Relevance of Multi-Omics Studies in Cardiovascular Diseases

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data

BACKGROUND

Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios.

METHODS

We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning.

RESULTS

Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively.

CONCLUSIONS

By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.

Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care

Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.

Cohort profile for the Tongji Cardiovascular Health Study: a prospective multiomics cohort study

PURPOSE

The Tongji Cardiovascular Health Study aimed to further explore the onset and progression mechanisms of cardiovascular disease (CVD) through a combination of traditional cohort studies and multiomics analysis, including genomics, metabolomics and metagenomics.

STUDY DESIGN AND PARTICIPANTS

This study included participants aged 20-70 years old from the Geriatric Health Management Centre of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology. After enrollment, each participant underwent a comprehensive series of traditional and novel cardiovascular risk factor assessments at baseline, including questionnaires, physical examinations, laboratory tests, cardiovascular health assessments and biological sample collection for subsequent multiomics analysis (whole genome sequencing, metabolomics study from blood samples and metagenomics study from stool samples). A biennial follow-up will be performed for 10 years to collect the information above and the outcome data.

FINDINGS TO DATE

A total of 2601 participants were recruited in this study (73.4% men), with a mean age of 51.5±11.5 years. The most common risk factor is overweight or obesity (54.8%), followed by hypertension (39.7%), hyperlipidaemia (32.4%), current smoking (23.9%) and diabetes (12.3%). Overall, 13.1% and 48.7% of men and women, respectively, did not have any of the CVD risk factors (hypertension, hyperlipidaemia, diabetes, cigarette smoking and overweight or obesity). Additionally, multiomics analyses of a subsample of the participants (n=938) are currently ongoing.

FUTURE PLANS

With the progress of the cohort follow-up work, it is expected to provide unique multidimensional and longitudinal data on cardiovascular health in China.

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.

Cardiac Development at a Single-Cell Resolution

Mammalian cardiac development is a complex, multistage process. Though traditional lineage tracing studies have characterized the broad trajectories of cardiac progenitors, the advent and rapid optimization of single-cell RNA sequencing methods have yielded an ever-expanding toolkit for characterizing heterogeneous cell populations in the developing heart. Importantly, they have allowed for a robust profiling of the spatiotemporal transcriptomic landscape of the human and mouse heart, revealing the diversity of cardiac cells-myocyte and non-myocyte-over the course of development. These studies have yielded insights into novel cardiac progenitor populations, chamber-specific developmental signatures, the gene regulatory networks governing cardiac development, and, thus, the etiologies of congenital heart diseases. Furthermore, single-cell RNA sequencing has allowed for the exquisite characterization of distinct cardiac populations such as the hard-to-capture cardiac conduction system and the intracardiac immune population. Therefore, single-cell profiling has also resulted in new insights into the regulation of cardiac regeneration and injury repair. Single-cell multiomics approaches combining transcriptomics, genomics, and epigenomics may uncover an even more comprehensive atlas of human cardiac biology. Single-cell analyses of the developing and adult mammalian heart offer an unprecedented look into the fundamental mechanisms of cardiac development and the complex diseases that may arise from it.

Methylomic, Proteomic, and Metabolomic Correlates of Traffic-Related Air Pollution in the Context of Cardiorespiratory Health: A Systematic Review, Pathway Analysis, and Network Analysis

A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead to cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease and highlight contemporary challenges and opportunities associated with such efforts.

