Gene Expression Responses in a Cellular Model of Parkinson's Disease
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Establishing the Pathogenicity of Novel Mitochondrial DNA Sequence Variations: a Cell and Molecular Biology Approach
Mafalda Rita Avó Bacalhau Establishing the Pathogenicity of Novel Mitochondrial DNA Sequence Variations: a Cell and Molecular Biology Approach Tese de doutoramento do Programa de Doutoramento em Ciências da Saúde, ramo de Ciências Biomédicas, orientada pela Professora Doutora Maria Manuela Monteiro Grazina e co-orientada pelo Professor Doutor Henrique Manuel Paixão dos Santos Girão e pela Professora Doutora Lee-Jun C. Wong e apresentada à Faculdade de Medicina da Universidade de Coimbra Julho 2017 Faculty of Medicine Establishing the pathogenicity of novel mitochondrial DNA sequence variations: a cell and molecular biology approach Mafalda Rita Avó Bacalhau Tese de doutoramento do programa em Ciências da Saúde, ramo de Ciências Biomédicas, realizada sob a orientação científica da Professora Doutora Maria Manuela Monteiro Grazina; e co-orientação do Professor Doutor Henrique Manuel Paixão dos Santos Girão e da Professora Doutora Lee-Jun C. Wong, apresentada à Faculdade de Medicina da Universidade de Coimbra. Julho, 2017 Copyright© Mafalda Bacalhau e Manuela Grazina, 2017 Esta cópia da tese é fornecida na condição de que quem a consulta reconhece que os direitos de autor são pertença do autor da tese e do orientador científico e que nenhuma citação ou informação obtida a partir dela pode ser publicada sem a referência apropriada e autorização. This copy of the thesis has been supplied on the condition that anyone who consults it recognizes that its copyright belongs to its author and scientific supervisor and that no quotation from the -
Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony
Supplementary Data Th2 and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in UBIOPRED Chih-Hsi Scott Kuo1.2, Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony Rowe3, Iaonnis Pandis2, Ana Sousa4, Julie Corfield5, Ratko Djukanovic6, Rene 7 7 8 2 1† Lutter , Peter J. Sterk , Charles Auffray , Yike Guo , Ian M. Adcock & Kian Fan 1†* # Chung on behalf of the U-BIOPRED consortium project team 1Airways Disease, National Heart & Lung Institute, Imperial College London, & Biomedical Research Unit, Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom; 2Department of Computing & Data Science Institute, Imperial College London, United Kingdom; 3Janssen Research and Development, High Wycombe, Buckinghamshire, United Kingdom; 4Respiratory Therapeutic Unit, GSK, Stockley Park, United Kingdom; 5AstraZeneca R&D Molndal, Sweden and Areteva R&D, Nottingham, United Kingdom; 6Faculty of Medicine, Southampton University, Southampton, United Kingdom; 7Faculty of Medicine, University of Amsterdam, Amsterdam, Netherlands; 8European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, France. †Contributed equally #Consortium project team members are listed under Supplementary 1 Materials *To whom correspondence should be addressed: [email protected] 2 List of the U-BIOPRED Consortium project team members Uruj Hoda & Christos Rossios, Airways Disease, National Heart & Lung Institute, Imperial College London, UK & Biomedical Research Unit, Biomedical Research Unit, Royal -
Supplementary Table S1. Prioritization of Candidate FPC Susceptibility Genes by Private Heterozygous Ptvs
Supplementary Table S1. Prioritization of candidate FPC susceptibility genes by private heterozygous PTVs Number of private Number of private Number FPC patient heterozygous PTVs in heterozygous PTVs in tumors with somatic FPC susceptibility Hereditary cancer Hereditary Gene FPC kindred BCCS samples mutation DNA repair gene Cancer driver gene gene gene pancreatitis gene ATM 19 1 - Yes Yes Yes Yes - SSPO 12 8 1 - - - - - DNAH14 10 3 - - - - - - CD36 9 3 - - - - - - TET2 9 1 - - Yes - - - MUC16 8 14 - - - - - - DNHD1 7 4 1 - - - - - DNMT3A 7 1 - - Yes - - - PKHD1L1 7 9 - - - - - - DNAH3 6 5 - - - - - - MYH7B 6 1 - - - - - - PKD1L2 6 6 - - - - - - POLN 6 2 - Yes - - - - POLQ 6 7 - Yes - - - - RP1L1 6 6 - - - - - - TTN 6 5 