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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. -
Genetic Association with Overall Survival of Taxane-Treated Lung
Genetic association with overall survival of taxane-treated lung cancer patients - a genome-wide association study in human lymphoblastoid cell lines followed by a clinical association study The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Niu, Nifang et al. “Genetic Association with Overall Survival of Taxane-treated Lung Cancer Patients - a Genome-wide Association Study in Human Lymphoblastoid Cell Lines Followed by a Clinical Association Study.” BMC Cancer 12.1 (2012): 422. CrossRef. Web. As Published http://dx.doi.org/10.1186/1471-2407-12-422 Publisher BioMed Central Ltd. Version Final published version Citable link http://hdl.handle.net/1721.1/77191 Terms of Use Creative Commons Attribution Detailed Terms http://creativecommons.org/licenses/by/2.0 Genetic variations and paclitaxel response Supplementary Figure 1. Imputation analysis for 8 SNPs associated with both paclitaxel IC50 in LCLs and overall survival in lung cancer patients. SNPs within 200kb up-/downstream of those 8 SNPs were imputed. Black circles indicate SNPs observed by genotyping, while red triangles indicate imputed SNPs. The y-axis represents –log10(p-value) for the association of each SNP with paclitaxel IC50, and the x-axis represents the chromosomal locations of the SNPs. Genetic variations in paclitaxel response 1 Supplementary Table 1. Top 1415 SNPs that were associated with paclitaxel IC50 with p-values <10-3 and top 147 SNPs that were significantly associated with paclitaxel IC50 -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Original Article a Database and Functional Annotation of NF-Κb Target Genes
Int J Clin Exp Med 2016;9(5):7986-7995 www.ijcem.com /ISSN:1940-5901/IJCEM0019172 Original Article A database and functional annotation of NF-κB target genes Yang Yang, Jian Wu, Jinke Wang The State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People’s Republic of China Received November 4, 2015; Accepted February 10, 2016; Epub May 15, 2016; Published May 30, 2016 Abstract: Backgrounds: The previous studies show that the transcription factor NF-κB always be induced by many inducers, and can regulate the expressions of many genes. The aim of the present study is to explore the database and functional annotation of NF-κB target genes. Methods: In this study, we manually collected the most complete listing of all NF-κB target genes identified to date, including the NF-κB microRNA target genes and built the database of NF-κB target genes with the detailed information of each target gene and annotated it by DAVID tools. Results: The NF-κB target genes database was established (http://tfdb.seu.edu.cn/nfkb/). The collected data confirmed that NF-κB maintains multitudinous biological functions and possesses the considerable complexity and diversity in regulation the expression of corresponding target genes set. The data showed that the NF-κB was a central regula- tor of the stress response, immune response and cellular metabolic processes. NF-κB involved in bone disease, immunological disease and cardiovascular disease, various cancers and nervous disease. NF-κB can modulate the expression activity of other transcriptional factors. Inhibition of IKK and IκBα phosphorylation, the decrease of nuclear translocation of p65 and the reduction of intracellular glutathione level determined the up-regulation or down-regulation of expression of NF-κB target genes. -
Gene Networks Activated by Specific Patterns of Action Potentials in Dorsal Root Ganglia Neurons Received: 10 August 2016 Philip R
