GIPC1 Antibody Cat

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GIPC1 Antibody Cat. No.: XW-8025 GIPC1 Antibody Specifications HOST SPECIES: Chicken SPECIES REACTIVITY: Human, Mouse, Rat IMMUNOGEN: 1-333 TESTED APPLICATIONS: WB GIPC PDZ domain-containing protein 1 antibody can be used for the detection of GIPC APPLICATIONS: PDZ domain-containing protein 1 by Western Blot. PREDICTED MOLECULAR 36 kDa (calculated) WEIGHT: Properties PURIFICATION: Immunoaffinity Purified CLONALITY: Polyclonal CONJUGATE: Unconjugated PHYSICAL STATE: Liquid BUFFER: Phosphate-Buffered Saline. No preservatives added. CONCENTRATION: 1 mg/mL September 27, 2021 1 https://www.prosci-inc.com/gipc1-antibody-8025.html GIPC1 antibody can be stored at 4˚C for short term (weeks). Long term storage should be STORAGE CONDITIONS: at -20˚C. As with all antibodies care should be taken to avoid repeated freeze thaw cycles. Antibodies should not be exposed to prolonged high temperatures. Additional Info OFFICIAL SYMBOL: GIPC1 C19orf3, GIPC, RGS19IP1, PDZ domain-containing protein GIPC1, GAIP C-terminus- interacting protein, TIP-2, NIP, IIP-1, SEMCAP, C19orf3, Hs.6454, GLUT1CBP, SYNECTIN, ALTERNATE NAMES: SYNECTIIN, GIPC PDZ domaincontaining protein 1, RGS19-interacting protein 1, RGS-GAIP- interacting protein, Tax interaction protein 2 ACCESSION NO.: NP_005707.1 PROTEIN GI NO.: 5031715 GENE ID: 10755 USER NOTE: Optimal dilutions for each application to be determined by the researcher. Background and References FUNCTION: May be involved in G protein-linked signaling. SUBUNIT: Interacts with RGS19 C-terminus and with HTLV-I Tax through the PDZ domain. BACKGROUND: SIMILARITY: Contains 1 PDZ (DHR) domain. SUBCELLULAR LOCATION: Cytoplasmic and membrane-associated. TISSUE SPECIFICITY: Widely expressed. ANTIBODIES FOR RESEARCH USE ONLY. For additional information, visit ProSci's Terms & Conditions Page. September 27, 2021 2 https://www.prosci-inc.com/gipc1-antibody-8025.html.

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Supplementary Figures 1-14 and Supplementary References
SUPPORTING INFORMATION Spatial Cross-Talk Between Oxidative Stress and DNA Replication in Human Fibroblasts Marko Radulovic,1,2 Noor O Baqader,1 Kai Stoeber,3† and Jasminka Godovac-Zimmermann1* 1Division of Medicine, University College London, Center for Nephrology, Royal Free Campus, Rowland Hill Street, London, NW3 2PF, UK. 2Insitute of Oncology and Radiology, Pasterova 14, 11000 Belgrade, Serbia 3Research Department of Pathology and UCL Cancer Institute, Rockefeller Building, University College London, University Street, London WC1E 6JJ, UK †Present Address: Shionogi Europe, 33 Kingsway, Holborn, London WC2B 6UF, UK TABLE OF CONTENTS 1. Supplementary Figures 1-14 and Supplementary References. Figure S-1. Network and joint spatial razor plot for 18 enzymes of glycolysis and the pentose phosphate shunt. Figure S-2. Correlation of SILAC ratios between OXS and OAC for proteins assigned to the SAME class. Figure S-3. Overlap matrix (r = 1) for groups of CORUM complexes containing 19 proteins of the 49-set. Figure S-4. Joint spatial razor plots for the Nop56p complex and FIB-associated complex involved in ribosome biogenesis. Figure S-5. Analysis of the response of emerin nuclear envelope complexes to OXS and OAC. Figure S-6. Joint spatial razor plots for the CCT protein folding complex, ATP synthase and V-Type ATPase. Figure S-7. Joint spatial razor plots showing changes in subcellular abundance and compartmental distribution for proteins annotated by GO to nucleocytoplasmic transport (GO:0006913). Figure S-8. Joint spatial razor plots showing changes in subcellular abundance and compartmental distribution for proteins annotated to endocytosis (GO:0006897). Figure S-9. Joint spatial razor plots for 401-set proteins annotated by GO to small GTPase mediated signal transduction (GO:0007264) and/or GTPase activity (GO:0003924).
