DOCSLIB.ORG

High Gene Expression Levels of VEGFA and CXCL8 in the Peritumoral Brain Zone Are Associated with the Recurrence of Glioblastoma: a Bioinformatics Analysis

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
High Gene Expression Levels of VEGFA and CXCL8 in the Peritumoral Brain Zone Are Associated with the Recurrence of Glioblastoma: a Bioinformatics Analysis ONCOLOGY LETTERS 18: 6171-6179, 2019 High gene expression levels of VEGFA and CXCL8 in the peritumoral brain zone are associated with the recurrence of glioblastoma: A bioinformatics analysis XIAOBIN LUO1, SHANGYI XU1, YALI ZHONG2, TIANQI TU1, YOULIN XU1, XIANGLONG LI1, BIN WANG1 and FUBING YANG1 1Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000; 2School of Nursing, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou 550000, P.R. China Received April 25, 2019; Accepted September 17, 2019 DOI: 10.3892/ol.2019.10988 Abstract. The present study aimed to identify differentially of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes regulated genes between the peritumoral brain zone (PBZ) and Genomes (KEGG) databases. The Search Tool for the and tumor core (TC) of glioblastoma (GBM), to elucidate the Retrieval of Interacting Genes/Proteins online tool was used underlying molecular mechanisms and provide a target for the to construct a protein-protein interaction (PPI) network of treatment of tumors. The GSE13276 and GSE116520 data- DEGs. Hub genes were identified using Cytohubba, a plug‑in sets were downloaded from the Gene Expression Omnibus for Cytoscape. The Gene Expression Profiling Interactive (GEO) database. Differentially expressed genes (DEGs) for Analysis (GEPIA) database was utilized to perform survival the PBZ and TC were obtained using the GEO2R tool. The analysis. In total, 75 DEGs, including 12 upregulated and bioinformatics and evolutionary genomics online tool Venn 63 downregulated genes, were identified. In the GO term was used to identify common DEGs between the two datasets. analysis, these DEGs were mainly enriched in ‘regulation The Database for Annotation, Visualization, and Integrated of angiogenesis’ and ‘central nervous system development’. Discovery online tool was used to analyze enriched pathways Furthermore, in the KEGG pathway analysis, the DEGs were mainly enriched in ‘bladder cancer’ and ‘endocytosis’. When filtering the results of the PPI network analysis using Cytohubba, a total of 10 hub genes, including proteolipid Correspondence to: Professor Fubing Yang, Department of protein 1, myelin associated oligodendrocyte basic protein, Neurosurgery, The Affiliated Hospital of Southwest Medical contactin 2, myelin oligodendrocyte glycoprotein, myelin basic University, 25 Taiping Street, Jiangyang, Luzhou, Sichuan 646000, protein, myelin associated glycoprotein, SRY-box transcription P. R. Ch i na factor 10, C-X-C motif chemokine ligand 8 (CXCL8), vascular E-mail: [email protected] endothelial growth factor A (VEGFA) and plasmolipin, were identified. These hub genes were further subjected to GO term Abbreviations: GBM, glioblastoma; PBZ, peritumoral brain and KEGG pathway analysis, and were revealed to be enriched zone; TC, tumor core; rGBM, recurrent glioblastoma; GEO, Gene Expression Omnibus; DEGs, differentially expressed genes; in ‘central nervous system development’, ‘bladder cancer’ and GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and ‘rheumatoid arthritis’. These hub genes were used to perform Genomes; PPI, protein‑protein interaction; UP, upregulated; survival analysis using the GEPIA database, and it was deter- DOWN, downregulated; GEPIA, Gene Expression Profiling mined that VEGFA and CXCL8 were significantly associated Interactive Analysis; TCGA, The Cancer Genome Atlas; GTEx, with a reduction in the overall survival of patients with GBM. Genotype‑Tissue Expression; HR, hazard ratio; PLP1, proteolipid In conclusion, the results suggest that the recurrence of GBM protein 1; MOBP, myelin‑associated oligodendrocyte basic protein; is associated with high gene expression levels VEGFA and CNTN2, contactin 2; CXCL8, C‑X‑C motif chemokine ligand 8; CXCL8, and the development of the central nervous system. MOG, myelin oligodendrocyte glycoprotein; VEGFA, vascular endothelial growth factor A; MBP, myelin basic protein; MAG, Introduction myelin associated glycoprotein; SOX10, SRY‑box 10; PLLP, plasmolipin; IL‑8, interleukin 8; MMP2, matrix metalloproteinase 2; CXCR, C‑X‑C chemokine receptor; B1R, B1 bradykinin receptor; Originating from adult brain tissue, glioblastoma (GBM) is TMZ, temozolomide a highly malignant primary tumor with numerous charac- teristics, including complicated pathological diagnosis, poor Key words: peritumoral brain zone, tumor core, bioinformatics prognosis and easy relapse (1). Tumor cells have a strong analysis, recurrent glioblastoma, vascular endothelial growth ability for proliferation and invasion, and can invade the