Systems pharmacology model to control cardiac fibrosis...see poster. Zeigler+ J Mol Cell Cardiol...
Transcript of Systems pharmacology model to control cardiac fibrosis...see poster. Zeigler+ J Mol Cell Cardiol...
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Systems pharmacology model to control cardiac fibrosis
Jeff SaucermanIMAG MSM March 2019
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Signaling complexity hinders targeting of fibrosis after myocardial infarction
Objective: Develop computational models that predict and mechanistically explain therapeutic strategies to control myofibroblast activation and fibrosis
Travers et al Circ Res 2016Stuart et al JMCC 2016
Every year, ~1M Americans have a myocardial infarction
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Reconstruction of the fibroblast signaling network
89 nodes, 129 reactions reconstructed from 177 papers2+ citations per reaction, emphasis on cardiac fibroblasts
Cytokines TGFβBeta-adrenergic
Integrin
Zeigler+ J Mol Cell Cardiol 2016
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From network structure to dynamics: logic-based differential equations
• Proteins/genes in units of normalized activity, not concentration• Reactions use normalized-Hill functions and AND/OR logic gates.• Default parameter values: EC50,(0.5), Hill coeff (1.4), rxn weight (1) time
constant (signaling: 10 s, transcription: 1 h, translation: 10 h)Kraeutler+ BMC Sys Bio 2010
𝑓𝑓𝑎𝑎𝑎𝑎𝑎𝑎 𝐴𝐴 =𝐴𝐴𝑛𝑛
𝐸𝐸𝐶𝐶50𝑛𝑛 + 𝐴𝐴𝑛𝑛𝑑𝑑𝐶𝐶𝑑𝑑𝑑𝑑
=1𝜏𝜏𝐶𝐶
(𝑊𝑊𝐴𝐴𝐶𝐶 𝑓𝑓𝑎𝑎𝑎𝑎𝑎𝑎 𝐴𝐴 𝑌𝑌𝑀𝑀𝐴𝐴𝑀𝑀,𝐶𝐶 − 𝐶𝐶)
𝐴𝐴𝐴𝐴𝐴𝐴 𝑋𝑋,𝑌𝑌 = 𝑓𝑓 𝑋𝑋 𝑓𝑓 𝑌𝑌𝑂𝑂𝑂𝑂 𝑋𝑋,𝑌𝑌 = 𝑓𝑓 𝑋𝑋 + 𝑓𝑓 𝑌𝑌 − 𝑓𝑓 𝑋𝑋 𝑓𝑓 𝑌𝑌
Z
ANDX Y
Z
ORX Y
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Predicted network response to TGFβTGFβ
Logic-based differential equation model
ET1IL6
Angela Zeigler
latent TGFβ
Methods: Kraeutler et al, BMC Sys Bio 2010
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Model accurately predicts 66 of 82 input-output relationships
other outputs
Cardiac fibroblast validation data from 34 papers not used for model buildingValidation accuracy = 80%; see poster
Zeigler+ J Mol Cell Cardiol 2016
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TGFβRi: SD208
Predicted and validated role for TGFβautocrine loop in mechano-induced αSMA
3D culture of cardiac fibroblasts in either mechanically restrained or floating collagen gelsGreen: αSMA; purple: DAPI
Will Richardson
Zeigler+ J Mol Cell Cardiol 2016
TGFβ stretch
αSMA
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Applying the network model to predict matrix remodeling dynamics after
myocardial infarction9 model inputs driven by experimentally measured cytokine/hormone dynamics after myocardial infarction
Post-MI experimental data from rator human hearts, from infarct zone where available
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Angiotensin II
Idealized CurveScheiffer 1994White 2001
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TGFBIdealized CurveHerskowitz 1995Hao 1999
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IL1Idealized CurveHerskowitz 1995Deten 2002
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TNFaIdealized CurveHerskowitz 1995Heba 2001
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NEIdealized CurveWhite 2001
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ET1Idealized CurveWhite 2001
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BNP
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PDGFIdealized CurveZhao 2011
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Experimental validation of predicted collagen mRNA and protein outputs after
myocardial infarction
Experimental data from rat infarcts:Deten 2001Zimmerman 2001Fomovsky 2010
Network model coupled to single ODE model with experimentally measured time-dependent MMP activity and fibroblast proliferation
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Collagen I mRNAModel PredictionRat Infarct Deten 2001Rat Infarct Zimmerman 2001
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Collagen III mRNAModel PredictionRat Infarct Deten 2001Rat Infarct Zimmerman 2001
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Simulating FDA approved drugs targeting fibroblast signaling network
25 FDA approved drugs that uniquely target 24 network nodes
Competitive inhibitors/activators
Non-competitive inhibitors/activators
shift [X] adjust reaction weights Dose: 80% of saturation
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Virtual screen for drugs that modulate fibroblasts after myocardial infarction
Time post-MI:
Fraccarollo et al Hypertension 2015
Chu et al Card Res 2012
Leuder+ Circ HF ‘15
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Network mechanisms contributing to fibrosis induced by arsenic trioxide
Ani Chandrabhatla
Chu et al Card Res 2012
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Network mechanisms contributing to fibrosis attenuated by Entresto
Ani Chandrabhatla
Entresto
Rats 4 weeks post-MI Trivedi+ JAHA 2018
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Model prediction after MI
Model prediction 1 week after MI
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Integrated agent-based and signaling network model of fibrosis
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Michaela Rikardw. Peirce-Cottler, Holmes
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Summary• Developed and validated a large-scale model of cardiac
fibroblast signaling• Validated model prediction that stretch-mediated
myofibroblast differentiation involves a TGFβ autocrine loop• Framework for predicting in vivo signaling dynamics and
effects of drugs after myocardial infarction• Predicted pro- and anti-fibrotic FDA approved drugs (e.g.
arsenic trioxide, nitrates, Entresto)
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AcknowledgementsSaucerman LabAni ChandrabhatlaBryan ChunSteve ChristiansenDeborah FrankMonika GrabowskaAli KhalilimeybodiCody NarcisoAnders NelsonBethany WissmannLaura WooAngela Zeigler
CollaboratorsJeff Holmes (UVA)Shayn Peirce-Cottler (UVA)Michaela Rikard (UVA)Will Richardson (Clemson)
Funding:
Posters on automated validation, fibrosis gene therapies
Grad student and post-doc positions availablehttp://bme.virginia.edu/saucerman
@sauce_lab