2021.07.003 Reference: JHEPAT 8358 To appear in: Journal of Hepatology Received Date: 21 December 2020 Revised Date: 22 June 2021 Accepted Date: 1 July 2021 Please cite this article as: Montaldo C, Terri M, Riccioni V, Battistelli C, Bordoni V, D’Offizi G, Prado MG, Trionfetti F, Vescovo T, Tartaglia E, Strippoli R, Agrati C, Tripodi M, Fibrogenic signals persist in DAA- treated HCV patients after sustained virological response, Journal of Hepatology (2021), doi: https:// doi.org/10.1016/j.jhep.2021.07.003. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2021 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved. Journal Pre-proof Fibrogenic signals persist in DAA-treated HCV patients after sustained virological response Claudia Montaldo1,*, Michela Terri1,2,*, Veronica Riccioni2, Cecilia Battistelli2, Veronica Bordoni1, Gianpiero D’Offizi1, Maria Giulia Prado2, Flavia Trionfetti1,2, Tiziana Vescovo1, Eleonora Tartaglia1, Raffaele Strippoli1,2, Chiara Agrati1 and Marco Tripodi1,2. 1National Institute for Infectious Diseases L. Spallanzani, IRCCS, 2Istituto Pasteur Italia-Fondazione Cenci Bolognetti, Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy. *These authors equally contributed Corresponding Author: Prof. Marco Tripodi, Department of Molecular Medicine, Sapienza University of Rome, Viale Regina Elena 324, Rome 00161, Italy. Tel. +390649918244; Fax: +39064462891; E-mail: [email protected]; Keywords: Fibrosis, HCV, DAA, Extracellular vesicles, miRNAs, Proteomics, nLC-MS/MS, DIAPH1, miR204-5p, miR181a-5p, miR93-5p, miR143-3p. Word count (main text): 6173 Figures and table number: 4 Figures, 4 Tables Supplementary data: 4 Figures, 3 Tables Supplementary material: 1 MS Office File Conflict of interests: The authors declare no potential conflicts of interest. Financial support: This study was funded by the EXO-DAA project, L.R. Lazio 13/08, Ministry for Health of Italy (Ricerca Corrente) and Sapienza University of Rome RG11916B6A9C42C7 (to M.T.), Italian Ministry of Health Ricerca Finalizzata GR-2013-02359524 to JournalCM and TV Pre-proof, Consorzio Interuniversitario Biotecnologie: fellowship to MGP. Authors’ contributions C. Montaldo: Investigation, conceptualization, data curation, formal analysis, validation, investigation, visualization, methodology. M. Terri: data curation, conceptualization, formal analysis, validation, investigation, visualization, methodology. V. Riccioni: data curation, formal analysis, 1 validation, investigation, visualization, methodology. C. Battistelli: data curation, formal analysis, validation, investigation, visualization, writing-original draft and editing. V. Bordoni: data curation, formal analysis, methodology. G.P D’Offizi: patients’ management, clinical data analysis. M.G. Prado: data curation, formal analysis, validation and methodology. F. Trionfetti: investigation, visualization, methodology. T. Vescovo: data curation, formal analysis, validation, investigation, visualization, methodology. E. Tartaglia: data curation, methodology. R. Strippoli: supervision, visualization, editing original draft. C. Agrati: conceptualization, resources, funding acquisition, visualization, project administration. M. Tripodi: conceptualization, resources, supervision, funding acquisition, visualization, writing-original draft, project administration and editing. Journal Pre-proof 2 Abstract Background & Aims: HCV SVR, achievable now by means of DAA therapy, identifies a new class of patients requiring medical surveillance to be designed in relation to the liver disease stage advancement. To this end, identification of both disease biomarkers and therapeutic targets appears necessary. Methods: Extracellular Vesicles (EVs) purified from plasma of 15 healthy donors (HD), and 16 HCV infected patients before (T0) and after (T6) DAA treatment have been utilised for functional and miRNA cargo analysis. EVs purified from plasma of 17 HD, 23 T0 and T6 patients have been employed for proteomic and western blot analysis. Functional analysis in