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Cyclin D1/Cyclin-Dependent Kinase 4 Interacts with Filamin a and Affects the Migration and Invasion Potential of Breast Cancer Cells
Published OnlineFirst February 28, 2010; DOI: 10.1158/0008-5472.CAN-08-1108 Tumor and Stem Cell Biology Cancer Research Cyclin D1/Cyclin-Dependent Kinase 4 Interacts with Filamin A and Affects the Migration and Invasion Potential of Breast Cancer Cells Zhijiu Zhong, Wen-Shuz Yeow, Chunhua Zou, Richard Wassell, Chenguang Wang, Richard G. Pestell, Judy N. Quong, and Andrew A. Quong Abstract Cyclin D1 belongs to a family of proteins that regulate progression through the G1-S phase of the cell cycle by binding to cyclin-dependent kinase (cdk)-4 to phosphorylate the retinoblastoma protein and release E2F transcription factors for progression through cell cycle. Several cancers, including breast, colon, and prostate, overexpress the cyclin D1 gene. However, the correlation of cyclin D1 overexpression with E2F target gene regulation or of cdk-dependent cyclin D1 activity with tumor development has not been identified. This suggests that the role of cyclin D1 in oncogenesis may be independent of its function as a cell cycle regulator. One such function is the role of cyclin D1 in cell adhesion and motility. Filamin A (FLNa), a member of the actin-binding filamin protein family, regulates signaling events involved in cell motility and invasion. FLNa has also been associated with a variety of cancers including lung cancer, prostate cancer, melanoma, human bladder cancer, and neuroblastoma. We hypothesized that elevated cyclin D1 facilitates motility in the invasive MDA-MB-231 breast cancer cell line. We show that MDA-MB-231 motility is affected by disturbing cyclin D1 levels or cyclin D1-cdk4/6 kinase activity. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Molecular Subtypes of Diffuse Large B-Cell Lymphoma Arise by Distinct Genetic Pathways
Molecular subtypes of diffuse large B-cell lymphoma arise by distinct genetic pathways Georg Lenza, George W. Wrightb, N. C. Tolga Emrea, Holger Kohlhammera, Sandeep S. Davea, R. Eric Davisa, Shannon Cartya, Lloyd T. Lama, A. L. Shaffera, Wenming Xiaoc, John Powellc, Andreas Rosenwaldd, German Ottd,e, Hans Konrad Muller-Hermelinkd, Randy D. Gascoynef, Joseph M. Connorsf, Elias Campog, Elaine S. Jaffeh, Jan Delabiei, Erlend B. Smelandj,k, Lisa M. Rimszal, Richard I. Fisherm,n, Dennis D. Weisenburgero, Wing C. Chano, and Louis M. Staudta,q aMetabolism Branch, bBiometric Research Branch, cCenter for Information Technology, and hLaboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892; dDepartment of Pathology, University of Wu¨rzburg, 97080 Wu¨rzburg, Germany; eDepartment of Clinical Pathology, Robert-Bosch-Krankenhaus, 70376 Stuttgart, Germany; fBritish Columbia Cancer Agency, Vancouver, British Columbia, Canada V5Z 4E6; gHospital Clinic, University of Barcelona, 08036 Barcelona, Spain; iPathology Clinic and jInstitute for Cancer Research, Rikshospitalet Hospital, Oslo, Norway; kCentre for Cancer Biomedicine, Faculty Division the Norwegian Radium Hospital, N-0310, Oslo, Norway; lDepartment of Pathology, University of Arizona, Tucson, AZ 85724; mSouthwest Oncology Group, 24 Frank Lloyd Wright Drive, Ann Arbor, MI 48106 ; nJames P. Wilmot Cancer Center, University of Rochester, Rochester, NY 14642; and oDepartments of Pathology and Microbiology, University of Nebraska, Omaha, NE 68198 Edited by Ira -
Identification of C3 As a Therapeutic Target for Diabetic Nephropathy By
www.nature.com/scientificreports OPEN Identifcation of C3 as a therapeutic target for diabetic nephropathy by bioinformatics analysis ShuMei Tang, XiuFen Wang, TianCi Deng, HuiPeng Ge & XiangCheng Xiao* The pathogenesis of diabetic nephropathy is not completely understood, and the efects of existing treatments are not satisfactory. Various public platforms already contain extensive data for deeper bioinformatics analysis. From the GSE30529 dataset based on diabetic nephropathy tubular samples, we identifed 345 genes through diferential expression analysis and weighted gene coexpression correlation network analysis. GO annotations mainly included neutrophil activation, regulation of immune efector process, positive regulation of cytokine production and neutrophil-mediated immunity. KEGG pathways