www.nature.com/scientificreports OPEN Gene networks activated by specific patterns of action potentials in dorsal root ganglia neurons Received: 10 August 2016 Philip R. Lee1,*, Jonathan E. Cohen1,*, Dumitru A. Iacobas2,3, Sanda Iacobas2 & Accepted: 23 January 2017 R. Douglas Fields1 Published: 03 March 2017 Gene regulatory networks underlie the long-term changes in cell specification, growth of synaptic connections, and adaptation that occur throughout neonatal and postnatal life. Here we show that the transcriptional response in neurons is exquisitely sensitive to the temporal nature of action potential firing patterns. Neurons were electrically stimulated with the same number of action potentials, but with different inter-burst intervals. We found that these subtle alterations in the timing of action potential firing differentially regulates hundreds of genes, across many functional categories, through the activation or repression of distinct transcriptional networks. Our results demonstrate that the transcriptional response in neurons to environmental stimuli, coded in the pattern of action potential firing, can be very sensitive to the temporal nature of action potential delivery rather than the intensity of stimulation or the total number of action potentials delivered. These data identify temporal kinetics of action potential firing as critical components regulating intracellular signalling pathways and gene expression in neurons to extracellular cues during early development and throughout life. Adaptation in the nervous system in response to external stimuli requires synthesis of new gene products in order to elicit long lasting changes in processes such as development, response to injury, learning, and memory1. Information in the environment is coded in the pattern of action-potential firing, therefore gene transcription must be regulated by the pattern of neuronal firing. -
Fig1-13Tab1-5.Pdf
Supplementary Information Promoter hypomethylation of EpCAM-regulated bone morphogenetic protein genes in advanced endometrial cancer Ya-Ting Hsu, Fei Gu, Yi-Wen Huang, Joseph Liu, Jianhua Ruan, Rui-Lan Huang, Chiou-Miin Wang, Chun-Liang Chen, Rohit R. Jadhav, Hung-Cheng Lai, David G. Mutch, Paul J. Goodfellow, Ian M. Thompson, Nameer B. Kirma, and Tim Hui-Ming Huang Tables of contents Page Table of contents 2 Supplementary Methods 4 Supplementary Figure S1. Summarized sequencing reads and coverage of MBDCap-seq 8 Supplementary Figure S2. Reproducibility test of MBDCap-seq 10 Supplementary Figure S3. Validation of MBDCap-seq by MassARRAY analysis 11 Supplementary Figure S4. Distribution of differentially methylated regions (DMRs) in endometrial tumors relative to normal control 12 Supplementary Figure S5. Network analysis of differential methylation loci by using Steiner-tree analysis 13 Supplementary Figure S6. DNA methylation distribution in early and late stage of the TCGA endometrial cancer cohort 14 Supplementary Figure S7. Relative expression of BMP genes with EGF treatment in the presence or absence of PI3K/AKT and Raf (MAPK) inhibitors in endometrial cancer cells 15 Supplementary Figure S8. Induction of invasion by EGF in AN3CA and HEC1A cell lines 16 Supplementary Figure S9. Knockdown expression of BMP4 and BMP7 in RL95-2 cells 17 Supplementary Figure S10. Relative expression of BMPs and BMPRs in normal endometrial cell and endometrial cancer cell lines 18 Supplementary Figure S11. Microfluidics-based PCR analysis of EMT gene panel in RL95-2 cells with or without EGF treatment 19 Supplementary Figure S12. Knockdown expression of EpCAM by different shRNA sequences in RL95-2 cells 20 Supplementary Figure S13. -
Egfr Activates a Taz-Driven Oncogenic Program in Glioblastoma
EGFR ACTIVATES A TAZ-DRIVEN ONCOGENIC PROGRAM IN GLIOBLASTOMA by Minling Gao A thesis submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland March 2020 ©2020 Minling Gao All rights reserved Abstract Hyperactivated EGFR signaling is associated with about 45% of Glioblastoma (GBM), the most aggressive and lethal primary brain tumor in humans. However, the oncogenic transcriptional events driven by EGFR are still incompletely understood. We studied the role of the transcription factor TAZ to better understand master transcriptional regulators in mediating the EGFR signaling pathway in GBM. The transcriptional coactivator with PDZ- binding motif (TAZ) and its paralog gene, the Yes-associated protein (YAP) are two transcriptional co-activators that play important roles in multiple cancer types and are regulated in a context-dependent manner by various upstream signaling pathways, e.g. the Hippo, WNT and GPCR signaling. In GBM cells, TAZ functions as an oncogene that drives mesenchymal transition and radioresistance. This thesis intends to broaden our understanding of EGFR signaling and TAZ regulation in GBM. In patient-derived GBM cell models, EGF induced TAZ and its known gene targets through EGFR and downstream tyrosine kinases (ERK1/2 and STAT3). In GBM cells with EGFRvIII, an EGF-independent and constitutively active mutation, TAZ showed EGF- independent hyperactivation when compared to EGFRvIII-negative cells. These results revealed a novel EGFR-TAZ signaling axis in GBM cells. The second contribution of this thesis is that we performed next-generation sequencing to establish the first genome-wide map of EGF-induced TAZ target genes. -
