Distinct Requirements for Energy Metabolism in Mouse Primordial Germ Cells and Their Reprogramming to Embryonic Germ Cells
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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 -
Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
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. -
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 -
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. -
ATP6V1B1 Gene Atpase H+ Transporting V1 Subunit B1
ATP6V1B1 gene ATPase H+ transporting V1 subunit B1 Normal Function The ATP6V1B1 gene provides instructions for making a part (subunit) of a large protein complex known as vacuolar H+-ATPase (V-ATPase). V-ATPases are a group of similar complexes that act as pumps to move positively charged hydrogen atoms (protons) across membranes. Because acids are substances that can "donate" protons to other molecules, this movement of protons helps regulate the relative acidity (pH) of cells and their surrounding environment. Tight control of pH is necessary for most biological reactions to proceed properly. The V-ATPase that includes the subunit produced from the ATP6V1B1 gene is found in the inner ear and in nephrons, which are the functional structures within the kidneys. The kidneys filter waste products from the blood and remove them in urine. They also reabsorb needed nutrients and release them back into the blood. Each nephron consists of two parts: a renal corpuscle (also known as a glomerulus) that filters the blood, and a renal tubule that reabsorbs substances that are needed and eliminates unneeded substances in urine. The V-ATPase is involved in regulating the amount of acid that is removed from the blood into the urine, and also in maintaining the proper pH of the fluid in the inner ear (endolymph). Health Conditions Related to Genetic Changes Renal tubular acidosis with deafness More than 25 ATP6V1B1 gene mutations have been identified in people with renal tubular acidosis with deafness, a disorder involving excess acid in the blood (metabolic acidosis), bone weakness, and hearing loss caused by changes in the inner ear ( sensorineural hearing loss). -
Multivariate Meta-Analysis of Differential Principal Components Underlying Human Primed and Naive-Like Pluripotent States
bioRxiv preprint doi: https://doi.org/10.1101/2020.10.20.347666; this version posted October 21, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. October 20, 2020 To: bioRxiv Multivariate Meta-Analysis of Differential Principal Components underlying Human Primed and Naive-like Pluripotent States Kory R. Johnson1*, Barbara S. Mallon2, Yang C. Fann1, and Kevin G. Chen2*, 1Intramural IT and Bioinformatics Program, 2NIH Stem Cell Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland 20892, USA Keywords: human pluripotent stem cells; naive pluripotency, meta-analysis, principal component analysis, t-SNE, consensus clustering *Correspondence to: Dr. Kory R. Johnson ([email protected]) Dr. Kevin G. Chen ([email protected]) 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.20.347666; this version posted October 21, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. ABSTRACT The ground or naive pluripotent state of human pluripotent stem cells (hPSCs), which was initially established in mouse embryonic stem cells (mESCs), is an emerging and tentative concept. To verify this important concept in hPSCs, we performed a multivariate meta-analysis of major hPSC datasets via the combined analytic powers of percentile normalization, principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and SC3 consensus clustering. -
Figure S1. Quality Control Validation of MS Data. (A‑C) Mass Error Distribution of All Peptides Identified in the Acetylome
Figure S1. Quality control validation of MS data. (A‑C) Mass error distribution of all peptides identified in the acetylome, succi- nylome and quantitative proteome, respectively. (D‑F) Length distribution of peptides identified in the acetylome, succinylome and quantitative proteome, respectively. Figure S2. Comparison of modification level between breast cancer tissue and normal tissue. Comparison of acetylation level (A) and succinylation level (B) between breast cancer tissue and normal tissue. Data are medians and were analyzed using Wilcoxon Signed Rank Test. **P<0.01. Table SI. Protein sites whose acetylation and succinylation levels were both significantly upregulated in breast cancer tissues (fold change ≥1.5 compared with normal tissues). Protein ID Protein name Modification site P54868 HMCS2 310K Q15063 POSTN 549K Q99715 COCA1 1601K P51572 BAP31 72K P07237 PDLA1 328K Q06830 PRDX1 192K P48735 IDHP 180K P30101 PDIA3 417K P0DMV9 HS71B 526K Q01995 TAGL 21K P06748 NPM1 27K Q00325 MPCP 209K P00488 F13A 69K P02545 LMNA 260K P08133 ANXA6 478K P02452 CO1A1 751K Table SII. Protein sites whose acetylation and succinylation levels were both significantly downregulated in breast cancer tissues (fold change ≥1.5 compared with normal tissues). Protein ID Protein name Modification site RET4 P02753 30K PSG2 P07585 142K HBA P69905 12K IGKC P01834 80K HBA P69905 8K Table SIII. All proteins whose expression level were significantly upregulated in breast cancer tissues (fold change ≥1.5 compared with normal tissues). Protein ID Protein description -
Proteomic and Metabolomic Analyses of Mitochondrial Complex I-Deficient
