Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease

Zebrafish Congenital Heart Disease Models: Opportunities and Challenges

Congenital heart defects (CHDs) are common human birth defects. Genetic mutations potentially cause the exhibition of various pathological phenotypes associated with CHDs, occurring alone or as part of certain syndromes. Zebrafish, a model organism with a strong molecular conservation similar to humans, is commonly used in studies on cardiovascular diseases owing to its advantageous features, such as a similarity to human electrophysiology, transparent embryos and larvae for observation, and suitability for forward and reverse genetics technology, to create various economical and easily controlled zebrafish CHD models. In this review, we outline the pros and cons of zebrafish CHD models created by genetic mutations associated with single defects and syndromes and the underlying pathogenic mechanism of CHDs discovered in these models. The challenges of zebrafish CHD models generated through gene editing are also discussed, since the cardiac phenotypes resulting from a single-candidate pathological gene mutation in zebrafish might not mirror the corresponding human phenotypes. The comprehensive review of these zebrafish CHD models will facilitate the understanding of the pathogenic mechanisms of CHDs and offer new opportunities for their treatments and intervention strategies.

Atrial proteomic profiling reveals a switch towards profibrotic gene expression program in CREM-IbΔC-X mice with persistent atrial fibrillation

BACKGROUND

Overexpression of the CREM (cAMP response element-binding modulator) isoform CREM-IbΔC-X in transgenic mice (CREM-Tg) causes the age-dependent development of spontaneous AF.

PURPOSE

To identify key proteome signatures and biological processes accompanying the development of persistent AF through integrated proteomics and bioinformatics analysis.

METHODS

Atrial tissue samples from three CREM-Tg mice and three wild-type littermates were subjected to unbiased mass spectrometry-based quantitative proteomics, differential expression and pathway enrichment analysis, and protein-protein interaction (PPI) network analysis.

RESULTS

A total of 98 differentially expressed proteins were identified. Gene ontology analysis revealed enrichment for biological processes regulating actin cytoskeleton organization and extracellular matrix (ECM) dynamics. Changes in ITGAV, FBLN5, and LCP1 were identified as being relevant to atrial fibrosis and structural based on expression changes, co-expression patterns, and PPI network analysis. Comparative analysis with previously published datasets revealed a shift in protein expression patterns from ion-channel and metabolic regulators in young CREM-Tg mice to profibrotic remodeling factors in older CREM-Tg mice. Furthermore, older CREM-Tg mice exhibited protein expression patterns reminiscent of those seen in humans with persistent AF.

CONCLUSIONS

This study uncovered distinct temporal changes in atrial protein expression patterns with age in CREM-Tg mice consistent with the progressive evolution of AF. Future studies into the role of the key differentially abundant proteins identified in this study in AF progression may open new therapeutic avenues to control atrial fibrosis and substrate development in AF.

Atrial Proteomic Profiling Reveals a Switch Towards Profibrotic Gene Expression Program in CREM-IbΔC-X Mice with Persistent Atrial Fibrillation

Background

Overexpression of the CREM (cAMP response element-binding modulator) isoform CREM-IbΔC-X in transgenic mice (CREM-Tg) causes the age-dependent development of spontaneous AF.

Purpose

To identify key proteome signatures and biological processes accompanying the development of persistent AF through integrated proteomics and bioinformatics analysis.

Methods

Atrial tissue samples from three CREM-Tg mice and three wild-type littermates were subjected to unbiased mass spectrometry-based quantitative proteomics, differential expression and pathway enrichment analysis, and protein-protein interaction (PPI) network analysis.

Results

A total of 98 differentially expressed proteins were identified. Gene ontology analysis revealed enrichment for biological processes regulating actin cytoskeleton organization and extracellular matrix (ECM) dynamics. Changes in ITGAV, FBLN5, and LCP1 were identified as being relevant to atrial fibrosis and remodeling based on expression changes, co-expression patterns, and PPI network analysis. Comparative analysis with previously published datasets revealed a shift in protein expression patterns from ion-channel and metabolic regulators in young CREM-Tg mice to profibrotic remodeling factors in older CREM-Tg mice. Furthermore, older CREM-Tg mice exhibited protein expression patterns that resembled those of humans with persistent AF.