Supplementary Table 3 Gene Microarray Analysis: PRL+E2 Vs
Supplementary Table 3 Gene microarray analysis: PRL+E2 vs. control ID1 Field1 ID Symbol Name M Fold P Value 69 15562 206115_at EGR3 early growth response 3 2,36 5,13 4,51E-06 56 41486 232231_at RUNX2 runt-related transcription factor 2 2,01 4,02 6,78E-07 41 36660 227404_s_at EGR1 early growth response 1 1,99 3,97 2,20E-04 396 54249 36711_at MAFF v-maf musculoaponeurotic fibrosarcoma oncogene homolog F 1,92 3,79 7,54E-04 (avian) 42 13670 204222_s_at GLIPR1 GLI pathogenesis-related 1 (glioma) 1,91 3,76 2,20E-04 65 11080 201631_s_at IER3 immediate early response 3 1,81 3,50 3,50E-06 101 36952 227697_at SOCS3 suppressor of cytokine signaling 3 1,76 3,38 4,71E-05 16 15514 206067_s_at WT1 Wilms tumor 1 1,74 3,34 1,87E-04 171 47873 238623_at NA NA 1,72 3,30 1,10E-04 600 14687 205239_at AREG amphiregulin (schwannoma-derived growth factor) 1,71 3,26 1,51E-03 256 36997 227742_at CLIC6 chloride intracellular channel 6 1,69 3,23 3,52E-04 14 15038 205590_at RASGRP1 RAS guanyl releasing protein 1 (calcium and DAG-regulated) 1,68 3,20 1,87E-04 55 33237 223961_s_at CISH cytokine inducible SH2-containing protein 1,67 3,19 6,49E-07 78 32152 222872_x_at OBFC2A oligonucleotide/oligosaccharide-binding fold containing 2A 1,66 3,15 1,23E-05 1969 32201 222921_s_at HEY2 hairy/enhancer-of-split related with YRPW motif 2 1,64 3,12 1,78E-02 122 13463 204015_s_at DUSP4 dual specificity phosphatase 4 1,61 3,06 5,97E-05 173 36466 227210_at NA NA 1,60 3,04 1,10E-04 117 40525 231270_at CA13 carbonic anhydrase XIII 1,59 3,02 5,62E-05 81 42339 233085_s_at OBFC2A oligonucleotide/oligosaccharide-binding -
Supp Data.Pdf
Supplementary Methods Verhaak sub-classification The Verhaak called tumor subtype was assigned as determined in the Verhaak et al. manuscript, for those TCGA tumors that were included at the time. TCGA data for expression (Affymetrix platform, level 1) and methylation (Infinium platform level 3) were downloaded from the TCGA portal on 09/29/2011. Affymetrix data were processed using R and Biocondutor with a RMA algorithm using quantile normalization and custom CDF. Level 3 methylation data has already been processed and converted to a beta-value. The genes that make up the signature for each of the 4 Verhaak groups (i.e. Proneural, Neural, Mesenchymal, and Classical) were downloaded. For each tumor, the averaged expression of the 4 genes signatures was calculated generating 4 ‘metagene’ scores, one for each subtype, (Colman et al.2010) for every tumor. Then each subtype metagene score was z-score corrected to allow comparison among the 4 metagenes. Finally, for each tumor the subtype with the highest metagene z-score among the four was used to assign subtype. Thus by this method, a tumor is assigned to the group to which it has the strongest expression signature. The shortcoming is that if a tumor has a ‘dual personality’ – for instance has strong expression of both classical and mesenchymal signature genes, there is some arbitrariness to the tumor’s assignment. Western Blot Analysis and cell fractionation Western blot analysis was performed using standard protocols. To determine protein expression we used the following antibodies: TAZ (Sigma and BD Biosciences), YAP (Santa Cruz Biotechnology), CD44 (Cell Signaling), FN1 (BD Biosciences), TEAD1, TEAD2, TEAD3, TEAD4 (Santa Cruz Biotechnology), MST1, p-MST1, MOB1, LATS1, LATS2, 14-3-3-, 1 ACTG2, (Cell Signaling), p-LATS1/2 (Abcam), Flag (Sigma Aldrich), Actin (Calbiochem), CAV2 (BD Biosciences), CTGF (Santa Cruz), RUNX2 (Sigma Aldrich), Cylin A, Cyclin E, Cyclin B1, p-cdk1, p-cdk4 (Cell Signaling Technologies). -
WO 2012/054896 Al
