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(P -Value<0.05, Fold Change≥1.4), 4 Vs. 0 Gy Irradiation
Table S1: Significant differentially expressed genes (P -Value<0.05, Fold Change≥1.4), 4 vs. 0 Gy irradiation Genbank Fold Change P -Value Gene Symbol Description Accession Q9F8M7_CARHY (Q9F8M7) DTDP-glucose 4,6-dehydratase (Fragment), partial (9%) 6.70 0.017399678 THC2699065 [THC2719287] 5.53 0.003379195 BC013657 BC013657 Homo sapiens cDNA clone IMAGE:4152983, partial cds. [BC013657] 5.10 0.024641735 THC2750781 Ciliary dynein heavy chain 5 (Axonemal beta dynein heavy chain 5) (HL1). 4.07 0.04353262 DNAH5 [Source:Uniprot/SWISSPROT;Acc:Q8TE73] [ENST00000382416] 3.81 0.002855909 NM_145263 SPATA18 Homo sapiens spermatogenesis associated 18 homolog (rat) (SPATA18), mRNA [NM_145263] AA418814 zw01a02.s1 Soares_NhHMPu_S1 Homo sapiens cDNA clone IMAGE:767978 3', 3.69 0.03203913 AA418814 AA418814 mRNA sequence [AA418814] AL356953 leucine-rich repeat-containing G protein-coupled receptor 6 {Homo sapiens} (exp=0; 3.63 0.0277936 THC2705989 wgp=1; cg=0), partial (4%) [THC2752981] AA484677 ne64a07.s1 NCI_CGAP_Alv1 Homo sapiens cDNA clone IMAGE:909012, mRNA 3.63 0.027098073 AA484677 AA484677 sequence [AA484677] oe06h09.s1 NCI_CGAP_Ov2 Homo sapiens cDNA clone IMAGE:1385153, mRNA sequence 3.48 0.04468495 AA837799 AA837799 [AA837799] Homo sapiens hypothetical protein LOC340109, mRNA (cDNA clone IMAGE:5578073), partial 3.27 0.031178378 BC039509 LOC643401 cds. [BC039509] Homo sapiens Fas (TNF receptor superfamily, member 6) (FAS), transcript variant 1, mRNA 3.24 0.022156298 NM_000043 FAS [NM_000043] 3.20 0.021043295 A_32_P125056 BF803942 CM2-CI0135-021100-477-g08 CI0135 Homo sapiens cDNA, mRNA sequence 3.04 0.043389246 BF803942 BF803942 [BF803942] 3.03 0.002430239 NM_015920 RPS27L Homo sapiens ribosomal protein S27-like (RPS27L), mRNA [NM_015920] Homo sapiens tumor necrosis factor receptor superfamily, member 10c, decoy without an 2.98 0.021202829 NM_003841 TNFRSF10C intracellular domain (TNFRSF10C), mRNA [NM_003841] 2.97 0.03243901 AB002384 C6orf32 Homo sapiens mRNA for KIAA0386 gene, partial cds. -
A Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic Buffering of Doxorubicin Sean M
Santos and Hartman Cancer & Metabolism (2019) 7:9 https://doi.org/10.1186/s40170-019-0201-3 RESEARCH Open Access A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin Sean M. Santos and John L. Hartman IV* Abstract Background: The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance. Methods: Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy. Results: Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context. -
Region Based Gene Expression Via Reanalysis of Publicly Available Microarray Data Sets
University of Louisville ThinkIR: The University of Louisville's Institutional Repository Electronic Theses and Dissertations 5-2018 Region based gene expression via reanalysis of publicly available microarray data sets. Ernur Saka University of Louisville Follow this and additional works at: https://ir.library.louisville.edu/etd Part of the Bioinformatics Commons, Computational Biology Commons, and the Other Computer Sciences Commons Recommended Citation Saka, Ernur, "Region based gene expression via reanalysis of publicly available microarray data sets." (2018). Electronic Theses and Dissertations. Paper 2902. https://doi.org/10.18297/etd/2902 This Doctoral Dissertation is brought to you for free and open access by ThinkIR: The University of Louisville's Institutional Repository. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of ThinkIR: The University of Louisville's Institutional Repository. This title appears here courtesy of the author, who has retained all other copyrights. For more information, please contact [email protected]. REGION BASED GENE EXPRESSION VIA REANALYSIS OF PUBLICLY AVAILABLE MICROARRAY DATA SETS By Ernur Saka B.S. (CEng), University of Dokuz Eylul, Turkey, 2008 M.S., University of Louisville, USA, 2011 A Dissertation Submitted To the J. B. Speed School of Engineering in Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Computer Science and Engineering Department of Computer Engineering and Computer Science University of Louisville Louisville, Kentucky May 2018 Copyright 2018 by Ernur Saka All rights reserved REGION BASED GENE EXPRESSION VIA REANALYSIS OF PUBLICLY AVAILABLE MICROARRAY DATA SETS By Ernur Saka B.S. (CEng), University of Dokuz Eylul, Turkey, 2008 M.S., University of Louisville, USA, 2011 A Dissertation Approved On April 20, 2018 by the following Committee __________________________________ Dissertation Director Dr. -
Title: a Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic
bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. 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 4.0 International license. 1 Title Page: 2 3 Title: A yeast phenomic model for the influence of Warburg metabolism on genetic 4 buffering of doxorubicin 5 6 Authors: Sean M. Santos1 and John L. Hartman IV1 7 1. University of Alabama at Birmingham, Department of Genetics, Birmingham, AL 8 Email: [email protected], [email protected] 9 Corresponding author: [email protected] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 1 bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. 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 4.0 International license. 26 Abstract: 27 Background: 28 Saccharomyces cerevisiae represses respiration in the presence of adequate glucose, 29 mimicking the Warburg effect, termed aerobic glycolysis. We conducted yeast phenomic 30 experiments to characterize differential doxorubicin-gene interaction, in the context of 31 respiration vs. glycolysis. The resulting systems level biology about doxorubicin 32 cytotoxicity, including the influence of the Warburg effect, was integrated with cancer 33 pharmacogenomics data to identify potentially causal correlations between differential 34 gene expression and anti-cancer efficacy. -
Table S1. 103 Ferroptosis-Related Genes Retrieved from the Genecards
Table S1. 103 ferroptosis-related genes retrieved from the GeneCards. Gene Symbol Description Category GPX4 Glutathione Peroxidase 4 Protein Coding AIFM2 Apoptosis Inducing Factor Mitochondria Associated 2 Protein Coding TP53 Tumor Protein P53 Protein Coding ACSL4 Acyl-CoA Synthetase Long Chain Family Member 4 Protein Coding SLC7A11 Solute Carrier Family 7 Member 11 Protein Coding VDAC2 Voltage Dependent Anion Channel 2 Protein Coding VDAC3 Voltage Dependent Anion Channel 3 Protein Coding ATG5 Autophagy Related 5 Protein Coding ATG7 Autophagy Related 7 Protein Coding NCOA4 Nuclear Receptor Coactivator 4 Protein Coding HMOX1 Heme Oxygenase 1 Protein Coding SLC3A2 Solute Carrier Family 3 Member 2 Protein Coding ALOX15 Arachidonate 15-Lipoxygenase Protein Coding BECN1 Beclin 1 Protein Coding PRKAA1 Protein Kinase AMP-Activated Catalytic Subunit Alpha 1 Protein Coding SAT1 Spermidine/Spermine N1-Acetyltransferase 1 Protein Coding NF2 Neurofibromin 2 Protein Coding YAP1 Yes1 Associated Transcriptional Regulator Protein Coding FTH1 Ferritin Heavy Chain 1 Protein Coding TF Transferrin Protein Coding TFRC Transferrin Receptor Protein Coding FTL Ferritin Light Chain Protein Coding CYBB Cytochrome B-245 Beta Chain Protein Coding GSS Glutathione Synthetase Protein Coding CP Ceruloplasmin Protein Coding PRNP Prion Protein Protein Coding SLC11A2 Solute Carrier Family 11 Member 2 Protein Coding SLC40A1 Solute Carrier Family 40 Member 1 Protein Coding STEAP3 STEAP3 Metalloreductase Protein Coding ACSL1 Acyl-CoA Synthetase Long Chain Family Member 1 Protein -
