Download x_SCAI--Mullins Lx--Loscalzo--2015

Network Medicine:
A Systems Approach to
Cardiovascular Disease
Diagnosis & Management
Joseph Loscalzo, M.D., Ph.D.
Brigham and Women’s Hospital
Harvard Medical School
Boston, MA
Outline
•Systems and networks in biomedicine
•Network medicine, disease modules, and
disease classification
•Network medicine and drug development
Systems and Networks
in
Biomedicine
Two Burning Questions
• Why has the rapidly expanding
knowledge of the human genome not
(yet) given us the expected epiphanies
about complex human disease?
• Why has new drug development been
limited despite a remarkable range of
technologies that can identify drug
targets and design pharmacologically
suitable candidate drugs?
Changing Scientific Paradigm
• Conventional scientific approach:
hold all variables constant except the
one of interest, and deduce its
importance (Cartesian reductionism
after Democritus).
• Inductive generalization can follow.
• Reductionist approach often
oversimplifies biological systems and
leads to linear thinking. A timely
example will follow….
Homocysteine Theory of
Atherothrombosis
• First proposed by McCully (Am.J.Path.
1969; 56:111)
• Evidence from over 30 studies
suggests that even mild-to-moderate
elevations of plasma homocysteine
confer a significant, independent risk for
atherothrombosis.
• Hyperhomocysteinemia found in 2040% of patients with vascular disease,
but in only 2% of unaffected individuals
Homocysteine Metabolism
Methionine
THF
5,10-Methylene
THF
2
SAM
DMG
Me-B12
5-Methyl-THF
3
1
Betaine
Homocysteine
4
1: Methionine Synthase
2: MTHF Reductase
3: Betaine-homocysteine
Methyltransferase
4: Cystathionine-betaSynthase
B6
Cystathionine
Cysteine
Sulfate
SAH
Vitamin Rx, Homocysteine, & CV Risk
NORVIT Trial
3749 pts. s/p AMI
Folic Acid (mg) 0.8 0.8 0 0
B12 (mg)
0.4 0.4 0 0
Pyridoxine (mg) 40 0 40 0
--Bonaa KH, et al., NEJM, 2006
HOPE-2 Trial
5522 pts. w/ CVD
or diabetes
2.5 mg Folic Acid
1.0 mg B12
50 mg pyridoxine
--HOPE-2 Investigators, NEJM, 2006
Folate, B12, and Homocysteine
Methionine
Folate, B12
Homocysteine
B6
Cystathionine
Biomedicine in Network Context
HCY Metabolism
Biomedicine in Network Context
Folate Metabolism
HCY Metabolism
Biomedicine in Network Context
Intermediary Metabolism
Folate Metabolism
HCY Metabolism
Folate, B12, and Homocysteine
DNA Synthesis
DHF
dTMP
Cell Proliferation
Increased Angiographic
Restenosis post-PCI
(Lange et al., NEJM, 2004)
Methionine
THF
SAM
Acceptor
DMG
dUMP
5,10
N
Me-B12
-CH2-THF
5
N -CH3-THF
MethylAcceptor
Betaine
Homocysteine
Modify Gene Expression
Impair NO Synthesis/EC Function
SAH
CpG DNA Methylation
ADMA Synthesis
Changing Scientific Paradigm
• Most biological systems respond to multiple
inputs that vary simultaneously and can
interact—i.e., these are complex systems
that form molecular networks.
• New quantitative approaches can be used
to construct these networks, identify
changes in them that lead to disease, and
examine their responses to perturbations
(including drug-induced perturbations).
Key Properties of Biological Networks
• Frequently clustered or scale-free
architecture
• Manifest emergent behavior
• Functional modularity follows structural
localization; modules as subnetworks
• Minimize transition time between states
• Comprise canonical structural and dynamic
motifs that reflect basic organizing principles
in biological systems
Generic Network Structures
Scale-free Network
Random Network
k=degree or #
nodal connections
Many nodes
sparsely linked
P(k) = k-g
P(k)
log P(k)
P(k) = e-k
<k>
k
Poisson Distribution
Few nodes
highly linked
m=g
log k
Power Law Distribution
Scale-free Networks: Biological
Implications
• Recapitulate natural selection and evolution
– Define difference between mutable nodes
(weakly connected) that engender diversity
and facilitate natural selection, and
immutable nodes (hubs), the loss of which is
lethal for the organism
• Accommodate pertubations to the network
with minimal effect on critical functions of the
organism
Network Medicine,
Disease Modules,
and
Disease Classification
Shortcomings of Conventional
Pathophenotype Classification
• Anachronistic—Malpighi (microscopic dx), Osler
(pathophysiologic dx)
• Organ-based approach reflects common end-stage
phenotypes determined by intermediate
pathophenotypes (e.g., inflammation).
