Page 1 Supplemental Table I Upin WTVG Upin Humlowfb FAR1
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Polymorphisms in FCGR2A (131H/R) and FCGR2B (232I/T) Are Associated with the Development of Inhibitors in Chinese Hemophilia a Patients
Polymorphisms in FCGR2A (131H/R) and FCGR2B (232I/T) are associated with the development of inhibitors in Chinese hemophilia A patients Hong Qu ( [email protected] ) PanYu Central Hospital https://orcid.org/0000-0003-0728-2744 Yongfang Chen Guangzhou Panyu Central Hospital Wenjing Zeng Guangzhou Panyu Central Hospital Xiaohua Huang Guangzhou Panyu Central Hospital Shuqin Cheng Guangzhou Panyu Central Hospital Primary research Keywords: Hemophilia A, FCGR2A, FCGR2B, FVIII inhibitors, polymorphisms Posted Date: June 15th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-35124/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/9 Abstract Background: Present study was to explore the association between gene polymorphisms in Fc gamma receptor IIa (FCGR2A) and Iib (FCGR2B) and factor VIII (FVIII) inhibitor development in patients with hemophili A (HA) in a Chinese Han population. Methods: FCGR2A (131H/R) and FCGR2B (232I/T) polymorphsims were genotyped using PCR and direct sequencing method in 108 HA patients, including 23 cases with inhibitors and 85 without inhibitors. Chi- square (c2) test was applied to compare the genotype and allele frequencies between two groups. Odds ratio (OR) and 95% condence interval (95%CI) were calculated to indicate the relative susceptibility of HA. Results: FCGR2A 131RH genotype frequency had a signicantly increased trend in inhibitor group compared with the non-inhibitor group, suggesting a momentous role of 131H/R polymorphism for inhibitor development in HA patients. Individuals carrying 131RH genotype showed higher risk to be attacked by the inhibitor development in HA patients (OR=4.929; 95%CI=1.029-23.605). -
Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse
Welcome to More Choice CD Marker Handbook For more information, please visit: Human bdbiosciences.com/eu/go/humancdmarkers Mouse bdbiosciences.com/eu/go/mousecdmarkers Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse CD3 CD3 CD (cluster of differentiation) molecules are cell surface markers T Cell CD4 CD4 useful for the identification and characterization of leukocytes. The CD CD8 CD8 nomenclature was developed and is maintained through the HLDA (Human Leukocyte Differentiation Antigens) workshop started in 1982. CD45R/B220 CD19 CD19 The goal is to provide standardization of monoclonal antibodies to B Cell CD20 CD22 (B cell activation marker) human antigens across laboratories. To characterize or “workshop” the antibodies, multiple laboratories carry out blind analyses of antibodies. These results independently validate antibody specificity. CD11c CD11c Dendritic Cell CD123 CD123 While the CD nomenclature has been developed for use with human antigens, it is applied to corresponding mouse antigens as well as antigens from other species. However, the mouse and other species NK Cell CD56 CD335 (NKp46) antibodies are not tested by HLDA. Human CD markers were reviewed by the HLDA. New CD markers Stem Cell/ CD34 CD34 were established at the HLDA9 meeting held in Barcelona in 2010. For Precursor hematopoetic stem cell only hematopoetic stem cell only additional information and CD markers please visit www.hcdm.org. Macrophage/ CD14 CD11b/ Mac-1 Monocyte CD33 Ly-71 (F4/80) CD66b Granulocyte CD66b Gr-1/Ly6G Ly6C CD41 CD41 CD61 (Integrin b3) CD61 Platelet CD9 CD62 CD62P (activated platelets) CD235a CD235a Erythrocyte Ter-119 CD146 MECA-32 CD106 CD146 Endothelial Cell CD31 CD62E (activated endothelial cells) Epithelial Cell CD236 CD326 (EPCAM1) For Research Use Only. -
Atlas Antibodies in Breast Cancer Research Table of Contents
