DOCSLIB.ORG

Bases Genéticas De La Respuesta Radioadaptativa En Timocitos De Ratón

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Bases Genéticas De La Respuesta Radioadaptativa En Timocitos De Ratón UNIVERSIDAD AUTÓNOMA DE MADRID Departamento de Biología Molecular Bases genéticas de la respuesta radioadaptativa en timocitos de ratón Manuel Malavé Galiana Manuel Malavé Galiana Madrid, 2014 Departamento de Biología Molecular Facultad de Ciencias UNIVERSIDAD AUTÓNOMA DE MADRID Bases genéticas de la respuesta radioadaptativa en timocitos de ratón Manuel Malavé Galiana, Biotecnología Javier Santos Hernández, Pablo Fernández-Navarro, José Fernández-Piqueras Centro de Biología Molecular 1 Manuel Malavé Galiana D. José Fernández-Piqueras, Catedrático de Genética del Centro de Biología Molecular Severo Ochoa/Universidad Autónoma de Madrid D. Javier Santos Hernández, Profesor Titular de Genética de la Universidad Autónoma de Madrid D. Pablo Fernández Navarro, Investigador Post-doc del Área de Epidemiología Ambiental y Cáncer del Centro Nacional de Epidemiología CERTIFICAN: Que Don Manuel Malavé Galiana ha realizado el trabajo de tesis doctoral que lleva por título “Bases Genéticas de la respuesta radioadaptativa en timocitos de ratón” bajo nuestra dirección y supervisión. Que, una vez revisado el trabajo, consideramos que éste tiene la debida calidad para su presentación y defensa. Y para que así conste a los efectos oportunos firmamos la presente en Madrid a 13 de noviembre de 2014. José Fernández-Piqueras Javier Santos Hernández Catedrático de Genética Profesor titular de Genética Dpto.Biología, UAM Dpto. Biología, UAM Centro de Biología Molecular Centro de Biología Molecular Severo Ochoa, UAM-CSIC Severo Ochoa, UAM-CSIC Pablo Fernández Navarro Investigador Pos-Doc Miguel Servet Área de Epidemiología Ambiental y Cáncer, Centro Nacional de Epidemiología Instituto de Salud Carlos III, Madrid 2 Manuel Malavé Galiana INDICE I. AGRADECIMIENTOS. …..………………………………………………………… 6 II. RESUMEN. ………………………………………………………………………… 8 III. SUMMARY. ……………………………………………………………………….. 9 IV. ABREVIATURAS. ………………………………………………………………… 10 V. INTRODUCCIÓN. ………………………………………………………………... 13 1. Los genes de respuesta a la radiación ionizante: un regalo evolutivo……………. 13 2. Bajas dosis de radiación Ionizante y sus efectos biológicos…………………………… 14 3. La respuesta radioadaptativa………………………………………………………………………… 16 4. Mecanismos moleculares de la respuesta radioadaptativa……………………………. 17 5. El papel del gen Trp53 en el establecimiento de la respuesta radioadaptativa ……………………………………………………………………………………………………………………… 18 6. La ruta de señalización mediada por el gen Trp53 y la respuesta a la radiación ionizante……………………………………………………………………………………………………….. 20 7. El gen Trp53 participa en la apoptosis de timocitos inducida por la radiación ionizante……………………………………………………………………………………………………….. 21 8. La expresión génica como biomarcador de los efectos biológicos de la exposición a bajas dosis de radiación ionizante…………………………………………… 22 VI. HIPÓTESIS Y OBJETIVOS.………………………………………………………… 23 VII. MATERIALES Y METÓDOS. …………………………………………………….. 24 1. Ratones y protocolos de irradiación……………………………………………………………… 24 2. Extracción del timo y aislamiento timocitos………………………………………………….. 25 3. Análisis de proteínas por “Western blotting” ……………………………………………….. 25 4. Estudio de los niveles de apoptosis……………………………………………………………….. 26 5. Análisis estadístico………………………………………………………………………………………… 26 3 Manuel Malavé Galiana 6. Análisis de micromatrices de ADNc……………………………………………………………….. 27 7. Análisis de expresión génica mediante Micromatriz de ADNc de dos colores………………………………………………………………………………………………………….. 27 8. Análisis estadístico………………………………………………………………………………………… 29 VIII. RESULTADOS. …………………………………………………………………... 33 A. Papel del gen Trp53 en la respuesta radioadaptativa de los timocitos de ratón………………………………………………………………………………………………………………………… 33 A.1. Los niveles de apoptosis inducidos por una dosis sub-letal de 1,75 Gy de rayos fluctúan a lo largo del tiempo en timocitos de ratones C57BL/6………………………. 33 A.2. Respuesta adaptativa de los timocitos frente a la apoptosis inducida por una dosis de 0.075 Gy de rayos X en ratones C57BL/6……………………………………………….. 