Estrogen-Related Receptor Agonism Reverses Mitochondrial Dysfunction and Inflammation in the Aging Kidney

A gradual decline in renal function occurs even in healthy aging individuals. In addition to aging, per se, concurrent metabolic syndrome and hypertension, which are common in the aging population, can induce mitochondrial dysfunction and inflammation, which collectively contribute to age-related kidney dysfunction and disease. This study examined the role of the nuclear hormone receptors, the estrogen-related receptors (ERRs), in regulation of age-related mitochondrial dysfunction and inflammation. The ERRs were decreased in both aging human and mouse kidneys and were preserved in aging mice with lifelong caloric restriction (CR). A pan-ERR agonist, SLU-PP-332, was used to treat 21-month-old mice for 8 weeks. In addition, 21-month-old mice were treated with a stimulator of interferon genes (STING) inhibitor, C-176, for 3 weeks. Remarkably, similar to CR, an 8-week treatment with a pan-ERR agonist reversed the age-related increases in albuminuria, podocyte loss, mitochondrial dysfunction, and inflammatory cytokines, via the cyclic GMP-AMP synthase-STING and STAT3 signaling pathways. A 3-week treatment of 21-month-old mice with a STING inhibitor reversed the increases in inflammatory cytokines and the senescence marker, p21/cyclin dependent kinase inhibitor 1A (Cdkn1a), but also unexpectedly reversed the age-related decreases in PPARG coactivator (PGC)-1α, ERRα, mitochondrial complexes, and medium chain acyl coenzyme A dehydrogenase (MCAD) expression. These studies identified ERRs as CR mimetics and as important modulators of age-related mitochondrial dysfunction and inflammation. These findings highlight novel druggable pathways that can be further evaluated to prevent progression of age-related kidney disease.

Host-microbe tryptophan partitioning in cardiovascular diseases

The functional interdependencies between the molecular components of a biological process demand for a network medicine platform that integrates systems biology and network science, to explore the interactions among biological components in health and disease. Access to large-scale omics datasets (genomics, transcriptomics, proteomics, metabolomics, metagenomics, phenomics, etc.) has significantly advanced our opportunity along this direction. Studies utilizing these techniques have begun to provide us with a deeper understanding of how the interaction between the intestinal microbes and their host affects the cardiovascular system in health and disease. Within the framework of a multiomics network approach, we highlight here how tryptophan metabolism may orchestrate the host-microbes interaction in cardiovascular diseases and the implications for precision medicine and therapeutics, including nutritional interventions.

Multi-Omics of Familial Thoracic Aortic Aneurysm and Dissection: Calcium Transport Impairment Predisposes Aortas to Dissection

Several genetic defects, including a mutation in myosin heavy chain 11 (Myh11), are reported to cause familial thoracic aortic aneurysm and dissection (FTAAD). We recently showed that mice lacking K1256 of Myh11 developed aortic dissection when stimulated with angiotensin II, despite the absence of major pathological phenotypic abnormalities prior to stimulation. In this study, we used a comprehensive, data-driven, unbiased, multi-omics approach to find underlying changes in transcription and metabolism that predispose the aorta to dissection in mice harboring the Myh11 K1256del mutation. Pathway analysis of transcriptomes showed that genes involved in membrane transport were downregulated in homozygous mutant (Myh11ΔK/ΔK) aortas. Furthermore, expanding the analysis with metabolomics showed that two mechanisms that raise the cytosolic Ca2+ concentration-multiple calcium channel expression and ADP-ribose synthesis-were attenuated in Myh11ΔK/ΔK aortas. We suggest that the impairment of the Ca2+ influx attenuates aortic contraction and that suboptimal contraction predisposes the aorta to dissection.

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.

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.

Methylomic, proteomic, and metabolomic correlates of traffic-related air pollution: A systematic review, pathway analysis, and network analysis relating traffic-related air pollution to subclinical and clinical cardiorespiratory outcomes

A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease, and highlight contemporary challenges and opportunities associated with such efforts.

Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWAS) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWAS) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWAS) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWAS and TWAS can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p-values across all the genes, which suggests a high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

Mechanisms regulating vascular and lymphatic regeneration in the heart after myocardial infarction

Myocardial infarction, caused by a thrombus or coronary vascular occlusion, leads to irreversible ischaemic injury. Advances in early reperfusion strategies have significantly reduced short-term mortality after myocardial infarction. However, survivors have an increased risk of developing heart failure, which confers a high risk of death at 1 year. The capacity of the injured neonatal mammalian heart to regenerate has stimulated extensive research into whether recapitulation of developmental regeneration programmes may be beneficial in adult cardiovascular disease. Restoration of functional blood and lymphatic vascular networks in the infarct and border regions via neovascularisation and lymphangiogenesis, respectively, is a key requirement to facilitate myocardial regeneration. An improved understanding of the endogenous mechanisms regulating coronary vascular and lymphatic expansion and function in development and in adult patients after myocardial infarction may inform future therapeutic strategies and improve translation from pre-clinical studies. In this review, we explore the underpinning research and key findings in the field of cardiovascular regeneration, with a focus on neovascularisation and lymphangiogenesis, and discuss the outcomes of therapeutic strategies employed to date. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Diversity of cells and signals in the cardiovascular system

This white paper is the outcome of the seventh UC Davis Cardiovascular Research Symposium on Systems Approach to Understanding Cardiovascular Disease and Arrhythmia. This biannual meeting aims to bring together leading experts in subfields of cardiovascular biomedicine to focus on topics of importance to the field. The theme of the 2022 Symposium was 'Cell Diversity in the Cardiovascular System, cell-autonomous and cell-cell signalling'. Experts in the field contributed their experimental and mathematical modelling perspectives and discussed emerging questions, controversies, and challenges in examining cell and signal diversity, co-ordination and interrelationships involved in cardiovascular function. This paper originates from the topics of formal presentations and informal discussions from the Symposium, which aimed to develop a holistic view of how the multiple cell types in the cardiovascular system integrate to influence cardiovascular function, disease progression and therapeutic strategies. The first section describes the major cell types (e.g. cardiomyocytes, vascular smooth muscle and endothelial cells, fibroblasts, neurons, immune cells, etc.) and the signals involved in cardiovascular function. The second section emphasizes the complexity at the subcellular, cellular and system levels in the context of cardiovascular development, ageing and disease. Finally, the third section surveys the technological innovations that allow the interrogation of this diversity and advancing our understanding of the integrated cardiovascular function and dysfunction.

Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility

Atrial fibrillation (AF) and heart failure (HF) contribute to about 45% of all cardiovascular disease (CVD) deaths in the USA and around the globe. Due to the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. To improve the deciphering of CVD mechanisms, we need to deeply investigate well-known and identify novel genes that are responsible for CVD development. With the advancements in sequencing technologies, genomic data have been generated at an unprecedented pace to foster translational research. Correct application of bioinformatics using genomic data holds the potential to reveal the genetic underpinnings of various health conditions. It can help in the identification of causal variants for AF, HF, and other CVDs by moving beyond the one-gene one-disease model through the integration of common and rare variant association, the expressed genome, and characterization of comorbidities and phenotypic traits derived from the clinical information. In this study, we examined and discussed variable genomic approaches investigating genes associated with AF, HF, and other CVDs. We collected, reviewed, and compared high-quality scientific literature published between 2009 and 2022 and accessible through PubMed/NCBI. While selecting relevant literature, we mainly focused on identifying genomic approaches involving the integration of genomic data; analysis of common and rare genetic variants; metadata and phenotypic details; and multi-ethnic studies including individuals from ethnic minorities, and European, Asian, and American ancestries. We found 190 genes associated with AF and 26 genes linked to HF. Seven genes had implications in both AF and HF, which are SYNPO2L, TTN, MTSS1, SCN5A, PITX2, KLHL3, and AGAP5. We listed our conclusion, which include detailed information about genes and SNPs associated with AF and HF.

