Supporting Information

Total Page:16

File Type:pdf, Size:1020Kb

Supporting Information Supporting Information SI Methods EWS-FLI1 interacting partners identified through MS. We developed an unbiased screen for potential partners that interact with EWS-FLI1 by adding ES nuclear lysate to immobilized EWS-FLI1, followed by elution and PAGE. Nuclear lysate from Ewing sarcoma (ES) cell line TC32 was added to two Hi-TRAP Ni charged columns in series, with only the second column containing EWS-FLI1. Following the addition of nuclear lysate and wash, the columns were separated and independently eluted with imidazole. Fractions were resolved on both high (14%) and low (6%) polyacrylamide gels, followed by Coomassie staining and protein bands of interest were manually excised and subjected to in-gel tryptic digestion. Protein digests separation was performed on an Aquity UPLC BEH 130 C18 column and followed by mass spectrometry (MS). Mass spectra were acquired online using a nano-ESI-LC-MS/MS QStar Elite (ABSCIEX) and operated with Analyst QS v2.0 data acquisition software. MS data were searched against the SWISS-PROT databases using ProteinPilot software 3.0 and Paragon Algorithm to identify the individual proteins. Positive identification of proteins eluting with EWS-FLI1 was rigorous including: (i) band from the gel located near estimated molecular weight, (ii) greater than 95% ion confidence interval, and (iii) 99% confidence in peptide assignment based upon greater than 3 peptides. Proteins eluted from a Ni-charged column without EWS-FLI1 were considered non- specific. Proteins were identified in three separate experiments starting from EWS-FLI1 and fresh nuclear lysate to insure reproducibility. Nuclear Co-Immunoprecipitation. Co-Immunoprecipitation (Co-IP) was used to pull down protein partners of a protein of interest using an antibody that specifically binds to this specific protein in order to test protein-protein interaction. Active motif universal magnetic Co-IP kit (Active Motif) was used to make nuclear extract from TC32 cells. The protein G magnetic beads were used for Co-IP and the IP was performed on 300 μg samples using 2 μg of FLI1/PRPF8/DDX5 polyclonal antibody and rabbit IgG (as a negative control). Western blot was then performed individually using the following antibodies FLI1 (Santa Cruz Biotechnology, USA), DDX5, PRPF6, hnRNP K, RHA, hnRNP U, CDC5L, DDX17, SF3B3, SF3B2, SF3A2, PRPF8, SFPQ (Abcam, USA) and snRNP200 (Sigma-Aldrich, USA), on 10 μg input nuclear extract, FLI1/PRPF8/DDX5 IP and the rabbit IgG IP. ELISA. ELISA was performed on maxi sorb 96 well plate. Recombinant full length EWS-FLI1 protein (300 ng) were coated on the bottom surface of the ELISA plate overnight and followed by blocked with 4% BSA. Spliceosomal proteins DDX5, PRPF6, hnRNP K, RHA, hnRNP U, CDC5L, DDX17, SF3B3 and SF3B2 were used as ligand to detect the specific protein interaction using anti-DDK monoclonal antibody with HRP conjugated (for RHA protein goat anti-RHA antibody (Everest)), protein binding was detected using 3,3’,5,5’-tetramethylbenzidine (TMB) peroxidase substrate kit (Bio-Rad, CA) per the manufacturer’s instructions. The substrate reaction is incubated at room temperature with periodic mixing (e.g., 2-3 seconds shaking in the plate reader) and the color development monitored by absorbance reading at 415 nm or 405 nm using a microplate reader. Negative controls for each ELISA included the absence of EWS-FLI1, absence of second recombinant protein, and blocking alone were used. For the positive control, ELISA plate wells were coated with their corresponding spliceosomal proteins and their corresponding secondary antibody. 