IPP17 Carcinoma IPMN Gene Symbol Transcript Accession Nucleotide
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Multiple Loci Modulate Opioid Therapy Response for Cancer Pain
Published OnlineFirst May 27, 2011; DOI: 10.1158/1078-0432.CCR-10-3028 Clinical Cancer Predictive Biomarkers and Personalized Medicine Research Multiple Loci Modulate Opioid Therapy Response for Cancer Pain Antonella Galvan1, Frank Skorpen2,Pal Klepstad2,3, Anne Kari Knudsen2, Torill Fladvad2, Felicia S. Falvella1, Alessandra Pigni1, Cinzia Brunelli1, Augusto Caraceni1,2, Stein Kaasa2,4, and Tommaso A. Dragani1 Abstract Purpose: Patients treated with opioid drugs for cancer pain experience different relief responses, raising the possibility that genetic factors play a role in opioid therapy outcome. In this study, we tested the hypothesis that genetic variations may control individual response to opioid drugs in cancer patients. Experimental Design: We tested 1 million single-nucleotide polymorphisms (SNP) in European cancer patients, selected in a first series, for extremely poor (pain relief 40%; n ¼ 145) or good (pain relief 90%; n ¼ 293) responses to opioid therapy using a DNA-pooling approach. Candidate SNPs identified by SNP- array were genotyped in individual samples constituting DNA pools as well as in a second series of 570 patients. Results: Association analysis in 1,008 cancer patients identified eight SNPs significantly associated with À pain relief at a statistical threshold of P < 1.0 Â 10 3, with rs12948783, upstream of the RHBDF2 gene, À showing the best statistical association (P ¼ 8.1 Â 10 9). Functional annotation analysis of SNP-tagged genes suggested the involvement of genes acting on processes of the neurologic system. Conclusion: Our results indicate that the identified SNP panel can modulate the response of cancer patients to opioid therapy and may provide a new tool for personalized therapy of cancer pain. -
Sex-Specific Transcriptome Differences in Human Adipose
G C A T T A C G G C A T genes Article Sex-Specific Transcriptome Differences in Human Adipose Mesenchymal Stem Cells 1, 2, 3 1,3 Eva Bianconi y, Raffaella Casadei y , Flavia Frabetti , Carlo Ventura , Federica Facchin 1,3,* and Silvia Canaider 1,3 1 National Laboratory of Molecular Biology and Stem Cell Bioengineering of the National Institute of Biostructures and Biosystems (NIBB)—Eldor Lab, at the Innovation Accelerator, CNR, Via Piero Gobetti 101, 40129 Bologna, Italy; [email protected] (E.B.); [email protected] (C.V.); [email protected] (S.C.) 2 Department for Life Quality Studies (QuVi), University of Bologna, Corso D’Augusto 237, 47921 Rimini, Italy; [email protected] 3 Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Via Massarenti 9, 40138 Bologna, Italy; fl[email protected] * Correspondence: [email protected]; Tel.: +39-051-2094114 These authors contributed equally to this work. y Received: 1 July 2020; Accepted: 6 August 2020; Published: 8 August 2020 Abstract: In humans, sexual dimorphism can manifest in many ways and it is widely studied in several knowledge fields. It is increasing the evidence that also cells differ according to sex, a correlation still little studied and poorly considered when cells are used in scientific research. Specifically, our interest is on the sex-related dimorphism on the human mesenchymal stem cells (hMSCs) transcriptome. A systematic meta-analysis of hMSC microarrays was performed by using the Transcriptome Mapper (TRAM) software. This bioinformatic tool was used to integrate and normalize datasets from multiple sources and allowed us to highlight chromosomal segments and genes differently expressed in hMSCs derived from adipose tissue (hADSCs) of male and female donors. -
Supplementary Data
