Probe Set I Title Gene Symbomap Locatiogo Bio Procgo Cell
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UBE2M (Mouse; Full Length), Pab
UBE2M (mouse; full length), pAb Alternate Names: Nedd8-conjugating enzyme, Ubc12, UBC-RS2, UBC12. Cat. No. 68-0025-100 Quantity: 100 µg Lot. No. 30262 Storage: -20˚C FOR RESEARCH USE ONLY NOT FOR USE IN HUMANS CERTIFICATE OF ANALYSIS Page 1 of 2 This antibody was developed and Physical Characteristics validated by the Medical Research Council Protein Phosphorylation and Quantity: 100 μg Formulation: phosphate-buffered Ubiquitylation Unit (University of saline Dundee, Dundee, UK). Concentration: to be provided on shipping Specificity:detects Ube2M at ~22 kDa Source: sheep polyclonal antibody Reactivity: mouse; other species not Background tested. Immunogen: mouse Ube2M (residues 1-183) [GST-tagged] Stability/Storage: 12 months at The enzymes of the NEDDylation pathway -20˚C; aliquot as required play a pivotal role in a number of cellular Purification:affinity-purified using processes including the indirect regula- immobilized immunogen tion and targeting of substrate proteins for proteasomal degradation. Three classes of enzymes are involved in the process of NEDDylation; the ubiquitin-like activating Research Applications and Quality Assurance enzyme APP-BP1/Uba3 (E1), the ubiquitin- Western Immunoblotting: Immunoprecipitation: like conjugating enzymes (E2s) and pro- Use 0.5 µg/ml Not tested tein ligases (E3s). UBE2M is a member of the E2 conjugating enzyme family and the gene for human UBE2M was first de- scribed by Osaka et al. (1998) and shares Dot Blotting Analysis: By dot blot assay the specific 42% sequence identity with yeast UBE2M. recognition of recombinant A trapped ubiquitin like activation complex Ube2M protein was observed has been described for the NEDD8 pathway under native and denaturing comprising, the E1 APP-BP1/Uba3, two conditions when probed with NEDD8 molecules, UBE2M and MgATP. -
The Title of the Article
Mechanism-Anchored Profiling Derived from Epigenetic Networks Predicts Outcome in Acute Lymphoblastic Leukemia Xinan Yang, PhD1, Yong Huang, MD1, James L Chen, MD1, Jianming Xie, MSc2, Xiao Sun, PhD2, Yves A Lussier, MD1,3,4§ 1Center for Biomedical Informatics and Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL 60637 USA 2State Key Laboratory of Bioelectronics, Southeast University, 210096 Nanjing, P.R.China 3The University of Chicago Cancer Research Center, and The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL 60637 USA 4The Institute for Genomics and Systems Biology, and the Computational Institute, The University of Chicago, Chicago, IL 60637 USA §Corresponding author Email addresses: XY: [email protected] YH: [email protected] JC: [email protected] JX: [email protected] XS: [email protected] YL: [email protected] - 1 - Abstract Background Current outcome predictors based on “molecular profiling” rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients – a paradigm shift towards accurate “mechanism-anchored profiling”. We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms. Genes termed as GEMs in this network meet all of the following criteria: (i) they are co-expressed with genes known to be involved in the biological mechanism of interest, (ii) they are also differentially expressed between distinct phenotypes relevant to the study, and (iii) as a biomodule, genes correlate with both the mechanism and the phenotype. -
Identification and Diagnostic Performance of a Small RNA Within the PCA3 and BMCC1 Gene Locus That Potentially Targets Mrna
Published OnlineFirst November 12, 2014; DOI: 10.1158/1055-9965.EPI-14-0377 Research Article Cancer Epidemiology, Biomarkers Identification and Diagnostic Performance of a & Prevention Small RNA within the PCA3 and BMCC1 Gene Locus That Potentially Targets mRNA Ross M. Drayton1, Ishtiaq Rehman1, Raymond Clarke2, Zhongming Zhao3,4, Karl Pang1, Saiful Miah1, Robert Stoehr5, Arndt Hartmann5, Sheila Blizard1, Martin Lavin2, Helen E. Bryant1, Elena S. Martens-Uzunova6, Guido Jenster6, Freddie C. Hamdy7, Robert A. Gardiner2, and James W.F. Catto1 Abstract Background: PCA3 is a long noncoding RNA (lncRNA) with malignant prostatic tissues, exfoliated urinary cells from men unknown function, upregulated in prostate cancer. LncRNAs may with prostate cancer (13–273 fold change; t test P < 0.003), and