expression of genes involved in oxidative stress responses in airway epithelial cells of copd...
Post on 19-Dec-2015
219 views
TRANSCRIPT
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.
astrazeneca.com
astrazeneca.se
David R Brennan, Chief Executive Officer
Employees around the world 2004
TOTAL 64,000
R&D 12,000
Sales and marketing 31,000
Other 6,000
Production 15,000
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 ®
• 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)
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
• Cardiovascular
• Neuroscience
• CNS
• Pain Control/Anaesthesia
• Oncology
• Respiratory and Inflammation
• Infection
The path to a new medicineYears 1 162 3 4 5 6 7 8 9 10 11 12 13 14 15
No. of compounds Up to 1,000,000 10-15
First patentapplication
Clinical trialapplication
Product licenceapplication
Drug Discovery Drug Development Target and leadidentification
Leadoptimisation Concept testing
Developmentfor launch Launch
Clinical DevelopmentPhase I50-150people
Phase II100-200people
Phase III500-5,000people
Phase IV studies continue
Product lifecycle support
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)
1-8 1-3 1
Preclinical studies Clinical studies
CHEMISTRY/ PHARMA-COLOGY
IND* PHASE I PHASE II PHASE III NDA** PHASE IV
Search for active
substances
Toxicology, efficacy
studies on various types
of animals
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*Investigational New Drug
Application for permission to
administer a new drug to humans
50–150persons
100–200patients
500–5,000patients
Registration, market
introduction
**New Drug ApplicationApplication for
permission to market a new drug
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
HIV
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
ls tr
eate
d w
ith C
igar
ette
sm
oke
extr
act r
esp
vehi
cle
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