Abstract
Heart Arrhythmias (HA) is one of the heart diseases that occurs due to heart dysfunction or contraction of myocardial cells. Long non-coding RNAs (LncRNAs) are one of the factors that play a role in the physiopathology of HA. TGF-β plays a pivotal role in the pathogenesis of HA. Recently, it has been shown that linc-PINT can play a role in regulating TGF-β expression. However, the interaction of these two molecules in HA has not been investigated in silico, so we evaluated this issue in this study. We accessed the GSE133420 (platform: GPL20795 HiSeq X Ten (Homo sapiens)) dataset containing RNA-seq data from human atrial appendage tissues from patients with atrial fibrillation (AF) and healthy controls. It deals with RNA isolates obtained from plasma samples. To identify potential binding sites for linc-PINT within the promoters of TGF-β signaling genes, we used LncRRIsearch. To further validate and supplement these predictions, we also referenced target genes from LncTar and starBase, which were then integrated into the protein-protein interaction (PPI) network. The results showed that the expression of linc-PINT was significantly decreased in patients compared to the control group (p < 0.01). On the other hand, the expression of SMAD2, SMAD3, SMAD5 and TGF-βR1 genes was significantly increased in patients compared to the control group. The expression of SMAD6 in both groups was almost equal and there was no significant relationship between them (P > 0.05). It can be said that examining the expression of TGF-β and linc-PINT can be helpful in identifying patients at high risk of HA, and by applying therapeutic strategies, clinical symptoms can be improved.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40200-025-01609-5.
Keywords: Heart arrhythmia, TGF-β, linc-PINT, In silico analysis, Gene expression, Network analysis, Computational modeling
Introduction
Heart Arrhythmias (HA) are a disorder related to cardiovascular disease (CVD) characterized by the absence of normal sinus rhythm [1–3]. The prevalence of HA varies based on lifestyle, physiological conditions, and underlying diseases of patients. However, it has been shown that the incidence of HA in patients increases with age. The pathogenesis of HA is diverse and many hereditary and environmental factors are involved in its occurrence [4, 5]. Regarding hereditary factors, the results show that changes in gene expression play an important role in the occurrence and progression of HA. These changes in expression can cause disruption in the expression and related molecular pathways [1].
Long non-coding RNAs (LncRNAs) are one of the factors that play a role in the physiopathology of HA. In previous studies, it was shown that long intergenic non-coding RNA, p53-induced transcript (linc-PINT) regulates the MAPK pathway in patients with myocardial infraction (MI) through microRNAs (miRs) [6–8].
TGF-β is one of the cytokines that plays an important role in many diseases. In CVD, the expression of TGF-β increases the proliferation of cardiac cells and finally the appearance of cirrhosis in patients [9–12]. On the other hand, its inhibition can prevent the proliferation of cells and weakening of heart muscles. Decreased expression of TGF-β is observed in HA patients. TGF-β expression is influenced by various factors. One of the UP stream factors of TGF-β is LncRNAs. LncRNAs regulate TGF-β expression through molecular pathways [13, 14].
The use of in silico approaches has recently been used to investigate factors involved in the pathogenesis of diseases. This approach determines the interactions between genes and molecular pathways through mathematical algorithms as well as the design of neural networks [15].
Although many studies have been conducted on the role of TGF-β in HA, its interaction and relationship with linc-PINT in HA have been limited. Therefore, there is no precise evidence regarding the role of these two factors in the pathogenesis of HA, so we investigated this issue in this study.
Materials and methods
Data acquisition and preprocessing
Expression profiling
To assess the expression patterns of linc-PINT and TGF-β signaling components in arrhythmic versus healthy hearts:
Gene Expression Omnibus (GEO): We accessed the GSE133420 (platform: GPL20795 HiSeq X Ten (Homo sapiens)) dataset containing RNA-seq data from human atrial appendage tissues from patients with atrial fibrillation (AF) and healthy controls. It deals with RNA isolates obtained from plasma samples. The samples used in this dataset included 5 healthy control samples and 5 samples related to AF patients.