1 Method for drawing networks. The proteins identified by MS as a potential interactive partner of EWS-FLI1 are included and followed by proteomic analysis were used as input for Ingenuity Pathway Analysis to identify networks of interacting proteins. Using IPA, clusters of proteins appearing in the top functional pathways and networks with the most highly significant p-values were selected for constructing the network. IPA uses a curated proprietary knowledgebase to identify protein interactions based on experimental evidence gathered from published reports. The resulting networks thus generated were filtered for direct and indirect interactions, and trimmed to remove nodes beyond the second level of interactions from the proteins of interest. Direct binding partners that appeared within this network were confirmed by experimental validations. Affymetrix Exon Array. Total RNA was extracted using Qiagen AllPrep DNA/RNA/Protein kit from each of the derived cell lines (9 cell lines with/without EWS-FLI1). The RNA quality was measured using Agilent 2100 bioanalyzer and RNA Integrity number (RIN) above 9.60 were used for Exon array experiments. cDNA was synthesized and hybridized to Affymetrix GeneChip Human Exon 1.0 ST microarray chips and .CEL files are generated. These raw data files were processed using two analytical tools, PGS and easyExon. All data CEL files were uploaded to GEO and their Accession number is GSE65941. Data preparation and analysis for PGS. Data was pre-processed using Partek Genomic Suite software (PGS v6.6, Partek Inc., St. Louis, MO) with the Affymetrix-annotated core probe set. The configuration consisted of a pre-background adjustment for GC content and probe sequence, Robust Multi-array Analysis (RMA) (1) for background correction, quantile normalization and probe set summarization using median polish. All signals were log2-transformed. Library files were those specified by Affymetrix (hg18). Differential expression was determined by ANOVA. The multiple test correction implemented the p-value method to determine a false discovery rate (FDR) < 0.05. Exon level data was filtered using Detection Above Background (DABG; (2)). Data was analyzed for alternative splicing by a repeated measure AS-ANOVA. The p-value method was used to control the FDR with a cutoff <0.05 to determine the presence of alternative exon usage. Data analysis for easyExon. easyExon uses RMA for background correction and probe set summarization. Microarray detection of alternative splicing (MIDAS) p-value <0.05 and pattern- based correlation (PAC) are used to determine significant AS transcripts (3). The degree of AS was visualized by the spicing index (SI) plot. In addition, the intensity fold change > ±2 was also calculated to show the differentially expressed genes. Exon array validation by Quantitative real-time polymerase chain reaction (qRT-PCR). Exon array findings (both expression and alternative splicing) were validated using qRT-PCR on the several genes focused on in this study. Validation of alternative splicing was performed using 2 different primer-pair combinations within the same transcript (RefSeq - release 61). One pair of primers target an area that can be used to differentiate between isoforms, and a second pair target an area common to all transcripts of the gene. The latter was used to normalize endogenous expression. Transcript expression validation used 18S as the internal normalizer gene with the primer set for the transcript of interest. 2 RNAseq analysis and MISO. 100 million paired-end reads were aligned to hg19 using TopHat2/Bowtie2 (4, 5) with default parameters for genome-guided transcriptome reconstruction. MISO version 0.5.2 was used to estimate PSI values (6). Version 2 reference annotation includes annotated splicing events found in Ensembl genes, UCSC knownGenes, and RefSeq genes. Version 2 includes annotation of skipped exons, retained introns, mutually exclusive exons, and alternative 5’/3’ splice sites. Discovered splicing events were filtered for those with a PSI difference greater than 20% as well as a Bayes factor greater than 10. Probability of overlap between sets of splicing events found in either shEWS-FLI1 or YK-4-279 was calculated using a hypergeometric distribution specific to the total number of annotated events of each type. RNA immunoprecipitation (RIP) and Cross-linked RNA immunoprecipitation (CLIP). RIP was carried out