Development, calibration, and validation of a novel human ventricular myocyte model in health, disease, and drug block

  1. Jakub Tomek  Is a corresponding author
  2. Alfonso Bueno-Orovio
  3. Elisa Passini
  4. Xin Zhou
  5. Ana Minchole
  6. Oliver Britton
  7. Chiara Bartolucci
  8. Stefano Severi
  9. Alvin Shrier
  10. Laszlo Virag
  11. Andras Varro
  12. Blanca Rodriguez  Is a corresponding author
  1. University of Oxford, United Kingdom
  2. University of Bologna, Italy
  3. McGill University, Canada
  4. University of Szeged, Hungary

Abstract

Human-based modelling and simulations are becoming ubiquitous in biomedical science due to their ability to augment experimental and clinical investigations. Cardiac electrophysiology is one of the most advanced areas, with cardiac modelling and simulation being considered for virtual testing of pharmacological therapies and medical devices. Current models present inconsistencies with experimental data, which limit further progress. In this study, we present the design, development, calibration and independent validation of a human-based ventricular model (ToR-ORd) for simulations of electrophysiology and excitation-contraction coupling, from ionic to whole-organ dynamics, including the electrocardiogram. Validation based on substantial multiscale simulations supports the credibility of the ToR-ORd model under healthy and key disease conditions, as well as drug blockade. In addition, the process uncovers new theoretical insights into the biophysical properties of the L-type calcium current, which are critical for sodium and calcium dynamics. These insights enable the reformulation of L-type calcium current, as well as replacement of the hERG current model.

Data availability

No new experimental data were created. However, codes for simulations are available at https://github.com/jtmff/torord.

Article and author information

Author details

  1. Jakub Tomek

    Department of Computer Science, University of Oxford, Oxford, United Kingdom
    For correspondence
    jakub.tomek@dpag.ox.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0157-4386
  2. Alfonso Bueno-Orovio

    Department of Computer Science, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Elisa Passini

    Department of Computer Science, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Xin Zhou

    Department of Computer Science, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Ana Minchole

    Department of Computer Science, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Oliver Britton

    Department of Computer Science, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Chiara Bartolucci

    Department of Electrical, Electronic, and Information Engineering 'Guglielmo Marconi', University of Bologna, Bologna, Italy
    Competing interests
    The authors declare that no competing interests exist.
  8. Stefano Severi

    Department of Electrical, Electronic, and Information Engineering 'Guglielmo Marconi', University of Bologna, Bologna, Italy
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4306-8294
  9. Alvin Shrier

    Department of Physiology, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  10. Laszlo Virag

    Department of Pharmacology and Pharmacotherapy, University of Szeged, Szeged, Hungary
    Competing interests
    The authors declare that no competing interests exist.
  11. Andras Varro

    Department of Pharmacology and Pharmacotherapy, University of Szeged, Szeged, Hungary
    Competing interests
    The authors declare that no competing interests exist.
  12. Blanca Rodriguez

    Department of Computer Science, University of Oxford, Oxford, United Kingdom
    For correspondence
    Blanca.Rodriguez@cs.ox.ac.uk
    Competing interests
    The authors declare that no competing interests exist.

Funding

Wellcome (100246/Z/12/Z)

  • Blanca Rodriguez

Amazon Web Services (Machine learning research award)

  • Blanca Rodriguez

Wellcome (214290/Z/18/Z)

  • Blanca Rodriguez

British Heart Foundation (FS/17/22/32644)

  • Alfonso Bueno-Orovio

European Commission (675451)

  • Blanca Rodriguez

National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC/P001076/1)

  • Blanca Rodriguez

TransQST (116030)

  • Blanca Rodriguez

BHF Centre of Research Excellence, Oxford (RE/13/1/30181)

  • Blanca Rodriguez

UK National Supercomputing (Archer RAP award (322 00180))

  • Blanca Rodriguez

UK National Supercomputing (PRACE (2017174226))

  • Blanca Rodriguez

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2019, Tomek et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 7,356
    views
  • 933
    downloads
  • 146
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Jakub Tomek
  2. Alfonso Bueno-Orovio
  3. Elisa Passini
  4. Xin Zhou
  5. Ana Minchole
  6. Oliver Britton
  7. Chiara Bartolucci
  8. Stefano Severi
  9. Alvin Shrier
  10. Laszlo Virag
  11. Andras Varro
  12. Blanca Rodriguez
(2019)
Development, calibration, and validation of a novel human ventricular myocyte model in health, disease, and drug block
eLife 8:e48890.
https://doi.org/10.7554/eLife.48890

Share this article

https://doi.org/10.7554/eLife.48890

Further reading

    1. Cell Biology
    Wonjo Jang, Kanishka Senarath ... Nevin A Lambert
    Tools and Resources

    Classical G-protein-coupled receptor (GPCR) signaling takes place in response to extracellular stimuli and involves receptors and heterotrimeric G proteins located at the plasma membrane. It has recently been established that GPCR signaling can also take place from intracellular membrane compartments, including endosomes that contain internalized receptors and ligands. While the mechanisms of GPCR endocytosis are well understood, it is not clear how well internalized receptors are supplied with G proteins. To address this gap, we use gene editing, confocal microscopy, and bioluminescence resonance energy transfer to study the distribution and trafficking of endogenous G proteins. We show here that constitutive endocytosis is sufficient to supply newly internalized endocytic vesicles with 20–30% of the G protein density found at the plasma membrane. We find that G proteins are present on early, late, and recycling endosomes, are abundant on lysosomes, but are virtually undetectable on the endoplasmic reticulum, mitochondria, and the medial-trans Golgi apparatus. Receptor activation does not change heterotrimer abundance on endosomes. Our findings provide a subcellular map of endogenous G protein distribution, suggest that G proteins may be partially excluded from nascent endocytic vesicles, and are likely to have implications for GPCR signaling from endosomes and other intracellular compartments.

    1. Cell Biology
    Mitsuhiro Abe, Masataka Yanagawa ... Yasushi Sako
    Research Article

    Anionic lipid molecules, including phosphatidylinositol-4,5-bisphosphate (PI(4,5)P2), are implicated in the regulation of epidermal growth factor receptor (EGFR). However, the role of the spatiotemporal dynamics of PI(4,5)P2 in the regulation of EGFR activity in living cells is not fully understood, as it is difficult to visualize the local lipid domains around EGFR. Here, we visualized both EGFR and PI(4,5)P2 nanodomains in the plasma membrane of HeLa cells using super-resolution single-molecule microscopy. The EGFR and PI(4,5)P2 nanodomains aggregated before stimulation with epidermal growth factor (EGF) through transient visits of EGFR to the PI(4,5)P2 nanodomains. The degree of coaggregation decreased after EGF stimulation and depended on phospholipase Cγ, the EGFR effector hydrolyzing PI(4,5)P2. Artificial reduction in the PI(4,5)P2 content of the plasma membrane reduced both the dimerization and autophosphorylation of EGFR after stimulation with EGF. Inhibition of PI(4,5)P2 hydrolysis after EGF stimulation decreased phosphorylation of EGFR-Thr654. Thus, EGFR kinase activity and the density of PI(4,5)P2 around EGFR molecules were found to be mutually regulated.