ATLAS ANTIBODIES IN BREAST CANCER RESEARCH TABLE OF CONTENTS The Human Protein Atlas, Triple A Polyclonals and PrecisA Monoclonals (4-5) Clinical markers (6) Antibodies used in breast cancer research (7-13) Antibodies against MammaPrint and other gene expression test proteins (14-16) Antibodies identified in the Human Protein Atlas (17-14) Finding cancer biomarkers, as exemplified by RBM3, granulin and anillin (19-22) Co-Development program (23) Contact (24) Page 2 (24) Page 3 (24) The Human Protein Atlas: a map of the Human Proteome The Human Protein Atlas (HPA) is a The Human Protein Atlas consortium cell types. All the IHC images for Swedish-based program initiated in is mainly funded by the Knut and Alice the normal tissue have undergone 2003 with the aim to map all the human Wallenberg Foundation. pathology-based annotation of proteins in cells, tissues and organs expression levels. using integration of various omics The Human Protein Atlas consists of technologies, including antibody- six separate parts, each focusing on References based imaging, mass spectrometry- a particular aspect of the genome- 1. Sjöstedt E, et al. (2020) An atlas of the based proteomics, transcriptomics wide analysis of the human proteins: protein-coding genes in the human, pig, and and systems biology. mouse brain. Science 367(6482) 2. Thul PJ, et al. (2017) A subcellular map of • The Tissue Atlas shows the the human proteome. Science. 356(6340): All the data in the knowledge resource distribution of proteins across all eaal3321 is open access to allow scientists both major tissues and organs in the 3. -
Machine-Learning and Chemicogenomics Approach Defi Nes and Predicts Cross-Talk of Hippo and MAPK Pathways
Published OnlineFirst November 18, 2020; DOI: 10.1158/2159-8290.CD-20-0706 RESEARCH ARTICLE Machine -Learning and Chemicogenomics Approach Defi nes and Predicts Cross-Talk of Hippo and MAPK Pathways Trang H. Pham 1 , Thijs J. Hagenbeek 1 , Ho-June Lee 1 , Jason Li 2 , Christopher M. Rose 3 , Eva Lin 1 , Mamie Yu 1 , Scott E. Martin1 , Robert Piskol 2 , Jennifer A. Lacap 4 , Deepak Sampath 4 , Victoria C. Pham 3 , Zora Modrusan 5 , Jennie R. Lill3 , Christiaan Klijn 2 , Shiva Malek 1 , Matthew T. Chang 2 , and Anwesha Dey 1 ABSTRACT Hippo pathway dysregulation occurs in multiple cancers through genetic and non- genetic alterations, resulting in translocation of YAP to the nucleus and activation of the TEAD family of transcription factors. Unlike other oncogenic pathways such as RAS, defi ning tumors that are Hippo pathway–dependent is far more complex due to the lack of hotspot genetic alterations. Here, we developed a machine-learning framework to identify a robust, cancer type–agnostic gene expression signature to quantitate Hippo pathway activity and cross-talk as well as predict YAP/TEAD dependency across cancers. Further, through chemical genetic interaction screens and multiomics analyses, we discover a direct interaction between MAPK signaling and TEAD stability such that knockdown of YAP combined with MEK inhibition results in robust inhibition of tumor cell growth in Hippo dysregulated tumors. This multifaceted approach underscores how computational models combined with experimental studies can inform precision medicine approaches including predictive diagnostics and combination strategies. SIGNIFICANCE: An integrated chemicogenomics strategy was developed to identify a lineage- independent signature for the Hippo pathway in cancers. -
View Is Portrayed Schematically in Figure 7B
BASIC RESEARCH www.jasn.org Recombination Signal Binding Protein for Ig-kJ Region Regulates Juxtaglomerular Cell Phenotype by Activating the Myo-Endocrine Program and Suppressing Ectopic Gene Expression † † ‡ Ruth M. Castellanos-Rivera,* Ellen S. Pentz,* Eugene Lin,* Kenneth W. Gross, † Silvia Medrano,* Jing Yu,§ Maria Luisa S. Sequeira-Lopez,* and R. Ariel Gomez* *Department of Pediatrics, School of Medicine, †Department of Biology, Graduate School of Arts and Sciences, and §Department of Cell Biology, University of Virginia, Charlottesville, Virginia; and ‡Department of Molecular and Cellular Biology, Roswell Park Cancer Institute, Buffalo, New York ABSTRACT Recombination signal binding protein for Ig-kJ region (RBP-J), the major downstream effector of