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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. -
Differential Patterns of Allelic Loss in Estrogen Receptor-Positive Infiltrating Lobular and Ductal Breast Cancer
GENES, CHROMOSOMES & CANCER 47:1049–1066 (2008) Differential Patterns of Allelic Loss in Estrogen Receptor-Positive Infiltrating Lobular and Ductal Breast Cancer L. W. M. Loo,1 C. Ton,1,2 Y.-W. Wang,2 D. I. Grove,2 H. Bouzek,1 N. Vartanian,1 M.-G. Lin,1 X. Yuan,1 T. L. Lawton,3 J. R. Daling,2 K. E. Malone,2 C. I. Li,2 L. Hsu,2 and P.L. Porter1,2,3* 1Division of Human Biology,Fred Hutchinson Cancer Research Center,Seattle,WA 2Division of Public Health Sciences,Fred Hutchinson Cancer Research Center,Seattle,WA 3Departmentof Pathology,Universityof Washington,Seattle,WA The two main histological types of infiltrating breast cancer, lobular (ILC) and the more common ductal (IDC) carcinoma are morphologically and clinically distinct. To assess the molecular alterations associated with these breast cancer subtypes, we conducted a whole-genome study of 166 archival estrogen receptor (ER)-positive tumors (89 IDC and 77 ILC) using the Affy- metrix GeneChip® Mapping 10K Array to identify sites of loss of heterozygosity (LOH) that either distinguished, or were shared by, the two phenotypes. We found single nucleotide polymorphisms (SNPs) of high-frequency LOH (>50%) common to both ILC and IDC tumors predominately in 11q, 16q, and 17p. Overall, IDC had a slightly higher frequency of LOH events across the genome than ILC (fractional allelic loss 5 0.186 and 0.156). By comparing the average frequency of LOH by chro- mosomal arm, we found IDC tumors with significantly (P < 0.05) higher frequency of LOH on 3p, 5q, 8p, 9p, 20p, and 20q than ILC tumors. -
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 -
Genetic Analysis of Indel Markers in Three Loci Associated with Parkinson’S Disease
RESEARCH ARTICLE Genetic analysis of indel markers in three loci associated with Parkinson's disease Zhixin Huo1, Xiaoguang Luo2, Xiaoni Zhan1, Qiaohong Chu1, Qin Xu1, Jun Yao1, Hao Pang1* 1 Department of Forensic Genetics and Biology, China Medical University, Shenyang North New Area, Shenyang, P.R., China, 2 Department of Neurology, 1st Affiliated Hospital of China Medical University, Shenyang, P.R., China * [email protected] Abstract a1111111111 a1111111111 The causal mutations and genetic polymorphisms associated with susceptibility to Parkin- a1111111111 son's disease (PD) have been extensively described. To explore the potential contribution a1111111111 a1111111111 of insertion (I)/deletion (D) polymorphisms (indels) to the risk of PD in a Chinese population, we performed genetic analyses of indel loci in ACE, DJ-1, and GIGYF2 genes. Genomic DNA was extracted from venous blood of 348 PD patients and 325 age- and sex-matched controls without neurodegenerative disease. Genotyping of the indel loci was performed by fragment length analysis after PCR and DNA sequencing. Our results showed a statistically OPEN ACCESS significant association for both allele X (alleles without 5) vs. 5 (odds ratio = 1.378, 95% con- Citation: Huo Z, Luo X, Zhan X, Chu Q, Xu Q, Yao fidence interval = 1.112±1.708, P = 0.003) and genotype 5/X+X/X vs. 5/5 (odds ratio = J, et al. (2017) Genetic analysis of indel markers in three loci associated with Parkinson's disease. 1.681, 95% confidence interval = 1.174±2.407, P = 0.004) in the GIGYF2 locus; however, PLoS ONE 12(9): e0184269. https://doi.org/ no significant differences were detected for the ACE and DJ-1 indels. -
Pan-Cancer Analysis of Homozygous Deletions in Primary Tumours Uncovers Rare Tumour Suppressors