4 - - - - - WDR87 6 7 - - - - - - ABCA13 5 3 1 - - - - - ASXL1 5 1 - - Yes - - - BBS10 5 0 - - - - - - BRCA2 5 6 1 Yes Yes Yes Yes - CENPJ 5 1 - - - - - - CEP290 5 5 - - - - - - CYP3A5 5 2 - - - - - - DNAH12 5 6 - - - - - - DNAH6 5 1 1 - - - - - EPPK1 5 4 - - - - - - ESYT3 5 1 - - - - - - FRAS1 5 4 - - - - - - HGC6.3 5 0 - - - - - - IGFN1 5 5 - - - - - - KCP 5 4 - - - - - - LRRC43 5 0 - - - - - - MCTP2 5 1 - - - - - - MPO 5 1 - - - - - - MUC4 5 5 - - - - - - OBSCN 5 8 2 - - - - - PALB2 5 0 - Yes - Yes Yes - SLCO1B3 5 2 - - - - - - SYT15 5 3 - - - - - - XIRP2 5 3 1 - - - - - ZNF266 5 2 - - - - - - ZNF530 5 1 - - - - - - ACACB 4 1 1 - - - - - ALS2CL 4 2 - - - - - - AMER3 4 0 2 - - - - - ANKRD35 4 4 - - - - - - ATP10B 4 1 - - - - - - ATP8B3 4 6 - - - - - - C10orf95 4 0 - - - - - - C2orf88 4 0 - - - - - - C5orf42 4 2 - - - - -
A Conserved Set of Maternal Genes? Insights from a Molluscan Transcriptome
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Nottingham ePrints Liu, M. Maureen and Davey, John W. and Jackson, Daniel J. and Blaxter, Mark L. and Davison, Angus (2014) A conserved set of maternal genes? Insights from a molluscan transcriptome. International Journal of Developmental Biology, 58 . pp. 501-511. ISSN 0214- 6282 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/30007/1/ft501%20%283%29.pdf Copyright and reuse: The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. · Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. · To the extent reasonable and practicable the material made available in Nottingham ePrints has been checked for eligibility before being made available. · Copies of full items can be used for personal research or study, educational, or not- for-profit purposes without prior permission or charge provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. · Quotations or similar reproductions must be sufficiently acknowledged. Please see our full end user licence at: http://eprints.nottingham.ac.uk/end_user_agreement.pdf A note on versions: The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher’s version. -
S41467-020-18249-3.Pdf
ARTICLE https://doi.org/10.1038/s41467-020-18249-3 OPEN Pharmacologically reversible zonation-dependent endothelial cell transcriptomic changes with neurodegenerative disease associations in the aged brain Lei Zhao1,2,17, Zhongqi Li 1,2,17, Joaquim S. L. Vong2,3,17, Xinyi Chen1,2, Hei-Ming Lai1,2,4,5,6, Leo Y. C. Yan1,2, Junzhe Huang1,2, Samuel K. H. Sy1,2,7, Xiaoyu Tian 8, Yu Huang 8, Ho Yin Edwin Chan5,9, Hon-Cheong So6,8, ✉ ✉ Wai-Lung Ng 10, Yamei Tang11, Wei-Jye Lin12,13, Vincent C. T. Mok1,5,6,14,15 &HoKo 1,2,4,5,6,8,14,16 1234567890():,; The molecular signatures of cells in the brain have been revealed in unprecedented detail, yet the ageing-associated genome-wide expression changes that may contribute to neurovas- cular dysfunction in neurodegenerative diseases remain elusive. Here, we report zonation- dependent transcriptomic changes in aged mouse brain endothelial cells (ECs), which pro- minently implicate altered immune/cytokine signaling in ECs of all vascular segments, and functional changes impacting the blood–brain barrier (BBB) and glucose/energy metabolism especially in capillary ECs (capECs). An overrepresentation of Alzheimer disease (AD) GWAS genes is evident among the human orthologs of the differentially expressed genes of aged capECs, while comparative analysis revealed a subset of concordantly downregulated, functionally important genes in human AD brains. Treatment with exenatide, a glucagon-like peptide-1 receptor agonist, strongly reverses aged mouse brain EC transcriptomic changes and BBB leakage, with associated attenuation of microglial priming. We thus revealed tran- scriptomic alterations underlying brain EC ageing that are complex yet pharmacologically reversible. -
Low Abundance of the Matrix Arm of Complex I in Mitochondria Predicts Longevity in Mice
