DOCSLIB.ORG

Supplementary Data

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Supplementary Data Supplemental figures Supplemental figure 1: Tumor sample selection. A total of 98 thymic tumor specimens were stored in Memorial Sloan-Kettering Cancer Center tumor banks during the study period. 64 cases corresponded to previously untreated tumors, which were resected upfront after diagnosis. Adjuvant treatment was delivered in 7 patients (radiotherapy in 4 cases, cyclophosphamide- doxorubicin-vincristine (CAV) chemotherapy in 3 cases). 34 tumors were resected after induction treatment, consisting of chemotherapy in 16 patients (cyclophosphamide-doxorubicin- cisplatin (CAP) in 11 cases, cisplatin-etoposide (PE) in 3 cases, cisplatin-etoposide-ifosfamide (VIP) in 1 case, and cisplatin-docetaxel in 1 case), in radiotherapy (45 Gy) in 1 patient, and in sequential chemoradiation (CAP followed by a 45 Gy-radiotherapy) in 1 patient. Among these 34 patients, 6 received adjuvant radiotherapy. 1 Supplemental Figure 2: Amino acid alignments of KIT H697 in the human protein and related orthologs, using (A) the Homologene database (exons 14 and 15), and (B) the UCSC Genome Browser database (exon 14). Residue H697 is highlighted with red boxes. Both alignments indicate that residue H697 is highly conserved. 2 Supplemental Figure 3: Direct comparison of the genomic profiles of thymic squamous cell carcinomas (n=7) and lung primary squamous cell carcinomas (n=6). (A) Unsupervised clustering analysis. Gains are indicated in red, and losses in green, by genomic position along the 22 chromosomes. (B) Genomic profiles and recurrent copy number alterations in thymic carcinomas and lung squamous cell carcinomas. Gains are indicated in red, and losses in blue. 3 Supplemental Methods Mutational profiling The exonic regions of interest (NCBI Human Genome Build 36.1) were broken into amplicons of 500 bp or less, and specific primers were designed using Primer 3 (on the World Wide Web for general users and for biologist programmers (see Supplemental Table 2) [1]. M13 tails were added to facilitate Sanger sequencing. PCR reactions were carried out in 384 well plates, in a Duncan DT-24 water bath thermal cycler, with 10 ng of DNA as template, using a touchdown PCR protocol with HotStart Taq (Kapa Biosystems, Cape Town, South Africa). The touchdown PCR method consisted of : 1 cycle of 95°C for 5 min; 3 cycles of 95°C for 30 sec, 64°C for 30 sec, 72°C for 60 sec; 3 cycles of 95°C for 30 sec, 62°C for 30 sec, 72°C for 60 sec; 3 cycles of 95°C for 30 sec, 60°C for 30 sec, 72°C for 60 sec; 37 cycles of 95°C for 30 sec, 58°C for 30 sec, 72°C for 60 sec; 1 cycle of 70°C for 5 min. Templates were purified using AMPure (Agencourt Biosciences, Beverly, MA). The purified PCR reactions were split into two, and sequenced bidirectionally with M13 forward and reverse primers and Big Dye Terminator Kit v.3.1 (Applied Biosystems, Foster City, CA), at Agencourt Biosciences. Dye terminators were removed using the CleanSEQ kit (Agencourt Biosciences), and sequence reactions were run on ABI PRISM 3730xl sequencing apparatus (Applied Biosystems, Foster City, CA). 1 Mutation detection Raw mass-spectrometry data were processed using the SpectroTYPER software (Sequenom, Sequenom, San Diego, CA). DNA sequencing chromatograms were analyzed using an institutional mutation detection pipeline. Briefly, bi-directional reads and mapping tables (to link read names to sample identifiers, gene names, read direction, and amplicon) were subjected to a QC filter which excludes reads that have an average phred score of < 10 for bases 100-200. Passing reads were assembled against the reference sequences for each gene, containing all coding and UTR exons including 5Kb upstream and downstream of the gene, using