LX2 cells measured fibrotic markers (mRNAs and proteins) in response to EVs. Structural analysis was performed by qPCR, label-free liquid chromatography-mass spectrometry (nLC-MS/MS) and Western blot. Results: On the basis of observations indicating functional differences (i.e. modulation of FN-1, ACTA2, Smad2/3 phosphorylation, collagen deposition) of plasma-derived EVs from HD, T0 and T6, we performed EVs structural analysis. We found consistent differences in terms of both miRNA and protein cargos: (i) antifibrogenic miR204-5p, miR181a-5p, miR143-3p, miR93-5p and miR122- 5p were found statistically underrepresented in T0 EVs with respect to HD whereas miR204-5p and miR143-3p were found statistically underrepresented between HD and T6 (p-value<0.05) (ii) proteomic analysis highlighted, in both T0 and T6, the modulation of several proteins with respect to HD; among them, the fibrogenic DIAPH1 was confirmed upregulated by western blot (4.4 Log2 fold change). Journal Pre-proof Conclusions: Taken together, these results highlight structural EVs modifications, conceivably causal for long-term liver disease progression in HCV patients that persists despite the DAA-mediated HCV SVR. 3 Introduction New direct antiviral agents (DAAs)-based therapy, available with first generation compounds since 2011, efficiently reaches the Hepatitis C Virus (HCV) eradication, thus providing a therapeutic opportunity for 71 million people (WHO estimated) affected by chronic HCV infection who eventually develop cirrhosis or liver cancer[1]. HCV elimination does not always result into a healing of liver disease, particularly in patients with advanced fibrosis or cirrhosis; emerging clinical studies with DAAs in patients with liver cirrhosis stirred a heated debate about the risk of HCC occurrence and recurrence after viral cure[2–4]. In this scenario, early non-invasive prognostic tools useful to highlight the long-term DAA therapy clinical output appear necessary in addressing patients’ management. Recently the scientific community has focused on circulating extracellular vesicles (EVs) for two reasons: i) EVs-delivered biological message may be causal to disease progression and ii) their analysis may provide prognostic and diagnostic evidence. Current technologies allow EV analysis for both informational content, (i.e. DNA, mRNAs, microRNAs, long non-coding RNAs and proteins) and functional analysis, assessed by means of target cell response. In fact, EV analysis has already provided an important contribution as in the case of EBV-associated tumours, where it was found that circulating EV cargo may include Epstein Barr Virus (EBV) miR-BART2-5p, a diagnostic and prognostic biomarker functionally able to protect latent cells from EBV reactivation[5]. Similarly, in antiretroviral therapy (ART)-treated HIV patients EVs were found to carry proteins related to immune activation and oxidative stress, functionally bringing immunomodulatory effects[6]. Specifically in the frame of DAA therapy, it has been previously described that the expression of specific miRNAs present in EVs is modulated by this therapyJournal as these changes Pre-proof correlate with the EV-mediated NK cell degranulation capability[7]. In this study, we address the DAA therapy impact on liver fibrosis progression analysing the functional abilities and the structure of circulating EVs derived from HCV-infected and DAA-cleared patients. 4 EVs functional analysis was performed on LX2 as recipient cells, an HSC/myofibroblast cell line partially mimicking the highly proliferative myofibroblast-like cells that derive from quiescent hepatic stellate cells (HSCs). Indeed, during chronic HCV infection, HSCs convert into highly proliferative myofibroblast-like cells expressing inflammatory and fibrogenic mediators responsible for ECM accumulation within the microenvironment, thus contributing to the fibrotic process leading to cirrhosis and liver failure in advanced stages[8,9]. EV structural analysis was performed for both miRNA and protein content. Data obtained show that EVs derived from HCV-infected patients, with respect to EVs derived from healthy donors (HD), increase the fibrogenic activity of LX2 cell line and that this functional data correlates with upregulation of HSC activators (e.g. of DIAPH1) and downregulation of some antifibrogenic miRNAs (e.g. miR204-5p, miR93-5p, miR143-3p, miR181a-5p and miR122-5p). Notably, longitudinal analysis highlights the persistent EV pro-fibrogenic activity in spite of the DAA- mediated HCV eradication,
