Photothermal Modulation of Human Stem Cells Using Light-Responsive 2D Nanomaterials
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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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. -
LDB3 Monoclonal Antibody (M06), Gene Alias: CYPHER, FLJ35865, KIAA01613, Clone 3C8 KIAA0613, ORACLE, PDLIM6, ZASP, Ldb3z1, Ldb3z4
LDB3 monoclonal antibody (M06), Gene Alias: CYPHER, FLJ35865, KIAA01613, clone 3C8 KIAA0613, ORACLE, PDLIM6, ZASP, ldb3z1, ldb3z4 Gene Summary: This gene encodes a PDZ Catalog Number: H00011155-M06 domain-containing protein. PDZ motifs are modular Regulatory Status: For research use only (RUO) protein-protein interaction domains consisting of 80-120 amino acid residues. PDZ domain-containing proteins Product Description: Mouse monoclonal antibody interact with each other in cytoskeletal assembly or with raised against a full-length recombinant LDB3. other proteins involved in targeting and clustering of membrane proteins. The protein encoded by this gene Clone Name: 3C8 interacts with alpha-actinin-2 through its N-terminal PDZ domain and with protein kinase C via its C-terminal LIM Immunogen: LDB3 (AAH10929, 1 a.a. ~ 283 a.a) domains. The LIM domain is a cysteine-rich motif full-length recombinant protein with GST tag. MW of the defined by 50-60 amino acids containing two GST tag alone is 26 KDa. zinc-binding modules. This protein also interacts with all three members of the myozenin family. Mutations in this Sequence: gene have been associated with myofibrillar myopathy MSYSVTLTGPGPWGFRLQGGKDFNMPLTISRITPGSK and dilated cardiomyopathy. Alternatively spliced AAQSQLSQGDLVVAIDGVNTDTMTHLEAQNKIKSASY transcript variants encoding different isoforms have been NLSLTLQKSKRPIPISTTAPPVQTPLPVIPHQKVVVNSP identified; all isoforms have N-terminal PDZ domains ANADYQERFNPSALKDSALSTHKPIEVKGLGGKATIIHA while only longer isoforms (1 and 2) have C-terminal -
Targeted Genes and Methodology Details for Neuromuscular Genetic Panels
Targeted Genes and Methodology Details for Neuromuscular Genetic Panels Reference transcripts based on build GRCh37 (hg19) interrogated by Neuromuscular Genetic Panels Next-generation sequencing (NGS) and/or Sanger sequencing is performed Motor Neuron Disease Panel to test for the presence of a mutation in these genes. Gene GenBank Accession Number Regions of homology, high GC-rich content, and repetitive sequences may ALS2 NM_020919 not provide accurate sequence. Therefore, all reported alterations detected ANG NM_001145 by NGS are confirmed by an independent reference method based on laboratory developed criteria. However, this does not rule out the possibility CHMP2B NM_014043 of a false-negative result in these regions. ERBB4 NM_005235 Sanger sequencing is used to confirm alterations detected by NGS when FIG4 NM_014845 appropriate.(Unpublished Mayo method) FUS NM_004960 HNRNPA1 NM_031157 OPTN NM_021980 PFN1 NM_005022 SETX NM_015046 SIGMAR1 NM_005866 SOD1 NM_000454 SQSTM1 NM_003900 TARDBP NM_007375 UBQLN2 NM_013444 VAPB NM_004738 VCP NM_007126 ©2018 Mayo Foundation for Medical Education and Research Page 1 of 14 MC4091-83rev1018 Muscular Dystrophy Panel Muscular Dystrophy Panel Gene GenBank Accession Number Gene GenBank Accession Number ACTA1 NM_001100 LMNA NM_170707 ANO5 NM_213599 LPIN1 NM_145693 B3GALNT2 NM_152490 MATR3 NM_199189 B4GAT1 NM_006876 MYH2 NM_017534 BAG3 NM_004281 MYH7 NM_000257 BIN1 NM_139343 MYOT NM_006790 BVES NM_007073 NEB NM_004543 CAPN3 NM_000070 PLEC NM_000445 CAV3 NM_033337 POMGNT1 NM_017739 CAVIN1 NM_012232 POMGNT2 -
1 1 Alpha-Smooth Muscle Actin (ACTA2) Is Required for Metastatic Potential of Human 1 Lung Adenocarcinoma 2 3 Hye Won Lee*1,2,3