command line Consed 16.0 (PMID: 9521923) [2]. Assemblies were passed on to Polyphred 6.02b (PMID: 9207020) which generated a list of putative candidate mutations, and to Polyscan 3.0 (PMID: 17416743) which generated a second list of putative mutations. The lists were merged together into a combined report, and the putative mutation calls were normalized to ‘+’ genomic coordinates and annotated using the Genomic Mutation Consequence Calculator (PMID: 17599934). The resulting list of annotated putative mutations was loaded into a Postgres database along with select assembly details for each mutation call (assembly position, coverage, and methods supporting mutation call). To reduce the number of false positives generated by the mutation detection software packages, only point mutations which are supported by at least one bi-directional read pair and at least one sample mutation called by Polyphred were considered, and only the putative mutations which are annotated as having non-synonymous coding effects, occur within 11 bp of an exon boundary, or have a conservation score > 0.699 (http://genome.ucsc.edu/cgi- bin/hgTrackUi?hgsid=108554407&g=multiz17way) were included in the final candidate 2 list. Indels called by any method were manually reviewed and included in the candidate list if found to hit an exon. All data were also manually reviewed to identify putative mutations. Synonymous variants and those with entries in the NCBI SNP database were excluded. All putative mutations were confirmed by a second PCR and sequencing reaction, in parallel with amplification and sequencing of matched normal tissue DNA. Genomic profiling data interpretation Significant transitions in copy number were identified from the calculated fluorescence ratios of the scanned images, using the circular binary segmentation algorithm [3]. Gains and losses for a subset of analyses were defined using sample- specific thresholds. Specifically, median absolute deviation (MAD) of the difference between the observed values and the segmentation means was used to estimate the within-sample variation. The sample-specific threshold to call gains and losses is then defined as 1.5 MAD above or below the sample median of the segmented values. Regions of high level amplification or deletion (amplitude higher than 3 MAD) were declared to be recurrent if present in at least two samples. For comparison with profiles from pulmonary primary squamous cell carcinomas, we used genomic profiles generated from a different Memorial Sloan-Kettering Cancer Center Institutional Review Board- approved study. Expression profiling data interpretation The robust multichip average (RMA) method was used to estimate expression of probe sets [4]. For unsupervised clustering, we applied a hierarchical clustering algorithm 3 on the most variable 1429 genes, with the Pearson correlation coefficient as the measure of similarity and average linkage as the method to join clusters. Immunohistochemistry A tissue microarray (TMA) was constructed using formalin-fixed, paraffin-embedded previously untreated tumor tissue specimens, which were reviewed by a single pathologist (M.F.Z.). Triplicate 0.6 mm biopsy cores were taken from selected areas containing a high ratio of tumor epithelial cells on parallel hematoxylin and eosin (H&E)- stained sections and arrayed onto master paraffin blocks using a manual tissue arrayer (Beecher Instruments, Sun Prairie, WI). For previously treated tumors, immunohistochemistry was done on individual paraffin-embedded tumor blocks. Five- micrometer sections from the tissue microarray or the tumor blocks were deparaffinized in xylene and dehydrated in graded alcohols. Ba/F3-KIT mutant transformant assays The KITV560del and KITH697Y mutations were generated using the QuikChange II XL site-directed Mutagenesis Kit (Stratagene, Agilent Technologies, Santa Clara, CA) and pMSCV-wtKIT-IRES-GFP, a retroviral expression vector (kindly provided by Dr. Gary Gilliland, Harvard Medical School) containing a wild-type human KIT cDNA (GNNK- isoform). Ba/F3 cells were co-transfected with the KITV560del or the KITH697Y cDNA (25 μg) and a hygromycin-resistance gene (25:1 ratio) by electroporation as previously described [5]. 