DOCSLIB.ORG

Mechanistic Pharmacodynamic Modeling for Cancer Precision Medicine

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mechanistic Pharmacodynamic Modeling for Cancer Precision Medicine Towards Mechanistic Pharmacodynamic Modeling for Cancer Precision Medicine Marc R. Birtwistle Associate Professor Department of Chemical and Biomolecular Engineering Clemson University Western South Carolina AIChE Meeting Greenville, SC 12 Feb 2019 Snapshot of Attrition During Drug Development 808 oral small- molecule compounds at their highest recorded phase of development Waring et al., Nat Rev Drug Discovery, 2015 Marc R. Birtwistle 2 Simulation is Typically an Integral Component of Design • Example of airplane building – Build many airplanes, see which ones don’t crash? – No! – Sufficient understanding of fluid dynamics and physics allows simulation to screen design ideas • Human biology is far more complex and less understood—even in how to simulate it – Need more basic research o Physiological and pathophysiological mechanisms o Modeling and simulation methods to capture said mechanisms Marc R. Birtwistle 3 Systems Pharmacology Drug Model Outcome 1. Modeling Drug ??? Outcome 2. Simulation Drug Model ??? 3. Control ??? Model Outcome 4 Targeted Therapy was found not to outperform Physician’s Choice Genomics ≠ Drugs 5 BRAF CRAF BRAF Dabrafenib MEK MEK Trametinib ERK ERK 6 What More Does Cancer Precision Medicine Need to Consider? One DriverOne TargetOne Drug 1. Systems • Driver may not be a good direct drug target • Drivers interact; 4-7 drivers per tumor (maybe more) 2. Polypharmacology • Multiple driversmultiple targetsmultiple drugs • Most targeted drugs are promiscuous 3. Dynamics • Tumors adapt and evolve on multiple time scales 4. Heterogeneity • Clonal cells show transient resistance • Cancers comprise multiple subclones with different drivers and microenvironments Marc R. Birtwistle 7 Marc R. Birtwistle 8 Quantitative Systems Pharmacology: Mechanistic Kinetic Modeling of Biochemical Networks ka g g* E-I ki kbm kdm m I E E-S P kbp k S dp p dp = k m - k p I E-S-I dt bp dp Stochastic Gene Expression dm = k g*-k m dt bm dm dg* = k g - k g* dt a i dg = k g*-k g dt i a Marc R. Birtwistle 9 Outline • Mechanistic Models of Cancer Cell Signaling – Formulation, Building, and Training – Stochastic Cell Cycle Entry – Stochastic Cell Death • Towards Training with Big Pharmacological Data • Reconstructing Cell Signaling Networks from Perturbation Time Course Data Marc R. Birtwistle 10 Marc R. Birtwistle 11 Defining Model Scope Pathways: • RTK-RAS-RAF-MAPK • PI3K-AKT-mTOR • Cell Cycle • p53-DNA Damage Adapted from Ciriello G et al., Nature Genetics 2013 Model is Composed of Pathway-Specific Models from the Literature Submodel Origin • Birtwistle MR et al., MSB 2007 Receptor • Bouhaddou M & Birtwistle MR, Tyrosine Mol Biosys 2014 Kinase • Many others • Birtwistle MR et al., MSB 2007 Proliferation & • Nakakuki T et al., Cell 2010 Growth • Kriegsheim A et al., Nat Cell Bio 2009 DNA Damage • Batchelor E et al., MSB, 2011 • Gerard C & Goldbeter A, PNAS Cell Cycle 2009 • Albeck JG et al., Plos Biology Apoptosis 2008 Expression The standard model System of ordinary differential equations (ODEs) ~800 species and ~2800 reactions A Prettier Picture EXTRACELLULAR SPACE CYTOPLASM RECEPTOR DEATH TYROSINE KINASE RECEPTORS EGFR, Erbb2, Erbb3, Erbb4, cMet, PDGFR, FGFR, IGFR, INSR, EGF, HRG, HGF, PDGF, FGF, IGF, Insulin MITOCHONDRIA GROWTH TSC1, TSC2, mTOR, APOPTOSIS Rictor, Raptor, S6K, PROLIFERATION EIF4EBP1, EIF4E, Grb-2, SOS, Cbl, Sprouty, TRAIL ligand, TRAIL Ribosomes Ras, C-Raf, B-Raf, MEK, receptors (DR4-DR5), ERK, cJun, cFos, NF1, Caspases 8/3/6/9, tBID, DUSP6, DUSP1, RSK, IRS, BIM, BAX, Cytochrome C, PLCg, GRP, PI3KC1, PI3KR1, APAF, XIAP, PARP, Flip, PI3K2, PTEN, PDK1, AKT, Bar, Smac, Bad, Bcl2, FOXO, GSK3-β, bCatenin, PUMA, NOXA