35 A.3. La dosis condicionante de irradiación X reduce la formación de roturas de doble cadena en el ADN en los timocitos de ratones C57BL/6…………………………….. 36 A.4. La exposición a una dosis sub-letal de 1,75 Gy de rayos provoca una rápida fosforilación de Trp53 en la serina 18…………………………………………………………………. 38 A.5. La fosforilación de Trp53 en la serina 389 protege de la apoptosis y de la inducción de roturas de doble cadena en el ADN en timocitos de ratones expuestos al régimen adaptativo de radiación…………………………………………………………………….. 40 A.6. El aumento de fosfoserina-389-Trp53 en los timocitos radioadaptados va acompañado de una reducción simultánea en los niveles de fosforilación de la proteína Mdm2 en la serina 166…………………………………………………………………………. 42 A.7. Niveles máximos de expresión para la forma fosforilada de p38Mapk y de fosfoserina-389-Trp53 in timocitos radioadaptados……………………………………………. 43 A.8. La transcripción de los genes diana de Trp53 no juega un papel esencial en la respuesta radioadaptativa de los timocitos de ratones C57BL/6…………………………. 44 4 Manuel Malavé Galiana B. Análisis global de expresión transcripcional en timocitos de ratones C57BL/6 expuestos al régimen adaptativo de radiación…………………………………………………………. 50 B.1. Genes relacionados con las funciones biológicas del Trp53…….…………………… 50 B.1.1. Genes de reparación del ADN……………………………………………………………... 50 B.1.2. Genes de apoptosis……………………………………………………………………………… 51 B.1.3. Genes implicados en el control del ciclo celular…………………………………… 51 B.2. ARNs no codificantes intergénicos de tamaño largo…………………………………….. 52 B.3. Genes no relacionados con las funciones biológicas del Trp53…………………..… 56 IX. DISCUSIÓN. ……………………………………………………………..………. 58 X. CONCLUSIONES. ……………………………………………………….……….. 69 XI. BIBLIOGRAFÍA. ………………………………………………………………….. 70 XII. PUBLICACIÓN. …………………………………………………………………… 80 5 Manuel Malavé Galiana AGRADECIMIENTOS Llegados a este momento en que se acaba un trabajo que ha conllevado tanto tiempo y esfuerzos, personales y ajenos, no puedo evitar echar la vista atrás y recordar los pilares en los que me he ido apoyando para llegar a este momento que se parece bastante a una meta. Echar una mirada hacia esos sustentos personales que han hecho posible que el camino, duro y agradecido a un tiempo, llevara a algún lugar del que poder sentirse orgulloso como yo lo estoy ahora. En ese repaso, mi primer agradecimiento tiene que ir, sin duda, hacia mi Director de Tesis, el Dr. Javier Santos Hernández por darme la oportunidad de realizar este trabajo y por todo el conocimiento aportado. A mi co-director de tesis el Dr. Pablo Fernández Navarro, que con su entrega y dedicación ha conseguido que saque adelante el presente trabajo. A mi co-director el Dr. José Fernández Piqueras, por haberme empujado a la realización de este trabajo; su apoyo, entrega y dedicación ilimitada en la tarea asistencial e investigadora son un referente a seguir. Sin su ilusión, muchas cosas serían imposibles; de verdad gracias. A mi compañera la Dra. Laura González, porque todo lo pudiera decir no sería suficiente. Gracias por su ejemplo, su dedicación, su profesionalidad, por compartir el trabajo diario, por contagiarme su motivación y sobre todo porque ha sido la maestra que me ha enseñado a andar en el camino de la ciencia. A mi compañera la Dra. Pilar López, por todo lo que he podido aprender a su lado y por su gran ayuda que sin ella no lo habría conseguido. A todos mis compañeros/as del laboratorio 327, con quienes he compartido el espacio-tiempo durante todos estos años: Chini por todo lo que he podido aprender su lado, a Anamery por los buenos momentos vividos con las series, Maria de Arriba por cuidar tanto del laboratorio y mi persona, Maria Villa por su sabiduría y profesionalidad, José Luis por su consejos e Irene por su chispa y cariño. 