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Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
1The screen versions of these slides have full details of copyright and acknowledgements
1
Proteomics in Cardiovascular Disease:Clinical Considerations
Dr. Christian DellesBHF Glasgow Cardiovascular Research Centre
Institute of Cardiovascular and Medical SciencesUniversity of Glasgow
2
Metabolomics
Proteomics
Transcriptomics
Systems Biology and "Omics"
DNA
mRNA
Protein
Metabolitessmall molecules
Genomics
miRNAs
Mat
urity
of r
esea
rch
3
Cardiovascular Continuum
Dzau V et al., Circulation2006
Risk factors
Oxidative and mechanical stressInflammation
Endothelial dysfunction
Atherothrombosis and progressive CV disease
Tissue injury(MI, stroke, renal
insufficiency,peripheral arterial
insufficiency)
Pathologicalremodeling
Target organ damage
End-organ failure(CHF, ESRD)
Death
Altered gene expressionAltered protein expression
Genome
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
2The screen versions of these slides have full details of copyright and acknowledgements
4
Proteomics
5
Proteomics (2)
Anderson NL & Anderson NG, Electrophoresis1998
The goal of proteomics is a comprehensive, quantitative description of protein expression and its changes
under the influence of biological perturbations such as disease or drug treatment
6
Publications on Proteomics
Van Eyk JE, Circ Res 2011
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
3The screen versions of these slides have full details of copyright and acknowledgements
7
Rationale for Proteomic Studies
• Research− Unravelling the pathophysiology
− Discovery of new pathways
• Clinical application: biomarker− Screening for disease
− Prediction of risk
− Diagnosis of (severity of) disease
− Monitoring of treatment
8
Samples
Tuñòn J et al., JACC 2010
9
Platforms
Tuñòn J et al., JACC 2010
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
4The screen versions of these slides have full details of copyright and acknowledgements
10Gerszten RE et al. Circ Res 2011
Platforms: LC-MS/MS
LC-MS/MSBiological samples (cases vs. control)
Data analysis
11Gerszten RE et al., Circ Res 2011
Technical ChallengesGenomics/transcriptomics Proteomics
• All possible features known
• Sample is static during analysis
• All features measured
• Robust means to amplify low numbers DNA or RNA (PCR)
• Signal not detected means feature not present
• All possible features not known
• Sample is dynamic during analysis
• 10-50% of features measured
• No protein PCR (analytics have to deal with enormous dynamic range)
• Signal not detected means either that feature not present or feature present but not detected
12
Post Translational Modifications
Van Eyk JE, Circ Res 2011
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
5The screen versions of these slides have full details of copyright and acknowledgements
13
Unravelling pathophysiology
Biomarker of disease
Pre-eclampsia
Organ damage
Biomarker of risk
14Iacobellis G et al., Nat Clin Pract Cardiovasc Med. 2005
Unravelling Pathophysiology: Epicardial Fat
15Salgado-Somoza A et al., Am J Physiol Heart Circ Physiol 2010
Unravelling Pathophysiology
Epicardial adipose tissue Subcutaneous adipose tissue
Catalase
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
6The screen versions of these slides have full details of copyright and acknowledgements
16Salgado-Somoza A et al., Am J Physiol Heart Circ Physiol 2010
Western Blot and IHCSAT
EAT
17
Function: NBT Assay of ROS
Salgado-Somoza A et al., Am J Physiol Heart Circ Physiol 2010
18
Unravelling pathophysiology
Biomarker of disease
Pre-eclampsia
Organ damage
Biomarker of risk
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
7The screen versions of these slides have full details of copyright and acknowledgements
19
Urinary Proteomics: CE/MS PlatformCapillary electrophoresis coupled to mass spectrometry
UrineSample
Capillary Electrophoresis
Mass Spectrometry
Ionization
Report
Data Storageand Evaluation
Diagnosis Disease specificBiomarker pattern
Separation and analysis
of proteins and peptides
(>1,000)
Run time ~60 min
CE
• Fast
• Robust
• Inexpensive
• Reproducible
MS
• Resolution
• Scan speed
20
• Easily accessible
• Non invasive sampling
• Available in large quantities
• Urinary polypeptides are stable, yielding comparable datasets
• Urinary polypeptides display the “status” of the kidney, bladder, prostate and vascular architecture, are capable of depicting systemic diseases
Why Urine?
