Differentially Over-Expressed Genes with a 2-Fold Higher Differe
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In Silico Analysis of Regulatory Elements of the Vitamin D Receptor
Open Access Baghdad Science Journal P-ISSN: 2078-8665 2020, 17(2):463-470 E-ISSN: 2411-7986 DOI: http://dx.doi.org/10.21123/bsj.2020.17.2.0463 In Silico Analysis of Regulatory Elements of the Vitamin D Receptor Shirin Farivar 1 Roya Amirinejad 2 Bahar Naghavi gargari 3 Seyedeh Batool Hassani 4 Zeinab Shirvani-Farsani 1* Received 26/2/2019, Accepted 5/1/2020, Published 1/6/2020 This work is licensed under a Creative Commons Attribution 4.0 International License. Abstract Vitamin D receptor (VDR) is a nuclear transcription factor that controls gene expression. Its impaired expression was found to be related to different diseases. VDR also acts as a regulator of different pathways including differentiation, inflammation, calcium and phosphate absorption, etc. but there is no sufficient knowledge about the regulation of the gene itself. Therefore, a better understanding of the genetic and epigenetic factors regulating the VDR may facilitate the improvement of strategies for the prevention and treatment of diseases associated with dysregulation of VDR. In the present investigation, a set of databases and methods were used to identify putative functional elements in the VDR locus. Histone modifications, CpG Islands, epigenetic marks at VDR locus were indicated. In addition, repeated sequences, enhancers, insulators, transcription factor binding sites and targets of the VDR gene, as well as protein- protein interactions with bioinformatics tools, were reported. Some of these genetic elements had overlapped with CpG Islands. These results revealed important new insight into the molecular mechanisms of the VDR gene regulation in human cells and tissues. Key words: CpG Islands, Epigenetics, In silico analysis, Regulation elements, VDR Introduction: Vitamin D is a steroid hormone that multiple sclerosis (10), Rheumatoid Arthritis and balances calcium and phosphate levels and is Systemic Lupus Erythematosus (11), cardiovascular crucial for bone structure. -
The Proximal Signaling Network of the BCR-ABL1 Oncogene Shows a Modular Organization
Oncogene (2010) 29, 5895–5910 & 2010 Macmillan Publishers Limited All rights reserved 0950-9232/10 www.nature.com/onc ORIGINAL ARTICLE The proximal signaling network of the BCR-ABL1 oncogene shows a modular organization B Titz, T Low, E Komisopoulou, SS Chen, L Rubbi and TG Graeber Crump Institute for Molecular Imaging, Institute for Molecular Medicine, Jonsson Comprehensive Cancer Center, California NanoSystems Institute, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA, USA BCR-ABL1 is a fusion tyrosine kinase, which causes signaling effects of BCR-ABL1 toward leukemic multiple types of leukemia. We used an integrated transformation. proteomic approach that includes label-free quantitative Oncogene (2010) 29, 5895–5910; doi:10.1038/onc.2010.331; protein complex and phosphorylation profiling by mass published online 9 August 2010 spectrometry to systematically characterize the proximal signaling network of this oncogenic kinase. The proximal Keywords: adaptor protein; BCR-ABL1; phospho- BCR-ABL1 signaling network shows a modular and complex; quantitative mass spectrometry; signaling layered organization with an inner core of three leukemia network; systems biology transformation-relevant adaptor protein complexes (Grb2/Gab2/Shc1 complex, CrkI complex and Dok1/ Dok2 complex). We introduced an ‘interaction direction- ality’ analysis, which annotates static protein networks Introduction with information on the directionality of phosphorylation- dependent interactions. In this analysis, the observed BCR-ABL1 is a constitutively active oncogenic fusion network structure was consistent with a step-wise kinase that arises through a chromosomal translocation phosphorylation-dependent assembly of the Grb2/Gab2/ and causes multiple types of leukemia. It is found in Shc1 and the Dok1/Dok2 complexes on the BCR-ABL1 many cases (B25%) of adult acute lymphoblastic core. -
Mutation-Specific and Common Phosphotyrosine Signatures of KRAS G12D and G13D Alleles Anticipated Graduation August 1St, 2018