Recommended publications
  • 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]
  • Mathematical Modeling of Noise and Discovery of Genetic Expression Classes in Gliomas
    Oncogene (2002) 21, 7164 – 7174 ª 2002 Nature Publishing Group All rights reserved 0950 – 9232/02 $25.00 www.nature.com/onc Mathematical modeling of noise and discovery of genetic expression classes in gliomas Hassan M Fathallah-Shaykh*,1, Mo Rigen1, Li-Juan Zhao1, Kanti Bansal1, Bin He1, Herbert H Engelhard3, Leonard Cerullo2, Kelvin Von Roenn2, Richard Byrne2, Lorenzo Munoz2, Gail L Rosseau2, Roberta Glick4, Terry Lichtor4 and Elia DiSavino1 1Department of Neurological Sciences, Rush Presbyterian – St. Lukes Medical Center, Chicago, Illinois, IL 60612, USA; 2Department of Neurosurgery, Rush Presbyterian – St. Lukes Medical Center, Chicago, Illinois, IL 60612, USA; 3Department of Neurosurgery, The University of Illinois at Chicago, Chicago, Illinois, IL 60612, USA; 4Department of Neurosurgery, The Cook County Hospital, Chicago, Illinois, IL 60612, USA The microarray array experimental system generates genetic repertoire in any disease-affected tissue. noisy data that require validation by other experimental However, genome-wide screening is still hampered by methods for measuring gene expression. Here we present the preponderance of false positive data in the gene an algebraic modeling of noise that extracts expression microarray experimental system (Ting Lee et al., 2000). measurements true to a high degree of confidence. This The following experiments are designed to profile the work profiles the expression of 19 200 cDNAs in 35 expression of 19 200 cDNAs in 35 human glioma human gliomas; the experiments are designed to generate samples. Here, we apply mathematical principles to four replicate spots/gene with switching of probes. The separate the noise and extract genes whose expression validity of the extracted measurements is confirmed by: levels are considered truly changed, to a high degree of (1) cluster analysis that generates a molecular classifica- confidence, in the tumor samples as compared to tion differentiating glioblastoma from lower-grade tumors normal brain.
    [Show full text]
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
    [Show full text]
  • Supplementary Material
    Supplementary Material Table S1: Significant downregulated KEGGs pathways identified by DAVID following exposure to five cinnamon- based phenylpropanoids (p < 0.05). p-value Term: Genes (Benjamini) Cytokine-cytokine receptor interaction: FASLG, TNFSF14, CXCL11, IL11, FLT3LG, CCL3L1, CCL3L3, CXCR6, XCR1, 2.43 × 105 RTEL1, CSF2RA, TNFRSF17, TNFRSF14, CCNL2, VEGFB, AMH, TNFRSF10B, INHBE, IFNB1, CCR3, VEGFA, CCR2, IL12A, CCL1, CCL3, CXCL5, TNFRSF25, CCR1, CSF1, CX3CL1, CCL7, CCL24, TNFRSF1B, IL12RB1, CCL21, FIGF, EPO, IL4, IL18R1, FLT1, TGFBR1, EDA2R, HGF, TNFSF8, KDR, LEP, GH2, CCL13, EPOR, XCL1, IFNA16, XCL2 Neuroactive ligand-receptor interaction: OPRM1, THRA, GRIK1, DRD2, GRIK2, TACR2, TACR1, GABRB1, LPAR4, 9.68 × 105 GRIK5, FPR1, PRSS1, GNRHR, FPR2, EDNRA, AGTR2, LTB4R, PRSS2, CNR1, S1PR4, CALCRL, TAAR5, GABRE, PTGER1, GABRG3, C5AR1, PTGER3, PTGER4, GABRA6, GABRA5, GRM1, PLG, LEP, CRHR1, GH2, GRM3, SSTR2, Chlorogenic acid Chlorogenic CHRM3, GRIA1, MC2R, P2RX2, TBXA2R, GHSR, HTR2C, TSHR, LHB, GLP1R, OPRD1 Hematopoietic cell lineage: IL4, CR1, CD8B, CSF1, FCER2, GYPA, ITGA2, IL11, GP9, FLT3LG, CD38, CD19, DNTT, 9.29 × 104 GP1BB, CD22, EPOR, CSF2RA, CD14, THPO, EPO, HLA-DRA, ITGA2B Cytokine-cytokine receptor interaction: IL6ST, IL21R, IL19, TNFSF15, CXCR3, IL15, CXCL11, TGFB1, IL11, FLT3LG, CXCL10, CCR10, XCR1, RTEL1, CSF2RA, IL21, CCNL2, VEGFB, CCR8, AMH, TNFRSF10C, IFNB1, PDGFRA, EDA, CXCL5, TNFRSF25, CSF1, IFNW1, CNTFR, CX3CL1, CCL5, TNFRSF4, CCL4, CCL27, CCL24, CCL25, CCL23, IFNA6, IFNA5, FIGF, EPO, AMHR2, IL2RA, FLT4, TGFBR2, EDA2R,
