Multi-omics approaches for revealing the complexity of cardiovascular disease

Effects of sevoflurane and isoflurane on acute myocardial infarction model establishment in mice

The selection of anesthetic drugs in the preparation of an acute myocardial infarction (AMI) model is very important. We specifically focus on various effects of sevoflurane and isoflurane in a murine AMI model, which have not been previously compared. Furthermore, we evaluated success of our AMI model using following methods: echocardiography, TTC staining, and PCR testing. The results show that compared to the isoflurane group, the sevoflurane group mice had shorter anesthetic induction(66.40 ± 2.90S vs. 125.10 ± 6.30S P < 0.0001) and recovery times(28.00 ± 1.07S vs. 56.88 ± 4.14S, P < 0.0001), lower incidence of respiratory depression (0 % vs. 50.00 %, P = 0.0325), and more successful models (93.33 % vs. 60.00 %, P = 0.0801). There were no significant differences in cardiac function, infarction area(49.41 ± 4.18 % vs. 48.66 ± 3.79 %, P = 0.5266), or inflammatory factors in the myocardial infarction area between the two groups. Sevoflurane may therefore be a better choice for the establishment of AMI models in mice.

Advances in humanoid organoid-based research on inter-organ communications during cardiac organogenesis and cardiovascular diseases

The intimate correlation between cardiovascular diseases and other organ pathologies, such as metabolic and kidney diseases, underscores the intricate interactions among these organs. Understanding inter-organ communications is crucial for developing more precise drugs and effective treatments for systemic diseases. While animal models have traditionally been pivotal in studying these interactions, human-induced pluripotent stem cells (hiPSCs) offer distinct advantages when constructing in vitro models. Beyond the conventional two-dimensional co-culture model, hiPSC-derived humanoid organoids have emerged as a substantial advancement, capable of replicating essential structural and functional attributes of internal organs in vitro. This breakthrough has spurred the development of multilineage organoids, assembloids, and organoids-on-a-chip technologies, which allow for enhanced physiological relevance. These technologies have shown great potential for mimicking coordinated organogenesis, exploring disease pathogenesis, and facilitating drug discovery. As the central organ of the cardiovascular system, the heart serves as the focal point of an extensively studied network of interactions. This review focuses on the advancements and challenges of hiPSC-derived humanoid organoids in studying interactions between the heart and other organs, presenting a comprehensive exploration of this cutting-edge approach in systemic disease research.

A Map of the Lipid-Metabolite-Protein Network to Aid Multi-Omics Integration

The integration of multi-omics data offers transformative potential for elucidating complex molecular mechanisms underlying biological processes and diseases. In this study, we developed a lipid-metabolite-protein network that combines a protein-protein interaction network and enzymatic and genetic interactions of proteins with metabolites and lipids to provide a unified framework for multi-omics integration. Using hyperbolic embedding, the network visualizes connections across omics layers, accessible through a user-friendly Shiny R (version 1.10.0) software package. This framework ranks molecules across omics layers based on functional proximity, enabling intuitive exploration. Application in a cardiovascular disease (CVD) case study identified lipids and metabolites associated with CVD-related proteins. The analysis confirmed known associations, like cholesterol esters and sphingomyelin, and highlighted potential novel biomarkers, such as 4-imidazoleacetate and indoleacetaldehyde. Furthermore, we used the network to analyze empagliflozin's temporal effects on lipid metabolism. Functional enrichment analysis of proteins associated with lipid signatures revealed dynamic shifts in biological processes, with early effects impacting phospholipid metabolism and long-term effects affecting sphingolipid biosynthesis. Our framework offers a versatile tool for hypothesis generation, functional analysis, and biomarker discovery. By bridging molecular layers, this approach advances our understanding of disease mechanisms and therapeutic effects, with broad applications in computational biology and precision medicine.

Digitalomics: Towards Artificial Intelligence / Machine Learning-Based Precision Cardiovascular Medicine

Recent advances in traditional "-omics" technologies have provided deeper insights into cardiovascular diseases through comprehensive molecular profiling. Accordingly, digitalomics has emerged as a novel transdisciplinary concept that integrates multimodal information with digitized physiological data, medical imaging, environmental data, electronic health records, environmental records, and biometric data from wearables. This digitalomics-driven augmented multiomics approach can provide more precise personalized health risk assessments and optimization when combined with conventional multiomics approaches. Artificial intelligence and machine learning (AI/ML) technologies, alongside statistical methods, serve as key comprehensive analytical tools in realizing this comprehensive framework. This review focuses on two promising AI/ML applications in cardiovascular medicine: digital phonocardiography (PCG) and AI text generators. Digital PCG uses AI/ML models to objectively analyze heart sounds and predict clinical parameters, potentially surpassing traditional auscultation capabilities. In addition, large language models, such as generative pretrained transformer, have demonstrated remarkable performance in assessing medical knowledge, achieving accuracy rates exceeding 80% in medical licensing examinations, although there are issues regarding knowledge accuracy and safety. Current challenges to the implementation of these technologies include maintaining up-to-date medical knowledge and ensuring consistent accuracy of outputs, but ongoing developments in fine-tuning and retrieval-augmented generation show promise in addressing these challenges. Integration of AI/ML technologies in clinical practice, guided by appropriate validation and implementation strategies, may notably advance precision cardiovascular medicine through the digitalomics framework.

Preventive Effects of Physical Activity on the Development of Atherosclerosis: A Narrative Review

Atherosclerosis, a major contributor to cardiovascular diseases (CVD), remains a leading cause of global mortality and morbidity. The pathogenesis of atherosclerosis involves a complex interplay of endothelial dysfunction, chronic inflammation, lipid accumulation, and arterial stiffness. Among the various preventive strategies, physical activity has emerged as a highly effective, non-pharmacological intervention. This review examines the preventive effects of different types of exercise-specifically aerobic exercise, resistance training, and combined training-on atherosclerosis development. Drawing on evidence from landmark studies, we explore the underlying mechanisms by which these exercise modalities improve endothelial function, reduce systemic inflammation, and enhance lipid profiles, thereby mitigating the progression of atherosclerosis. Additionally, the review discusses the dose-response relationship between physical activity and cardiovascular health, the differential effects of exercise intensities, and the potential risks associated with high-intensity training. The synergistic benefits of combined aerobic and resistance training are highlighted, particularly in populations with metabolic syndrome or other high-risk conditions. Emerging trends in personalized exercise medicine and the use of wearable technology for monitoring physical activity are also addressed, underscoring the potential for tailored exercise prescriptions to maximize cardiovascular health. By integrating current research findings, this review provides insights into effective exercise strategies for reducing cardiometabolic risk and emphasizes the importance of personalized approaches in exercise interventions.