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number ι (43) International Publication Date ¾ ί t 2 6 April 2012 (26.04.2012) WO 2012/054896 Al (51) International Patent Classification: AO, AT, AU, AZ, BA, BB, BG, BH, BR, BW, BY, BZ, C12N 5/00 (2006.01) C12N 15/00 (2006.01) CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, C12N 5/02 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, (21) International Application Number: KR, KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, PCT/US201 1/057387 ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, (22) International Filing Date: NO, NZ, OM, PE, PG, PH, PL, PT, QA, RO, RS, RU, 2 1 October 201 1 (21 .10.201 1) RW, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, (25) Filing Language: English ZM, ZW. (26) Publication Language: English (84) Designated States (unless otherwise indicated, for every (30) Priority Data: kind of regional protection available): ARIPO (BW, GH, 61/406,064 22 October 2010 (22.10.2010) US GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, SZ, TZ, 61/415,244 18 November 2010 (18.1 1.2010) US UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, MD, RU, TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, (71) Applicant (for all designated States except US): BIO- DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, TIME INC. -
Preview of “Supplement Table S4.Xls”
Annotations for all recurrently lost regions cytoband 1p36.12 3p14.2 3q13.13 q value 0,01352 0,1513 0,085307 residual q value 0,01352 0,1513 0,085307 wide peak boundaries chr1:23457835-23714048 chr3:60396160-60637030 chr3:110456383-110657226 genes in wide peak E2F2 FHIT DPPA4 ID3 DPPA2 TCEA3 DDEFL1 TBC1D19 PI4K2B STIM2 GBA3 KCNIP4 DKFZp761B107 C4orf28 GPR125 FLJ45721 hsa-mir-218-1 CXCL3 GRSF1 HNRPD HTN1 HTN3 IBSP IGFBP7 IGJ IL8 CXCL10 KDR CXCL9 AFF1 MUC7 NKX6-1 PF4 PF4V1 PKD2 POLR2B PPEF2 PPAT PPBP PRKG2 MAPK10 PTPN13 REST CXCL6 CXCL11 CXCL5 SPINK2 SPP1 SRP72 STATH SULT1E1 UGT2B4 UGT2B7 UGT2B10 UGT2B15 UGT2B17 SPARCL1 VDP SLC4A4 HERC3 GENX-3414 CDKL2 TMPRSS11D ABCG2 ADAMTS3 CLOCK CEP135 G3BP2 HNRPDL ENAM FAM13A1 CXCL13 PAICS UGT2B11 HPSE NMU SMR3B NPFFR2 UGT2A1 CCNI hsa-mir-491 hsa-mir-31 4p15.32 4q13.1 4q13.1 (continued) 6q14.1 0,14888 0,17878 0,093693 0,14888 0,17878 0,093693 chr4:17969802-29966659 chr4:55756035-90966136 chr6:76830186-107898353 CCKAR AFM SEC31A AIM1 DHX15 AFP RUFY3 BCKDHB RBPSUH ALB WDFY3 PRDM1 SOD3 AMBN LPHN3 CCNC SLIT2 ANXA3 DKFZP564O0823 CGA SLC34A2 AREG RCHY1 CNR1 PPARGC1A ART3 ANKRD17 EPHA7 KIAA0746 BMP3 BRDG1 GABRR1 ANAPC4 BTC SMR3A GABRR2 SLA/LP CCNG2 ASAHL GRIK2 LGI2 SCARB2 COQ2 HTR1B TBC1D19 CDS1 SULT1B1 HTR1E PI4K2B CENPC1 TMPRSS11E IMPG1 STIM2 CSN1S1 MRPS18C ME1 GBA3 CSN2 COPS4 NT5E KCNIP4 CSN3 HSD17B11 PGM3 DKFZp761B107 DCK HERC5 POU3F2 C4orf28 DMP1 PLAC8 PREP GPR125 DSPP NUDT9 SIM1 FLJ45721 EPHA5 NUP54 ELOVL4 hsa-mir-218-1 EREG UGT2B28 MAP3K7 FGF5 ODAM TPBG GC HERC6 TTK GK2 SDAD1 RNGTT GNRHR UBE1L2 TBX18 -
Figure S1. Gene Ontology Classification of Abeliophyllum Distichum Leaves Extract-Induced Degs
Figure S1. Gene ontology classification of Abeliophyllum distichum leaves extract-induced DEGs. The results are summarized in three main categories: Biological process, Cellular component and Molecular function. Figure S2. KEGG pathway enrichment analysis using Abeliophyllum distichum leaves extract-DEGs (A). Venn diagram analysis of DEGs involved in PI3K/Akt signaling pathway and Rap1 signaling pathway (B). Figure S3. The expression (A) and protein levels (B) of Akt3 in AL-treated SK-MEL2 cells. Values with different superscripted letters are significantly different (p < 0.05). Table S1. Abeliophyllum distichum leaves extract-induced DEGs. log2 Fold Gene name Gene description Change A2ML1 alpha-2-macroglobulin-like protein 1 isoform 2 [Homo sapiens] 3.45 A4GALT lactosylceramide 4-alpha-galactosyltransferase [Homo sapiens] −1.64 ABCB4 phosphatidylcholine translocator ABCB4 isoform A [Homo sapiens] −1.43 ABCB5 ATP-binding cassette sub-family B member 5 isoform 1 [Homo sapiens] −2.99 ABHD17C alpha/beta hydrolase domain-containing protein 17C [Homo sapiens] −1.62 ABLIM2 actin-binding LIM protein 2 isoform 1 [Homo sapiens] −2.53 ABTB2 ankyrin repeat and BTB/POZ domain-containing protein 2 [Homo sapiens] −1.48 ACACA acetyl-CoA carboxylase 1 isoform 1 [Homo sapiens] −1.76 ACACB acetyl-CoA carboxylase 2 precursor [Homo sapiens] −2.03 ACSM1 acyl-coenzyme A synthetase ACSM1, mitochondrial [Homo sapiens] −3.05 disintegrin and metalloproteinase domain-containing protein 19 preproprotein [Homo ADAM19 −1.65 sapiens] disintegrin and metalloproteinase