Integration of Cistromic and Transcriptomic Analyses Identifies Nphs2, Mafb, and Magi2 As Wilms' Tumor 1 Target Genes in Podoc
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A Genetic Screening Identifies a Component of the SWI/SNF Complex, Arid1b As a Senescence Regulator
A genetic screening identifies a component of the SWI/SNF complex, Arid1b as a senescence regulator Sadaf Khan A thesis submitted to Imperial College London for the degree of Doctor in Philosophy MRC Clinical Sciences Centre Imperial College London, School of Medicine July 2013 Statement of originality All experiments included in this thesis were performed by myself unless otherwise stated. Copyright Declaration The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives license. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the license terms of this work. 2 Abstract Senescence is an important tumour suppressor mechanism, which prevents the proliferation of stressed or damaged cells. The use of RNA interference to identify genes with a role in senescence is an important tool in the discovery of novel cancer genes. In this work, a protocol was established for conducting bypass of senescence screenings, using shRNA libraries together with next-generation sequencing. Using this approach, the SWI/SNF subunit Arid1b was identified as a regulator of cellular lifespan in MEFs. SWI/SNF is a large multi-subunit complex that remodels chromatin. Mutations in SWI/SNF proteins are frequently associated with cancer, suggesting that SWI/SNF components are tumour suppressors. Here the role of ARID1B during senescence was investigated. Depletion of ARID1B extends the proliferative capacity of primary mouse and human fibroblasts. -
Protein Interaction Network of Alternatively Spliced Isoforms from Brain Links Genetic Risk Factors for Autism
ARTICLE Received 24 Aug 2013 | Accepted 14 Mar 2014 | Published 11 Apr 2014 DOI: 10.1038/ncomms4650 OPEN Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism Roser Corominas1,*, Xinping Yang2,3,*, Guan Ning Lin1,*, Shuli Kang1,*, Yun Shen2,3, Lila Ghamsari2,3,w, Martin Broly2,3, Maria Rodriguez2,3, Stanley Tam2,3, Shelly A. Trigg2,3,w, Changyu Fan2,3, Song Yi2,3, Murat Tasan4, Irma Lemmens5, Xingyan Kuang6, Nan Zhao6, Dheeraj Malhotra7, Jacob J. Michaelson7,w, Vladimir Vacic8, Michael A. Calderwood2,3, Frederick P. Roth2,3,4, Jan Tavernier5, Steve Horvath9, Kourosh Salehi-Ashtiani2,3,w, Dmitry Korkin6, Jonathan Sebat7, David E. Hill2,3, Tong Hao2,3, Marc Vidal2,3 & Lilia M. Iakoucheva1 Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases. -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Genetic Basis of Simple and Complex Traits with Relevance to Avian Evolution
Genetic basis of simple and complex traits with relevance to avian evolution Małgorzata Anna Gazda Doctoral Program in Biodiversity, Genetics and Evolution D Faculdade de Ciências da Universidade do Porto 2019 Supervisor Miguel Jorge Pinto Carneiro, Auxiliary Researcher, CIBIO/InBIO, Laboratório Associado, Universidade do Porto Co-supervisor Ricardo Lopes, CIBIO/InBIO Leif Andersson, Uppsala University FCUP Genetic basis of avian traits Nota Previa Na elaboração desta tese, e nos termos do número 2 do Artigo 4º do Regulamento Geral dos Terceiros Ciclos de Estudos da Universidade do Porto e do Artigo 31º do D.L.74/2006, de 24 de Março, com a nova redação introduzida pelo D.L. 230/2009, de 14 de Setembro, foi efetuado o aproveitamento total de um conjunto coerente de trabalhos de investigação já publicados ou submetidos para publicação em revistas internacionais indexadas e com arbitragem científica, os quais integram alguns dos capítulos da presente tese. Tendo em conta que os referidos trabalhos foram realizados com a colaboração de outros autores, o candidato esclarece que, em todos eles, participou ativamente na sua conceção, na obtenção, análise e discussão de resultados, bem como na elaboração da sua forma publicada. Este trabalho foi apoiado pela Fundação para a Ciência e Tecnologia (FCT) através da atribuição de uma bolsa de doutoramento (PD/BD/114042/2015) no âmbito do programa doutoral em Biodiversidade, Genética e Evolução (BIODIV). 