Background
Tetralogy of Fallot is a rare CHD that affects about 3.5% of infants born with CHD. Reference Apitz, Webb and Redington1 Currently, the most effective treatment for ToF in children remains is the surgery with cardiopulmonary bypass. Reference Chai, Jacobs and Quintessenza2 Despite surgical repair of defects in patients with ToF, these patients are still at significant long-term risk of heart failure, arrhythmias, exercise intolerance, and sudden death. Reference Yim, Riesenkampff, Caro-Dominguez, Yoo, Seed and Grosse-Wortmann3 One study has shown that there is a close link between heart failure and myocardial fibrosis. Reference Kong, Christia and Frangogiannis4 Inflammation is an important pathological factor leading to myocardial fibrosis. Inflammatory cytokines (TNF-α, AngII, IL-6, etc.) can be activated directly or indirectly through a variety of molecular signalling pathways allowing fibroblasts to increase the synthesis of extracellular matrix proteins such that tissue in the damaged area and the surrounding non-damaged area undergo different degrees of fibrosis. Reference Dobaczewski, de Haan and Frangogiannis5 Moreover, Takayoshi et al Reference Tsutamoto, Hisanaga and Wada6 have proposed that interleukin-6 can act as a post-independent factor in heart failure. Despite efforts to protect organs, CPB can still cause a systemic inflammatory response, which contributes to post-operative complications and myocardial failure. Reference Bronicki and Hall7–Reference Calza, Lerzo and Perfumo9
In this study, we aimed to explore the potential mechanism of the association between CPB and myocardial fibrosis at the genetic level. We analysed the differential gene expression in right atrium biopsies acquired from children affected by ToF undergoing primary surgical defect repair pre- and post-CPB. Based on a previous study, Reference Tsutamoto, Hisanaga and Wada6 we focused our attention on expression differences in IL-6. We hope that our findings will contribute to improving management strategies for cardiopulmonary bypass and identify new targets for existing drugs or predict new drugs for known targets. The workflow of our study is shown in Fig 1.
Material and methods
Data processing
We downloaded the expression profile datasets GSE132176 (Platform: GPL13158) from Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) database. Reference Barrett, Wilhite and Ledoux10 We closely analysed the GSE132176 data to identify any patients with ToF. The data we extracted included atrial tissue samples from 10 children pre- and post-CPB during TOF surgery. R (v3.6) package limma Reference Ritchie, Phipson and Wu11 was used to normalise the raw data.
Gene Set Enrichment Analysis
We set out to study the effect of CPB on biological function in patients with ToF by comparing gene expression levels pre- and post-CPB. A Gene Set Enrichment Analysis Reference Subramanian, Tamayo and Mootha12,Reference Mootha, Lindgren and Eriksson13 was used to analyse the differences of the two groups using KEGG pathways. GSEA was performed using GSEA 3.0 (Java) software (http://www.broadinstitute.org/gsea/). The c2.cp.kegg.v6.2.symbols.gmt datasets from the Molecular Signatures Database Reference Liberzon, Subramanian, Pinchback, Thorvaldsdóttir, Tamayo and Mesirov14 were used as reference gene sets. An enrichment analysis was considered statistically significant when meeting a nominal p-value cut-off (NOM p-value) of < 0.05.
Screening for differentially expressed genes
DEGs were identified using the limma package in R. A volcano map and a heatmap of DGEs were plotted using the ggplot2package and pheatmap package, respectively. DEGs with p < 0.05 and |log2FC|> 1 were considered as being significantly different. Boxplots and paired plots were drawn using the beeswarm and ggpubr packages to show the different expression levels of IL-6 in the pre- and post-CPB groups.
Weighted gene correlation network analysis
A weighted correlation network analysis can be used to find clusters (modules) of highly correlated genes, which can be used to identify candidate biomarkers or therapeutic targets. Reference Langfelder and Horvath15 We use DEGs to perform WGCNA. The best power was automatically selected by the software, and then the co-expression networks were constructed using the WGCNA R package.
GO enrichment analysis and protein–protein interaction network
A GO enrichment analysis (learn what biological processes genes are involved in and how they function at the molecular level) Reference Nota16 and the identification of the PPI network (a tool to understand cell functions) Reference Lin and Lai17 were carried out for the module with the strongest correlation in WGCNA. The enrichment analysis was performed using org. Hs.eg.db and enrichplot packages in R, and the PPI was based on STRING (https://string-db.org/) with the species limited to “Homo sapiens”.