f cMyc TRANSLATION DEATH EXPRESSION NUCLEUS Genes and mRNAs TRANSCRIPTION CELL CYCLE f Cyclins D/E/A/B, CDKs DNA DAMAGE 4/6/2/1, Chk1, p21, ATM, ATR, p53, WIP1, EPIGENETICS p27, E2F, Rb, Wee1, MDM2, MDM4, ARF, CDH1, CDC25A, BRCA2, MSH6, MGMT CDC25B, CDC25C, Skp2, Cdc20 DIVISION Model considers 141 genes Single Cells Have Stochastic Response to Drugs Adapted from Gascoigne and Taylor, Cancer Cell 2008 Cell-to-cell variability: Simulating Stochastic Gene Expression STOCHASTIC g g* mRNA production m f Transcriptional p f Activators and Repressors SIGNALING (TARs) DETERMINISTIC Increasing Confidence in Models INTEGRATED UNIT TESTING UNIT TESTING ANALYSIS Evaluate input-output Evaluate input- Use model to reason behavior of individual output behavior of about biological sub-models model as a whole observations Cell context: Start with non-transformed MCF10A • Predictable phenotypic behaviors • Few alterations • Extensive literature data and widely studied Marc R. Birtwistle 17 Unit Testing—Expression: Tailoring Model to Quantitative Expression Context Define “expression context”: 1. GENOME: Gene copy number 2. TRANSCRIPTOME: mRNA levels Post-Initialization 3. PROTEOME: Protein levels Concentration (nM) Concentration PROTEOMICS (Mass Spectrometry) PROTEIN Serum- LEVELS Starved MCF10A Cells TRANSCRIPTOMICS (RNA-seq) mRNA SAMPLE INITIALIZATION LEVELS GENOMICS* (Sequencing) GENE COPY RUN SIMULATIONS NUMBER* * Bessette et al., Plos One 2015 Unit Testing – Receptor Tyrosine Kinase (RTK) Ligand-Receptor EGFR ErbB2 ErbB3 ErbB4 Cooperativity Behavior EGF HRG HGF cMET PDGFR FGFR IGFR INSR PDGF LITERATURE DICTATES Negative cooperativity (n<1) FGF No cooperativity (n=1) Positive cooperativity (n>1) [Receptor Complex] (nM) Complex] [Receptor IGF INS [Ligand Dose] Unit Testing – Proliferation & Growth EGF and Insulin ERK Micro-Western Blot ERK AKT mTOR AKT mTOR Experiments by Rick Koch and Anne Marie Barrette Unit Testing Submodel Required Properties for Each Submodel Receptor • Ligand-receptor cooperativity matches experimental observations. Tyrosine Kinase • Receptor trafficking kinetics reflects experimental observations. Proliferation & • Receptor pathway preferences match experimental observations. • Basal activity fluxes through ERK and AKT pathways exist, tailored to the serum-starved state. Growth • Dynamic dose responses of ERK, AKT, and mTOR signaling matches experimental western blot data. Cell Cycle • Cell cycle entry is driven by induction of cyclin D mRNA. • Order and timing of cyclin/cdk complexes matches established observations. • Cell cycle duration matches that in MCF10A cells. • Upregulation of p21 arrests the cell cycle. Apoptosis • Robustness against small death signals. • Model exhibits all-or-nothing death response when apoptosis signaling surpasses threshold. • Dose and dynamics of TRAIL-induced extrinsic apoptosis matches experimental observations. • Intrinsic apoptosis signaling responds to interrupted survival signaling and DNA damage induced upregulation of pro-apoptotic proteins. DNA Damage • Convert original delayed differential equations into ordinary differential equations. • p53 dynamics corresponding to single- and double-stranded DNA breaks matches experimental observations. • Rate of DNA damage repair is dependent on levels of repair enzymes. • p53 activation dynamics exhibit “digital” and not “analog” behavior, whereby the number of p53 pulses, but not pulse height or width, scales to magnitude of DNA damage. • Etoposide-induced DNA damage is dependent on the cell cycle stage (S-phase). Expression • Model is tailored to genomic, transcriptomic, and proteomic context of MCF10A cells. • Stochastic gene expression is simulated with a computationally efficient algorithm. • Cell-to-cell variability in mRNA and protein levels matches