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Expression of genes involved in oxidative stress responses in airway epithelial cells of COPD Expression of genes involved in oxidative stress responses in airway epithelial cells of COPD smokers Per Broberg Biological Sciences Astra. Zeneca R&D Lund

Outline • Astra. Zeneca • Introduction to Chronic Obstructive Pulmonary Disease (COPD; KOL = Outline • Astra. Zeneca • Introduction to Chronic Obstructive Pulmonary Disease (COPD; KOL = Kronisk Obstruktiv Lungesygdom), disease classification, pathobiology. • Comparison of Affymetrix studies on epithelial brushings. • A set of genes induced by smoke. Transcription factors.

astrazeneca. com astrazeneca. se David R Brennan, Chief Executive Officer astrazeneca. com astrazeneca. se David R Brennan, Chief Executive Officer

Employees around the world 2004 Production 15, 000 R&D 12, 000 Other 6, 000 Employees around the world 2004 Production 15, 000 R&D 12, 000 Other 6, 000 Sales and marketing 31, 000 TOTAL 64, 000

Top 10 pharmaceutical companies Sales (MUSD) Source: IMS Health, IMS MIDAS, 46 countries MAT/Qtr Top 10 pharmaceutical companies Sales (MUSD) Source: IMS Health, IMS MIDAS, 46 countries MAT/Qtr 4 2004

Sales (MUSD) Sales (MUSD)

Sales per therapeutic area Other 6% Respiratory and Inflammation 12% Gastrointestinal 28% Oncology 16% Sales per therapeutic area Other 6% Respiratory and Inflammation 12% Gastrointestinal 28% Oncology 16% Neuroscience 16% Cardiovascular 22%

Sales by therapeutic area (MUSD) % growth CER Gastrointestinal -4% Cardiovascular +17% Neuroscience +19% Sales by therapeutic area (MUSD) % growth CER Gastrointestinal -4% Cardiovascular +17% Neuroscience +19% Oncology +16% Respiratory and Inflammation +8% Infection +7% 2004 2003

The Gastric Proton Pump - Losec The Gastric Proton Pump - Losec

Nexium ® • The first acid pump inhibitor to show superiority over Losec ® Nexium ® • The first acid pump inhibitor to show superiority over Losec ® • Effective healing and fast symptom relief of reflux oesophagitis • Healing of duodenal ulcers after one week

Sales of major products 2004 (MUSD) % growth CER Nexium +15% Seroquel +33% Losec/Prilosec Sales of major products 2004 (MUSD) % growth CER Nexium +15% Seroquel +33% Losec/Prilosec -30% Seloken +6% Pulmicort +4% Casodex +11% Zoladex -1% Crestor >100% Atacand +10% Arimidex +48% 2004 2003

R&D expenditures (MUSD) Expenditures as % of sales 17. 2% 18. 3% 17. 7% R&D expenditures (MUSD) Expenditures as % of sales 17. 2% 18. 3% 17. 7%

Our research areas • • • Gastrointestinal Cardiovascular Neuroscience • • • CNS Pain Our research areas • • • Gastrointestinal Cardiovascular Neuroscience • • • CNS Pain Control/Anaesthesia Oncology Respiratory and Inflammation Infection

The path to a new medicine Years 1 2 3 4 First patent application The path to a new medicine Years 1 2 3 4 First patent application Drug Discovery Target and lead Lead identification optimisation 5 6 7 8 9 Clinical trial application 10 11 12 13 14 15 Product licence application Drug Development Concept testing for launch Clinical Development Phase III 50 -150 100 -200 500 -5, 000 people Product life Launch cycle support Phase IV studies continue Toxicology and pharmacokinetic studies (absorption, distribution, metabolism, excretion) Pharmaceutical and analytical development Process chemistry and manufacturing Registration and regulatory affairs Sales and marketing (preparation, promotion, advertising and selling) No. of compounds Up to 1, 000 10 -15 1 -8 1 -3 1 16