Conclusions

This study uncovered distinct temporal changes in atrial protein expression patterns with age in CREM-Tg mice consistent with the progressive evolution of AF. Future studies into the role of the key differentially abundant proteins identified in this study in AF progression may open new therapeutic avenues to control atrial fibrosis and substrate development in AF.

Mapping of Neuro-Cardiac Electrophysiology: Interlinking Epilepsy and Arrhythmia

The interplay between neurology and cardiology has gained significant attention in recent years, particularly regarding the shared pathophysiological mechanisms and clinical comorbidities observed in epilepsy and arrhythmias. Neuro-cardiac electrophysiology mapping involves the comprehensive assessment of both neural and cardiac electrical activity, aiming to unravel the intricate connections and potential cross-talk between the brain and the heart. The emergence of artificial intelligence (AI) has revolutionized the field by enabling the analysis of large-scale data sets, complex signal processing, and predictive modeling. AI algorithms have been applied to neuroimaging, electroencephalography (EEG), electrocardiography (ECG), and other diagnostic modalities to identify subtle patterns, classify disease subtypes, predict outcomes, and guide personalized treatment strategies. In this review, we highlight the potential clinical implications of neuro-cardiac mapping and AI in the management of epilepsy and arrhythmias. We address the challenges and limitations associated with these approaches, including data quality, interpretability, and ethical considerations. Further research and collaboration between neurologists, cardiologists, and AI experts are needed to fully unlock the potential of this interdisciplinary field.

Beyond the Rhythm: In Silico Identification of Key Genes and Therapeutic Targets in Atrial Fibrillation

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia worldwide and is characterized by a high risk of thromboembolism, ischemic stroke, and fatality. The precise molecular mechanisms of AF pathogenesis remain unclear. The purpose of this study was to use bioinformatics tools to identify novel key genes in AF, provide deeper insights into the molecular pathogenesis of AF, and uncover potential therapeutic targets. Four publicly available raw RNA-Seq datasets obtained through the ENA Browser, as well as proteomic analysis results, both derived from atrial tissues, were used in this analysis. Differential gene expression analysis was performed and cross-validated with proteomics results to identify common genes/proteins between them. A functional enrichment pathway analysis was performed. Cross-validation analysis revealed five differentially expressed genes, namely FGL2, IGFBP5, NNMT, PLA2G2A, and TNC, in patients with AF compared with those with sinus rhythm (SR). These genes play crucial roles in various cardiovascular functions and may be part of the molecular signature of AF. Furthermore, functional enrichment analysis revealed several pathways related to the extracellular matrix, inflammation, and structural remodeling. This study highlighted five key genes that constitute promising candidates for further experimental exploration as biomarkers as well as therapeutic targets for AF.

Alterations in cytoskeletal and Ca2+ cycling regulators in atria lacking the obscurin Ig58/59 module

Introduction

Obscurin (720-870 kDa) is a giant cytoskeletal and signaling protein that possesses both structural and regulatory functions in striated muscles. Immunoglobulin domains 58/59 (Ig58/59) of obscurin bind to a diverse set of proteins that are essential for the proper structure and function of the heart, including giant titin, novex-3, and phospholamban (PLN). Importantly, the pathophysiological significance of the Ig58/59 module has been further underscored by the discovery of several mutations within Ig58/59 that are linked to various forms of myopathy in humans. We previously generated a constitutive deletion mouse model, Obscn-ΔIg58/59, that expresses obscurin lacking Ig58/59, and characterized the effects of this deletion on cardiac morphology and function through aging. Our findings demonstrated that Obscn-ΔIg58/59 male animals develop severe arrhythmia, primarily manifesting as episodes of junctional escape and spontaneous loss of regular p-waves, reminiscent of human atrial fibrillation, accompanied by significant atrial enlargement that progresses in severity with aging.

Methods and Results

To comprehensively characterize the molecular alterations responsible for these pathologies, we performed proteomic and phospho-proteomic analyses in aging Obscn-ΔIg58/59 atria. Our studies revealed extensive and novel alterations in the expression and phosphorylation profile of major cytoskeletal proteins, Ca2+ regulators, and Z-disk associated protein complexes in the Obscn-ΔIg58/59 atria through aging.

Discussion

These studies implicate obscurin, particularly the Ig58/59 module, as an essential regulator of the Z-disk associated cytoskeleton and Ca2+ cycling in the atria and provide new molecular insights into the development of atrial fibrillation and remodeling.