experimental observations. • EIF4E levels possess extrinsic control over the translation rate. • Ribosomes double during the course of one cell cycle. Integrated Unit Testing – Cell Cycle How are synergistic EGF and Insulin signals integrated by the cell? mitogens kinase inhibitors PROLIFERATION APOPTOSIS DEATH CELL DNA CYCLE DAMAGE DIVISION chemotherapy Spatiotemporal Dynamics of Signaling? PC-12 Cells Sustained ppERK Transient Adapted from Marshall, Cell, 1995 HEK293 cells Short times (under 15 min) Marc R. Birtwistle 24 Integrated Unit Testing – Cell Cycle How are synergistic EGF and Insulin signals integrated by mitogens kinase inhibitors the cell? Cell Fate Biochemistry PROLIFERATION APOPTOSIS SIMULATIONS EXPERIMENT EGF (μ-western blot) DEATH @6hrs CELL DNA INS CYCLE DAMAGE Cyclin D (AU) EGF+INS DIVISION chemotherapy SIMULATION [CYC [CYC A/Cdk2] @6hrs % Proliferating Cells % Proliferating EGF+INS EXPERIMENT +MEKi (BrdU incorporation/ Flow cytometry) Cyclin Cyclin D (nM) @ 24hrs EGF+INS +AKTi Time (hours) @ 24hrs % Proliferating Analysis: Prolonged AKT Activation Explains EGF and Insulin S-Phase Synergy EXPERIMENT (μ-western blot) E 10nM E 1nM EGF Alone E 0.1nM E 0.01nM I 1721nM I 17nM Insulin Alone I 1.7nM ppERK ppERK (AU) I 0.17nM ppAKT (AU) High/High (E/I) High/Low Low/High Combos Low/Low ERK AKT Dynamics Dynamics 26 Inhibitor Time Course Experiments post GF post GF post GF (~24h post post GF starvation) 0 h ~ 18 h ~42 h add ~66 h Seed cells starve treat cells Inhibitors (24 h post GF harvest cells with GF treatment) cells ERK Pathway Inhibitor Akt Inhibitor Fold Proliferation Analysis – Cell Cycle: Phospho-ERK levels dictate stochastic cell cycle entry SIMULATION: In response to EGF + Insulin Initial Levels Cycling (C) *** Not cycling (NC) ppERK ppERK (nM) ppERK C NC Time (hours) * ppAKT ppAKT (nM) ppAKT C NC Time (hours) Analysis – Cell Cycle: Can Stochastic Cell Cycle Response Be Predicted? In MCF10A cells: 1. Lasso regression Predictors: Total initial protein levels SOS Responses: Cycling or non-cycling BRaf and CRaf were top hits ERK 2. Train SVM classifier
Load more

Recommended publications
  • Gene Symbol Gene Description ACVR1B Activin a Receptor, Type IB
    Table S1. Kinase clones included in human kinase cDNA library for yeast two-hybrid screening Gene Symbol Gene Description ACVR1B activin A receptor, type IB ADCK2 aarF domain containing kinase 2 ADCK4 aarF domain containing kinase 4 AGK multiple substrate lipid kinase;MULK AK1 adenylate kinase 1 AK3 adenylate kinase 3 like 1 AK3L1 adenylate kinase 3 ALDH18A1 aldehyde dehydrogenase 18 family, member A1;ALDH18A1 ALK anaplastic lymphoma kinase (Ki-1) ALPK1 alpha-kinase 1 ALPK2 alpha-kinase 2 AMHR2 anti-Mullerian hormone receptor, type II ARAF v-raf murine sarcoma 3611 viral oncogene homolog 1 ARSG arylsulfatase G;ARSG AURKB aurora kinase B AURKC aurora kinase C BCKDK branched chain alpha-ketoacid dehydrogenase kinase BMPR1A bone morphogenetic protein receptor, type IA BMPR2 bone morphogenetic protein receptor, type II (serine/threonine kinase) BRAF v-raf murine sarcoma viral oncogene homolog B1 BRD3 bromodomain containing 3 BRD4 bromodomain containing 4 BTK Bruton agammaglobulinemia tyrosine kinase BUB1 BUB1 budding uninhibited by benzimidazoles 1 homolog (yeast) BUB1B BUB1 budding uninhibited by benzimidazoles 1 homolog beta (yeast) C9orf98 chromosome 9 open reading frame 98;C9orf98 CABC1 chaperone, ABC1 activity of bc1 complex like (S. pombe) CALM1 calmodulin 1 (phosphorylase kinase, delta) CALM2 calmodulin 2 (phosphorylase kinase, delta) CALM3 calmodulin 3 (phosphorylase kinase, delta) CAMK1 calcium/calmodulin-dependent protein kinase I CAMK2A calcium/calmodulin-dependent protein kinase (CaM kinase) II alpha CAMK2B calcium/calmodulin-dependent
    [Show full text]
  • In Vitro Pharmacological Profiling of R406 Identifies Molecular Targets