The R&D process Preclinical studies Clinical studies Discovery Development CHEMISTRY/ PHARMACOLOGY IND* PHASE III The R&D process Preclinical studies Clinical studies Discovery Development CHEMISTRY/ PHARMACOLOGY IND* PHASE III NDA** PHASE IV Search for active substances Regulatory review Efficacy studies on healthy volunteers Clinical studies on a limited scale Comparative studies on a large number of patients Regulatory review Continued comparative studies 50– 150 persons 100– 200 patients Toxicology, efficacy studies on various types of animals *Investigational New Drug Application for permission to administer a new drug to humans LEVEL 500– 5, 000 patients KNOWLEDGE LEVEL KNOWLEDGE Registration, market introduction **New Drug Application for permission to market a new drug TIME SPAN 2– 4 yrs. 2– 6 mos. 3– 6 yrs. 1– 3 yrs. Approximately 10 years from idea to marketable drug

Future Global Mortality 1. Ischaemic heart disease 1990 2020 2. Cerebrovascular disease 3. Lower Future Global Mortality 1. Ischaemic heart disease 1990 2020 2. Cerebrovascular disease 3. Lower respiratory infection 3 rd 4. Diarrhoeal disease 5. Perinatal disorders 6. COPD 6 th Murray & Lopez: WHO/World Bank Global Predictions Nat Med 1998 7. Tuberculosis 8. Measles Stomach cancer 9. Road traffic accidents HIV 10. Lung cancer Suicide

Prevalence COPD – smoking habits Males Stang P. Chest 2000; 117: 354 S Prevalence COPD – smoking habits Males Stang P. Chest 2000; 117: 354 S

FEV 1 = how much you can exhale in 1 sec. FEV 1/FVC = FEV 1 = how much you can exhale in 1 sec. FEV 1/FVC = how large proportion you can exhale. Measures obstruction. FVC

COPD: lung function decline Fletcher & Peto, BMJ, 1977 COPD: lung function decline Fletcher & Peto, BMJ, 1977

GOLD Management guidelines of COPD GOLD workshop report update 2003 GOLD Management guidelines of COPD GOLD workshop report update 2003

A “typical” COPD patient ? A “typical” COPD patient ?

Pathophysiological changes in COPD small airways Pathophysiological changes in COPD small airways

COPD pathobiology and current treatment hypotheses Barnes and Hansel, Lancet, 2004 COPD pathobiology and current treatment hypotheses Barnes and Hansel, Lancet, 2004

Airway epithelial cell function is dysregulated in COPD • Barrier function • Mucus hyperplasia/metaplasia Airway epithelial cell function is dysregulated in COPD • Barrier function • Mucus hyperplasia/metaplasia • Proliferation, differentiation and repair • Inflammatory mediator production • Interactions with inflammatory cells

AZ - U. of Southampton collaboration (Holgate, Djukanovic, Davies, Wilson, Richter, O´Donnell, Angco) Aims AZ - U. of Southampton collaboration (Holgate, Djukanovic, Davies, Wilson, Richter, O´Donnell, Angco) Aims of the study • • • Investigate airway epithelial gene expression in non-smokers, healthy smokers and smokers with COPD in relation to clinical phenotype Establish relevant in vitro cell models to study in detail the effects of cigarette smoke on epithelial cells Increase understanding of molecular mechansisms underlying epithelial pathophysiology in COPD and provide novel targets or pathways for therapeutic intervention

Cellular Composition of Brush Biopsies N = 79 Cellular Composition of Brush Biopsies N = 79

Subject characteristics N = 70 (9 samples excluded because of impurities) Subject characteristics N = 70 (9 samples excluded because of impurities)

Affymetrix U 133 A, B microarray analysis • • 70 samples assayed Software • Affymetrix U 133 A, B microarray analysis • • 70 samples assayed Software • • • ZAM: low level analysis, in-house SAGx: differential expression, Bioconductor Clustering and visualisation: Spotfire, Dchip Contrast normalisation RMA type of index Penalised t-test to compare subject categories Close to 45000 probesets Gene Sets from KEGG and Biocarta Roughly 150 clinical variables

Penalised t-test and FDR • • • Low expressed genes less accurately assayed: higher Penalised t-test and FDR • • • Low expressed genes less accurately assayed: higher risk of false positives Solution: add a penalty to the denomimator of the t-test statistica To control false positive rate: estimate False Discovery Rate and threshold