factor A, C-X-C motif chemokine ligand 8 central nervous system (2). In 2018, patients with GBM had a poor prognosis, and the median survival time of patients was ~17 months (3). In 2019, following standard treatment, the 6172 LUO et al: VEGFA AND CXCL8 GENES ARE ASSOCIATED WITH THE RECURRENCE OF GLIOBLASTOMA 6-month progression-free survival rate was 25.2% in patients Screening of DEGs. DEGs were screened using GEO2R with recurrent glioblastoma (rGBM) (4). Traditional treatment (https://www.ncbi.nlm.nih.gov/geo/geo2r/), which allowed a for GBM includes surgical resection, radiation and chemo- comparison of the differences between two different sets of therapy with temozolomide (5). Following surgical resection samples in a series. In the GSE13276 dataset, |log fold change of GBM, substantial invading tumor cells are located in the (FC)| ≥2 and P<0.05 were defined as statistically significant near resection edge of the tumor or within 2-3 cm from the for the DEGs. Similarly, in the GSE116520 dataset, |log FC| resection cavity, and these are the most common recurrence ≥1 (and P<0.05 were defined as statistically significant for the sites (6). DEGs). If the same criteria in the two datasets were selected, the In previous years, with the development of medicine and resulting genes would be limited, therefore in order to obtain a bioinformatics, gene expression information has been generated larger number of target genes different criteria were selected. and stored in the Gene Expression Omnibus (GEO) database. Excel software (Office 2016; Microsoft Corporation) was used Bioinformatics analysis allows an improved understanding to delete all genes without gene symbols and duplicate genes of the alterations in essential genes in the process of tumor corresponding to multiple probe IDs in the GSE13276 and development. Furthermore, it can provide a basis for target GSE116520 datasets. A Venn diagram of the DEGs obtained treatment and tumor diagnosis (7). A previous study identified from the bioinformatics and evolutionary genomics online tool that both the peritumoral brain zone (PBZ) and tumor core ‘Calculate and draw custom Venn diagrams’ (http://bioinfor- (TC) are heterogeneous in the same patients, and tumor cells matics.psb.ugent.be/webtools/Venn/) was constructed, and the in the PBZ exhibit increased invasiveness compared with cells common DEGs were identified. in the TC (8). Van Meter et al (9) demonstrated that there were a number of different gene expression products between the GO and KEGG enrichment analysis. GO (http://www. PBZ and TC. Even in the absence of tumor cells in the PBZ, geneontology.org) is a database used to describe the charac- it is possible to detect the expression of genes associated with teristics of genes and gene products. KEGG (https://www. invasion and neo-angiogenesis (10). Therefore, identifying kegg.jp/) is a database containing information on genome, differentially expressed genes (DEGs) between the PBZ chemistry and system functions (12). DAVID (version 6.8; and TC may reveal the causes of GBM recurrence, and may http://david.ncifcrf.gov/) is an online database, and was used provide a target for tumor treatment. to perform GO term and KEGG pathway enrichment anal- In the present study, the gene expression profiles of the yses (13,14). P<0.05 was considered to indicate a statistically PBZ were compared with the gene expression profiles of significant difference. the TC to identify DEGs. The DEGs of the PBZ and TC were screened by GEO2R online analysis, and the Database Interaction network analysis and hub gene identification. for Annotation, Visualization, and Integrated Discovery The STRING (https://string-db.org/) database is a tool for (DAVID) was used for Gene Ontology (GO) and Kyoto analyzing the interrelationships between genes, including direct Encyclopedia of Genes and Genomes (KEGG) enrichment (physical) and indirect (functional) links, and constructing PPI analyses. A protein-protein interaction (PPI) network was networks. STRING version 11.0 contains 24,584,628 proteins constructed using Search Tool for the Retrieval of Interacting from 5,090 organisms (15). The PPI network association Genes/Proteins (STRING) and Cytoscape software tools, among DEGs was analyzed using the STRING database and and the hub genes were obtained using the Cytoscape plug-in the default minimum required interaction score was set as Cytohubba. Furthermore, the Gene Expression Profiling >0.4. Subsequently, the Cytoscape (version 3.7.1; http://www. Interactive Analysis (GEPIA) database was used to analyze cytoscape.org/) plug‑in Cytohubba (version was 0.1; http://apps. the survival rate of patients with aberrant expression levels cytoscape.org/apps/cytohubba) was used to screen the PPI of the hub genes. network, and the top10 genes, which were the hub genes in the PBZ, were obtained using the Degree algorithm. Materials and methods Survival analysis.