Corrected: Author correction; Corrected: Author correction ARTICLE DOI: 10.1038/s41467-017-01355-0 OPEN Pan-cancer analysis of homozygous deletions in primary tumours uncovers rare tumour suppressors Jiqiu Cheng1,2, Jonas Demeulemeester 3,4, David C. Wedge5,6, Hans Kristian M. Vollan2,3, Jason J. Pitt7,8, Hege G. Russnes2,9, Bina P. Pandey1, Gro Nilsen10, Silje Nord2, Graham R. Bignell5, Kevin P. White7,11,12,13, Anne-Lise Børresen-Dale2, Peter J. Campbell5, Vessela N. Kristensen2, Michael R. Stratton5, Ole Christian Lingjærde 10, Yves Moreau1 & Peter Van Loo 3,4 1234567890 Homozygous deletions are rare in cancers and often target tumour suppressor genes. Here, we build a compendium of 2218 primary tumours across 12 human cancer types and sys- tematically screen for homozygous deletions, aiming to identify rare tumour suppressors. Our analysis defines 96 genomic regions recurrently targeted by homozygous deletions. These recurrent homozygous deletions occur either over tumour suppressors or over fragile sites, regions of increased genomic instability. We construct a statistical model that separates fragile sites from regions showing signatures of positive selection for homozygous deletions and identify candidate tumour suppressors within those regions. We find 16 established tumour suppressors and propose 27 candidate tumour suppressors. Several of these genes (including MGMT, RAD17, and USP44) show prior evidence of a tumour suppressive function. Other candidate tumour suppressors, such as MAFTRR, KIAA1551, and IGF2BP2, are novel. Our study demonstrates how rare tumour suppressors can be identified through copy number meta-analysis. 1 Department of Electrical Engineering (ESAT) and iMinds Future Health Department, University of Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium. -
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. -
Open Data for Differential Network Analysis in Glioma
International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. -
"The Genecards Suite: from Gene Data Mining to Disease Genome Sequence Analyses". In: Current Protocols in Bioinformat
The GeneCards Suite: From Gene Data UNIT 1.30 Mining to Disease Genome Sequence Analyses Gil Stelzer,1,5 Naomi Rosen,1,5 Inbar Plaschkes,1,2 Shahar Zimmerman,1 Michal Twik,1 Simon Fishilevich,1 Tsippi Iny Stein,1 Ron Nudel,1 Iris Lieder,2 Yaron Mazor,2 Sergey Kaplan,2 Dvir Dahary,2,4 David Warshawsky,3 Yaron Guan-Golan,3 Asher Kohn,3 Noa Rappaport,1 Marilyn Safran,1 and Doron Lancet1,6 1Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel 2LifeMap Sciences Ltd., Tel Aviv, Israel 3LifeMap Sciences Inc., Marshfield, Massachusetts 4Toldot Genetics Ltd., Hod Hasharon, Israel 5These authors contributed equally to the paper 6Corresponding author GeneCards, the human gene compendium, enables researchers to effectively navigate and inter-relate the wide universe of human genes, diseases, variants, proteins, cells, and biological pathways. Our recently launched Version 4 has a revamped infrastructure facilitating faster data updates, better-targeted data queries, and friendlier user experience. It also provides a stronger foundation for the GeneCards suite of companion databases and analysis tools. Improved data unification includes gene-disease links via MalaCards and merged biological pathways via PathCards, as well as drug information and proteome expression. VarElect, another suite member, is a phenotype prioritizer for next-generation sequencing, leveraging the GeneCards and MalaCards knowledgebase. It au- tomatically infers direct and indirect scored associations between hundreds or even thousands of variant-containing genes and disease phenotype terms. Var- Elect’s capabilities, either independently or within TGex, our comprehensive variant analysis pipeline, help prepare for the challenge of clinical projects that involve thousands of exome/genome NGS analyses. -
Supplemental Table S1. Primers for Sybrgreen Quantitative RT-PCR Assays
Supplemental Table S1. Primers for SYBRGreen quantitative RT-PCR assays. Gene Accession Primer Sequence Length Start Stop Tm GC% GAPDH NM_002046.3 GAPDH F TCCTGTTCGACAGTCAGCCGCA 22 39 60 60.43 59.09 GAPDH R GCGCCCAATACGACCAAATCCGT 23 150 128 60.12 56.52 Exon junction 131/132 (reverse primer) on template NM_002046.3 DNAH6 NM_001370.1 DNAH6 F GGGCCTGGTGCTGCTTTGATGA 22 4690 4711 59.66 59.09% DNAH6 R TAGAGAGCTTTGCCGCTTTGGCG 23 4797 4775 60.06 56.52% Exon junction 4790/4791 (reverse primer) on template NM_001370.1 DNAH7 NM_018897.2 DNAH7 F TGCTGCATGAGCGGGCGATTA 21 9973 9993 59.25 57.14% DNAH7 R AGGAAGCCATGTACAAAGGTTGGCA 25 10073 10049 58.85 48.00% Exon junction 9989/9990 (forward primer) on template NM_018897.2 DNAI1 NM_012144.2 DNAI1 F AACAGATGTGCCTGCAGCTGGG 22 673 