4 Ba/F3 cells were maintained with 10 ng/ml recombinant murine IL-3 and 200 μg hygromycin for 10 days, and then selected for IL-3-independent growth. The IL3- independent BaF3 KITV560del and KITH697Y cells reached optimal growth with 20 ng/ml human stem cell factor (hSCF). KIT cell surface expression was confirmed by flow cytometry using PE-conjugated anti-CD117 (BD Biosciences, San Jose, CA) antibody. Total KIT expression was confirmed by immunoblots using an anti-KIT (Oncogene, San Diego, CA) antibody (data not shown). IL-3-independent clones were verified by direct sequencing to harbor mutant cDNAs. For drug sensitivity assays, Ba/F3 KITV560del and KITH697Y cells were starved from hSCF for 4 hours. 15 minutes prior to drug administration, 20ng/ml of hSCF was added. References 1. Krawetz S, Misener S (eds): Bioinformatics Methods and Protocols: Methods in Molecular Biology. Humana Press, Totowa, NJ, 2000;pp 365-6. 2. Gordon D, Abajian C, Green P. Consed: A Graphical Tool for Sequence Finishing. Genome Res 1998;8:195-202. 3. Olshen AB, Venkatraman ES, Lucito R, Wigler M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 2004;5:557-72. 4. Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003;4:249-64. 5 5. Guo T, Agaram NP, Wong GC, et al. Sorafenib inhibits the imatinib-resistant KITT670I gatekeeper mutation in gastrointestinal stromal tumor. Clin Cancer Res 2007;13:4874-81. 6 Supplemental Tables Supplemental Table 1: List of mutations screened using mass spectrometry-based
Load more

Recommended publications
  • Comprehensive Analysis of Mrna Expression Profiles in Head and Neck Cancer by Using Robust Rank Aggregation and Weighted Gene Coexpression Network Analysis
    Hindawi BioMed Research International Volume 2020, Article ID 4908427, 21 pages https://doi.org/10.1155/2020/4908427 Research Article Comprehensive Analysis of mRNA Expression Profiles in Head and Neck Cancer by Using Robust Rank Aggregation and Weighted Gene Coexpression Network Analysis Zaizai Cao , Yinjie Ao , Yu Guo , and Shuihong Zhou Department of Otolaryngology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang Province 310003, China Correspondence should be addressed to Shuihong Zhou; [email protected] Received 3 June 2020; Revised 2 November 2020; Accepted 23 November 2020; Published 10 December 2020 Academic Editor: Hassan Dariushnejad Copyright © 2020 Zaizai Cao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Head and neck squamous cell cancer (HNSCC) is the sixth most common cancer in the world; its pathogenic mechanism remains to be further clarified. Methods. Robust rank aggregation (RRA) analysis was utilized to identify the metasignature dysregulated genes, which were then used for potential drug prediction. Weighted gene coexpression network analysis (WGCNA) was performed on all metasignature genes to find hub genes. DNA methylation analysis, GSEA, functional annotation, and immunocyte infiltration analysis were then performed on hub genes to investigate their potential role in HNSCC. Result. A total of 862 metasignature genes were identified, and 6 potential drugs were selected based on these genes. Based on the result of WGCNA, six hub genes (ITM2A, GALNTL1, FAM107A, MFAP4, PGM5, and OGN) were selected (GS > 0:1, MM > 0:75,GSp value < 0.05, and MM p value < 0.05).
    [Show full text]
  • 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.