SUPPLEMENTARY METHODS 1) Characterisation of OCCC cell line gene expression profiles using Prediction Analysis for Microarrays (PAM) The ovarian cancer dataset from Hendrix et al (25) was used to predict the phenotypes of the cell lines used in this study. Hendrix et al (25) analysed a series of 103 ovarian samples using the Affymetrix U133A array platform (GEO: GSE6008). This dataset comprises clear cell (n=8), endometrioid (n=37), mucinous (n=13) and serous epithelial (n=41) primary ovarian carcinomas and samples from 4 normal ovaries. To build the predictor, the Prediction Analysis of Microarrays (PAM) package in R environment was employed (http://rss.acs.unt.edu/Rdoc/library/pamr/html/00Index.html). When more than one probe described the expression of a given gene, we used the probe with the highest median absolute deviation across the samples. The dataset from Hendrix et al. (25) and the dataset of OCCC cell lines described in this manuscript were then overlaid on the basis of 11536 common unique HGNC gene symbols. Only the 99 primary ovarian cancers samples and the four normal ovary samples were used to build the predictor. Following leave one out cross-validation, a predictor based upon 126 genes was able to identify correctly the four distinct phenotypes of primary ovarian tumour samples with a misclassification rate of 18.3%. This predictor was subsequently applied to the expression data from the 12 OCCC cell lines to determine the likeliest phenotype of the OCCC cell lines compared to primary ovarian cancers. Posterior probabilities were estimated for each cell line in comparison to the following phenotypes: clear cell, endometrioid, mucinous and serous epithelial. -
Us 2018 / 0305689 A1
US 20180305689A1 ( 19 ) United States (12 ) Patent Application Publication ( 10) Pub . No. : US 2018 /0305689 A1 Sætrom et al. ( 43 ) Pub . Date: Oct. 25 , 2018 ( 54 ) SARNA COMPOSITIONS AND METHODS OF plication No . 62 /150 , 895 , filed on Apr. 22 , 2015 , USE provisional application No . 62/ 150 ,904 , filed on Apr. 22 , 2015 , provisional application No. 62 / 150 , 908 , (71 ) Applicant: MINA THERAPEUTICS LIMITED , filed on Apr. 22 , 2015 , provisional application No. LONDON (GB ) 62 / 150 , 900 , filed on Apr. 22 , 2015 . (72 ) Inventors : Pål Sætrom , Trondheim (NO ) ; Endre Publication Classification Bakken Stovner , Trondheim (NO ) (51 ) Int . CI. C12N 15 / 113 (2006 .01 ) (21 ) Appl. No. : 15 /568 , 046 (52 ) U . S . CI. (22 ) PCT Filed : Apr. 21 , 2016 CPC .. .. .. C12N 15 / 113 ( 2013 .01 ) ; C12N 2310 / 34 ( 2013. 01 ) ; C12N 2310 /14 (2013 . 01 ) ; C12N ( 86 ) PCT No .: PCT/ GB2016 /051116 2310 / 11 (2013 .01 ) $ 371 ( c ) ( 1 ) , ( 2 ) Date : Oct . 20 , 2017 (57 ) ABSTRACT The invention relates to oligonucleotides , e . g . , saRNAS Related U . S . Application Data useful in upregulating the expression of a target gene and (60 ) Provisional application No . 62 / 150 ,892 , filed on Apr. therapeutic compositions comprising such oligonucleotides . 22 , 2015 , provisional application No . 62 / 150 ,893 , Methods of using the oligonucleotides and the therapeutic filed on Apr. 22 , 2015 , provisional application No . compositions are also provided . 62 / 150 ,897 , filed on Apr. 22 , 2015 , provisional ap Specification includes a Sequence Listing . SARNA sense strand (Fessenger 3 ' SARNA antisense strand (Guide ) Mathew, Si Target antisense RNA transcript, e . g . NAT Target Coding strand Gene Transcription start site ( T55 ) TY{ { ? ? Targeted Target transcript , e . -
UC San Diego UC San Diego Electronic Theses and Dissertations