as previously published (7). CLIP was carried out using FLI1 antibody from Innovagen (Sweden) raised against recombinant FLI1 C- terminal protein in rabbit. TC32 cells were UV crosslinked at 150 mJ/cm2 in Stratalinker (Invitrogen). The CLIP protocol was followed as published previously (8). CLIPseq analysis and MEME suite. CLIPseq reads were trimmed for barcode and CLIP adapter sequences and PCR duplicated were removed. Filtered reads were then aligned to hg19 using Bowtie version 1.1.1 (9, 10). Those reads that aligned unambiguously were used in downstream processing. Sequence of forward and reverse reads were treated separately as MEME inputs (11). The top motif with a corresponding reverse complement motif in the reverse set was considered as the lead motif for further analysis. Motif localization and secondary structure MFE prediction. All higher-level data analysis was done in R using Bioconductor packages or at the UNIX command line (12, 13). The BSgenome and GenomicRanges R packages were used to extract genomic sequence +/-125bp of the 5’ and 3’ ends of all exons annotated in hg19. These sets were searched for occurrences of the lead motif with ≥ 90% match to the motif position weight matrix. Frequency of hits were graphed with respect to the exon-intron boundary for the 5’ and 3’ sets separately. Local secondary structure prediction was performed using RNALFold version 2.1.8. Local secondary structure was defined as 200nt or less, not including g-quadruplex formation. Minimum free energy for each read sequenced and aligned greater than 20nt long was recorded. A null set was generated by taking random exonic sequences of the same length as each CLIP read. These two minimum free energy density profiles were plotted together to compare local secondary structure preference of EWS-FLI1 RNA binding. Minigene cloning and transfection. TERT minigene was synthesized using the three exons and two introns (300 bp on the either side of exon-intron junction) and subcloned them into pcDNA3.1+ vector (GenScript). We transfected a total of 6 μg of minigene plasmid into 50% confluent TC32 cells using X-treme Gene 9 DNA transfection reagent (Roche Diagnostics) (14).
Recommended publications
  • Activation of ATM Depends on Chromatin Interactions Occurring Before Induction of DNA Damage
    LETTERS Activation of ATM depends on chromatin interactions occurring before induction of DNA damage Yong-Chul Kim1,6, Gabi Gerlitz1,6, Takashi Furusawa1, Frédéric Catez1,5, Andre Nussenzweig2, Kyu-Seon Oh3, Kenneth H. Kraemer3, Yosef Shiloh4 and Michael Bustin1,7 Efficient and correct responses to double-stranded breaks the KAP-1 protein9. The local and global changes in chromatin organiza- (DSB) in chromosomal DNA are crucial for maintaining genomic tion facilitate recruitment of damage-response proteins and remodelling stability and preventing chromosomal alterations that lead to factors, which further modify chromatin in the vicinity of the DSB and cancer1,2. The generation of DSB is associated with structural propagate the DNA damage response. Given the extensive changes in chro- changes in chromatin and the activation of the protein kinase matin structure associated with the DSB response, it could be expected ataxia-telangiectasia mutated (ATM), a key regulator of the that architectural chromatin-binding proteins, such as the high mobility signalling network of the cellular response to DSB3,4. The group (HMG) proteins, would be involved in this process10–12. interrelationship between DSB-induced changes in chromatin HMG is a superfamily of nuclear proteins that bind to chromatin architecture and the activation of ATM is unclear4. Here we show without any obvious specificity for a particular DNA sequence and affect that the nucleosome-binding protein HMGN1 modulates the the structure and activity of the chromatin fibre10,12,13. We have previ- interaction of ATM with chromatin both before and after DSB ously reported that HMGN1, an HMG protein that binds specifically to formation, thereby optimizing its activation.
