DOCSLIB.ORG

Jorge Martinez Romero 2019

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Jorge Martinez Romero 2019 UNIVERSIDAD AUTÓNOMA DE MADRID SCHOOL OF SCIENCES. DEPARTAMENT OF BIOLOGY Molecular anD computational approach to the link between nutrition anD cancer Jorge Martinez Romero 2019 Doctoral Thesis UNIVERSIDAD AUTÓNOMA DE MADRID SCHOOL OF SCIENCES DEPARTAMENT OF BIOLOGY CELLULAR BIOLOGY Imdea Food Research Institute Molecular Oncology Group TITLE: Molecular and computational approach to the link between nutrition and cancer Jorge Martínez Romero Doctoral THESIS Madrid, 2019 1 UNIVERSIDAD AUTÓNOMA DE MADRID FACULTAD DE CIENCIAS DEPARTAMENTO DE BIOLOGIA BIOLOGIA CELULAR Instituto Imdea Alimentación Grupo de Oncología Molecular TITULO: Molecular and computational approach to the link between nutrition and cancer Memoria presentada por: Jorge Martínez Romero Para optar al grado de DOCTOR EN BIOLOGÍA Directora: Dra. Ana Ramírez de Molina Codirector: Prof. Dr. Guillermo Reglero Rada Tutor: Prof. María José Hazen de San Juan Madrid, 2019 3 Drs. Ana Ramírez de Molina, PhD in Biochemistry and Molecular Biology at the University Autónoma de Madrid, and currently Deputy Director of the IMDEA Food Research Institute, Director of the Precision Nutrition and Cancer Program and Principal Investigator of the Molecular Oncology Group. As Director of this Thesis, Prof. Guillermo Reglero Rada, PhD in Chemistry, Full Professor of Food Sciences at the University Autónoma de Madrid and Senior Researcher of the Spanish National Research Council (CSIC). Currently he is the Director of the IMDEA Food Research Institute and Principal Investigator of the Food Products for Precision Nutrition Program. As co-director of this Thesis, INFORM: That the present work entitled “Molecular and computational approach to the link between nutrition and cancer” and that constitutes the Thesis presented by Mr. Jorge Martínez Romero to apply for the degree of Doctor in Biology, has been carried out at the Madrid Institute for Advanced Studies in Food (IMDEA Food),Spain; the Biomedical Research Center, National Institutes of Health (NIH), Baltimore, MD, USA; and the Cancer Research Center, Salamanca, Spain, under their direction. They consider that the experimental study is original, and the results have sufficient scientific quality for presentation as a Doctoral Thesis in the School of Science, Department of Biology, of the University Autónoma de Madrid. Thesis Director: PhD Ana Ramírez de Molina Thesis Codirector: Professor Guillermo Reglero Rada 5 Index 9 INDEX ACKNOWLEDGEMENTS .............................................................................................................................................................. 15 INDEX OF FIGURES AND TABLES ............................................................................................................................................. 21 LIST OF ABBREVIATIONS ............................................................................................................................................................ 27 SUMMARY ....................................................................................................................................................................................... 33 1. INTRODUCTION ............................................................................................................................................................... 39 1.1. CANCER. ..................................................................................................................................................................... 41 1.1.1. Cancer overview .................................................................................................................................................. 41 1.1.2. Colorectal cancer (CRC) ..................................................................................................................................... 48 1.1.3. Breast Cancer (BC) ............................................................................................................................................. 57 1.2. BIOINFORMATICS APPLIED TO GENOME-WIDE EXPRESSION TO ANALYZE THE CANCER GENOME ALTERATIONS. ...... 62 1.2.1. Functional genomics in cancer ........................................................................................................................... 62 1.3. NUTRITIONAL STRATEGIES IN CANCER BASED ON MOLECULAR EFFECTS: .................................................................. 69 1.3.1. Bioactive compounds: Phitochemicals, Phenolic compound Ellagic acid and derivatives in CRC. ............... 72 1.3.2. Caloric restriction and fasting in breast cancer. ................................................................................................ 78 2. HYPOTHESIS ....................................................................................................................................................................... 89 3. OBJECTIVES ........................................................................................................................................................................ 91 3.1. IDENTIFICATION OF GENES INVOLVED IN NUTRIENT SENSING OR CELL METABOLISM ASSOCIATED WITH CRC PROGNOSIS AND PATIENT SURVIVAL. ........................................................................................................................................... 91 3.2. IDENTIFICATION OF POTENTIAL PRECISION STRATEGIES IN CANCER FOCUSED ON MOLECULAR NUTRITION. .......... 91 3.2.1. Nutritional strategies based on the inclusion of bioactive compounds: Analysis of bioactive compounds with potential beneficial effect in CRC ........................................................................................................................................ 91 3.2.2. Nutritional strategies based on the inhibition of tumor nutrient requirements: Analysis of the inhibition of tumor progression and metastatic burden in BC through fasting strategies. .................................................................... 91 4. MATERIALS AND METHODS .......................................................................................................................................... 93 4.1. IN SILICO ANALYSIS: IDENTIFICATION OF GENES INVOLVED IN NUTRIENT SENSING OR CELL METABOLISM ASSOCIATED WITH CRC PROGNOSIS AND PATIENT SURVIVAL. ........................................................................................................................ 