HUB gene identification
The data obtained from STRING were then imported into Cytoscape3.70, an open software for visualising and analysing interaction networks. Reference Shannon, Markiel and Ozier18 The top 10 genes of Maximal Clique Centrality Reference Chin, Chen, Wu, Ho, Ko and Lin19 were selected and visualised using the CytoHubba Reference Chin, Chen, Wu, Ho, Ko and Lin19 plugin. The top six genes were identified as HUB genes ranked by MCC.
Cell types associated with the HUB genes
We identified the cell types associated with the HUB genes using PanglaoDB (https://panglaodb.se/index.html), which is a database for the scientific community interested in the exploration of single-cell RNA sequencing experiments from mouse and human. Reference Franzén, Gan and Björkegren20
Results
Identification of DEGs
Through a differential gene expression analysis comparing pre- and post-CPB, we found there were 119 DEGs, of which 112 were up-regulated and 7 were down-regulated (Fig 2a). The top 50 with the most significant differences genes were visualised using a heatmap (Fig 2b). Particularly, we found that IL-6 was significantly up-regulated in the post-CPB group (p = 4.871e−04) (Fig 2c). A paired analysis showed that IL-6 expression was significantly up-regulated in all the post-CBP samples (Fig 2d).
WGCNA
Through a WGCNA, a total of five modules were identified (Fig 3a). The turquoise module included 116 genes and was positively correlated with the post-CPB group (correlation = 0.84, p = 4e-6; Fig 3b). Furthermore, a biological processes enrichment analysis showed that the turquoise module genes were significantly involved in response to lipopolysaccharide, molecules of bacterial origin, leukocyte cell−cell adhesion, epithelial cell proliferation, and regulation of epithelial cell proliferation (Fig 3c). An enrichment for molecular function showed that turquoise module genes were enriched in DNA-binding transcription activator activity, RNA polymerase II-specific, transcription corepressor activity, and DNA-binding transcription repressor activity, and RNA polymerase II-specific (Fig 3c).
PPI and the identification of HUB genes
We imported the genes in the turquoise module into STRING to build a network with 111 nodes and 556 edges (Fig 4a). The resulting network was then imported into Cytoscape in order to analyse and visualise the top 10 MMC genes (Fig 4b). Next, we selected the top six genes with the most significant MMC as HUB genes (JUN, FOS, ATF3, EGR1, IL-6, and PTGS2).
Gene Set Enrichment Analysis
A GSEA showed that compared to the before CPB group, neuroactive ligand–receptor interactions, MAPK signalling pathway, Wnt signalling pathway, Jak-STAT signalling pathway, colorectal cancer, T-cell receptor signalling pathway, ErbB signalling pathway, and bladder cancer were enriched in the post-CPB group (Fig 5a). These pathways were also enriched in the IL-6-high group (Fig 5b). Furthermore, we found that up-regulation of IL-6 affects the myocardium during CPB mainly through activation of the Jak-STAT signalling pathway (Fig 5c and d).
Cell types associated with HUB genes
Data downloaded from PanglaoDB showed that IL-6, ATF3, EGR1, and PTGS2 are closely associated with fibroblasts (Fig 6).
Discussion
ToF is a serious CHD, and early post-operative complications have a long-term impact on the quality of life and survival of children after surgical treatment. Reference Apitz, Webb and Redington1,Reference Yim, Riesenkampff, Caro-Dominguez, Yoo, Seed and Grosse-Wortmann3 The CPB-induced inflammatory response is an important factor leading to post-operative complications in ToF. Reference Bronicki and Hall7 IL-6 is a critical inflammatory cytokine that plays an important role in myocardial fibrosis. Reference Dobaczewski, de Haan and Frangogiannis5 At the same time, myocardial fibrosis is an important factor leading to heart failure. Reference Kong, Christia and Frangogiannis4 Therefore, it is necessary to explore the molecular mechanism of CPB leading to myocardial fibrosis, focusing on IL-6. In our study, we first found that IL-6 was generally up-regulated in the post-CPB group (Fig 2d), suggesting that CPB directly causes high expression levels of IL-6. The WGCNA and PPI network analysis identified six genes that were identified as HUB genes (JUN, FOS, ATF3, EGR1, IL-6, and PTGS2). Amongst these, FOS, ATGF3, EGR1, and IL-6 are known to be closely related to myocardial fibrosis. Reference Yim, Riesenkampff, Caro-Dominguez, Yoo, Seed and Grosse-Wortmann3,Reference Pan, Sun and Shan21–Reference Shen, Xing and Gong23 JUN has been reported to play an important role in Inflammation, Reference Xiao, Wang and Lahusen24 and as the target of non-steroidal anti-inflammatory drugs, PTGS2 plays a critical role in regulating inflammation and pain. Reference Hata and Breyer25–Reference Smyth, Grosser, Wang, Yu and FitzGerald27 One study showed that inhibition of PTGS2 prolonged allograft survival and reduced inflammation and myocardial damage during acute cardiac allograft rejection in a rat model. Reference Ma, Szabolcs and Sun28 Thus, inflammation-related genes appear to be significantly up-regulated post-CPB during ToF surgery. It is known that inflammation is an important inducer of myocardial fibrosis. Reference Leslie, Schwarz, Simpson and Huber29 IL-6 is generally regarded as an inflammatory biomarker, and is commonly used to assess the presence and severity of low-grade inflammation. Reference Baumeister, Akhtar, Ciufolini, Pariante and Mondelli30