UC San Diego UC San Diego Electronic Theses and Dissertations Title Systems Biology of Liver Regeneration and Pathologies Permalink https://escholarship.org/uc/item/05d214b4 Author Min, Jun SungJun Publication Date 2015 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, SAN DIEGO Systems Biology of Liver Regeneration and Pathologies A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Bioengineering by Jun SungJun Min Committee in charge: Professor Shankar Subramaniam, Chair Professor Pedro Cabrales Professor Daniel Tartakovsky Professor Shyni Varghese Professor Yingxiao Wang 2015 Copyright Jun SungJun Min, 2015 All rights reserved. The Dissertation of Jun SungJun Min is approved, and it is acceptable in quality and form for publication on microfilm and electronically: ______________________________________________________________ ______________________________________________________________ ______________________________________________________________ ______________________________________________________________ ______________________________________________________________ Chair University of California, San Diego 2015 iii DEDICATION To my friends and family With their love and support iv TABLE OF CONTENTS Signature Page ..................................................................................................... iii Dedication ........................................................................................................... -
Explorations in Olfactory Receptor Structure and Function by Jianghai
Explorations in Olfactory Receptor Structure and Function by Jianghai Ho Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Hiroaki Matsunami, Supervisor ___________________________ Jorg Grandl, Chair ___________________________ Marc Caron ___________________________ Sid Simon ___________________________ [Committee Member Name] Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2014 ABSTRACT Explorations in Olfactory Receptor Structure and Function by Jianghai Ho Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Hiroaki Matsunami, Supervisor ___________________________ Jorg Grandl, Chair ___________________________ Marc Caron ___________________________ Sid Simon ___________________________ [Committee Member Name] An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2014 Copyright by Jianghai Ho 2014 Abstract Olfaction is one of the most primitive of our senses, and the olfactory receptors that mediate this very important chemical sense comprise the largest family of genes in the mammalian genome. It is therefore surprising that we understand so little of how olfactory receptors work. In particular we have a poor idea of what chemicals are detected by most of the olfactory receptors in the genome, and for those receptors which we have paired with ligands, we know relatively little about how the structure of these ligands can either activate or inhibit the activation of these receptors. Furthermore the large repertoire of olfactory receptors, which belong to the G protein coupled receptor (GPCR) superfamily, can serve as a model to contribute to our broader understanding of GPCR-ligand binding, especially since GPCRs are important pharmaceutical targets. -
Supplementary Tables
Supplementary Tables Supplementary Table 1. Differentially methylated genes in correlation with their expression pattern in the A4 progression model A. Hypomethylated–upregulated Genes (n= 76) ALOX5 RRAD RTN4R DSCR6 FGFR3 HTR7 WNT3A POGK PLCD3 ALPPL2 RTEL1 SEMA3B DUSP5 FOSB ITGB4 MEST PPL PSMB8 ARHGEF4 BST2 SEMA7A SLC12A7 FOXQ1 KCTD12 LETM2 PRPH PXMP2 ARNTL2 CDH3 SHC2 SLC20A2 HSPA2 KIAA0182 LIMK2 NAB1 RASIP1 ASRGL1 CLDN3 DCBLD1 SNX10 SSH1 KREMEN2 LIPE NDRG2 ATF3 CLU DCHS1 SOD3 ST3GAL4 MAL LRRC1 NR3C2 ATP8B3 CYC1 DGCR8 EBAG9 SYNGR1 TYMS MCM2 NRG2 RHOF DAGLA DISP2 FAM19A5 TNNI3 UNC5B MYB PAK6 RIPK4 DAZAP1 DOCK3 FBXO6 HSPA4L WHSC1 PNMT PCDH1 B. Hypermethylated-downregulated Genes (n= 31) ARHGAP22 TNFSF9 KLF6 LRP8 NRP1 PAPSS2 SLC43A2 TBC1D16 ASB2 DZIP1 TPM1 MDGA1 NRP2 PIK3CD SMARCA2 TLL2 C18orf1 FBN1 LHFPL2 TRIO NTNG2 PTGIS SOCS2 TNFAIP8 DIXDC1 KIFC3 LMO1 NR3C1 ODZ3 PTPRM SYNPO Supplementary Table 2. Genes enriched for different histone methylation marks in A4 progression model identified through ChIP-on-chip a. H3K4me3 (n= 978) AATF C20orf149 CUL3 FOXP1 KATNA1 NEGR1 RAN SPIN2B ABCA7 C20orf52 CWF19L1 FRK KBTBD10 NEIL1 RANBP2 SPPL2A ABCC9 C21orf13-SH3BGR CXCL3 FSIP1 KBTBD6 NELF RAPGEF3 SPRY4 ABCG2 C21orf45 CYC1 FUK KCMF1 NFKB2 RARB SPRYD3 ABHD7 C22orf32 CYorf15A FXR2 KCNH7 NGDN RASAL2 SPTLC2 ACA15 C2orf18 DAXX FZD9 KCNMB4 NKAP RASD1 SRFBP1 ACA26 C2orf29 DAZ3 G6PD KCTD18 NKTR RASEF SRI ACA3 C2orf32 DBF4 GABPB2 KDELR2 NNT RASGRF1 SRM ACA48 C2orf55 DBF4B GABRA5 KIAA0100 NOL5A RASSF1 SSH2 ACAT1 C3orf44 DBI GADD45B KIAA0226 NOLC1 RASSF3 SSH3 ACSL5 -