2 FCUP Genetic basis of avian traits Acknowledgements Firstly, I would like to thank to my all supervisors Miguel Carneiro, Ricardo Lopes and Leif Andersson, for the demanding task of supervising myself last four years. -
The Role and Mechanisms of Action of Micrornas in Cancer Drug Resistance Wengong Si1,2,3, Jiaying Shen4, Huilin Zheng1,5 and Weimin Fan1,6*
Si et al. Clinical Epigenetics (2019) 11:25 https://doi.org/10.1186/s13148-018-0587-8 REVIEW Open Access The role and mechanisms of action of microRNAs in cancer drug resistance Wengong Si1,2,3, Jiaying Shen4, Huilin Zheng1,5 and Weimin Fan1,6* Abstract MicroRNAs (miRNAs) are small non-coding RNAs with a length of about 19–25 nt, which can regulate various target genes and are thus involved in the regulation of a variety of biological and pathological processes, including the formation and development of cancer. Drug resistance in cancer chemotherapy is one of the main obstacles to curing this malignant disease. Statistical data indicate that over 90% of the mortality of patients with cancer is related to drug resistance. Drug resistance of cancer chemotherapy can be caused by many mechanisms, such as decreased antitumor drug uptake, modified drug targets, altered cell cycle checkpoints, or increased DNA damage repair, among others. In recent years, many studies have shown that miRNAs are involved in the drug resistance of tumor cells by targeting drug-resistance-related genes or influencing genes related to cell proliferation, cell cycle, and apoptosis. A single miRNA often targets a number of genes, and its regulatory effect is tissue-specific. In this review, we emphasize the miRNAs that are involved in the regulation of drug resistance among different cancers and probe the mechanisms of the deregulated expression of miRNAs. The molecular targets of miRNAs and their underlying signaling pathways are also explored comprehensively. A holistic understanding of the functions of miRNAs in drug resistance will help us develop better strategies to regulate them efficiently and will finally pave the way toward better translation of miRNAs into clinics, developing them into a promising approach in cancer therapy. -
ARID1B Is a Specific Vulnerability in ARID1A-Mutant Cancers The
ARID1B is a specific vulnerability in ARID1A-mutant cancers The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. Citation Helming, K. C., X. Wang, B. G. Wilson, F. Vazquez, J. R. Haswell, H. E. Manchester, Y. Kim, et al. 2014. “ARID1B is a specific vulnerability in ARID1A-mutant cancers.” Nature medicine 20 (3): 251-254. doi:10.1038/nm.3480. http://dx.doi.org/10.1038/nm.3480. Published Version doi:10.1038/nm.3480 Accessed February 16, 2015 10:04:32 PM EST Citable Link http://nrs.harvard.edu/urn-3:HUL.InstRepos:12987227 Terms of Use This article was downloaded from Harvard University's DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA (Article begins on next page) NIH Public Access Author Manuscript Nat Med. Author manuscript; available in PMC 2014 September 01. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: Nat Med. 2014 March ; 20(3): 251–254. doi:10.1038/nm.3480. ARID1B is a specific vulnerability in ARID1A-mutant cancers Katherine C. Helming1,2,3,4,*, Xiaofeng Wang1,2,3,*, Boris G. Wilson1,2,3, Francisca Vazquez5, Jeffrey R. Haswell1,2,3, Haley E. Manchester1,2,3, Youngha Kim1,2,3, Gregory V. Kryukov5, Mahmoud Ghandi5, Andrew J. Aguirre5,6,7, Zainab Jagani8, Zhong Wang9, Levi A. Garraway6, William C. Hahn6,7, and Charles W. -