Author Manuscript Published OnlineFirst on August 30, 2013; DOI: 10.1158/1078-0432.CCR-13-1181 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 1 Alpha-smooth muscle actin (ACTA2) is required for metastatic potential of human 2 lung adenocarcinoma 3 4 Hye Won Lee*1,2,3, Young Mi Park*3,4, Se Jeong Lee1,4,5, Hyun Jung Cho1,2, Duk-Hwan Kim6,7, 5 Jung-Il Lee2, Myung-Soo Kang3, Ho Jun Seol2, Young Mog Shim8, Do-Hyun Nam1,2,3, Hyeon 6 Ho Kim3,4, Kyeung Min Joo1,3,4,5 7 8 Authors’ Affiliations: 9 1Cancer Stem Cell Research Center, 2Department of Neurosurgery, 3Department of Health 10 Sciences and Technology, Samsung Advanced Institute for Health Sciences and 11 Technology (SAIHST), 4Samsung Biomedical Research Institute, 5Department of Anatomy 12 and Cell Biology, 6Department of Molecular Cell Biology, 7Center for Genome Research, 13 8Department of Thoracic Surgery, Samsung Medical Center, Sungkyunkwan University 14 School of Medicine, Seoul, Korea 15 16 *These authors contributed equally to this work. 17 18 Running title: ACTA2 confers metastatic potential on lung adenocarcinoma 19 Keywords: Non-small cell lung adenocarcinoma, alpha-smooth muscle actin, migration, 20 invasion, metastasis 1 Downloaded from clincancerres.aacrjournals.org on September 26, 2021. © 2013 American Association for Cancer Research. Author Manuscript Published OnlineFirst on August 30, 2013; DOI: 10.1158/1078-0432.CCR-13-1181 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 2 1 Financial support: This study was supported by a grant from the Korea Healthcare 2 Technology R&D Project, Ministry for Health & Welfare Affairs, Republic of Korea (A092255), 3 and the Basic Science Research Program, National Research Foundation of Korea by the 4 Ministry of Education, Science, and Technology (2011-009329 to H. -
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. -
Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
The Role of Z-Disc Proteins in Myopathy and Cardiomyopathy
International Journal of Molecular Sciences Review The Role of Z-disc Proteins in Myopathy and Cardiomyopathy Kirsty Wadmore 1,†, Amar J. Azad 1,† and Katja Gehmlich 1,2,* 1 Institute of Cardiovascular Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK; [email protected] (K.W.); [email protected] (A.J.A.) 2 Division of Cardiovascular Medicine, Radcliffe Department of Medicine and British Heart Foundation Centre of Research Excellence Oxford, University of Oxford, Oxford OX3 9DU, UK * Correspondence: [email protected]; Tel.: +44-121-414-8259 † These authors contributed equally. Abstract: The Z-disc acts as a protein-rich structure to tether thin filament in the contractile units, the sarcomeres, of striated muscle cells. Proteins found in the Z-disc are integral for maintaining the architecture of the sarcomere. They also enable it to function as a (bio-mechanical) signalling hub. Numerous proteins interact in the Z-disc to facilitate force transduction and intracellular signalling in both cardiac and skeletal muscle. This review will focus on six key Z-disc proteins: α-actinin 2, filamin C, myopalladin, myotilin, telethonin and Z-disc alternatively spliced PDZ-motif (ZASP), which have all been linked to myopathies and cardiomyopathies. We will summarise pathogenic variants identified in the six genes coding for these proteins and look at their involvement in myopathy and cardiomyopathy. Listing the Minor Allele Frequency (MAF) of these variants in the Genome Aggregation Database (GnomAD) version 3.1 will help to critically re-evaluate pathogenicity based on variant frequency in normal population cohorts. -
List of Genes Used in Cell Type Enrichment Analysis