    [Show full text]
  • IMGT-ONTOLOGY and IMGT Databases, Tools and Web
    Molecular Immunology 40 (2004) 647–660 Review IMGT-ONTOLOGY and IMGT databases, tools and Web resources for immunogenetics and immunoinformatics Marie-Paule Lefranc a,b,∗ a Laboratoire d’ImmunoGénétique Moléculaire, LIGM, Institut de Génétique Humaine IGH, Université Montpellier II, UPR CNRS 1142, 141 rue de la Cardonille, 34396 Montpellier Cedex 5, France b Institut Universitaire de France, France Received 18 June 2003; received in revised form 2 September 2003; accepted 16 September 2003 Abstract The international ImMunoGeneTics information system® (IMGT; http://imgt.cines.fr), is a high quality integrated information system specialized in immunoglobulins (IG), T cell receptors (TR), major histocompatibility complex (MHC), and related proteins of the immune system (RPI) of human and other vertebrates, created in 1989, by the Laboratoire d’ImmunoGénétique Moléculaire (LIGM; Université Montpellier II and CNRS) at Montpellier, France. IMGT provides a common access to standardized data which include nucleotide and protein sequences, oligonucleotide primers, gene maps, genetic polymorphisms, specificities, 2D and 3D structures. IMGT consists of several sequence databases (IMGT/LIGM-DB, IMGT/MHC-DB, IMGT/PRIMER-DB), one genome database (IMGT/GENE-DB) and one 3D structure database (IMGT/3Dstructure-DB), interactive tools for sequence analysis (IMGT/V-QUEST, IMGT/JunctionAnalysis, IMGT/PhyloGene, IMGT/Allele-Align), for genome analysis (IMGT/GeneSearch, IMGT/GeneView, IMGT/LocusView) and for 3D struc- ture analysis (IMGT/StructuralQuery), and
    [Show full text]
  • 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
    [Show full text]
  • Primepcr™Assay Validation Report
    PrimePCR™Assay Validation Report Gene Information Gene Name epithelial membrane protein 2 Gene Symbol Emp2 Organism Rat Gene Summary Description Not Available Gene Aliases Not Available RefSeq Accession No. Not Available UniGene ID Rn.21730 Ensembl Gene ID ENSRNOG00000002664 Entrez Gene ID 360468 Assay Information Unique Assay ID qRnoCIP0024746 Assay Type Probe - Validation information is for the primer pair using SYBR® Green detection Detected Coding Transcript(s) ENSRNOT00000003615 Amplicon Context Sequence CCTGCTGCCTTCGCTGCCCTGTGAACATGTTGGTGATTCTTGCCTTCATCATCGT CTTCCACATCGTGTCTACGGCACTCTTGTTCATTTCAACCATTGACAATGCCTGG TGGGTAGGAGATGGCTTCTCAGCT Amplicon Length (bp) 104 Chromosome Location 10:4276703-4281842 Assay Design Intron-spanning Purification Desalted Validation Results Efficiency (%) 99 R2 0.9989 cDNA Cq 20.98 cDNA Tm (Celsius) 82.5 gDNA Cq Specificity (%) 100 Information to assist with data interpretation is provided at the end of this report. Page 1/4 PrimePCR™Assay Validation Report Emp2, Rat Amplification Plot Amplification of cDNA generated from 25 ng of universal reference RNA Melt Peak Melt curve analysis of above amplification Standard Curve Standard curve generated using 20 million copies of template diluted 10-fold to 20 copies Page 2/4 PrimePCR™Assay Validation Report Products used to generate validation data Real-Time PCR Instrument CFX384 Real-Time PCR Detection System Reverse Transcription Reagent iScript™ Advanced cDNA Synthesis Kit for RT-qPCR Real-Time PCR Supermix SsoAdvanced™ SYBR® Green Supermix Experimental Sample qPCR Reference Total RNA Data Interpretation Unique Assay ID This is a unique identifier that can be used to identify the assay in the literature and online. Detected Coding Transcript(s) This is a list of the Ensembl transcript ID(s) that this assay will detect.
    [Show full text]
  • 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.