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APPL1 Associates with Trka and GIPC1 and Is Required for Nerve Growth Factor-Mediated Signal Transduction
University of Montana ScholarWorks at University of Montana Biomedical and Pharmaceutical Sciences Faculty Publications Biomedical and Pharmaceutical Sciences 12-2006 APPL1 Associates with TrkA and GIPC1 and is Required for Nerve Growth Factor-Mediated Signal Transduction D. C. Lin C. Quevedo N. E. Brewer A. Bell J. R. Testa See next page for additional authors Follow this and additional works at: https://scholarworks.umt.edu/biopharm_pubs Part of the Medical Sciences Commons, and the Pharmacy and Pharmaceutical Sciences Commons Let us know how access to this document benefits ou.y Recommended Citation Lin, D. C.; Quevedo, C.; Brewer, N. E.; Bell, A.; Testa, J. R.; Grimes, Mark L.; Miller, F. D.; and Kaplan, D. R., "APPL1 Associates with TrkA and GIPC1 and is Required for Nerve Growth Factor-Mediated Signal Transduction" (2006). Biomedical and Pharmaceutical Sciences Faculty Publications. 13. https://scholarworks.umt.edu/biopharm_pubs/13 This Article is brought to you for free and open access by the Biomedical and Pharmaceutical Sciences at ScholarWorks at University of Montana. It has been accepted for inclusion in Biomedical and Pharmaceutical Sciences Faculty Publications by an authorized administrator of ScholarWorks at University of Montana. For more information, please contact [email protected]. Authors D. C. Lin, C. Quevedo, N. E. Brewer, A. Bell, J. R. Testa, Mark L. Grimes, F. D. Miller, and D. R. Kaplan This article is available at ScholarWorks at University of Montana: https://scholarworks.umt.edu/biopharm_pubs/13 MOLECULAR AND CELLULAR BIOLOGY, Dec. 2006, p. 8928–8941 Vol. 26, No. 23 0270-7306/06/$08.00ϩ0 doi:10.1128/MCB.00228-06 Copyright © 2006, American Society for Microbiology.
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Table S1. Identified Proteins with Exclusive Expression in Cerebellum of Rats of Control, 10Mg F/L and 50Mg F/L Groups
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Confirmation of Pathogenic Mechanisms by SARS-Cov-2–Host
Messina et al. Cell Death and Disease (2021) 12:788 https://doi.org/10.1038/s41419-021-03881-8 Cell Death & Disease ARTICLE Open Access Looking for pathways related to COVID-19: confirmation of pathogenic mechanisms by SARS-CoV-2–host interactome Francesco Messina 1, Emanuela Giombini1, Chiara Montaldo1, Ashish Arunkumar Sharma2, Antonio Zoccoli3, Rafick-Pierre Sekaly2, Franco Locatelli4, Alimuddin Zumla5, Markus Maeurer6,7, Maria R. Capobianchi1, Francesco Nicola Lauria1 and Giuseppe Ippolito 1 Abstract In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level, but the mechanisms of interaction between host and SARS-CoV-2, determining the grade of COVID-19 severity, are still unknown. We provide a network analysis on protein–protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus interactome was inferred, applying an explorative algorithm (Random Walk with Restart, RWR) triggered by 28 proteins of SARS-CoV-2. The analysis of PPI allowed to estimate the distribution of SARS-CoV-2 proteins in the host cell. Interactome built around one single viral protein allowed to define a different response, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, the network-based approach highlighted a possible direct action of ORF3a and NS7b to enhancing Bradykinin Storm. This network-based representation of SARS-CoV-2 infection could be a framework for pathogenic evaluation of specific 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; clinical outcomes.
