Gene Expression Profiling in Hepatocellular Carcinoma: Upregulation of Genes in Amplified Chromosome Regions
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Recombinant Human ARFIP2 Protein Catalog Number: ATGP1695
Recombinant human ARFIP2 protein Catalog Number: ATGP1695 PRODUCT INPORMATION Expression system E.coli Domain 1-341aa UniProt No. P53365 NCBI Accession No. NP_036534 Alternative Names Arfaptin 2, POR1 PRODUCT SPECIFICATION Molecular Weight 40.2 kDa (364aa) confirmed by MALDI-TOF Concentration 0.25mg/ml (determined by Bradford assay) Formulation Liquid in. 20mM Tris-HCl buffer (pH 8.0) containing 0.2M NaCl, 40% glycerol, 1mM DTT Purity > 90% by SDS-PAGE Tag His-Tag Application SDS-PAGE Storage Condition Can be stored at +2C to +8C for 1 week. For long term storage, aliquot and store at -20C to -80C. Avoid repeated freezing and thawing cycles. BACKGROUND Description Arfaptin 2, also known as ARFIP2, is a Rac1 binding protein necessary for Rac-mediated actin polymerization and the subsequent formation of membrane ruffles and lamellipodia. ARFIP2 has also been shown to interact with the ADP ribosylation factor ARF6, a GTPase that associates with the plasma membrane and intracellular endosome vesicles, in a GTP dependent manner. Arfaptin 2 also regulates the aggregation of mutant Huntingtin protein by possibly impairing proteasome function. Expression of ARFIP2 was shown to be increased at sites of neurodegeneration. Recombinant human ARFIP2 protein, fused to His-tag at N-terminus, was expressed in E. coli 1 Recombinant human ARFIP2 protein Catalog Number: ATGP1695 and purified by using conventional chromatography techniques. Amino acid Sequence MGSSHHHHHH SSGLVPRGSH MGSMTDGILG KAATMEIPIH GNGEARQLPE DDGLEQDLQQ VMVSGPNLNE TSIVSGGYGG SGDGLIPTGS GRHPSHSTTP SGPGDEVARG IAGEKFDIVK KWGINTYKCT KQLLSERFGR GSRTVDLELE LQIELLRETK RKYESVLQLG RALTAHLYSL LQTQHALGDA FADLSQKSPE LQEEFGYNAE TQKLLCKNGE TLLGAVNFFV SSINTLVTKT MEDTLMTVKQ YEAARLEYDA YRTDLEELSL GPRDAGTRGR LESAQATFQA HRDKYEKLRG DVAIKLKFLE ENKIKVMHKQ LLLFHNAVSA YFAGNQKQLE QTLQQFNIKL RPPGAEKPSW LEEQ General References D'Souza Schorey C., et al. -
University of California, San Diego
UNIVERSITY OF CALIFORNIA, SAN DIEGO The post-terminal differentiation fate of RNAs revealed by next-generation sequencing A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Biomedical Sciences by Gloria Kuo Lefkowitz Committee in Charge: Professor Benjamin D. Yu, Chair Professor Richard Gallo Professor Bruce A. Hamilton Professor Miles F. Wilkinson Professor Eugene Yeo 2012 Copyright Gloria Kuo Lefkowitz, 2012 All rights reserved. The Dissertation of Gloria Kuo Lefkowitz is approved, and it is acceptable in quality and form for publication on microfilm and electronically: __________________________________________________________________ __________________________________________________________________ __________________________________________________________________ __________________________________________________________________ __________________________________________________________________ Chair University of California, San Diego 2012 iii DEDICATION Ma and Ba, for your early indulgence and support. Matt and James, for choosing more practical callings. Roy, my love, for patiently sharing the ups and downs of this journey. iv EPIGRAPH It is foolish to tear one's hair in grief, as though sorrow would be made less by baldness. ~Cicero v TABLE OF CONTENTS Signature Page .............................................................................................................. iii Dedication .................................................................................................................... -
Biobin User Guide Current Version: Biobin 2.0
___________________________________ BioBin User Guide Current version: BioBin 2.0 October 2012 Last modified: October 2012 Ritchie Lab, Center for Systems Genomics Pennsylvania State University, University Park, PA 16802 URL: https://ritchielab.psu.edu/ritchielab/software/ Email: [email protected] Table of Contents Overview ............................................................................................................................................................................... 3 BioBin ................................................................................................................................................................................................................ 