    [Show full text]
  • MALE Protein Name Accession Number Molecular Weight CP1 CP2 H1 H2 PDAC1 PDAC2 CP Mean H Mean PDAC Mean T-Test PDAC Vs. H T-Test
    MALE t-test t-test Accession Molecular H PDAC PDAC vs. PDAC vs. Protein Name Number Weight CP1 CP2 H1 H2 PDAC1 PDAC2 CP Mean Mean Mean H CP PDAC/H PDAC/CP - 22 kDa protein IPI00219910 22 kDa 7 5 4 8 1 0 6 6 1 0.1126 0.0456 0.1 0.1 - Cold agglutinin FS-1 L-chain (Fragment) IPI00827773 12 kDa 32 39 34 26 53 57 36 30 55 0.0309 0.0388 1.8 1.5 - HRV Fab 027-VL (Fragment) IPI00827643 12 kDa 4 6 0 0 0 0 5 0 0 - 0.0574 - 0.0 - REV25-2 (Fragment) IPI00816794 15 kDa 8 12 5 7 8 9 10 6 8 0.2225 0.3844 1.3 0.8 A1BG Alpha-1B-glycoprotein precursor IPI00022895 54 kDa 115 109 106 112 111 100 112 109 105 0.6497 0.4138 1.0 0.9 A2M Alpha-2-macroglobulin precursor IPI00478003 163 kDa 62 63 86 72 14 18 63 79 16 0.0120 0.0019 0.2 0.3 ABCB1 Multidrug resistance protein 1 IPI00027481 141 kDa 41 46 23 26 52 64 43 25 58 0.0355 0.1660 2.4 1.3 ABHD14B Isoform 1 of Abhydrolase domain-containing proteinIPI00063827 14B 22 kDa 19 15 19 17 15 9 17 18 12 0.2502 0.3306 0.7 0.7 ABP1 Isoform 1 of Amiloride-sensitive amine oxidase [copper-containing]IPI00020982 precursor85 kDa 1 5 8 8 0 0 3 8 0 0.0001 0.2445 0.0 0.0 ACAN aggrecan isoform 2 precursor IPI00027377 250 kDa 38 30 17 28 34 24 34 22 29 0.4877 0.5109 1.3 0.8 ACE Isoform Somatic-1 of Angiotensin-converting enzyme, somaticIPI00437751 isoform precursor150 kDa 48 34 67 56 28 38 41 61 33 0.0600 0.4301 0.5 0.8 ACE2 Isoform 1 of Angiotensin-converting enzyme 2 precursorIPI00465187 92 kDa 11 16 20 30 4 5 13 25 5 0.0557 0.0847 0.2 0.4 ACO1 Cytoplasmic aconitate hydratase IPI00008485 98 kDa 2 2 0 0 0 0 2 0 0 - 0.0081 - 0.0
    [Show full text]
  • Limits of Variation, Specific Infectivity, and Genome Packaging Of
    Limits of variation, specific infectivity, and genome PNAS PLUS packaging of massively recoded poliovirus genomes Yutong Songa,1,2, Oleksandr Gorbatsevycha,1, Ying Liua,b, JoAnn Mugaveroa, Sam H. Shena,c, Charles B. Wardd,e, Emmanuel Asarea, Ping Jianga, Aniko V. Paula, Steffen Muellera,f, and Eckard Wimmera,2 aDepartment of Molecular Genetics and Microbiology, Stony Brook University, Stony Brook, NY, 11794; bPathology and Laboratory Medicine, Staten Island University Hospital, Staten Island, NY 10305; cDepartment of Chemistry, University of Iowa, Iowa City, IA 52242; dGoogle, Inc., Mountain View, CA 94043; eDepartment of Computer Science, Stony Brook University, Stony Brook, NY, 11794; and fCodagenix Inc., Stony Brook, NY 11794 Contributed by Eckard Wimmer, August 17, 2017 (sent for review May 17, 2017; reviewed by Alexander Gorbalenya and Richard J. Kuhn) Computer design and chemical synthesis generated viable vari- could have coevolved that would be optimal to specify 881 capsid ants of poliovirus type 1 (PV1), whose ORF (6,189 nucleotides) car- residues? ried up to 1,297 “Max” mutations (excess of overrepresented syn- If PV, a member of the genus Enterovirus of Picornaviridae, onymous codon pairs) or up to 2,104 “SD” mutations (randomly is an evolutionary descendant of C-cluster Coxsackie viruses scrambled synonymous codons). “Min” variants (excess of under- (C-CAVs) (12), the evolution of PV nucleotide sequences was represented synonymous codon pairs) are nonviable except for constrained as it adhered to the basic architecture of C-CAVs, P2Min, a variant temperature-sensitive at 33 and 39.5 ◦C. Com- its evolutionary parents (13). A second well-known restriction of pared with WT PV1, P2Min displayed a vastly reduced specific sequence variability in ORFs is “codon bias” (14), the unequal infectivity (si) (WT, 1 PFU/118 particles vs.
