Quantitative proteomics of changes in succinylated proteins expression profiling in left appendages tissue from valvular heart disease patients with atrial fibrillation

Cellular and molecular biology of posttranslational modifications in cardiovascular disease

Cardiovascular disease (CVD) has now become the leading cause of death worldwide, and its high morbidity and mortality rates pose a great threat to society. Although numerous studies have reported the pathophysiology of CVD, the exact pathogenesis of all types of CVD is not fully understood. Therefore, much more research is still needed to explore the pathogenesis of CVD. With the development of proteomics, many studies have successfully identified the role of posttranslational modifications in the pathogenesis of CVD, including key processes such as apoptosis, cell metabolism, and oxidative stress. In this review, we summarize the progress in the understanding of posttranslational modifications in cardiovascular diseases, including novel protein posttranslational modifications such as succinylation and nitrosylation. Furthermore, we summarize the currently identified histone deacetylase (HDAC) inhibitors used to treat CVD, providing new perspectives on CVD treatment modalities. We critically analyze the roles of posttranslational modifications in the pathogenesis of CVD-related diseases and explore future research directions related to posttranslational modifications in cardiovascular diseases.

NR4A3 prevents diabetes induced atrial cardiomyopathy by maintaining mitochondrial energy metabolism and reducing oxidative stress

BACKGROUND

Atrial cardiomyopathy (ACM) is responsible for atrial fibrillation (AF) and thromboembolic events. Diabetes mellitus (DM) is an important risk factor for ACM. However, the potential mechanism between ACM and DM remains elusive.

METHODS

Atrial tissue samples were obtained from patients diagnosed with AF or sinus rhythm (SR) to assess alterations in NR4A3 expression, and then two distinct animal models were generated by subjecting Nr4a3-/- mice and WT mice to a high-fat diet (HFD) and Streptozotocin (STZ), while db/db mice were administered AAV9-Nr4a3 or AAV9-ctrl. Subsequently, in vivo and in vitro experiments were conducted to assess the impact of NR4A3 on diabetes-induced atrial remodeling through electrophysiological, biological, and histological analyses. RNA sequencing (RNA-seq) and metabolomics analysis were employed to unravel the downstream mechanisms.

FINDINGS

The expression of NR4A3 was significantly decreased in atrial tissues of both AF patients and diabetic mice compared to their respective control groups. NR4A3 deficiency exacerbated atrial hypertrophy and atrial fibrosis, and increased susceptibility to pacing-induced AF. Conversely, overexpression of NR4A3 alleviated atrial structural remodeling and reduced AF induction rate. Mechanistically, we confirmed that NR4A3 improves mitochondrial energy metabolism and reduces oxidative stress injury by preserving the transcriptional expression of Sdha, thereby exerting a protective influence on atrial remodeling induced by diabetes.

INTERPRETATION

Our data confirm that NR4A3 plays a protective role in atrial remodeling caused by diabetes, so it may be a new target for treating ACM.

FUNDING

This study was supported by the major research program of National Natural Science Foundation of China (NSFC) No: 82370316 (to Q-S. W.), No. 81974041 (to Y-P. W.), and No. 82270447 (to Y-P. W.) and Fundation of Shanghai Hospital Development Center (No. SHDC2022CRD044 to Q-S. W.).

Post-translational modifications of proteins in cardiovascular diseases examined by proteomic approaches

Over 400 different types of post-translational modifications (PTMs) have been reported and over 200 various types of PTMs have been discovered using mass spectrometry (MS)-based proteomics. MS-based proteomics has proven to be a powerful method capable of global PTM mapping with the identification of modified proteins/peptides, the localization of PTM sites and PTM quantitation. PTMs play regulatory roles in protein functions, activities and interactions in various heart related diseases, such as ischemia/reperfusion injury, cardiomyopathy and heart failure. The recognition of PTMs that are specific to cardiovascular pathology and the clarification of the mechanisms underlying these PTMs at molecular levels are crucial for discovery of novel biomarkers and application in a clinical setting. With sensitive MS instrumentation and novel biostatistical methods for precise processing of the data, low-abundance PTMs can be successfully detected and the beneficial or unfavorable effects of specific PTMs on cardiac function can be determined. Moreover, computational proteomic strategies that can predict PTM sites based on MS data have gained an increasing interest and can contribute to characterization of PTM profiles in cardiovascular disorders. More recently, machine learning- and deep learning-based methods have been employed to predict the locations of PTMs and explore PTM crosstalk. In this review article, the types of PTMs are briefly overviewed, approaches for PTM identification/quantitation in MS-based proteomics are discussed and recently published proteomic studies on PTMs associated with cardiovascular diseases are included.