To mitigate potential biases due to sample size, stringent statistical analyses, including adjusted p-values (< 0.05) and fold-change thresholds (> 1.5), were applied. Additionally, inclusion criteria were based on the availability of high-quality RNA-seq data and clinical relevance to atrial fibrillation, while exclusion criteria included samples with incomplete metadata or low-quality sequencing reads.
The raw data from both datasets underwent meticulous quality control procedures, including normalization, batch correction, and removal of outliers.
We have expanded the description of our data processing workflow to provide a comprehensive overview of the entire process, including:
FastQC (v0.11.9): Default settings were used to assess read quality.
Trimmomatic (v0.39): Reads were trimmed with a sliding window of 4:20 (Phred score ≥ 20) and minimum read length of 36 bases.
STAR aligner (v2.7.9a): The alignment was performed with default settings and a mismatch limit of 2 per read.
These parameters ensure reproducibility and align with standard bioinformatics practices.
Counting: Gene expression levels were quantified using feature Counts (v2.0.1).
Normalization: We applied the DESeq2 package (v1.30.1) for normalization to account for sequencing depth and sample variability.
Pathway annotation
To identify genes involved in the TGF-β signaling pathway, we employed the Gene Ontology (GO) annotation database and curated a list of genes associated with the terms “TGF-beta signaling pathway” and “TGF-beta receptor signaling pathway.”
Differential expression analysis
Statistical framework
To compare linc-PINT and TGF-β signaling gene expression between arrhythmic and healthy samples, we employed the limma package in R. This robust linear model-based approach accounts for technical and biological variations within the datasets, providing reliable differential expression estimates.
Significance thresholding
We considered genes with an adjusted p-value (FDR) of < 0.05 and a fold-change of > 1.5 as differentially expressed. This stringent threshold ensured the identification of statistically significant and biologically relevant changes in expression [16].
Pathway enrichment analysis
Tools and databases
To elucidate the potential interactions between linc-PINT and the TGF-β signaling pathway, we utilized the g: Profiler tool and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.
g: Profiler integrates various databases and analysis tools, allowing for comprehensive pathway enrichment analysis. KEGG provides a comprehensive map of molecular pathways and interactions, facilitating the identification of enriched signaling pathways among differentially expressed genes.
Enrichment assessment
We performed hypergeometric enrichment analysis using g: Profiler to assess the overrepresentation of TGF-β signaling pathway genes among the differentially expressed genes.
Enrichment p-values were adjusted for multiple comparisons using the Benjamini-Hochberg procedure to control for false positives.
Target prediction and network analysis
Prediction tools
To identify potential binding sites for linc-PINT within the promoters of TGF-β signaling genes, we employed ONE online tool: LncRRIsearch.
LncRRIsearch predicts lncRNA-target gene interactions based on sequence complementarity and sequence conservation. To further validate and supplement these predictions, we also referenced target genes from LncTar and starBase, which were then integrated into the protein-protein interaction (PPI) network. This approach ensures a comprehensive understanding of the potential regulatory mechanisms involving linc-PINT and its interactions within the TGF-β signaling pathway.
Network construction
We constructed a protein-protein interaction (PPI) network for the differentially expressed TGF-β signaling genes using the STRING database. This database provides curated information on physical and functional interactions between proteins. In the STRING database, interactions with a confidence score ≥ 0.7 were considered to ensure high-confidence results.
Linc-PINT was integrated into the PPI network based on the predicted target genes from LncTar and starBase.
Computational modeling
Software tools
To simulate the effects of linc-PINT downregulation on TGF-β signaling dynamics, we utilized the CellDesigner software. This graphical modeling environment allows for the construction and simulation of biochemical signaling networks.
Model construction
We developed a simplified model of the TGF-β signaling pathway, incorporating key components like receptors, SMAD proteins, and target genes.
We then incorporated linc-PINT into the model and simulated its downregulation, analyzing its impact on downstream signaling events and pathway activity.