    [Show full text]
  • Fig1-13Tab1-5.Pdf
    Supplementary Information Promoter hypomethylation of EpCAM-regulated bone morphogenetic protein genes in advanced endometrial cancer Ya-Ting Hsu, Fei Gu, Yi-Wen Huang, Joseph Liu, Jianhua Ruan, Rui-Lan Huang, Chiou-Miin Wang, Chun-Liang Chen, Rohit R. Jadhav, Hung-Cheng Lai, David G. Mutch, Paul J. Goodfellow, Ian M. Thompson, Nameer B. Kirma, and Tim Hui-Ming Huang Tables of contents Page Table of contents 2 Supplementary Methods 4 Supplementary Figure S1. Summarized sequencing reads and coverage of MBDCap-seq 8 Supplementary Figure S2. Reproducibility test of MBDCap-seq 10 Supplementary Figure S3. Validation of MBDCap-seq by MassARRAY analysis 11 Supplementary Figure S4. Distribution of differentially methylated regions (DMRs) in endometrial tumors relative to normal control 12 Supplementary Figure S5. Network analysis of differential methylation loci by using Steiner-tree analysis 13 Supplementary Figure S6. DNA methylation distribution in early and late stage of the TCGA endometrial cancer cohort 14 Supplementary Figure S7. Relative expression of BMP genes with EGF treatment in the presence or absence of PI3K/AKT and Raf (MAPK) inhibitors in endometrial cancer cells 15 Supplementary Figure S8. Induction of invasion by EGF in AN3CA and HEC1A cell lines 16 Supplementary Figure S9. Knockdown expression of BMP4 and BMP7 in RL95-2 cells 17 Supplementary Figure S10. Relative expression of BMPs and BMPRs in normal endometrial cell and endometrial cancer cell lines 18 Supplementary Figure S11. Microfluidics-based PCR analysis of EMT gene panel in RL95-2 cells with or without EGF treatment 19 Supplementary Figure S12. Knockdown expression of EpCAM by different shRNA sequences in RL95-2 cells 20 Supplementary Figure S13.
    [Show full text]
  • The Glycoproteins of Porcine Reproductive and Respiratory Syndrome Virus and Their Role in Infection and Immunity
    University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Dissertations & Theses in Veterinary and Veterinary and Biomedical Sciences, Biomedical Science Department of 8-2010 THE GLYCOPROTEINS OF PORCINE REPRODUCTIVE AND RESPIRATORY SYNDROME VIRUS AND THEIR ROLE IN INFECTION AND IMMUNITY Phani B. Das University of Nebraska-Lincoln, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/vetscidiss Part of the Veterinary Medicine Commons, and the Virology Commons Das, Phani B., "THE GLYCOPROTEINS OF PORCINE REPRODUCTIVE AND RESPIRATORY SYNDROME VIRUS AND THEIR ROLE IN INFECTION AND IMMUNITY" (2010). Dissertations & Theses in Veterinary and Biomedical Science. 3. https://digitalcommons.unl.edu/vetscidiss/3 This Article is brought to you for free and open access by the Veterinary and Biomedical Sciences, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Dissertations & Theses in Veterinary and Biomedical Science by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. THE GLYCOPROTEINS OF PORCINE REPRODUCTIVE AND RESPIRATORY SYNDROME VIRUS AND THEIR ROLE IN INFECTION AND IMMUNITY by Phani Bhusan Das A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfilment of Requirements For the Degree of Doctor of Philosophy Major: Integrative Biomedical Sciences Under the Supervision of Professor Asit K. Pattnaik Lincoln, Nebraska August, 2010 THE GLYCOPROTEINS OF PORCINE REPRODUCTIVE AND RESPIRATORY SYNDROME VIRUS AND THEIR ROLE IN INFECTION AND IMMUNITY Phani Bhusan Das, Ph.D. University of Nebraska, 2010 Adviser: Asit K. Pattnaik The porcine reproductive and respiratory syndrome virus (PRRSV) is an economically important pathogen of swine and is known to cause abortion and infertility in pregnant sows and respiratory distress in piglets.