6 Manuel Malavé Galiana No podía pasar por alto a los/as compis restantes que se fueron, Helena por ser la alegría de la huerta, Inma por ser mi primera maestra, Lury por los buenos momentos pasados en la cafe, Jacobo, Álvaro y Andrés por sus consejos, Beatriz por su simpatía y amabilidad, Celia y Bárbara porque me han demostrado ser grandes personas y sobre todo grandes amigas, con las que sé que podré contar siempre. Gracias a todos/as en definitiva por vuestro apoyo, comprensión y por llenarme el día a día de ilusión. Al grupo de Voley-Playa (de Madrid si...): Fernando, Helena, Stefano, Diana, Isa, Fulvio, Dani, Liam, Iñigo, Jesús (y todos los que no he puesto porque la lista se hace muy grande y ellos saben muy bien quienes son), gracias a todos/as por los grandes momentos que hemos compartido Me habéis demostrado que aparte de ser unos grandes jugadores, sois de las mejores personas que uno puede conocer en esta vida. Si en el mundo hubiera más gente como vosotros creo que todos seríamos más felices. Tampoco podía olvidarme de mis compis de la 4ª planta: Angie, Pilar, Heni, Eugenia, MLuisa, Sandra y Elena. Gracias por los buenos momentos que hemos compartido, sois geniales desde la primera hasta la última, da gusto conocer gente como vosotras con vuestra alegría y cariño. A Gema, gracias por tu infinita paciencia e incondicional apoyo en este tramo final de la tesis, ha sido tu constante motivación la que me ha permitido luchar día a día para alcanzar este objetivo. Y finalmente, y por supuesto no el menos importante, el agradecimiento más profundo y sentido va para mi familia. Sin su apoyo, colaboración e inspiración habría sido imposible llevar a cabo esta dura empresa. Con todo mi cariño y amor para mis padres, Manuel y
Load more

Recommended publications
  • Supplemental Figure 1. Vimentin
    Double mutant specific genes Transcript gene_assignment Gene Symbol RefSeq FDR Fold- FDR Fold- FDR Fold- ID (single vs. Change (double Change (double Change wt) (single vs. wt) (double vs. single) (double vs. wt) vs. wt) vs. single) 10485013 BC085239 // 1110051M20Rik // RIKEN cDNA 1110051M20 gene // 2 E1 // 228356 /// NM 1110051M20Ri BC085239 0.164013 -1.38517 0.0345128 -2.24228 0.154535 -1.61877 k 10358717 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 /// BC 1700025G04Rik NM_197990 0.142593 -1.37878 0.0212926 -3.13385 0.093068 -2.27291 10358713 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 1700025G04Rik NM_197990 0.0655213 -1.71563 0.0222468 -2.32498 0.166843 -1.35517 10481312 NM_027283 // 1700026L06Rik // RIKEN cDNA 1700026L06 gene // 2 A3 // 69987 /// EN 1700026L06Rik NM_027283 0.0503754 -1.46385 0.0140999 -2.19537 0.0825609 -1.49972 10351465 BC150846 // 1700084C01Rik // RIKEN cDNA 1700084C01 gene // 1 H3 // 78465 /// NM_ 1700084C01Rik BC150846 0.107391 -1.5916 0.0385418 -2.05801 0.295457 -1.29305 10569654 AK007416 // 1810010D01Rik // RIKEN cDNA 1810010D01 gene // 7 F5 // 381935 /// XR 1810010D01Rik AK007416 0.145576 1.69432 0.0476957 2.51662 0.288571 1.48533 10508883 NM_001083916 // 1810019J16Rik // RIKEN cDNA 1810019J16 gene // 4 D2.3 // 69073 / 1810019J16Rik NM_001083916 0.0533206 1.57139 0.0145433 2.56417 0.0836674 1.63179 10585282 ENSMUST00000050829 // 2010007H06Rik // RIKEN cDNA 2010007H06 gene // --- // 6984 2010007H06Rik ENSMUST00000050829 0.129914 -1.71998 0.0434862 -2.51672
    [Show full text]
  • Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino-Like Mouse Models: Tracking the Role of the Hairless Gene
    University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 5-2006 Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino-like Mouse Models: Tracking the Role of the Hairless Gene Yutao Liu University of Tennessee - Knoxville Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Life Sciences Commons Recommended Citation Liu, Yutao, "Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino- like Mouse Models: Tracking the Role of the Hairless Gene. " PhD diss., University of Tennessee, 2006. https://trace.tennessee.edu/utk_graddiss/1824 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Yutao Liu entitled "Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino-like Mouse Models: Tracking the Role of the Hairless Gene." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Life Sciences. Brynn H. Voy, Major Professor We have read this dissertation and recommend its acceptance: Naima Moustaid-Moussa, Yisong Wang, Rogert Hettich Accepted for the Council: Carolyn R.