    ORIGINAL ARTICLE In vitro pharmacological profiling of R406 identifies molecular targets underlying the clinical effects of fostamatinib Michael G. Rolf, Jon O. Curwen, Margaret Veldman-Jones, Cath Eberlein, Jianyan Wang, Alex Harmer, Caroline J. Hellawell & Martin Braddock AstraZeneca R&D Alderley Park, Macclesfield, Cheshire SK10 4TG, United Kingdom Keywords Abstract Blood pressure elevation, fostamatinib, in vitro pharmacological profiling, R406, SYK Off-target pharmacology may contribute to both adverse and beneficial effects of a new drug. In vitro pharmacological profiling is often applied early in drug Correspondence discovery; there are fewer reports addressing the relevance of broad profiles to Michael G. Rolf, AstraZeneca R&D Molndal,€ clinical adverse effects. Here, we have characterized the pharmacological profile Pepparedsleden 1, 431 83 Mo¨ lndal, Sweden. of the active metabolite of fostamatinib, R406, linking an understanding of drug Tel: +46 31 776 60 40; Fax: +46 31 776 37 selectivity to the increase in blood pressure observed in clinical studies. R406 60; E-mail: [email protected] was profiled in a broad range of in vitro assays to generate a comprehensive Funding Information pharmacological profile and key targets were further investigated using func- No funding information provided. tional and cellular assay systems. A combination of traditional literature searches and text-mining approaches established potential mechanistic links Received: 9 April 2015; Accepted: 14 July between the profile of R406 and clinical side effects. R406 was selective outside 2015 the kinase domain, with only antagonist activity at the adenosine A3 receptor in the range relevant to clinical effects. R406 was less selective in the kinase Pharma Res Per, 3(5), 2015, e00175, doi: 10.1002/prp2.175 domain, having activity at many protein kinases at therapeutically relevant con- centrations when tested in multiple in vitro systems.
    [Show full text]
  • Phospho-RPS6KA1(T359) Antibody Peptide Affinity Purified Rabbit Polyclonal Antibody (Pab) Catalog # Ap3497a
    9765 Clairemont Mesa Blvd, Suite C San Diego, CA 92124 Tel: 858.875.1900 Fax: 858.622.0609 Phospho-RPS6KA1(T359) Antibody Peptide Affinity Purified Rabbit Polyclonal Antibody (Pab) Catalog # AP3497a Specification Phospho-RPS6KA1(T359) Antibody - Product Information Application WB, DB,E Primary Accession Q15418 Other Accession P10666, P10665, P18652 Reactivity Human Predicted Chicken, Xenopus Host Rabbit Clonality Polyclonal Isotype Rabbit Ig Calculated MW 82723 Phospho-RPS6KA1(T359) Antibody - Additional Information Gene ID 6195 Other Names Western blot analysis of extracts from Hela Ribosomal protein S6 kinase alpha-1, cells,untreated or treated with S6K-alpha-1, 90 kDa ribosomal protein S6 TPA,200nM，using phospho RPS6KA1-T359 (left) kinase 1, p90-RSK 1, p90RSK1, p90S6K, MAP or RPS6KA1 antibody(right) kinase-activated protein kinase 1a, MAPK-activated protein kinase 1a, MAPKAP kinase 1a, MAPKAPK-1a, Ribosomal S6 kinase 1, RSK-1, RPS6KA1, MAPKAPK1A, RSK1 Target/Specificity This RPS6KA1 Antibody is generated from rabbits immunized with a KLH conjugated synthetic phosphopeptide corresponding to amino acid residues surrounding T359 of human RPS6KA1. Dilution WB~~1:2000 DB~~1:500 Format Purified polyclonal antibody supplied in PBS with 0.09% (W/V) sodium azide. This antibody is purified through a protein A column, followed by peptide affinity purification. Dot blot analysis of anti-RPS6KA1-pT359 Pab (RB13385) on nitrocellulose membrane. 50ng Storage of Phospho-peptide or Non Phospho-peptide per Maintain refrigerated at 2-8°C for up to 6 dot were adsorbed. Antibody working months. For long term storage store at -20°C concentrations are 0.5ug per ml. in small aliquots to prevent freeze-thaw cycles.