Notch signaling, is necessary to maintain the number of renin-positive juxtaglomerular cells and the plasticity of arteriolar smooth muscle cells to re-express renin when homeostasis is threatened. We hypothesized that RBP-J controls a repertoire of genes that defines the phenotype of the renin cell. Mice bearing a bacterial artificial chromosome reporter with a mutated RBP-J binding site in the renin promoter had markedly reduced reporter expression at the basal state and in response to a homeostatic challenge. Mice with conditional deletion of RBP-J in renin cells had decreased expression of endocrine (renin and Akr1b7)and smooth muscle (Acta2, Myh11, Cnn1,andSmtn) genes and regulators of smooth muscle expression (miR- 145, SRF, Nfatc4, and Crip1). To determine whether RBP-J deletion decreased the endowment of renin cells, we traced the fate of these cells in RBP-J conditional deletion mice. Notably, the lineage staining patterns in mutant and control kidneys were identical, although mutant kidneys had fewer or no renin- expressing cells in the juxtaglomerular apparatus. -
The Role of Genetic Variants in FCGR2A on the Risk of Rheumatoid Arthritis in the Han Chinese Population
The role of genetic variants in FCGR2A on the risk of rheumatoid arthritis in the Han Chinese population Yonghui Yang Clinical laboratory,Xi'an 630 hospital Linna Peng Xizang Minzu University Chunjuan He Xizang Minzu University Shishi Xing Xizang Minzu University Dandan Li Xizang Minzu University Tianbo Jin Xizang Minzu University Li Wang ( [email protected] ) Xizang Minzu University Research Keywords: Rheumatoid arthritis (RA), single nucleotide polymorphisms (SNPs), FCGR2A Posted Date: September 2nd, 2020 DOI: https://doi.org/10.21203/rs.3.rs-63617/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/21 Abstract Background: Rheumatoid arthritis (RA) is the most common inammatory arthritis and is characterized by irreversible joint damage and deformities, which is largely caused by genetic factors. The aim of this study was to explore the role of FCGR2A polymorphisms with the susceptibility to RA in the Han Chinese cohort. Methods: We enrolled 506 RA patients and 509 healthy controls, with four single nucleotide polymorphisms (SNPs) successfully genotyped using Agena MassARRAY. Genetic models, haplotype analyses were applied to assess the association between FCGR2A polymorphisms and RA. And we evaluated the relative risk by odds ratios (ORs) and 95% condence intervals (95% CIs) using logistic regression analysis. Results: The results revealed that FCGR2A rs6668534 was signicantly related to an increased risk of RA in the overall (OR = 1.24, 95%CI = 1.04 – 1.48, p = 0.014). There was no any association found between the polymorphisms and RA risk at age > 54 years, while the two (rs6671847 and rs1801274) of the four SNPs possibly contributed to the susceptibility to RA at age ≤ 54 years. -
An Extracellular Site on Tetraspanin CD151 Determines Α3 and Α6
JCBArticle An extracellular site on tetraspanin CD151 determines ␣3 and ␣6 integrin–dependent cellular morphology Alexander R. Kazarov, Xiuwei Yang, Christopher S. Stipp, Bantoo Sehgal, and Martin E. Hemler Dana-Farber Cancer Institute and Department of Pathology, Harvard Medical School, Boston, MA 02115 he ␣31 integrin shows strong, stoichiometric, direct Brij 96–resistant) were independent of the QRD/TS151r lateral association with the tetraspanin CD151. As site, occurred late in biosynthesis, and involved mature T shown here, an extracellular CD151 site (QRD194–196) integrin subunits. Presence of the CD151–QRD194–196→INF is required for strong (i.e., Triton X-100–resistant) ␣31 mutant disrupted ␣3 and ␣6 integrin–dependent formation association and for maintenance of a key CD151 epitope of a network of cellular cables by Cos7 or NIH3T3 cells on (defined by monoclonal antibody TS151r) that is blocked basement membrane Matrigel and markedly altered cell upon ␣3 integrin association. Strong CD151 association spreading. These results provide definitive evidence that with