Phenomic Studies on Diseases: Potential and Challenges

The rapid development of such research field as multi-omics and artificial intelligence (AI) has made it possible to acquire and analyze the multi-dimensional big data of human phenomes. Increasing evidence has indicated that phenomics can provide a revolutionary strategy and approach for discovering new risk factors, diagnostic biomarkers and precision therapies of diseases, which holds profound advantages over conventional approaches for realizing precision medicine: first, the big data of patients' phenomes can provide remarkably richer information than that of the genomes; second, phenomic studies on diseases may expose the correlations among cross-scale and multi-dimensional phenomic parameters as well as the mechanisms underlying the correlations; and third, phenomics-based studies are big data-driven studies, which can significantly enhance the possibility and efficiency for generating novel discoveries. However, phenomic studies on human diseases are still in early developmental stage, which are facing multiple major challenges and tasks: first, there is significant deficiency in analytical and modeling approaches for analyzing the multi-dimensional data of human phenomes; second, it is crucial to establish universal standards for acquirement and management of phenomic data of patients; third, new methods and devices for acquirement of phenomic data of patients under clinical settings should be developed; fourth, it is of significance to establish the regulatory and ethical guidelines for phenomic studies on diseases; and fifth, it is important to develop effective international cooperation. It is expected that phenomic studies on diseases would profoundly and comprehensively enhance our capacity in prevention, diagnosis and treatment of diseases.

Integration of epigenetic regulatory mechanisms in heart failure

The number of "omics" approaches is continuously growing. Among others, epigenetics has appeared as an attractive area of investigation by the cardiovascular research community, notably considering its association with disease development. Complex diseases such as cardiovascular diseases have to be tackled using methods integrating different omics levels, so called "multi-omics" approaches. These approaches combine and co-analyze different levels of disease regulation. In this review, we present and discuss the role of epigenetic mechanisms in regulating gene expression and provide an integrated view of how these mechanisms are interlinked and regulate the development of cardiac disease, with a particular attention to heart failure. We focus on DNA, histone, and RNA modifications, and discuss the current methods and tools used for data integration and analysis. Enhancing the knowledge of these regulatory mechanisms may lead to novel therapeutic approaches and biomarkers for precision healthcare and improved clinical outcomes.

Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography

OBJECTIVE

Machine learning (ML) approaches have the potential to uncover regular patterns in multi-layered data. Here we applied self-organizing maps (SOMs) to detect such patterns with the aim to better predict in-stent restenosis (ISR) at surveillance angiography 6 to 8 months after percutaneous coronary intervention with stenting.

METHODS

In prospectively collected data from 10,004 patients receiving percutaneous coronary intervention (PCI) for 15,004 lesions, we applied SOMs to predict ISR angiographically 6-8 months after index procedure. SOM findings were compared with results of conventional uni- and multivariate analyses. The predictive value of both approaches was assessed after random splitting of patients into training and test sets (50:50).

RESULTS

Conventional multivariate analyses revealed 10, mostly known, predictors for restenosis after coronary stenting: balloon-to-vessel ratio, complex lesion morphology, diabetes mellitus, left main stenting, stent type (bare metal vs. first vs. second generation drug eluting stent), stent length, stenosis severity, vessel size reduction, and prior bypass surgery. The SOM approach identified all these and nine further predictors, including chronic vessel occlusion, lesion length, and prior PCI. Moreover, the SOM-based model performed well in predicting ISR (AUC under ROC: 0.728); however, there was no meaningful advantage in predicting ISR at surveillance angiography in comparison with the conventional multivariable model (0.726, p = 0.3).

CONCLUSIONS

The agnostic SOM-based approach identified-without clinical knowledge-even more contributors to restenosis risk. In fact, SOMs applied to a large prospectively sampled cohort identified several novel predictors of restenosis after PCI. However, as compared with established covariates, ML technologies did not improve identification of patients at high risk for restenosis after PCI in a clinically relevant fashion.