    [Show full text]
  • Thiadiazole Derivatives As Anticancer Agents
    Pharmacological Reports (2020) 72:1079–1100 https://doi.org/10.1007/s43440-020-00154-7 REVIEW Thiadiazole derivatives as anticancer agents Monika Szeliga1 Received: 15 June 2020 / Revised: 13 August 2020 / Accepted: 20 August 2020 / Published online: 3 September 2020 © The Author(s) 2020 Abstract In spite of substantial progress made toward understanding cancer pathogenesis, this disease remains one of the leading causes of mortality. Thus, there is an urgent need to develop novel, more efective anticancer therapeutics. Thiadiazole ring is a versatile scafold widely studied in medicinal chemistry. Mesoionic character of this ring allows thiadiazole-containing compounds to cross cellular membrane and interact strongly with biological targets. Consequently, these compounds exert a broad spectrum of biological activities. This review presents the current state of knowledge on thiadiazole derivatives that demonstrate in vitro and/or in vivo efcacy across the cancer models with an emphasis on targets of action. The infuence of the substituent on the compounds’ activity is depicted. Furthermore, the results from clinical trials assessing thiadiazole- containing drugs in cancer patients are summarized. Keywords Thiadiazole derivatives · Cancer · Anticancer therapy · Clinical trials Introduction antiparasitic, anti-infammatory and anticancer activities [2]. Due to the mesoionic nature, thiadiazoles are able to According to the most recent data provided by the Interna- cross the cellular membranes. Their relatively good lipo- tional Agency for Research on Cancer (IARC), 18.1 mil- solubility is most likely attributed to the presence of the sul- lion new cases and 9.6 million cancer deaths were regis- phur atom [3]. The thiadiazole-containing drugs, including tered worldwide in 2018 [1].
    [Show full text]
  • 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.
    [Show full text]
  • Katalog 2015 Cover Paul Lin *Hinweis Förderung.Indd
    Product List 2015 WE LIVE SERVICE Certificates quartett owns two productions sites that are certified according to EN ISO 9001:2008 Quality management systems - Requirements EN ISO 13485:2012 + AC:2012 Medical devices - Quality management systems - Requirements for regulatory purposes GMP Conformity Our quality management guarantees products of highest quality! 2 Foreword to the quartett product list 2015 quartett Immunodiagnostika, Biotechnologie + Kosmetik Vertriebs GmbH welcomes you as one of our new business partners as well as all of our previous loyal clients. You are now member of quartett´s worldwide customers. First of all we would like to introduce ourselves to you. Founded as a family-run company in 1986, quartett ensures for more than a quarter of a century consistent quality of products. Service and support of our valued customers are our daily businesses. And we will continue! In the end 80´s quartett offered radioimmunoassay and enzyme immunoassay kits from different manufacturers in the USA. In the beginning 90´s the company changed its strategy from offering products for routine diagnostic to the increasing field of research and development. Setting up a production plant in 1997 and a second one in 2011 supported this decision. The company specialized its product profile in the field of manufacturing synthetic peptides for antibody production, peptides such as protease inhibitors, biochemical reagents and products for histology, cytology and immunohistology. All products are exclusively manufactured in Germany without outsourcing any production step. Nowadays, we expand into all other diagnostic and research fields and supply our customers in universities, government institutes, pharmaceutical and biotechnological companies, hospitals, and private doctor offices.
    [Show full text]
  • Table 2. Significant
    Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S.
    [Show full text]
  • Proteomic Identification of the Transcription Factors Ikaros And
    European School of Molecular Medicine (SEMM) University of Milan and University of Naples “Federico II” PhD degree in Systems Medicine (curriculum in Molecular Oncology) Settore disciplinare: BIO/11 Proteomic identification of the transcription factors Ikaros and Aiolos as new Myc interactors on chromatin Chiara Veronica Locarno Matricola: R10755 Center for Genomic Science IIT@SEMM, Milan Supervisor: Bruno Amati, PhD IEO, Milan Added Supervisor: Arianna Sabò, PhD IEO, Milan Academic year 2017-2018 Table of contents List of abbreviations ........................................................................................................... 4 List of figures ....................................................................................................................... 8 List of tables ....................................................................................................................... 11 Abstract .............................................................................................................................. 12 1. INTRODUCTION ......................................................................................................... 13 1.1 Myc ........................................................................................................................................ 13 1.1.1 Myc discovery and structure ........................................................................................... 13 1.1.2. Role of Myc in physiological and pathological conditions ...........................................