THE JOURNAL OF BIOLOGICAL CHEMISTRY VOL. 287, NO. 24, pp. 20652–20663, June 8, 2012 © 2012 by The American Society for Biochemistry and Molecular Biology, Inc. Published in the U.S.A. Proteomic and Metabolomic Analyses of Mitochondrial Complex I-deficient Mouse Model Generated by Spontaneous B2 Short Interspersed Nuclear Element (SINE) Insertion into NADH Dehydrogenase (Ubiquinone) Fe-S Protein 4 (Ndufs4) Gene*□S Received for publication, November 25, 2011, and in revised form, April 5, 2012 Published, JBC Papers in Press, April 25, 2012, DOI 10.1074/jbc.M111.327601 Dillon W. Leong,a1 Jasper C. Komen,b1 Chelsee A. Hewitt,a Estelle Arnaud,c Matthew McKenzie,d Belinda Phipson,e Melanie Bahlo,e,f Adrienne Laskowski,b Sarah A. Kinkel,a,g,h Gayle M. Davey,g William R. Heath,g Anne K. Voss,a,h René P. Zahedi,i James J. Pitt,j Roman Chrast,c Albert Sickmann,i,k Michael T. Ryan,l Gordon K. Smyth,e,f,h b2 a,h,m,n3 David R. Thorburn, and Hamish S. Scott Downloaded from From the aMolecular Medicine Division, gImmunology Division, and eBioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia, the bMurdoch Childrens Research Institute, Royal Children’s Hospital and Department of Paediatrics, University of Melbourne, Parkville, Victoria 3052, Australia, the cDépartement de Génétique Médicale, Université de Lausanne, 1005 Lausanne, Switzerland, the dCentre for Reproduction and Development, Monash Institute of Medical Research, Clayton, Victoria 3168, Australia, the hDepartment of Medical Biology -
Inherited Renal Tubulopathies—Challenges and Controversies
G C A T T A C G G C A T genes Review Inherited Renal Tubulopathies—Challenges and Controversies Daniela Iancu 1,* and Emma Ashton 2 1 UCL-Centre for Nephrology, Royal Free Campus, University College London, Rowland Hill Street, London NW3 2PF, UK 2 Rare & Inherited Disease Laboratory, London North Genomic Laboratory Hub, Great Ormond Street Hospital for Children National Health Service Foundation Trust, Levels 4-6 Barclay House 37, Queen Square, London WC1N 3BH, UK; [email protected] * Correspondence: [email protected]; Tel.: +44-2381204172; Fax: +44-020-74726476 Received: 11 February 2020; Accepted: 29 February 2020; Published: 5 March 2020 Abstract: Electrolyte homeostasis is maintained by the kidney through a complex transport function mostly performed by specialized proteins distributed along the renal tubules. Pathogenic variants in the genes encoding these proteins impair this function and have consequences on the whole organism. Establishing a genetic diagnosis in patients with renal tubular dysfunction is a challenging task given the genetic and phenotypic heterogeneity, functional characteristics of the genes involved and the number of yet unknown causes. Part of these difficulties can be overcome by gathering large patient cohorts and applying high-throughput sequencing techniques combined with experimental work to prove functional impact. This approach has led to the identification of a number of genes but also generated controversies about proper interpretation of variants. In this article, we will highlight these challenges and controversies. Keywords: inherited tubulopathies; next generation sequencing; genetic heterogeneity; variant classification. 1. Introduction Mutations in genes that encode transporter proteins in the renal tubule alter kidney capacity to maintain homeostasis and cause diseases recognized under the generic name of inherited tubulopathies. -
Aplicación De La Biología De Sistemas Al Estudio De La Malaria Y Búsqueda De Biomarcadores Y Dianas Terapéuticas
Aplicación de la biología de sistemas al estudio de la malaria y búsqueda de biomarcadores y dianas terapéuticas Autora: Mireia Ferrer Almirall Máster en Bioinformática y Bioestadística Area 1-Bioinformática farmacéutica Tutores: Melchor Sánchez Martínez y Alex Sánchez Pla Profesor responsable de la asignatura: Carles Ventura Royo 02/01/2019 Esta obra está sujeta a una licencia de Reconocimiento-NoComercial- SinObraDerivada 3.0 España de Creative Commons FICHA DEL TRABAJO FINAL Aplicación de la biología de sistemas al Título del trabajo: estudio de la malaria y búsqueda de biomarcadores y dianas terapéuticas Nombre del autor: Mireia Ferrer Almirall Melchor Sánchez Martínez y Nombre del consultor/a: Alex Sánchez Pla Nombre del PRA: Carles Ventura Royo Fecha de entrega (mm/aaaa): 01/2019 Titulación: Máster en Bioinformática y Bioestadística Área del Trabajo Final: 1-Bioinformática farmacéutica Idioma del trabajo: castellano Malaria, Biología-de-sistemas, Palabras clave dianas-terapéuticas Resumen del Trabajo (máximo 250 palabras): Con la finalidad, contexto de aplicación, metodología, resultados i conclusiones del trabajo. La finalidad de este trabajo es aplicar herramientas de biología de sistemas para investigar los mecanismos implicados en la infección por el parásito de la malaria e identificar posibles biomarcadores y dianas terapéuticas. Se ha partido de una serie temporal de datos de microarrays del bazo de ratones infectados con dos cepas del parásito (NL y L) para determinar los genes que se encuentran diferencialmente expresados (DEG) respecto a ratones control. A partir de las listas de DEG obtenidas, se han utilizado herramientas de biología de sistemas en combinación con análisis de significación biológica para obtener una visión integrada de los procesos biológicos que se encuentran alterados en la enfermedad e identificar posibles biomarcadores/dianas terapéuticas. -
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.