    [Show full text]
  • Personalized Prediction of Acquired Resistance to EGFR-Targeted Inhibitors Using a Pathway-Based Machine Learning Approach
    Cancers 2019, 11, x S1 of S9 Supplementary Materials: Personalized Prediction of Acquired Resistance to EGFR-Targeted Inhibitors Using a Pathway-Based Machine Learning Approach Young Rae Kim, Yong Wan Kim, Suh Eun Lee, Hye Won Yang and Sung Young Kim Table S1. Characteristics of individual studies. Sample Size Origin of Cancer Drug Dataset Platform S AR (Cell Lines) Lung cancer Agilent-014850 Whole Human GSE34228 26 26 (PC9) Genome Microarray 4x44K Gefitinib Epidermoid carcinoma Affymetrix Human Genome U133 GSE10696 3 3 (A431) Plus 2.0 Head and neck cancer Illumina HumanHT-12 V4.0 GSE62061 12 12 (Cal-27, SSC-25, FaDu, expression beadchip SQ20B) Erlotinib Head and neck cancer Illumina HumanHT-12 V4.0 GSE49135 3 3 (HN5) expression beadchip Lung cancer (HCC827, Illumina HumanHT-12 V3.0 GSE38310 3 6 ER3, T15-2) expression beadchip Lung cancer Illumina HumanHT-12 V3.0 GSE62504 1 2 (HCC827) expression beadchip Afatinib Lung cancer * Illumina HumanHT-12 V4.0 GSE75468 1 3 (HCC827) expression beadchip Head and neck cancer Affymetrix Human Genome U133 Cetuximab GSE21483 3 3 (SCC1) Plus 2.0 Array GEO, gene expression omnibus; GSE, gene expression series; S, sensitive; AR, acquired EGFR-TKI resistant; * Lung Cancer Cells Derived from Tumor Xenograft Model. Table S2. The performances of four penalized regression models. Model ACC precision recall F1 MCC AUROC BRIER Ridge 0.889 0.852 0.958 0.902 0.782 0.964 0.129 Lasso 0.944 0.957 0.938 0.947 0.889 0.991 0.042 Elastic Net 0.978 0.979 0.979 0.979 0.955 0.999 0.023 EPSGO Elastic Net 0.989 1.000 0.979 0.989 0.978 1.000 0.018 AUROC, area under curve of receiver operating characteristic; ACC, accuracy; MCC, Matthews correlation coefficient; EPSGO, Efficient Parameter Selection via Global Optimization algorithm.