Machine Learning in Tissue Engineering

Machine learning (ML) and artificial intelligence have accelerated scientific discovery, augmented clinical practice, and deepened fundamental understanding of many biological phenomena. ML technologies have now been applied to diverse areas of tissue engineering research, including biomaterial design, scaffold fabrication, and cell/tissue modeling. Emerging ML-empowered strategies include machine-optimized polymer synthesis, predictive modeling of scaffold fabrication processes, complex analyses of structure-function relationships, and deep learning of spatialized cell phenotypes and tissue composition. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex and multivariate analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research. Impact statement Machine learning (ML) has accelerated scientific discovery and augmented clinical practice across multiple fields. Now, ML has driven exciting new paradigms in tissue engineering research, including machine-optimized biomaterial design, predictive modeling of scaffold fabrication, and spatiotemporal analysis of cell and tissue systems. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research.

The functional verification and analysis of Fugu promoter of cardiac gene tnni1a in zebrafish

Troponin I type 1b (Tnni1b) is thought to be a novel isoform that is expressed only in the zebrafish heart. Knocking down of tnni1b can lead to cardiac defects in zebrafish. Although both the zebrafish tnni1b and human troponin I1 (TNNI1) genes are thought to be closely associated with fatal cardiac development, the regulatory molecular mechanisms of these genes are poorly understood. Analyzing the functionally conserved sequence, especially in the noncoding regulatory region involved in gene expression, clarified these mechanisms. In this study, we isolated a 3 kb fragment upstream of Fugu tnni1a that can regulate green fluorescence protein (GFP) expression in a heart-specific manner, similar to the pattern of zebrafish homologue expression. Three evolutionarily conserved regions (ECRs) in the 5'-flanking sequence of Fugu tnni1a were identified by sequence alignment. Deletion analysis led to the identification of ECR2 as a core sequence that affects the heart-specific expression function of the Fugu tnni1a promoter. Interestingly, both the Fugu tnni1a promoter and ECR2 sequence were functionally conserved in zebrafish, although they shared no sequence similarity. Together, the findings of our study provided further evidence for the important role of tnni1a homologous in cardiac development and demonstrated that two functionally conserved sequences in the zebrafish and Fugu genomes may be ECRs, despite their lack of similarity.

Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data

Bladder cancer has been increasing globally. Urinary cytology is considered a major screening method for bladder cancer, but it has poor sensitivity. This study aimed to utilize clinical laboratory data and machine learning methods to build predictive models of bladder cancer. A total of 1336 patients with cystitis, bladder cancer, kidney cancer, uterus cancer, and prostate cancer were enrolled in this study. Two-step feature selection combined with WEKA and forward selection was performed. Furthermore, five machine learning models, including decision tree, random forest, support vector machine, extreme gradient boosting (XGBoost), and light gradient boosting machine (GBM) were applied. Features, including calcium, alkaline phosphatase (ALP), albumin, urine ketone, urine occult blood, creatinine, alanine aminotransferase (ALT), and diabetes were selected. The lightGBM model obtained an accuracy of 84.8% to 86.9%, a sensitivity 84% to 87.8%, a specificity of 82.9% to 86.7%, and an area under the curve (AUC) of 0.88 to 0.92 in discriminating bladder cancer from cystitis and other cancers. Our study provides a demonstration of utilizing clinical laboratory data to predict bladder cancer.

Protein phosphatase 2A in the healthy and failing heart: New insights and therapeutic opportunities

Protein phosphatases have emerged as critical regulators of phosphoprotein homeostasis in settings of health and disease. Protein phosphatase 2A (PP2A) encompasses a large subfamily of enzymes that remove phosphate groups from serine/threonine residues within phosphoproteins. The heterogeneity in PP2A structure, which arises from the grouping of different catalytic, scaffolding and regulatory subunit isoforms, creates distinct populations of catalytically active enzymes (i.e. holoenzymes) that localise to different parts of the cell. This structural complexity, combined with other regulatory mechanisms, such as interaction of PP2A heterotrimers with accessory proteins and post-translational modification of the catalytic and/or regulatory subunits, enables PP2A holoenzymes to target phosphoprotein substrates in a highly specific manner. In this review, we summarise the roles of PP2A in cardiac physiology and disease. PP2A modulates numerous processes that are vital for heart function including calcium handling, contractility, β-adrenergic signalling, metabolism and transcription. Dysregulation of PP2A has been observed in human cardiac disease settings, including heart failure and atrial fibrillation. Efforts are underway, particularly in the cancer field, to develop therapeutics targeting PP2A activity. The development of small molecule activators of PP2A (SMAPs) and other compounds that selectively target specific PP2A holoenzymes (e.g. PP2A/B56α and PP2A/B56ε) will improve understanding of the function of different PP2A species in the heart, and may lead to the development of therapeutics for normalising aberrant protein phosphorylation in settings of cardiac remodelling and dysfunction.

