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Rôle Fonctionnel Des Pentoses Phosphates Et Glutamine Dans Le Métabolisme Des Cellules Cancéreuses

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Rôle Fonctionnel Des Pentoses Phosphates Et Glutamine Dans Le Métabolisme Des Cellules Cancéreuses THÈSE Pour obtenir le grade de DOCTEUR DE LA COMMUNAUTÉ UNIVERSITÉ GRENOBLE ALPES préparée dans le cadre d’une cotutelle entre la Communauté Université Grenoble Alpes et Université de Barcelona Spécialité : BIS-Biotechnologie, instrumentation, signal et imagerie pour la biologie, la médecine et l’environnement Arrêté ministériel : 25 mai 2016 Présentée par « Ibrahim Halil Polat » Thèse dirigée par « Philippe Sabatier » codirigée par « Marta Cascante » préparée au sein des Laboratoires « TIMC-IMAG » et « BQI » dans les Écoles Doctorales « EDISCE » et « Ecole Doctorale de l’Université de Barcelone » Rôle fonctionnel des pentoses phosphates et glutamine dans le métabolisme des cellules cancéreuses Thèse soutenue publiquement le « 4 novembre 2016 », devant le jury composé de : M. Santiago Imperial Rodenas Professeur à l’Université de Barcelone (Président) Mme Loranne Agius Professeur à l’Université de Newcastle Upon Tyne (Rapporteur) M. Jose Antonio Lupiañez Professeur à l’Université de Grenade (Rapporteur) M. Philippe Sabatier Professeur à l’Université Grenoble Alpes (Membre) Functional Role of Pentose Phosphate Pathway and Glutamine in Cancer Cell Metabolism Ibrahim Halil Polat Doctoral Thesis 2016 To Christian, my family and all the people who have shared my life experience during the last years. My sincere thanks to Dr. Marta Cascante, Dr. Philippe Sabatier, Dr. Roldan Cortes and Dr. Silvia Marin for their support to make this Ph.D Thesis possible. Also, special thanks to Dr. Miriam Tarrado, Dr. Anusha Jayaraman, Joseph Tarrago, Erika Zodda (que me sueña), and Dr. Esther Aguilar (canari@s) for always being there for me. “Un científico es una persona a la que el sistema educativo no ha logrado destruir su curiosidad de niño y sigue preguntándose cosas como por qué el cielo es azul” León Lederman (Premio Nobel de Física 1988) TABLE OF CONTENTS 1. ABBREVIATIONS ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 13 2. INTRODUCTION ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 19 2.1. CANCER ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 21 2.1.1. Breast Cancer ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 21 2.1.2. Colorectal Cancer ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 22 2.2. HALLMARKS OF CANCER ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 23 2.3. UNDERSTANDING CANCER METABOLISM ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 28 2.3.1. An Emerging Hallmark: Tumor Metabolic Reprogramming ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 29 2.3.2. Glycolysis and Learnings from Warburg ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 32 2.3.3. Pentose Phosphate Pathway ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 34 2.3.4. Mitochondrial Metabolism ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 38 2.3.4.1. Glutamine Metabolism ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 39 2.3.5. Amino Acid Metabolism ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 41 2.3.6. Lipid Metabolism ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 42 2.3.7. Cell Metabolism Plays a Key Role in the Regulation of Redox Status, Cell Cycle and Apoptosis ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 43 2.3.8. Crosstalk between Signaling Events and Cancer Metabolism ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 47 2.3.9. Current Approaches in Cancer Therapy Targeting Metabolic Reprogramming ‐‐‐‐‐‐ 50 2.4. TOOLS USED TO STUDY CANCER METABOLISM ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 54 2.4.1. Metabolomics ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 54 Adapted from Frontiers, an Open Access Publisher. Vernocchi, P., et al., Integration of datasets from different analytical techniques to assess the impact of nutrition on human metabolome. Front Cell Infect Microbiol, 2012. 2: p. 156. Copyright © 2012 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 56 2.4.2. Fluxomics ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 57 2.4.2.1. Parallel Labeling Approach in 13C Assisted Metabolomics Experiments ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 59 2.4.2.2. Mass Isotopomer Distribution Analysis ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 62 2.4.2.3. Computational Modeling Based Flux Estimation ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 63 3. OBJECTIVES ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 65 4. MATERIALS AND METHODS ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 69 4.1. Cell Culture ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 71 4.2. siRNA Transfection ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 71 4.3. RNA isolation and gene expression analysis ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 72 4.4. Cell proliferation and Viability Assays ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 73 4.5. Cell Cycle distribution analysis. ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 74 4.6. Apoptosis measurement. ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 74 4.7. Enzyme Activity Assays ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 75 4.7.1. 6‐Phosphogluconate Dehydrogenase (6PGD), Glucose‐6‐Phosphate Dehydrogenase (G6PD) Malic Enzyme (ME) and Isocitrate Dehydrogenase (IDH) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 75 4.7.2. Lactate dehydrogenase ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 76 4.8. Mammosphere formation assay (3D cell culture) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 76 4.9. Total Protein Extraction from Cell Culture ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 76 4.10. Western Blot ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 77 4.11. ROS level Measurement ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 77 4.12. Biochemical Assays ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 78 4.13. Transcriptomic analysis ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 79 4.14. 13C Mediated Metabolomics ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 80 4.14.1. Glucose ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 81 4.14.2. Lactate ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 82 4.14.3. Glutamate ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 82 4.14.4. Alanine, Glycine, Aspartate/Aspargine, Glutamate/Glutamine, Serine, Proline, Methionine ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 83 4.14.5. Ribose ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 84 4.14.6. Palmitate and Stereate ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 84 4.14.7. TCA Cycle Intermediates ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 85 4.15. GC‐MS Data Reduction ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 86 4.16. Data Analysis and modeling by using Isodyn ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 86 4.17. Mass isotopomer distribution analysis (MIDA) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 87 4.17.1. Calculation of pathway specific production of lactate ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 88 4.17.2. Calculation of glucose contribution to Mitochondria (via PC or PDH) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 90 4.17.3. Calculation of glucose and glutamine contribution to fatty acid synthesis ‐‐‐‐‐‐‐‐‐‐‐ 91 4.17.4. Calculation of Approximate Flux ratio of Glutamate Dehydrogenase (GDH) to Isocitrate Dehdrogenase (IDH) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 92 4.18. Statistical analysis ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 93 5. RESULTS AND DISCUSSION ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 95 5.1. The Role of 6‐Phosphogluconate Dehydrogenase (6PGD) in Breast Cancer Cells: A Novel Therapeutic Target Against Breast Cancer ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 97 5.1.1. Introduction ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 97 5.1.2. Results ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 99 5.1.2.1. 6PGD Knockdown in MCF7 and T47D Cells ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 99 5.1.2.2. Inhibition of 6PGD Reduces in vitro Proliferation of Breast Cancer Cells ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 100 5.1.2.3. 6PGD Knockdown Leads to Cell Cycle Arrest and Apoptosis Induction ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐
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    Downloaded from http://www.jimmunol.org/ by guest on October 2, 2021 is online at: average * The Journal of Immunology published online 20 April 2012 from submission to initial decision 4 weeks from acceptance to publication http://www.jimmunol.org/content/early/2012/04/20/jimmun ol.1103760 Complement Protein C1q Directs Macrophage Polarization and Limits Inflammasome Activity during the Uptake of Apoptotic Cells Marie E. Benoit, Elizabeth V. Clarke, Pedro Morgado, Deborah A. Fraser and Andrea J. Tenner J Immunol Submit online. Every submission reviewed by practicing scientists ? is published twice each month by http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://www.jimmunol.org/content/suppl/2012/04/20/jimmunol.110376 0.DC1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2012 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of October 2, 2021. Published April 20, 2012, doi:10.4049/jimmunol.1103760 The Journal of Immunology Complement Protein C1q Directs Macrophage Polarization and Limits Inflammasome Activity during the Uptake of Apoptotic Cells Marie E. Benoit, Elizabeth V. Clarke, Pedro Morgado, Deborah A. Fraser, and Andrea J. Tenner Deficiency in C1q, the recognition component of the classical complement cascade and a pattern recognition receptor involved in apoptotic cell clearance, leads to lupus-like autoimmune diseases characterized by auto-antibodies to self proteins and aberrant innate immune cell activation likely due to impaired clearance of apoptotic cells.