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And Pancreatic Cancer: from the Role of Evs to the Interference with EV-Mediated Reciprocal Communication
biomedicines Review Extracellular Vesicles (EVs) and Pancreatic Cancer: From the Role of EVs to the Interference with EV-Mediated Reciprocal Communication 1, 1, 1 1 1 Sokviseth Moeng y, Seung Wan Son y, Jong Sun Lee , Han Yeoung Lee , Tae Hee Kim , Soo Young Choi 1, Hyo Jeong Kuh 2 and Jong Kook Park 1,* 1 Department of Biomedical Science and Research Institute for Bioscience & Biotechnology, Hallym University, Chunchon 24252, Korea; [email protected] (S.M.); [email protected] (S.W.S.); [email protected] (J.S.L.); [email protected] (H.Y.L.); [email protected] (T.H.K.); [email protected] (S.Y.C.) 2 Department of Medical Life Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-33-248-2114 These authors contributed equally. y Received: 29 June 2020; Accepted: 1 August 2020; Published: 3 August 2020 Abstract: Pancreatic cancer is malignant and the seventh leading cause of cancer-related deaths worldwide. However, chemotherapy and radiotherapy are—at most—moderately effective, indicating the need for new and different kinds of therapies to manage this disease. It has been proposed that the biologic properties of pancreatic cancer cells are finely tuned by the dynamic microenvironment, which includes extracellular matrix, cancer-associated cells, and diverse immune cells. Accumulating evidence has demonstrated that extracellular vesicles (EVs) play an essential role in communication between heterogeneous subpopulations of cells by transmitting multiplex biomolecules. EV-mediated cell–cell communication ultimately contributes to several aspects of pancreatic cancer, such as growth, angiogenesis, metastasis and therapeutic resistance.
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The GIPC1‐Akt1 Pathway Is Required for the Specification of The
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Anti-GIPC1 / NIP Antibody (ARG63385)
Product datasheet [email protected] ARG63385 Package: 100 μg anti-GIPC1 / NIP antibody Store at: -20°C Summary Product Description Goat Polyclonal antibody recognizes GIPC1 / NIP Tested Reactivity Hu Predict Reactivity Ms, Rat, Cow Tested Application WB Specificity This antibody is expected to recognize both isoform 1 (NP_005707.1, NP_974197.1 and NP_974199.1) and isoform 2 (NP_974196.1, NP_974198.1 and NP_974223.1). Host Goat Clonality Polyclonal Isotype IgG Target Name GIPC1 / NIP Antigen Species Human Immunogen C-GAIGDAKVGRY Conjugation Un-conjugated Alternate Names GLUT1CBP; RGS-GAIP-interacting protein; RGS19IP1; C19orf3; IIP-1; Tax interaction protein 2; GIPC; Hs.6454; SYNECTIIN; GAIP C-terminus-interacting protein; Synectin; RGS19-interacting protein 1; TIP-2; NIP; PDZ domain-containing protein GIPC1; SEMCAP; SYNECTIN Application Instructions Application table Application Dilution WB 0.03 - 0.1 µg/ml Application Note WB: Recommend incubate at RT for 1h. * The dilutions indicate recommended starting dilutions and the optimal dilutions or concentrations should be determined by the scientist. Calculated Mw 36 kDa Properties Form Liquid Purification Purified from goat serum by ammonium sulphate precipitation followed by antigen affinity chromatography using the immunizing peptide. Buffer Tris saline (pH 7.3), 0.02% Sodium azide and 0.5% BSA Preservative 0.02% Sodium azide Stabilizer 0.5% BSA Concentration 0.5 mg/ml www.arigobio.com 1/2 Storage instruction For continuous use, store undiluted antibody at 2-8°C for up to a week. For long-term storage, aliquot and store at -20°C or below. Storage in frost free freezers is not recommended. Avoid repeated freeze/thaw cycles.
