ELISA Kit Catalog
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
Comprehensive Study of Nuclear Receptor DNA Binding Provides a Revised Framework for Understanding Receptor Specificity
ARTICLE https://doi.org/10.1038/s41467-019-10264-3 OPEN Comprehensive study of nuclear receptor DNA binding provides a revised framework for understanding receptor specificity Ashley Penvose 1,2,4, Jessica L. Keenan 2,3,4, David Bray2,3, Vijendra Ramlall 1,2 & Trevor Siggers 1,2,3 The type II nuclear receptors (NRs) function as heterodimeric transcription factors with the retinoid X receptor (RXR) to regulate diverse biological processes in response to endogenous 1234567890():,; ligands and therapeutic drugs. DNA-binding specificity has been proposed as a primary mechanism for NR gene regulatory specificity. Here we use protein-binding microarrays (PBMs) to comprehensively analyze the DNA binding of 12 NR:RXRα dimers. We find more promiscuous NR-DNA binding than has been reported, challenging the view that NR binding specificity is defined by half-site spacing. We show that NRs bind DNA using two distinct modes, explaining widespread NR binding to half-sites in vivo. Finally, we show that the current models of NR specificity better reflect binding-site activity rather than binding-site affinity. Our rich dataset and revised NR binding models provide a framework for under- standing NR regulatory specificity and will facilitate more accurate analyses of genomic datasets. 1 Department of Biology, Boston University, Boston, MA 02215, USA. 2 Biological Design Center, Boston University, Boston, MA 02215, USA. 3 Bioinformatics Program, Boston University, Boston, MA 02215, USA. 4These authors contributed equally: Ashley Penvose, Jessica L. Keenan. Correspondence -
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. -
New York Chapter American College of Physicians Annual
New York Chapter American College of Physicians Annual Scientific Meeting Poster Presentations Saturday, October 12, 2019 Westchester Hilton Hotel 699 Westchester Avenue Rye Brook, NY New York Chapter American College of Physicians Annual Scientific Meeting Medical Student Clinical Vignette 1 Medical Student Clinical Vignette Adina Amin Medical Student Jessy Epstein, Miguel Lacayo, Emmanuel Morakinyo Touro College of Osteopathic Medicine A Series of Unfortunate Events - A Rare Presentation of Thoracic Outlet Syndrome Venous thoracic outlet syndrome, formerly known as Paget-Schroetter Syndrome, is a condition characterized by spontaneous deep vein thrombosis of the upper extremity. It is a very rare syndrome resulting from anatomical abnormalities of the thoracic outlet, causing thrombosis of the deep veins draining the upper extremity. This disease is also called “effort thrombosis― because of increased association with vigorous and repetitive upper extremity activities. Symptoms include severe upper extremity pain and swelling after strenuous activity. A 31-year-old female with a history of vascular thoracic outlet syndrome, two prior thrombectomies, and right first rib resection presented with symptoms of loss of blood sensation, dull pain in the area, and sharp pain when coughing/sneezing. When the patient had her first blood clot, physical exam was notable for swelling, venous distension, and skin discoloration. The patient had her first thrombectomy in her right upper extremity a couple weeks after the first clot was discovered. Thrombolysis with TPA was initiated, and percutaneous mechanical thrombectomy with angioplasty of the axillary and subclavian veins was performed. Almost immediately after the thrombectomy, the patient had a rethrombosis which was confirmed by ultrasound. -
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. -
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). -
Role of Cornification and Triglyceride Synthesis Genes
