Expression of genes involved in oxidative stress responses

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 = Kronisk Obstruktiv Lungesygdom), disease classification, pathobiology. • Comparison of Affymetrix studies on epithelial brushings. • A set of genes induced by smoke. Transcription factors.

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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

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

GOLD Management guidelines of COPD GOLD workshop report update 2003

A “typical” COPD patient ?

Pathophysiological changes in COPD small airways

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

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 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

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

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 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

PCA based on oxidative stress related genes NS HS COPD

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 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 sets related to oxidative stress ranked high

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 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 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

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

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. , 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