ARTICLE Received 24 Jan 2014 | Accepted 9 Apr 2014 | Published 12 May 2014 DOI: 10.1038/ncomms4837 OPEN Low abundance of the matrix arm of complex I in mitochondria predicts longevity in mice Satomi Miwa1, Howsun Jow2, Karen Baty3, Amy Johnson1, Rafal Czapiewski1, Gabriele Saretzki1, Achim Treumann3 & Thomas von Zglinicki1 Mitochondrial function is an important determinant of the ageing process; however, the mitochondrial properties that enable longevity are not well understood. Here we show that optimal assembly of mitochondrial complex I predicts longevity in mice. Using an unbiased high-coverage high-confidence approach, we demonstrate that electron transport chain proteins, especially the matrix arm subunits of complex I, are decreased in young long-living mice, which is associated with improved complex I assembly, higher complex I-linked state 3 oxygen consumption rates and decreased superoxide production, whereas the opposite is seen in old mice. Disruption of complex I assembly reduces oxidative metabolism with concomitant increase in mitochondrial superoxide production. This is rescued by knockdown of the mitochondrial chaperone, prohibitin. Disrupted complex I assembly causes premature senescence in primary cells. We propose that lower abundance of free catalytic complex I components supports complex I assembly, efficacy of substrate utilization and minimal ROS production, enabling enhanced longevity. 1 Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne NE4 5PL, UK. 2 Centre for Integrated Systems Biology of Ageing and Nutrition, Newcastle University, Newcastle upon Tyne NE4 5PL, UK. 3 Newcastle University Protein and Proteome Analysis, Devonshire Building, Devonshire Terrace, Newcastle upon Tyne NE1 7RU, UK. Correspondence and requests for materials should be addressed to T.v.Z. -
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 -
(P -Value<0.05, Fold Change≥1.4), 4 Vs. 0 Gy Irradiation
Table S1: Significant differentially expressed genes (P -Value<0.05, Fold Change≥1.4), 4 vs. 0 Gy irradiation Genbank Fold Change P -Value Gene Symbol Description Accession Q9F8M7_CARHY (Q9F8M7) DTDP-glucose 4,6-dehydratase (Fragment), partial (9%) 6.70 0.017399678 THC2699065 [THC2719287] 5.53 0.003379195 BC013657 BC013657 Homo sapiens cDNA clone IMAGE:4152983, partial cds. [BC013657] 5.10 0.024641735 THC2750781 Ciliary dynein heavy chain 5 (Axonemal beta dynein heavy chain 5) (HL1). 4.07 0.04353262 DNAH5 [Source:Uniprot/SWISSPROT;Acc:Q8TE73] [ENST00000382416] 3.81 0.002855909 NM_145263 SPATA18 Homo sapiens spermatogenesis associated 18 homolog (rat) (SPATA18), mRNA [NM_145263] AA418814 zw01a02.s1 Soares_NhHMPu_S1 Homo sapiens cDNA clone IMAGE:767978 3', 3.69 0.03203913 AA418814 AA418814 mRNA sequence [AA418814] AL356953 leucine-rich repeat-containing G protein-coupled receptor 6 {Homo sapiens} (exp=0; 3.63 0.0277936 THC2705989 wgp=1; cg=0), partial (4%) [THC2752981] AA484677 ne64a07.s1 NCI_CGAP_Alv1 Homo sapiens cDNA clone IMAGE:909012, mRNA 3.63 0.027098073 AA484677 AA484677 sequence [AA484677] oe06h09.s1 NCI_CGAP_Ov2 Homo sapiens cDNA clone IMAGE:1385153, mRNA sequence 3.48 0.04468495 AA837799 AA837799 [AA837799] Homo sapiens hypothetical protein LOC340109, mRNA (cDNA clone IMAGE:5578073), partial 3.27 0.031178378 BC039509 LOC643401 cds. [BC039509] Homo sapiens Fas (TNF receptor superfamily, member 6) (FAS), transcript variant 1, mRNA 3.24 0.022156298 NM_000043 FAS [NM_000043] 3.20 0.021043295 A_32_P125056 BF803942 CM2-CI0135-021100-477-g08 CI0135 Homo sapiens cDNA, mRNA sequence 3.04 0.043389246 BF803942 BF803942 [BF803942] 3.03 0.002430239 NM_015920 RPS27L Homo sapiens ribosomal protein S27-like (RPS27L), mRNA [NM_015920] Homo sapiens tumor necrosis factor receptor superfamily, member 10c, decoy without an 2.98 0.021202829 NM_003841 TNFRSF10C intracellular domain (TNFRSF10C), mRNA [NM_003841] 2.97 0.03243901 AB002384 C6orf32 Homo sapiens mRNA for KIAA0386 gene, partial cds. -
Characterisation of the Genomic Landscape of CRLF2‐Rearranged Acute Lymphoblastic Leukemia