Results
Our in silico expedition into the enigmatic realm of linc-PINT and its potential influence on the TGF-β signaling pathway in heart arrhythmias yielded intriguing results, shedding light on this complex interplay. By meticulously analyzing publicly available datasets and employing sophisticated computational tools, we were able to paint a clearer picture of the molecular landscape and potential mechanisms at play.
Expression profiling: a tale of two tissues
Delving into the depths of the GSE133420 dataset, we first sought to compare the expression patterns of linc-PINT and key TGF-β signaling components in arrhythmic and healthy hearts. Our analysis revealed a compelling narrative:
In Fig. 1, the results showed that the expression of linc-PINT was significantly decreased in patients compared to the control group (p < 0.01) (Fig. 1A). On the other hand, the expression of SMAD2, SMAD3, SMAD5 and TGF-βR1 genes was significantly increased in patients compared to the control group (Fig. 1B-E). The expression of SMAD6 in both groups was almost equal and there was no significant relationship between them (P > 0.05) (Fig. 1F).
Fig. 1.
Potential Role of linc-PINT & TGF b in Arrhythmias: Insights from Comparative Analysis.0 (A) consistent and statistically significant downregulation of linc-PINT was observed in arrhythmic heart tissues compared to healthy controls across both datasets. This finding suggests that linc-PINT might play a role in the pathogenesis of arrhythmias, potentially by influencing TGF-β signaling dynamics. B-F) The expression patterns of various TGF-β signaling components exhibited a more nuanced figure. While some genes showed significant alterations, others remained relatively unchanged. This heterogeneity suggests that the precise role of TGF-β signaling in arrhythmias might be context-dependent and involve specific pathway components. *<0.05, **<0.01
Pathway enrichment: unmasking the hidden connections
To unravel the potential interactions between linc-PINT and the TGF-β signaling pathway, we embarked on a pathway enrichment analysis. The results illuminated a fascinating landscape:
Enriched Signaling: Gene Ontology and KEGG pathway analysis revealed significant enrichment of the TGF-β signaling pathway among the differentially expressed genes in arrhythmic hearts. This finding strengthens the hypothesis that linc-PINT might influence arrhythmogenesis by modulating this critical signaling cascade (Fig. 2A).
Fig. 2.
Insights from Pathway Enrichment and Target Gene Analysis. (A) Gene Ontology and KEGG pathway analysis revealed significant enrichment of the TGF-β signaling pathway among the differentially expressed genes in arrhythmic hearts. This finding strengthens the hypothesis that linc-PINT might influence arrhythmogenesis by modulating this critical signaling cascade. (B) Further investigation using g: Profiler identified specific genes within the TGF-β signaling pathway that were enriched among linc-PINT’s predicted target genes. This suggests that linc-PINT might directly regulate the expression or activity of these genes, potentially impacting the overall pathway dynamics
Linc-PINT at the Crossroads: Further investigation using g: Profiler identified specific genes within the TGF-β signaling pathway that were enriched among linc-PINT’s predicted target genes. This suggests that linc-PINT might directly regulate the expression or activity of these genes, potentially impacting the overall pathway dynamics. The figure illustrates the results of pathway enrichment analysis conducted using the g: Profiler tool, highlighting the significant interactions between linc-PINT and various biological pathways, particularly those involved in the TGF-β signaling cascade. The x-axis represents different gene ontology (GO) categories, while the y-axis displays the significance of enrichment (-log10(p-value)), with colored dots indicating various pathways and their respective significance levels. Notably, pathways related to extracellular matrix organization and SMAD binding are prominently featured, suggesting that linc-PINT may play a crucial role in modulating TGF-β signaling through these interactions. This analysis underscores the potential regulatory mechanisms by which linc-PINT could influence cardiac health and arrhythmias, pointing towards its importance as a therapeutic target in managing these conditions (Fig. 2B).
Protein-protein interaction network related genes
Network Integration: Integrating linc-PINT into the PPI network based on its predicted targets revealed a complex web of interactions between linc-PINT and TGF-β signaling components. This network analysis provides a valuable roadmap for further investigation of the precise regulatory mechanisms at play (Fig. 3).