be processed into smaller active species. We hypothesized this for closely correlated to PCA3 expression (r ¼ 0.84–0.93; P < 0.001). PCA3. Urinary PCA3-shRNA2 (C-index, 0.75–0.81) and PCA3 (C-index, Methods: We computed feasible RNA hairpins within the 0.78) could predict the presence of cancer in most men. PCA3- BMCC1 gene (encompassing PCA3) and searched a prostate shRNA2 knockup altered the expression of predicted target transcriptome for these. We measured expression using qRT- mRNAs, including COPS2, SOX11, WDR48, TEAD1, and Noggin. PCR in three cohorts of prostate cancer tissues (n ¼ 60), PCA3-shRNA2 expression was negatively correlated with COPS2 exfoliated urinary cells (n ¼ 484 with cancer and n ¼ 166 in patient samples (r ¼0.32; P < 0.001). controls), and in cell lines (n ¼ 22). We used in silico predictions Conclusion: We identified a short RNA within PCA3, whose and RNA knockup to identify potential mRNA targets of short expression is correlated to PCA3, which may target mRNAs transcribed RNAs. -
Entrez Symbols Name Termid Termdesc 117553 Uba3,Ube1c
Entrez Symbols Name TermID TermDesc 117553 Uba3,Ube1c ubiquitin-like modifier activating enzyme 3 GO:0016881 acid-amino acid ligase activity 299002 G2e3,RGD1310263 G2/M-phase specific E3 ubiquitin ligase GO:0016881 acid-amino acid ligase activity 303614 RGD1310067,Smurf2 SMAD specific E3 ubiquitin protein ligase 2 GO:0016881 acid-amino acid ligase activity 308669 Herc2 hect domain and RLD 2 GO:0016881 acid-amino acid ligase activity 309331 Uhrf2 ubiquitin-like with PHD and ring finger domains 2 GO:0016881 acid-amino acid ligase activity 316395 Hecw2 HECT, C2 and WW domain containing E3 ubiquitin protein ligase 2 GO:0016881 acid-amino acid ligase activity 361866 Hace1 HECT domain and ankyrin repeat containing, E3 ubiquitin protein ligase 1 GO:0016881 acid-amino acid ligase activity 117029 Ccr5,Ckr5,Cmkbr5 chemokine (C-C motif) receptor 5 GO:0003779 actin binding 117538 Waspip,Wip,Wipf1 WAS/WASL interacting protein family, member 1 GO:0003779 actin binding 117557 TM30nm,Tpm3,Tpm5 tropomyosin 3, gamma GO:0003779 actin binding 24779 MGC93554,Slc4a1 solute carrier family 4 (anion exchanger), member 1 GO:0003779 actin binding 24851 Alpha-tm,Tma2,Tmsa,Tpm1 tropomyosin 1, alpha GO:0003779 actin binding 25132 Myo5b,Myr6 myosin Vb GO:0003779 actin binding 25152 Map1a,Mtap1a microtubule-associated protein 1A GO:0003779 actin binding 25230 Add3 adducin 3 (gamma) GO:0003779 actin binding 25386 AQP-2,Aqp2,MGC156502,aquaporin-2aquaporin 2 (collecting duct) GO:0003779 actin binding 25484 MYR5,Myo1e,Myr3 myosin IE GO:0003779 actin binding 25576 14-3-3e1,MGC93547,Ywhah -
Mass Spectrometry-Based Proteomics Techniques and Their Application in Ovarian Cancer Research Agata Swiatly, Szymon Plewa, Jan Matysiak and Zenon J
Swiatly et al. Journal of Ovarian Research (2018) 11:88 https://doi.org/10.1186/s13048-018-0460-6 REVIEW Open Access Mass spectrometry-based proteomics techniques and their application in ovarian cancer research Agata Swiatly, Szymon Plewa, Jan Matysiak and Zenon J. Kokot* Abstract Ovarian cancer has emerged as one of the leading cause of gynecological malignancies. So far, the measurement of CA125 and HE4 concentrations in blood and transvaginal ultrasound examination are essential ovarian cancer diagnostic methods. However, their sensitivity and specificity are still not sufficient to detect disease at the early stage. Moreover, applied treatment may appear to be ineffective due to drug-resistance. Because of a high mortality rate of ovarian cancer, there is a pressing need to develop innovative strategies leading to a full understanding of complicated molecular pathways related to cancerogenesis. Recent studies have shown the great potential of clinical proteomics in the characterization of many diseases, including ovarian cancer. Therefore, in this review, we summarized achievements of proteomics in ovarian cancer management. Since the development of mass spectrometry has caused a breakthrough in systems biology, we decided to focus on studies based on this technique. According to PubMed engine, in the years 2008–2010 the number of studies concerning OC proteomics was increasing, and since 2010 it has reached a plateau. Proteomics as a rapidly evolving branch of science may be essential in novel biomarkers discovery, therapy decisions, progression predication, monitoring of drug response or resistance. Despite the fact that proteomics has many to offer, we also discussed some limitations occur in ovarian cancer studies. -
Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
Broad Poster Vivek
A novel computational method for finding regions with copy number abnormalities in cancer cells Vivek, Manuel Garber, and Mike Zody Broad Institute of MIT and Harvard, Cambridge, MA, USA Introduction Results Cancer can result from the over expression of oncogenes, genes which control and regulate cell growth. Sometimes oncogenes increase in 1 2 3 activity due to a specific genetic mutation called a translocation (Fig 1). SMAD4 – a gene known to be deleted in pancreatic COX10 – a gene deleted in cytochrome c oxidase AK001392 – a hereditary prostate cancer protein This translocation allows the oncogene to remain as active as its paired carcinoma deficiency, known to be related to cell proliferation gene. Amplification of this mutation can occur, thereby creating the proper conditions for uncontrolled cell growth; consequently, each Results from Analysis Program Results from Analysis Program Results from Analysis Program component of the translocation will amplify in similar quantities. In this mutation, the chromosomal region containing the oncogene displaces to Region 1 Region 2 R2 Region 1 Region 2 R2 Region 1 Region 2 R2 a region on another chromosome containing a gene that is expressed Chr18:47044749-47311978 Chr17:13930739-14654741 0.499070821478475 Chr17:13930739-14654741 Chr18:26861790-27072166 0.47355172850856 Chr17:12542326-13930738 Chr8:1789292-1801984 0.406208680312004 frequently. Actual region containing gene Actual region containing gene Actual region containing gene chr18: 45,842,214 - 48,514,513 chr17: 13,966,862 - 14,068,461 chr17: 12,542,326 - 13,930,738 Fig 1. Two chromosomal regions (abcdef and ghijk) are translocating to create two new regions (abckl and ghijedf). -
1 ICR-Geneset Gene List
ICR-geneset Gene List. IMAGE ID UniGene Locus Name Cluster 20115 Hs.62185 SLC9A6 solute carrier family 9 (sodium/hydrogen exchanger), isoform 6 21738 21899 Hs.78353 SRPK2 SFRS protein kinase 2 21908 Hs.79133 CDH8 cadherin 8, type 2 22040 Hs.151738 MMP9 matrix metalloproteinase 9 (gelatinase B, 92kD gelatinase, 92kD type IV collagenase) 22411 Hs.183 FY Duffy blood group 22731 Hs.1787 PHRET1 PH domain containing protein in retina 1 22859 Hs.356487 ESTs 22883 Hs.150926 FPGT fucose-1-phosphate guanylyltransferase 22918 Hs.346868 EBNA1BP2 EBNA1 binding protein 2 23012 Hs.158205 BLZF1 basic leucine zipper nuclear factor 1 (JEM-1) 23073 Hs.284244 FGF2 fibroblast growth factor 2 (basic) 23173 Hs.151051 MAPK10 mitogen-activated protein kinase 10 23185 Hs.289114 TNC tenascin C (hexabrachion) 23282 Hs.8024 IK IK cytokine, down-regulator of HLA II 23353 23431 Hs.50421 RB1CC1 RB1-inducible coiled-coil 1 23514 23548 Hs.71848 Human clone 23548 mRNA sequence 23629 Hs.135587 Human clone 23629 mRNA sequence 23658 Hs.265855 SETMAR SET domain and mariner transposase fusion gene 23676 Hs.100841 Homo sapiens clone 23676 mRNA sequence 23772 Hs.78788 LZTR1 leucine-zipper-like transcriptional regulator, 1 23776 Hs.75438 QDPR quinoid dihydropteridine reductase 23804 Hs.343586 ZFP36 zinc finger protein 36, C3H type, homolog (mouse) 23831 Hs.155247 ALDOC aldolase C, fructose-bisphosphate 23878 Hs.99902 OPCML opioid binding protein/cell adhesion molecule-like 23903 Hs.12526 Homo sapiens clone 23903 mRNA sequence 23932 Hs.368063 Human clone 23932 mRNA sequence 24004 -
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. -
Exploring Prostate Cancer Genome Reveals Simultaneous Losses of PTEN, FAS and PAPSS2 in Patients with PSA Recurrence After Radical Prostatectomy
