Expression of genes involved in oxidative stress responses in airway epithelial cells of COPD smokers

Per BrobergBiological SciencesAstraZeneca R&D Lund

Outline

• AstraZeneca

• 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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David R Brennan, Chief Executive Officer

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TOTAL 64,000

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Sales and marketing 31,000

Other 6,000

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Top 10 pharmaceutical companiesSales (MUSD)

13 991

15 172

17 269

21 255

21 955

23 425

24 297

26 162

31 887

49 986

Wyeth

Bristol-Myers Squibb

Roche

AstraZeneca

Novartis

Merck & Co

Johnson & Johnson

Sanofi-Aventis

GlaxoSmithKline

Pfizer

Source: IMS Health, IMS MIDAS, 46 countries MAT/Qtr4 2004

Sales(MUSD)

17 841 18 84921 426

0

5 000

10 000

15 000

20 000

25 000

2002 2003 2004

Sales per therapeutic area

Respiratory andInflammation 12%

Other 6%

Gastrointestinal 28%

Cardiovascular 22%

Oncology 16%

Neuroscience 16%

Sales by therapeutic area(MUSD)

Neuroscience +19%

Oncology +16%

Respiratory and Inflammation

Gastrointestinal -4%

Cardiovascular +17%

Infection +7%

+8%

% growth CER

476

2 261

2 743

2 833

3 910

5 943

539

2 583

3 376

3 496

4 777

5 918

2003

2004

The Gastric Proton Pump - Losec

Nexium ®

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Sales of major products 2004(MUSD)

519

750

129

869

854

968

1 280

2 565

1 487

3 302

811

879

908

917

1 012

1 050

1 387

1 947

2 027

3 883

Losec/Prilosec -30%

Seloken +6%

Nexium +15%

Seroquel +33%

Zoladex -1%

Crestor >100%

Pulmicort +4%

Casodex +11%

Atacand +10%

Arimidex +48%

% growth CER

2003

2004

R&D expenditures(MUSD)

Expenditures as % of sales

17.2% 18.3% 17.7%

3 0693 451

3 803

0

500

1 000

1 5002 000

2 500

3 000

3 500

4 000

2002 2003 2004

Our research areas

• Gastrointestinal

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• Oncology

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Preclinical studies Clinical studies

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Toxicology, efficacy

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introduction

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KNOWLEDGE

LEVEL

KNOWLEDGE

LEVEL

2–4 yrs. 2–6 mos. 3–6 yrs. 1–3 yrs.

Approximately 10 years from idea to marketable drug

TIME SPAN

Discovery Development

The R&D process

Future Global Mortality

1. Ischaemic heart disease

2. Cerebrovascular disease

3. Lower respiratory infection

4. Diarrhoeal disease

5. Perinatal disorders

6. COPD

7. Tuberculosis

8. Measles

9. Road traffic accidents

10.Lung cancer

Stomach cancer

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Suicide

Murray & Lopez:WHO/World Bank Global PredictionsNat Med 1998

1990 2020

6th

3rd

Prevalence COPD – smoking habits

Males

Stang P. Chest 2000; 117:354S

Normal Flow Volume LoopNormal Flow Volume Loop

FVC

FEV1 = how much you can exhalein 1 sec.FEV1/FVC = how large proportionyou can exhale- Measures obstruction.

100

75

50

25

025 50 75

Sm okedregularly andsusceptible toits e ffects

Never sm okedor notsusceptibleto sm oke

Stoppedat 45

Stopped at 65

D isability

Death

AG E (Y EARS )

FE

V (

% o

f va

lue

at

ag

e 2

5)

1

† †

Fletcher & Peto, BMJ, 1977

COPD: lung function decline

GOLD Management guidelines of COPDGOLD Management guidelines of COPD

GOLD workshop report update 2003

A “typical” COPD patient ?