• Major therapeutic emphasis is on non-specific
intermediate pathophenotypes (e.g., antithrombotics).
• Many distinct diseases have common phenotypes
(e.g., hepatitides).
• Common treatments, disease co-occurrence, and
GWAS suggest common underlying mechanisms.
Complex Pathophenotypes
There is (genetic) overlap among common complex human pathophenotypes.
--Rzhetsky et al., PNAS 2007;104:11694-9
Genome-wide Association Study
Different complex chronic diseases have common GWAS loci.
14,000 cases of 7 common diseases, 3,000 shared controls, 500,000 SNPs
--Wellcome Trust Case Control Consortium, Nature 2007;447:661-78
The Genome:
Linear Narrative of Disease
Manhattan Plot
--Wellcome Trust Case Control Consortium, Nature 2007;447:661-78
“All science is either
physics or stamp collecting.”
--Ernest Rutherford, 1920
Can Molecular Networks Give
Unique Insight into Disease
Pathogenesis and Therapy?
Essential vs. Disease Genes
Disease genes are largely nonessential and do not encode hubs.
Interactome
--Barabasi et al., Nat Revs Genet 2011;12:56-68
Essential vs. Disease Genes
Disease genes are largely nonessential and do not encode hubs.
Interactome
--Barabasi et al., Nat Revs Genet 2011;12:56-68
The Consolidated Interactome
•Protein-protein interactions from yeast 2H
screens) & protein complexes (CORUM)
•Regulatory protein-DNA interactions
(TRANSFAC)
•Metabolic enzyme-coupled interactions
(MCIs)
•Protein kinase network (only PTM
incorporated to date)
Disease Modules, Disease
Neighborhoods, and the Interactome
Disease Module
Interactome
Disease
Neighborhood
Disease Modules in the Interactome
•We first compiled a corpus of 299 diseases defined by
13,470 Nodes
Medical Subject Headings (MeSH) ontology.
141,296 Edges
•These 299 diseases also have at least 20 associated genes
in the current Online Mendelian Inheritance in Man (OMIM)
and GWAS databases.
•This compilation totals 2,436 disease-associated proteins.
•The derived disease modules are incomplete and, on
average, comprise only ~20% of the respective disease
genes.
•Used DiseAse MOdule Detection Algorithm (DIAMOnD) to
expand the seed gene (protein) pool by incorporating
additional interactome-linked proteins with a significant
number of connections (Sharma et al., BMC Bioinformatics, in
press).
--Menche et al., Science 2015;347:1257601
Disease Modules in the Interactome
13,470 Nodes
141,296 Edges
Peroxisomal
Disorders
Rheumatoid
Arthritis
Multiple
Sclerosis
--Menche et al., Science 2015;347:1257601
Topological Localization and Biological
Consequences
The more localized a disease is topologically, the greater the
functional similarity of the genes in the disease module.
--Menche et al., Science 2015;347:1257601
Disease Overlap and Separation
in the Interactome
--Menche et al., Science 2015;347:1257601
Module Overlap and Function
Disease module overlap is associated with functional similarity.
--Menche et al., Science 2015;347:1257601
PAH Disease Module Derivation
• Identify disease phenotype of interest.
• Ascertain disease network
components.
• Construct disease network (i.e.,
determine the structural or functional
linkages among module components).
• Identify disease module(s) within
network.
PAH Network Derivation
PAH Network
Components
(131 Nodes,
26 Functional
Pathways)
Disease components derived from curated literature, or gene, protein, or metabolite
(expression) profiles.
PAH Network Derivation
PAH Network
Components
(131 Nodes,
26 Functional
Pathways)
Consolidated
Interactome
(11,643
Nodes
100,791
Edges)
Consolidated interactome of all known physical interations: PPIs and Protein
Complexes (CORUM), Regulatory Protein-DNA Interaction (TRANSFAC),
Metabolic Enzyme-coupled Interactions (MCIs), Kinase Network
PAH Network Derivation
PAH Network
Components
(131 Nodes,
26 Functional
Pathways)
Map
Consolidated
Interactome
(11,643
Nodes
100,791
Edges)
PAH Network Derivation
PAH Network
Components
(131 Nodes,
26 Functional
Pathways)
Map
Consolidated
Interactome
(11,643
Nodes
100,791
Edges)
PAH Network Derivation
PAH Network
(115 Nodes,
255 Edges,
Largest
Connected
Component =
82 Nodes)
Interactome-derived PAH Network
--Parikh et al., Circulation 2012;125:1520-1532
PAH and miR-21 Disease Module
miR-21 serves as a negative regulator of pathogenic pulmonary vascular responses in PAH.