integrin ␣61 also required the QRD194–196 site and strong lateral CD151–integrin association is functionally masked the TS151r epitope. For both ␣3 and ␣6 integrins, important, identify CD151 as a key player during ␣3 and strong QRD/TS151r-dependent CD151 association occurred ␣6 integrin–dependent matrix remodeling and cell spreading, early in biosynthesis and involved ␣ subunit precursor and support a model of CD151 as a transmembrane linker forms. In contrast, weaker associations of CD151 with itself, between extracellular integrin domains and intracellular integrins, or other tetraspanins (Triton X-100–sensitive but cytoskeleton/signaling molecules. -
The Platelet Fc Receptor, Fcγ
Jianlin Qiao The platelet Fc receptor, FccRIIa Mohammad Al-Tamimi Ross I. Baker Robert K. Andrews Elizabeth E. Gardiner Authors’ addresses Summary: Human platelets express FccRIIa, the low-affinity receptor Jianlin Qiao1, Mohammad Al-Tamimi2, Ross I. Baker3, Robert K. for the constant fragment (Fc) of immunoglobulin (Ig) G that is also Andrews1, Elizabeth E. Gardiner1 found on neutrophils, monocytes, and macrophages. Engagement of 1The Australian Centre for Blood Diseases, Monash this receptor on platelets by immune complexes triggers intracellular University, Melbourne, VIC, Australia. signaling events that lead to platelet activation and aggregation. Impor- 2Department of Basic Medical Sciences, Hashemite tantly these events occur in vivo, particularly in response to pathological University, Zarqa, Jordan. immune complexes, and engagement of this receptor on platelets has 3Western Australian Centre for Thrombosis and been causally linked to disease pathology. In this review, we will high- Haemostasis, Murdoch University, Perth, WA, Australia. light some of the key features of this receptor in the context of the pla- telet surface, and examine the functions of platelet FccRIIa in normal Correspondence to: hemostasis and in response to injury and infection. This review will Elizabeth Gardiner also highlight pathological consequences of engagement of this recep- Australian Centre for Blood Diseases tor in platelet-based autoimmune disorders. Finally, we present some Monash University new data investigating whether levels of the extracellular -
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). -
CB1 and GPR55 Receptors Are Co-Expressed and Form Heteromers in Rat 3 and Monkey Striatum
YEXNR-11769; No. of pages: 9; 4C: Experimental Neurology xxx (2014) xxx–xxx Contents lists available at ScienceDirect Experimental Neurology journal homepage: www.elsevier.com/locate/yexnr 1 Regular Article 2 CB1 and GPR55 receptors are co-expressed and form heteromers in rat 3 and monkey striatum 4 E. Martínez-Pinilla a,⁎, I. Reyes-Resina e, A. Oñatibia-Astibia a,M.Zamarbidea,A.Ricobarazad,G.Navarroe, 5 E. Moreno e,I.G.Dopeso-Reyesb,c, S. Sierra b,c, A.J. Rico b,c,E.Rodab,c,J.L.Lanciegob,c,1,R.Francoa,e,1 6 a Laboratory of Cell and Molecular Neuropharmacology, Neurosciences Division, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain 7 b Laboratory of Basal Ganglia Neuroanatomy, Neurosciences Division, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain 8 c Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Spain 9 d Laboratoire de Plasticité du Cerveau, ESPCI-ParisTech, Paris, France 10 e Department of Biochemistry and Molecular Biology, Faculty of Biology, University of Barcelona, Barcelona, Spain 11 article info abstract 12 Article history: Heteromerization of G-protein-coupled receptors is an important event as they integrate the actions 22 13 Received 6 May 2014 of extracellular signals to give heteromer-selective ligand binding and signaling, opening new ave- 23 14 Revised 13 June 2014 nues in the development of potential drug targets in pharmacotherapy. A further aim of the present 24 15 Accepted 17 June 2014 paper was to check for cannabinoid CB –GPR55 receptor heteromers in the central nervous system 25 16 Available online xxxx 1 (CNS), specifically in striatum. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,