Reprogramming macrophage metabolism following myocardial infarction: A neglected piece of a therapeutic opportunity

Given the global prevalence of myocardial infarction (MI) as the leading cause of mortality, there is an urgent need to devise novel strategies that target reducing infarct size, accelerating cardiac tissue repair, and preventing detrimental left ventricular (LV) remodeling. Macrophages, as a predominant type of innate immune cells, undergo metabolic reprogramming following MI, resulting in alterations in function and phenotype that significantly impact the progression of MI size and LV remodeling. This article aimed to delineate the characteristics of macrophage metabolites during reprogramming in MI and elucidate their targets and functions in cardioprotection. Furthermore, we summarize the currently proposed regulatory mechanisms of macrophage metabolic reprogramming and identify the regulators derived from endogenous products and natural small molecules. Finally, we discussed the challenges of macrophage metabolic reprogramming in the treatment of MI, with the goal of inspiring further fundamental and clinical research into reprogramming macrophage metabolism and validating its potential therapeutic targets for MI.

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Screening for Heart Failure: Biomarkers to Detect Heightened Risk in the General Population

PURPOSE OF REVIEW

Heart failure (HF) represents a growing global burden of morbidity and mortality. Identifying individuals at risk for HF development is increasingly important, particularly given the advent of disease-modifying therapies for HF as well as its major risk factors such as obesity actalnd diabetes. We aim to review the key circulating biomarkers associated with future HF which may contribute to HF risk prediction.

RECENT FINDINGS

While current guidelines recommend the use of natriuretic peptides and cardiac troponins in HF risk stratification, there are a diverse array of other emerging protein, metabolic, transcriptomic, and genomic biomarkers of future HF development. These biomarkers not only lend insight into the underlying pathophysiology of HF, which spans inflammation to cardiac fibrosis, but also offer an opportunity to further refine HF risk in addition to established biomarkers. As evolving techniques in molecular biology enable an increased understanding of the complex biologic contributions to HF pathophysiology, there is an important opportunity to construct integrated clinical and multi-omic models to best capture HF risk. Moving forward, future studies should seek to understand the contributions of sex differences, underlying comorbidity burden, and HF subtypes to an individual's HF risk. Further studies are necessary to fully define the clinical utility of biomarker screening approaches to refine HF risk assessment, as well as to link risk assessment directly to preventive strategies for HF.

Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data

Tropical diseases can often be caused by viruses, bacteria, parasites, and fungi. They can be spread over vectors. Analysis of multiple omics data types can be utilized in providing comprehensive insights into biological system functions and disease progression. To this end, bioinformatics tools and diverse AI techniques are pivotal in identifying and understanding tropical diseases through the analysis of omics data. In this article, we provide a thorough review of opportunities, challenges, and future directions of utilizing Bioinformatics tools and AI-assisted models on tropical disease identification using various omics data types. We conducted the review from 2015 to 2024 considering reliable databases of peer-reviewed journals and conference articles. Several keywords were taken for the article searching and around 40 articles were reviewed. According to the review, we observed that utilization of omics data with Bioinformatics tools like BLAST, and Clustal Omega can make significant outcomes in tropical disease identification. Further, the integration of multiple omics data improves biomarker identification, and disease predictions including disease outbreak predictions. Moreover, AI-assisted models can improve the precision, cost-effectiveness, and efficiency of CRISPR-based gene editing, optimizing gRNA design, and supporting advanced genetic correction. Several AI-assisted models including XAI can be used to identify diseases and repurpose therapeutic targets and biomarkers efficiently. Furthermore, recent advancements including Transformer-based models such as BERT and GPT-4, have been mainly applied for sequence analysis and functional genomics. Finally, the most recent GeneViT model, utilizing Vision Transformers, and other AI techniques like Generative Adversarial Networks, Federated Learning, Transfer Learning, Reinforcement Learning, Automated ML and Attention Mechanism have shown significant performance in disease classification using omics data.

Uncovering SPP1+ Macrophage, Neutrophils and Their Related Diagnostic Biomarkers in Intracranial Aneurysm and Subarachnoid Hemorrhage

Background

Intracranial aneurysms (IA) frequently cause subarachnoid hemorrhage (SAH) and have poor prognosis. However, the molecular mechanisms and diagnostic biomarkers associated with IA and ruptured IA (rIA) remain poorly understood.

Methods

In this study, single-cell and transcriptome datasets were obtained from the GEO database. The cell populations were annotated to identify potential pathogenic subpopulations, followed by intercellular communication, pseudotime, and SCENIC analyses. Proteome-wide and transcriptome-wide Mendelian randomization (MR) analyses were conducted to identify risk factors for IA and SAH. The major pathological changes and diagnostic biomarkers of IA and SAH were identified based on the transcriptome datasets. A clinical cohort was established to identify the diagnostic biomarkers and validate the results.

Results

Macrophages and neutrophils were predominantly increased in IA and rIA tissues, and neutrophils were markedly upregulated in the blood of SAH patients. SPP1+ Macrophage was progressively elevated in aneurysms, promoting vascular smooth muscle cell (VSMC) phenotypic transformation and collagen matrix remodeling through the SPP1 and TGF-β pathways. Furthermore, HIF1α regulon was enriched in SPP1+ Macrophage, mediating inflammation and metabolic reprogramming, which contributed to IA progression. Integrated MR analysis identified CD36 as a risk factor for both IA and SAH, and it has been recognized as an effective blood biomarker for SAH. Neutrophils and their related indicators have emerged as excellent biomarkers of SAH in clinical cohorts.

Conclusion

This study highlighted the detrimental role of SPP1+ Macrophage in IA and SAH using single-cell sequencing and MR analyses. CD36 was identified as a risk factor for IA and SAH and was also an efficient blood biomarker for SAH. In a clinical cohort, neutrophils and related indicators were valuable for the early diagnosis of SAH.

Machine Learning and Omics Analysis in Aortic Aneurysm

Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellular and molecular pathways involved. Omics generate a large amount of data and several studies have highlighted that artificial intelligence (AI) and techniques such as machine learning (ML)/deep learning (DL) can be of use in analyzing such complex datasets. However, only a few studies have so far reported the use of ML/DL for omics analysis in aortic aneurysms. The aim of this study is to summarize recent advances on the use of ML/DL for omics analysis to decipher aortic aneurysm pathophysiology and develop patient-tailored risk prediction models. In the light of current knowledge, we discuss current limits and highlight future directions in the field.

