Network Medicine: A Systems Approach to Cardiovascular Disease Diagnosis & Management Joseph...

Network Medicine:A Systems Approach to Cardiovascular Disease

Diagnosis & Management

Joseph Loscalzo, M.D., Ph.D.Brigham and Women’s Hospital

Harvard Medical SchoolBoston, MA

Outline

•Systems and networks in biomedicine

•Network medicine, disease modules, and disease classification

•Network medicine and drug development

Systems and Networksin

Biomedicine

• 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?

Two Burning Questions

• 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….

Changing Scientific Paradigm

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 20-40% of patients with vascular disease, but in only 2% of unaffected individuals

Homocysteine Metabolism

Methionine

Cystathionine

Homocysteine

Cysteine

SAH

SAM

Betaine

DMG

THF

5-Methyl-THF

5,10-Methylene

THF

Sulfate

Me-B12

B6

1

4

2

3

1: Methionine Synthase

2: MTHF Reductase

3: Betaine-homocysteine

Methyltransferase

4: Cystathionine-beta-

Synthase

Vitamin Rx, Homocysteine, & CV Risk

--HOPE-2 Investigators, NEJM, 2006--Bonaa KH, et al., NEJM, 2006

NORVIT Trial HOPE-2 Trial

3749 pts. s/p AMI 5522 pts. w/ CVD

or diabetes

2.5 mg Folic Acid

1.0 mg B12

50 mg pyridoxine

Folic Acid (mg) 0.8 0.8 0 0

B12 (mg) 0.4 0.4 0 0

Pyridoxine (mg) 40 0 40 0

Folate, B12, and Homocysteine

Methionine

Homocysteine

Folate, B12

Cystathionine

B6

HCY Metabolism

Biomedicine in Network Context


Folate Metabolism

HCY Metabolism

Intermediary Metabolism

Folate Metabolism

HCY Metabolism


Methionine

Homocysteine SAH

SAM

Betaine

DMG

THF

N -CH3-THF

N -CH2-THFMe-B12

Folate, B12, and Homocysteine

5

5,10

DHF

dUMP

dTMPAcceptor

Methyl-

Acceptor

DNA Synthesis Cell Proliferation

CpG DNA Methylation

ADMA Synthesis

Modify Gene Expression

Impair NO Synthesis/EC Function

Increased AngiographicRestenosis post-PCI(Lange et al., NEJM, 2004)

• 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).

Changing Scientific Paradigm

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 StructuresRandom Network Scale-free Network

P(k) = e-k P(k) = k-g

m = g

Poisson Distribution Power Law Distribution

P(k

)

log

P(k

)

log kk

<k>

Few nodeshighly linked

Many nodessparsely linked

k=degree or # nodal connections

• 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

Scale-free Networks: Biological Implications

Network Medicine,Disease Modules,

and Disease 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.

Shortcomings of Conventional Pathophenotype Classification

Complex PathophenotypesThere is (genetic) overlap among common complex human pathophenotypes.

--Rzhetsky et al., PNAS 2007;104:11694-9

Genome-wide Association Study14,000 cases of 7 common diseases, 3,000 shared controls, 500,000 SNPs

--Wellcome Trust Case Control Consortium, Nature 2007;447:661-78

Different complex chronic diseases have common GWAS loci.

The Genome:Linear Narrative of Disease

--Wellcome Trust Case Control Consortium, Nature 2007;447:661-78

Manhattan Plot

“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 GenesDisease genes are largely nonessential and do not encode hubs.

--Barabasi et al., Nat Revs Genet 2011;12:56-68

Interactome

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

13,470 Nodes141,296 Edges

--Menche et al., Science 2015;347:1257601

• We first compiled a corpus of 299 diseases defined by Medical Subject Headings (MeSH) ontology.

• 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).

Disease Modules in the Interactome

PeroxisomalDisorders

RheumatoidArthritis

Multiple Sclerosis

13,470 Nodes141,296 Edges


Topological Localization and Biological Consequences


The more localized a disease is topologically, the greater the functional similarity of the genes in the disease module.

Disease Overlap and Separation in the Interactome


Module Overlap and Function


Disease module overlap is associated with functional similarity.

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 DerivationPAH Network Components (131 Nodes, 26 Functional Pathways)

Disease components derived from curated literature, or gene, protein, or metabolite (expression) profiles.

PAH Network Derivation

Consolidated Interactome(11,643 Nodes100,791 Edges)

PAH Network Components (131 Nodes, 26 Functional Pathways)

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 Components (131 Nodes, 26 Functional Pathways)

Map



PAH Network Components(131 Nodes, 26 Functional Pathways)

Map


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.

miR-21-/- Mice and PH


Network Analysis Informs GWAS


Disease-Tissue Network

--Kitsak et al., submitted

Construction of tissue-specific interactome

Disease-Tissue Network

--Kitsak et al., submitted

The integrity and completeness of expression of the disease module determines the tissue specificity of disease expression.

Network Medicineand

Drug Development

FDA Approved Drugs: 2000-2012

0

20

40

60

80

100

Ap

pro

ve

d D

rug

s

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 Target

Target Function

Drug targets are typically characterized in isolation from the disease module.

--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

--Swinney & Anthony, Nature Rev Drug Disc 2011;10:507-519

Drug Discovery Strategies:Success Rates

Per

cent

age

of N

ME

s

Phenotype

Target-based

Screen

N=83

N=30N=17

N=28

Disease Modules and TherapeuticsDrug targets are better characterized with regard to their effects on phenotype.

Drug TargetFunctionalPhenotype

--Modified from Barabasi et al., Nat Revs Genet 2011;12:56-68

The Promise of Personalized Medicine: Find the Target

Before Rx

--Wagle et al., J Clin Oncol 2011;29:3085-3096

BRAFmut

MEK1C121S

http://jco.ascopubs.org.ezp-prod1.hul.harvard.edu/content/29/22/3085/F1.large.jpg

The Promise of Personalized Medicine: Inhibit the Target

Before Rx Vemurafenib—15 wks


BRAFmut

MEK1C121S


The Peril of Personalized Medicine with Conventional Strategy

Before Rx Vemurafenib—15 wks Vemurafenib—23 wks


BRAFmut

MEK1C121S


Pathway Targeting: Combination RxCombination 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

http://hugenavigator.net/

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)


• (e)MI drug targets have significant overlap with MI disease genes.

• We found 12 drug-target-gene (DTG) modules with more than 5 proteins.


MI Drug Target-Gene Modules

MI Drug Target-Gene Module Example


Blue: MI Disease GeneYellow: MI-related Drug TargetRed: MI Drug or Drug Target

Asthma Module

--Sharma et al., Hum Mol Genet, 2015, e-pub

Novel Regulatory Pathway in Asthma: GAB1

--Sharma et al., Hum Mol Genet, 2015, e-pub

GAB1 Signalsome

Novel inflammatory pathway in asthma derived from disease module

--From Proteostasis Web Site: http://www.proteostasis.com/science/proteostasis_network.php

Systems Pharmacology:Visualizing Therapeutic Actions

• Laszlo Barabasi• Dan Chasman• Zak Kohane• Paul Ridker

• Masanori Aikawa• Stefan Blankenberg• Brad Maron• Marc Vidal• Scott Weiss• Tania Zeller

Acknowledgements

• Dina Ghiassian• Maksim Kitsak• Joerg Menche• Piero Ricchiuto• Amitabh Sharma• Ruisheng Wang

Network Medicine: A Systems Approach to Cardiovascular Disease Diagnosis & Management Joseph...

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