3 Library of Knowledge Integration (LOKI) Database .................................................................................................................... 3 Quick Reference ............................................................................................................................................................................................ 5 Installation ............................................................................................................................................................................ 6 Prerequisites .................................................................................................................................................................................................. 6 Unpacking ...................................................................................................................................................................................................... -
CCT3 Rabbit Pab
Leader in Biomolecular Solutions for Life Science CCT3 Rabbit pAb Catalog No.: A6547 3 Publications Basic Information Background Catalog No. The protein encoded by this gene is a molecular chaperone that is a member of the A6547 chaperonin containing TCP1 complex (CCT), also known as the TCP1 ring complex (TRiC). This complex consists of two identical stacked rings, each containing eight different Observed MW proteins. Unfolded polypeptides enter the central cavity of the complex and are folded 60kDa in an ATP-dependent manner. The complex folds various proteins, including actin and tubulin. Alternate transcriptional splice variants have been characterized for this gene. Calculated MW In addition, a pseudogene of this gene has been found on chromosome 8. 56kDa/60kDa Category Primary antibody Applications WB,IHC,IF Cross-Reactivity Human, Mouse, Rat Recommended Dilutions Immunogen Information WB 1:500 - 1:2000 Gene ID Swiss Prot 7203 P49368 IHC 1:50 - 1:200 Immunogen 1:50 - 1:200 IF Recombinant fusion protein containing a sequence corresponding to amino acids 1-300 of human CCT3 (NP_005989.3). Synonyms CCT3;CCT-gamma;CCTG;PIG48;TCP-1-gamma;TRIC5 Contact Product Information www.abclonal.com Source Isotype Purification Rabbit IgG Affinity purification Storage Store at -20℃. Avoid freeze / thaw cycles. Buffer: PBS with 0.02% sodium azide,50% glycerol,pH7.3. Validation Data Western blot analysis of extracts of various cell lines, using CCT3 antibody (A6547) at 1:1000 dilution. Secondary antibody: HRP Goat Anti-Rabbit IgG (H+L) (AS014) at 1:10000 dilution. Lysates/proteins: 25ug per lane. Blocking buffer: 3% nonfat dry milk in TBST. -
Hypoxia and Oxygen-Sensing Signaling in Gene Regulation and Cancer Progression
International Journal of Molecular Sciences Review Hypoxia and Oxygen-Sensing Signaling in Gene Regulation and Cancer Progression Guang Yang, Rachel Shi and Qing Zhang * Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; [email protected] (G.Y.); [email protected] (R.S.) * Correspondence: [email protected]; Tel.: +1-214-645-4671 Received: 6 October 2020; Accepted: 29 October 2020; Published: 31 October 2020 Abstract: Oxygen homeostasis regulation is the most fundamental cellular process for adjusting physiological oxygen variations, and its irregularity leads to various human diseases, including cancer. Hypoxia is closely associated with cancer development, and hypoxia/oxygen-sensing signaling plays critical roles in the modulation of cancer progression. The key molecules of the hypoxia/oxygen-sensing signaling include the transcriptional regulator hypoxia-inducible factor (HIF) which widely controls oxygen responsive genes, the central members of the 2-oxoglutarate (2-OG)-dependent dioxygenases, such as prolyl hydroxylase (PHD or EglN), and an E3 ubiquitin ligase component for HIF degeneration called von Hippel–Lindau (encoding protein pVHL). In this review, we summarize the current knowledge about the canonical hypoxia signaling, HIF transcription factors, and pVHL. In addition, the role of 2-OG-dependent enzymes, such as DNA/RNA-modifying enzymes, JmjC domain-containing enzymes, and prolyl hydroxylases, in gene regulation of cancer progression, is specifically reviewed. We also discuss the therapeutic advancement of targeting hypoxia and oxygen sensing pathways in cancer. Keywords: hypoxia; PHDs; TETs; JmjCs; HIFs 1. Introduction Molecular oxygen serves as a co-factor in many biochemical processes and is fundamental for aerobic organisms to maintain intracellular ATP levels [1,2]. -