Oxidative stress related genes go up in Smokers and further increase in COPD Oxidative stress related genes go up in Smokers and further increase in COPD

PCA based on oxidative stress related genes NS HS COPD PCA based on oxidative stress related genes NS HS COPD

Expression of Oxidative Stress related genes Principal Components Analysis (PCA) High Figure produced in Expression of Oxidative Stress related genes Principal Components Analysis (PCA) High Figure produced in Gene Data Viewer

Gene Set Enrichment Analysis schematically ES = enrichment score MES = maximum ES Calculation Gene Set Enrichment Analysis schematically ES = enrichment score MES = maximum ES Calculation of MES 1) Order genes by expression difference 2) For each gene set: Calculate running sum of ES along all genes. Add to sum if gene belongs to gene set, otherwise subtract 3) Take maximum (partial) sum From Mootha et al (2003)

Gene Set Enrichment Analysis Implemented in R based on Mootha et al. (2003) Gene Gene Set Enrichment Analysis Implemented in R based on Mootha et al. (2003) Gene sets related to oxidative stress ranked high

Transcription Factor Binding Sites (TFBSs) • • A transcription factor (TF) is a protein Transcription Factor Binding Sites (TFBSs) • • A transcription factor (TF) is a protein that mediates the binding of RNA polymerase and the initiation of transcription TRANSFAC is a database on eukaryotic TFs, their genomic binding sites and DNA-binding Which TFBSs are overrepresented in a set of regulated genes? Elkon et al. (2003) presents an algorithm to score upstream regions in terms of TF binding affinity From Jayneway et al.

Position weight matrix Sequence logo representation of the binding specificity of the transcription factor Position weight matrix Sequence logo representation of the binding specificity of the transcription factor Elk-1, copied from the Jaspar web site , http: //jaspar. cgb. ki. se Roepcke, S. et al. Nucl. Acids Res. 2005 33: W 438 -W 441; doi: 10. 1093/nar/gki 590 Denote by p(i, j) the frequency of base i at position j in the PWM P Given a promoter subsequence s 1 s 2. . . sl, define its similarity to P as follows: sim(P, s 1 s 2…sl) > some large T(P) will be called a ‘hit’

Over-representation of Transcripion Factor Binding Sites Idea: compare distribution of hits among genes under Over-representation of Transcripion Factor Binding Sites Idea: compare distribution of hits among genes under study to a background set. Let n 1, n 2, and n 3 denote the number of background promoters containing one, two, or at least three hits, respectively. Assuming that T is randomly chosen out of B, the analytical score for the probability of observing at least h hits in T is: From Elkon et al. (2003)

Clustering of Genes with respect to TF binding sites Clustering of Genes with respect to TF binding sites

Cells treated with Cigarette smoke extract resp vehicle Correspondance between cell cultures and humans Cells treated with Cigarette smoke extract resp vehicle Correspondance between cell cultures and humans Healthy Smokers/Nonsmokers

Link between gene expression and clinical variables PLS analysis Link between gene expression and clinical variables PLS analysis

Validation • • • RT-PCR Genetic Association studies in separate cohorts Cell and other Validation • • • RT-PCR Genetic Association studies in separate cohorts Cell and other models Localisation in disease tissue Protein

References • • Pierrou, S. , Broberg, P. , O'Donnell, R. , Pawlowski, K. References • • Pierrou, S. , Broberg, P. , O'Donnell, R. , Pawlowski, K. , Virtala, R. , Lindqvist, E. , Richter, A. , Wilson, S. , Angco, G. , Möller, S. , Bergstrand, H. , Koopmann, W. , Wieslander, E. , Strömstedt, P. -E. , Holgate, S. , Davies, D. , Lund, J. , Djukanovic, R. (2006) Expression of genes involved in oxidative stress responses in airway epithelial cells of COPD smokers, AJRCCM Jayneway et al. , Immunobiology Elkon, R. , Linhart, C. , Sharan, R. , Shamir, R. , and Shiloh, Y. , Genomewide In-silico Identification of Transcriptional Regulators Controlling Cell Cycle in Human Cells, Genome Research, Vol. 13(5), pp. 773 -780, 2003 Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC, PGC-1 alpharesponsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes, Nat Genet. 2003 Jul; 34(3): 267 -73