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  • Transcriptome Profiling Reveals the Complexity of Pirfenidone Effects in IPF
    ERJ Express. Published on August 30, 2018 as doi: 10.1183/13993003.00564-2018 Early View Original article Transcriptome profiling reveals the complexity of pirfenidone effects in IPF Grazyna Kwapiszewska, Anna Gungl, Jochen Wilhelm, Leigh M. Marsh, Helene Thekkekara Puthenparampil, Katharina Sinn, Miroslava Didiasova, Walter Klepetko, Djuro Kosanovic, Ralph T. Schermuly, Lukasz Wujak, Benjamin Weiss, Liliana Schaefer, Marc Schneider, Michael Kreuter, Andrea Olschewski, Werner Seeger, Horst Olschewski, Malgorzata Wygrecka Please cite this article as: Kwapiszewska G, Gungl A, Wilhelm J, et al. Transcriptome profiling reveals the complexity of pirfenidone effects in IPF. Eur Respir J 2018; in press (https://doi.org/10.1183/13993003.00564-2018). This manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Copyright ©ERS 2018 Copyright 2018 by the European Respiratory Society. Transcriptome profiling reveals the complexity of pirfenidone effects in IPF Grazyna Kwapiszewska1,2, Anna Gungl2, Jochen Wilhelm3†, Leigh M. Marsh1, Helene Thekkekara Puthenparampil1, Katharina Sinn4, Miroslava Didiasova5, Walter Klepetko4, Djuro Kosanovic3, Ralph T. Schermuly3†, Lukasz Wujak5, Benjamin Weiss6, Liliana Schaefer7, Marc Schneider8†, Michael Kreuter8†, Andrea Olschewski1,
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  • (NF1) As a Breast Cancer Driver
    INVESTIGATION Comparative Oncogenomics Implicates the Neurofibromin 1 Gene (NF1) as a Breast Cancer Driver Marsha D. Wallace,*,† Adam D. Pfefferle,‡,§,1 Lishuang Shen,*,1 Adrian J. McNairn,* Ethan G. Cerami,** Barbara L. Fallon,* Vera D. Rinaldi,* Teresa L. Southard,*,†† Charles M. Perou,‡,§,‡‡ and John C. Schimenti*,†,§§,2 *Department of Biomedical Sciences, †Department of Molecular Biology and Genetics, ††Section of Anatomic Pathology, and §§Center for Vertebrate Genomics, Cornell University, Ithaca, New York 14853, ‡Department of Pathology and Laboratory Medicine, §Lineberger Comprehensive Cancer Center, and ‡‡Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27514, and **Memorial Sloan-Kettering Cancer Center, New York, New York 10065 ABSTRACT Identifying genomic alterations driving breast cancer is complicated by tumor diversity and genetic heterogeneity. Relevant mouse models are powerful for untangling this problem because such heterogeneity can be controlled. Inbred Chaos3 mice exhibit high levels of genomic instability leading to mammary tumors that have tumor gene expression profiles closely resembling mature human mammary luminal cell signatures. We genomically characterized mammary adenocarcinomas from these mice to identify cancer-causing genomic events that overlap common alterations in human breast cancer. Chaos3 tumors underwent recurrent copy number alterations (CNAs), particularly deletion of the RAS inhibitor Neurofibromin 1 (Nf1) in nearly all cases. These overlap with human CNAs including NF1, which is deleted or mutated in 27.7% of all breast carcinomas. Chaos3 mammary tumor cells exhibit RAS hyperactivation and increased sensitivity to RAS pathway inhibitors. These results indicate that spontaneous NF1 loss can drive breast cancer. This should be informative for treatment of the significant fraction of patients whose tumors bear NF1 mutations.
