Identification of Differential Modules in Ankylosing Spondylitis Using Systemic Module Inference and the Attract Method
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PSMA4 Antibody (Monoclonal) (M01) Mouse Monoclonal Antibody Raised Against a Full Length Recombinant PSMA4
10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 PSMA4 Antibody (monoclonal) (M01) Mouse monoclonal antibody raised against a full length recombinant PSMA4. Catalog # AT3454a Specification PSMA4 Antibody (monoclonal) (M01) - Product Information Application IF, WB, E Primary Accession P25789 Other Accession BC005361 Reactivity Human Host mouse Clonality Monoclonal Isotype IgG2b kappa Calculated MW 29484 PSMA4 Antibody (monoclonal) (M01) - Additional Information Immunofluorescence of monoclonal antibody to PSMA4 on HeLa cell. [antibody concentration 10 ug/ml] Gene ID 5685 Other Names Proteasome subunit alpha type-4, Macropain subunit C9, Multicatalytic endopeptidase complex subunit C9, Proteasome component C9, Proteasome subunit L, PSMA4, HC9, PSC9 Target/Specificity PSMA4 (AAH05361, 1 a.a. ~ 261 a.a) full-length recombinant protein with GST tag. MW of the GST tag alone is 26 KDa. Dilution WB~~1:500~1000 Antibody Reactive Against Recombinant Protein.Western Blot detection against Format Immunogen (54.45 KDa) . Clear, colorless solution in phosphate buffered saline, pH 7.2 . Storage Store at -20°C or lower. Aliquot to avoid repeated freezing and thawing. Precautions PSMA4 Antibody (monoclonal) (M01) is for research use only and not for use in diagnostic or therapeutic procedures. Page 1/3 10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 PSMA4 Antibody (monoclonal) (M01) - Protocols Provided below are standard protocols that you may find useful for product applications. • Western Blot • Blocking Peptides • Dot Blot • Immunohistochemistry • Immunofluorescence • Immunoprecipitation • Flow Cytomety • Cell Culture PSMA4 monoclonal antibody (M01), clone 2A10-E4 Western Blot analysis of PSMA4 expression in Hela ( (Cat # AT3454a ) Detection limit for recombinant GST tagged PSMA4 is approximately 3ng/ml as a capture antibody. -
View of HER2: Human Epidermal Growth Factor Receptor 2; TNBC: Triple-Negative Breast Resistance to Systemic Therapy in Patients with Breast Cancer
Wen et al. Cancer Cell Int (2018) 18:128 https://doi.org/10.1186/s12935-018-0625-9 Cancer Cell International PRIMARY RESEARCH Open Access Sulbactam‑enhanced cytotoxicity of doxorubicin in breast cancer cells Shao‑hsuan Wen1†, Shey‑chiang Su2†, Bo‑huang Liou3, Cheng‑hao Lin1 and Kuan‑rong Lee1* Abstract Background: Multidrug resistance (MDR) is a major obstacle in breast cancer treatment. The predominant mecha‑ nism underlying MDR is an increase in the activity of adenosine triphosphate (ATP)-dependent drug efux trans‑ porters. Sulbactam, a β-lactamase inhibitor, is generally combined with β-lactam antibiotics for treating bacterial infections. However, sulbactam alone can be used to treat Acinetobacter baumannii infections because it inhibits the expression of ATP-binding cassette (ABC) transporter proteins. This is the frst study to report the efects of sulbactam on mammalian cells. Methods: We used the breast cancer cell lines as a model system to determine whether sulbactam afects cancer cells. The cell viabilities in the present of doxorubicin with or without sulbactam were measured by MTT assay. Protein identities and the changes in protein expression levels in the cells after sulbactam and doxorubicin treatment were determined using LC–MS/MS. Real-time reverse transcription polymerase chain reaction (real-time RT-PCR) was used to analyze the change in mRNA expression levels of ABC transporters after treatment of doxorubicin with or without sulbactam. The efux of doxorubicin was measures by the doxorubicin efux assay. Results: MTT assay revealed that sulbactam enhanced the cytotoxicity of doxorubicin in breast cancer cells. The results of proteomics showed that ABC transporter proteins and proteins associated with the process of transcription and initiation of translation were reduced. -