Pathophysiological changes in COPD small airways

COPD pathobiology andcurrent 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

EPITHELIAL NEUTROPHIL EOSINOPHIL OTHER

NSHSCOPD0COPD1COPD2

02

04

06

08

01

00

Mean cell type compostion in brushings (%)

N = 79

Subject characteristics

Parameter NS HS COPD0 COPD1 COPD2 N (F/M) 15 (10/5) 19 (9/10) 18 (12/6) 9 (3/6) 16 (4/12) AGE 54

(40 - 64) 44 (26 - 63)

50 (40 - 64)

58 (44 – 65)

55 (43- 64)

Fev1% 107 (92 – 136)

104 (88 – 128)

98 (76 – 132)

91 (82 – 101)

56 (25 - 79)

Fev1/FVC (%) 75 (67 – 86)

80 (69 – 90)

76 (70 – 82)

67 (60 – 70)

55 (30 – 69)

Packyears 0 (0 – 0)

32 (10 – 48)

50 (19 – 160)

42 (30 – 66)

56 (30 – 86)

Tlco (%) 81 (61 – 100)

68 (38 – 91)

63 (38 – 89)

63 (41 – 89)

57 (32 – 87)

Total SGRQ 5 (0 – 39)

7 (0 – 17)

21 (0 – 45)

28 (3 – 42)

36 (12 – 67)

N = 70 (9 samples excluded because of impurities)

Affymetrix U133A,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

samrocn.html pava.fdr.html

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

High

Figure produced in Gene Data Viewer

Principal Components Analysis (PCA)

ES = enrichment scoreMES = maximum ES

Gene Set Enrichment Analysis schematically

From Mootha et al (2003)

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

comparison of NS and HS

Gene set Database identifier GSEA p-value Source of gene set

Ribosome Map03010 0.0009 KEGG

Automated set, subset:

Metallothioneins None 0.0016 Authors

The Role of Eosinophils in the

Chemokine Network of Allergy

H_eosinophilsPathw

ay 0.0023 Biocarta

Automated set (full set) None 0.0042 Authors

Manual set (oxidant responsive) None 0.0126 Authors

Fibrinolysis Pathway H_fibrinolysisPathw

ay 0.0130 Biocarta

Comparison of COPD and healthy smokers

Gene set Database identifier GSEA p-value Source of

gene set

Oxidative phosphorylation map00190 0.000489 KEGG

Manual set (oxidant responsive) None 0.002027 Authors

ATP synthesis Map00193 0.002566 KEGG

Proteasome Map03050 0.002604 KEGG

Automated set, subset: Thioredoxins None 0.005926 Authors

Glycolysis Pathway h_glycolysisPathwa

y 0.012183 Biocarta

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:W438-W441; doi:10.1093/nar/gki590

Denote by p(i, j) the frequency of base i at position j in the PWM P

Given a promoter subsequence s1s2 ... sl, define its similarity to P as follows:

sim(P, s1s2…sl) > some large T(P) will be called a ‘hit’

TF site TF site accession

(Transfac)

p-value

Promoters of genes that are down-regulated in

smokers (HS/NS) and also up-regulated in COPD

(COPD/HS).

Oct-1 M00136 0.0012

E2F M00425 0.0099

NF-kappaB M00054 0.011

FOXO4 M00472 0.013

Nrf2 M00821 0.015

c-Myc/Max M00322 0.016

GR M00921 0.016

Promoters of genes that are up-regulated both in

smokers (HS/NS) and in COPD (COPD/HS).

Pax M00808 0.0003

P53 M00761 0.0016

AP-2 M00189 0.0023

HNF-4 M00764 0.0031

p53 M00272 0.0032

Nrf2 M00821 0.0051

AP-1 M00172 0.0093

COUP-TF M00158 0.010

Lhx3 M00510 0.011

NF-AT M00935 0.017

AP-2alpha M00469 0.018

Over-representation of Transcripion Factor Binding Sites

Idea: compare distribution of hits among genes under study to a background set.Let n1,

n2, and n3 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

Correspondance between cell cultures and humans

Healthy Smokers/NonsmokersCel

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d w

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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., Genome-

wide 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-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes, Nat Genet. 2003 Jul;34(3):267-73