    [Show full text]
  • Defining Functional Interactions During Biogenesis of Epithelial Junctions
    ARTICLE Received 11 Dec 2015 | Accepted 13 Oct 2016 | Published 6 Dec 2016 | Updated 5 Jan 2017 DOI: 10.1038/ncomms13542 OPEN Defining functional interactions during biogenesis of epithelial junctions J.C. Erasmus1,*, S. Bruche1,*,w, L. Pizarro1,2,*, N. Maimari1,3,*, T. Poggioli1,w, C. Tomlinson4,J.Lees5, I. Zalivina1,w, A. Wheeler1,w, A. Alberts6, A. Russo2 & V.M.M. Braga1 In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases. 1 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 2 Computing Department, Imperial College London, London SW7 2AZ, UK. 3 Bioengineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK. 4 Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK.
    [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]
  • Supplementary Table 3 Complete List of RNA-Sequencing Analysis of Gene Expression Changed by ≥ Tenfold Between Xenograft and Cells Cultured in 10%O2
    Supplementary Table 3 Complete list of RNA-Sequencing analysis of gene expression changed by ≥ tenfold between xenograft and cells cultured in 10%O2 Expr Log2 Ratio Symbol Entrez Gene Name (culture/xenograft) -7.182 PGM5 phosphoglucomutase 5 -6.883 GPBAR1 G protein-coupled bile acid receptor 1 -6.683 CPVL carboxypeptidase, vitellogenic like -6.398 MTMR9LP myotubularin related protein 9-like, pseudogene -6.131 SCN7A sodium voltage-gated channel alpha subunit 7 -6.115 POPDC2 popeye domain containing 2 -6.014 LGI1 leucine rich glioma inactivated 1 -5.86 SCN1A sodium voltage-gated channel alpha subunit 1 -5.713 C6 complement C6 -5.365 ANGPTL1 angiopoietin like 1 -5.327 TNN tenascin N -5.228 DHRS2 dehydrogenase/reductase 2 leucine rich repeat and fibronectin type III domain -5.115 LRFN2 containing 2 -5.076 FOXO6 forkhead box O6 -5.035 ETNPPL ethanolamine-phosphate phospho-lyase -4.993 MYO15A myosin XVA -4.972 IGF1 insulin like growth factor 1 -4.956 DLG2 discs large MAGUK scaffold protein 2 -4.86 SCML4 sex comb on midleg like 4 (Drosophila) Src homology 2 domain containing transforming -4.816 SHD protein D -4.764 PLP1 proteolipid protein 1 -4.764 TSPAN32 tetraspanin 32 -4.713 N4BP3 NEDD4 binding protein 3 -4.705 MYOC myocilin -4.646 CLEC3B C-type lectin domain family 3 member B -4.646 C7 complement C7 -4.62 TGM2 transglutaminase 2 -4.562 COL9A1 collagen type IX alpha 1 chain -4.55 SOSTDC1 sclerostin domain containing 1 -4.55 OGN osteoglycin -4.505 DAPL1 death associated protein like 1 -4.491 C10orf105 chromosome 10 open reading frame 105 -4.491
    [Show full text]
  • NBN Gene Analysis and It's Impact on Breast Cancer
    Journal of Medical Systems (2019) 43: 270 https://doi.org/10.1007/s10916-019-1328-z IMAGE & SIGNAL PROCESSING NBN Gene Analysis and it’s Impact on Breast Cancer P. Nithya1 & A. ChandraSekar1 Received: 8 March 2019 /Accepted: 7 May 2019 /Published online: 5 July 2019 # Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Single Nucleotide Polymorphism (SNP) researches have become essential in finding out the congenital relationship of structural deviations with quantitative traits, heritable diseases and physical responsiveness to different medicines. NBN is a protein coding gene (Breast Cancer); Nibrin is used to fix and rebuild the body from damages caused because of strand breaks (both singular and double) associated with protein nibrin. NBN gene was retrieved from dbSNP/NCBI database and investigated using computational SNP analysis tools. The encrypted region in SNPs (exonal SNPs) were analyzed using software tools, SIFT, Provean, Polyphen, INPS, SNAP and Phd-SNP. The 3’ends of SNPs in un-translated region were also investigated to determine the impact of binding. The association of NBN gene polymorphism leads to several diseases was studied. Four SNPs were predicted to be highly damaged in coding regions which are responsible for the diseases such as, Aplastic Anemia, Nijmegan breakage syndrome, Microsephaly normal intelligence, immune