Proteasome System of Protein Degradation and Processing
ISSN 0006-2979, Biochemistry (Moscow), 2009, Vol. 74, No. 13, pp. 1411-1442. © Pleiades Publishing, Ltd., 2009. Original Russian Text © A. V. Sorokin, E. R. Kim, L. P. Ovchinnikov, 2009, published in Uspekhi Biologicheskoi Khimii, 2009, Vol. 49, pp. 3-76. REVIEW Proteasome System of Protein Degradation and Processing A. V. Sorokin*, E. R. Kim, and L. P. Ovchinnikov Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Moscow Region, Russia; E-mail: [email protected]; [email protected] Received February 5, 2009 Abstract—In eukaryotic cells, degradation of most intracellular proteins is realized by proteasomes. The substrates for pro- teolysis are selected by the fact that the gate to the proteolytic chamber of the proteasome is usually closed, and only pro- teins carrying a special “label” can get into it. A polyubiquitin chain plays the role of the “label”: degradation affects pro- teins conjugated with a ubiquitin (Ub) chain that consists at minimum of four molecules. Upon entering the proteasome channel, the polypeptide chain of the protein unfolds and stretches along it, being hydrolyzed to short peptides. Ubiquitin per se does not get into the proteasome, but, after destruction of the “labeled” molecule, it is released and labels another molecule. This process has been named “Ub-dependent protein degradation”. In this review we systematize current data on the Ub–proteasome system, describe in detail proteasome structure, the ubiquitination system, and the classical ATP/Ub- dependent mechanism of protein degradation, as well as try to focus readers’ attention on the existence of alternative mech- anisms of proteasomal degradation and processing of proteins. -
Role of Phytochemicals in Colon Cancer Prevention: a Nutrigenomics Approach
Role of phytochemicals in colon cancer prevention: a nutrigenomics approach Marjan J van Erk Promotor: Prof. Dr. P.J. van Bladeren Hoogleraar in de Toxicokinetiek en Biotransformatie Wageningen Universiteit Co-promotoren: Dr. Ir. J.M.M.J.G. Aarts Universitair Docent, Sectie Toxicologie Wageningen Universiteit Dr. Ir. B. van Ommen Senior Research Fellow Nutritional Systems Biology TNO Voeding, Zeist Promotiecommissie: Prof. Dr. P. Dolara University of Florence, Italy Prof. Dr. J.A.M. Leunissen Wageningen Universiteit Prof. Dr. J.C. Mathers University of Newcastle, United Kingdom Prof. Dr. M. Müller Wageningen Universiteit Dit onderzoek is uitgevoerd binnen de onderzoekschool VLAG Role of phytochemicals in colon cancer prevention: a nutrigenomics approach Marjan Jolanda van Erk Proefschrift ter verkrijging van graad van doctor op gezag van de rector magnificus van Wageningen Universiteit, Prof.Dr.Ir. L. Speelman, in het openbaar te verdedigen op vrijdag 1 oktober 2004 des namiddags te vier uur in de Aula Title Role of phytochemicals in colon cancer prevention: a nutrigenomics approach Author Marjan Jolanda van Erk Thesis Wageningen University, Wageningen, the Netherlands (2004) with abstract, with references, with summary in Dutch ISBN 90-8504-085-X ABSTRACT Role of phytochemicals in colon cancer prevention: a nutrigenomics approach Specific food compounds, especially from fruits and vegetables, may protect against development of colon cancer. In this thesis effects and mechanisms of various phytochemicals in relation to colon cancer prevention were studied through application of large-scale gene expression profiling. Expression measurement of thousands of genes can yield a more complete and in-depth insight into the mode of action of the compounds. -