Online Supporting Information S2: Proteins in Each Negative Pathway
Online Supporting Information S2: Proteins in each negative pathway Index Proteins ADO,ACTA1,DEGS2,EPHA3,EPHB4,EPHX2,EPOR,EREG,FTH1,GAD1,HTR6, IGF1R,KIR2DL4,NCR3,NME7,NOTCH1,OR10S1,OR2T33,OR56B4,OR7A10, Negative_1 OR8G1,PDGFC,PLCZ1,PROC,PRPS2,PTAFR,SGPP2,STMN1,VDAC3,ATP6V0 A1,MAPKAPK2 DCC,IDS,VTN,ACTN2,AKR1B10,CACNA1A,CHIA,DAAM2,FUT5,GCLM,GNAZ Negative_2 ,ITPA,NEU4,NTF3,OR10A3,PAPSS1,PARD3,PLOD1,RGS3,SCLY,SHC1,TN FRSF4,TP53 Negative_3 DAO,CACNA1D,HMGCS2,LAMB4,OR56A3,PRKCQ,SLC25A5 IL5,LHB,PGD,ADCY3,ALDH1A3,ATP13A2,BUB3,CD244,CYFIP2,EPHX2,F CER1G,FGD1,FGF4,FZD9,HSD17B7,IL6R,ITGAV,LEFTY1,LIPG,MAN1C1, Negative_4 MPDZ,PGM1,PGM3,PIGM,PLD1,PPP3CC,TBXAS1,TKTL2,TPH2,YWHAQ,PPP 1R12A HK2,MOS,TKT,TNN,B3GALT4,B3GAT3,CASP7,CDH1,CYFIP1,EFNA5,EXTL 1,FCGR3B,FGF20,GSTA5,GUK1,HSD3B7,ITGB4,MCM6,MYH3,NOD1,OR10H Negative_5 1,OR1C1,OR1E1,OR4C11,OR56A3,PPA1,PRKAA1,PRKAB2,RDH5,SLC27A1 ,SLC2A4,SMPD2,STK36,THBS1,SERPINC1 TNR,ATP5A1,CNGB1,CX3CL1,DEGS1,DNMT3B,EFNB2,FMO2,GUCY1B3,JAG Negative_6 2,LARS2,NUMB,PCCB,PGAM1,PLA2G1B,PLOD2,PRDX6,PRPS1,RFXANK FER,MVD,PAH,ACTC1,ADCY4,ADCY8,CBR3,CLDN16,CPT1A,DDOST,DDX56 ,DKK1,EFNB1,EPHA8,FCGR3A,GLS2,GSTM1,GZMB,HADHA,IL13RA2,KIR2 Negative_7 DS4,KLRK1,LAMB4,LGMN,MAGI1,NUDT2,OR13A1,OR1I1,OR4D11,OR4X2, OR6K2,OR8B4,OXCT1,PIK3R4,PPM1A,PRKAG3,SELP,SPHK2,SUCLG1,TAS 1R2,TAS1R3,THY1,TUBA1C,ZIC2,AASDHPPT,SERPIND1 MTR,ACAT2,ADCY2,ATP5D,BMPR1A,CACNA1E,CD38,CYP2A7,DDIT4,EXTL Negative_8 1,FCER1G,FGD3,FZD5,ITGAM,MAPK8,NR4A1,OR10V1,OR4F17,OR52D1,O R8J3,PLD1,PPA1,PSEN2,SKP1,TACR3,VNN1,CTNNBIP1 APAF1,APOA1,CARD11,CCDC6,CSF3R,CYP4F2,DAPK1,FLOT1,GSTM1,IL2 -
The Effects of TAR DNA Binding Protein Mutations on RNA Processing Associated with Amyotrophic Lateral Sclerosis
The effects of TAR DNA binding protein mutations on RNA processing associated with Amyotrophic Lateral Sclerosis By Afnan Ali Al Sultan Submitted for the degree of Doctor of Philosophy (PhD) Sheffield Institute for Translational Neuroscience University of Sheffield November, 2016 This PhD thesis is dedicated to my dear loving husband, Ahmed Alamer, Without your tremendous support, encouragement and love my dream would not have been possible 1 Abstract Introduction: Amyotrophic Lateral Sclerosis (ALS) is a devastating, chronic progressive neurodegenerative disorder, characterized by the loss of the upper motor neurons in the motor cortex and the lower motor neurons of the brainstem and spinal cord. This leads to muscle weakness, atrophy and paralysis. Death usually occurs 3-5 years from onset. In familial ALS, mutations in TARDBP, encoding the RNA binding protein TDP-43, cause 5% of cases. TDP-43 is mainly localized in the nucleus and has multiple functions, of which the best characterised is regulation of splicing/alternative splicing of hnRNA. In ALS TDP- 43 mislocates to the cytoplasm causing the characteristic protein aggregations. The current work investigates the possible effects of both TARDBP missense mutations and a truncation mutation on RNA processing. This was approached by examining the changes in gene expression in both the cytoplasm and nucleus in fibroblasts derived from familial ALS-TARDBP patients. Hypothesis: The cytoplasmic and nuclear transcriptomic profile from mutant TARDBP fibroblasts will generate different transcriptomic profiles than control fibroblasts and will establish transcripts and pathways dysregulated in the presence of mutations in TARDBP. The objectives were 1) to optimize the separation of nuclear and cytoplasmic RNA from patient and control fibroblasts, 2) to compare the expression profiles of the cytoplasmic and nuclear compartments from control and mutant fibroblasts and 3) to determine the effect of both mutation types on gene expression in fALS. -