Pathway and Network Analysis for Mrna and Protein Profiling Data
Pathway and network analysis for mRNA and protein profiling data Bing Zhang, Ph.D. Professor of Molecular and Human Genetics Lester & Sue Smith Breast Center Baylor College of Medicine [email protected] VU workshop, 2016 Gene expression DNA Transcription Transcriptome Transcriptome RNA mRNA decay profiling Translation Proteome Protein Proteome Protein degradation profiling Phenotype Networks VU workshop, 2016 Overall workflow of gene expression studies Biological question Experimental design Microarray RNA-Seq Shotgun proteomics Image analysis Reads mapping Peptide/protein ID Signal intensities Read counts Spectral counts; Intensities Data Analysis Experimental Hypothesis validation VU workshop, 2016 Data matrix Samples probe_set_id HNE0_1 HNE0_2 HNE0_3 HNE60_1 HNE60_2 HNE60_3 1007_s_at 8.6888 8.5025 8.5471 8.5412 8.5624 8.3073 1053_at 9.1558 9.1835 9.4294 9.2111 9.1204 9.2494 117_at 7.0700 7.0034 6.9047 9.0414 8.6382 9.2663 121_at 9.7174 9.7440 9.6120 9.7581 9.7422 9.7345 1255_g_at 4.2801 4.4669 4.2360 4.3700 4.4573 4.2979 1294_at 6.3556 6.2381 6.2053 6.4290 6.5074 6.2771 Genes 1316_at 6.5759 6.5330 6.4709 6.6636 6.6438 6.4688 1320_at 6.5497 6.5388 6.5410 6.6605 6.5987 6.7236 1405_i_at 4.3260 4.4640 4.1438 4.3462 4.3876 4.6849 1431_at 5.2191 5.2070 5.2657 5.2823 5.2522 5.1808 1438_at 7.0155 6.9359 6.9241 7.0248 7.0142 7.0971 1487_at 8.6361 8.4879 8.4498 8.4470 8.5311 8.4225 1494_f_at 7.3296 7.3901 7.0886 7.2648 7.6058 7.2949 1552256_a_at 10.6245 10.5235 10.6522 10.4205 10.2344 10.3144 1552257_a_at 10.3224 10.1749 10.1992 10.2464 10.2191 -
Gene List HTG Edgeseq Immune Response Panel
Gene List HTG EdgeSeq Immune Response Panel A2M AGER ARAF BIRC6 CAVIN1 CD1D CDK4 COPS2 CXCL5 AAMP AGO1 ARF1 BIRC7 CAVIN2 CD2 CDK6 COPS5 CXCL6 ABCA1 AGO2 ARF5 BLK CBL CD209 CDK8 CPA3 CXCL8 ABCB1 AGT ARG1 BLNK CBLB CD22 CDK9 CPEB4 CXCL9 ABCC2 AHI1 ARHGAP30 BMP8A CCDC116 CD226 CDKAL1 CPT1A CXCR1 ABCC3 AHR ARHGEF1 BMPER CCDC60 CD24 CDKN1A CPT1B CXCR2 ABCC5 AHSA1 ARID5B BORCS5 CCDC88B CD244 CDKN1B CPT2 CXCR3 ABCC8 AHSA2P ARL3 BRAF CCL11 CD247 CDKN1C CR2 CXCR4 ABCF1 AICDA ARPC2 BRD8 CCL13 CD27 CDKN2A CRCP CXCR5 ABHD6 AIG1 ARRB2 BRF1 CCL14 CD274 CDKN2B CREB1 CXCR6 ABL1 AIM2 ASPA BRWD1 CCL15 CD276 CDKN2C CREB3 CXorf21 ACAA2 AIRE ATF1 BSN CCL16 CD28 CDKN2D CREB3L2 CYBA ACADL AKT1 ATF2 BST2 CCL17 CD33 CEACAM1 CREB3L4 CYBB ACADM AKT1S1 ATF3_activating BTC CCL18 CD34 CEACAM3 CREBBP CYC1 ACE AKT2 ATF3_repressing BTK CCL19 CD36 CEACAM4 CREM CYLD ACE2 AKT3 ATF4 BTLA CCL2 CD38 CEACAM6 CRK CYP1B1 ACIN1 ALAS1 ATF6 BTNL2 CCL20 CD3D CEACAM8 CRLF2 CYP27A1 ACKR1 ALAS2 ATG101 BUB1 CCL21 CD3E CEBPA CRLS1 CYP4A11_ ACKR2 ALOX15 ATG16L1 BUB1B CCL22 CD3G CEBPB CRP CYP4A22 ACLY ALOX5 ATG4B C19orf33 CCL23 CD4 CEBPD CRTC3 CYP51A1 ACOT1_ACOT2 ALOX5AP ATG5 C1orf53 CCL24 CD40 CEBPG CRYGD CYP7A1 ACOT13 ALPL ATG9A C1QA CCL26 CD40LG CELF1 CSF1 DAB2IP ACOX1 AMIGO3 ATM C1QB CCL27 CD44 CELF2 CSF1R DACT1 ACOX3 ANG ATP2A2 C1QBP CCL3 CD46 CENPO CSF2 DAP ACOXL ANGPT1 ATP2B4 C1QTNF6 CCL3L_family CD48 CEP250 CSF2RA DAXX ACP5 ANGPT2 ATP5F1B C1S CCL4 CD5 CEP57 CSF2RB DBP ACPP ANGPTL1 ATP6V0A1 C2 CCL5 CD52 CFB CSF3 DCLRE1B ACSL1 ANGPTL4 ATP6V0C C3 CCL7 CD55 CFD CSF3R DDAH1 ACSL3 ANK3 -
Network-Assisted Analysis of Primary Sjögren's Syndrome GWAS Data In