Multi-Omics Analysis of Gene and Protein Candidates Possibly Related to Tetrodotoxin Accumulation in the Skin of Takifugu flavidus

Pufferfish is increasingly regarded by many as a delicacy. However, the tetrodotoxin (TTX) that accumulates in its body can be lethal upon consumption by humans. TTX is known to mainly accumulate in pufferfish skin, but the accumulation mechanisms are poorly understood. In this study, we aimed to explore the possible mechanism of TTX accumulation in the skin of the pufferfish Takifugu flavidus following treatment with TTX. Through liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, we detected 37.3% of toxin accumulated in the skin at the end of the rearing period (168 h). Transcriptome and proteome analyses revealed the mechanism and pathways of TTX accumulation in the skin of T. flavidus in detail. Gene ontology and the Kyoto Encyclopedia of Genes and Genomes analyses strongly suggest that cardiac muscle contraction and adrenergic signaling in cardiomyocyte pathways play an important role in TTX accumulation. Moreover, some upregulated and downregulated genes, which were determined via RNA-Seq, were verified with qPCR analysis. This study is the first to use multi-omics profiling data to identify novel regulatory network mechanisms of TTX accumulation in the skin of pufferfish.

Are Interactions between Epicardial Adipose Tissue, Cardiac Fibroblasts and Cardiac Myocytes Instrumental in Atrial Fibrosis and Atrial Fibrillation?

Atrial fibrillation is very common among the elderly and/or obese. While myocardial fibrosis is associated with atrial fibrillation, the exact mechanisms within atrial myocytes and surrounding non-myocytes are not fully understood. This review considers the potential roles of myocardial fibroblasts and myofibroblasts in fibrosis and modulating myocyte electrophysiology through electrotonic interactions. Coupling with (myo)fibroblasts in vitro and in silico prolonged myocyte action potential duration and caused resting depolarization; an optogenetic study has verified in vivo that fibroblasts depolarized when coupled myocytes produced action potentials. This review also introduces another non-myocyte which may modulate both myocardial (myo)fibroblasts and myocytes: epicardial adipose tissue. Epicardial adipocytes are in intimate contact with myocytes and (myo)fibroblasts and may infiltrate the myocardium. Adipocytes secrete numerous adipokines which modulate (myo)fibroblast and myocyte physiology. These adipokines are protective in healthy hearts, preventing inflammation and fibrosis. However, adipokines secreted from adipocytes may switch to pro-inflammatory and pro-fibrotic, associated with reactive oxygen species generation. Pro-fibrotic adipokines stimulate myofibroblast differentiation, causing pronounced fibrosis in the epicardial adipose tissue and the myocardium. Adipose tissue also influences myocyte electrophysiology, via the adipokines and/or through electrotonic interactions. Deeper understanding of the interactions between myocytes and non-myocytes is important to understand and manage atrial fibrillation.

A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.

A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery

Assessment of risk before lung resection surgery can provide anesthesiologists with information about whether a patient can be weaned from the ventilator immediately after surgery. However, it is difficult for anesthesiologists to perform a complete integrated risk assessment in a time-limited pre-anesthetic clinic. We retrospectively collected the electronic medical records of 709 patients who underwent lung resection between 1 January 2017 and 31 July 2019. We used the obtained data to construct an artificial intelligence (AI) prediction model with seven supervised machine learning algorithms to predict whether patients could be weaned immediately after lung resection surgery. The AI model with Naïve Bayes Classifier algorithm had the best testing result and was therefore used to develop an application to evaluate risk based on patients' previous medical data, to assist anesthesiologists, and to predict patient outcomes in pre-anesthetic clinics. The individualization and digitalization characteristics of this AI application could improve the effectiveness of risk explanations and physician-patient communication to achieve better patient comprehension.