Multi-omic analyses and network biology in cardiovascular disease

Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.

Proteomics and β-hydroxybutyrylation Modification Characterization in the Hearts of Naturally Senescent Mice

Aging is widely accepted as an independent risk factor for cardiovascular disease (CVD), which contributes to increasing morbidity and mortality in the elderly population. Lysine β-hydroxybutyrylation (Kbhb) is a novel post-translational modification (PTM), wherein β-hydroxybutyrate is covalently attached to lysine ε-amino groups. Recent studies have revealed that histone Kbhb contributes to tumor progression, diabetic cardiomyopathy progression, and postnatal heart development. However, no studies have yet reported a global analysis of Kbhb proteins in aging hearts or elucidated the mechanisms underlying this modification in the process. Herein, we conducted quantitative proteomics and Kbhb PTM omics to comprehensively elucidate the alterations of global proteome and Kbhb modification in the hearts of aged mice. The results revealed a decline in grip strength and cardiac diastolic function in 22-month-old aged mice compared to 3-month-old young mice. High-throughput liquid chromatogram-mass spectrometry analysis identified 1710 β-hydroxybutyrylated lysine sites in 641 proteins in the cardiac tissue of young and aged mice. Additionally, 183 Kbhb sites identified in 134 proteins exhibited significant differential modification in aged hearts (fold change (FC) > 1.5 or <1/1.5, p < 0.05). Notably, the Kbhb-modified proteins were primarily detected in energy metabolism pathways, such as fatty acid elongation, glyoxylate and dicarboxylate metabolism, tricarboxylic acid cycle, and oxidative phosphorylation. Furthermore, these Kbhb-modified proteins were predominantly localized in the mitochondria. The present study, for the first time, provides a global proteomic profile and Kbhb modification landscape of cardiomyocytes in aged hearts. These findings put forth novel possibilities for treating cardiac aging and aging-related CVDs by reversing abnormal Kbhb modifications.

Plasma Proteome Profiling of Patients With In-stent Restenosis by Tandem Mass Tag-Based Quantitative Proteomics Approach

Background

Despite the widespread application of new drug-eluting stents, a considerable portion of patients experience in-stent restenosis (ISR). To date, the pathophysiologic mechanisms of ISR remain poorly understood.

Methods

In this study, we collected plasma samples from ISR patients (n = 29) and non-ISR patients (n = 36) after drug-eluting stent implantation, as well as from healthy controls (HCs) (n = 32). Our goal was to investigate differences in plasma protein profiles using tandem mass tag (TMT) labeling coupled with liquid chromatography and tandem mass spectrometry. The proteomic data were validated by enzyme-linked immunosorbent assay (ELISA). Bioinformatic analyses were conducted to analyze potential pathways and protein-protein interaction (PPI) involved in ISR.

Results

A total of 1,696 proteins were identified, of which 278 differed in protein abundance between non-ISR and HCs, 497 between ISR and HCs, and 387 between ISR and non-ISR, respectively. Bioinformatic analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and PPI, further demonstrated that differentially abundant proteins between ISR and non-ISR are involved in several crucial biological processes and signaling pathways, such as focal adhesion, platelet activation, Rap1 signaling, regulation of actin cytoskeleton, and cholesterol metabolism. Among the identified differentially abundant proteins in ISR, 170 were increased in abundance relative to both non-ISR patients and HCs. Some of these proteins were identified to have critical functions for atherosclerosis development and might be involved in ISR pathology. Among these proteins, 3 proteins with increased abundance including fetuin-B, apolipoprotein C-III (APOC3), and cholesteryl ester transfer protein (CETP) were confirmed by ELISA.

Conclusions

This is the first study provided a comprehensive proteomic profile to understand ISR pathology, which may help identify early diagnostic biomarkers and therapeutic targets.

The growing landscape of succinylation links metabolism and heart disease

Post-translational modification of proteins is an important biochemical process that occurs at the protein level. Succinylation is a newly discovered post-translational modification with the hallmark of a significant chemical and structural change. Succinylation has many similarities with other modifications, but succinylation may lead to more functional changes. Although the physiological significance of succinylation has not been well characterized, the lysine succinylation modification shows great potentials during disease processes. The discovery of SIRT5 has made great progress in exploring the role of succinylation in energy metabolism, heart disease and tumorigenesis. In this review, we focus on the discovery of succinylation in organisms and mechanism of succinylation. We are also concerned with the metabolic reactions and heart diseases associated with succinylation.