95 4.1.1. Dataset Integration and batch effect removal. ................................................................................................. 101 4.1.2. Batch effect removal evaluation. ....................................................................................................................... 102 4.1.3. Differential expression analysis......................................................................................................................... 103 4.1.4. Survival analysis ................................................................................................................................................. 103 4.1.5. Gene expression profiles of epithelial CRC samples vs CRC tumor samples. .............................................. 104 4.1.6. Geneset enrichment analysis (GSEA) .............................................................................................................. 105 4.2. IN VITRO ANALYSIS: ANALYSIS OF BIOACTIVE COMPOUNDS WITH POTENTIAL BENEFICIAL EFFECT IN CRC........... 106 4.2.1. Phenolic Compounds and Derived Metabolites .............................................................................................. 106 4.2.2. Cell Culture ....................................................................................................................................................... 106 4.2.3. Cell Viability Assays .......................................................................................................................................... 107 4.2.4. RNA Extraction and Quantification ................................................................................................................. 107 4.2.5. Gene Expression Assays .................................................................................................................................... 108 4.2.6. Real time qPCR ................................................................................................................................................. 108 4.2.7. Top-Fop tranfection assay ................................................................................................................................. 109 4.2.8. miRNAs Expression Assay ................................................................................................................................ 111 4.2.9. Cell Culture, Protein Extraction and Quantification ...................................................................................... 111 11 4.2.10. Western Blot analysis .................................................................................................................................. 111 4.2.11. Cell Migration Assay ................................................................................................................................... 113 4.2.12. Mitochondrial respiration and glycolytic function monitoring. ............................................................... 113 4.3. IN VIVO ANALYSIS: ANALYSIS OF THE INHIBITION OF TUMOR PROGRESSION AND METASTATIC BURDEN IN BC THROUGH
Load more

Recommended publications
  • Batch Effects in Single-Cell RNA Sequencing Data Are Corrected by Matching Mutual Nearest Neighbours
    Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours Laleh Haghverdi1,2, Aaron T. L. Lun3, Michael D. Morgan4, John C. Marioni1,3,4 1 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom. 2 Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany. 3 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom. 4 Wellcome Trust Sanger Institute, Cambridge, United Kingdom. Correspondence should be addressed to J.C.M.: [email protected] Editor Summary: Differences in gene expression between individual cells of the same type are measured across batches and used to correct technical artefacts in single-cell RNA sequencing data Large-scale single-cell RNA sequencing (scRNA-seq) datasets that are produced in different laboratories and at different times contain batch effects that could compromise integration and interpretation of these data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known, or the same, across batches. We present a strategy for batch correction that is based on the detection of mutual nearest neighbours (MNN) in the high-dimensional expression space. Our approach does not rely on pre-defined or equal population compositions across batches, and only requires that a subset of the population be shared between batches. We demonstrate the superiority of our approach over existing methods using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect correction method scales to large numbers of cells.
    [Show full text]
  • 1 Towards Best Practice in Single-Cell Sequencing
    Towards Best Practice in Single-cell Sequencing: A Comprehensive Multi-Center Cross-platform Benchmarking Study of Single-cell RNA Sequencing Using Reference Samples Wanqiu Chen1#, Yongmei Zhao2,8#, Xin Chen1,3#, Zhaowei Yang4,1#, Xiaojiang Xu5, Yingtao Bi6, Vicky Chen2,8, Jing Li4, Hannah Choi1, Ben Ernest7, Bao Tran8, Monika Mehta8, ParimaL Kumar8, Andrew Farmer9, ALain Mir9, Urvashi Mehra7, Jian-Liang Li5, MaLcoLm Moos Jr.10, Wenming Xiao11*, Charles Wang3,1* 1Center for Genomics, SchooL of Medicine, Loma Linda University, 11021 Campus St., Loma Linda, CA 92350. 2CCR-SF Bioinformatics Group, Advanced BiomedicaL and ComputationaL Sciences, BiomedicaL Informatics and Data Science Directorate, Frederick NationaL Laboratory for Cancer Research, 8560 Progress Drive, Frederick, MD 21701. 3Department of Basic Sciences, SchooL of Medicine, Loma Linda University, 11021 Campus St., Loma Linda, CA 92350. 4Department of AlLergy and ClinicaL ImmunoLogy, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory HeaLth, the First AffiLiated HospitaL of Guangzhou MedicaL University, Guangzhou, Guangdong, 510182, P. R. China. 5Integrative Bioinformatics Group, NationaL Institute of Environment Health Sciences, NIH, Department of Health and Human Services, Research Triangle Park, NC 27709. 6Abbvie Cambridge Research Center, 200 Sidney Street, Cambridge, MA 02139. 7Digicon Corporation, 7926 Jones Branch Drive, Suite 615, McLean, VA 22102. 8Sequencing Facility, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, 8560 Progress Drive, Frederick, MD 21701. 9Takara Bio USA, Inc., Mountain View, CA 94043. 10Center for BioLogics EvaLuation and Research & Division of CeLLular and Gene Therapies, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, SiLver Spring, MD 20993. 11The Center for Devices and RadioLogicaL HeaLth, U.S.