    [Show full text]
  • Gene Expression Is Related to Parental Origin and Regional Coordinate Control
    Journal of Human Genetics (2009) 54, 193–198 & 2009 The Japan Society of Human Genetics All rights reserved 1434-5161/09 $32.00 www.nature.com/jhg ORIGINAL ARTICLE William’s syndrome: gene expression is related to parental origin and regional coordinate control Jeremy C Collette1, Xiao-Ning Chen1, Debra L Mills2, Albert M Galaburda3, Allan L Reiss4, Ursula Bellugi5 and Julie R Korenberg1,6 William’s syndrome (WS) features a spectrum of neurocognitive and behavioral abnormalities due to a rare 1.5 MB deletion that includes about 24–28 genes on chromosome band 7q11.23. Study of the expression of these genes from the single normal copy provides an opportunity to elucidate the genetic and epigenetic controls on these genes as well as their roles in both WS and normal brain development and function. We used quantitative RT-PCR to determine the transcriptional level of 14 WS gene markers in a cohort of 77 persons with WS and 48 normal controls. Results reported here: (1) show that the expression of the genes deleted in WS is decreased in some but not all cases, (2) demonstrate that the parental origin of the deletion contributes to the level of expression of GTF2I independently of age and gender and (3) indicate that the correlation of expression between GTF2I and some other genes in the WS region differs in WS subjects and normal controls, which in turn points toward a regulatory role for this gene. Interspecies comparisons suggest GTF2I may play a key role in normal brain development. Journal of Human Genetics (2009) 54, 193–198; doi:10.1038/jhg.2009.5; published online 13 March 2009 Keywords: William’s syndrome; gene expression; RT-PCR; parental origin; GTF2I INTRODUCTION As an approach toward understanding the role of the deleted genes William’s syndrome (WS) is a neurogenetic disorder affecting human in WS, we have characterized WS subjects according to genetic, social/ development and adult cognition.
    [Show full text]
  • Molecular and Cellular Mechanisms of the Angiogenic Effect of Poly(Methacrylic Acid-Co-Methyl Methacrylate) Beads
    Molecular and Cellular Mechanisms of the Angiogenic Effect of Poly(methacrylic acid-co-methyl methacrylate) Beads by Lindsay Elizabeth Fitzpatrick A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Institute of Biomaterials and Biomedical Engineering University of Toronto © Copyright by Lindsay Elizabeth Fitzpatrick 2012 Molecular and Cellular Mechanisms of the Angiogenic Effect of Poly(methacrylic acid-co-methyl methacrylate) Beads Lindsay Elizabeth Fitzpatrick Doctorate of Philosophy Institute of Biomaterials and Biomedical Engineering University of Toronto 2012 Abstract Poly(methacrylic acid -co- methyl methacrylate) beads were previously shown to have a therapeutic effect on wound closure through the promotion of angiogenesis. However, it was unclear how this polymer elicited its beneficial properties. The goal of this thesis was to characterize the host response to MAA beads by identifying molecules of interest involved in MAA-mediated angiogenesis (in comparison to poly(methyl methacrylate) beads, PMMA). Using a model of diabetic wound healing and a macrophage-like cell line (dTHP-1), eight molecules of interest were identified in the host response to MAA beads. Gene and/or protein expression analysis showed that MAA beads increased the expression of Shh, IL-1β, IL-6, TNF- α and Spry2, but decreased the expression of CXCL10 and CXCL12, compared to PMMA and no beads. MAA beads also appeared to modulate the expression of OPN. In vivo, the global gene expression of OPN was increased in wounds treated with MAA beads, compared to PMMA and no beads. In contrast, dTHP-1 decreased OPN gene expression compared to PMMA and no beads, but expressed the same amount of secreted OPN, suggesting that the cells decreased the expression of the intracellular isoform of OPN.