Mapping the lymphatic system across body scales and expertise domains: A report from the 2021 National Heart, Lung, and Blood Institute workshop at the Boston Lymphatic Symposium

Enhancing our understanding of lymphatic anatomy from the microscopic to the anatomical scale is essential to discern how the structure and function of the lymphatic system interacts with different tissues and organs within the body and contributes to health and disease. The knowledge of molecular aspects of the lymphatic network is fundamental to understand the mechanisms of disease progression and prevention. Recent advances in mapping components of the lymphatic system using state of the art single cell technologies, the identification of novel biomarkers, new clinical imaging efforts, and computational tools which attempt to identify connections between these diverse technologies hold the potential to catalyze new strategies to address lymphatic diseases such as lymphedema and lipedema. This manuscript summarizes current knowledge of the lymphatic system and identifies prevailing challenges and opportunities to advance the field of lymphatic research as discussed by the experts in the workshop.

Reducing the Threshold of Primary Prevention of Cardiovascular Disease to 10% Over 10 Years: The Implications of Altered Intensity "Statin" Therapy Guidance

Cardiovascular disease (CVD) is a significant noncommunicable disease associated with high long-term mortality. In addition to more effective secondary therapies, the primary prevention of CVD has developed markedly in the past several years. This study aims to investigate the evidence and impact of reducing the threshold for primary CVD risk management to 10% over 10 years with "statin" therapy. To conduct research a systematic review utilizing 5 electronic database searches was completed for studies, analyzing the clinical effect of reducing the threshold of CVD risk to 10% over 10 years for primary prevention with statin therapy. The study included six (6) trials. Statin therapy was allocated to 31,018 participants. The mean age was 61 years and the mean follow-up was 4.6 years. The mean relative reduction in total cholesterol was 19% (from an average of), low-density lipoprotein cholesterol was 28.3% (from mmol/L to mmol/L) and triglycerides were 14.8% (from mmol/L to mmol/L). High-density lipoprotein cholesterol was observed to increase by a mean of 3.3% (from mmol/L to mmol/L). When examining all-cause mortality, statin therapy was associated with a 12% relative risk reduction compared with control, where overall rates were reduced from 1.4% to 1. % There is a 30% risk reduction in general major coronary events (from to %). There is a 19% risk reduction in general major cerebrovascular events with the statin group. While there is undoubtedly statistical evidence that supports the observation of the effectiveness of statin therapy for primary prevention, there is a risk that many hundreds of patients need to be treated to avoid a single adverse clinical outcome.

Detectable Lipidomes and Metabolomes by Different Plasma Exosome Isolation Methods in Healthy Controls and Patients with Advanced Prostate and Lung Cancer

Circulating exosomes in the blood are promising tools for biomarker discovery in cancer. Due to their heterogeneity, different isolation methods may enrich distinct exosome cargos generating different omic profiles. In this study, we evaluated the effects of plasma exosome isolation methods on detectable multi-omic profiles in patients with non-small cell lung cancer (NSCLC), castration-resistant prostate cancer (CRPC), and healthy controls, and developed an algorithm to quantify exosome enrichment. Plasma exosomes were isolated from CRPC (n = 10), NSCLC (n = 14), and healthy controls (n = 10) using three different methods: size exclusion chromatography (SEC), lectin binding, and T-cell immunoglobulin domain and mucin domain-containing protein 4 (TIM4) binding. Molecular profiles were determined by mass spectrometry of extracted exosome fractions. Enrichment analysis of uniquely detected molecules was performed for each method with MetaboAnalyst. The exosome enrichment index (EEI) scores methods based on top differential molecules between patient groups. The lipidomic analysis detected 949 lipids using exosomes from SEC, followed by 246 from lectin binding and 226 from TIM4 binding. The detectable metabolites showed SEC identifying 191 while lectin binding and TIM4 binding identified 100 and 107, respectively. When comparing uniquely detected molecules, different methods showed preferential enrichment of different sets of molecules with SEC enriching the greatest diversity. Compared to controls, SEC identified 28 lipids showing significant difference in NSCLC, while only 1 metabolite in NSCLC and 5 metabolites in CRPC were considered statistically significant (FDR < 0.1). Neither lectin-binding- nor TIM4-binding-derived exosome lipids or metabolites demonstrated significant differences between patient groups. We observed the highest EEI from SEC in lipids (NSCLC: 871.33) which was also noted in metabolites. These results support that the size exclusion method of exosome extraction implemented by SBI captures more heterogeneous exosome populations. In contrast, lectin-binding and TIM4-binding methods bind surface glycans or phosphatidylserine moieties of the exosomes. Overall, these findings suggest that specific isolation methods select subpopulations which may significantly impact cancer biomarker discovery.