Network-assisted analysis of primary Sjögren’s syndrome GWAS data in Han Chinese Kechi Fang, Kunlin Zhang, Jing Wang* Address: Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China. Email: Kechi Fang – [email protected]; Kunlin Zhang – [email protected]; Jing Wang – [email protected] *Corresponding author 1 Supplementary materials Page 3 – Page 5: Supplementary Figure S1. The direct network formed by the module genes from DAPPLE. Page 6: Supplementary Figure S2. Transcript expression heatmap. Page 7: Supplementary Figure S3. Transcript enrichment heatmap. Page 8: Supplementary Figure S4. Workflow of network-assisted analysis of pSS GWAS data to identify candidate genes. Page 9 – Page 734: Supplementary Table S1. A full list of PPI pairs involved in the node-weighted pSS interactome. Page 735 – Page 737: Supplementary Table S2. Detailed information about module genes and sigMHC-genes. Page 738: Supplementary Table S3. GO terms enriched by module genes. 2 NFKBIE CFLAR NFKB1 STAT4 JUN HSF1 CCDC90B SUMO2 STAT1 PAFAH1B3 NMI GTF2I 2e−04 CDKN2C LAMA4 8e−04 HDAC1 EED 0.002 WWOX PSMD7 0.008 TP53 PSMA1 HR 0.02 RPA1 0.08 UBC ARID3A PTTG1 0.2 TSC22D4 ERH NIF3L1 0.4 MAD2L1 DMRTB1 1 ERBB4 PRMT2 FXR2 MBL2 CBS UHRF2 PCNP VTA1 3 DNMT3B DNMT1 RBBP4 DNMT3A RFC3 DDB1 THRA CBX5 EED NR2F2 RAD9A HUS1 RFC4 DDB2 HDAC2 HCFC1 CDC45L PPP1CA MLLSMARCA2 PGR SP3 EZH2 CSNK2B HIST1H4C HIST1H4F HNRNPUL1 HR HIST4H4 TAF1C HIST1H4A ENSG00000206300 APEX1 TFDP1 RHOA ENSG00000206406 RPF2 E2F4 HIST1H4IHIST1H4B HIST1H4D -
Pntgs1-Like Expression During Reproductive Development Supports a Role for RNA Methyltransferases in the Aposporous Pathway Lorena Siena, Juan Pablo A
PnTgs1-like expression during reproductive development supports a role for RNA methyltransferases in the aposporous pathway Lorena Siena, Juan Pablo A. Ortiz, Olivier Leblanc, Silvina Pessino To cite this version: Lorena Siena, Juan Pablo A. Ortiz, Olivier Leblanc, Silvina Pessino. PnTgs1-like expression during reproductive development supports a role for RNA methyltransferases in the aposporous pathway. BMC Plant Biology, 2014, 14 (1), 10.1186/s12870-014-0297-0. hal-03165273 HAL Id: hal-03165273 https://hal.archives-ouvertes.fr/hal-03165273 Submitted on 10 Mar 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Siena et al. BMC Plant Biology 2014, 14:297 http://www.biomedcentral.com/1471-2229/14/297 RESEARCH ARTICLE Open Access PnTgs1-like expression during reproductive development supports a role for RNA methyltransferases in the aposporous pathway Lorena A Siena1, Juan Pablo A Ortiz1,2, Olivier Leblanc3 and Silvina Pessino1* Abstract Background: In flowering plants, apomixis (asexual reproduction via seeds) is widely believed to result from failure of key regulators of the sexual female reproductive pathway. In the past few years, both differential display and RNA-seq comparative approaches involving reproductive organs of sexual plants and their apomictic counterparts have yielded extensive lists of candidate genes. -
A Plant-Specific TGS1 Homolog Influences Gametophyte Development in Sexual Tetraploid Paspalum Notatum Ovules
ORIGINAL RESEARCH published: 29 November 2019 doi: 10.3389/fpls.2019.01566 A Plant-SpecificTGS1 Homolog Influences Gametophyte Development in Sexual Tetraploid Paspalum notatum Ovules Carolina Colono 1, Juan Pablo A. Ortiz 1, Hugo R. Permingeat 1, Eduardo Daniel Souza Canada 1, Lorena A. Siena 1, Nicolás Spoto 1, Florencia Galdeano 2, Francisco Espinoza 2, Olivier Leblanc 3 and Silvina C. Pessino 1* 1 Molecular Biology Laboratory, IICAR, CONICET, Universidad Nacional de Rosario, Rosario, Argentina, 2 Genetics Laboratory, IBONE, CONICET, Universidad Nacional del Nordeste, Corrientes, Argentina, 3 UMR DIADE, IRD, Univ. Montpellier, Montpellier, France Edited by: Tzung-Fu Hsieh, North Carolina State University, Aposporous apomictic plants form clonal maternal seeds by inducing the emergence of United States non-reduced (2n) embryo sacs in the ovule nucellus and the development of embryos by Reviewed by: parthenogenesis. In previous work, we reported a plant-specificTRIMETHYLGUANOSINE John G. Carman, Utah State University, United States SYNTHASE 1 (TGS1) gene (PN_TGS1-like) showing expression levels positively Ross Andrew Bicknell, correlated with sexuality rates in