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. -
A General Binding Mechanism for All Human Sulfatases by the Formylglycine-Generating Enzyme
A general binding mechanism for all human sulfatases by the formylglycine-generating enzyme Dirk Roeser*, Andrea Preusser-Kunze†, Bernhard Schmidt†, Kathrin Gasow*, Julia G. Wittmann*, Thomas Dierks‡, Kurt von Figura†, and Markus Georg Rudolph*§ *Department of Molecular Structural Biology, University of Go¨ttingen, Justus-von-Liebig-Weg 11, D-37077 Go¨ttingen, Germany; †Department of Biochemistry II, Heinrich-Du¨ker-Weg 12, University of Go¨ttingen, D-37073 Go¨ttingen, Germany; and ‡Department of Biochemistry I, Universita¨tsstrasse 25, University of Bielefeld, D-33615 Bielefeld, Germany Edited by Carolyn R. Bertozzi, University of California, Berkeley, CA, and approved November 8, 2005 (received for review September 1, 2005) The formylglycine (FGly)-generating enzyme (FGE) uses molecular tases, suggesting a general binding mechanism of substrate sulfa- oxygen to oxidize a conserved cysteine residue in all eukaryotic tases by FGE. sulfatases to the catalytically active FGly. Sulfatases degrade and The details of how O2-dependent cysteine oxidation is mediated remodel sulfate esters, and inactivity of FGE results in multiple by FGE are unknown. As a first step toward the elucidation of the sulfatase deficiency, a fatal disease. The previously determined FGE molecular mechanism of FGly formation, we have previously crystal structure revealed two crucial cysteine residues in the active determined crystal structures of FGE in various oxidation states site, one of which was thought to be implicated in substrate (8). FGE adopts a novel fold with surprisingly little regular sec- 2ϩ binding. The other cysteine residue partakes in a novel oxygenase ondary structure and contains two structural Ca ions and two mechanism that does not rely on any cofactors. -
NICU Gene List Generator.Xlsx
Neonatal Crisis Sequencing Panel Gene List Genes: A2ML1 - B3GLCT A2ML1 ADAMTS9 ALG1 ARHGEF15 AAAS ADAMTSL2 ALG11 ARHGEF9 AARS1 ADAR ALG12 ARID1A AARS2 ADARB1 ALG13 ARID1B ABAT ADCY6 ALG14 ARID2 ABCA12 ADD3 ALG2 ARL13B ABCA3 ADGRG1 ALG3 ARL6 ABCA4 ADGRV1 ALG6 ARMC9 ABCB11 ADK ALG8 ARPC1B ABCB4 ADNP ALG9 ARSA ABCC6 ADPRS ALK ARSL ABCC8 ADSL ALMS1 ARX ABCC9 AEBP1 ALOX12B ASAH1 ABCD1 AFF3 ALOXE3 ASCC1 ABCD3 AFF4 ALPK3 ASH1L ABCD4 AFG3L2 ALPL ASL ABHD5 AGA ALS2 ASNS ACAD8 AGK ALX3 ASPA ACAD9 AGL ALX4 ASPM ACADM AGPS AMELX ASS1 ACADS AGRN AMER1 ASXL1 ACADSB AGT AMH ASXL3 ACADVL AGTPBP1 AMHR2 ATAD1 ACAN AGTR1 AMN ATL1 ACAT1 AGXT AMPD2 ATM ACE AHCY AMT ATP1A1 ACO2 AHDC1 ANK1 ATP1A2 ACOX1 AHI1 ANK2 ATP1A3 ACP5 AIFM1 ANKH ATP2A1 ACSF3 AIMP1 ANKLE2 ATP5F1A ACTA1 AIMP2 ANKRD11 ATP5F1D ACTA2 AIRE ANKRD26 ATP5F1E ACTB AKAP9 ANTXR2 ATP6V0A2 ACTC1 AKR1D1 AP1S2 ATP6V1B1 ACTG1 AKT2 AP2S1 ATP7A ACTG2 AKT3 AP3B1 ATP8A2 ACTL6B ALAS2 AP3B2 ATP8B1 ACTN1 ALB AP4B1 ATPAF2 ACTN2 ALDH18A1 AP4M1 ATR ACTN4 ALDH1A3 AP4S1 ATRX ACVR1 ALDH3A2 APC AUH ACVRL1 ALDH4A1 APTX AVPR2 ACY1 ALDH5A1 AR B3GALNT2 ADA ALDH6A1 ARFGEF2 B3GALT6 ADAMTS13 ALDH7A1 ARG1 B3GAT3 ADAMTS2 ALDOB ARHGAP31 B3GLCT Updated: 03/15/2021; v.3.6 1 Neonatal Crisis Sequencing Panel Gene List Genes: B4GALT1 - COL11A2 B4GALT1 C1QBP CD3G CHKB B4GALT7 C3 CD40LG CHMP1A B4GAT1 CA2 CD59 CHRNA1 B9D1 CA5A CD70 CHRNB1 B9D2 CACNA1A CD96 CHRND BAAT CACNA1C CDAN1 CHRNE BBIP1 CACNA1D CDC42 CHRNG BBS1 CACNA1E CDH1 CHST14 BBS10 CACNA1F CDH2 CHST3 BBS12 CACNA1G CDK10 CHUK BBS2 CACNA2D2 CDK13 CILK1 BBS4 CACNB2 CDK5RAP2 -
How Asbestos Drives the Tissue Towards Tumors: YAP Activation, Macrophage and Mesothelial Precursor Recruitment, RNA Editing, and Somatic Mutations