Identifying Hub Genes and Metabolic Pathways in Collagen VI-Related Dystrophies: A Roadmap to Therapeutic Intervention

Collagen VI-related dystrophies (COL6RD) are a group of rare muscle disorders caused by mutations in specific genes responsible for type VI collagen production. It affects muscles, joints, and connective tissues, leading to weakness, joint problems, and structural issues. Currently, there is no effective treatment for COL6RD; its management typically addresses symptoms and complications. Therefore, it is essential to decipher the disease's molecular mechanisms, identify drug targets, and develop effective treatment strategies to treat COL6RD. In this study, we employed differential gene expression analysis, weighted gene co-expression network analysis, and genome-scale metabolic modeling to investigate gene expression patterns in COL6RD patients, uncovering key genes, significant metabolites, and disease-related pathophysiological pathways. First, we performed differential gene expression and weighted gene co-expression network analyses, which led to the identification of 12 genes (CHCHD10, MRPS24, TRIP10, RNF123, MRPS15, NDUFB4, COX10, FUNDC2, MDH2, RPL3L, NDUFB11, PARVB) as potential hub genes involved in the disease. Second, we utilized a drug repurposing strategy to identify pharmaceutical candidates that could potentially modulate these genes and be effective in the treatment. Next, we utilized context-specific genome-scale metabolic models to compare metabolic variations between healthy individuals and COL6RD patients. Finally, we conducted reporter metabolite analysis to identify reporter metabolites (e.g., phosphatidates, nicotinate ribonucleotide, ubiquinol, ferricytochrome C). In summary, our analysis revealed critical genes and pathways associated with COL6RD and identified potential targets, reporter metabolites, and candidate drugs for therapeutic interventions.

Biomarker identification and risk assessment of cardiovascular disease based on untargeted metabolomics and machine learning

Cardiovascular disease (CVD) is the leading cause of mortality, disability, and healthcare costs, with a significant impact on the elderly and contributing to premature deaths across various age groups, including those below age 70. Despite decades of transformative discoveries and clinical efforts, the challenges of diagnosis, prevention, and treatment of CVD persist on a massive scale. This study aimed to unravel potential CVD-associated biomarkers and establish a machine learning model for the risk assessment of CVD. Untargeted metabolic assay with ultra-high performance liquid chromatography-tandem mass spectrometry and routine clinical biochemistry test were undertaken on the fasting venous blood specimens from 57 subjects. Four relevant clinical traits and 164 CVD-associated metabolites were identified, especially those related to glycerophospholipid metabolism and biosynthesis of unsaturated fatty acids. The machine learning model achieved from an integrated biomarker panel of palmitic amide, oleic acid, 138-pos (the 138th detected metabolomic feature in positive ion mode), phosphatidylcholine, linoleic acid, age, direct bilirubin, and inorganic phosphate, was able to improve the accuracy of CVD risk assessment up to a high satisfactory value of 0.91. The findings indicate that disorders in the metabolic processes of biological membranes and energy are significantly associated with increased risk of vascular damage in CVD patients. With machine learning methods, the pivotal metabolites and clinical biomarkers offer a promising potential for the efficient risk assessment and diagnosis of CVD.

Molecular remodeling in comorbidities associated with heart failure: a current update

Recent advances in genomics and proteomics have helped in understanding the molecular mechanisms and pathways of comorbidities and heart failure. In this narrative review, we reviewed molecular alterations in common comorbidities associated with heart failure such as obesity, diabetes mellitus, systemic hypertension, pulmonary hypertension, coronary artery disease, hypercholesteremia and lipoprotein abnormalities, chronic kidney disease, and atrial fibrillation. We searched the electronic databases, PubMed, Ovid, EMBASE, Google Scholar, CINAHL, and PhysioNet for articles without time restriction. Although the association between comorbidities and heart failure is already well established, recent studies have explored the molecular pathways in much detail. These molecular pathways demonstrate how novels drugs for heart failure works with respect to the pathways associated with comorbidities. Understanding the altered molecular milieu in heart failure and associated comorbidities could help to develop newer medications and targeted therapies that incorporate these molecular alterations as well as key molecular variations across individuals to improve therapeutic outcomes. The molecular alterations described in this study could be targeted for novel and personalized therapeutic approaches in the future. This knowledge is also critical for developing precision medicine strategies to improve the outcomes for patients living with these conditions.

Multi-omics analysis reveals the key factors involved in the severity of the Alzheimer's disease

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder with a global impact, yet its pathogenesis remains poorly understood. While age, metabolic abnormalities, and accumulation of neurotoxic substances are potential risk factors for AD, their effects are confounded by other factors. To address this challenge, we first utilized multi-omics data from 87 well phenotyped AD patients and generated plasma proteomics and metabolomics data, as well as gut and saliva metagenomics data to investigate the molecular-level alterations accounting the host-microbiome interactions. Second, we analyzed individual omics data and identified the key parameters involved in the severity of the dementia in AD patients. Next, we employed Artificial Intelligence (AI) based models to predict AD severity based on the significantly altered features identified in each omics analysis. Based on our integrative analysis, we found the clinical relevance of plasma proteins, including SKAP1 and NEFL, plasma metabolites including homovanillate and glutamate, and Paraprevotella clara in gut microbiome in predicting the AD severity. Finally, we validated the predictive power of our AI based models by generating additional multi-omics data from the same group of AD patients by following up for 3 months. Hence, we observed that these results may have important implications for the development of potential diagnostic and therapeutic approaches for AD patients.

A Multiplexed Quantitative Proteomics Approach to the Human Plasma Protein Signature

Despite the plasma proteome being able to provide a unique insight into the health and disease status of individuals, holding singular promise as a source of protein biomarkers that could be pivotal in the context of personalized medicine, only around 100 proteins covering a few human conditions have been approved as biomarkers by the US Food and Drug Administration (FDA) so far. Mass spectrometry (MS) currently has enormous potential for high-throughput analysis in clinical research; however, plasma proteomics remains challenging mainly due to the wide dynamic range of plasma protein abundances and the time-consuming procedures required. We applied a new MS-based multiplexed proteomics workflow to quantitate proteins, encompassing 67 FDA-approved biomarkers, in >1300 human plasma samples from a clinical cohort. Our results indicate that this workflow is suitable for large-scale clinical studies, showing good accuracy and reproducibility (coefficient of variation (CV) < 20 for 90% of the proteins). Furthermore, we identified plasma signature proteins (stable in time on an individual basis), stable proteins (exhibiting low biological variability and high temporal stability), and highly variable proteins (with low temporal stability) that can be used for personalized health monitoring and medicine.