Anti-Inflammatory Role of Curcumin in LPS Treated A549 Cells at Global Proteome Level and on Mycobacterial Infection
Anti-inflammatory Role of Curcumin in LPS Treated A549 cells at Global Proteome level and on Mycobacterial infection. Suchita Singh1,+, Rakesh Arya2,3,+, Rhishikesh R Bargaje1, Mrinal Kumar Das2,4, Subia Akram2, Hossain Md. Faruquee2,5, Rajendra Kumar Behera3, Ranjan Kumar Nanda2,*, Anurag Agrawal1 1Center of Excellence for Translational Research in Asthma and Lung Disease, CSIR- Institute of Genomics and Integrative Biology, New Delhi, 110025, India. 2Translational Health Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110067, India. 3School of Life Sciences, Sambalpur University, Jyoti Vihar, Sambalpur, Orissa, 768019, India. 4Department of Respiratory Sciences, #211, Maurice Shock Building, University of Leicester, LE1 9HN 5Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia- 7003, Bangladesh. +Contributed equally for this work. S-1 70 G1 S 60 G2/M 50 40 30 % of cells 20 10 0 CURI LPSI LPSCUR Figure S1: Effect of curcumin and/or LPS treatment on A549 cell viability A549 cells were treated with curcumin (10 µM) and/or LPS or 1 µg/ml for the indicated times and after fixation were stained with propidium iodide and Annexin V-FITC. The DNA contents were determined by flow cytometry to calculate percentage of cells present in each phase of the cell cycle (G1, S and G2/M) using Flowing analysis software. S-2 Figure S2: Total proteins identified in all the three experiments and their distribution betwee curcumin and/or LPS treated conditions. The proteins showing differential expressions (log2 fold change≥2) in these experiments were presented in the venn diagram and certain number of proteins are common in all three experiments. -
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. -
Table 1. Swine Proteins Identified As Differentially Expressed at 24Dpi in OURT 88/3 Infected Animals
Table 1. swine proteins identified as differentially expressed at 24dpi in OURT 88/3 infected animals. Gene name Protein ID Protein Name -Log p-value control vs A_24DPI Difference control Vs A_24DPI F8 K7GL28 Coagulation factor VIII 2.123919902 5.42533493 PPBP F1RUL6 C-X-C motif chemokine 3.219079808 4.493174871 SDPR I3LDR9 Caveolae associated protein 2 2.191007299 4.085711161 IGHG L8B0X5 IgG heavy chain 2.084611488 -4.282530149 LOC100517145 F1S3H9 Complement C3 (LOC100517145) 3.885740476 -4.364484406 GOLM1 F1S4I1 Golgi membrane protein 1 1.746130664 -4.767168681 FCN2 I3L5W3 Ficolin-2 2.937884686 -6.029483795 Table 2. swine proteins identified as differentially expressed at 7dpi in Benin ΔMGF infected animals. Gene name Protein ID Protein Name -Log p-value control vs B_7DPI Difference control Vs B_7DPI A0A075B7I5 Ig-like domain-containing protein 1.765578164 -3.480728149 ATP5A1 F1RPS8_PIG ATP synthase subunit alpha 2.270386995 3.270935059 LOC100627396 F1RX35_PIG Fibrinogen C-terminal domain-containing protein 2.211242648 3.967363358 LOC100514666;LOC102158263 F1RX36_PIG Fibrinogen alpha chain 2.337934993 3.758180618 FGB F1RX37_PIG Fibrinogen beta chain 2.411948004 4.03753376 PSMA8 F1SBA5_PIG Proteasome subunit alpha type 1.473601007 -3.815182686 ACAN F1SKR0_PIG Aggrecan core protein 1.974489764 -3.726634026 TFG F1SL01_PIG PB1 domain-containing protein 1.809215274 -3.131304741 LOC100154408 F1SSL6_PIG Proteasome subunit alpha type 1.701949053 -3.944885254 PSMA4 F2Z528_PIG Proteasome subunit alpha type 2.045768185 -4.502977371 PSMA5 F2Z5K2_PIG -