--Parikh et al., Circulation 2012;125:1520-1532
miR-21-/- Mice and PH
--Parikh et al., Circulation 2012;125:1520-1532
Network Analysis Informs GWAS
--Menche et al., Science 2015;347:1257601
Network Analysis Informs GWAS
--Menche et al., Science 2015;347:1257601
Disease-Tissue Network
Construction of tissue-specific interactome
--Kitsak et al., submitted
Disease-Tissue Network
The integrity and completeness of expression of the disease module
determines the tissue specificity of disease expression.
--Kitsak et al., submitted
Network Medicine
and
Drug Development
FDA Approved Drugs: 2000-2012
Approved Drugs
100
80
60
40
20
0
00 01 02 03 04 05 06 07 08 09 10 11 12
20 20 20 20 20 20 20 20 20 20 20 20 20
Reasons for Declining Productivity
•
•
•
•
Regulatory environment
Increasing need to explore novel targets
Easy targets have been exhausted.
Increasing attrition rate for developing
drugs
• The intrinsically flawed reductionist
approach to drug development, i.e., the
need to identify a single drug target with
a single “magic bullet”
Disease Modules and Therapeutics
Drug targets are typically characterized in isolation from the disease module.
Target
Function
Drug Target
--Modified from Barabasi et al., Nat Revs Genet 2011;12:56-68
Target-based Screening
Facilitated by:
• Genomic datasets for target
identification
• Structural tools, including protein X-ray
crystallogaphy, NMR spectroscopy,
computational modeling
• Large real and virtual compound
libraries
• High-throughput screening
technologies
Drug Discovery Strategies:
Success Rates
Screen
Target-based
Percentage of NMEs
N=83
Phenotype
N=28
N=30
N=17
--Swinney & Anthony, Nature Rev Drug Disc 2011;10:507-519
Disease Modules and Therapeutics
Drug targets are better characterized with regard to their effects on phenotype.
Drug Target
Functional
Phenotype
--Modified from Barabasi et al., Nat Revs Genet 2011;12:56-68
The Promise of Personalized
Medicine: Find the Target
Before Rx
BRAFmut
MEK1C121S
--Wagle et al., J Clin Oncol 2011;29:3085-3096
The Promise of Personalized
Medicine: Inhibit the Target
Before Rx
Vemurafenib—15 wks
BRAFmut
MEK1C121S
--Wagle et al., J Clin Oncol 2011;29:3085-3096
The Peril of Personalized Medicine
with Conventional Strategy
Before Rx
Vemurafenib—15 wks
Vemurafenib—23 wks
BRAFmut
MEK1C121S
--Wagle et al., J Clin Oncol 2011;29:3085-3096
Pathway Targeting: Combination Rx
Combination therapy: dabrafenib (BRAF inhibitor) & trametinib (MEK inhibitor)
--Flaherty et al., NEJM 2012;367:1694-1703
Cardiovascular Drug Data Sets
•
•
•
•
[1] F.J. Azuaje etal . Drug-target network in myocardial infarction reveals
multiple side effects of unrelated drugs. Scientific Reports 1, 52, 2011
From [1] we obtained 38 drugs used for acute myocardial infarction (MI) and
344 non-MI drugs that interact with MI drugs.
These drugs involves 425 drug targets (denoted by eMIDTs), among which
67 (denoted by MIDTs) are targeted specifically by MI drugs.
We further collected 431 myocardial infarction disease genes from
Phenopedia in HuGE Navigator (http://hugenavigator.net). We then mapped
eMIDTs and MI disease genes onto the human interactome.
--Wang et al., in preparation
Proximity of eMIDTs to MI Genes in the
Interactome
eMIDTs and MIDTs (inset)have significantly more interactions with MI disease
genes than random expectation under Null Model I (left) and Null Model II (right)
--Wang et al., in preparation
MI Drug Target-Gene Modules
• (e)MI drug targets have
significant overlap with MI
disease genes.
• We found 12 drug-target-gene
(DTG) modules with more than
5 proteins.
--Wang et al., in preparation
MI Drug Target-Gene Module Example
Blue: MI Disease Gene
Yellow: MI-related Drug Target
Red: MI Drug or Drug Target
--Wang et al., in preparation
Asthma Module
--Sharma et al., Hum Mol Genet, 2015, e-pub
Novel Regulatory Pathway in
Asthma: GAB1
Novel inflammatory pathway in asthma derived from disease module
GAB1 Signalsome
--Sharma et al., Hum Mol Genet, 2015, e-pub
Systems Pharmacology:
Visualizing Therapeutic Actions
--From Proteostasis Web Site:
http://www.proteostasis.com/science/proteostasis_network.php
Acknowledgements
•
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•
•
Laszlo Barabasi
Dan Chasman
Zak Kohane
Paul Ridker
•
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•
•
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Masanori Aikawa
Stefan Blankenberg
Brad Maron
Marc Vidal
Scott Weiss
Tania Zeller
•
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•
•
Dina Ghiassian
Maksim Kitsak
Joerg Menche
Piero Ricchiuto
Amitabh Sharma
Ruisheng Wang