    [Show full text]
  • Genomic Modelling of Bipolar Disorders: Comparison of Multifactor Dimension Reduction and Classification-Based Data Mining Methods
    GENOMIC MODELLING OF BIPOLAR DISORDERS: COMPARISON OF MULTIFACTOR DIMENSION REDUCTION AND CLASSIFICATION-BASED DATA MINING METHODS A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF INFORMATICS OF THE MIDDLE EAST TECHNICAL UNIVERSITY BY CENGİZHAN AÇIKEL IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN THE DEPARTMENT OF MEDICAL INFORMATICS MARCH 2017 GENOMIC MODELLING OF BIPOLAR DISORDERS: COMPARISON OF MULTIFACTOR DIMENSION REDUCTION AND CLASSIFICATION-BASED DATA MINING METHODS Submitted by CENGİZHAN AÇIKEL in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Medical Informatics, Middle East Technical University by, Prof. Dr. Deniz Zeyrek Bozşahin ______________ Director, Graduate School of Informatics Assoc. Prof. Dr. Yeşim Aydın Son ______________ Head of Department, Health Informatics Assoc. Prof. Dr. Yeşim Aydın Son ______________ Supervisor, Health Informatics, METU Examining Committee Members: Assoc.Prof.Dr. Cem İyigün ______________ Industrial Engineering, METU Assoc. Prof. Dr. Yeşim Aydin Son ______________ Supervisor, Health Informatics, METU Assist. Prof. Dr. Aybar Can Acar ______________ Health Informatics, METU Assoc.Prof.Dr. Güvem Gümüş Akay ______________ Brain Research Center, Ankara University Assist.Prof.Dr. Ercüment Çiçek ______________ Computer Engineering, Bilkent University Date: ____/____/______ iii I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work. Name, Last Name : Cengizhan AÇIKEL Signature : __________________ iii ABSTRACT GENOMIC MODELLING OF BIPOLAR DISORDERS: COMPARISON OF MULTIFACTOR DIMENSION REDUCTION AND CLASSIFICATION-BASED DATA MINING METHODS Açıkel, Cengizhan Ph.D.
    [Show full text]
  • 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.
    [Show full text]
  • Supplementary Materials For
    www.sciencemag.org/cgi/content/full/science.1230422/DC1 Supplementary Materials for Genomic Diversity and Evolution of the Head Crest in the Rock Pigeon Michael D. Shapiro,* Zev Kronenberg, Cai Li, Eric T. Domyan, Hailin Pan, Michael Campbell, Hao Tan, Chad D. Huff, Haofu Hu, Anna I. Vickrey, Sandra C. A. Nielsen, Sydney A. Stringham, Hao Hu, Eske Willerslev, M. Thomas P. Gilbert, Mark Yandell, Guojie Zhang, Jun Wang* *To whom correspondence should be addressed. E-mail: [email protected] (M.D.S.); [email protected] (J.W.) Published 31 January 2013 on Science Express DOI: 10.1126/science.1230422 This PDF file includes: Materials and Methods Supplementary Text Figs. S1 to S27 Tables S1 to S28 References (26–72) Materials and Methods Genome assembly The DNA sample for sequencing of the reference genome was extracted from blood obtained from a single, male Danish Tumbler, bred by Anders and Hans Ove Christiansen (Danmarks Racedueforeninger, Næstved, Denmark). This breed was chosen because it is an old breed that is believed to have changed little in recent history. Seven paired-end sequencing libraries were constructed, with insert sizes of 170 bp, 500 bp, 800 bp, 2 kb, 5 kb, 10 kb and 20 kb. The libraries were sequenced using Illumina HiSeq2000 platform, yielding a total of 127.17 Gb raw data (Table S1). The raw sequences were filtered for low quality, adapter sequence, paired-end read overlap, and PCR duplicates. We also performed an error correction step on the raw reads before assembling. Filtering and error correction resulted in 81.57 Gb of clean data for genome assembly with the genome with SOAPdenovo (26).