    [Show full text]
  • The P90 RSK Family Members: Common Functions and Isoform Specificity
    Published OnlineFirst August 22, 2013; DOI: 10.1158/0008-5472.CAN-12-4448 Cancer Review Research The p90 RSK Family Members: Common Functions and Isoform Specificity Romain Lara, Michael J. Seckl, and Olivier E. Pardo Abstract The p90 ribosomal S6 kinases (RSK) are implicated in various cellular processes, including cell proliferation, survival, migration, and invasion. In cancer, RSKs modulate cell transformation, tumorigenesis, and metastasis. Indeed, changes in the expression of RSK isoforms have been reported in several malignancies, including breast, prostate, and lung cancers. Four RSK isoforms have been identified in humans on the basis of their high degree of sequence homology. Although this similarity suggests some functional redundancy between these proteins, an increasing body of evidence supports the existence of isoform-based specificity among RSKs in mediating particular cellular processes. This review briefly presents the similarities between RSK family members before focusing on the specific function of each of the isoforms and their involvement in cancer progression. Cancer Res; 73(17); 1–8. Ó2013 AACR. Introduction subsequently cloned throughout the Metazoan kingdom (2). The extracellular signal–regulated kinase (ERK)1/2 pathway The genomic analysis of several cancer types suggests that fi is involved in key cellular processes, including cell prolifera- these genes are not frequently ampli ed or mutated, with some tion, differentiation, survival, metabolism, and migration. notable exceptions (e.g., in the case of hepatocellular carcino- More than 30% of all human cancers harbor mutations within ma; ref. 6). Table 1 summarizes reported genetic changes in this pathway, mostly resulting in gain of function and conse- RSK genes.
    [Show full text]
  • RPS6KA1 Antibody Mouse Monoclonal Antibody (Mab) Catalog # Am1882b
    10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 RPS6KA1 Antibody Mouse Monoclonal Antibody (Mab) Catalog # AM1882b Specification RPS6KA1 Antibody - Product Information Application WB,E Primary Accession Q15418 Other Accession NP_001006666.1, NP_002944.2 Reactivity Human, Mouse Host Mouse Clonality Monoclonal Isotype IgG1,K RPS6KA1 Antibody - Additional Information Gene ID 6195 RPS6KA1 antibody (Cat. #AM1882b) western Other Names blot analysis in K562 cell line lysates Ribosomal protein S6 kinase alpha-1, (35μg/lane).This demonstrates the RPS6KA1 S6K-alpha-1, 90 kDa ribosomal protein S6 antibody detected the RPS6KA1 protein kinase 1, p90-RSK 1, p90RSK1, p90S6K, (arrow). MAP kinase-activated protein kinase 1a, MAPK-activated protein kinase 1a, MAPKAP kinase 1a, MAPKAPK-1a, Ribosomal S6 kinase 1, RSK-1, RPS6KA1, MAPKAPK1A, RSK1 Target/Specificity This RPS6KA1 monoclonal antibody is generated from mouse immunized with RPS6KA1 recombinant protein. Dilution WB~~1:1000 Format Purified monoclonal antibody supplied in PBS with 0.09% (W/V) sodium azide. This antibody is purified through a protein G column, followed by dialysis against PBS. "All lanes : Anti-RPS6KA1 Antibody at 1:1000 Storage dilution Lane 1: mouse brain lysate Lane 2: Maintain refrigerated at 2-8°C for up to 2 mouse cerebellum lysate Secondary Goat weeks. For long term storage store at -20°C Anti-mouse IgG, (H+L),Peroxidase conjugated in small aliquots to prevent freeze-thaw at 1/10000 dilution. Predicted band size : cycles. 82723 Da Blocking/Dilution buffer: 5% NFDM/TBST." Precautions RPS6KA1 Antibody is for research use only Page 1/3 10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 and not for use in diagnostic or therapeutic RPS6KA1 Antibody - Background procedures.
    [Show full text]
  • Characterization of the Small Molecule Kinase Inhibitor SU11248 (Sunitinib/ SUTENT in Vitro and in Vivo
    TECHNISCHE UNIVERSITÄT MÜNCHEN Lehrstuhl für Genetik Characterization of the Small Molecule Kinase Inhibitor SU11248 (Sunitinib/ SUTENT in vitro and in vivo - Towards Response Prediction in Cancer Therapy with Kinase Inhibitors Michaela Bairlein 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 genehmigten Dissertation. Vorsitzender: Univ. -Prof. Dr. K. Schneitz Prüfer der Dissertation: 1. Univ.-Prof. Dr. A. Gierl 2. Hon.-Prof. Dr. h.c. A. Ullrich (Eberhard-Karls-Universität Tübingen) 3. Univ.-Prof. A. Schnieke, Ph.D. Die Dissertation wurde am 07.01.2010 bei der Technischen Universität München eingereicht und durch die Fakultät Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt am 19.04.2010 angenommen. FOR MY PARENTS 1 Contents 2 Summary ................................................................................................................................................................... 5 3 Zusammenfassung .................................................................................................................................................... 6 4 Introduction .............................................................................................................................................................. 8 4.1 Cancer ..............................................................................................................................................................
