Integrative transcriptomic analysis of tissue-specific metabolic crosstalk after myocardial infarction

  1. Muhammad Arif
  2. Martina Klevstig
  3. Rui Benfeitas
  4. Stephen Doran
  5. Hasan Turkez
  6. Mathias Uhlén
  7. Maryam Clausen
  8. Johannes Wikström
  9. Damla Etal
  10. Cheng Zhang
  11. Malin Levin
  12. Adil Mardinoglu  Is a corresponding author
  13. Jan Boren  Is a corresponding author
  1. KTH Royal Institute of Technology, Sweden
  2. University of Gothenburg, Sweden
  3. KTH - Royal Institute of Technology, Sweden
  4. King's College London, United Kingdom
  5. Atatürk University, Turkey
  6. AstraZeneca, Sweden

Abstract

Myocardial infarction (MI) promotes a range of systemic effects, many of which are unknown. Here, we investigated the alterations associated with MI progression in heart and other metabolically active tissues (liver, skeletal muscle, and adipose) in a mouse model of MI (induced by ligating the left ascending coronary artery) and sham-operated mice. We performed a genome-wide transcriptomic analysis on tissue samples obtained 6- and 24-hours post MI or sham operation. By generating tissue-specific biological networks, we observed: (1) dysregulation in multiple biological processes (including immune system, mitochondrial dysfunction, fatty-acid beta-oxidation, and RNA and protein processing) across multiple tissues post MI; and (2) tissue-specific dysregulation in biological processes in liver and heart post MI. Finally, we validated our findings in two independent MI cohorts. Overall, our integrative analysis highlighted both common and specific biological responses to MI across a range of metabolically active tissues.

Data availability

All raw RNA-sequencing data generated from this study can be accessed through accession number GSE153485.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Muhammad Arif

    Systems Biology, KTH Royal Institute of Technology, Stockholm, Sweden
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2261-0881
  2. Martina Klevstig

    Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden
    Competing interests
    No competing interests declared.
  3. Rui Benfeitas

    Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7972-0083
  4. Stephen Doran

    Centre for Host-Microbiome Interactions, King's College London, London, United Kingdom
    Competing interests
    No competing interests declared.
  5. Hasan Turkez

    Department of Medical Biology, Atatürk University, Erzurum, Turkey
    Competing interests
    No competing interests declared.
  6. Mathias Uhlén

    Systems Biology, KTH Royal Institute of Technology, Stockholm, Sweden
    Competing interests
    No competing interests declared.
  7. Maryam Clausen

    Discovery Sciences, Innovative Medicines and Early Development Biotech unit, AstraZeneca, Mölndal, Sweden
    Competing interests
    Maryam Clausen, employee at AstraZeneca.
  8. Johannes Wikström

    Bioscience Cardiovascular, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), AstraZeneca, Gothenburg, Sweden
    Competing interests
    Johannes Wikström, employee at AstraZeneca.
  9. Damla Etal

    Translational Genomics, AstraZeneca, Gothenburg, Sweden
    Competing interests
    Damla Etal, employee at AstraZeneca.
  10. Cheng Zhang

    Systems Biology, KTH Royal Institute of Technology, Stockholm, Sweden
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3721-8586
  11. Malin Levin

    Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden
    Competing interests
    No competing interests declared.
  12. Adil Mardinoglu

    Systems Biology, KTH Royal Institute of Technology, Stockholm, Sweden
    For correspondence
    adil.mardinoglu@kcl.ac.uk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4254-6090
  13. Jan Boren

    Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden
    For correspondence
    Jan.Boren@wlab.gu.se
    Competing interests
    No competing interests declared.

Funding

Knut och Alice Wallenbergs Stiftelse

  • Adil Mardinoglu
  • Jan Boren

Vetenskapsrådet

  • Jan Boren

Hjärt-Lungfonden

  • Jan Boren

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

Ethics

Animal experimentation: This study were approved by the local animal ethics committee and conform to the guidelines from Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes.

Copyright

© 2021, Arif 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

  • 3,176
    views
  • 410
    downloads
  • 30
    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. Muhammad Arif
  2. Martina Klevstig
  3. Rui Benfeitas
  4. Stephen Doran
  5. Hasan Turkez
  6. Mathias Uhlén
  7. Maryam Clausen
  8. Johannes Wikström
  9. Damla Etal
  10. Cheng Zhang
  11. Malin Levin
  12. Adil Mardinoglu
  13. Jan Boren
(2021)
Integrative transcriptomic analysis of tissue-specific metabolic crosstalk after myocardial infarction
eLife 10:e66921.
https://doi.org/10.7554/eLife.66921

Share this article

https://doi.org/10.7554/eLife.66921

Further reading

    1. Computational and Systems Biology
    Nobuhisa Umeki, Yoshiyuki Kabashima, Yasushi Sako
    Research Article

    The RAS-MAPK system plays an important role in regulating various cellular processes, including growth, differentiation, apoptosis, and transformation. Dysregulation of this system has been implicated in genetic diseases and cancers affecting diverse tissues. To better understand the regulation of this system, we employed information flow analysis based on transfer entropy (TE) between the activation dynamics of two key elements in cells stimulated with EGF: SOS, a guanine nucleotide exchanger for the small GTPase RAS, and RAF, a RAS effector serine/threonine kinase. TE analysis allows for model-free assessment of the timing, direction, and strength of the information flow regulating the system response. We detected significant amounts of TE in both directions between SOS and RAF, indicating feedback regulation. Importantly, the amount of TE did not simply follow the input dose or the intensity of the causal reaction, demonstrating the uniqueness of TE. TE analysis proposed regulatory networks containing multiple tracks and feedback loops and revealed temporal switching in the reaction pathway primarily responsible for reaction control. This proposal was confirmed by the effects of an MEK inhibitor on TE. Furthermore, TE analysis identified the functional disorder of a SOS mutation associated with Noonan syndrome, a human genetic disease, of which the pathogenic mechanism has not been precisely known yet. TE assessment holds significant promise as a model-free analysis method of reaction networks in molecular pharmacology and pathology.

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Eric V Strobl, Eric Gamazon
    Research Article

    Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.