Transcript et al

Expression of genes involved in
oxidative stress responses in airway
epithelial cells of COPD smokers
Per Broberg
Biological Sciences
AstraZeneca 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
Production 15,000
R&D 12,000
Other 6,000
Sales and
marketing 31,000
TOTAL 64,000
Top 10 pharmaceutical companies
Sales (MUSD)
Pfizer
49 986
GlaxoSmithKline
31 887
Sanofi-Aventis
26 162
Johnson & Johnson
24 297
Merck & Co
23 425
Novartis
21 955
AstraZeneca
21 255
Roche
17 269
Bristol-Myers Squibb
Wyeth
15 172
13 991
Source: IMS Health, IMS MIDAS, 46 countries MAT/Qtr4 2004
Sales
(MUSD)
25 000
20 000
15 000
21 426
17 841
18 849
10 000
5 000
0
2002
2003
2004
Sales per therapeutic area
Other 6%
Respiratory and
Inflammation 12%
Gastrointestinal 28%
Oncology 16%
Cardiovascular 22%
Neuroscience 16%
Sales by therapeutic area
(MUSD)
% growth CER
Gastrointestinal
-4%
Cardiovascular
+17%
Neuroscience
+19%
Oncology
+16%
Respiratory
and Inflammation
+8%
Infection
+7%
5 918
5 943
4 777
3 910
3 496
2 833
3 376
2 743
2 583
2 261
539
2004
476
2003
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)
% growth CER
Nexium
+15%
Seroquel
+33%
Losec/Prilosec
-30%
Seloken
+6%
Pulmicort
+4%
Casodex
+11%
Zoladex
-1%
Crestor
>100%
Atacand
+10%
Arimidex
+48%
3 883
3 302
2 027
1 487
1 947
2 565
1 387
1 280
1 050
968
1 012
854
917
869
908
2004
879
750
2003
129
811
519
R&D expenditures
(MUSD)
4 000
3 500
3 000
2 500
2 000
1 500
1 000
500
0
Expenditures
as % of sales
3 803
3 451
3 069
2002
2003
2004
17.2%
18.3%
17.7%
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
Drug Discovery
Target and lead Lead
identification
optimisation
5
6
7
8
9
Clinical trial
application
10
12
13
14
15
Product licence
application
Drug Development
Development
Concept testing for launch
Clinical
Phase I
50-150
people
11
Development
Phase II Phase III
100-200 500-5,000
people
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
1,000,000 10-15
1-8
1-3
1
16
The R&D process
Preclinical studies
Clinical studies
Discovery
Development
CHEMISTRY/
PHARMACOLOGY
IND*
PHASE I
PHASE II
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
KNOWLEDGE
LEVEL
500–5,000
patients
KNOWLEDGE
Registration,
market
introduction
**New Drug Application
Application for
permission to market a
new drug
LEVEL
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
1990
2020
1. Ischaemic heart disease
2. Cerebrovascular disease
3. Lower respiratory infection
3rd
4. Diarrhoeal disease
5. Perinatal disorders
6. COPD
6th
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; 117:354S
FEV1 = how
much you can exhale
in 1 sec.
FEV1/FVC =
how large proportion
you can exhaleMeasures obstruction.
FVC
COPD: lung function decline
Never smoked
or not
susceptible
to smoke
1
FEV (% of value at age 25)
100
75
Smoked
regularly and
susceptible to
its effects
50
Stopped
at 45
Disability
25
Stopped at 65
Death
†
†
0
25
50
75
AGE (YEARS)
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
100
M ean cell type compostion in brushings (%)
0
20
40
60
80
NS
HS
COPD0
COPD1
COPD2
EPITHELIAL
NEUTROPHIL
EOSINOPHIL
OTHER
Subject characteristics
N = 70 (9 samples excluded because of impurities)
Parameter
N (F/M)
AGE
Fev1%
Fev1/FVC (%)
Packyears
Tlco (%)
Total SGRQ
NS
15 (10/5)
54
(40 - 64)
107
(92 – 136)
75
(67 – 86)
0
(0 – 0)
81
(61 – 100)
5
(0 – 39)
HS
19 (9/10)
44
(26 - 63)
104
(88 – 128)
80
(69 – 90)
32
(10 – 48)
68
(38 – 91)
7
(0 – 17)
COPD0
18 (12/6)
50
(40 - 64)
98
(76 – 132)
76
(70 – 82)
50
(19 – 160)
63
(38 – 89)
21
(0 – 45)
COPD1
9 (3/6)
58
(44 – 65)
91
(82 – 101)
67
(60 – 70)
42
(30 – 66)
63
(41 – 89)
28
(3 – 42)
COPD2
16 (4/12)
55
(43- 64)
56
(25 - 79)
55
(30 – 69)
56
(30 – 86)
57
(32 – 87)
36
(12 – 67)
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
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
comparison of NS and HS
Gene set
Database identifier GSEA p-value
Source of gene set
Ribosome
Map03010
0.0009
KEGG
None
0.0016
Authors
0.0023
Biocarta
Automated set, subset:
Metallothioneins
The Role of Eosinophils in the
H_eosinophilsPathw
Chemokine Network of Allergy
ay
Automated set (full set)
None
0.0042
Authors
Manual set (oxidant responsive)
None
0.0126
Authors
0.0130
Biocarta
H_fibrinolysisPathw
Fibrinolysis Pathway
ay
Comparison of COPD and healthy smokers
Source of
Gene set
Database identifier GSEA p-value
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
0.005926
Authors
0.012183
Biocarta
Automated set, subset: Thioredoxins None
h_glycolysisPathwa
Glycolysis Pathway
y
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’
Over-representation of Transcripion Factor Binding Sites
TF site
TF site accession
p-value
(Transfac)
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)
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
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-1alpharesponsive genes involved in oxidative phosphorylation are
coordinately downregulated in human diabetes, Nat Genet. 2003
Jul;34(3):267-73