Cardiovascular disease and risk assessment in people living with HIV: Current practices and novel perspectives

Human immunodeficiency virus (HIV) infection represents a major cardiovascular risk factor, and the cumulative cardiovascular disease (CVD) burden among aging people living with HIV (PLWH) constitutes a leading cause of morbidity and mortality. To date, CVD risk assessment in PLWH remains challenging. Therefore, it is necessary to evaluate and stratify the cardiovascular risk in PLWH with appropriate screening and risk assessment tools and protocols to correctly identify which patients are at a higher risk for CVD and will benefit most from prevention measures and timely management. This review aims to accumulate the current evidence on the association between HIV infection and CVD, as well as the risk factors contributing to CVD in PLWH. Furthermore, considering the need for cardiovascular risk assessment in daily clinical practice, the purpose of this review is also to report the current practices and novel perspectives in cardiovascular risk assessment of PLWH and provide further insights into the development and implementation of appropriate CVD risk stratification and treatment strategies, particularly in countries with high HIV burden and limited resources.

An integrated signature of extracellular matrix proteins and a diastolic function imaging parameter predicts post-MI long-term outcomes

Background

Patients suffering from acute myocardial infarction (AMI) are at risk of secondary outcomes including major adverse cardiovascular events (MACE) and heart failure (HF). Comprehensive molecular phenotyping and cardiac imaging during the post-discharge time window may provide cues for risk stratification for the outcomes.

Materials and methods

In a prospective AMI cohort in New Zealand (N = 464), we measured plasma proteins and lipids 30 days after hospital discharge and inferred a unified partial correlation network with echocardiographic variables and established clinical biomarkers (creatinine, c-reactive protein, cardiac troponin I and natriuretic peptides). Using a network-based data integration approach (iOmicsPASS+), we identified predictive signatures of long-term secondary outcomes based on plasma protein, lipid, imaging markers and clinical biomarkers and assessed the prognostic potential in an independent cohort from Singapore (N = 190).

Results

The post-discharge levels of plasma proteins and lipids showed strong correlations within each molecular type, reflecting concerted homeostatic regulation after primary MI events. However, the two molecular types were largely independent with distinct correlation structures with established prognostic imaging parameters and clinical biomarkers. To deal with massively correlated predictive features, we used iOmicsPASS + to identify subnetwork signatures of 211 and 189 data features (nodes) predictive of MACE and HF events, respectively (160 overlapping). The predictive features were primarily imaging parameters, including left ventricular and atrial parameters, tissue Doppler parameters, and proteins involved in extracellular matrix (ECM) organization, cell differentiation, chemotaxis, and inflammation. The network signatures contained plasma protein pairs with area-under-the-curve (AUC) values up to 0.74 for HF prediction in the validation cohort, but the pair of NT-proBNP and fibulin-3 (EFEMP1) was the best predictor (AUC = 0.80). This suggests that there were a handful of plasma proteins with mechanistic and functional roles in predisposing patients to the secondary outcomes, although they may be weaker prognostic markers than natriuretic peptides individually. Among those, the diastolic function parameter (E/e' - an indicator of left ventricular filling pressure) and two ECM proteins, EFEMP1 and follistatin-like 3 (FSTL3) showed comparable performance to NT-proBNP and outperformed left ventricular measures as benchmark prognostic factors for post-MI HF.

Conclusion

Post-discharge levels of E/e', EFEMP1 and FSTL3 are promising complementary markers of secondary adverse outcomes in AMI patients.