Functional Gene Clusters in Global Pathogenesis of Clear Cell Carcinoma of the Ovary Discovered by Integrated Analysis of Transcriptomes
International Journal of Environmental Research and Public Health Article Functional Gene Clusters in Global Pathogenesis of Clear Cell Carcinoma of the Ovary Discovered by Integrated Analysis of Transcriptomes Yueh-Han Hsu 1,2, Peng-Hui Wang 1,2,3,4,5 and Chia-Ming Chang 1,2,* 1 Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei 112, Taiwan; [email protected] (Y.-H.H.); [email protected] (P.-H.W.) 2 School of Medicine, National Yang-Ming University, Taipei 112, Taiwan 3 Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan 4 Department of Medical Research, China Medical University Hospital, Taichung 440, Taiwan 5 Female Cancer Foundation, Taipei 104, Taiwan * Correspondence: [email protected]; Tel.: +886-2-2875-7826; Fax: +886-2-5570-2788 Received: 27 April 2020; Accepted: 31 May 2020; Published: 2 June 2020 Abstract: Clear cell carcinoma of the ovary (ovarian clear cell carcinoma (OCCC)) is one epithelial ovarian carcinoma that is known to have a poor prognosis and a tendency for being refractory to treatment due to unclear pathogenesis. Published investigations of OCCC have mainly focused only on individual genes and lack of systematic integrated research to analyze the pathogenesis of OCCC in a genome-wide perspective. Thus, we conducted an integrated analysis using transcriptome datasets from a public domain database to determine genes that may be implicated in the pathogenesis involved in OCCC carcinogenesis. We used the data obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) DataSets. We found six interactive functional gene clusters in the pathogenesis network of OCCC, including ribosomal protein, eukaryotic translation initiation factors, lactate, prostaglandin, proteasome, and insulin-like growth factor. -
Correction1 4784..4785
Correction Correction: PCI-24781 Induces Caspase and Reactive Oxygen Species-Dependent Apoptosis In the article on PCI-24781 induces caspase and reactive oxygen species-dependent apoptosis published in the May 15, 2009 issue of Clinical Cancer Research, there was an error in Table 1. Down-regulated genes were incorrectly labeled as up-regulated genes. The correct table appears here. Bhalla S, Balasubramanian S, David K, et al. PCI-24781 induces caspase and reactive oxygen species-dependent apoptosis through NF-nB mechanisms and is synergistic with bortezomib in lymphoma cells. Clin Cancer Res 2009;15:3354–65. Table 1. Selected genes from expression analysis following 24-h treatment with PCI-24781, bortezomib, or the combination (in Ramos cells) Accn # Down-regulated genes 0.25 Mmol/L PCI/3 nmol/L Bor Name PCI-24781 Bortezomib Combination* Cell cycle-related NM_000075 Cyclin-dependent kinase 4 (CDK4) 0.49 0.83 0.37 NM_001237 Cyclin A2 (CCNA2) 0.43 0.87 0.37 NM_001950 E2F transcription factor 4, p107/p130-binding (E2F4) 0.48 0.79 0.40 NM_001951 E2F transcription factor 5, p130-binding (E2F5) 0.46 0.98 0.43 NM_003903 CDC16 cell division cycle 16 homolog (S cerevisiae) (CDC16) 0.61 0.78 0.43 NM_031966 Cyclin B1 (CCNB1) 0.55 0.90 0.43 NM_001760 Cyclin D3 (CCND3) 0.48 1.02 0.46 NM_001255 CDC20 cell division cycle 20 homolog (S cerevisiae; CDC20) 0.61 0.82 0.46 NM_001262 Cyclin-dependent kinase inhibitor 2C (p18, inhibits CDK4; CDKN2C) 0.61 1.15 0.56 NM_001238 Cyclin E1 (CCNE1) 0.56 1.05 0.60 NM_001239 Cyclin H (CCNH) 0.74 0.90 0.64 NM_004701 -
Dynamics of Protein Ubiquitination Upon Proteasome Modulation Karen Alexandra Sap
