Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate

Abstract

Cardiac magnetic resonance imaging (MRI) has revealed fibrosis in embolic stroke of undetermined source (ESUS) patients comparable to levels seen in atrial fibrillation (AFib). We used computational modeling to understand the absence of arrhythmia in ESUS despite the presence of putatively pro-arrhythmic fibrosis. MRI-based atrial models were reconstructed for 45 ESUS and 45 AFib patients. The fibrotic substrate's arrhythmogenic capacity in each patient was assessed computationally. Reentrant drivers were induced in 24/45 (53%) ESUS and 22/45 (49%) AFib models. Inducible models had more fibrosis (16.7±5.45%) than non-inducible models (11.07±3.61%; P<0.0001); however, inducible subsets of ESUS and AFib models had similar fibrosis levels (P=0.90), meaning the intrinsic pro-arrhythmic substrate properties of fibrosis in ESUS and AFib are indistinguishable. This suggests some ESUS patients have latent pre-clinical fibrotic substrate that could be a future source of arrhythmogenicity. Thus, our work prompts the hypothesis that ESUS patients with fibrotic atria are spared from AFib due to an absence of arrhythmia triggers.

Data availability

Where possible (Figs. 2, 3, 5, 6), raw numerical data underlying figures are available via figshare: https://doi.org/10.6084/m9.figshare.14348042. Patient-derived data related to this article, including processed versions thereof, are not publicly available out of respect for the privacy of the patients involved. Interested parties wishing to obtain these data for non-commercial reuse should contact the co-corresponding authors via email. Upon all reasonable requests for access to these data, the co-corresponding authors will work to pursue negotiation of a Data Transfer and Use Agreement with the requesting party; administrators at the requesting party's institution, the University of Washington, and Klinikum Coburg; and relevant Institutional Review Boards at all the latter institutions. Source files for a complete example of computational modeling and simulation of the fibrotic atria, using publicly available data sets and software tools only, can be found via the following permanent link: https://doi.org/10.6084/m9.figshare.14347979. Documentation provided with this example includes instructions on the use of the openCARP cardiac electrophysiology simulator and the meshalyzer visualization software (both available via https://opencarp.org/) to precisely reproduce the computational protocol applied to patient-specific left atria models in this study.

Article and author information

Author details

  1. Savannah F Bifulco

    Bioengineering, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Griffin D Scott

    Bioengineering, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Sakher Sarairah

    Cardiology, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Zeinab Birjandian

    Cardiology, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Caroline H Roney

    School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Steven A Niederer

    School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Christian Mahnkopf

    Cardiology, Klinikum Coburg, Coburg, Germany
    Competing interests
    The authors declare that no competing interests exist.
  8. Peter Kuhnlein

    Cardiology, Klinikum Coburg, Coburg, Germany
    Competing interests
    The authors declare that no competing interests exist.
  9. Marcel Mitlacher

    Cardiology, Klinikum Coburg, Coburg, Germany
    Competing interests
    The authors declare that no competing interests exist.
  10. David Tirschwell

    Neurology, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. W T Longstreth Jr

    Neurology, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Nazem Akoum

    Cardiology, University of Washington, Seattle, United States
    For correspondence
    nakoum@cardiology.washington.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2001-6806
  13. Patrick M Boyle

    Bioengineering, University of Washington, Seattle, United States
    For correspondence
    pmjboyle@uw.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9048-1239

Funding

Achievement Rewards for College Scientists Foundation

  • Savannah F Bifulco

National Institutes of Health (T32-EB001650)

  • Savannah F Bifulco

Medical Research Council (MR/S015086/1)

  • Caroline H Roney

National Institutes of Health (R01-HL152256)

  • Steven A Niederer

H2020 European Research Council (PREDICT-HF (864055))

  • Steven A Niederer

British Heart Foundation (RG/20/4/34803)

  • Steven A Niederer

Engineering and Physical Sciences Research Council (EP/P01268X/1)

  • Steven A Niederer

Wellcome Trust (203148/Z/16/Z)

  • Steven A Niederer

National Institutes of Health (NIH 5-U01-NS095869)

  • David Tirschwell
  • W T Longstreth Jr

John Locke Charitable Trust

  • Nazem Akoum

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

Ethics

Human subjects: This study was approved by the Institutional Review Board (IRB) of the University of Washington (UW) and the Ethikkommission der Bayerischen Ländesärztekammer München, Bayern, Deutschland; all participants provided written informed consent. Associated reference numbers: IRB5350 for ESUS patients; IRB8763 for AFib patients.

Copyright

© 2021, Bifulco 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.

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. Savannah F Bifulco
  2. Griffin D Scott
  3. Sakher Sarairah
  4. Zeinab Birjandian
  5. Caroline H Roney
  6. Steven A Niederer
  7. Christian Mahnkopf
  8. Peter Kuhnlein
  9. Marcel Mitlacher
  10. David Tirschwell
  11. W T Longstreth Jr
  12. Nazem Akoum
  13. Patrick M Boyle
(2021)
Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate
eLife 10:e64213.
https://doi.org/10.7554/eLife.64213

Share this article

https://doi.org/10.7554/eLife.64213

Further reading

    1. Cancer Biology
    2. Computational and Systems Biology
    Rosalyn W Sayaman, Masaru Miyano ... Mark A LaBarge
    Research Article Updated

    Effects from aging in single cells are heterogenous, whereas at the organ- and tissue-levels aging phenotypes tend to appear as stereotypical changes. The mammary epithelium is a bilayer of two major phenotypically and functionally distinct cell lineages: luminal epithelial and myoepithelial cells. Mammary luminal epithelia exhibit substantial stereotypical changes with age that merit attention because these cells are the putative cells-of-origin for breast cancers. We hypothesize that effects from aging that impinge upon maintenance of lineage fidelity increase susceptibility to cancer initiation. We generated and analyzed transcriptomes from primary luminal epithelial and myoepithelial cells from younger <30 (y)ears old and older >55 y women. In addition to age-dependent directional changes in gene expression, we observed increased transcriptional variance with age that contributed to genome-wide loss of lineage fidelity. Age-dependent variant responses were common to both lineages, whereas directional changes were almost exclusively detected in luminal epithelia and involved altered regulation of chromatin and genome organizers such as SATB1. Epithelial expression variance of gap junction protein GJB6 increased with age, and modulation of GJB6 expression in heterochronous co-cultures revealed that it provided a communication conduit from myoepithelial cells that drove directional change in luminal cells. Age-dependent luminal transcriptomes comprised a prominent signal that could be detected in bulk tissue during aging and transition into cancers. A machine learning classifier based on luminal-specific aging distinguished normal from cancer tissue and was highly predictive of breast cancer subtype. We speculate that luminal epithelia are the ultimate site of integration of the variant responses to aging in their surrounding tissue, and that their emergent phenotype both endows cells with the ability to become cancer-cells-of-origin and represents a biosensor that presages cancer susceptibility.

    1. Computational and Systems Biology
    David B Blumenthal, Marta Lucchetta ... Martin H Schaefer
    Research Article Updated

    Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations, and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.