De Hortus SanitatisMainz, Germany, 1491
21
Urinary Proteomics: CE/MS Platform
Migration time (min)
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
8The screen versions of these slides have full details of copyright and acknowledgements
22
Methods
Cases ControlsCE/MSCE/MSDiagnostic pattern
Discriminatory biomarker
Discriminatory biomarker
Compiledpattern
Compiled pattern
Kolch et al., RCM 2004, 18: 2635-2636
Neuhoff et al., RCM 2004, 18: 149-156
Mischak et al., PROTEOMICS - Clinical Applications2007, 1: 148-156
23
CE/MS
Methods (2)
Cases ControlsDiagnostic pattern
Kolch et al., RCM 2004, 18: 2635-2636
Neuhoff et al., RCM 2004, 18: 149-156
Mischak et al., PROTEOMICS - Clinical Applications2007, 1: 148-156
24
Study cohort Samples CAD Control Primary Usage Secondary Usage
Biomarker discovery 586 204 382
CAD [9,10] (N=120†) 183 151 32 CAD markers SVM modelingUAP [10] (N=59) 59 35 24 SVM modeling n.a.CACTI [11] (N=33) 33 18 15 SVM modeling n.a.Additional controls [14] (N=153) 229 0 229 SVM modeling n.a.TRENDY, baseline [9,12] (N=17†) 14 0 14 Medication markers SVM modelingTRENDY, follow -up [9,12] 16 0 16 Medication markers SVM modelingFenofibrate, baseline [13] (N=26) 26 0 26 Medication markers SVM modelingFenofibrate, follow-up 26 0 26 Medication markers SVM modeling
Blinded cohort (N=138) 138 71 67 Validation n.a.
Short-term treatment effects [15] 193 n.a. n.a.
HIB 0 mg (N=55‡) 55 n.a. n.a. Drug interference n.a.HIB 300 mg 48 n.a. n.a. Drug interference n.a.HIB 600 mg 45 n.a. n.a. Drug interference n.a.HIB 900 mg 45 n.a. n.a. Drug interference n.a.
Long-term treatment effects [16] 44 n.a. n.a.
IRMA -2 Irbesartan baseline (N=11†) 11 n.a. n.a. Therapy monitoring n.a.IRMA -2 Irbesartan follow-up 11 n.a. n.a. Therapy monitoring n.a.IRMA -2 Placebo baseline(N=11†) 11 n.a. n.a. Therapy monitoring n.a.IRMA -2 Placebo follow -up 11 n.a. n.a. Therapy monitoring n.a.
Total (N=623) 961
Patients
Discovery
Adjustment for drug treatment
Effect of treatment
Blinded cohort
Delles C et al., J Hypertens 2010
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
9The screen versions of these slides have full details of copyright and acknowledgements
25
238 Biomarker Panel
Delles C et al., J Hypertens 2010
26
Identification of Proteins
• Collagen type 1
• Collagen type 3
• Alpha-1-antitrypsin (AAT)
• Granin-like neuroendocrine peptide precursor (ProSAAS)
• Membrane associated progesterone receptor component 1
• Sodium/potassium-transporting ATPase gamma chain
• Fibrinogen-alpha-chain
Delles C et al., J Hypertens 2010
27
Single Marker
Marker 1
CaseControl
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
10The screen versions of these slides have full details of copyright and acknowledgements
28
Two Markers
Marker 1
Marker 2
CaseControl
29
Three Markers
Marker 1
Marker 2
CaseControl
30 1 - Specificity1.00.80.60.40.20.0
Sen
sitiv
ity
1.0
0.8
0.6
0.4
0.2
0.0
CAD Controls
24 M
arke
rs
AUC 0.786
50 M
arke
rs
AUC 0.786AUC 0.882
238
Mar
kers
Better Discrimination with More Markers
CAD Controls
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
11The screen versions of these slides have full details of copyright and acknowledgements
31
Effect of Drug Therapy
10-week treatment with irbesartan
2-year treatment with irbesartan
Delles C et al., J Hypertens 2010
32
Unravelling pathophysiology
Biomarker of disease
Pre-eclampsia
Organ damage
Biomarker of risk
33
Pre-eclampsia
Hypertension
Proteinuria
> 20 weeks gestation
Without pre-existing hypertension
Maternal
• Oedema
• Headaches/blurred vision/seizures
• Renal failure
• Coagulation problems