Predictions of Response to Cancer Immunotherapy Via Tumour Mutational Burden and Genomic Resistance Markers Jacob Bradley1 and Nirmesh Patel Phd2
Predictions of Response to Cancer Immunotherapy via Tumour Mutational Burden and Genomic Resistance Markers Jacob Bradley1 and Nirmesh Patel PhD2 1Student: Part III Systems Biology, University of Cambridge, UK 2Supervisor: Cambridge Cancer Genomics, Cambridge, UK ABSTRACT The field of immuno-oncology (IO) is making huge advances translating immunological research into successful therapies, in particular Immune Checkpoint Blockade (ICB). The best predictor of treatment effectiveness in most cancers is the metric of Tumour Mutation Burden (TMB). We consider here methods for constructing a cost-effectively concise gene panel to predict TMB. We then investigate the extent to which it is feasible to produce a single gene panel capable of predicting TMB across a range of cancer types, and methods by which one may attempt to do so. We also look into IO resistance mechanisms and genes associated with poor response to ICB, so that we can ensure the best treatment monitoring possible. Finally, we exhibit an IO monitoring panel for Non-Small Cell Lung Cancer, and analyse its performance in comparison to other commercially available assays. Contents 1 List of Abbreviations 2 2 Introduction 3 2.1 Cancer is a Disease of the Genome.......................................................3 2.2 Immune Responses to Cancer are Mediated by Checkpoints.......................................3 2.3 Immunotherapy is an Emerging Field......................................................4 2.4 Tumour Mutational Burden is a Genomic Biomarker for Immunnotherapy Response........................4 2.5 TMB Varies Across and Within Cancer Types.................................................4 3 Results 5 3.1 TMB Estimation in a Single Cancer Type...................................................5 Genome-Wide Association • Gene Oriented Methods 3.2 TMB Estimation Across Cancer Types.................................................... -
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Supplementary Methods
doi: 10.1038/nature06162 SUPPLEMENTARY INFORMATION Supplementary Methods Cloning of human odorant receptors 423 human odorant receptors were cloned with sequence information from The Olfactory Receptor Database (http://senselab.med.yale.edu/senselab/ORDB/default.asp). Of these, 335 were predicted to encode functional receptors, 45 were predicted to encode pseudogenes, 29 were putative variant pairs of the same genes, and 14 were duplicates. We adopted the nomenclature proposed by Doron Lancet 1. OR7D4 and the six intact odorant receptor genes in the OR7D4 gene cluster (OR1M1, OR7G2, OR7G1, OR7G3, OR7D2, and OR7E24) were used for functional analyses. SNPs in these odorant receptors were identified from the NCBI dbSNP database (http://www.ncbi.nlm.nih.gov/projects/SNP) or through genotyping. OR7D4 single nucleotide variants were generated by cloning the reference sequence from a subject or by inducing polymorphic SNPs by site-directed mutagenesis using overlap extension PCR. Single nucleotide and frameshift variants for the six intact odorant receptors in the same gene cluster as OR7D4 were generated by cloning the respective genes from the genomic DNA of each subject. The chimpanzee OR7D4 orthologue was amplified from chimpanzee genomic DNA (Coriell Cell Repositories). Odorant receptors that contain the first 20 amino acids of human rhodopsin tag 2 in pCI (Promega) were expressed in the Hana3A cell line along with a short form of mRTP1 called RTP1S, (M37 to the C-terminal end), which enhances functional expression of the odorant receptors 3. For experiments with untagged odorant receptors, OR7D4 RT and S84N variants without the Rho tag were cloned into pCI.