Transcriptomic data meta-analysis reveals common and injury model specific gene expression changes in the regenerating zebrafish heart

Abstract

Zebrafish have the capacity to fully regenerate the heart after an injury, which lies in sharp contrast to the irreversible loss of cardiomyocytes after a myocardial infarction in humans. Transcriptomics analysis has contributed to dissect underlying signaling pathways and gene regulatory networks in the zebrafish heart regeneration process. This process has been studied in response to different types of injuries namely: ventricular resection, ventricular cryoinjury, and genetic ablation of cardiomyocytes. However, there exists no database to compare injury specific and core cardiac regeneration responses. Here, we present a meta-analysis of transcriptomic data of regenerating zebrafish hearts in response to these three injury models at 7 days post injury (7dpi). We reanalyzed 36 samples and analyzed the differentially expressed genes (DEG) followed by downstream Gene Ontology Biological Processes (GO:BP) analysis. We found that the three injury models share a common core of DEG encompassing genes involved in cell proliferation, the Wnt signaling pathway and genes that are enriched in fibroblasts. We also found injury-specific gene signatures for resection and genetic ablation, and to a lower extent the cryoinjury model. Finally, we present our data in a user-friendly web interface that displays gene expression signatures across different injury types and highlights the importance to consider injury-specific gene regulatory networks when interpreting the results related to cardiac regeneration in the zebrafish. The analysis is freely available at: https://mybinder.org/v2/gh/MercaderLabAnatomy/PUB_Botos_et_al_2022_shinyapp_binder/HEAD?urlpath=shiny/bus-dashboard/ .

Figure 1

Transcriptomic meta-analysis of the cardiac regenerative response upon distinct types of injuries. (A) Workflow of the process used to analyze the data. Selected datasets differed in conditions of the transcriptomic data such as the capture techniques, sequencing platforms and reads structure. Data were downloaded from GEO and the processing steps to find the differentially expressed genes were performed. This was followed by downstream analysis to interpret the results. (B) PCA plots of the raw data before any processing. (C) Batch corrected, after removing the platform sequencing variable giving the highest batch effect. (D) DESeq2 normalized data.

Figure 2

Unique and common differentially expressed genes (DEGs) in the 7 dpi regenerating zebrafish heart after resection, cryoinjury, or genetic ablation. (A) Volcano plot of the different conditions showing the DEGs. Grey, sham; orange, genetic ablation; magenta, ventricular resection; green, uninjured; blue, cryoinjury. Black area stands for non-significant genes with an adjusted p value > 0.05 or a log2FoldChange value > − 1 and <  + 1 which were not considered for the analysis. (B) Venn diagram of the zebrafish DEGs converted to the respective mouse orthologs, for each condition.

Figure 3

Biological process analysis of DEGs unique to different regeneration models. On the left, Venn diagrams of enriched Gene Ontology Biological Processes showing the specific GO:BP for each condition of interest analyzed labelled in color. On the right, Cytoscape representations of most enriched processes. Shown are data from the comparisons of the different injury models. (A) Uninjured vs sham, (B) Resection vs sham, (C) Ablation vs sham, (D) Cryoinjury vs sham, revealing no significantly enriched terms.

Figure 4

Biological pathways enriched in the core regeneration DEG set. (A) Venn diagram of the Mus musculus converted genes from the DEG analysis. Highlighting the converted genes involved in the “core regeneration” process of a heart injury despite the model used. (B) Gene Ontology Biological Processes associated to these genes when performing annotation and the clustering of these in a network.