    [Show full text]
  • Multiple Deleted Regions on the Long Arm of Chromosome 6 in Astrocytic
    British Journal of Cancer (2000) 82(3), 543–549 © 2000 Cancer Research Campaign DOI: 10.1054/ bjoc.1999.0961, available online at http://www.idealibrary.com on Multiple deleted regions on the long arm of chromosome 6 in astrocytic tumours A Miyakawa1,2,3,4, K Ichimura1,2, EE Schmidt1,2, S Varmeh-Ziaie1,2 and VP Collins1,2 1Department of Pathology, Division of Molecular Histopathology, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 2QQ, UK; 2Department of Tumour Pathology, Karolinska Institute, Stockholm 171 76, Sweden; 3Department of Urology, Faculty of Medicine, University of Ryukyus, Naha City, Okinawa 903-0804, Japan; 4Department of Urology, School of Medicine, Keio University, Tokyo, Japan Summary Chromosome 6 deletions are common in human neoplasms including gliomas. In order to study the frequency and identify commonly deleted regions of chromosome 6 in astrocytomas, 159 tumours (106 glioblastomas, 39 anaplastic astrocytomas and 14 astrocytomas malignancy grade II) were analysed using 31 microsatellite markers that span the chromosome. Ninety-five per cent of cases with allelic losses had losses affecting 6q. Allelic losses were infrequent in astrocytomas malignancy grade II (14%) but more usual in anaplastic astrocytomas (38%) and glioblastomas (37%). Evidence for clonal heterogeneity in the astrocytomas and anaplastic astrocytomas was frequently observed (i.e. co-existence of subpopulations with and without chromosome 6 deletions). Clonal heterogeneity was less common in glioblastomas. Five commonly deleted regions were identified on 6q. These observations suggest that a number of tumour suppressor genes are located on 6q and that these genes may be involved in the progression of astrocytic tumours.
    [Show full text]
  • CD29 Identifies IFN-Γ–Producing Human CD8+ T Cells With
    + CD29 identifies IFN-γ–producing human CD8 T cells with an increased cytotoxic potential Benoît P. Nicoleta,b, Aurélie Guislaina,b, Floris P. J. van Alphenc, Raquel Gomez-Eerlandd, Ton N. M. Schumacherd, Maartje van den Biggelaarc,e, and Monika C. Wolkersa,b,1 aDepartment of Hematopoiesis, Sanquin Research, 1066 CX Amsterdam, The Netherlands; bLandsteiner Laboratory, Oncode Institute, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; cDepartment of Research Facilities, Sanquin Research, 1066 CX Amsterdam, The Netherlands; dDivision of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands; and eDepartment of Molecular and Cellular Haemostasis, Sanquin Research, 1066 CX Amsterdam, The Netherlands Edited by Anjana Rao, La Jolla Institute for Allergy and Immunology, La Jolla, CA, and approved February 12, 2020 (received for review August 12, 2019) Cytotoxic CD8+ T cells can effectively kill target cells by producing therefore developed a protocol that allowed for efficient iso- cytokines, chemokines, and granzymes. Expression of these effector lation of RNA and protein from fluorescence-activated cell molecules is however highly divergent, and tools that identify and sorting (FACS)-sorted fixed T cells after intracellular cytokine + preselect CD8 T cells with a cytotoxic expression profile are lacking. staining. With this top-down approach, we performed an un- + Human CD8 T cells can be divided into IFN-γ– and IL-2–producing biased RNA-sequencing (RNA-seq) and mass spectrometry cells. Unbiased transcriptomics and proteomics analysis on cytokine- γ– – + + (MS) analyses on IFN- and IL-2 producing primary human producing fixed CD8 T cells revealed that IL-2 cells produce helper + + + CD8 Tcells.