Int. J. Mol. Sci. 2015, 16, 3856-3869; doi:10.3390/ijms16023856 OPEN ACCESS International Journal of Molecular Sciences ISSN 1422-0067 www.mdpi.com/journal/ijms Article Exploring Prostate Cancer Genome Reveals Simultaneous Losses of PTEN, FAS and PAPSS2 in Patients with PSA Recurrence after Radical Prostatectomy Chinyere Ibeawuchi 1, Hartmut Schmidt 2, Reinhard Voss 3, Ulf Titze 4, Mahmoud Abbas 5, Joerg Neumann 6, Elke Eltze 7, Agnes Marije Hoogland 8, Guido Jenster 9, Burkhard Brandt 10 and Axel Semjonow 1,* 1 Prostate Center, Department of Urology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebaeude 1A, Muenster D-48149, Germany; E-Mail: [email protected] 2 Center for Laboratory Medicine, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebaeude 1A, Muenster D-48149, Germany; E-Mail: [email protected] 3 Interdisciplinary Center for Clinical Research, University of Muenster, Albert-Schweitzer-Campus 1, Gebaeude D3, Domagkstrasse 3, Muenster D-48149, Germany; E-Mail: [email protected] 4 Pathology, Lippe Hospital Detmold, Röntgenstrasse 18, Detmold D-32756, Germany; E-Mail: [email protected] 5 Institute of Pathology, Mathias-Spital-Rheine, Frankenburg Street 31, Rheine D-48431, Germany; E-Mail: [email protected] 6 Institute of Pathology, Klinikum Osnabrueck, Am Finkenhuegel 1, Osnabrueck D-49076, Germany; E-Mail: [email protected] 7 Institute of Pathology, Saarbrücken-Rastpfuhl, Rheinstrasse 2, Saarbrücken D-66113, Germany; E-Mail: [email protected] 8 Department -
Primate Specific Retrotransposons, Svas, in the Evolution of Networks That Alter Brain Function
Title: Primate specific retrotransposons, SVAs, in the evolution of networks that alter brain function. Olga Vasieva1*, Sultan Cetiner1, Abigail Savage2, Gerald G. Schumann3, Vivien J Bubb2, John P Quinn2*, 1 Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, U.K 2 Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK 3 Division of Medical Biotechnology, Paul-Ehrlich-Institut, Langen, D-63225 Germany *. Corresponding author Olga Vasieva: Institute of Integrative Biology, Department of Comparative genomics, University of Liverpool, Liverpool, L69 7ZB, [email protected] ; Tel: (+44) 151 795 4456; FAX:(+44) 151 795 4406 John Quinn: Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK, [email protected]; Tel: (+44) 151 794 5498. Key words: SVA, trans-mobilisation, behaviour, brain, evolution, psychiatric disorders 1 Abstract The hominid-specific non-LTR retrotransposon termed SINE–VNTR–Alu (SVA) is the youngest of the transposable elements in the human genome. The propagation of the most ancient SVA type A took place about 13.5 Myrs ago, and the youngest SVA types appeared in the human genome after the chimpanzee divergence. Functional enrichment analysis of genes associated with SVA insertions demonstrated their strong link to multiple ontological categories attributed to brain function and the disorders. SVA types that expanded their presence in the human genome at different stages of hominoid life history were also associated with progressively evolving behavioural features that indicated a potential impact of SVA propagation on a cognitive ability of a modern human. -
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PROBING THE INTERACTION OF ASPERGILLUS FUMIGATUS CONIDIA AND HUMAN AIRWAY EPITHELIAL CELLS BY TRANSCRIPTIONAL PROFILING IN BOTH SPECIES by POL GOMEZ B.Sc., The University of British Columbia, 2002 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Experimental Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2010 © Pol Gomez, 2010 ABSTRACT The cells of the airway epithelium play critical roles in host defense to inhaled irritants, and in asthma pathogenesis. These cells are constantly exposed to environmental factors, including the conidia of the ubiquitous mould Aspergillus fumigatus, which are small enough to reach the alveoli. A. fumigatus is associated with a spectrum of diseases ranging from asthma and allergic bronchopulmonary aspergillosis to aspergilloma and invasive aspergillosis. Airway epithelial cells have been shown to internalize A. fumigatus conidia in vitro, but the implications of this process for pathogenesis remain unclear. We have developed a cell culture model for this interaction using the human bronchial epithelium cell line 16HBE and a transgenic A. fumigatus strain expressing green fluorescent protein (GFP). Immunofluorescent staining and nystatin protection assays indicated that cells internalized upwards of 50% of bound conidia. Using fluorescence-activated cell sorting (FACS), cells directly interacting with conidia and cells not associated with any conidia were sorted into separate samples, with an overall accuracy of 75%. Genome-wide transcriptional profiling using microarrays revealed significant responses of 16HBE cells and conidia to each other. Significant changes in gene expression were identified between cells and conidia incubated alone versus together, as well as between GFP positive and negative sorted cells.