    [Show full text]
  • GSK3 and Its Interactions with the PI3K/AKT/Mtor Signalling Network
    Heriot-Watt University Research Gateway GSK3 and its interactions with the PI3K/AKT/mTOR signalling network Citation for published version: Hermida, MA, Kumar, JD & Leslie, NR 2017, 'GSK3 and its interactions with the PI3K/AKT/mTOR signalling network', Advances in Biological Regulation, vol. 65, pp. 5-15. https://doi.org/10.1016/j.jbior.2017.06.003 Digital Object Identifier (DOI): 10.1016/j.jbior.2017.06.003 Link: Link to publication record in Heriot-Watt Research Portal Document Version: Peer reviewed version Published In: Advances in Biological Regulation Publisher Rights Statement: © 2017 Elsevier B.V. General rights Copyright for the publications made accessible via Heriot-Watt Research Portal is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy Heriot-Watt University has made every reasonable effort to ensure that the content in Heriot-Watt Research Portal complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 27. Sep. 2021 Accepted Manuscript GSK3 and its interactions with the PI3K/AKT/mTOR signalling network Miguel A. Hermida, J. Dinesh Kumar, Nick R. Leslie PII: S2212-4926(17)30124-0 DOI: 10.1016/j.jbior.2017.06.003 Reference: JBIOR 180 To appear in: Advances in Biological Regulation Received Date: 13 June 2017 Accepted Date: 23 June 2017 Please cite this article as: Hermida MA, Dinesh Kumar J, Leslie NR, GSK3 and its interactions with the PI3K/AKT/mTOR signalling network, Advances in Biological Regulation (2017), doi: 10.1016/ j.jbior.2017.06.003.
    [Show full text]
  • Cells Phenotype of Human Tolerogenic Dendritic Glycolytic
    High Mitochondrial Respiration and Glycolytic Capacity Represent a Metabolic Phenotype of Human Tolerogenic Dendritic Cells This information is current as of September 26, 2021. Frano Malinarich, Kaibo Duan, Raudhah Abdull Hamid, Au Bijin, Wu Xue Lin, Michael Poidinger, Anna-Marie Fairhurst and John E. Connolly J Immunol published online 27 April 2015 http://www.jimmunol.org/content/early/2015/04/25/jimmun Downloaded from ol.1303316 Supplementary http://www.jimmunol.org/content/suppl/2015/04/25/jimmunol.130331 http://www.jimmunol.org/ Material 6.DCSupplemental Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists by guest on September 26, 2021 • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2015 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. Published April 27, 2015, doi:10.4049/jimmunol.1303316 The Journal of Immunology High Mitochondrial Respiration and Glycolytic Capacity Represent a Metabolic Phenotype of Human Tolerogenic Dendritic Cells Frano Malinarich,*,† Kaibo Duan,† Raudhah Abdull Hamid,*,† Au Bijin,*,† Wu Xue Lin,*,† Michael Poidinger,† Anna-Marie Fairhurst,† and John E.
    [Show full text]
  • The Mtor Substrate S6 Kinase 1 (S6K1)
    The Journal of Neuroscience, July 26, 2017 • 37(30):7079–7095 • 7079 Cellular/Molecular The mTOR Substrate S6 Kinase 1 (S6K1) Is a Negative Regulator of Axon Regeneration and a Potential Drug Target for Central Nervous System Injury X Hassan Al-Ali,1,2,3* Ying Ding,5,6* Tatiana Slepak,1* XWei Wu,5 Yan Sun,5,7 Yania Martinez,1 Xiao-Ming Xu,5 Vance P. Lemmon,1,3 and XJohn L. Bixby1,3,4 1Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, Florida 33136, 2Peggy and Harold Katz Family Drug Discovery Center, University of Miami Miller School of Medicine, Miami, Florida 33136, 3Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida 33136, 4Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida 33136, 5Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana 46202, 6Department of Histology and Embryology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China, and 7Department of Anatomy, Histology and Embryology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China Themammaliantargetofrapamycin(mTOR)positivelyregulatesaxongrowthinthemammaliancentralnervoussystem(CNS).Althoughaxon regeneration and functional recovery from CNS injuries are typically limited, knockdown or deletion of PTEN, a negative regulator of mTOR, increases mTOR activity and induces robust axon growth and regeneration. It has been suggested that inhibition of S6 kinase 1 (S6K1, gene symbol: RPS6KB1), a prominent mTOR target, would blunt mTOR’s positive effect on axon growth. In contrast to this expectation, we demon- strate that inhibition of S6K1 in CNS neurons promotes neurite outgrowth in vitro by twofold to threefold.