Proteomic Profile of Human Spermatozoa in Healthy And
Cao et al. Reproductive Biology and Endocrinology (2018) 16:16 https://doi.org/10.1186/s12958-018-0334-1 REVIEW Open Access Proteomic profile of human spermatozoa in healthy and asthenozoospermic individuals Xiaodan Cao, Yun Cui, Xiaoxia Zhang, Jiangtao Lou, Jun Zhou, Huafeng Bei and Renxiong Wei* Abstract Asthenozoospermia is considered as a common cause of male infertility and characterized by reduced sperm motility. However, the molecular mechanism that impairs sperm motility remains unknown in most cases. In the present review, we briefly reviewed the proteome of spermatozoa and seminal plasma in asthenozoospermia and considered post-translational modifications in spermatozoa of asthenozoospermia. The reduction of sperm motility in asthenozoospermic patients had been attributed to factors, for instance, energy metabolism dysfunction or structural defects in the sperm-tail protein components and the differential proteins potentially involved in sperm motility such as COX6B, ODF, TUBB2B were described. Comparative proteomic analysis open a window to discover the potential pathogenic mechanisms of asthenozoospermia and the biomarkers with clinical significance. Keywords: Proteome, Spermatozoa, Sperm motility, Asthenozoospermia, Infertility Background fertilization failure [4] and it has become clear that iden- Infertility is defined as the lack of ability to achieve a tifying the precise proteins and the pathways involved in clinical pregnancy after one year or more of unprotected sperm motility is needed [5]. and well-timed intercourse with the same partner [1]. It is estimated that around 15% of couples of reproductive age present with infertility, and about half of the infertil- Application of proteomic techniques in male ity is associated with male partner [2, 3]. -
Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice
Loyola University Chicago Loyola eCommons Biology: Faculty Publications and Other Works Faculty Publications 2013 Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice Mihaela Palicev Gunter P. Wagner James P. Noonan Benedikt Hallgrimsson James M. Cheverud Loyola University Chicago, [email protected] Follow this and additional works at: https://ecommons.luc.edu/biology_facpubs Part of the Biology Commons Recommended Citation Palicev, M, GP Wagner, JP Noonan, B Hallgrimsson, and JM Cheverud. "Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice." Genome Biology and Evolution 5(10), 2013. This Article is brought to you for free and open access by the Faculty Publications at Loyola eCommons. It has been accepted for inclusion in Biology: Faculty Publications and Other Works by an authorized administrator of Loyola eCommons. For more information, please contact [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. © Palicev et al., 2013. GBE Genomic Correlates of Relationship QTL Involved in Fore- versus Hind Limb Divergence in Mice Mihaela Pavlicev1,2,*, Gu¨ nter P. Wagner3, James P. Noonan4, Benedikt Hallgrı´msson5,and James M. Cheverud6 1Konrad Lorenz Institute for Evolution and Cognition Research, Altenberg, Austria 2Department of Pediatrics, Cincinnati Children‘s Hospital Medical Center, Cincinnati, Ohio 3Yale Systems Biology Institute and Department of Ecology and Evolutionary Biology, Yale University 4Department of Genetics, Yale University School of Medicine 5Department of Cell Biology and Anatomy, The McCaig Institute for Bone and Joint Health and the Alberta Children’s Hospital Research Institute for Child and Maternal Health, University of Calgary, Calgary, Canada 6Department of Anatomy and Neurobiology, Washington University *Corresponding author: E-mail: [email protected]. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Trait Locus Chr Position P-Value Effect Effect SE Ref MA MAF Gene Gene Function FP Rs137787931 14 1880378 4.27E-07