mostly included phagosome, complement and coagulation cascades, cell adhesion molecules and the AGE-RAGE signalling pathway in diabetic complications. Additional datasets were analysed to understand the mechanisms of diferential gene expression from an epigenetic perspective. Diferentially expressed miRNAs were obtained to construct a miRNA-mRNA network from the miRNA profles in the GSE57674 dataset. The miR-1237-3p/SH2B3, miR-1238-5p/ ZNF652 and miR-766-3p/TGFBI axes may be involved in diabetic nephropathy. The methylation levels of the 345 genes were also tested based on the gene methylation profles of the GSE121820 dataset. The top 20 hub genes in the PPI network were discerned using the CytoHubba tool. Correlation analysis with GFR showed that SYK, CXCL1, LYN, VWF, ANXA1, C3, HLA-E, RHOA, SERPING1, EGF and KNG1 may be involved in diabetic nephropathy. Eight small molecule compounds were identifed as potential therapeutic drugs using Connectivity Map. It is estimated that a total of 451 million people sufered from diabetes by 2017, and the number is speculated to be 693 million by 2045 1. -
Early Growth Response 1 Regulates Hematopoietic Support and Proliferation in Human Primary Bone Marrow Stromal Cells
Hematopoiesis SUPPLEMENTARY APPENDIX Early growth response 1 regulates hematopoietic support and proliferation in human primary bone marrow stromal cells Hongzhe Li, 1,2 Hooi-Ching Lim, 1,2 Dimitra Zacharaki, 1,2 Xiaojie Xian, 2,3 Keane J.G. Kenswil, 4 Sandro Bräunig, 1,2 Marc H.G.P. Raaijmakers, 4 Niels-Bjarne Woods, 2,3 Jenny Hansson, 1,2 and Stefan Scheding 1,2,5 1Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden; 2Lund Stem Cell Center, Depart - ment of Laboratory Medicine, Lund University, Lund, Sweden; 3Division of Molecular Medicine and Gene Therapy, Department of Labora - tory Medicine, Lund University, Lund, Sweden; 4Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands and 5Department of Hematology, Skåne University Hospital Lund, Skåne, Sweden ©2020 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol. 2019.216648 Received: January 14, 2019. Accepted: July 19, 2019. Pre-published: August 1, 2019. Correspondence: STEFAN SCHEDING - [email protected] Li et al.: Supplemental data 1. Supplemental Materials and Methods BM-MNC isolation Bone marrow mononuclear cells (BM-MNC) from BM aspiration samples were isolated by density gradient centrifugation (LSM 1077 Lymphocyte, PAA, Pasching, Austria) either with or without prior incubation with RosetteSep Human Mesenchymal Stem Cell Enrichment Cocktail (STEMCELL Technologies, Vancouver, Canada) for lineage depletion (CD3, CD14, CD19, CD38, CD66b, glycophorin A). BM-MNCs from fetal long bones and adult hip bones were isolated as reported previously 1 by gently crushing bones (femora, tibiae, fibulae, humeri, radii and ulna) in PBS+0.5% FCS subsequent passing of the cell suspension through a 40-µm filter. -
SUPPLEMENTARY DATA Supplementary Table 1. Top Ten
SUPPLEMENTARY DATA Supplementary Table 1. Top ten most highly expressed protein-coding genes in the EndoC-βH1 cell line. Expression levels provided for non-mitochondrial genes in EndoC-βH1 and the corresponding expression levels in sorted primary human β-cells (1). Ensembl gene ID Gene Name EndoC-βH1 [RPKM] Primary β cells [RPKM] ENSG00000254647.2 INS 8012.452 166347.111 ENSG00000087086.9 FTL 3090.7454 2066.464 ENSG00000100604.8 CHGA 2853.107 1113.162 ENSG00000099194.5 SCD 1411.631 238.714 ENSG00000118271.5 TTR 1312.8928 1488.996 ENSG00000184009.5 ACTG1 1108.0277 839.681 ENSG00000124172.5 ATP5E 863.42334 254.779 ENSG00000156508.13 EEF1A1 831.17316 637.281 ENSG00000112972.10 HMGCS1 719.7504 22.104 ENSG00000167552.9 TUBA1A 689.1415 511.699 ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0361/-/DC1 SUPPLEMENTARY DATA Supplementary Table 2. List of genes selected for inclusion in the primary screen. Expression levels in EndoC-βH1 and sorted primary human β-cells are shown for all genes targeted for silencing in the primary screen, ordered by locus association (1). For gene selection, the following criteria were applied: we first considered (1) all protein-coding genes within 1 Mb of a type 2 diabetes association signal that (2) had non-zero expression (RPKM > 0) in both EndoC-βH1 and primary human β-cells. Previous studies have shown cis-eQTLs to form a relatively tight, symmetrical distribution around the target-gene transcription start site, and a 1 Mb cut-off is thus likely to capture most effector transcripts subject to cis regulation (2-5). -