The Role of Posttranslational Modification and Mitochondrial Quality Control in Cardiovascular Diseases

Cardiovascular disease (CVD) is the leading cause of death in the world. The mechanism behind CVDs has been studied for decades; however, the pathogenesis is still controversial. Mitochondrial homeostasis plays an essential role in maintaining the normal function of the cardiovascular system. The alterations of any protein function in mitochondria may induce abnormal mitochondrial quality control and unexpected mitochondrial dysfunction, leading to CVDs. Posttranslational modifications (PTMs) affect protein function by reversibly changing their conformation. This review summarizes how common and novel PTMs influence the development of CVDs by regulating mitochondrial quality control. It provides not only ideas for future research on the mechanism of some types of CVDs but also ideas for CVD treatments with therapeutic potential.

TMT-based quantitative proteomic analysis reveals defense mechanism of wheat against the crown rot pathogen Fusarium pseudograminearum

BACKGROUND

Fusarium crown rot is major disease in wheat. However, the wheat defense mechanisms against this disease remain poorly understood.

RESULTS

Using tandem mass tag (TMT) quantitative proteomics, we evaluated a disease-susceptible (UC1110) and a disease-tolerant (PI610750) wheat cultivar inoculated with Fusarium pseudograminearum WZ-8A. The morphological and physiological results showed that the average root diameter and malondialdehyde content in the roots of PI610750 decreased 3 days post-inoculation (dpi), while the average number of root tips increased. Root vigor was significantly increased in both cultivars, indicating that the morphological, physiological, and biochemical responses of the roots to disease differed between the two cultivars. TMT analysis showed that 366 differentially expressed proteins (DEPs) were identified by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment in the two comparison groups, UC1110_3dpi/UC1110_0dpi (163) and PI610750_3dpi/PI610750_0dpi (203). It may be concluded that phenylpropanoid biosynthesis (8), secondary metabolite biosynthesis (12), linolenic acid metabolites (5), glutathione metabolism (8), plant hormone signal transduction (3), MAPK signaling pathway-plant (4), and photosynthesis (12) contributed to the defense mechanisms in wheat. Protein-protein interaction network analysis showed that the DEPs interacted in both sugar metabolism and photosynthesis pathways. Sixteen genes were validated by real-time quantitative polymerase chain reaction and were found to be consistent with the proteomics data.

CONCLUSION

The results provided insight into the molecular mechanisms of the interaction between wheat and F. pseudograminearum.

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

Background

Atrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learning approach.

Methods

At the transcriptomic level, four microarray datasets (GSE41177, GSE79768, GSE115574, GSE14975) were downloaded from the Gene Expression Omnibus database, which included 130 available atrial samples from AF and sinus rhythm (SR) patients with valvular heart disease. Microarray meta-analysis was adopted to identified differentially expressed genes (DEGs). At the proteomic level, a qualitative and quantitative analysis of proteomics in the left atrial appendage of 18 patients (9 with AF and 9 with SR) who underwent cardiac valvular surgery was conducted. The machine learning correlation-based feature selection (CFS) method was introduced to selected feature genes of AF using the training set of 130 samples involved in the microarray meta-analysis. The Naive Bayes (NB) based classifier constructed using training set was evaluated on an independent validation test set GSE2240.

Results

863 DEGs with FDR < 0.05 and 482 differentially expressed proteins (DEPs) with FDR < 0.1 and fold change > 1.2 were obtained from the transcriptomic and proteomic study, respectively. The DEGs and DEPs were then analyzed together which identified 30 biomarkers with consistent trends. Further, 10 features, including 8 upregulated genes (CD44, CHGB, FHL2, GGT5, IGFBP2, NRAP, SEPTIN6, YWHAQ) and 2 downregulated genes (TNNI1, TRDN) were selected from the 30 biomarkers through machine learning CFS method using training set. The NB based classifier constructed using the training set accurately and reliably classify AF from SR samples in the validation test set with a precision of 87.5% and AUC of 0.995.

Conclusion

Taken together, our present work might provide novel insights into the molecular mechanism and provide some promising diagnostic and therapeutic targets of AF.