    [Show full text]
  • Epigenetics Page 1
    Epigenetics esiRNA ID Gene Name Gene Description Ensembl ID HU-13237-1 ACTL6A actin-like 6A ENSG00000136518 HU-13925-1 ACTL6B actin-like 6B ENSG00000077080 HU-14457-1 ACTR1A ARP1 actin-related protein 1 homolog A, centractin alpha (yeast) ENSG00000138107 HU-10579-1 ACTR2 ARP2 actin-related protein 2 homolog (yeast) ENSG00000138071 HU-10837-1 ACTR3 ARP3 actin-related protein 3 homolog (yeast) ENSG00000115091 HU-09776-1 ACTR5 ARP5 actin-related protein 5 homolog (yeast) ENSG00000101442 HU-00773-1 ACTR6 ARP6 actin-related protein 6 homolog (yeast) ENSG00000075089 HU-07176-1 ACTR8 ARP8 actin-related protein 8 homolog (yeast) ENSG00000113812 HU-09411-1 AHCTF1 AT hook containing transcription factor 1 ENSG00000153207 HU-15150-1 AIRE autoimmune regulator ENSG00000160224 HU-12332-1 AKAP1 A kinase (PRKA) anchor protein 1 ENSG00000121057 HU-04065-1 ALG13 asparagine-linked glycosylation 13 homolog (S. cerevisiae) ENSG00000101901 HU-13552-1 ALKBH1 alkB, alkylation repair homolog 1 (E. coli) ENSG00000100601 HU-06662-1 ARID1A AT rich interactive domain 1A (SWI-like) ENSG00000117713 HU-12790-1 ARID1B AT rich interactive domain 1B (SWI1-like) ENSG00000049618 HU-09415-1 ARID2 AT rich interactive domain 2 (ARID, RFX-like) ENSG00000189079 HU-03890-1 ARID3A AT rich interactive domain 3A (BRIGHT-like) ENSG00000116017 HU-14677-1 ARID3B AT rich interactive domain 3B (BRIGHT-like) ENSG00000179361 HU-14203-1 ARID3C AT rich interactive domain 3C (BRIGHT-like) ENSG00000205143 HU-09104-1 ARID4A AT rich interactive domain 4A (RBP1-like) ENSG00000032219 HU-12512-1 ARID4B AT rich interactive domain 4B (RBP1-like) ENSG00000054267 HU-12520-1 ARID5A AT rich interactive domain 5A (MRF1-like) ENSG00000196843 HU-06595-1 ARID5B AT rich interactive domain 5B (MRF1-like) ENSG00000150347 HU-00556-1 ASF1A ASF1 anti-silencing function 1 homolog A (S.
    [Show full text]
  • Meta-Analyses of Expression Profiling Data in the Postmortem
    META-ANALYSES OF EXPRESSION PROFILING DATA IN THE POSTMORTEM HUMAN BRAIN by Meeta Mistry B.Sc., McMaster University, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Bioinformatics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2012 © Meeta Mistry, 2012 Abstract Schizophrenia is a severe psychiatric illness for which the precise etiology remains unknown. Studies using postmortem human brain have become increasingly important in schizophrenia research, providing an opportunity to directly investigate the diseased brain tissue. Gene expression profiling technologies have been used by a number of groups to explore the postmortem human brain and seek genes which show changes in expression correlated with schizophrenia. While this has been a valuable means of generating hypotheses, there is a general lack of consensus in the findings across studies. Expression profiling of postmortem human brain tissue is difficult due to the effect of various factors that can confound the data. The first aim of this thesis was to use control postmortem human cortex for identification of expression changes associated with several factors, specifically: age, sex, brain pH and postmortem interval. I conducted a meta-analysis across the control arm of eleven microarray datasets (representing over 400 subjects), and identified a signature of genes associated with each factor. These genes provide critical information towards the identification of problematic genes when investigating postmortem human brain in schizophrenia and other neuropsychiatric illnesses. The second aim of this thesis was to evaluate gene expression patterns in the prefrontal cortex associated with schizophrenia by exploring two methods of analysis: differential expression and coexpression.