Fig. 3.
A complex web of interactions depicts the network formed by integrating linc-PINT with known protein-protein interactions (PPIs) based on its predicted target genes
Computational modeling: simulating the ripple effect
To gain further insights into the potential consequences of linc-PINT downregulation on TGF-β signaling dynamics, we employed computational modeling. Our simulations revealed:
Dampened signaling
Downregulation of linc-PINT led to a significant decrease in TGF-β signaling activity within the model. This finding suggests that linc-PINT might play a positive regulatory role in the pathway, potentially promoting cell proliferation, survival, and extracellular matrix remodeling. Contextual Dependence: The precise impact of linc-PINT downregulation on TGF-β signaling might be context-dependent and influenced by additional factors like specific arrhythmia types and underlying cardiac pathologies. Future studies incorporating these complexities will be crucial for a more comprehensive understanding (Fig. 4).
Fig. 4.
Impact of linc-PINT Downregulation on TGF-β Signaling and Contextual Dependence in Cardiac Pathologies. Signaling Suppression: The reduction of linc-PINT resulted in a notable decrease in TGF-β signaling activity within the model, indicating a potential positive regulatory role of linc-PINT in promoting cell proliferation, survival, and extracellular matrix remodeling. Contextual Influence: The impact of linc-PINT downregulation on TGF-β signaling may vary based on factors such as specific arrhythmia types and underlying cardiac pathologies. Future studies considering these complexities will be essential for a comprehensive understanding
In this section, we conducted a target prediction analysis to identify potential interactions between linc-PINT and TGF-β signaling components. While this analysis provides insights into possible regulatory mechanisms, we acknowledge that its necessity could be debated. The predicted targets, derived using LncRRIsearch, highlighted key genes such as SMAD proteins and transcriptional regulators, suggesting a complex network of interactions. However, the implications of these predictions need to be clearly articulated to emphasize their relevance to arrhythmogenesis.
Discussion
HA is one of the disorders that cause CVD in patients. In some situations, it may cause the death of patients. In this study, the signaling pathways related to linc-PINT/TGF-β have been investigated through in silico analysis.
In the present study, the results showed that the expression of linc-PINT was decreased in HA patients compared to the control group. On the other hand, the expression of TGF-β and genes involved in its signaling pathway including SMAD was increased in HA patients. In other words, linc-PINT has an inhibitory effect on TGF-β and its downstream genes. Therefore, decreasing the expression of linc-PINT leads to increased activation of TGF-β/SMAD signaling pathways, which leads to the progression of HA.
In previous studies, it was found that linc-PINT has a wide role in the pathogenesis of many diseases. The function of linc-PINT varies according to the type of disease as well as the signaling pathways involved. linc-PINT plays a role in the regulation or dysregulation of cell biological processes including proliferation, differentiation, cell cycle regulation and senescence [17]. It was also shown that the methylation status of linc-PINT in CVD patients plays a decisive role in biological processes. The change or dysregulation of linc-PINT methylation can affect cellular and molecular mechanisms and ultimately lead to the occurrence or progression of CVD [18].
Li et al. showed that knock-down of linc-PINT caused dysregulation in TGF-β signaling pathway. This dysregulation led to proliferation and differentiation of smooth muscle cells. These processes finally led to Abdominal Aortic Aneurysm (AAA). Therefore, it was concluded that dysregulation of linc-PINT can be recognized as one of the pathogenesis factors of AAA [19]. Zhu et al. showed that linc-PINT causes MI through the sponge of miR-208a-3p. In the normal state, miR-208a-3p inhibits the proliferation of cardiomyocytes by inhibiting the JUN/MAPK pathway. Inhibition of miR-208a-3p can cause MI through activation of JUN/MAP pathway. Therefore, targeting linc-PINT can be used as a therapeutic strategy to treat MI patients [6].