Load more

Recommended publications
  • FK506-Binding Protein 12.6/1B, a Negative Regulator of [Ca2+], Rescues Memory and Restores Genomic Regulation in the Hippocampus of Aging Rats
    This Accepted Manuscript has not been copyedited and formatted. The final version may differ from this version. A link to any extended data will be provided when the final version is posted online. Research Articles: Neurobiology of Disease FK506-Binding Protein 12.6/1b, a negative regulator of [Ca2+], rescues memory and restores genomic regulation in the hippocampus of aging rats John C. Gant1, Eric M. Blalock1, Kuey-Chu Chen1, Inga Kadish2, Olivier Thibault1, Nada M. Porter1 and Philip W. Landfield1 1Department of Pharmacology & Nutritional Sciences, University of Kentucky, Lexington, KY 40536 2Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL 35294 DOI: 10.1523/JNEUROSCI.2234-17.2017 Received: 7 August 2017 Revised: 10 October 2017 Accepted: 24 November 2017 Published: 18 December 2017 Author contributions: J.C.G. and P.W.L. designed research; J.C.G., E.M.B., K.-c.C., and I.K. performed research; J.C.G., E.M.B., K.-c.C., I.K., and P.W.L. analyzed data; J.C.G., E.M.B., O.T., N.M.P., and P.W.L. wrote the paper. Conflict of Interest: The authors declare no competing financial interests. NIH grants AG004542, AG033649, AG052050, AG037868 and McAlpine Foundation for Neuroscience Research Corresponding author: Philip W. Landfield, [email protected], Department of Pharmacology & Nutritional Sciences, University of Kentucky, 800 Rose Street, UKMC MS 307, Lexington, KY 40536 Cite as: J. Neurosci ; 10.1523/JNEUROSCI.2234-17.2017 Alerts: Sign up at www.jneurosci.org/cgi/alerts to receive customized email alerts when the fully formatted version of this article is published.
    [Show full text]
  • Pentraxin-2 Suppresses C-Jun/AP-1 Signaling to Inhibit Progressive Fibrotic Disease
    Pentraxin-2 suppresses c-Jun/AP-1 signaling to inhibit progressive fibrotic disease Naoki Nakagawa, … , Sina A. Gharib, Jeremy S. Duffield JCI Insight. 2016;1(20):e87446. https://doi.org/10.1172/jci.insight.87446. Research Article Immunology Inflammation Pentraxin-2 (PTX-2), also known as serum amyloid P component (SAP/APCS), is a constitutive, antiinflammatory, innate immune plasma protein whose circulating level is decreased in chronic human fibrotic diseases. Here we show that recombinant human PTX-2 (rhPTX-2) retards progression of chronic kidney disease in Col4a3 mutant mice with Alport syndrome, reducing blood markers of kidney failure, enhancing lifespan by 20%, and improving histological signs of disease. Exogenously delivered rhPTX-2 was detected in macrophages but also in tubular epithelial cells, where it counteracted macrophage activation and was cytoprotective for the epithelium. Computational analysis of genes regulated by rhPTX-2 identified the transcriptional regulator c-Jun along with its activator protein–1 (AP-1) binding partners as a central target for the function of rhPTX-2. Accordingly, PTX-2 attenuates c-Jun and AP-1 activity, and reduces expression of AP-1–dependent inflammatory genes in both monocytes and epithelium. Our studies therefore identify rhPTX-2 as a potential therapy for chronic fibrotic disease of the kidney and an important inhibitor of pathological c-Jun signaling in this setting. Find the latest version: https://jci.me/87446/pdf RESEARCH ARTICLE Pentraxin-2 suppresses c-Jun/AP-1 signaling to inhibit progressive fibrotic disease Naoki Nakagawa,1,2,3 Luke Barron,4 Ivan G. Gomez,1,2,4 Bryce G.