Gillespie et al. BMC Genomics 2013, 14:169 http://www.biomedcentral.com/1471-2164/14/169 RESEARCH ARTICLE Open Access Transcriptome analysis of pigeon milk production – role of cornification and triglyceride synthesis genes Meagan J Gillespie1,2*, Tamsyn M Crowley1,3, Volker R Haring1, Susanne L Wilson1, Jennifer A Harper1, Jean S Payne1, Diane Green1, Paul Monaghan1, John A Donald2, Kevin R Nicholas3 and Robert J Moore1 Abstract Background: The pigeon crop is specially adapted to produce milk that is fed to newly hatched young. The process of pigeon milk production begins when the germinal cell layer of the crop rapidly proliferates in response to prolactin, which results in a mass of epithelial cells that are sloughed from the crop and regurgitated to the young. We proposed that the evolution of pigeon milk built upon the ability of avian keratinocytes to accumulate intracellular neutral lipids during the cornification of the epidermis. However, this cornification process in the pigeon crop has not been characterised. Results: We identified the epidermal differentiation complex in the draft pigeon genome scaffold and found that, like the chicken, it contained beta-keratin genes. These beta-keratin genes can be classified, based on sequence similarity, into several clusters including feather, scale and claw keratins. The cornified cells of the pigeon crop express several cornification-associated genes including cornulin, S100-A9 and A16-like, transglutaminase 6-like and the pigeon ‘lactating’ crop-specific annexin cp35. Beta-keratins play an important role in ‘lactating’ crop, with several claw and scale keratins up-regulated. Additionally, transglutaminase 5 and differential splice variants of transglutaminase 4 are up-regulated along with S100-A10. -
Genes in Eyecare Geneseyedoc 3 W.M
Genes in Eyecare geneseyedoc 3 W.M. Lyle and T.D. Williams 15 Mar 04 This information has been gathered from several sources; however, the principal source is V. A. McKusick’s Mendelian Inheritance in Man on CD-ROM. Baltimore, Johns Hopkins University Press, 1998. Other sources include McKusick’s, Mendelian Inheritance in Man. Catalogs of Human Genes and Genetic Disorders. Baltimore. Johns Hopkins University Press 1998 (12th edition). http://www.ncbi.nlm.nih.gov/Omim See also S.P.Daiger, L.S. Sullivan, and B.J.F. Rossiter Ret Net http://www.sph.uth.tmc.edu/Retnet disease.htm/. Also E.I. Traboulsi’s, Genetic Diseases of the Eye, New York, Oxford University Press, 1998. And Genetics in Primary Eyecare and Clinical Medicine by M.R. Seashore and R.S.Wappner, Appleton and Lange 1996. M. Ridley’s book Genome published in 2000 by Perennial provides additional information. Ridley estimates that we have 60,000 to 80,000 genes. See also R.M. Henig’s book The Monk in the Garden: The Lost and Found Genius of Gregor Mendel, published by Houghton Mifflin in 2001 which tells about the Father of Genetics. The 3rd edition of F. H. Roy’s book Ocular Syndromes and Systemic Diseases published by Lippincott Williams & Wilkins in 2002 facilitates differential diagnosis. Additional information is provided in D. Pavan-Langston’s Manual of Ocular Diagnosis and Therapy (5th edition) published by Lippincott Williams & Wilkins in 2002. M.A. Foote wrote Basic Human Genetics for Medical Writers in the AMWA Journal 2002;17:7-17. A compilation such as this might suggest that one gene = one disease. -
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 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, -
Computational and Experimental Approaches for Evaluating the Genetic Basis of Mitochondrial Disorders
Computational and Experimental Approaches For Evaluating the Genetic Basis of Mitochondrial Disorders The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Lieber, Daniel Solomon. 2013. Computational and Experimental Approaches For Evaluating the Genetic Basis of Mitochondrial Disorders. Doctoral dissertation, Harvard University. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:11158264 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Computational and Experimental Approaches For Evaluating the Genetic Basis of Mitochondrial Disorders A dissertation presented by Daniel Solomon Lieber to The Committee on Higher Degrees in Systems Biology in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Systems Biology Harvard University Cambridge, Massachusetts April 2013 © 2013 - Daniel Solomon Lieber All rights reserved. Dissertation Adviser: Professor Vamsi K. Mootha Daniel Solomon Lieber Computational and Experimental Approaches For Evaluating the Genetic Basis of Mitochondrial Disorders Abstract Mitochondria are responsible for some of the cell’s most fundamental biological pathways and metabolic processes, including aerobic ATP production by the mitochondrial respiratory chain. In humans, mitochondrial dysfunction can lead to severe disorders of energy metabolism, which are collectively referred to as mitochondrial disorders and affect approximately 1:5,000 individuals. These disorders are clinically heterogeneous and can affect multiple organ systems, often within a single individual. Symptoms can include myopathy, exercise intolerance, hearing loss, blindness, stroke, seizures, diabetes, and GI dysmotility. -
Xenobiotic-Sensing Nuclear Receptors Involved in Drug Metabolism: a Structural Perspective
HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Drug Metab Manuscript Author Rev. Author Manuscript Author manuscript; available in PMC 2016 May 24. Published in final edited form as: Drug Metab Rev. 2013 February ; 45(1): 79–100. doi:10.3109/03602532.2012.740049. Xenobiotic-sensing nuclear receptors involved in drug metabolism: a structural perspective Bret D. Wallace and Matthew R. Redinbo Departments of Chemistry, Biochemistry, and Microbiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA Abstract Xenobiotic compounds undergo a critical range of biotransformations performed by the phase I, II, and III drug-metabolizing enzymes. The oxidation, conjugation, and transportation of potentially harmful xenobiotic and endobiotic compounds achieved by these catalytic systems are significantly regulated, at the gene expression level, by members of the nuclear receptor (NR) family of ligand-modulated transcription factors. Activation of NRs by a variety of endo- and exogenous chemicals are elemental to induction and repression of drug-metabolism pathways. The master xenobiotic sensing NRs, the promiscuous pregnane X receptor and less-promiscuous constitutive androstane receptor are crucial to initial ligand recognition, jump-starting the metabolic process. Other receptors, including farnesoid X receptor, vitamin D receptor, hepatocyte nuclear factor 4 alpha, peroxisome proliferator activated receptor, glucocorticoid receptor, liver X receptor, and RAR-related orphan receptor, are not directly linked to promiscuous xenobiotic binding, but clearly play important roles in the modulation of metabolic gene expression. Crystallographic studies of the ligand-binding domains of nine NRs involved in drug metabolism provide key insights into ligand-based and constitutive activity, coregulator recruitment, and gene regulation. -
Cloud-Clone 16-17
Cloud-Clone - 2016-17 Catalog Description Pack Size Supplier Rupee(RS) ACB028Hu CLIA Kit for Anti-Albumin Antibody (AAA) 96T Cloud-Clone 74750 AEA044Hu ELISA Kit for Anti-Growth Hormone Antibody (Anti-GHAb) 96T Cloud-Clone 74750 AEA255Hu ELISA Kit for Anti-Apolipoprotein Antibodies (AAHA) 96T Cloud-Clone 74750 AEA417Hu ELISA Kit for Anti-Proteolipid Protein 1, Myelin Antibody (Anti-PLP1) 96T Cloud-Clone 74750 AEA421Hu ELISA Kit for Anti-Myelin Oligodendrocyte Glycoprotein Antibody (Anti- 96T Cloud-Clone 74750 MOG) AEA465Hu ELISA Kit for Anti-Sperm Antibody (AsAb) 96T Cloud-Clone 74750 AEA539Hu ELISA Kit for Anti-Myelin Basic Protein Antibody (Anti-MBP) 96T Cloud-Clone 71250 AEA546Hu ELISA Kit for Anti-IgA Antibody 96T Cloud-Clone 71250 AEA601Hu ELISA Kit for Anti-Myeloperoxidase Antibody (Anti-MPO) 96T Cloud-Clone 71250 AEA747Hu ELISA Kit for Anti-Complement 1q Antibody (Anti-C1q) 96T Cloud-Clone 74750 AEA821Hu ELISA Kit for Anti-C Reactive Protein Antibody (Anti-CRP) 96T Cloud-Clone 74750 AEA895Hu ELISA Kit for Anti-Insulin Receptor Antibody (AIRA) 96T Cloud-Clone 74750 AEB028Hu ELISA Kit for Anti-Albumin Antibody (AAA) 96T Cloud-Clone 71250 AEB264Hu ELISA Kit for Insulin Autoantibody (IAA) 96T Cloud-Clone 74750 AEB480Hu ELISA Kit for Anti-Mannose Binding Lectin Antibody (Anti-MBL) 96T Cloud-Clone 88575 AED245Hu ELISA Kit for Anti-Glutamic Acid Decarboxylase Antibodies (Anti-GAD) 96T Cloud-Clone 71250 AEK505Hu ELISA Kit for Anti-Heparin/Platelet Factor 4 Antibodies (Anti-HPF4) 96T Cloud-Clone 71250 CCA005Hu CLIA Kit for Angiotensin II