facultative apomictic Paspalum notatum. PN_ TGS1-like The New Zealand Institute for Plant & displayed contrasting in situ hybridization patterns in apomictic and sexual plant ovules Food Research Ltd, New Zealand Diego Hojsgaard, from premeiosis to anthesis. Here we transformed sexual P. notatum with a TGS1-like University of Göttingen, Germany antisense construction under a constitutive promoter, in order to produce lines with *Correspondence: reduced transcript representation. Antisense plants developed prominent trichomes on Silvina C. Pessino [email protected] the adaxial leaf surface, a trait absent from control genotypes. Reproductive development analysis revealed occasional formation of twin ovules. -
Composition and Function of Telomerase—A Polymerase Associated with the Origin of Eukaryotes
biomolecules Review Composition and Function of Telomerase—A Polymerase Associated with the Origin of Eukaryotes Petra Procházková Schrumpfová 1,2,* and Jiˇrí Fajkus 1,2,3 1 Laboratory of Functional Genomics and Proteomics, National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kotláˇrská 2, CZ-61137 Brno, Czech Republic; [email protected] 2 Mendel Centre for Plant Genomics and Proteomics, Central European Institute of Technology, Masaryk University, Kamenice 5, CZ-62500 Brno, Czech Republic 3 The Czech Academy of Sciences, Institute of Biophysics, Královopolská 135, 612 65 Brno, Czech Republic * Correspondence: [email protected] Received: 3 September 2020; Accepted: 1 October 2020; Published: 8 October 2020 Abstract: The canonical DNA polymerases involved in the replication of the genome are unable to fully replicate the physical ends of linear chromosomes, called telomeres. Chromosomal termini thus become shortened in each cell cycle. The maintenance of telomeres requires telomerase—a specific RNA-dependent DNA polymerase enzyme complex that carries its own RNA template and adds telomeric repeats to the ends of chromosomes using a reverse transcription mechanism. Both core subunits of telomerase—its catalytic telomerase reverse transcriptase (TERT) subunit and telomerase RNA (TR) component—were identified in quick succession in Tetrahymena more than 30 years ago. Since then, both telomerase subunits have been described in various organisms including yeasts, mammals, birds, reptiles and fish. Despite the fact that telomerase activity in plants was described 25 years ago and the TERT subunit four years later, a genuine plant TR has only recently been identified by our group. In this review, we focus on the structure, composition and function of telomerases. -
T Lymphocytes Mrna, and Protein Expression in Activated
Downloaded from http://www.jimmunol.org/ by guest on September 27, 2021 is online at: average * The Journal of Immunology , 31 of which you can access for free at: 2011; 187:2233-2243; Prepublished online 25 from submission to initial decision 4 weeks from acceptance to publication July 2011; doi: 10.4049/jimmunol.1101233 http://www.jimmunol.org/content/187/5/2233 MicroRNA Regulation of Molecular Networks Mapped by Global MicroRNA, mRNA, and Protein Expression in Activated T Lymphocytes Yevgeniy A. Grigoryev, Sunil M. Kurian, Traver Hart, Aleksey A. Nakorchevsky, Caifu Chen, Daniel Campbell, Steven R. Head, John R. Yates III and Daniel R. Salomon J Immunol cites 83 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription http://www.jimmunol.org/content/suppl/2011/07/25/jimmunol.110123 3.DC1 This article http://www.jimmunol.org/content/187/5/2233.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 © 2011 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 27, 2021. The Journal of Immunology MicroRNA Regulation of Molecular Networks Mapped by Global MicroRNA, mRNA, and Protein Expression in Activated T Lymphocytes Yevgeniy A.