694 59.67 59.09 DNAI1 R TCTCGATCCCGGACAGGGTTGT 22 822 801 59.07 59.09 Exon junction 814/815 (reverse primer) on template NM_012144.2 RPGRIP1L NM_015272.2 RPGRIP1L F TCCCAAGGTTTCACAAGAAGGCAGT 25 3118 3142 58.5 48.00% RPGRIP1L R TGCCAAGCTTTGTTCTGCAAGCTGA 25 3238 3214 60.06 48.00% Exon junction 3124/3125 (forward primer) on template NM_015272.2 Supplemental Table S2. Transcripts that differentiate IPF/UIP from controls at 5%FDR Fold- p-value Change Transcript Gene p-value p-value p-value (IPF/UIP (IPF/UIP Cluster ID RefSeq Symbol gene_assignment (Age) (Gender) (Smoking) vs. C) vs. C) NM_001178008 // CBS // cystathionine-beta- 8070632 NM_001178008 CBS synthase // 21q22.3 // 875 /// NM_0000 0.456642 0.314761 0.418564 4.83E-36 -2.23 NM_003013 // SFRP2 // secreted frizzled- 8103254 NM_003013 -
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 -
Download Special Issue
BioMed Research International Novel Bioinformatics Approaches for Analysis of High-Throughput Biological Data Guest Editors: Julia Tzu-Ya Weng, Li-Ching Wu, Wen-Chi Chang, Tzu-Hao Chang, Tatsuya Akutsu, and Tzong-Yi Lee Novel Bioinformatics Approaches for Analysis of High-Throughput Biological Data BioMed Research International Novel Bioinformatics Approaches for Analysis of High-Throughput Biological Data Guest Editors: Julia Tzu-Ya Weng, Li-Ching Wu, Wen-Chi Chang, Tzu-Hao Chang, Tatsuya Akutsu, and Tzong-Yi Lee Copyright © 2014 Hindawi Publishing Corporation. All rights reserved. This is a special issue published in “BioMed Research International.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Contents Novel Bioinformatics Approaches for Analysis of High-Throughput Biological Data,JuliaTzu-YaWeng, Li-Ching Wu, Wen-Chi Chang, Tzu-Hao Chang, Tatsuya Akutsu, and Tzong-Yi Lee Volume2014,ArticleID814092,3pages Evolution of Network Biomarkers from Early to Late Stage Bladder Cancer Samples,Yung-HaoWong, Cheng-Wei Li, and Bor-Sen Chen Volume 2014, Article ID 159078, 23 pages MicroRNA Expression Profiling Altered by Variant Dosage of Radiation Exposure,Kuei-FangLee, Yi-Cheng Chen, Paul Wei-Che Hsu, Ingrid Y. Liu, and Lawrence Shih-Hsin Wu Volume2014,ArticleID456323,10pages EXIA2: Web Server of Accurate and Rapid Protein Catalytic Residue Prediction, Chih-Hao Lu, Chin-Sheng -
Mapping the Mammalian Ribosome Quality Control Complex Interactome Using Proximity Labeling Approaches
M BoC | ARTICLE Mapping the mammalian ribosome quality control complex interactome using proximity labeling approaches Nathan Zuzow, Arit Ghosh, Marilyn Leonard, Jeffrey Liao, Bing Yang, and Eric J. Bennett* Section of Cell and Developmental Biology, Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093 ABSTRACT Previous genetic and biochemical studies from Saccharomyces cerevisiae have Monitoring Editor identified a critical ribosome-associated quality control complex (RQC) that facilitates resolu- Sandra Wolin tion of stalled ribosomal complexes. While components of the mammalian RQC have been National Cancer Institute, NIH examined in vitro, a systematic characterization of RQC protein interactions in mammalian Received: Dec 12, 2017 cells has yet to be described. Here we utilize both proximity-labeling proteomic approaches, Revised: Mar 6, 2018 BioID and APEX, and traditional affinity-based strategies to both identify interacting proteins Accepted: Mar 9, 2018 of mammalian RQC members and putative substrates for the RQC resident E3 ligase, Ltn1. Surprisingly, validation studies revealed that a subset of substrates are ubiquitylated by Ltn1 in a regulatory manner that does not result in subsequent substrate degradation. We demon- strate that Ltn1 catalyzes the regulatory ubiquitylation of ribosomal protein S6 kinase 1 and 2 (RPS6KA1, RPS6KA3). Further, loss of Ltn1 function results in hyperactivation of RSK1/2 signaling without impacting RSK1/2 protein turnover. These results suggest that Ltn1-medi- ated RSK1/2 ubiquitylation is inhibitory and establishes a new role for Ltn1 in regulating mi- togen-activated kinase signaling via regulatory RSK1/2 ubiquitylation. Taken together, our results suggest that mammalian RQC interactions are difficult to observe and may be more transient than the homologous complex in S.