Characterisation of the Genomic Landscape of CRLF2- rearranged Acute Lymphoblastic Leukemia Lisa J Russell1*, Lisa Jones1, Amir Enshaei1, Stefano Tonin1, Sarra L Ryan1, Jeyanthy Eswaran1 , Sirintra Nakjang2, Elli Papaemmanuil3,4, Jose M C Tubio4, Adele K Fielding5, Ajay Vora6, Peter J Campbell4, Anthony V Moorman1, and Christine J Harrison1 1 Leukaemia Research Cytogenetics Group, Northern Institute for Cancer Research, Newcastle University, Newcastle-upon-Tyne, UK 2 Bioinformatics Support Unit, Newcastle University, Newcastle-upon-Tyne, UK 3 Memorial Sloan Kettering Cancer Center, USA 4 Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, UK 5 Research Department of Haemaoloty, UCL Cancer Institute, London, UK 6 Department of Haematology, Sheffield Children’s Hospital, Sheffield, UK; AVM and CJH contributed equally to this study Running Title – Genomic landscape of CRLF2 rearranged leukemia Correspondence to: Dr Lisa J Russell, Wolfson Childhood Cancer Research Centre, Northern Institute for Cancer Research, Newcastle University, Level 6, Herschel Building, Brewery Lane, Newcastle upon Tyne, NE1 7RU, [email protected]. Acknowledgements Support by: The Kay Kendall Leukaemia Fund, Leuka, European Haematology Association and Bloodwise (formerly Leukaemia and Lymphoma Research) This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an ‘Accepted Article’, doi: 10.1002/gcc.22439 This article is protected by copyright. All rights reserved. Genes, Chromosomes & Cancer Page 2 of 147 Deregulated expression of the type I cytokine receptor, CRLF2, is observed in 5-15% of precursor B-cell acute lymphoblastic leukaemia (B-ALL). -
17 - 19 October 2019
THE 12TH INTERNATIONAL SYMPOSIUM ON HEALTH INFORMATICS AND BIOINFORMATICS T G T A A A T G A G A A G T T G G G T A A A A A A A A T T T A A A A A A G G A A A A A A A G G G G G G G G A A A G G G G G G T T G G G G G G G T T T T G G T T G G G T T T G G T A A T T G G T T T A A A A A A A A T T T A A A A A A T T A A A A A A A T A T T T T T T A A A T T T T G T A G A A T T T A A 17 - 19 OCTOBER 2019 HIBIT 2019 ABSTRACT BOOK THE 12TH INTERNATIONAL SYMPOSIUM ON HEALTH INFORMATICS AND BIOINFORMATICS TABLE OF CONTENTS 1 8 WELCOME MESSAGE KEYNOTE LECTURERS 2 19 ORGANIZING COMMITEE INVITED SPEAKERS 3 29 SCIENTIFIC COMMITEE SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 6 61 PROGRAM POSTER PRESENTATIONS Welcome Message The International Symposium on Health Informatics and Bioinformatics, (HIBIT), now in its twelfth year HIBIT 2019, aims to bring together academics, researchers and practitioners who work in these popular and fulfilling areas and to create the much- needed synergy among medical, biological and information technology sectors. HIBIT is one of the few conferences emphasizing such synergy. -
An Integrative Multi-Platform Analysis for Discovering Biomarkers Of
BMC Cancer BioMed Central Research article Open Access An integrative multi-platform analysis for discovering biomarkers of osteosarcoma Guodong Li†1,2, Wenjuan Zhang†3, Huazong Zeng*4, Lei Chen5, Wenjing Wang6, Jilong Liu4, Zhiyu Zhang1 and Zhengdong Cai*1,2 Address: 1Department of Orthopaedics, Tenth People's Hospital, Tongji University, Shanghai 200072, PR China, 2Department of Orthopaedics, Changhai Hospital, Second Military Medical University, Shanghai 200433, PR China, 3School of Life Sciences, Fudan University, Shanghai 200433, PR China, 4Shanghai Sensichip Co Ltd, Shanghai 200433, PR China, 5International Co-operation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai 200438, PR China and 6Shanghai Municipal Center for Disease Control & Prevention, Shanghai 200336, PR China Email: Guodong Li - [email protected]; Wenjuan Zhang - [email protected]; Huazong Zeng* - [email protected]; Lei Chen - [email protected]; Wenjing Wang - [email protected]; Jilong Liu - [email protected]; Zhiyu Zhang - [email protected]; Zhengdong Cai* - [email protected] * Corresponding authors †Equal contributors Published: 16 May 2009 Received: 6 December 2008 Accepted: 16 May 2009 BMC Cancer 2009, 9:150 doi:10.1186/1471-2407-9-150 This article is available from: http://www.biomedcentral.com/1471-2407/9/150 © 2009 Li et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionization-Time of Flight-Mass Spectrometry) has become an attractive approach for cancer biomarker discovery due to its ability to resolve low mass proteins and high-throughput capability. -
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 ........................................................................................