deficiency and hereditary cancer predisposing syndrome (clivar). The present study will be helpful in finding the suitable drugs in future for various diseases especially for breast cancer. Keywords NBN . Single nucleotide polymorphism . Double strand breaks . nsSNP . Associated diseases Introduction NBN has a more complex structure due to its interaction with large proteins formed from the ATM gene which is NBN (Nibrin) is a protein coding gene, it is also known as highly essential in identifying damaged strands of DNA NBS1, Cell cycle regulatory Protein P95, is situated on and facilitating their repair [1].
    [Show full text]
  • Transcriptional Control of Tissue-Resident Memory T Cell Generation
    Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 © 2019 Filip Cvetkovski All rights reserved ABSTRACT Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Tissue-resident memory T cells (TRM) are a non-circulating subset of memory that are maintained at sites of pathogen entry and mediate optimal protection against reinfection. Lung TRM can be generated in response to respiratory infection or vaccination, however, the molecular pathways involved in CD4+TRM establishment have not been defined. Here, we performed transcriptional profiling of influenza-specific lung CD4+TRM following influenza infection to identify pathways implicated in CD4+TRM generation and homeostasis. Lung CD4+TRM displayed a unique transcriptional profile distinct from spleen memory, including up-regulation of a gene network induced by the transcription factor IRF4, a known regulator of effector T cell differentiation. In addition, the gene expression profile of lung CD4+TRM was enriched in gene sets previously described in tissue-resident regulatory T cells. Up-regulation of immunomodulatory molecules such as CTLA-4, PD-1, and ICOS, suggested a potential regulatory role for CD4+TRM in tissues. Using loss-of-function genetic experiments in mice, we demonstrate that IRF4 is required for the generation of lung-localized pathogen-specific effector CD4+T cells during acute influenza infection. Influenza-specific IRF4−/− T cells failed to fully express CD44, and maintained high levels of CD62L compared to wild type, suggesting a defect in complete differentiation into lung-tropic effector T cells.
    [Show full text]
  • A SARS-Cov-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing
    A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing Supplementary Information Supplementary Discussion All SARS-CoV-2 protein and gene functions described in the subnetwork appendices, including the text below and the text found in the individual bait subnetworks, are based on the functions of homologous genes from other coronavirus species. These are mainly from SARS-CoV and MERS-CoV, but when available and applicable other related viruses were used to provide insight into function. The SARS-CoV-2 proteins and genes listed here were designed and researched based on the gene alignments provided by Chan et. al. 1 2020 .​ Though we are reasonably sure the genes here are well annotated, we want to note that not every ​ protein has been verified to be expressed or functional during SARS-CoV-2 infections, either in vitro or in vivo. ​ ​ In an effort to be as comprehensive and transparent as possible, we are reporting the sub-networks of these functionally unverified proteins along with the other SARS-CoV-2 proteins. In such cases, we have made notes within the text below, and on the corresponding subnetwork figures, and would advise that more caution be taken when examining these proteins and their molecular interactions. Due to practical limits in our sample preparation and data collection process, we were unable to generate data for proteins corresponding to Nsp3, Orf7b, and Nsp16. Therefore these three genes have been left out of the following literature review of the SARS-CoV-2 proteins and the protein-protein interactions (PPIs) identified in this study.
    [Show full text]