• HELLP syndrome
Foetal
• Intra-uterine growth restriction
• Preterm delivery
• Death
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
12The screen versions of these slides have full details of copyright and acknowledgements
34
Normal Pregnancy
http://www.sgul.ac.uk/depts/immunology/~dash/troph/troph.htm
Unmodified artery Trophoblast-modified artery
Creation of a high-flow, low-resistance zone
Replacement of smooth muscle and endothelium
with trophoblasts
Invading trophoblasts
Loss of spirality
Endothelium
Smooth muscle (tunica media)
35
Maternal Risk Factors
Duckitt K & Harrington D, BMJ 2005
• Antiphospholipid syndrome, previous pre-eclampsia, pre-existing diabetes, twins vs. singleton, nulliparity, family history, raised BMI, age > 40 yrs, raised DBP
36
Delivery
n = 2385
Study Design
2407 pregnant women
Blood/urine/risk factors
Booking (Week 12-14)
Delivery
Outcome PE - yes/no
"Risk-factor" subgroup
n = 132
Weeks16 and 28
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
13The screen versions of these slides have full details of copyright and acknowledgements
37
Clinical Characteristics
Carty DM et al., Hypertension2011
Preeclampsian = 45
Controlsn = 86 P-valueMaternal characteristics
Age (years) 29±5 28±5 0.69Ethnicity
CaucasianOther
42 (93%)3 (7%)
81 (94%)5 (6%)
1.00
Body mass index (kg/m2) 29±5 29±5 0.76Nulliparous 39 (87%) 55 (64%) 0.01Smoker 4 (9%) 21 (24%) 0.04Previous pre-eclampsia 1 (2%) 6 (7%) 0.42Family history of pre-eclampsia (mother or sister) 8 (17%) 7 (8%) 0.14At bookingSystolic blood pressure (mmHg) 121±10 114±12 0.002Diastolic blood pressure (mmHg) 76±9 69±10 0.001Proteinuria (³ + on dipstick) 1 (2%) 2 (2%) 1.00Gestation at sampling (wks) 13.8±1.6 13.7±1.3 0.61Pregnancy OutcomeHighest systolic blood pressure (mmHg) 165±11 125±12 <0.001Highest diastolic blood pressure (mmHg) 101±8 71±9 <0.001Caesarean section 20 (44%) 10 (12%) <0.001Gestation at delivery (wks) 38.3±2 40±1.4 <0.001Birthweight (g) 3155±621 3558±416 <0.001Small for gestational age(<10th customised birthweight centile)
12 (27%) 3 (3%) <0.001
38
Pregnancy Specific Proteomic Biomarkers
Carty DM et al., Hypertension2011
39
Week 28: Pre-eclampsia Specific Biomarkers
Carty DM et al., Hypertension2011
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
14The screen versions of these slides have full details of copyright and acknowledgements
40
Week 28: Pre-eclampsia Specific Biomarkers (2)
Controlsn = 17
Casesn = 18
P<0.001
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0C
lass
ifica
tion
fact
or
Carty DM et al., Hypertension2011
41
-2.5
-2.0
-1.5
-1.0
-0.50.0
0.5
1.0
1.5
2.0
Cla
ssifi
catio
n fa
ctor
Week 12-16 Week 28
P = 0.031
Controls
-2.5
-2.0
-1.5
-1.0-0.5
0.0
0.5
1.0
1.5
2.0
Cla
ssifi
catio
n fa
ctor
P < 0.001
Week 12-16 Week 28
Cases
Change in Classification Factor
Carty DM et al., Hypertension2011
42
• Collagen alpha-1 (I) chain
• Collagen alpha-1 (III) chain
• Collagen alpha-2 (I) chain
• Collagen alpha-2 (I) chain
• Collagen alpha-3 (IX) chain
• Fibrinogen alpha chain
• Ig kappa chain V-IV region B17
• Retinol-binding protein 4
• Uromodulin
Sequencing
Carty DM et al., Hypertension2011
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
15The screen versions of these slides have full details of copyright and acknowledgements
43Blumenstein M et al., Proteomics2009
Plasma Proteome: DIGE and LC-MS/MS
Samples from20 weeks gestation
44
Pre-eclampsia and Cardiovascular Risk
Carty DM et al., J Hypertens 2010
45
Serum Proteomics: 2D-LC-MS/MS
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
