1. Computational and Systems Biology
  2. Medicine
Download icon

Heart Attack: Working together

  1. Regan Odongo
  2. Tunahan Çakır  Is a corresponding author
  1. Gebze Technical University, Turkey
Insight
  • Cited 0
  • Views 394
  • Annotations
Cite this article as: eLife 2021;10:e69863 doi: 10.7554/eLife.69863

Abstract

Heart attacks have a ripple effect on how other organs exchange biomolecules that help the heart respond to injury.

Main text

A healthy heart relies on a close network of organs to work properly. Indeed, its cells require high levels of energy which is provided by biomolecules transported in the blood from other organs. If the blood supply is blocked, this can result in a heart attack which damages the heart’s muscle cells leading to the organ beating irregularly or, in some cases, failing all together. Our current understanding of heart attacks is limited to examining the alterations to tissues in the heart, but less is known about what happens in other organs.

Recent advances in molecular methods have begun to shed light on this issue, showing, for instance, that gene expression in the spleen and liver of pigs is significantly modified following a heart attack (Zimmermann et al., 2017). Computer simulations can predict the molecular alterations taking place during complex diseases, and could be harnessed to examine how different tissues respond to a heart attack (Priest and Tontonoz, 2019). Now, in eLife, Adil Mardinoglu (Royal Institute of Technology), Jan Boren (Sahlgrenska University Hospital) and colleagues – including Muhammad Arif and Martina Klevstig as joint first authors – report how they used this method, and other techniques, to investigate how heart attacks alter fat, skeletal muscle and liver tissues (Arif et al., 2021).

To identify which genes are altered in these tissues, the team (who are based in Sweden, Turkey and the United Kingdom) triggered acute heart attacks in mice by surgically blocking some of the arteries that supply blood to muscle cells in the heart (Figure 1). They then collected samples from the mice’s heart, skeletal muscle, fat tissue and liver, and measured how every single protein-coding gene was expressed using a highly sensitive technique called RNA sequencing. Comparing results from the heart attack model to tissue samples from control mice revealed striking changes in a number of genes, including four that had not previously been associated with this disease (Flnc, Lgals3, Prkaca and Pprc1). Most of these molecular changes took place after 24 hours, with the most profound effects occurring in the heart’s tissues.

Heart attacks cause molecular changes in multiple tissues.

Acute heart attacks were induced in mice by blocking some of the arteries that supply blood to the heart, resulting in an irregular heartbeat. Samples were then collected from the heart, skeletal muscle, liver and adipose, or 'fat', tissues of these mice (red heart) and control animals that had not undergone a heart attack (green heart). RNA sequencing revealed that the heart attack altered genes expressed in all four of these tissues. Further computational simulations suggest that these genetic changes alter the production of metabolites that are then taken up by the other tissues (represented by arrows).

Image credit: Regan Odongo.

Further analysis showed that genes associated with the immune system, RNA processes and mitochondrial function were commonly altered in the heart, skeletal muscle and fat tissues. Arif et al. also found groups of genes that were only modified in specific tissues. These unique clusters had a variety of roles, ranging from reacting to the heart attack to breaking down metabolites (biomolecules derived from glucose) so muscle cells in the heart can adequately respond to the injury. These findings suggest that the molecular changes induced by a heart attack have a ripple effect that changes how genes are expressed in other organs.

Next, Arif et al. performed computational simulations to predict which of the metabolites produced by these tissues might have an important role in supplying energy to the heart. To do this, they used a technique called reporter metabolite analysis which identifies key metabolites based on the genes expressed (Patil and Nielsen, 2005). Some of the metabolites were detected in all four tissues, while others were only found in either the heart, skeletal muscle, fat or liver. These metabolites were consistent with physiological changes commonly observed in patients that have experienced a heart attack (Doenst et al., 2013).

Advanced computational simulations were then conducted on a model in which all four tissues are linked together by blood vessels. This predicted that some of the metabolites synthesised by the liver are released into the blood and taken up by the heart’s tissues (Figure 1). The model also showed that some of the metabolites frequently found in the blood samples of heart attack patients are released from fat tissues and taken up by the liver and skeletal muscles. Taken together, these findings suggest that the heart, skeletal muscles, fat and liver all have a distinct molecular response to a heart attack. However, these genetic changes allow the tissues to ‘talk to each other’ and exchange metabolites that help the heart respond adequately to the damage caused by the heart attack.

This work provides novel insights into how spatially separated tissues with different embryonic origins can still coordinate after a heart attack. It also highlights how multi-tissue computational simulations can be used to unravel complex diseases. In future studies it will be critical to determine whether a similar multi-tissue crosstalk occurs in chronic heart attacks (which are more common), and if drugs that alter these molecular processes could improve the outcome for patients.

References

Article and author information

Author details

  1. Regan Odongo

    Regan Odongo is in the Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey

    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7200-7477
  2. Tunahan Çakır

    Tunahan Çakır is in the Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey

    For correspondence
    tcakir@gtu.edu.tr
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8262-4420

Publication history

  1. Version of Record published: June 8, 2021 (version 1)

Copyright

© 2021, Odongo and Çakır

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

Metrics

  • 394
    Page views
  • 20
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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)

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

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

Further reading

    1. Computational and Systems Biology
    Pierpaolo Sorrentino et al.
    Short Report Updated

    Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r = 0.37, p<0.0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics.

    1. Computational and Systems Biology
    2. Neuroscience
    Matthew Rosenberg et al.
    Research Article Updated

    Animals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in the labyrinth makes ~2000 navigation decisions per hour. The animal explores the maze, quickly discovers the location of a reward, and executes correct 10-bit choices after only 10 reward experiences — a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The underlying search algorithm does not require a global memory of places visited and is largely explained by purely local turning rules.