    [Show full text]
  • Actin Binding LIM 1 (Ablim1) Negatively Controls Osteoclastogenesis by Regulating Cell Migration and Fusion
    Received: 14 September 2017 | Accepted: 16 March 2018 DOI: 10.1002/jcp.26605 ORIGINAL RESEARCH ARTICLE Actin binding LIM 1 (abLIM1) negatively controls osteoclastogenesis by regulating cell migration and fusion Haruna Narahara1,2 | Eiko Sakai1 | Yu Yamaguchi1 | Shun Narahara1 | Mayumi Iwatake1,* | Kuniaki Okamoto1,† | Noriaki Yoshida2 | Takayuki Tsukuba1 1 Department of Dental Pharmacology, Graduate School of Biomedical Sciences, Actin binding LIM 1 (abLIM1) is a cytoskeletal actin-binding protein that has been Nagasaki University, Nagasaki, Japan implicated in interactions between actin filaments and cytoplasmic targets. Previous 2 Department of Orthodontics and Dentofacial Orthopedics, Graduate School of Biomedical biochemical and cytochemical studies have shown that abLIM1 interacts and co- Sciences, Nagasaki University, Nagasaki, Japan localizes with F-actin in the retina and muscle. However, whether abLIM1 regulates Correspondence osteoclast differentiation has not yet been elucidated. In this study, we examined the Takayuki Tsukuba, Department of Dental role of abLIM1 in osteoclast differentiation and function. We found that abLIM1 Pharmacology, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki 852- expression was upregulated during receptor activator of nuclear factor kappa-B ligand 8588, Japan. (RANKL)-induced osteoclast differentiation, and that a novel transcript of abLIM1 was Email: [email protected] exclusively expressed in osteoclasts. Overexpression of abLIM1 in the murine Funding information monocytic cell line, RAW-D suppressed osteoclast differentiation and decreased JSPS KAKENHI, Grant numbers: 15H05298, 16K15790, 17H04379 expression of several osteoclast-marker genes. By contrast, small interfering RNA-induced knockdown of abLIM1 enhanced the formation of multinucleated osteoclasts and markedly increased the expression of the osteoclast-marker genes. Mechanistically, abLIM1 regulated the localization of tubulin, migration, and fusion in osteoclasts.
    [Show full text]
  • Macropinocytosis Requires Gal-3 in a Subset of Patient-Derived Glioblastoma Stem Cells
    ARTICLE https://doi.org/10.1038/s42003-021-02258-z OPEN Macropinocytosis requires Gal-3 in a subset of patient-derived glioblastoma stem cells Laetitia Seguin1,8, Soline Odouard2,8, Francesca Corlazzoli 2,8, Sarah Al Haddad2, Laurine Moindrot2, Marta Calvo Tardón3, Mayra Yebra4, Alexey Koval5, Eliana Marinari2, Viviane Bes3, Alexandre Guérin 6, Mathilde Allard2, Sten Ilmjärv6, Vladimir L. Katanaev 5, Paul R. Walker3, Karl-Heinz Krause6, Valérie Dutoit2, ✉ Jann N. Sarkaria 7, Pierre-Yves Dietrich2 & Érika Cosset 2 Recently, we involved the carbohydrate-binding protein Galectin-3 (Gal-3) as a druggable target for KRAS-mutant-addicted lung and pancreatic cancers. Here, using glioblastoma patient-derived stem cells (GSCs), we identify and characterize a subset of Gal-3high glio- 1234567890():,; blastoma (GBM) tumors mainly within the mesenchymal subtype that are addicted to Gal-3- mediated macropinocytosis. Using both genetic and pharmacologic inhibition of Gal-3, we showed a significant decrease of GSC macropinocytosis activity, cell survival and invasion, in vitro and in vivo. Mechanistically, we demonstrate that Gal-3 binds to RAB10, a member of the RAS superfamily of small GTPases, and β1 integrin, which are both required for macro- pinocytosis activity and cell survival. Finally, by defining a Gal-3/macropinocytosis molecular signature, we could predict sensitivity to this dependency pathway and provide proof-of- principle for innovative therapeutic strategies to exploit this Achilles’ heel for a significant and unique subset of GBM patients. 1 University Côte d’Azur, CNRS UMR7284, INSERM U1081, Institute for Research on Cancer and Aging (IRCAN), Nice, France. 2 Laboratory of Tumor Immunology, Department of Oncology, Center for Translational Research in Onco-Hematology, Swiss Cancer Center Léman (SCCL), Geneva University Hospitals, University of Geneva, Geneva, Switzerland.
    [Show full text]