Oncogene (2018) 37:2645–2659 https://doi.org/10.1038/s41388-018-0153-z ARTICLE How asbestos drives the tissue towards tumors: YAP activation, macrophage and mesothelial precursor recruitment, RNA editing, and somatic mutations 1 2 3 3 3 4 Hubert Rehrauer ● Licun Wu ● Walter Blum ● Lazslo Pecze ● Thomas Henzi ● Véronique Serre-Beinier ● 1 5 2 3 6 Catherine Aquino ● Bart Vrugt ● Marc de Perrot ● Beat Schwaller ● Emanuela Felley-Bosco Received: 1 September 2017 / Revised: 11 December 2017 / Accepted: 30 December 2017 / Published online: 6 March 2018 © The Author(s) 2018. This article is published with open access Abstract Chronic exposure to intraperitoneal asbestos triggered a marked response in the mesothelium well before tumor development. Macrophages, mesothelial precursor cells, cytokines, and growth factors accumulated in the peritoneal lavage. Transcriptome profiling revealed YAP/TAZ activation in inflamed mesothelium with further activation in tumors, paralleled by increased levels of cells with nuclear YAP/TAZ. Arg1 was one of the highest upregulated genes in inflamed tissue and tumor. Inflamed tissue showed increased levels of single-nucleotide variations, with an RNA-editing signature, which were 1234567890();,: even higher in the tumor samples. Subcutaneous injection of asbestos-treated, but tumor-free mice with syngeneic mesothelioma tumor cells resulted in a significantly higher incidence of tumor growth when compared to naïve mice supporting the role of the environment in tumor progression. Introduction The association of exposure to asbestos with development of mesothelioma has been demonstrated in the seminal experimental work of Wagner in the 1960s [1]. In 1987, Kane and co-workers [2] observed that already a single dose These authors contributed equally: Hubert Rehrauer, Licun Wu. -
140503 IPF Signatures Supplement Withfigs Thorax
Supplementary material for Heterogeneous gene expression signatures correspond to distinct lung pathologies and biomarkers of disease severity in idiopathic pulmonary fibrosis Daryle J. DePianto1*, Sanjay Chandriani1⌘*, Alexander R. Abbas1, Guiquan Jia1, Elsa N. N’Diaye1, Patrick Caplazi1, Steven E. Kauder1, Sabyasachi Biswas1, Satyajit K. Karnik1#, Connie Ha1, Zora Modrusan1, Michael A. Matthay2, Jasleen Kukreja3, Harold R. Collard2, Jackson G. Egen1, Paul J. Wolters2§, and Joseph R. Arron1§ 1Genentech Research and Early Development, South San Francisco, CA 2Department of Medicine, University of California, San Francisco, CA 3Department of Surgery, University of California, San Francisco, CA ⌘Current address: Novartis Institutes for Biomedical Research, Emeryville, CA. #Current address: Gilead Sciences, Foster City, CA. *DJD and SC contributed equally to this manuscript §PJW and JRA co-directed this project Address correspondence to Paul J. Wolters, MD University of California, San Francisco Department of Medicine Box 0111 San Francisco, CA 94143-0111 [email protected] or Joseph R. Arron, MD, PhD Genentech, Inc. MS 231C 1 DNA Way South San Francisco, CA 94080 [email protected] 1 METHODS Human lung tissue samples Tissues were obtained at UCSF from clinical samples from IPF patients at the time of biopsy or lung transplantation. All patients were seen at UCSF and the diagnosis of IPF was established through multidisciplinary review of clinical, radiological, and pathological data according to criteria established by the consensus classification of the American Thoracic Society (ATS) and European Respiratory Society (ERS), Japanese Respiratory Society (JRS), and the Latin American Thoracic Association (ALAT) (ref. 5 in main text). Non-diseased normal lung tissues were procured from lungs not used by the Northern California Transplant Donor Network. -
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