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Low Incidence of GIPC3 Variants Among the Prelingual Hearing Impaired from Southern India
Journal of Genetics (2020)99:74 Ó Indian Academy of Sciences https://doi.org/10.1007/s12041-020-01234-6 (0123456789().,-volV)(0123456789().,-volV) RESEARCH NOTE Low incidence of GIPC3 variants among the prelingual hearing impaired from southern India MURUGESAN KALAIMATHI, MAHALINGAM SUBATHRA, JUSTIN MARGRET JEFFREY , MATHIYALAGAN SELVAKUMARI, JAYASANKARAN CHANDRU , NARASIMHAN SHARANYA, VANNIYA S. PARIDHY and C. R. SRIKUMARI SRISAILAPATHY* Dr. ALM PG Institute of Basic Medical Sciences, University of Madras, Chennai 600 113, India *For correspondence. E-mail: [email protected]. Received 24 October 2019; revised 24 June 2020; accepted 24 August 2020 Abstract. The broad spectrum of causal variants in the newly discovered GIPC3 gene is well reflected in worldwide studies. Except for one missense variant, none of the reported variants had reoccurred, thus reflecting the intragenic heterogeneity. We screened all the six coding exons of GIPC3 gene in a large cohort of 177 unrelated prelingual hearing impaired after excluding the common GJB2, GJB6 nuclear and A1555G mitochondrial variants. We observed a single homozygous pathogenic frameshift variant c.685dupG (p.A229GfsX10), accounting for a low incidence (0.56%) of GIPC3 variants in south Indian population. GIPC3 being a rare gene as a causative for deafness, the allelic spectra perhaps became much more diverse from population to population, thus resulting in a minimal recurrence of the variants in our study, that were reported by authors from other parts of the globe. Keywords. prelingual deafness; c.685dupG; consanguinity; south India; GIPC3 gene. Introduction PDZ domain and flanking conserved GIPC homology (GH1 and GH2) domains (Katoh 2002). Deafness variants are recurrently identified in GJB2, In the past two decades, tremendous efforts have been SLC26A4, TMC1, OTOF and CDH23 genes (Hilgert et al.
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Hair Cells Use Active Zones with Different Voltage Dependence Of
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Network Mining Approach to Cancer Biomarker Discovery
NETWORK MINING APPROACH TO CANCER BIOMARKER DISCOVERY THESIS Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Praneeth Uppalapati, B.E. Graduate Program in Computer Science and Engineering The Ohio State University 2010 Thesis Committee: Dr. Kun Huang, Advisor Dr. Raghu Machiraju Copyright by Praneeth Uppalapati 2010 ABSTRACT With the rapid development of high throughput gene expression profiling technology, molecule profiling has become a powerful tool to characterize disease subtypes and discover gene signatures. Most existing gene signature discovery methods apply statistical methods to select genes whose expression values can differentiate different subject groups. However, a drawback of these approaches is that the selected genes are not functionally related and hence cannot reveal biological mechanism behind the difference in the patient groups. Gene co-expression network analysis can be used to mine functionally related sets of genes that can be marked as potential biomarkers through survival analysis. We present an efficient heuristic algorithm EigenCut that exploits the properties of gene co- expression networks to mine functionally related and dense modules of genes. We apply this method to brain tumor (Glioblastoma Multiforme) study to obtain functionally related clusters. If functional groups of genes with predictive power on patient prognosis can be identified, insights on the mechanisms related to metastasis in GBM can be obtained and better therapeutical plan can be developed. We predicted potential biomarkers by dividing the patients into two groups based on their expression profiles over the genes in the clusters and comparing their survival outcome through survival analysis.
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