Dynamics of Protein Ubiquitination Upon Proteasome Modulation Karen Alexandra Sap Dynamics of Protein Ubiquitination Upon Proteasome Modulation A Quantitative Mass Spectrometry Approach Karen Alexandra Sap Dynamics of Protein Ubiquitination upon Proteasome Modulation A Quantitative Mass Spectrometry Approach Karen Alexandra Sap © 2018 Karen Sap Cover design: Karen Sap & Frank Sap ISBN: 978-94-93019-55-3 Printed by: ProefschriftMaken.nl || www.proefschriftmaken.nl Published by: ProefschriftMaken.nl || www.proefschriftmaken.nl The studies described in this thesis were performed in the Proteomics Center which is embedded in the Department of Biochemistry of the Erasmus University Medical Center in Rotterdam, The Netherlands The research described in this thesis was financially supported by the Netherlands Proteomics Center (project number 184.032.201) Dynamics of Protein Ubiquitination upon Proteasome Modulation A Quantitative Mass Spectrometry Approach Dynamiek van eiwit ubiquitinatie als gevolg van proteasoom modulatie Onderzocht door middel van kwantitatieve massaspectrometrie Proefschrift ter verkrijging van de graad van doctor aan de Erasmus Universiteit Rotterdam op gezag van de rector magnificus Prof.dr. R.C.M.E. Engels en volgens besluit van het College voor Promoties. De openbare verdediging zal plaatsvinden op donderdag 20 september 2018 om 11.30 uur door Karen Alexandra Sap geboren te Rotterdam Promotiecommissie Promotor: Prof.dr. C.P. Verrijzer Overige leden: Prof.dr. J.H. Gribnau Dr. J.A.F Marteijn Dr. A.C.O Vertegaal Copromotor: -
Open Data for Differential Network Analysis in Glioma
International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. -
Role of Genomic Variants in the Response to Biologics Targeting Common Autoimmune Disorders
Role of genomic variants in the response to biologics targeting common autoimmune disorders by Gordana Lenert, PhD The thesis submitted to the Faculty of Graduate and Postdoctoral Affairs in partial fulfillment of the requirements for the degree of Master of Science Ottawa-Carleton Joint Program in Bioinformatics Carleton University Ottawa, Canada © 2016 Gordana Lenert Abstract Autoimmune diseases (AID) are common chronic inflammatory conditions initiated by the loss of the immunological tolerance to self-antigens. Chronic immune response and uncontrolled inflammation provoke diverse clinical manifestations, causing impairment of various tissues, organs or organ systems. To avoid disability and death, AID must be managed in clinical practice over long periods with complex and closely controlled medication regimens. The anti-tumor necrosis factor biologics (aTNFs) are targeted therapeutic drugs used for AID management. However, in spite of being very successful therapeutics, aTNFs are not able to induce remission in one third of AID phenotypes. In our research, we investigated genomic variability of AID phenotypes in order to explain unpredictable lack of response to aTNFs. Our hypothesis is that key genetic factors, responsible for the aTNFs unresponsiveness, are positioned at the crossroads between aTNF therapeutic processes that generate remission and pathogenic or disease processes that lead to AID phenotypes expression. In order to find these key genetic factors at the intersection of the curative and the disease pathways, we combined genomic variation data collected from publicly available curated AID genome wide association studies (AID GWAS) for each disease. Using collected data, we performed prioritization of genes and other genomic structures, defined the key disease pathways and networks, and related the results with the known data by the bioinformatics approaches.