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. 2021 May 11:10:e66921.
doi: 10.7554/eLife.66921.

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

Muhammad Arif #  1 , Martina Klevstig #  2 , Rui Benfeitas  3 , Stephen Doran  4 , Hasan Turkez  5 , Mathias Uhlén  1 , Maryam Clausen  6 , Johannes Wikström  7 , Damla Etal  6 , Cheng Zhang  1 , Malin Levin  2 , Adil Mardinoglu  1   4 , Jan Boren  2
Affiliations

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

Muhammad Arif et al. Elife. .

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 hr 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.

Keywords: Systems biology; computational biology; medicine; metabolically active tissues; mouse; multi-tissue; myocardial infarction; network analysis; systems biology; whole-body modelling.

Plain language summary

The human body is like a state-of-the-art car, where each part must work together with all the others. When a car breaks down, most of the time the problem is not isolated to only one part, as it is an interconnected system. Diseases in the human body can also have systemic effects, so it is important to study their implications throughout the body. Most studies of heart attacks focus on the direct impact on the heart and the cardiovascular system. Learning more about how heart attacks affect rest of the body may help scientists identify heart attacks early or create improved treatments. Arif and Klevstig et al. show that heart attacks affect the metabolism throughout the body. In the experiments, mice underwent a procedure that mimics either a heart attack or a fake procedure. Then, Arif and Klevstig et al. compared the activity of genes in the heart, muscle, liver and fat tissue of the two groups of mice 6- and 24-hours after the operations. This revealed disruptions in the immune system, metabolism and the production of proteins. The experiments also showed that changes in the activity of four important genes are key to these changes. This suggests that this pattern of changes could be used as a way to identify heart attacks. The experiments show that heart attacks have important effects throughout the body, especially on metabolism. These discoveries may help scientists learn more about the underlying biological processes and develop new treatments that prevent the harmful systemic effects of heart attacks and boost recovery.

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Conflict of interest statement

MA, MK, RB, SD, HT, MU, CZ, ML, AM, JB No competing interests declared, MC, JW, DE employee at AstraZeneca

Figures

Figure 1.
Figure 1.. Study overview and transcriptional changes 24 hours after MI.
(A) Overview of this study (B) Number of differentially expressed genes for each tissue at each time point. Effect of MI shown to be more pronounced after 24 hr. (C) UpSet plot to show intersection between differentially expressed genes (FDR < 5%) in different tissues. The plot showed that each tissue has its specific set of genes that were affected by MI. (D) KEGG pathway analysis (FDR < 0.05 in at least three tissues) for 24 hours post MI compared to its control for each tissue. We observed that 141 (5 upregulated) and 125 (14 upregulated) pathways are significantly altered in heart 6 and 24 hr after infarction, respectively. For other tissues, we found that 24 (9 upregulated), 61 (54 upregulated), and 48 (15 upregulated) pathways are altered in liver, muscle, and adipose, respectively.
Figure 1—figure supplement 1.
Figure 1—figure supplement 1.. Data exploration of the samples.
PCA plots of each tissue showing data from mice 6 and 24 hr after an MI or sham operation. The plot showed that heart was affected the most by the change in conditions and the rest were most affected by time shifts.
Figure 1—figure supplement 2.
Figure 1—figure supplement 2.. KEGG pathway analysis results for Heart 6- and 24 hr post MI.
Figure 1—figure supplement 3.
Figure 1—figure supplement 3.. KEGG pathway analysis results for each tissue liver, muscle, and adipose tissue 24 hr post MI.
Figure 1—figure supplement 4.
Figure 1—figure supplement 4.. KEGG pathways related to cardiac problems show activation after an MI.
Figure 2.
Figure 2.. Gene ontology and reporter metabolites analysis results.
(A) Functional analysis with GO (FDR < 0.05% in at least three tissues) revealed that 944 (919 upregulated) and 1019 (970 upregulation) BPs are significantly altered in heart 6 and 24 hr after infarction, respectively. The results also showed 38 (16 upregulated), 376 (357 upregulated), and 193 (116 upregulated) BPs are significantly altered 24 hr after infarction in liver, muscle and adipose, respectively. Most tissues show significant alterations in multiple biological processes, including mitochondrial functions, RNA processes, cell adhesion, ribosome, and immune systems. The results of this analysis showed alterations concordant with those observed for KEGG pathways. (B) Reporter metabolites analysis shows significant alteration in important metabolites. Our analysis revealed that 169, 324, 118, and 51 reporter metabolites are significantly altered in heart, liver, skeletal muscle and adipose tissues, respectively, at 24 hr post-infarction (Table S4).
Figure 3.
Figure 3.. Tissue-specific gene co-expression network analyses.
(A) Heart co-expression network clusters with superimposed DEGs 24 h post-infarction (Blue = downregulated, Red = upregulated) marked with the cluster numbers. The edges between the clusters were aggregation of the inter-cluster edges (B) Liver. (C) Muscle. (D) Adipose. (E) Intersection of the most central clusters in all tissues shows that the central architecture of the network was conserved in all tissues. We found four sub-clusters within the network intersection. Top 10 most connected genes are marked in black. (F) Enriched GO BP in heart-specific cluster generated by Revigo.
Figure 4.
Figure 4.. Functional analysis of network clusters and hypothesized metabolites flow.
(A) Similarity of functions in the most central cluster and specific functions of each tissue-specific cluster. (B) Functional analysis for each tissue and hypothesized flow of metabolites.
Figure 4—figure supplement 1.
Figure 4—figure supplement 1.. cGMP-PKG with overlay data from differential expression and reporter metabolites analysis.
Figure 4—figure supplement 2.
Figure 4—figure supplement 2.. HIF-1 signaling pathway with overlay data from differential expression and reporter metabolites analysis.
Figure 5.
Figure 5.. Central DEGs in fatty acid and lipid metabolism.
(A) Significantly differentially expressed central genes of each tissue-specific cluster to fatty acid metabolism, as one of the most affected metabolic process. (B) Lipid metabolism. Red = upregulated, blue = downregulated.
Figure 6.
Figure 6.. Comparison of our analysis results with the independent validation cohorts.
(A) DEGs intersection of our data and validation cohort (B) and (C) intersection of functional analysis results (GO BP and KEGG Pathways) of our data and validation cohort.
See this image and copyright information in PMC

Comment in

  • Working together.
    Odongo R, Çakır T. Odongo R, et al. Elife. 2021 Jun 8;10:e69863. doi: 10.7554/eLife.69863. Elife. 2021. PMID: 34100721 Free PMC article.

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