    [Show full text]
  • Statistical Data Integration in Translational Medicine
    TECHNISCHE UNIVERSITAT¨ MUNCHEN¨ Wissenschaftszentrum Weihenstephan f¨urErn¨ahrung,Landnutzung und Umwelt Statistical data integration in translational medicine Linda Carina Krause Vollständiger Abdruck der von der Fakultät Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigten Dissertation. Vorsitzende/-r: Prof. Dr. Dietmar Zehn Pr¨ufende/-rder Dissertation: 1. Prof. Dr. Dr. Fabian Theis 2. Prof. Dr. Bernhard K¨uster Die Dissertation wurde am 28.03.2019 bei der Technischen Universit¨atM¨unchen eingereicht und durch die Fakult¨atWissenschaftszentrum Weihenstephan f¨ur Ern¨ahrung,Landnutzung und Umwelt am 27.09.2019 angenommen. Acknowledgment I would like to thank Fabian J. Theis for being my doctoral supervisor and inviting me to come to Munich to work as a doctoral researcher in the rich scientific environment at his institute. I am deeply grateful to my supervisor Nikola S. Mueller for her continuous guidance, her steady support and scientific input. I thank my co-supervisor Stefanie Eyerich for discussing all my results in detail, being open to my ideas and introducing me to the exciting world of immunology. Further, I would like to thank Prof. Dr. Bernhard K¨usterfor being in my thesis committee and Prof. Dr. Dietmar Zehn for being the chair of this committee. I would like to thank all former and current members of the computational cell maps group for great discussion rounds, interesting journal clubs and also for the fun we had together during our events and retreats. A special thanks to all the long-term great room mates I had over the years: beginning with Ivan Kondoversky, Julia S¨ollner and Norbert Krautenbacher, followed by Eudes Barbosa and Karolina Worf and finally Christoph Ogris joined us in room 022.
    [Show full text]
  • Batch Effects in Single-Cell RNA-Sequencing Data Are Corrected by Matching Mutual Nearest Neighbors
    ANALYSIS Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors Laleh Haghverdi1,2, Aaron T L Lun3 , Michael D Morgan4 & John C Marioni1,3,4 Large-scale single-cell RNA sequencing (scRNA-seq) data sets the coefficient for each blocking term is set to zero, and the expres- that are produced in different laboratories and at different sion values are computed from the remaining terms and residuals, times contain batch effects that may compromise the thus yielding a new expression matrix without batch effects. The integration and interpretation of the data. Existing scRNA-seq ComBat method8 uses a similar strategy but performs an additional analysis methods incorrectly assume that the composition of step involving empirical Bayes shrinkage of the blocking coefficient cell populations is either known or identical across batches. estimates. This procedure stabilizes the estimates in the presence of We present a strategy for batch correction based on the limited replicates by sharing information across genes. Other meth- detection of mutual nearest neighbors (MNNs) in the ods, such as RUVseq9 and svaseq10, are also frequently used for batch high-dimensional expression space. Our approach does correction, but their focus is primarily on identifying unknown fac- not rely on predefined or equal population compositions tors of variation, for example, those due to unrecorded experimental across batches; instead, it requires only that a subset of the differences in cell processing. After these factors are identified, their population be shared between batches. We demonstrate the effects can be regressed out as described previously. superiority of our approach compared with existing methods Existing batch-correction methods were specifically designed for by using both simulated and real scRNA-seq data sets.
    [Show full text]
  • Bioinformatic Analysis of Complex, High-Throughput Genomic And
    Bioinformatic analysis of complex, high-throughput genomic and epigenomic data in the context of CD4+ T-cell differentiation and diagnosis and treatment of transplant rejection A thesis presented by Ryan C. Thompson to The Scripps Research Institute Graduate Program in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Biology for The Scripps Research Institute La Jolla, California October 2019 © 2019 by Ryan C. Thompson All rights reserved. i For Dan, who helped me through the hard times again and again. He is, and will always be, fondly remembered and sorely missed. ii Acknowledgements My path through graduate school has been a long and winding one, and I am grateful to all the mentors I have had through the years – Drs. Terry Gaasterland, Daniel Salomon, and Andrew Su – all of whose encouragement and support have been vital to my development into the scientist I am today. I am also thankful for my collaborators in the Salomon lab: Drs. Sarah Lamere, Sunil Kurian, Thomas Whisenant, Padmaja Natarajan, Katie Podshivalova, and Heather Kiyomi Komori; as well as the many other lab members I have worked with in small ways over the years. In addition, Steven Head, Dr. Phillip Ordoukhanian, and Terri Gelbart from the Scripps Genomics core have also been instrumental in supporting my work. And of course, I am thankful for the guidance and expertise provided by my committee, Drs. Nicholas Schork, Ali Torkamani, Michael Petrascheck, and Luc Teyton. Finally, I wish to thank my parents, for instilling in me a love of science and learning from an early age and encouraging me to pursue that love as a career as I grew up.
    [Show full text]