Table S1. The SNPs which reached significance in single-locus association for fat percentages at suggestive threshold (P < 2.22 × 10-5). Trait Locus Chr Position P-Value Effect Effect SE Ref MA MAF Gene Gene function FP rs137787931 14 1,880,378 4.27E-07 -0.06 0.01 T C 0.42 MROH1 - rs134432442 14 1,736,599 8.69E-07 0.06 0.01 C T 0.49 CPSF1 mRNA polyadenylation FP = fat percentage, Chr = chromosome, Ref = reference allele, MA = minor allele, MAF = minor allele frequency, MROH1 = maestro heat like repeat family member 1, CPSF1 = cleavage and polyadenylation specific factor 1. Table S2. The 1-Mb SNP windows surpass suggestive significance level that is proportion of genetic variance (PVE) at 0.19 % and their window posterior probability of association (WPPA) for percentages of milk fat (FP) and crude protein (CPP), milk urea (MU) and efficiency of crude protein utilization (ECPU). Trait Window Chr Start-end window (Mb) Start SNP End SNP No. of SNP PVE (%) WPPA Gene Gene function FP 1849 18 15-16 15,039,844 15,954,290 21 0.31 0.22 GPT2 Regulation of biosynthesis CPP 323 3 26-27 26,020,004 26,925,312 18 0.36 0.22 TRIM45 Protein ubiquitination 1567 14 62-63 62,081,472 62,960,995 17 0.34 0.12 UBR5 Protein ubiquitination MU 563 5 23-24 23,019,369 23,949,571 19 0.34 0.08 UBE2N Protein ubiquitination 1084 9 75-76 75,026,578 75,935,285 19 0.33 0.14 TNFAIP3 Protein ubiquitination 1677 16 1-2 1,033,239 1,972,109 24 0.32 0.14 ATP2B4, Urinary bladder smooth muscle contraction, REN Kidney development 4 1 3-4 3,079,342 3,987,104 19 0.31 0.18 UBR1 Protein -
Mutant IDH, (R)-2-Hydroxyglutarate, and Cancer
Downloaded from genesdev.cshlp.org on October 1, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW What a difference a hydroxyl makes: mutant IDH, (R)-2-hydroxyglutarate, and cancer Julie-Aurore Losman1 and William G. Kaelin Jr.1,2,3 1Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02215, USA; 2Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA Mutations in metabolic enzymes, including isocitrate whether altered cellular metabolism is a cause of cancer dehydrogenase 1 (IDH1) and IDH2, in cancer strongly or merely an adaptive response of cancer cells in the face implicate altered metabolism in tumorigenesis. IDH1 of accelerated cell proliferation is still a topic of some and IDH2 catalyze the interconversion of isocitrate and debate. 2-oxoglutarate (2OG). 2OG is a TCA cycle intermediate The recent identification of cancer-associated muta- and an essential cofactor for many enzymes, including tions in three metabolic enzymes suggests that altered JmjC domain-containing histone demethylases, TET cellular metabolism can indeed be a cause of some 5-methylcytosine hydroxylases, and EglN prolyl-4-hydrox- cancers (Pollard et al. 2003; King et al. 2006; Raimundo ylases. Cancer-associated IDH mutations alter the enzymes et al. 2011). Two of these enzymes, fumarate hydratase such that they reduce 2OG to the structurally similar (FH) and succinate dehydrogenase (SDH), are bone fide metabolite (R)-2-hydroxyglutarate [(R)-2HG]. Here we tumor suppressors, and loss-of-function mutations in FH review what is known about the molecular mechanisms and SDH have been identified in various cancers, in- of transformation by mutant IDH and discuss their im- cluding renal cell carcinomas and paragangliomas. -
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. -
DF6216-CSRP1 Antibody
Affinity Biosciences website:www.affbiotech.com order:[email protected] CSRP1 Antibody Cat.#: DF6216 Concn.: 1mg/ml Mol.Wt.: 21kDa Size: 50ul,100ul,200ul Source: Rabbit Clonality: Polyclonal Application: WB 1:500-1:2000, IHC 1:50-1:200, ELISA(peptide) 1:20000-1:40000 *The optimal dilutions should be determined by the end user. Reactivity: Human,Mouse,Rat Purification: The antiserum was purified by peptide affinity chromatography using SulfoLink™ Coupling Resin (Thermo Fisher Scientific). Specificity: CSRP1 Antibody detects endogenous levels of total CSRP1. Immunogen: A synthesized peptide derived from human CSRP1, corresponding to a region within the internal amino acids. Uniprot: P21291 Description: This gene encodes a member of the cysteine-rich protein (CSRP) family. This gene family includes a group of LIM domain proteins, which may be involved in regulatory processes important for development and cellular differentiation. The LIM/double zinc-finger motif found in this gene product occurs in proteins with critical functions in gene regulation, cell growth, and somatic differentiation. Alternatively spliced transcript variants have been described. [provided by RefSeq, Aug 2010] Storage Condition and Rabbit IgG in phosphate buffered saline , pH 7.4, 150mM Buffer: NaCl, 0.02% sodium azide and 50% glycerol.Store at -20 °C.Stable for 12 months from date of receipt. Western blot analysis of CSRP1 expression in Mouse lung lysate 1 / 2 Affinity Biosciences website:www.affbiotech.com order:[email protected] DF6216 at 1/100 staining Mouse brain tissue by IHC-P. The sample was formaldehyde fixed and a heat mediated antigen retrieval step in citrate buffer was performed.