    [Show full text]
  • Analysis of Trans Esnps Infers Regulatory Network Architecture
    Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation.
    [Show full text]
  • MLR) Model and Filtration
    SUPPLEMENT Contents 1. Extended description of the Data sets. 2. Extended description of the multiple linear regression (MLR) model and filtration. 3. Extended description of the random-gene classifiers. 4. Extended description of the comparison with Dakhova et al. (1)and Richardson et al. (2) 5. Extended description of preparation of RNA and the XP_PCR protocol. 6. Table S1. Comparison of 131-probe set Diagnostic Classifier to classifiers generated with ‘random’ genes. 7. Table S2. Concordance of 38 overlapping genes/probe sets of the 339 probe sets ( basis) of the Diagnostic Classifier with the sign of differential change of Dakhova et al. (1). 8. Table S3. Function enrichment analysis. 9. Table S4. PCR validation of preferential expression in stroma by representative genes of the Diagnostic Classifier. 10. Figure S1. The incidence numbers of 339 probe sets obtained by 105-fold permutation procedure for gene selection. 11. Figure S2. Heatmap using the Diagnostic Classifier to categorize all training cases. 12. Figure S3. Heatmap of all 364 test samples used in this study as categorized by the 131 probe set Diagnostic Classifier. 13. Figure S4. Cluster diagram of the cases of Dakhova et al. (1) using only the 38 overlapping genes. 14. References for the Supplement. 1 1. Extended description of the Data Sets. Datasets 1 and 2 (Table 1) are based on post-prostatectomy frozen tissue samples obtained by informed consent using IRB-approved and HIPPA-compliant protocols. All tissues, except where noted, were collected at surgery and escorted to pathology for expedited review, dissection, and snap freezing in liquid nitrogen.
    [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]
  • Cell Type-Specific Gene Expression Patterns Associated with Posttraumatic Stress Disorder in World Trade Center Responders
    Kuan et al. Translational Psychiatry (2019) 9:1 https://doi.org/10.1038/s41398-018-0355-8 Translational Psychiatry ARTICLE Open Access Cell type-specific gene expression patterns associated with posttraumatic stress disorder in World Trade Center responders Pei-Fen Kuan1, Xiaohua Yang2,SeanClouston 3,XuRen1,RomanKotov4, Monika Waszczuk4,PrashantK.Singh5, Sean T. Glenn5, Eduardo Cortes Gomez 6, Jianmin Wang6,EvelynBromet4 and Benjamin J. Luft2 Abstract Posttraumatic stress disorder (PTSD), a chronic disorder resulting from severe trauma, has been linked to immunologic dysregulation. Gene expression profiling has emerged as a promising tool for understanding the pathophysiology of PTSD. However, to date, all but one gene expression study was based on whole blood or unsorted peripheral blood mononuclear cell (PBMC), a complex tissue consisting of several populations of cells. The objective of this study was to utilize RNA sequencing to simultaneously profile the gene expression of four immune cell subpopulations (CD4T, CD8T, B cells, and monocytes) in 39 World Trade Center responders (20 with and 19 without PTSD) to determine which immune subsets play a role in the transcriptomic changes found in whole blood. Transcriptome-wide analyses identified cell-specific and shared differentially expressed genes across the four cell types. FKBP5 and PI4KAP1 genes were consistently upregulated across all cell types. Notably, REST and SEPT4, genes linked to neurodegeneration, were among the top differentially expressed genes in monocytes. Pathway analyses identified differentially expressed gene sets involved in mast cell activation and regulation in CD4T, interferon-beta production in CD8T, and neutrophil- fi 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; related gene sets in monocytes.