Detection of pharmacolipidodynamic effects following the intravenous and oral administration of gefitinib to C57Bl/6JRj mice by rapid UHPLC-MS analysis of plasma

Omics-based biomarker technologies, including metabolic profiling (metabolomics/metabonomics) and lipidomics, are making a significant impact on disease understanding, drug development, and translational research. A wide range of patho-physiological processes involve lipids and monitoring changes in lipid abundance can give valuable insights into mechanisms of drug action, off target pharmacology and toxicity. Here we report changes, detected by untargeted LC-MS, in the plasma lipid profiles of male C57Bl/6JRj mice following the PO and IV administration of the epidermal growth factor receptor (EGFR) inhibitor gefitinib. Statistical analysis of the data obtained for both the IV and PO samples showed time-related changes in the amounts of lipids from several different classes. The largest effects were associated with a rapid onset of these changes following gefitinib administration followed by a gradual return by 24 h post dose to the type of lipid profile seen in predose samples. Investigation of the lipids responsible for the variance observed in the data showed that the PI, PC, LPC, PE and TG were subject to the largest disruption with both transient increases and decreases in relative amounts seen in response to administration of the drug. The pattern of the changes in the relative abundances of those lipids subject to variation appeared to be correlated to the pharmacokinetics of gefitinib (and its major metabolites). These observations support the concept of a distinct pharmacolipidodynamic relationship between drug exposure and plasma lipid abundance.

Unraveling interindividual variation of trimethylamine N-oxide and its precursors at the population level

Trimethylamine N-oxide (TMAO) is a circulating microbiome-derived metabolite implicated in the development of atherosclerosis and cardiovascular disease (CVD). We investigated whether plasma levels of TMAO, its precursors (betaine, carnitine, deoxycarnitine, choline), and TMAO-to-precursor ratios are associated with clinical outcomes, including CVD and mortality. This was followed by an in-depth analysis of their genetic, gut microbial, and dietary determinants. The analyses were conducted in five Dutch prospective cohort studies including 7834 individuals. To further investigate association results, Mendelian Randomization (MR) was also explored. We found only plasma choline levels (hazard ratio [HR] 1.17, [95% CI 1.07; 1.28]) and not TMAO to be associated with CVD risk. Our association analyses uncovered 10 genome-wide significant loci, including novel genomic regions for betaine (6p21.1, 6q25.3), choline (2q34, 5q31.1), and deoxycarnitine (10q21.2, 11p14.2) comprising several metabolic gene associations, for example, CPS1 or PEMT. Furthermore, our analyses uncovered 68 gut microbiota associations, mainly related to TMAO-to-precursors ratios and the Ruminococcaceae family, and 16 associations of food groups and metabolites including fish-TMAO, meat-carnitine, and plant-based food-betaine associations. No significant association was identified by the MR approach. Our analyses provide novel insights into the TMAO pathway, its determinants, and pathophysiological impact on the general population.

Using Omics to Identify Novel Therapeutic Targets in Heart Failure

Omics refers to the measurement and analysis of the totality of molecules or biological processes involved within an organism. Examples of omics data include genomics, transcriptomics, epigenomics, proteomics, metabolomics, and more. In this review, we present the available literature reporting omics data on heart failure that can inform the development of novel treatments or innovative treatment strategies for this disease. This includes polygenic risk scores to improve prediction of genomic data and the potential of multiomics to more efficiently identify potential treatment targets for further study. We also discuss the limitations of omic analyses and the barriers that must be overcome to maximize the utility of these types of studies. Finally, we address the current state of the field and future opportunities for using multiomics to better personalize heart failure treatment strategies.

A study protocol to characterise pathophysiological and molecular markers of rheumatic heart disease and degenerative aortic stenosis using multiparametric cardiovascular imaging and multiomics techniques

INTRODUCTION

Rheumatic heart disease (RHD), degenerative aortic stenosis (AS), and congenital valve diseases are prevalent in sub-Saharan Africa. Many knowledge gaps remain in understanding disease mechanisms, stratifying phenotypes, and prognostication. Therefore, we aimed to characterise patients through clinical profiling, imaging, histology, and molecular biomarkers to improve our understanding of the pathophysiology, diagnosis, and prognosis of RHD and AS.

METHODS

In this cross-sectional, case-controlled study, we plan to recruit RHD and AS patients and compare them to matched controls. Living participants will undergo clinical assessment, echocardiography, CMR and blood sampling for circulatory biomarker analyses. Tissue samples will be obtained from patients undergoing valve replacement, while healthy tissues will be obtained from cadavers. Immunohistology, proteomics, metabolomics, and transcriptome analyses will be used to analyse circulatory- and tissue-specific biomarkers. Univariate and multivariate statistical analyses will be used for hypothesis testing and identification of important biomarkers. In summary, this study aims to delineate the pathophysiology of RHD and degenerative AS using multiparametric CMR imaging. In addition to discover novel biomarkers and explore the pathomechanisms associated with RHD and AS through high-throughput profiling of the tissue and blood proteome and metabolome and provide a proof of concept of the suitability of using cadaveric tissues as controls for cardiovascular disease studies.

UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review

Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk. This review proposes two hypotheses: (i) RBBM and GBBM biomarkers have a strong correlation and can be used to detect the severity of CVD and stroke precisely, and (ii) introduces a proposed artificial intelligence (AI)-based preventive, precision, and personalized ( aiP 3 ) CVD/Stroke risk model. The PRISMA search selected 246 studies for the CVD/Stroke risk. It showed that using the RBBM and GBBM biomarkers, deep learning (DL) modelscould be used for CVD/Stroke risk stratification in the aiP 3 framework. Furthermore, we present a concise overview of platelet function, complete blood count (CBC), and diagnostic methods. As part of the AI paradigm, we discuss explainability, pruning, bias, and benchmarking against previous studies and their potential impacts. The review proposes the integration of RBBM and GBBM, an innovative solution streamlined in the DL paradigm for predicting CVD/Stroke risk in the aiP 3 framework. The combination of RBBM and GBBM introduces a powerful CVD/Stroke risk assessment paradigm. aiP 3 model signifies a promising advancement in CVD/Stroke risk assessment.

Cardiovascular disease detection using a novel stack-based ensemble classifier with aggregation layer, DOWA operator, and feature transformation

Due to their widespread prevalence and impact on quality of life, cardiovascular diseases (CVD) pose a considerable global health burden. Early detection and intervention can reduce the incidence, severity, and progression of CVD and prevent premature death. The application of machine learning (ML) techniques to early CVD detection is therefore a valuable approach. In this paper, A stack-based ensemble classifier with an aggregation layer and the dependent ordered weighted averaging (DOWA) operator is proposed for detecting cardiovascular diseases. We propose transforming features using the Johnson transformation technique and normalizing feature distributions. Three diverse first-level classifiers are selected based on their accuracy, and predictions are combined using the aggregation layer and DOWA. A linear support vector machine (SVM) meta-classifier makes the final classification. Adding the aggregation layer to the stacking classifier improves classification accuracy significantly, according to the study. The accuracy is enhanced by 5%, resulting in an impressive overall accuracy of 94.05%. Moreover, the proposed system significantly increases the area under the receiver operating characteristic (ROC) curve compared to recent studies, reaching 97.14%. It further reinforces the classifier's reliability and effectiveness in classifying cardiovascular disease by distinguishing between positive and negative instances. With improved accuracy and a high area under the curve (AUC), the proposed classifier exhibits robustness and superior performance in the detection of cardiovascular diseases.