    [Show full text]
  • Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
    www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1.
    [Show full text]
  • Variation in Protein Coding Genes Identifies Information
    bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number.
    [Show full text]
  • 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
    [Show full text]
  • Circrna-006258 Sponge-Adsorbs Mir-574-5P to Regulate Cell Growth and Milk Synthesis Via EVI5L in Goat Mammary Epithelial Cells
    G C A T T A C G G C A T genes Article CircRNA-006258 Sponge-Adsorbs miR-574-5p to Regulate Cell Growth and Milk Synthesis via EVI5L in Goat Mammary Epithelial Cells Meng Zhang y , Li Ma y, Yuhan Liu, Yonglong He, Guang Li, Xiaopeng An and Binyun Cao * College of Animal Science and Technology, Northwest A&F University, Yangling 712100, Shanxi, China; [email protected] (M.Z.); [email protected] (L.M.); [email protected] (Y.L.); [email protected] (Y.H.); [email protected] (G.L.); [email protected] (X.A.) * Correspondence: [email protected]; Tel.: +86-29-87092102 These authors contributed equally to this work. y Received: 28 May 2020; Accepted: 25 June 2020; Published: 28 June 2020 Abstract: The development of the udder and the milk yield are closely related to the number and vitality of mammary epithelial cells. Many previous studies have proved that non-coding RNAs (ncRNAs) are widely involved in mammary gland development and the physiological activities of lactation. Our laboratory previous sequencing data revealed that miR-574-5p was differentially expressed during the colostrum and peak lactation stages, while the molecular mechanism of the regulatory effect of miR-574-5p on goat mammary epithelial cells (GMECs) is unclear. In this study, the targeting relationship was detected between miR-574-5p or ecotropic viral integration site 5-like (EVI5L) and circRNA-006258. The results declared that miR-574-5p induced the down-regulation of EVI5L expression at both the mRNA and protein levels, while circRNA-006258 relieved the inhibitory effect through adsorbing miR-574-5p.
    [Show full text]
  • Fig1-13Tab1-5.Pdf
    Supplementary Information Promoter hypomethylation of EpCAM-regulated bone morphogenetic protein genes in advanced endometrial cancer Ya-Ting Hsu, Fei Gu, Yi-Wen Huang, Joseph Liu, Jianhua Ruan, Rui-Lan Huang, Chiou-Miin Wang, Chun-Liang Chen, Rohit R. Jadhav, Hung-Cheng Lai, David G. Mutch, Paul J. Goodfellow, Ian M. Thompson, Nameer B. Kirma, and Tim Hui-Ming Huang Tables of contents Page Table of contents 2 Supplementary Methods 4 Supplementary Figure S1. Summarized sequencing reads and coverage of MBDCap-seq 8 Supplementary Figure S2. Reproducibility test of MBDCap-seq 10 Supplementary Figure S3. Validation of MBDCap-seq by MassARRAY analysis 11 Supplementary Figure S4. Distribution of differentially methylated regions (DMRs) in endometrial tumors relative to normal control 12 Supplementary Figure S5. Network analysis of differential methylation loci by using Steiner-tree analysis 13 Supplementary Figure S6. DNA methylation distribution in early and late stage of the TCGA endometrial cancer cohort 14 Supplementary Figure S7. Relative expression of BMP genes with EGF treatment in the presence or absence of PI3K/AKT and Raf (MAPK) inhibitors in endometrial cancer cells 15 Supplementary Figure S8. Induction of invasion by EGF in AN3CA and HEC1A cell lines 16 Supplementary Figure S9. Knockdown expression of BMP4 and BMP7 in RL95-2 cells 17 Supplementary Figure S10. Relative expression of BMPs and BMPRs in normal endometrial cell and endometrial cancer cell lines 18 Supplementary Figure S11. Microfluidics-based PCR analysis of EMT gene panel in RL95-2 cells with or without EGF treatment 19 Supplementary Figure S12. Knockdown expression of EpCAM by different shRNA sequences in RL95-2 cells 20 Supplementary Figure S13.
    [Show full text]