"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. -
SEC22A CRISPR/Cas9 KO Plasmid (M): Sc-435547
SANTA CRUZ BIOTECHNOLOGY, INC. SEC22A CRISPR/Cas9 KO Plasmid (m): sc-435547 BACKGROUND APPLICATIONS The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and SEC22A CRISPR/Cas9 KO Plasmid (m) is recommended for the disruption of CRISPR-associated protein (Cas9) system is an adaptive immune response gene expression in mouse cells. defense mechanism used by archea and bacteria for the degradation of foreign genetic material (4,6). This mechanism can be repurposed for other 20 nt non-coding RNA sequence: guides Cas9 functions, including genomic engineering for mammalian systems, such as to a specific target location in the genomic DNA gene knockout (KO) (1,2,3,5). CRISPR/Cas9 KO Plasmid products enable the U6 promoter: drives gRNA scaffold: helps Cas9 identification and cleavage of specific genes by utilizing guide RNA (gRNA) expression of gRNA bind to target DNA sequences derived from the Genome-scale CRISPR Knock-Out (GeCKO) v2 library developed in the Zhang Laboratory at the Broad Institute (3,5). Termination signal Green Fluorescent Protein: to visually REFERENCES verify transfection CRISPR/Cas9 Knockout Plasmid CBh (chicken β-Actin 1. Cong, L., et al. 2013. Multiplex genome engineering using CRISPR/Cas hybrid) promoter: drives expression of Cas9 systems. Science 339: 819-823. 2A peptide: allows production of both Cas9 and GFP from the 2. Mali, P., et al. 2013. RNA-guided human genome engineering via Cas9. same CBh promoter Science 339: 823-826. Nuclear localization signal 3. Ran, F.A., et al. 2013. Genome engineering using the CRISPR-Cas9 system. Nuclear localization signal SpCas9 ribonuclease Nat. Protoc. 8: 2281-2308. -
ADHD) Gene Networks in Children of Both African American and European American Ancestry
G C A T T A C G G C A T genes Article Rare Recurrent Variants in Noncoding Regions Impact Attention-Deficit Hyperactivity Disorder (ADHD) Gene Networks in Children of both African American and European American Ancestry Yichuan Liu 1 , Xiao Chang 1, Hui-Qi Qu 1 , Lifeng Tian 1 , Joseph Glessner 1, Jingchun Qu 1, Dong Li 1, Haijun Qiu 1, Patrick Sleiman 1,2 and Hakon Hakonarson 1,2,3,* 1 Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; [email protected] (Y.L.); [email protected] (X.C.); [email protected] (H.-Q.Q.); [email protected] (L.T.); [email protected] (J.G.); [email protected] (J.Q.); [email protected] (D.L.); [email protected] (H.Q.); [email protected] (P.S.) 2 Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA 3 Department of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA * Correspondence: [email protected]; Tel.: +1-267-426-0088 Abstract: Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with poorly understood molecular mechanisms that results in significant impairment in children. In this study, we sought to assess the role of rare recurrent variants in non-European populations and outside of coding regions. We generated whole genome sequence (WGS) data on 875 individuals, Citation: Liu, Y.; Chang, X.; Qu, including 205 ADHD cases and 670 non-ADHD controls. The cases included 116 African Americans H.-Q.; Tian, L.; Glessner, J.; Qu, J.; Li, (AA) and 89 European Americans (EA), and the controls included 408 AA and 262 EA. -
Ejhg2009157.Pdf
European Journal of Human Genetics (2010) 18, 342–347 & 2010 Macmillan Publishers Limited All rights reserved 1018-4813/10 $32.00 www.nature.com/ejhg ARTICLE Fine mapping and association studies of a high-density lipoprotein cholesterol linkage region on chromosome 16 in French-Canadian subjects Zari Dastani1,2,Pa¨ivi Pajukanta3, Michel Marcil1, Nicholas Rudzicz4, Isabelle Ruel1, Swneke D Bailey2, Jenny C Lee3, Mathieu Lemire5,9, Janet Faith5, Jill Platko6,10, John Rioux6,11, Thomas J Hudson2,5,7,9, Daniel Gaudet8, James C Engert*,2,7, Jacques Genest1,2,7 Low levels of high-density lipoprotein cholesterol (HDL-C) are an independent risk factor for cardiovascular disease. To identify novel genetic variants that contribute to HDL-C, we performed genome-wide scans and quantitative association studies in two study samples: a Quebec-wide