A critical appreciation of pathway analysis in atherosclerotic disease. Cellular phenotypic plasticity as an illustrative example

The rapid advancements in genome-scale (omics) techniques has created significant opportunities to investigate complex disease mechanisms in tissues and cells. Nevertheless, interpreting -omics data can be challenging, and pathway enrichment analysis is a frequently used method to identify candidate molecular pathways that drive gene expression changes. With a growing number of -omics studies dedicated to atherosclerosis, there has been a significant increase in studies and hypotheses relying on enrichment analysis. This brief review discusses the benefits and limitations of pathway enrichment analysis within atherosclerosis research. We highlight the challenges of identifying complex biological processes, such as cell phenotypic switching, within -omics data. Additionally, we emphasize the need for more comprehensive and curated gene sets that reflect the biological complexity of atherosclerosis. Pathway enrichment analysis is a valuable tool for gaining insights into the molecular mechanisms of atherosclerosis. Nevertheless, it is crucial to remain aware of the intrinsic limitations of this approach. By addressing these weaknesses, enrichment analysis in atherosclerosis can lead to breakthroughs in identifying the mechanisms of disease progresses, the identification of key driver genes, and consequently, advance personalized patient care.

Derivatization-free conventional and synchronous spectrofluorimetric estimation of atenolol and amlodipine

Fixed-dose combinations for treatment of hypertension are observed in many dosages in the global market because of their high efficacy compared to single component dosage forms. One of these effective combinations is atenolol/amlodipine which is usually administered to patients with hypertension. Hence, development of facile, accurate, and sensitive methods for simultaneous estimation of atenolol and amlodipine is of great importance for quality control testing and pharmacokinetic studies. In our study, we developed two spectrofluorimetric methods to estimate both compounds in different pharmaceuticals. The first method is based on the estimation of atenolol and amlodipine by double-scan conventional spectrofluorimetry where the fluorescence intensities of atenolol and amlodipine were measured at 299 and 434 nm after excitation at 274 and 358 nm, respectively. The second method depends on synchronous spectrofluorimetric measurements at Δλ = 70 nm, where atenolol is assayed at 266 nm and amlodipine is assayed at 363 nm. Methods' optimizations were carried out to select the optimum conditions that render high selectivity and sensitivity. Such optimizations included assessment of solvents, surfactants, buffer volumes and pHs. The conventional spectrofluorimetric method was rectilinear over concentration range of 30.0-300.0 ng mL-1 for atenolol and 0.25-7.00 µg mL-1 for amlodipine while the synchronous spectrofluorimetric method showed linearity over the ranges of 0.60-6.00 µg mL-1 for atenolol and 0.25-7.00 µg mL-1 for amlodipine with low detection limits (≤0.12 µg mL-1) for both compounds in the two methods. It is the first work that demonstrates estimation of atenolol and amlodipine in their combinations by conventional and synchronous spectrofluorimetry. Both methods were applied to estimate atenolol and amlodipine in different pharmaceuticals with high %recovery and low %RSD.

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients. After robust gene expression data pre-processing, we utilized three statistical tests (Pearson correlation, Chi-square test, and ANOVA) to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, the recursive feature elimination classifier assigned rankings to transcriptomic features based on their relation to the case-control variable. The top ten percent of commonly observed significant biomarkers were evaluated using four unique ML classifiers (Random Forest, Support Vector Machine, Xtreme Gradient Boosting Decision Trees, and k-Nearest Neighbors). After optimizing hyperparameters, the ensembled models, which were implemented using a soft voting classifier, accurately differentiated between patients and healthy individuals. We have uncovered 18 transcriptomic biomarkers that are highly significant in the CVD population that were used to predict disease with up to 96% accuracy. Additionally, we cross-validated our results with clinical records collected from patients in our cohort. The identified biomarkers served as potential indicators for early detection of CVDs. With its successful implementation, our newly developed predictive engine provides a valuable framework for identifying patients with CVDs based on their biomarker profiles.

Myocardial infarction complexity: A multi-omics approach

Myocardial infarction (MI), a prevalent cardiovascular disease, is fundamentally precipitated by thrombus formation in the coronary arteries, which subsequently decreases myocardial perfusion and leads to cellular necrosis. The intricacy of MI pathogenesis necessitates extensive research to elucidate the disease's root cause, thereby addressing the limitations present in its diagnosis and prognosis. With the continuous advancement of genomics technology, genomics, proteomics, metabolomics and transcriptomics are widely used in the study of MI, which provides an excellent way to identify new biomarkers that elucidate the complex mechanisms of MI. This paper provides a detailed review of various genomics studies of MI, including genomics, proteomics, transcriptomics, metabolomics and multi-omics studies. The metabolites and proteins involved in the pathogenesis of MI are investigated through integrated protein-protein interactions and multi-omics analysis by STRING and Metascape platforms. In conclusion, the future of omics research in myocardial infarction offers significant promise.

Advances in screening, synthesis, modification, and biomedical applications of peptides and peptide aptamers

Peptides and peptide aptamers have emerged as promising molecules for a wide range of biomedical applications due to their unique properties and versatile functionalities. The screening strategies for identifying peptides and peptide aptamers with desired properties are discussed, including high-throughput screening, display screening technology, and in silico design approaches. The synthesis methods for the efficient production of peptides and peptide aptamers, such as solid-phase peptide synthesis and biosynthesis technology, are described, along with their advantages and limitations. Moreover, various modification techniques are explored to enhance the stability, specificity, and pharmacokinetic properties of peptides and peptide aptamers. This includes chemical modifications, enzymatic modifications, biomodifications, genetic engineering modifications, and physical modifications. Furthermore, the review highlights the diverse biomedical applications of peptides and peptide aptamers, including targeted drug delivery, diagnostics, and therapeutic. This review provides valuable insights into the advancements in screening, synthesis, modification, and biomedical applications of peptides and peptide aptamers. A comprehensive understanding of these aspects will aid researchers in the development of novel peptide-based therapeutics and diagnostic tools for various biomedical challenges.