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  • Table S1. 103 Ferroptosis-Related Genes Retrieved from the Genecards
    Table S1. 103 ferroptosis-related genes retrieved from the GeneCards. Gene Symbol Description Category GPX4 Glutathione Peroxidase 4 Protein Coding AIFM2 Apoptosis Inducing Factor Mitochondria Associated 2 Protein Coding TP53 Tumor Protein P53 Protein Coding ACSL4 Acyl-CoA Synthetase Long Chain Family Member 4 Protein Coding SLC7A11 Solute Carrier Family 7 Member 11 Protein Coding VDAC2 Voltage Dependent Anion Channel 2 Protein Coding VDAC3 Voltage Dependent Anion Channel 3 Protein Coding ATG5 Autophagy Related 5 Protein Coding ATG7 Autophagy Related 7 Protein Coding NCOA4 Nuclear Receptor Coactivator 4 Protein Coding HMOX1 Heme Oxygenase 1 Protein Coding SLC3A2 Solute Carrier Family 3 Member 2 Protein Coding ALOX15 Arachidonate 15-Lipoxygenase Protein Coding BECN1 Beclin 1 Protein Coding PRKAA1 Protein Kinase AMP-Activated Catalytic Subunit Alpha 1 Protein Coding SAT1 Spermidine/Spermine N1-Acetyltransferase 1 Protein Coding NF2 Neurofibromin 2 Protein Coding YAP1 Yes1 Associated Transcriptional Regulator Protein Coding FTH1 Ferritin Heavy Chain 1 Protein Coding TF Transferrin Protein Coding TFRC Transferrin Receptor Protein Coding FTL Ferritin Light Chain Protein Coding CYBB Cytochrome B-245 Beta Chain Protein Coding GSS Glutathione Synthetase Protein Coding CP Ceruloplasmin Protein Coding PRNP Prion Protein Protein Coding SLC11A2 Solute Carrier Family 11 Member 2 Protein Coding SLC40A1 Solute Carrier Family 40 Member 1 Protein Coding STEAP3 STEAP3 Metalloreductase Protein Coding ACSL1 Acyl-CoA Synthetase Long Chain Family Member 1 Protein
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  • Microscaled Proteogenomic Methods for Precision Oncology
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  • Arxiv:2105.12807V2 [Q-Bio.GN] 4 Jul 2021 CE6T Odn Kand UK ∗ London, 6BT, WC1E 1 Nfr Aiodapoiainadpoeto (UMAP) Projection and Other and 2008] Data
    arXiv, 2021, pp. 1–12 Preprint XOmiVAE Problem Solving Protocol PROBLEM SOLVING PROTOCOL XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data Eloise Withnell,1,2,† Xiaoyu Zhang,1,∗,† Kai Sun1 and Yike Guo1,3 1Data Science Institute, Imperial College London, SW7 2AZ, London, UK, 2Department of Health Informatics, University College London, WC1E 6BT, London, UK and 3Department of Computer Science, Hong Kong Baptist University, Hong Kong, China ∗Corresponding author. [email protected]†The first two authors contributed equally to this paper. Abstract The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This “black box” problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE) based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction, and the correlation between each gene and each latent dimension. It is also demonstrated that XOmiVAE can explain not only the supervised classification but the unsupervised clustering results from the deep learning network. To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE. The explainable results generated by XOmiVAE were validated by both the performance of downstream tasks and the biomedical knowledge. In our experiments, XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and academic literature, which shows great potential for novel biomedical knowledge discovery from deep learning models.
