Integrative transcriptomic analysis of tissue-specific metabolic crosstalk after myocardial infarction
- KTH Royal Institute of Technology, Sweden
- University of Gothenburg, Sweden
- KTH - Royal Institute of Technology, Sweden
- King's College London, United Kingdom
- Atatürk University, Turkey
- 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.
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Revealing tissue-specific metabolic crosstalk after a myocardial infarctionNCBI Gene Expression Omnibus, GSE153485.
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Analysis of early changes in mouse heart transcriptome after myocardial infarctionNCBI Gene Expression Omnibus, GSE104187.
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Long RNA Seq on Sham vs MI Adult Mouse HeartNCBI Gene Expression Omnibus, GSE52313.
Article and author information
Author details
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.
Reviewing Editor
- Edward D Janus, University of Melbourne, Australia
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.
Version history
- Received: January 26, 2021
- Accepted: April 25, 2021
- Accepted Manuscript published: May 11, 2021 (version 1)
- Version of Record published: June 8, 2021 (version 2)
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.
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Integrative transcriptomic analysis of tissue-specific metabolic crosstalk after myocardial infarction
eLife 10:e66921.
https://doi.org/10.7554/eLife.66921
Further reading
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- Computational and Systems Biology
- Genetics and Genomics
Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).
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- Computational and Systems Biology
Transcriptomic profiling became a standard approach to quantify a cell state, which led to accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here we present Phantasus - a user-friendly web-application for interactive gene expression analysis which provides a streamlined access to more than 96000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed on-line at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under MIT license.