    [Show full text]
  • Engineered Type 1 Regulatory T Cells Designed for Clinical Use Kill Primary
    ARTICLE Acute Myeloid Leukemia Engineered type 1 regulatory T cells designed Ferrata Storti Foundation for clinical use kill primary pediatric acute myeloid leukemia cells Brandon Cieniewicz,1* Molly Javier Uyeda,1,2* Ping (Pauline) Chen,1 Ece Canan Sayitoglu,1 Jeffrey Mao-Hwa Liu,1 Grazia Andolfi,3 Katharine Greenthal,1 Alice Bertaina,1,4 Silvia Gregori,3 Rosa Bacchetta,1,4 Norman James Lacayo,1 Alma-Martina Cepika1,4# and Maria Grazia Roncarolo1,2,4# Haematologica 2021 Volume 106(10):2588-2597 1Department of Pediatrics, Division of Stem Cell Transplantation and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA; 2Stanford Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA; 3San Raffaele Telethon Institute for Gene Therapy, Milan, Italy and 4Center for Definitive and Curative Medicine, Stanford School of Medicine, Stanford, CA, USA *BC and MJU contributed equally as co-first authors #AMC and MGR contributed equally as co-senior authors ABSTRACT ype 1 regulatory (Tr1) T cells induced by enforced expression of interleukin-10 (LV-10) are being developed as a novel treatment for Tchemotherapy-resistant myeloid leukemias. In vivo, LV-10 cells do not cause graft-versus-host disease while mediating graft-versus-leukemia effect against adult acute myeloid leukemia (AML). Since pediatric AML (pAML) and adult AML are different on a genetic and epigenetic level, we investigate herein whether LV-10 cells also efficiently kill pAML cells. We show that the majority of primary pAML are killed by LV-10 cells, with different levels of sensitivity to killing. Transcriptionally, pAML sensitive to LV-10 killing expressed a myeloid maturation signature.
    [Show full text]
  • Analysis of the Genetic Diversity and Mrna Expression Level in Porcine
    Cortey et al. Vet Res (2018) 49:19 https://doi.org/10.1186/s13567-018-0514-1 SHORT REPORT Open Access Analysis of the genetic diversity and mRNA expression level in porcine reproductive and respiratory syndrome virus vaccinated pigs that developed short or long viremias after challenge Martí Cortey1*† , Gaston Arocena1†, Tahar Ait‑Ali2, Anna Vidal1, Yanli Li1, Gerard Martín‑Valls1, Alison D. Wilson2, Allan L. Archibald2, Enric Mateu1,3 and Laila Darwich1,3 Abstract Porcine reproductive and respiratory syndrome virus (PRRSv) infection alters the host’s cellular and humoral immune response. Immunity against PRRSv is multigenic and vary between individuals. The aim of the present study was to compare several genes that encode for molecules involved in the immune response between two groups of vacci‑ nated pigs that experienced short or long viremic periods after PRRSv challenge. These analyses include the sequenc‑ ing of four SLA Class I, two Class II allele groups, and CD163, plus the analysis by quantitative realtime qRT-PCR of the constitutive expression of TLR2, TLR3, TLR4, TLR7, TLR8 and TLR9 mRNA and other molecules in peripheral blood mononuclear cells. Introduction, methods and results ORF5a protein and E) and three major (GP5, M and N) Introduction structural proteins [3]. Porcine reproductive and respiratory syndrome (PRRS) is Te primary target of PRRSv are CD163­ + macrophages one of the most economically important swine diseases that are common in lungs and lymphoid organs, par- worldwide [1]. Currently, two species of PRRS virus are ticularly tonsil [4]. Te entry of the virus into target recognized: PRRSv-1 and PRRSv-2 both within the genus cells involves: (i) an initial attachment to the cell sur- Porartevirus, Family Arteriviridae.