MUTATION-SPECIFIC AND COMMON PHOSPHOTYROSINE SIGNATURES OF KRAS G12D AND G13D ALLELES by Raiha Tahir A dissertation submitted to The Johns Hopkins University in conformity with the requirement of the degree of Doctor of Philosophy Baltimore, MD August 2018 © 2018 Raiha Tahir All Rights Reserved ABSTRACT KRAS is one of the most frequently mutated genes across all cancer subtypes. Two of the most frequent oncogenic KRAS mutations observed in patients result in glycine to aspartic acid substitution at either codon 12 (G12D) or 13 (G13D). Although the biochemical differences between these two predominant mutations are not fully understood, distinct clinical features of the resulting tumors suggest involvement of disparate signaling mechanisms. When we compared the global phosphotyrosine proteomic profiles of isogenic colorectal cancer cell lines bearing either G12D or G13D KRAS mutations, we observed both shared as well as unique signaling events induced by the two KRAS mutations. Remarkably, while the G12D mutation led to an increase in membrane proximal and adherens junction signaling, the G13D mutation led to activation of signaling molecules such as non-receptor tyrosine kinases, MAPK kinases and regulators of metabolic processes. The importance of one of the cell surface molecules, MPZL1, which found to be hyperphosphorylated in G12D cells, was confirmed by cellular assays as its knockdown led to a decrease in proliferation of G12D but not G13D expressing cells. Overall, our study reveals important signaling differences across two common KRAS mutations and highlights the utility of our approach to systematically dissect the subtle differences between related oncogenic mutants and potentially lead to individualized treatments. -
Defining Functional Interactions During Biogenesis of Epithelial Junctions
ARTICLE Received 11 Dec 2015 | Accepted 13 Oct 2016 | Published 6 Dec 2016 | Updated 5 Jan 2017 DOI: 10.1038/ncomms13542 OPEN Defining functional interactions during biogenesis of epithelial junctions J.C. Erasmus1,*, S. Bruche1,*,w, L. Pizarro1,2,*, N. Maimari1,3,*, T. Poggioli1,w, C. Tomlinson4,J.Lees5, I. Zalivina1,w, A. Wheeler1,w, A. Alberts6, A. Russo2 & V.M.M. Braga1 In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases. 1 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 2 Computing Department, Imperial College London, London SW7 2AZ, UK. 3 Bioengineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK. 4 Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. -
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. -
Prediction of Meiosis-Essential Genes Based Upon the Dynamic Proteomes Responsive to Spermatogenesis
bioRxiv preprint doi: https://doi.org/10.1101/2020.02.05.936435; this version posted February 6, 2020. 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. Prediction of meiosis-essential genes based upon the dynamic proteomes responsive to spermatogenesis Kailun Fang1,2,3,8, Qidan Li3,4,5,8, Yu Wei2,3,8, Jiaqi Shen6,8, Wenhui Guo3,4,5,7,8, Changyang Zhou2,3,8, Ruoxi Wu1, Wenqin Ying2, Lu Yu1,2, Jin Zi5, Yuxing Zhang3,4,5, Hui Yang2,3,9*, Siqi Liu3,4,5,9*, Charlie Degui Chen1,3,9* 1. State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China. 2. State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Institute of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. 3. University of the Chinese Academy of Sciences, Beijing 100049, China. 4. CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China. 5. BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China. 6. United World College Changshu China, Jiangsu 215500, China. 7. Malvern College Qingdao, Shandong, 266109, China 8. -
CD93 and Dystroglycan Cooperation in Human Endothelial Cell Adhesion and Migration