Here, we further explored biological processes associated with CPB. A biological process enrichment analysis shows that genes with a strong correlation with CPB are significantly involved in the response to lipopolysaccharide, molecules of bacterial origin, leukocyte cell−cell adhesion, epithelial cell proliferation, and regulation of epithelial cell proliferation. Furthermore, a molecular function enrichment analysis and GSEA analysis showed neuroactive ligand–receptor interaction and a number of immune-related pathways are enriched in the post-CPB group and the IL-6-high group. One study has shown that neuroactive ligand–receptor interaction is associated with human arrhythmogenic right ventricular cardiomyopathy. Reference Chen, Long, Xu, Wu and Zhang31 Another study has suggested that the myocardial protective effect of sevoflurane is through neuroactive ligand–receptor interactions in patients undergoing coronary artery bypass graft surgery; Reference Wang, Cheng, Zhang and Li32 we, therefore, speculate that neuroactive ligand–receptor interactions are closely related to cardiac function.
It is well known that the MAPK signalling pathway and the T-cell receptor signalling pathway are associated with inflammatory responses. Reference Wang, Chen and Cao33,Reference Rudd34 In addition, activation of the Wnt signalling pathway leads to adverse remodelling after ischaemic injury, whereas inhibition of Wnt signalling improves cardiac function. Reference Zelarayán, Noack and Sekkali35 The ErbB signalling pathway is closely associated with heart failure, and it is a target for drugs to treat heart failure. Reference Lemmens, Doggen and De Keulenaer36 The JAK/STAT signalling pathway is an important cytokine signal transduction pathway, which regulates diverse physiological and pathological processes including cellular inflammation, differentiation, proliferation, apoptosis, and immunity. Reference Wang, Li and Li37 Activation of the JAK/STAT signalling pathway as a result of the up-regulation of IL-6 results in pro-inflammatory responses that promote myocardial fibrosis (Fig 5c and d).
From the PanglaoDB analysis, we found that IL-6, ATF3, EGR1, and PTGS2 are closely related to fibroblasts (Fig 6), which further supports our findings. These data suggest that using drugs during CPB procedures that can prevent a pro-inflammatory response may improve prognosis. In this regard, aprotinin and ulinastatin are common anti-inflammatory drugs that are used in CPB, procedures. Propofol, sevoflurane, and dexmedetomidine have also been found to have anti-inflammatory and myocardial protective effects during CPB. Reference Wang, Cheng, Zhang and Li32,Reference Biccard, Goga and de Beurs38–Reference Mikawa, Akamatsu and Nishina41
Although we have uncovered several interesting results in this study, it does have significant limitations. First, the sample size is small and so this may affect the validity of the findings, and second, the proposed molecular mechanisms are theoretical and so require further experimental verification.
Conclusions
In the present study, we identified several HUB genes (JUN, FOS, ATF3, EGR1, IL-6, and PTGS2) that are associated with myocardial inflammation and fibrosis caused by CPB during ToF surgery in children. High expression of IL-6 would be expected to activate JAK-STATA signalling pathway during CPB, and therefore may be a potential mechanism underlying myocardial fibrosis. It can be speculated that the high levels of IL-6 are an important factor leading to heart failure after ToF surgery. Our study provides a theoretical basis for the formulation of cardiac protective strategies for CPB and to provide a direction for the future development of drugs to address CPB-mediated fibrosis.
Acknowledgements
Not applicable.
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Financial support
This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Conflicts of interest
None.
Ethical standards
The research does not involve ethics, and ethics approval was not required.
Authors’ contributions
H.L. analysed the data. Z.W. designed the study and revised the manuscript. Y.Z. prepared figures. L.Z. authored or reviewed drafts of the paper, and approved the final draft. All authors read and approved the final manuscript.
Patient consent for publication
Not applicable.
Supplementary material
To view supplementary material for this article, please visit https://doi.org/10.1017/S1047951121001803.