    [Show full text]
  • Hypoxia Alters Epigenetic and N-Glycosylation Profiles of Ovarian
    ORIGINAL RESEARCH published: 29 July 2020 doi: 10.3389/fonc.2020.01218 Hypoxia Alters Epigenetic and N-Glycosylation Profiles of Ovarian and Breast Cancer Cell Lines in-vitro Edited by: Gordon Greville 1,2, Esther Llop 3,4, Chengnan Huang 1†, Jack Creagh-Flynn 2†, Massimiliano Berretta, Stephanie Pfister 2†, Roisin O’Flaherty 1, Stephen F. Madden 5, Rosa Peracaula 3,4, Centro di Riferimento Oncologico di Pauline M. Rudd 1,6, Amanda McCann 2,7 and Radka Saldova 1,2* Aviano (IRCCS), Italy 1 2 Reviewed by: GlycoScience Group, The National Institute for Bioprocessing Research and Training (NIBRT), Dublin, Ireland, UCD School Keith R. Laderoute, of Medicine, College of Health and Agricultural Science (CHAS), University College Dublin (UCD), Dublin, Ireland, 3 4 SRI International, United States Biochemistry and Molecular Biology Unit, Department of Biology, University of Girona, Girona, Spain, Biochemistry of 5 Parvez Khan, Cancer Group, Girona Biomedical Research Institute (IDIBGI), Girona, Spain, Data Science Centre, Division of Population 6 University of Nebraska Medical Health Sciences, Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland, Analytics Group, Bioprocessing Technology 7 Center, United States Institute, Astar, Singapore, UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin (UCD), Dublin, Ireland *Correspondence: Radka Saldova [email protected] Background: Glycosylation is one of the most fundamental post-translational †Present address: modifications. Importantly, glycosylation is altered in many cancers. These alterations Chengnan Huang, have been proven to impact on tumor progression and to promote tumor cell survival. School of Pharmaceutical Sciences, From the literature, it is known that there is a clear link between chemoresistance and Tsinghua University, Beijing, China Jack Creagh-Flynn, hypoxia, hypoxia and epigenetics and more recently glycosylation and epigenetics.
    [Show full text]
  • Cd42d (GP5) (NM 004488) Human Tagged ORF Clone Product Data
    OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for RC218870 CD42d (GP5) (NM_004488) Human Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: CD42d (GP5) (NM_004488) Human Tagged ORF Clone Tag: Myc-DDK Symbol: GP5 Synonyms: CD42d; GPV Vector: pCMV6-Entry (PS100001) E. coli Selection: Kanamycin (25 ug/mL) Cell Selection: Neomycin This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 4 CD42d (GP5) (NM_004488) Human Tagged ORF Clone – RC218870 ORF Nucleotide >RC218870 representing NM_004488 Sequence: Red=Cloning site Blue=ORF Green=Tags(s) TTTTGTAATACGACTCACTATAGGGCGGCCGGGAATTCGTCGACTGGATCCGGTACCGAGGAGATCTGCC GCCGCGATCGCC ATGCTGAGGGGGACTCTACTGTGCGCGGTGCTCGGGCTTCTGCGCGCCCAGCCCTTCCCCTGTCCGCCAG CTTGCAAGTGTGTCTTCCGGGACGCCGCGCAGTGCTCGGGGGGCGACGTGGCGCGCATCTCCGCGCTGGG CCTGCCCACCAACCTCACGCACATCCTGCTCTTCGGAATGGGCCGCGGCGTCCTGCAGAGCCAGAGCTTC AGCGGCATGACCGTCCTGCAGCGCCTCATGATCTCCGACAGCCACATTTCCGCCGTTGCCCCCGGCACCT TCAGTGACCTGATAAAACTGAAAACCCTGAGGCTGTCGCGCAACAAAATCACGCATCTTCCAGGTGCGCT GCTGGATAAGATGGTGCTCCTGGAGCAGTTGTTTTTGGACCACAATGCGCTAAGGGGCATTGACCAAAAC ATGTTTCAGAAACTGGTTAACCTGCAGGAGCTCGCTCTGAACCAGAATCAGCTCGATTTCCTTCCTGCCA GTCTCTTCACGAATCTGGAGAACCTGAAGTTGTTGGATTTATCGGGAAACAACCTGACCCACCTGCCCAA GGGGTTGCTTGGAGCACAGGCTAAGCTCGAGAGACTTCTGCTCCACTCGAACCGCCTTGTGTCTCTGGAT
    [Show full text]