Effects of Mechanical Stress on Endothelial Cells In Situ and In Vitro

Endothelial cells lining blood vessels are essential for maintaining vascular homeostasis and mediate several pathological and physiological processes. Mechanical stresses generated by blood flow and other biomechanical factors significantly affect endothelial cell activity. Here, we review how mechanical stresses, both in situ and in vitro, affect endothelial cells. We review the basic principles underlying the cellular response to mechanical stresses. We also consider the implications of these findings for understanding the mechanisms of mechanotransducer and mechano-signal transduction systems by cytoskeletal components.

Multi-Omics Integration for the Design of Novel Therapies and the Identification of Novel Biomarkers

Multi-omics is a cutting-edge approach that combines data from different biomolecular levels, such as DNA, RNA, proteins, metabolites, and epigenetic marks, to obtain a holistic view of how living systems work and interact. Multi-omics has been used for various purposes in biomedical research, such as identifying new diseases, discovering new drugs, personalizing treatments, and optimizing therapies. This review summarizes the latest progress and challenges of multi-omics for designing new treatments for human diseases, focusing on how to integrate and analyze multiple proteome data and examples of how to use multi-proteomics data to identify new drug targets. We also discussed the future directions and opportunities of multi-omics for developing innovative and effective therapies by deciphering proteome complexity.

Functional mutation, splice, distribution, and divergence analysis of impactful genes associated with heart failure and other cardiovascular diseases

Cardiovascular disease (CVD) is caused by a multitude of complex and largely heritable conditions. Identifying key genes and understanding their susceptibility to CVD in the human genome can assist in early diagnosis and personalized treatment of the relevant patients. Heart failure (HF) is among those CVD phenotypes that has a high rate of mortality. In this study, we investigated genes primarily associated with HF and other CVDs. Achieving the goals of this study, we built a cohort of thirty-five consented patients, and sequenced their serum-based samples. We have generated and processed whole genome sequence (WGS) data, and performed functional mutation, splice, variant distribution, and divergence analysis to understand the relationships between each mutation type and its impact. Our variant and prevalence analysis found FLNA, CST3, LGALS3, and HBA1 linked to many enrichment pathways. Functional mutation analysis uncovered ACE, MME, LGALS3, NR3C2, PIK3C2A, CALD1, TEK, and TRPV1 to be notable and potentially significant genes. We discovered intron, 5' Flank, 3' UTR, and 3' Flank mutations to be the most common among HF and other CVD genes. Missense mutations were less common among HF and other CVD genes but had more of a functional impact. We reported HBA1, FADD, NPPC, ADRB2, ADBR1, MYH6, and PLN to be consequential based on our divergence analysis.

Pro-inflammatory cytokines mediating senescence of vascular endothelial cells in atherosclerosis

Atherosclerosis (AS) is a chronic inflammatory vascular disease, and aging is a major risk factor. The accumulation of senescent vascular endothelial cells (VECs) often leads to chronic inflammation and oxidative stress and induces endothelial dysfunction, contributing to the occurrence and development of AS. Senescent cells can secrete a variety of pro-inflammatory cytokines to induce the senescence of adjacent cells in a paracrine manner, leading to the transmission of signaling of cellular senescence to neighboring cells and the accumulation of senescent cells. Recent studies have demonstrated that several pro-inflammatory cytokines, including IL-17, TNF-α, and IFN-γ, can induce the senescence of VECs. This review summarizes and focuses on the pro-inflammatory cytokines that often induce the senescence of VECs and the molecular mechanisms of these pro-inflammatory cytokines inducing senescence of VECs. Targeting the senescence of VECs induced by pro-inflammatory cytokines may provide a potential and novel strategy for the prevention and treatment of AS.

Association of systemic immune inflammation index and system inflammation response index with clinical risk of acute myocardial infarction

Background

Systemic immune inflammatory index (SII) and systemic inflammatory response index (SIRI) are combinations of non-specific inflammatory and adaptive immune response impairments associated with cardiovascular disease. Yet little analysis has been done on SII, SIRI and acute myocardial infarction (AMI) prognosis. The purpose of this study was to investigate the correlation of SII and SIRI with clinical risk factors such as GRACE, Gensini, and QTc after acute myocardial infarction.

Methods

This study enrolled 310 patients with AMI from February 1, 2018, to December 31, 2022, at our institution. Routine blood items calculated SII and SIRI. Two groups were divided according to whether MACE occurred: the MACE group (81 cases) and the NMACE group (229 cases); each group was divided into three groups according to the SII and SIRI tertiles. The relationship between SII, SIRI and MACE was analyzed using multifactorial logistic regression analysis after adjusting for confounders; ROC curves were plotted to examine the predictive value of SII and SIRI for MACE. The correlation between SII and SIRI and potential risk factors such as Gensini, QTc and GRACE was further analyzed.

Results

The study enrolled 310 patients, comprising 248 men (80%, mean age 60.73 ± 13.695 years) and 62 women (20%, mean age 69.79 ± 11.555 years). In the regression model completely adjusted for confounders, the risk of MACE was higher in AMI patients with SII > 11.00 [OR = 1.061,95% CI (1.018,1.105)] than in SII < 5.98; the risk of MACE was 115.3% higher in AMI patients with SIRI (1.72-3.68) [OR = 2.153, 95% CI (1.251, 3.705)] was 115.3% higher in AMI patients with SIRI < 1.72 and the risk of MACE was 25.1% higher in AMI patients with SIRI > 3.68 [OR = 1.251, 95% CI (1.123, 1.394)] than in AMI patients with SIRI < 1.72. In addition, SII, SIRI, and potential post-infarction risk factors (Gensini, QTc, and GRACE) were also associated.

Conclusion

SII and SIRI have been significantly associated with post-myocardial infarction MACE and the predictive potential clinically integrated risk factors in AMI patients, for which more attention should be paid to targeted anti-inflammatory therapy in AMI patients to further reduce the incidence of prognostic MACE in AMI patients.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Multimodal plasma metabolomics and lipidomics in elucidating metabolic perturbations in tuberculosis patients with concurrent type 2 diabetes

Type 2 diabetes mellitus (DM) poses a major burden for the treatment and control of tuberculosis (TB). Characterization of the underlying metabolic perturbations in DM patients with TB infection would yield insights into the pathophysiology of TB-DM, thus potentially leading to improvements in TB treatment. In this study, a multimodal metabolomics and lipidomics workflow was applied to investigate plasma metabolic profiles of patients with TB and TB-DM. Significantly different biological processes and biomarkers in TB-DM vs. TB were identified using a data-driven, knowledge-based framework. Changes in metabolic and signaling pathways related to carbohydrate and amino acid metabolism were mainly captured by amide HILIC column metabolomics analysis, while perturbations in lipid metabolism were identified by the C18 metabolomics and lipidomics analysis. Compared to TB, TB-DM exhibited elevated levels of bile acids and molecules related to carbohydrate metabolism, as well as the depletion of glutamine, retinol, lysophosphatidylcholine, and phosphatidylcholine. Moreover, arachidonic acid metabolism was determined as a potential important factor in the interaction between TB and DM pathophysiology. In a correlation network of the significantly altered molecules, among the central nodes, chenodeoxycholic acid was robustly associated with TB and DM. Fatty acid (22:4) was a component of all significant modules. In conclusion, the integration of multimodal metabolomics and lipidomics provides a thorough picture of the metabolic changes associated with TB-DM. The results obtained from this comprehensive profiling of TB patients with DM advance the current understanding of DM comorbidity in TB infection and contribute to the development of more effective treatment.