    [Show full text]
  • Circular RNA Hsa Circ 0005114‑Mir‑142‑3P/Mir‑590‑5P‑ Adenomatous
    ONCOLOGY LETTERS 21: 58, 2021 Circular RNA hsa_circ_0005114‑miR‑142‑3p/miR‑590‑5p‑ adenomatous polyposis coli protein axis as a potential target for treatment of glioma BO WEI1*, LE WANG2* and JINGWEI ZHAO1 1Department of Neurosurgery, China‑Japan Union Hospital of Jilin University, Changchun, Jilin 130033; 2Department of Ophthalmology, The First Hospital of Jilin University, Jilin University, Changchun, Jilin 130021, P.R. China Received September 12, 2019; Accepted October 22, 2020 DOI: 10.3892/ol.2020.12320 Abstract. Glioma is the most common type of brain tumor APC expression with a good overall survival rate. UALCAN and is associated with a high mortality rate. Despite recent analysis using TCGA data of glioblastoma multiforme and the advances in treatment options, the overall prognosis in patients GSE25632 and GSE103229 microarray datasets showed that with glioma remains poor. Studies have suggested that circular hsa‑miR‑142‑3p/hsa‑miR‑590‑5p was upregulated and APC (circ)RNAs serve important roles in the development and was downregulated. Thus, hsa‑miR‑142‑3p/hsa‑miR‑590‑5p‑ progression of glioma and may have potential as therapeutic APC‑related circ/ceRNA axes may be important in glioma, targets. However, the expression profiles of circRNAs and their and hsa_circ_0005114 interacted with both of these miRNAs. functions in glioma have rarely been studied. The present study Functional analysis showed that hsa_circ_0005114 was aimed to screen differentially expressed circRNAs (DECs) involved in insulin secretion, while APC was associated with between glioma and normal brain tissues using sequencing the Wnt signaling pathway. In conclusion, hsa_circ_0005114‑ data collected from the Gene Expression Omnibus database miR‑142‑3p/miR‑590‑5p‑APC ceRNA axes may be potential (GSE86202 and GSE92322 datasets) and explain their mecha‑ targets for the treatment of glioma.
    [Show full text]
  • Protein Kinase A-Mediated Septin7 Phosphorylation Disrupts Septin Filaments and Ciliogenesis
    cells Article Protein Kinase A-Mediated Septin7 Phosphorylation Disrupts Septin Filaments and Ciliogenesis Han-Yu Wang 1,2, Chun-Hsiang Lin 1, Yi-Ru Shen 1, Ting-Yu Chen 2,3, Chia-Yih Wang 2,3,* and Pao-Lin Kuo 1,2,4,* 1 Department of Obstetrics and Gynecology, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan; [email protected] (H.-Y.W.); [email protected] (C.-H.L.); [email protected] (Y.-R.S.) 2 Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan; [email protected] 3 Department of Cell Biology and Anatomy, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan 4 Department of Obstetrics and Gynecology, National Cheng-Kung University Hospital, Tainan 704, Taiwan * Correspondence: [email protected] (C.-Y.W.); [email protected] (P.-L.K.); Tel.: +886-6-2353535 (ext. 5338); (C.-Y.W.)+886-6-2353535 (ext. 5262) (P.-L.K.) Abstract: Septins are GTP-binding proteins that form heteromeric filaments for proper cell growth and migration. Among the septins, septin7 (SEPT7) is an important component of all septin filaments. Here we show that protein kinase A (PKA) phosphorylates SEPT7 at Thr197, thus disrupting septin filament dynamics and ciliogenesis. The Thr197 residue of SEPT7, a PKA phosphorylating site, was conserved among different species. Treatment with cAMP or overexpression of PKA catalytic subunit (PKACA2) induced SEPT7 phosphorylation, followed by disruption of septin filament formation. Constitutive phosphorylation of SEPT7 at Thr197 reduced SEPT7-SEPT7 interaction, but did not affect SEPT7-SEPT6-SEPT2 or SEPT4 interaction.
    [Show full text]
  • Supplemental Data
    MOLECULAR PHARMACOLOGY Supplemental Data Regulation of M3 Muscarinic Receptor Expression and Function by Transmembrane Protein 147 Erica Rosemond, Mario Rossi, Sara M. McMillin, Marco Scarselli, Julie G. Donaldson, and Jürgen Wess Supplemental Table 1 M3R-interacting proteins identified in a membrane-based yeast two-hybrid screen Accession Protein No. Tetraspanin family CD82 AAB23825 CD9 P21926 CD63 AAV38940 Ubiquitin-associated proteins Small ubiquitin-related modifier 2 precursor (SUMO-1) AAC50996 Small ubiquitin-related modifier 2 precursor (SUMO-2) P61956 UBC protein AAH08955 Ubiquitin C NP_066289 Receptor proteins/Transmembrane proteins AAH01118 / Transmembrane protein 147 BC001118 G protein-coupled receptor 37 NP_005293 NM_016235 / Homo sapiens G protein-coupled receptor, family C, group 5, member B (GPRC5B) AAF05331 Rhodopsin NP_000530 Aquaporin-4 (AQP-4) P55087 Glutamate receptor, ionotropic, N-methyl D-aspartate-associated protein 1 NP_001009184 Sodium channel, voltage gated, type VIII, alpha NP_055006 Transmembrane 9 superfamily member 2 CAH71381 Transmembrane protein 14A AAH19328 Signaling molecules Phosphatidic acid phosphatase type 2C AAP35667 Calmodulin 2 AAH08437 Protein kinase Njmu-R1 AAH54035 2',3'-Cyclic nucleotide 3' phosphodiesterase (CNP) AAH06392 Solute carrier proteins Solute carrier family 39 (zinc transporter), member 3 isoform a NP_653165 Solute carrier family 22 (organic cation transporter), member 17 isoform b NP_057693 Solute carrier family 31 (copper transporters), member 2 NP_001851 Solute carrier family