16The screen versions of these slides have full details of copyright and acknowledgements
46 Rasanen J et al., J Proteome Res 2010
Serum Proteomics: 2D-LC-MS/MS (2)
47 Buhimschi IA et al., AJOG 2008
Urinary Proteomics: SELDI
48Buhimschi IA et al., AJOG 2008
Urinary Proteomics: SELDI (2)
Samples at time of severe diseaseSome longitudinal samples
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
17The screen versions of these slides have full details of copyright and acknowledgements
49
Unravelling pathophysiology
Biomarker of disease
Pre-eclampsia
Organ damage
Biomarker of risk
50
Stroke
Dawson J et al., PloS One 2012
51
Stroke (2)
Dawson J et al., PloS One 2012
Diagnostic accuracy Stroke severity
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
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52
Heart Failure
Kuznetsova T et al. Eur Heart J 2012
53
Unravelling pathophysiology
Biomarker of disease
Pre-eclampsia
Organ damage
Biomarker of risk
54
Chronic Kidney Disease PatternControlsCKD
CE migration time [min] CE migration time [min]
Training set
Cases Controls
n = 23030 ANCA,30 MGN,22 MCD,44 IgAN,25 FSGS,
58 DN,
21 SLE
n = 379379 C
CKD pattern (n = 273 biomarkers):
Fragments of• Various collagens
• Plasma proteins (serum albumin, transthyretin, alpha-1-antitrypsin, alpha-1B-glycoprotein, alpha-2-HS-glycoprotein, antithrombin-III, apolipoprotein A-I, beta-2-microglobulin, fibrinogen alpha)
• Clusterin
• Uromodulin
• Na/K-transporting ATPase gamma chain
• Psoriasis susceptibility 1 candidate gene 2
• Prostaglandin-H2 D-isomerase
• Proprotein convertase subtilisin/kexin type 1 inhibitor
• Polymeric-immunoglobulin receptor
• Osteopontin
• Neurosecretory protein VGF
• Membrane associated progesteronereceptor component 1
• CD99 antigen
• Ig lambda chain C regions
Good DM et al., Mol Cell Protomics 2010, Jantos-Siwy J et al., J Proteome Res 2009
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
19The screen versions of these slides have full details of copyright and acknowledgements
55
Diabetic Nephropathy
Normal Diabetic
Increase in ECM and collagen in diabetes and diabetic nephropathy
Normal Thick
56
Diabetic Nephropathy (2)
early detection?
57
Study Flow ChartProteomic prediction and renin angiotensin aldosterone system inhibition prevention
of early diabetic nephropathy in type 2 diabetic patients with normoalbuminuria
TEST
Spironolactone Placebo
Type 2 diabetesNormoalbuminuria
N = 3280
Positive (at risk)n = 656 (approx)
Negative (low risk)n = 2624 (approx)
Standard
Screened populationN = 7000
End of trial
156 weeksRandomization
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
20The screen versions of these slides have full details of copyright and acknowledgements
58
Integrating "omics" data
59
Systems Biology and "Omics"
Metabolomics
Proteomics
Transcriptomics
Genomics
DNA
mRNA
Protein
Metabolitessmall molecules
miRNAs
Mat
urity
of r
esea
rch
60
Summary and Conclusions
• Proteomics has a potential to elucidate pathophysiological mechanisms and as a clinical biomarker of disease and risk
• A range of different proteomic techniques is available; Results from experiments using different platforms do not necessarily overlap
• Rigorous standards for phenotypic characterisation and replication of results have to be applied to clinical proteomic studies
• Integration of data across "omics" modalitiesremains challenging
Proteomics in Cardiovascular Disease:Clinical Considerations
Christian Delles
21The screen versions of these slides have full details of copyright and acknowledgements
61
BHF Glasgow Cardiovascular Research Centre
62