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; P_HEIDI>0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR<0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including "cell aging", "metal ion binding" and "oxidative damage". Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.

Cardiometabolic health, diet and the gut microbiome: a meta-omics perspective

Cardiometabolic diseases have become a leading cause of morbidity and mortality globally. They have been tightly linked to microbiome taxonomic and functional composition, with diet possibly mediating some of the associations described. Both the microbiome and diet are modifiable, which opens the way for novel therapeutic strategies. High-throughput omics techniques applied on microbiome samples (meta-omics) hold the unprecedented potential to shed light on the intricate links between diet, the microbiome, the metabolome and cardiometabolic health, with a top-down approach. However, effective integration of complementary meta-omic techniques is an open challenge and their application on large cohorts is still limited. Here we review meta-omics techniques and discuss their potential in this context, highlighting recent large-scale efforts and the novel insights they provided. Finally, we look to the next decade of meta-omics research and discuss various translational and clinical pathways to improving cardiometabolic health.

A urinary peptidomics approach for early stages of cardiovascular disease risk: The African-PREDICT study

Cardiovascular disease (CVD) affects individuals across the lifespan, with multiple cardiovascular (CV) risk factors increasingly present in young populations. The underlying mechanisms in early cardiovascular disease development are complex and still poorly understood. We therefore employed urinary proteomics as a novel approach to gain better insight into early CVD-related molecular pathways based on a CVD risk stratification approach. This study included 964 apparently healthy (no self-reported chronic illnesses, free from clinical symptoms of CVD) black and white men and women (aged 20-30 years old) from the African Prospective study on the Early Detection and Identification of Cardiovascular disease and Hypertension (African-PREDICT) study. Cardiovascular risk factors used for stratification included obesity, physical inactivity, tobacco use, high alcohol intake, hyperglycemia, dyslipidemia and hypertension. Participants were divided into low (0 risk factors), medium (1-2 risk factors) and high (≥3 risk factors) CV risk groups. We analyzed urinary peptidomics by capillary electrophoresis time-of-flight mass spectrometry. After adjusting for ethnicity, sex and age, 65 sequenced urinary peptides were differentially expressed between the CV risk groups (all q-values ≤ 0.01). These peptides included a lower abundance of collagen type I- and III-derived peptides in the high compared to the low CV risk group. With regard to noncollagen peptides, we found a lower abundance of alpha-1-antitrypsin fragments in the high compared to the low CV risk group (all q-values ≤ 0.01). Our findings indicate lower abundances of collagen types I and III in the high compared to the low CV risk group, suggesting potential early alterations in the CV extracellular matrix.

Applying multi-omics techniques to the discovery of biomarkers for acute aortic dissection

Acute aortic dissection (AAD) is a cardiovascular disease that manifests suddenly and fatally. Due to the lack of specific early symptoms, many patients with AAD are often overlooked or misdiagnosed, which is undoubtedly catastrophic for patients. The particular pathogenic mechanism of AAD is yet unknown, which makes clinical pharmacological therapy extremely difficult. Therefore, it is necessary and crucial to find and employ unique biomarkers for Acute aortic dissection (AAD) as soon as possible in clinical practice and research. This will aid in the early detection of AAD and give clear guidelines for the creation of focused treatment agents. This goal has been made attainable over the past 20 years by the quick advancement of omics technologies and the development of high-throughput tissue specimen biomarker screening. The primary histology data support and add to one another to create a more thorough and three-dimensional picture of the disease. Based on the introduction of the main histology technologies, in this review, we summarize the current situation and most recent developments in the application of multi-omics technologies to AAD biomarker discovery and emphasize the significance of concentrating on integration concepts for integrating multi-omics data. In this context, we seek to offer fresh concepts and recommendations for fundamental investigation, perspective innovation, and therapeutic development in AAD.

Tackling inflammation in atherosclerosis: Are we there yet and what lies beyond?

Atherosclerosis is a lipid-driven disease of the artery characterized by chronic non-resolving inflammation. Despite availability of excellent lipid-lowering therapies, atherosclerosis remains the leading cause of disability and death globally. The demonstration that suppressing inflammation prevents the adverse clinical manifestations of atherosclerosis in recent clinical trials has led to heightened interest in anti-inflammatory therapies. In this review, we briefly highlight some key anti-inflammatory and pro-resolution pathways, which could be targeted to modulate pathogenesis and stall atherosclerosis progression. We also highlight key challenges that must be overcome to turn the concept of inflammation targeting therapies into clinical reality for atherosclerotic heart disease.

Integrated multi-omic data reveal the potential molecular mechanisms of the nutrition and flavor in Liancheng white duck meat

The Liancheng white (LW) duck is one of the most valued Chinese indigenous poultry breeds. Its meat is rich in nutrients and has distinct flavors, but the molecular mechanisms behind them are unknown. To address this issue, we measured and compared multi-omic data (genome, transcriptome, and metabolome) of breast meat from LW ducks and the Mianyang Shelduck (MS) ducks. We found that the LW duck has distinct breed-specific genetic features, including numerous mutant genes with differential expressions associated with amino acid metabolism and transport activities. The metabolome driven by genetic materials was also seen to differ between the two breeds. For example, several amino acids that are beneficial for human health, such as L-Arginine, L-Ornithine, and L-lysine, were found in considerably higher concentrations in LW muscle than in MS duck muscle (p < 0.05). SLC7A6, a mutant gene, was substantially upregulated in the LW group (p < 0.05), which may lead to excessive L-arginine and L-ornithine accumulation in LW duck meat through transport regulation. Further, guanosine monophosphate (GMP), an umami-tasting molecule, was considerably higher in LW muscle (p < 0.05), while L-Aspartic acid was significantly abundant in MS duck meat (p < 0.05), showing that the LW duck has a different umami formation. Overall, this study contributed to our understanding of the molecular mechanisms driving the enriched nutrients and distinct umami of LW duck meat, which will provide a useful reference for duck breeding.