List of genes used in cell type enrichment analysis Metagene Cell type Immunity ADAM28 Activated B cell Adaptive CD180 Activated B cell Adaptive CD79B Activated B cell Adaptive BLK Activated B cell Adaptive CD19 Activated B cell Adaptive MS4A1 Activated B cell Adaptive TNFRSF17 Activated B cell Adaptive IGHM Activated B cell Adaptive GNG7 Activated B cell Adaptive MICAL3 Activated B cell Adaptive SPIB Activated B cell Adaptive HLA-DOB Activated B cell Adaptive IGKC Activated B cell Adaptive PNOC Activated B cell Adaptive FCRL2 Activated B cell Adaptive BACH2 Activated B cell Adaptive CR2 Activated B cell Adaptive TCL1A Activated B cell Adaptive AKNA Activated B cell Adaptive ARHGAP25 Activated B cell Adaptive CCL21 Activated B cell Adaptive CD27 Activated B cell Adaptive CD38 Activated B cell Adaptive CLEC17A Activated B cell Adaptive CLEC9A Activated B cell Adaptive CLECL1 Activated B cell Adaptive AIM2 Activated CD4 T cell Adaptive BIRC3 Activated CD4 T cell Adaptive BRIP1 Activated CD4 T cell Adaptive CCL20 Activated CD4 T cell Adaptive CCL4 Activated CD4 T cell Adaptive CCL5 Activated CD4 T cell Adaptive CCNB1 Activated CD4 T cell Adaptive CCR7 Activated CD4 T cell Adaptive DUSP2 Activated CD4 T cell Adaptive ESCO2 Activated CD4 T cell Adaptive ETS1 Activated CD4 T cell Adaptive EXO1 Activated CD4 T cell Adaptive EXOC6 Activated CD4 T cell Adaptive IARS Activated CD4 T cell Adaptive ITK Activated CD4 T cell Adaptive KIF11 Activated CD4 T cell Adaptive KNTC1 Activated CD4 T cell Adaptive NUF2 Activated CD4 T cell Adaptive PRC1 Activated -
Finding Drug Targeting Mechanisms with Genetic Evidence for Parkinson’S Disease
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.24.208975; this version posted July 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Finding drug targeting mechanisms with genetic evidence for Parkinson’s disease Catherine S. Storm1,*, Demis A. Kia1, Mona Almramhi1, Sara Bandres-Ciga2, Chris Finan3, Aroon D. Hingorani3,4,5, International Parkinson’s Disease Genomics Consortium (IPDGC), Nicholas W. Wood1,6,* 1 Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, WC1N 3BG, United Kingdom 2 Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, United States of America 3 Institute of Cardiovascular Science, Faculty of Population Health, University College London, London WC1E 6BT, United Kingdom 4 University College London British Heart Foundation Research Accelerator Centre, New Delhi, India 5 Health Data Research UK, 222 Euston Road, London, United Kingdom 6 Lead Contact * Correspondence: [email protected] (CSS), [email protected] (NWW) Summary Parkinson’s disease (PD) is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation using human evidence. Here, we use Mendelian randomization to investigate more than 3000 genes that encode druggable proteins, seeking to predict their efficacy as drug targets for PD. We use expression and protein quantitative trait loci for druggable genes to mimic exposure to medications, and we examine the causal effect on PD risk (in two large case-control cohorts), PD age at onset and progression. -
Supplemental Table S1
Entrez Gene Symbol Gene Name Affymetrix EST Glomchip SAGE Stanford Literature HPA confirmed Gene ID Profiling profiling Profiling Profiling array profiling confirmed 1 2 A2M alpha-2-macroglobulin 0 0 0 1 0 2 10347 ABCA7 ATP-binding cassette, sub-family A (ABC1), member 7 1 0 0 0 0 3 10350 ABCA9 ATP-binding cassette, sub-family A (ABC1), member 9 1 0 0 0 0 4 10057 ABCC5 ATP-binding cassette, sub-family C (CFTR/MRP), member 5 1 0 0 0 0 5 10060 ABCC9 ATP-binding cassette, sub-family C (CFTR/MRP), member 9 1 0 0 0 0 6 79575 ABHD8 abhydrolase domain containing 8 1 0 0 0 0 7 51225 ABI3 ABI gene family, member 3 1 0 1 0 0 8 29 ABR active BCR-related gene 1 0 0 0 0 9 25841 ABTB2 ankyrin repeat and BTB (POZ) domain containing 2 1 0 1 0 0 10 30 ACAA1 acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3-oxoacyl-Coenzyme A thiol 0 1 0 0 0 11 43 ACHE acetylcholinesterase (Yt blood group) 1 0 0 0 0 12 58 ACTA1 actin, alpha 1, skeletal muscle 0 1 0 0 0 13 60 ACTB actin, beta 01000 1 14 71 ACTG1 actin, gamma 1 0 1 0 0 0 15 81 ACTN4 actinin, alpha 4 0 0 1 1 1 10700177 16 10096 ACTR3 ARP3 actin-related protein 3 homolog (yeast) 0 1 0 0 0 17 94 ACVRL1 activin A receptor type II-like 1 1 0 1 0 0 18 8038 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 1 0 0 0 0 19 8751 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 1 0 0 0 0 20 8728 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 1 0 0 0 0 21 81792 ADAMTS12 ADAM metallopeptidase with thrombospondin type 1 motif, 12 1 0 0 0 0 22 9507 ADAMTS4 ADAM metallopeptidase with thrombospondin type 1 -