    [Show full text]
  • Cellular and Molecular Signatures in the Disease Tissue of Early
    Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of
    [Show full text]
  • Background Methods Conclusions Results
    Gene Expression Profiles of Pediatric Tuberculosis Patients and Exposed Controls from India Jeffrey A Tornheim,1 Mandar Paradkar,2 Anil Madugundu,4,5 Nikhil Gupte,2 Vandana Kulkarni,2 Sreelakshmi Sreenivasamurthy,4,5 Remya Raja,4 Neeta Pradhan,2 Shri Vijay Bala Yogendra Shivakumar,6 Chhaya Valvi,3 Rewa Kohli,2 Padmapriyadarsini Chandrasekaran,7 Vidya Mave,2 Akhilesh Pandey,4,5 and Amita Gupta1 1Center for Clinical Global Health Education, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA, 2Byramjee Jeejeebhoy Government Medical College – Johns Hopkins University Clinical Research Site, Pune, Maharashtra, India, 3Byramjee Jeejeebhoy Government Medical College, Pune, Maharashtra, India, 4National Institute of Mental Health and Neurosciences - Institute of Bioinformatics Lab, Bangalore, India, 5Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA, 6Johns Hopkins University – India office (CCGHE), Pune, Maharashtra, India, 7National Institute for Research in Tuberculosis, Chennai, Tamil Nadu, India BACKGROUND RESULTS—Differential Gene Expression Tuberculosis (TB) is the #1 infectious disease killer worldwide, and 27% of cases are in India (WHO 2016). Aim 1: Differential gene expression between pediatric cases (N=16) and controls (N=32) at enrollment Children frequently have either paucibacillary disease, are too young to provide adequate sputum Gene Counts Fold Change Distribution samples, or develop extrapulmonary disease, forcing clinicians to rely on diagnostic tests with poor sensitivity and frequent presumptive treatment for TB in children (Perez-Velez NEJM 2012). Several studies have attempted to use biomarkers of host response to infection as an alternative means of Number of Differentially Expressed Genes between Cases and Controls, diagnosing TB, but these studies have not included many children, Indian patients, or microbiologically by False Discovery Rate (FDR) and Log2-Fold Change Any Log2 >0.5 Log2 >1.0 Log2 >1.5 Log2 >2.0 confirmed patients.
    [Show full text]
  • Translational Research of the Quaking Gene
    Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1381 Translational research of the quaking gene Focusing on the conjunction between development and disease BRYN FARNSWORTH ACTA UNIVERSITATIS UPSALIENSIS ISSN 1651-6214 ISBN 978-91-554-9595-4 UPPSALA urn:nbn:se:uu:diva-287408 2016 Dissertation presented at Uppsala University to be publicly examined in Zootisalen, EBC, Norbyvägen 18A, Uppsala, Tuesday, 14 June 2016 at 13:00 for the degree of Doctor of Philosophy. The examination will be conducted in English. Faculty examiner: Dr. Roman Chrast (Department of Neuroscience, Karolinska University). Abstract Farnsworth, B. 2016. Translational research of the quaking gene. Focusing on the conjunction between development and disease. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1381. 61 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-554-9595-4. Quaking (QKI) is an RNA binding protein involved in the post-transcriptional regulation of gene expression. Originally identified as the cause of hypomyelination in a mouse mutant, it has since been consistently implicated in a wide range of neurological diseases. As a gene exclusively expressed in glial cells of the central nervous system, such associations emphasise the importance of an indirect, or non-neuronal link to aberrant neural function. A role in early neural development has also been suggested from the viable and embryonic lethal mouse mutants, yet detailed and in vivo study has been precluded thus far by the murine uterine gestation, and mutant lethality prior to oligodendrogenesis. This thesis examines the role of QKI in human neurological disease, and explores the use of the zebrafish as a model organism to allow the unimpeded study of neural development.
    [Show full text]