REGLIV: Molecular regulation data of diverse living systems facilitating current multiomics research

Multiomics is a powerful technique in molecular biology that facilitates the identification of new associations among different molecules (genes, proteins & metabolites). It has attracted tremendous research interest from the scientists worldwide and has led to an explosive number of published studies. Most of these studies are based on the regulation data provided in available databases. Therefore, it is essential to have molecular regulation data that are strictly validated in the living systems of various cell lines and in vivo models. However, no database has been developed yet to provide comprehensive molecular regulation information validated by living systems. Herein, a new database, Molecular Regulation Data of Living System Facilitating Multiomics Study (REGLIV) is introduced to describe various types of molecular regulation tested by the living systems. (1) A total of 2996 regulations describe the changes in 1109 metabolites triggered by alterations in 284 genes or proteins, and (2) 1179 regulations describe the variations in 926 proteins induced by 125 endogenous metabolites. Overall, REGLIV is unique in (a) providing the molecular regulation of a clearly defined regulatory direction other than simple correlation, (b) focusing on molecular regulations that are validated in a living system not simply in an in vitro test, and (c) describing the disease/tissue/species specific property underlying each regulation. Therefore, REGLIV has important implications for the future practice of not only multiomics, but also other fields relevant to molecular regulation. REGLIV is freely accessible at: https://idrblab.org/regliv/.

Biologics and cardiac disease: challenges and opportunities

Biologics are revolutionizing the treatment of chronic diseases, such as cancer and monogenic disorders, by overcoming the limits of classic therapeutic approaches using small molecules. However, the clinical use of biologics is limited for cardiovascular diseases (CVDs) , which are the primary cause of morbidity and mortality worldwide. Here, we review the state-of-the-art use of biologics for cardiac disorders and provide a framework for understanding why they still struggle to enter the field. Some limitations are common and intrinsic to all biological drugs, whereas others depend on the complexity of cardiac disease. In our opinion, delineating these struggles will be valuable in developing and accelerating the approval of a new generation of biologics for CVDs.

Proteomics Reveals Long-Term Alterations in Signaling and Metabolic Pathways Following Both Myocardial Infarction and Chemically Induced Denervation

Myocardial infraction (MI) is the principal risk factor for the onset of heart failure (HF). Investigations regarding the physiopathology of MI progression to HF have revealed the concerted engagement of other tissues, such as the autonomic nervous system and the medulla oblongata (MO), giving rise to systemic effects, important in the regulation of heart function. Cardiac sympathetic afferent denervation following application of resiniferatoxin (RTX) attenuates cardiac remodelling and restores cardiac function following MI. While the physiological responses are well documented in numerous species, the underlying molecular responses during the initiation and progression from MI to HF remains unclear. We obtained multi-tissue time course proteomics with a murine model of HF induced by MI in conjunction with RTX application. We isolated tissue sections from the left ventricle (LV), MO, cervical spinal cord and cervical vagal nerves at four time points over a 12-week study. Bioinformatic analyses consistently revealed a high statistical enrichment for metabolic pathways in all tissues and treatments, implicating a central role of mitochondria in the tissue-cellular response to both MI and RTX. In fact, the additional functional pathways found to be enriched in these tissues, involving the cytoskeleton, vesicles and signal transduction, could be downstream of responses initiated by mitochondria due to changes in neuronal pulse frequency after a shock such as MI or the modification of such frequency communication from the heart to the brain after RTX application. Development of future experiments, based on our proteomic results, should enable the dissection of more precise mechanisms whereby metabolic changes in neuronal and cardiac tissues can effectively ameliorate the negative physiological effects of MI via RTX application.

OUP accepted manuscript

Cardiovascular diseases (CVDs) arise from a complex interplay among genomic, proteomic, and metabolomic abnormalities. Emerging evidence has recently consolidated the presence of robust DNA damage in a variety of cardiovascular disorders. DNA damage triggers a series of cellular responses termed DNA damage response (DDR) including detection of DNA lesions, cell cycle arrest, DNA repair, cellular senescence, and apoptosis, in all organ systems including hearts and vasculature. Although transient DDR in response to temporary DNA damage can be beneficial for cardiovascular function, persistent activation of DDR promotes the onset and development of CVDs. Moreover, therapeutic interventions that target DNA damage and DDR have the potential to attenuate cardiovascular dysfunction and improve disease outcome. In this review, we will discuss molecular mechanisms of DNA damage and repair in the onset and development of CVDs, and explore how DDR in specific cardiac cell types contributes to CVDs. Moreover, we will highlight the latest advances regarding the potential therapeutic strategies targeting DNA damage signalling in CVDs.

MIKB: A manually curated and comprehensive knowledge base for myocardial infarction

Myocardial infarction knowledge base (MIKB; http://www.sysbio.org.cn/mikb/; latest update: December 31, 2020) is an open-access and manually curated database dedicated to integrating knowledge about MI to improve the efficiency of translational MI research. MIKB is an updated and expanded version of our previous MI Risk Knowledge Base (MIRKB), which integrated MI-related risk factors and risk models for providing help in risk assessment or diagnostic prediction of MI. The updated MIRKB includes 9701 records with 2054 single factors, 209 combined factors, 243 risk models, 37 MI subtypes and 3406 interactions between single factors and MIs collected from 4817 research articles. The expanded functional module, i.e. MIGD, is a database including not only MI associated genetic variants, but also the other multi-omics factors and the annotations for their functional alterations. The goal of MIGD is to provide a multi-omics level understanding of the molecular pathogenesis of MI. MIGD includes 1782 omics factors, 28 MI subtypes and 2347 omics factor-MI interactions as well as 1253 genes and 6 chromosomal alterations collected from 2647 research articles. The functions of MI associated genes and their interaction with drugs were analyzed. MIKB will be continuously updated and optimized to provide precision and comprehensive knowledge for the study of heterogeneous and personalized MI.

The "Common Soil Hypothesis" Revisited-Risk Factors for Type 2 Diabetes and Cardiovascular Disease

The prevalence and the incidence of type 2 diabetes (T2D), representing >90% of all cases of diabetes, are increasing rapidly worldwide. Identification of individuals at high risk of developing diabetes is of great importance, as early interventions might delay or even prevent full-blown disease. T2D is a complex disease caused by multiple genetic variants in interaction with lifestyle and environmental factors. Cardiovascular disease (CVD) is the major cause of morbidity and mortality. Detailed understanding of molecular mechanisms underlying in CVD events is still largely missing. Several risk factors are shared between T2D and CVD, including obesity, insulin resistance, dyslipidemia, and hyperglycemia. CVD can precede the development of T2D, and T2D is a major risk factor for CVD, suggesting that both conditions have common genetic and environmental antecedents and that they share “common soil”. We analyzed the relationship between the risk factors for T2D and CVD based on genetics and population-based studies with emphasis on Mendelian randomization studies.

Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.