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. Science for Life Laboratory, KTH - Royal Institute of Technology, Sweden
  2. Department of Molecular and Clinical Medicine, University of Gothenburg, The Wallenberg Laboratory, Sahlgrenska University Hospital, Sweden
  3. National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Sweden
  4. Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, United Kingdom
  5. Department of Medical Biology, Faculty of Medicine, Atatürk University, Turkey
  6. Translational Genomics, BioPharmaceuticals R&D, Discovery Sciences, AstraZeneca, Sweden
  7. Bioscience Cardiovascular, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Sweden
6 figures, 2 tables and 10 additional files

Figures

Figure 1 with 4 supplements
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
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
KEGG pathway analysis results for Heart 6- and 24 hr post MI.
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
KEGG pathways related to cardiac problems show activation after an MI.
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).

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 with 2 supplements
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
cGMP-PKG with overlay data from differential expression and reporter metabolites analysis.
Figure 4—figure supplement 2
HIF-1 signaling pathway with overlay data from differential expression and reporter metabolites analysis.
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.

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.

Tables

Table 1
Properties of the co-expression network.
Tissue# of Genes# of Edges# of ClustersModularity scores
Heart8793157089870.540
Liver7760110358960.577
Muscle8834166060370.521
Adipose10790263637880.495
Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
Commercial assay or kitRNeasy Fibrous Tissue Mini KitQiagenHeart and Skeletal Muscle Tissue
Commercial assay or kitRNeasy Mini KitQiagenLiver Tissue
Commercial assay or kitRNeasy Lipid Tissue Mini KitQiagenAdipose Tissue
Commercial assay or kitcDNA Reverse Transcription KitApplied Biosystems
Commercial assay or kitTaqMan real-time PCR in a ViiA seven systemApplied Biosystems
Commercial assay or kitNovaSeq6000Illumina
Software, algorithmNovaSeq Control Software 1.6.0/RNA v3.4.4Illumina
Software, algorithmCASAVA Software SuiteIllumina
Software, algorithmKallistoRRID:SCR_016582
Software, algorithmPython 3.7Python Programming LanguageRRID:SCR_008394
Software, algorithmsklearnPython PackageRRID:SCR_019053
Software, algorithmRR Project for Statistical ComputingRRID:SCR_001905
Software, algorithmRpy2Python Packagehttps://rpy2.github.io/
Software, algorithmDESeq2R PackageRRID:SCR_015687
Software, algorithmPIANOR PackageRRID:SCR_003200
Software, algorithmSciPyPython PackageRRID:SCR_008058
Software, algorithmStatsmodelPython PackageRRID:SCR_016074
Software, algorithmiGraphPython PackageRRID:SCR_019225
Software, algorithmLeiden ClusteringPython Packagehttps://github.com/vtraag/leidenalg
Software, algorithmMatlabMathworksRRID:SCR_001622

Additional files

Supplementary file 1

Differential expression analysis results.

https://cdn.elifesciences.org/articles/66921/elife-66921-supp1-v2.xlsx
Supplementary file 2

KEGG pathways.

https://cdn.elifesciences.org/articles/66921/elife-66921-supp2-v2.xlsx
Supplementary file 3

Gene ontology biological processes.

https://cdn.elifesciences.org/articles/66921/elife-66921-supp3-v2.xlsx
Supplementary file 4

DEG comparison between liver and other tissues.

https://cdn.elifesciences.org/articles/66921/elife-66921-supp4-v2.xlsx
Supplementary file 5

Reporter metabolite analysis.

https://cdn.elifesciences.org/articles/66921/elife-66921-supp5-v2.xlsx
Supplementary file 6

Enrichment analyses of clusters, clusters properties.

https://cdn.elifesciences.org/articles/66921/elife-66921-supp6-v2.xlsx
Supplementary file 7

Food intake, energy expenditure, and flux balance analysis (FBA) of whole-body modeling.

https://cdn.elifesciences.org/articles/66921/elife-66921-supp7-v2.xlsx
Supplementary file 8

Validation result (differential expression and functional analysis).

https://cdn.elifesciences.org/articles/66921/elife-66921-supp8-v2.xlsx
Supplementary file 9

Detailed information of 16 key genes that are DEGs in at least three tissues and neighbors and functional analysis results of The Neighbors of 4 key genes.

https://cdn.elifesciences.org/articles/66921/elife-66921-supp9-v2.xlsx
Transparent reporting form
https://cdn.elifesciences.org/articles/66921/elife-66921-transrepform-v2.docx

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  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