study consisting of 11 multigenerational families and a study of 61 families from the Saguenay– Lac St-Jean (SLSJ) region of Quebec. The heritability of HDL-C in these study samples was 0.73 and 0.49, respectively. Variance components linkage methods identified a LOD score of 2.61 at 98 cM near the marker D16S515 in Quebec-wide families and an LOD score of 2.96 at 86 cM near the marker D16S2624 in SLSJ families. In the Quebec-wide sample, four families showed segregation over a 25.5-cM (18 Mb) region, which was further reduced to 6.6 Mb with additional markers. The coding regions of all genes within this region were sequenced. A missense variant in CHST6 segregated in four families and, with additional families, we observed a P value of 0.015 for this variant. -
Cell Culture-Based Profiling Across Mammals Reveals DNA Repair And
1 Cell culture-based profiling across mammals reveals 2 DNA repair and metabolism as determinants of 3 species longevity 4 5 Siming Ma1, Akhil Upneja1, Andrzej Galecki2,3, Yi-Miau Tsai2, Charles F. Burant4, Sasha 6 Raskind4, Quanwei Zhang5, Zhengdong D. Zhang5, Andrei Seluanov6, Vera Gorbunova6, 7 Clary B. Clish7, Richard A. Miller2, Vadim N. Gladyshev1* 8 9 1 Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard 10 Medical School, Boston, MA, 02115, USA 11 2 Department of Pathology and Geriatrics Center, University of Michigan Medical School, 12 Ann Arbor, MI 48109, USA 13 3 Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, 14 MI 48109, USA 15 4 Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 16 48109, USA 17 5 Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10128, USA 18 6 Department of Biology, University of Rochester, Rochester, NY 14627, USA 19 7 Broad Institute, Cambridge, MA 02142, US 20 21 * corresponding author: Vadim N. Gladyshev ([email protected]) 22 ABSTRACT 23 Mammalian lifespan differs by >100-fold, but the mechanisms associated with such 24 longevity differences are not understood. Here, we conducted a study on primary skin 25 fibroblasts isolated from 16 species of mammals and maintained under identical cell culture 26 conditions. We developed a pipeline for obtaining species-specific ortholog sequences, 27 profiled gene expression by RNA-seq and small molecules by metabolite profiling, and 28 identified genes and metabolites correlating with species longevity. Cells from longer-lived 29 species up-regulated genes involved in DNA repair and glucose metabolism, down-regulated 30 proteolysis and protein transport, and showed high levels of amino acids but low levels of 31 lysophosphatidylcholine and lysophosphatidylethanolamine. -
Differential Co-Expression Analysis of Obesity-Associated Networks in Human Subcutaneous Adipose Tissue
Europe PMC Funders Group Author Manuscript Int J Obes (Lond). Author manuscript; available in PMC 2012 July 01. Published in final edited form as: Int J Obes (Lond). 2012 January ; 36(1): 137–147. doi:10.1038/ijo.2011.22. Europe PMC Funders Author Manuscripts Differential co-expression analysis of obesity-associated networks in human subcutaneous adipose tissue A.J. Walley1,*, P. Jacobson2,*, M. Falchi1,*, L. Bottolo1,3, J.C. Andersson1,2, E. Petretto3,4, A. Bonnefond5, E. Vaillant5, C. Lecoeur5, V. Vatin5, M. Jernas2, D. Balding1,6, M. Petteni1, Y.S. Park1, T. Aitman4, S. Richardson3, L. Sjostrom2, L. M. S. Carlsson2,*, and P. Froguel1,5,*,† 1 Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK 2 Department of Molecular and Clinical Medicine, The Sahlgrenska Academy, Gothenburg University, SE-413 07 Gothenburg, Sweden 3 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Marys Hospital, 161 Norfolk Place, London, UK 4 MRC Clinical Sciences Centre, Division of Clinical Sciences, Imperial College London, Commonwealth Building, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK 5CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France. 6Institute of Genetics, University College London, Kathleen Lonsdale Building, 5 Gower Place, London, WC1 E6B, UK Abstract Europe PMC Funders Author Manuscripts Objective—To use a unique obesity-discordant sib-pair study design to combine differential expression analysis, expression quantitative trait loci (eQTLs) mapping, and a co-expression regulatory network approach in subcutaneous human adipose tissue to identify genes relevant to the obese state.