    [Show full text]
  • The Mtor Substrate S6 Kinase 1 (S6K1) Is a Negative Regulator of Axon Regeneration and a Potential Drug Target for Central Nervous System Injury
    This Accepted Manuscript has not been copyedited and formatted. The final version may differ from this version. Research Articles: Cellular/Molecular The mTOR substrate S6 Kinase 1 (S6K1) is a negative regulator of axon regeneration and a potential drug target for Central Nervous System injury Hassan Al-Ali1,2,3, Ying Ding5,6, Tatiana Slepak1, Wei Wu5, Yan Sun5,7, Yania Martinez1, Xiao-Ming Xu5, V.P. Lemmon1,3 and J.l. Bixby1,3,4 1The Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, FL 33136, USA. 2Peggy and Harold Katz Family Drug Discovery Center, University of Miami Miller School of Medicine, Miami, FL, 33136, USA. 3Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA. 4Department of Molecular & Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA. 5Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN 46202, USA. 6Department of Histology and Embryology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China. 7Department of Anatomy, Histology and Embryology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China. DOI: 10.1523/JNEUROSCI.0931-17.2017 Received: 4 April 2017 Revised: 18 May 2017 Accepted: 27 May 2017 Published: 16 June 2017 Author contributions: H.A.-A., Y.D., T.S., W.W., Y.S., X.-M.X., V.P.L., and J.B. designed research; H.A.-A., Y.D., T.S., W.W., Y.S., and Y.M.
    [Show full text]
  • Activation of Diverse Signalling Pathways by Oncogenic PIK3CA Mutations
    ARTICLE Received 14 Feb 2014 | Accepted 12 Aug 2014 | Published 23 Sep 2014 DOI: 10.1038/ncomms5961 Activation of diverse signalling pathways by oncogenic PIK3CA mutations Xinyan Wu1, Santosh Renuse2,3, Nandini A. Sahasrabuddhe2,4, Muhammad Saddiq Zahari1, Raghothama Chaerkady1, Min-Sik Kim1, Raja S. Nirujogi2, Morassa Mohseni1, Praveen Kumar2,4, Rajesh Raju2, Jun Zhong1, Jian Yang5, Johnathan Neiswinger6, Jun-Seop Jeong6, Robert Newman6, Maureen A. Powers7, Babu Lal Somani2, Edward Gabrielson8, Saraswati Sukumar9, Vered Stearns9, Jiang Qian10, Heng Zhu6, Bert Vogelstein5, Ben Ho Park9 & Akhilesh Pandey1,8,9 The PIK3CA gene is frequently mutated in human cancers. Here we carry out a SILAC-based quantitative phosphoproteomic analysis using isogenic knockin cell lines containing ‘driver’ oncogenic mutations of PIK3CA to dissect the signalling mechanisms responsible for oncogenic phenotypes induced by mutant PIK3CA. From 8,075 unique phosphopeptides identified, we observe that aberrant activation of PI3K pathway leads to increased phosphorylation of a surprisingly wide variety of kinases and downstream signalling networks. Here, by integrating phosphoproteomic data with human protein microarray-based AKT1 kinase assays, we discover and validate six novel AKT1 substrates, including cortactin. Through mutagenesis studies, we demonstrate that phosphorylation of cortactin by AKT1 is important for mutant PI3K-enhanced cell migration and invasion. Our study describes a quantitative and global approach for identifying mutation-specific signalling events and for discovering novel signalling molecules as readouts of pathway activation or potential therapeutic targets. 1 McKusick-Nathans Institute of Genetic Medicine and Department of Biological Chemistry, Johns Hopkins University School of Medicine, 733 North Broadway, BRB 527, Baltimore, Maryland 21205, USA.
    [Show full text]