www.impactjournals.com/oncotarget/ Oncotarget, Vol. 7, No. 9 CD93 and dystroglycan cooperation in human endothelial cell adhesion and migration Federico Galvagni1,*, Federica Nardi1,*, Marco Maida1, Giulia Bernardini1, Silvia Vannuccini2, Felice Petraglia2, Annalisa Santucci1, Maurizio Orlandini1 1 Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 2-53100 Siena, Italy 2 Department of Molecular and Developmental Medicine, Obstetrics and Gynecology, University of Siena, 53100 Siena, Italy *These authors contributed equally to this work Correspondence to: Maurizio Orlandini, e-mail: [email protected] Keywords: angiogenesis, signal transduction, C1qRp, Src, Cbl Received: June 29, 2015 Accepted: January 22, 2016 Published: February 02, 2016 ABSTRACT CD93 is a transmembrane glycoprotein predominantly expressed in endothelial cells. Although CD93 displays proangiogenic activity, its molecular function in angiogenesis still needs to be clarified. To get molecular insight into the biological role of CD93 in the endothelium, we performed proteomic analyses to examine changes in the protein profile of endothelial cells after CD93 silencing. Among differentially expressed proteins, we identified dystroglycan, a laminin-binding protein involved in angiogenesis, whose expression is increased in vascular endothelial cells within malignant tumors. Using immunofluorescence, FRET, and proximity ligation analyses, we observed a close interaction between CD93 and β-dystroglycan. Moreover, silencing experiments showed that CD93 and dystroglycan promoted endothelial cell migration and organization into capillary-like structures. CD93 proved to be phosphorylated on tyrosine 628 and 644 following cell adhesion on laminin through dystroglycan. This phosphorylation was shown to be necessary for a proper endothelial migratory phenotype. Moreover, we showed that during cell spreading phosphorylated CD93 recruited the signaling protein Cbl, which in turn was phosphorylated on tyrosine 774. -
SKIP Counteracts P53-Mediated Apoptosis Via Selective Regulation of P21cip1 Mrna Splicing
Downloaded from genesdev.cshlp.org on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press SKIP counteracts p53-mediated apoptosis via selective regulation of p21Cip1 mRNA splicing Yupeng Chen, Lirong Zhang, and Katherine A. Jones1 Regulatory Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA The Ski-interacting protein SKIP/SNW1 functions as both a splicing factor and a transcriptional coactivator for induced genes. We showed previously that transcription elongation factors such as SKIP are dispensable in cells subjected to DNA damage stress. However, we report here that SKIP is critical for both basal and stress-induced expression of the cell cycle arrest factor p21Cip1. RNAi chromatin immunoprecipitation (RNAi-ChIP) and RNA immunoprecipitation (RNA-IP) experiments indicate that SKIP is not required for transcription elongation of the gene under stress, but instead is critical for splicing and p21Cip1 protein expression. SKIP interacts with the 39 splice site recognition factor U2AF65 and recruits it to the p21Cip1 gene and mRNA. Remarkably, SKIP is not required for splicing or loading of U2AF65 at other investigated p53-induced targets, including the proapoptotic gene PUMA. Consequently, depletion of SKIP induces a rapid down-regulation of p21Cip1 and predisposes cells to undergo p53-mediated apoptosis, which is greatly enhanced by chemotherapeutic DNA damage agents. ChIP experiments reveal that SKIP is recruited to the p21Cip1, and not PUMA, gene promoters, indicating that p21Cip1 gene-specific splicing is predominantly cotranscriptional. The SKIP-associated factors DHX8 and Prp19 are also selectively required for p21Cip1 expression under stress. Together, these studies define a new step that controls cancer cell apoptosis. -
Essential Genes and Their Role in Autism Spectrum Disorder
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2017 Essential Genes And Their Role In Autism Spectrum Disorder Xiao Ji University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, and the Genetics Commons Recommended Citation Ji, Xiao, "Essential Genes And Their Role In Autism Spectrum Disorder" (2017). Publicly Accessible Penn Dissertations. 