A Molecular Switch from STAT2-IRF9 to ISGF3 Underlies Interferon-Induced Gene Transcription
ARTICLE https://doi.org/10.1038/s41467-019-10970-y OPEN A molecular switch from STAT2-IRF9 to ISGF3 underlies interferon-induced gene transcription Ekaterini Platanitis 1, Duygu Demiroz1,5, Anja Schneller1,5, Katrin Fischer1, Christophe Capelle1, Markus Hartl 1, Thomas Gossenreiter 1, Mathias Müller2, Maria Novatchkova3,4 & Thomas Decker 1 Cells maintain the balance between homeostasis and inflammation by adapting and inte- grating the activity of intracellular signaling cascades, including the JAK-STAT pathway. Our 1234567890():,; understanding of how a tailored switch from homeostasis to a strong receptor-dependent response is coordinated remains limited. Here, we use an integrated transcriptomic and proteomic approach to analyze transcription-factor binding, gene expression and in vivo proximity-dependent labelling of proteins in living cells under homeostatic and interferon (IFN)-induced conditions. We show that interferons (IFN) switch murine macrophages from resting-state to induced gene expression by alternating subunits of transcription factor ISGF3. Whereas preformed STAT2-IRF9 complexes control basal expression of IFN-induced genes (ISG), both type I IFN and IFN-γ cause promoter binding of a complete ISGF3 complex containing STAT1, STAT2 and IRF9. In contrast to the dogmatic view of ISGF3 formation in the cytoplasm, our results suggest a model wherein the assembly of the ISGF3 complex occurs on DNA. 1 Max Perutz Labs (MPL), University of Vienna, Vienna 1030, Austria. 2 Institute of Animal Breeding and Genetics, University of Veterinary Medicine Vienna, Vienna 1210, Austria. 3 Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna 1030, Austria. 4 Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna 1030, Austria. -
Paxillin Binding to the Cytoplasmic Domain of CD103 Promotes Cell Adhesion and Effector
Author Manuscript Published OnlineFirst on October 11, 2017; DOI: 10.1158/0008-5472.CAN-17-1487 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Paxillin binding to the cytoplasmic domain of CD103 promotes cell adhesion and effector functions for CD8+ resident memory T cells in tumors Ludiane Gauthier1, Stéphanie Corgnac1, Marie Boutet1, Gwendoline Gros1, Pierre Validire2, Georges Bismuth3 and Fathia Mami-Chouaib1 1 INSERM UMR 1186, Integrative Tumor Immunology and Genetic Oncology, Gustave Roussy, EPHE, Fac. de médecine - Univ. Paris-Sud, Université Paris-Saclay, 94805, Villejuif, France 2 Institut Mutualiste Montsouris, Service d’Anatomie pathologique, 75014 Paris, France. 3 INSERM U1016, CNRS UMR8104, Université Paris Descartes, Institut Cochin, 75014 Paris. S Corgnac, M Boutet and G Gros contributed equally to this work. M Boutet current address: Department of Microbiology and Immunology Albert Einstein College of Medecine, NY 10461 USA. Corresponding author: Fathia Mami-Chouaib, INSERM UMR 1186, Gustave Roussy. 39, rue Camille Desmoulins, F-94805 Villejuif. Phone: +33 1 42 11 49 65, Fax: +33 1 42 11 52 88, e-mail: [email protected] and [email protected] Running title: CD103 signaling in human TRM cells Key words: TRM cells, CD103 integrin, T-cell function and signaling, paxillin. Abbreviations: IS: immune synapse; LFA: leukocyte function-associated antigen; FI: fluorescence intensity; mAb: monoclonal antibody; phospho: phosphorylated; Pyk2: proline- rich tyrosine kinase-2; NSCLC: non-small-cell lung carcinoma; r: recombinant; sh-pxn: shorthairpin RNA-paxillin; TCR: T-cell receptor; TIL: tumor-infiltrating lymphocyte; TRM: tissue-resident memory T.