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  • Comparative Oncogenomics Implicates the Neurofibromin 1 Gene (NF1) As A
    Genetics: Published Articles Ahead of Print, published on July 30, 2012 as 10.1534/genetics.112.142802 Comparative oncogenomics implicates the Neurofibromin 1 gene (NF1) as a breast cancer driver Marsha D. Wallace1,2, Adam D. Pfefferle3,4 ^, Lishuang Shen1^, Adrian J. McNairn1, Ethan G. Cerami5, Barbara L. Fallon1, Vera D. Rinaldi1, Teresa L. Southard1,6, Charles M. Perou3,4,7, John C. Schimenti1,2,8 1Department of Biomedical Sciences 2Department of Molecular Biology and Genetics 6Section of Anatomic Pathology 8Center for Vertebrate Genomics Cornell University, Ithaca, NY 14853 3Department of Pathology and Laboratory Medicine 4Lineberger Comprehensive Cancer Center 7Department of Genetics University of North Carolina, Chapel Hill, NC 27514 5Memorial Sloan-Kettering Cancer Center, New York, NY 10065 1 Copyright 2012. ^ Contributed equally to this paper Running Title: NF1 deletion in breast cancer Key Words: breast cancer; mouse genetics; neurofibromin; NF1; genomic instability. Corresponding author: John Schimenti, Ph.D. Cornell Univ. College of Veterinary Medicine Ithaca, NY 14850 email: [email protected] phone 607-253-3636 Fax 607-253-3789 2 ABSTRACT Identifying genomic alterations driving breast cancer is complicated by tumor diversity and genetic heterogeneity. Relevant mouse models are powerful for untangling this problem because such heterogeneity can be controlled. Inbred Chaos3 mice exhibit high levels of genomic instability leading to mammary tumors that have tumor gene expression profiles closely resembling mature human mammary luminal cell signatures. We genomically characterized mammary adenocarcinomas from these mice to identify cancer-causing genomic events that overlap common alterations in human breast cancer. Chaos3 tumors underwent recurrent copy number alterations (CNAs), particularly deletion of the RAS inhibitor Neurofibromin 1 (Nf1) in nearly all cases.
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  • Supplementary Information
    Supplementary Information Figure S1: Normalization strategy of BioID results. Normalization of protein MS-Label Free Quantification (LFQ) values between NHA9-BioID and BirAR118G and SN214-BioID and BirAR118G. A two-step normalization approach was used to account for the differences in protein expression between the BioID fusion proteins and the control BirAR118G. 1. Internal normalization (LFQi): for each condition, the ratio between the LFQ value of each protein (protein A) and the LFQ value of the BirAR118G within the same condition (i.e. the BirA portion from the respective BioID fusion protein). 2. External normalization (LFQe): the LFQi value of each protein (protein A) in NHA9-BioID or SN214-BioID was divided by the corresponding LFQi value in the control condition (BirA). The results are expressed as a fold-change, which corresponds to log2. Figure S2: Clustered Pathway Analysis of NUP98-HOXA9-BioID proximal interactors. Functionally grouped NHA9- BioID interactors. Statistical analysis was performed with the Cytoscape plugin ClueGo (v2.5.5) using hypergeometric test and the following parameters: p <0.01, kappa-score (k) = 0.4; (min/%) genes = 3/4%, GO tree levels: 3-11. Ontology databases: GOBP, GOCC, and GOMF, Reactome Pathways and KEGG. Figure S3: Most represented cellular compartments (GOCC) among SN214-BioID proximal interactors. Statistical analysis of the overrepresented proteins in the SN214-BioID fraction (total 1125 proteins) with PANTHER classification online software (v14.1) using Fisher’s exact test. Results are displayed for FDR p <0.05. Figure S4: Most represented biological processes (GOBP) among SN214-BioID proximal interactors. Statistical analysis of the overrepresented proteins in the SN214-BioID fraction (total 1125 proteins) with PANTHER classification online software (v14.1) using Fisher’s exact test.
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