2369. https://repository.upenn.edu/edissertations/2369 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2369 For more information, please contact [email protected]. Essential Genes And Their Role In Autism Spectrum Disorder Abstract Essential genes (EGs) play central roles in fundamental cellular processes and are required for the survival of an organism. EGs are enriched for human disease genes and are under strong purifying selection. This intolerance to deleterious mutations, commonly observed haploinsufficiency and the importance of EGs in pre- and postnatal development suggests a possible cumulative effect of deleterious variants in EGs on complex neurodevelopmental disorders. Autism spectrum disorder (ASD) is a heterogeneous, highly heritable neurodevelopmental syndrome characterized by impaired social interaction, communication and repetitive behavior. More and more genetic evidence points to a polygenic model of ASD and it is estimated that hundreds of genes contribute to ASD. The central question addressed in this dissertation is whether genes with a strong effect on survival and fitness (i.e. EGs) play a specific oler in ASD risk. I compiled a comprehensive catalog of 3,915 mammalian EGs by combining human orthologs of lethal genes in knockout mice and genes responsible for cell-based essentiality. -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
NCK1 Antibody (Monoclonal) (M01) Mouse Monoclonal Antibody Raised Against a Partial Recombinant NCK1
10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 NCK1 Antibody (monoclonal) (M01) Mouse monoclonal antibody raised against a partial recombinant NCK1. Catalog # AT2980a Specification NCK1 Antibody (monoclonal) (M01) - Product Information Application WB, E Primary Accession P16333 Other Accession BC006403 Reactivity Human Host mouse Clonality Monoclonal Isotype IgG1 Kappa Calculated MW 42864 NCK1 Antibody (monoclonal) (M01) - Additional Information Antibody Reactive Against Recombinant Protein.Western Blot detection against Gene ID 4690 Immunogen (37.73 KDa) . Other Names Cytoplasmic protein NCK1, NCK adaptor protein 1, Nck-1, SH2/SH3 adaptor protein NCK-alpha, NCK1, NCK Target/Specificity NCK1 (AAH06403, 185 a.a. ~ 294 a.a) partial recombinant protein with GST tag. MW of the GST tag alone is 26 KDa. Dilution WB~~1:500~1000 Format Clear, colorless solution in phosphate NCK1 monoclonal antibody (M01), clone 1A1 buffered saline, pH 7.2 . Western Blot analysis of NCK1 expression in Hela S3 NE ( (Cat # AT2980a ) Storage Store at -20°C or lower. Aliquot to avoid repeated freezing and thawing. Precautions NCK1 Antibody (monoclonal) (M01) is for research use only and not for use in diagnostic or therapeutic procedures. NCK1 Antibody (monoclonal) (M01) - Protocols Page 1/3 10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 Provided below are standard protocols that you may find useful for product applications. • Western Blot • Blocking Peptides • Dot Blot • Immunohistochemistry • Immunofluorescence • Immunoprecipitation • Flow Cytomety • Cell Culture Western Blot analysis of NCK1 expression in transfected 293T cell line by NCK1 monoclonal antibody (M01), clone 1A1. -
Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients with Stable Coronary Heart Disease
Supplementary Online Content Ganz P, Heidecker B, Hveem K, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. doi: 10.1001/jama.2016.5951 eTable 1. List of 1130 Proteins Measured by Somalogic’s Modified Aptamer-Based Proteomic Assay eTable 2. Coefficients for Weibull Recalibration Model Applied to 9-Protein Model eFigure 1. Median Protein Levels in Derivation and Validation Cohort eTable 3. Coefficients for the Recalibration Model Applied to Refit Framingham eFigure 2. Calibration Plots for the Refit Framingham Model eTable 4. List of 200 Proteins Associated With the Risk of MI, Stroke, Heart Failure, and Death eFigure 3. Hazard Ratios of Lasso Selected Proteins for Primary End Point of MI, Stroke, Heart Failure, and Death eFigure 4. 9-Protein Prognostic Model Hazard Ratios Adjusted for Framingham Variables eFigure 5. 9-Protein Risk Scores by Event Type This supplementary material has been provided by the authors to give readers additional information about their work. Downloaded From: https://jamanetwork.com/ on 10/02/2021 Supplemental Material Table of Contents 1 Study Design and Data Processing ......................................................................................................... 3 2 Table of 1130 Proteins Measured .......................................................................................................... 4 3 Variable Selection and Statistical Modeling ........................................................................................