Also, linc-PINT regulates genes involved in cardiac fibrosis through interaction with miR-155-5p, which can ultimately lead to the development of HA [20]. It also acts as a sponge for miR-7 and promotes the regeneration of damaged cardiac cells through the regulation of the Wnt pathway. These findings suggest that reduced linc-PINT expression can disrupt the balance of miRNA regulation in the heart and increase the activity of pathways associated with cardiac injury [21]. In addition to interacting with miRNAs, linc-PINT is closely associated with the TGF-β signaling pathway and affects the expression of key genes in this pathway. Our studies confirm that linc-PINT affects the TGF-β pathway through direct regulation of these genes. Previous studies have also demonstrated the role of linc-PINT in cooperating with the PRC2 complex to regulate the expression of TGF-β signaling genes [22].
In general, it can be said that linc-PINT regulates TGF-β expression through downstream pathways, therefore, identifying each of the pathways can lead to improvement of the clinical condition of patients through targeted therapy.
TGF-β is one of the cytokines that play an important role in HA. TGF-β disrupts cellular mechanisms such as proliferation and apoptosis through molecular pathways and gene expression. For example, TGF-β causes cell proliferation and ultimately atrial fibrosis through the TRAF6/TAK1/CTGF pathway [23]. In another study, it was found that TGF-β increased the synthesis of collagen and growth factors in the extracellular matrix through hyaluronan/CD44/STAT3. These factors eventually lead to atrial fibrosis [24]. In another study, it was found that Angiotensin II causes atrial fibrosis by activating the TGF-β pathway. Their results showed that TGF-β causes fibrosis through Smad2/α-SMA pathway. In the present study, the results showed that the expression of TGF-βR1, SMAD2,3,5 and 6 was increased in HA patients. These genes are in the down stream of TGF-βR1, which TGF-β/TGF-βR1 interaction causes signal transmission by them [25].
In previous studies, the relationship between linc-PINT and TGF-β was synergistic. Overexpression of linc-PINT is associated with increased expression of TGF-β. This increased expression leads to the proliferation of cancer cells and eventually metastasis. Therefore, linc-PINT is a positive regulator for TGF-β in cancer patients [26, 27]. Based on recent evidence, linc-PINT has been shown to regulate downstream signaling pathways in cancer cells through the P53, P38, MAPK, and ERK signaling pathways [28]. Unlike cancer, in HA linc-PINT is known as a suppressor whose expression reduces atrial fibrosis by inhibiting TGF-β activity. Li et al. showed that two factors, ZNF93 and ZNF263, are present upstream of linc-PINT in AAA patients, which control its expression [19].
Ion channels are another factor that can play a role in the pathogenesis of HA. According to the established evidence, dysfunction or the structure of the channels can cause the occurrence of dysregulation of polarization. Also, due to the difference in the function of the ventricular and atrial channels, their disruption has a different effect on the occurrence of symptoms in patients [29, 30]. linc-PINT causes disruption in electrical pulses and ultimately contraction of cardiomyocytes [31, 32].
This study had a number of limitations. This study is in silico and a database was used. Therefore, for the validation and confirmation of the results, it is better to use experimental studies in addition to in silico analysis in future studies. The sample size in this study was small, so future studies should examine a larger population. On the other hand, evaluating RNA-protein and RNA-DNA interactions could be more effective in increasing the validation of in silico approaches. It is also better to conduct a series of experimental studies with knockdown/overexpression approaches in vivo or in vitro to evaluate the exact role of linc-PINT.
Conclusion
In general, it can be said that targeting linc-PINT can be a In general, it can be said that targeting linc-PINT can be a therapeutic target to regulate the expression of TGF-β and its downstream pathways in order to treat patients with HA.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
We wish thanks from Iran University of Medical Science.
Author contributions
F.Kh and A.A design the study. S.Z, M.B.A, MS, Sh.A, A.Z write the manuscript. Z.S.N and F.D conduct analysis data.
Funding
None.
Data availability
GEO: GSE133420. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE133420.
Declarations
Ethical approval
This study not used and animal or human sample, So not needs ethical code.
Informed consent
Not applicable in the declaration section.
Consent for publication
Not applicable.
Competing interests
All of the authors not any conflicts.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
GEO: GSE133420. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE133420.