Liquid Biopsy in Coronary Heart Disease

Cardiovascular disease (CVD) remains the major cause of morbidity and mortality globally. Accumulating evidence indicates that coronary heart disease (CHD) contributes to the majority of cardiovascular deaths. With the development of precision medicine, the diagnosis and treatment of coronary heart disease are becoming more refined and individualized. Molecular diagnosis technology and individualized treatment are gradually applied to the clinical diagnosis and treatment of CHD. It is great significance to seek sensitive biological indicators to help early diagnosis and improve prognosis of CHD. Liquid biopsy is a minimally invasive technique, which is widely used to detect molecular biomarkers of tumors without invasive biopsy. Compared with the field of oncology, it is not easy to get the diseased tissue in CVD, especially CHD. Therefore, the idea of "fluid biopsy" is very attractive, and its progress may provide new and useful noninvasive indicators for CHD. By analyzing circulating cells or their products in blood, saliva, and urine samples, we can investigate the molecular changes that occur in each patient at a specific point in time, thus continuously monitoring the evolution of CHD. For example, the assessment of cell-free DNA (cfDNA) levels may help predict the severity of acute myocardial infarction and diagnose heart transplant rejection. Moreover, the unmethylated FAM101A gene may specifically track the cfDNA derived from cardiomyocyte death, which provides a powerful diagnostic biomarker for apoptosis during ischemia. In addition, the changes of plasma circulating miR-92 levels may predict the occurrence of acute coronary syndrome (ACS) onset in patients with diabetes. Liquid biopsy can reflect the disease state through patients' body fluids and may noninvasively provide dynamic and rich molecular information related to CHD. It has great application potential in early warning and auxiliary diagnosis, real-time monitoring of curative effect, medication guidance and exploration of drug resistance mechanism, prognosis judgment, and risk classification of CHD. This chapter will review the latest progress of liquid biopsy in accurate diagnosis and treatment of CHD, meanwhile explore the application status and clinical prospect of liquid biopsy in CHD, in order to improve the importance of precision medicine and personalized treatment in this field.

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.

Flavanol Consumption in Healthy Men Preserves Integrity of Immunological-Endothelial Barrier Cell Functions: Nutri(epi)genomic Analysis

SCOPE

While cocoa flavanol (CF) consumption improves cardiovascular risk biomarkers, molecular mechanisms underlying their protective effects are not understood.

OBJECTIVE

To investigate nutri(epi)genomic effects of CF and identify regulatory networks potential mediating vascular health benefits.

METHODS AND RESULTS

Twenty healthy middle-aged men consume CF (bi-daily 450 mg) or control drinks for 1 month. Microarray analysis identifies 2235 differentially expressed genes (DEG) involved in processes regulating immune response, cell adhesion, or cytoskeleton organization. Distinct patterns of DEG correlate with CF-related changes in endothelial function, arterial stiffness, and blood pressure. DEG profile negatively correlates with expression profiles of cardiovascular disease patients. CF modulated DNA methylation profile of genes implicates in cell adhesion, actin cytoskeleton organization, or cell signaling. In silico docking analyses indicate that CF metabolites have the potential of binding to cell signaling proteins and transcription factors. Incubation of plasma obtained after CF consumption decrease monocyte to endothelial adhesion and dose-dependently increase nitric oxide-dependent chemotaxis of circulating angiogenic cells further validating the biological functions of CF metabolites.

CONCLUSION

In healthy humans, CF consumption may mediate vascular protective effects by modulating gene expression and DNA methylation towards a cardiovascular protective effect, in agreement with clinical results, by preserving integrity of immunological-endothelial barrier functions.

Whole-plant microbiome profiling reveals a novel geminivirus associated with soybean stay-green disease

Microbiota colonize every accessible plant tissue and play fundamental roles in plant growth and health. Soybean stay-green syndrome (SGS), a condition that causes delayed leaf senescence (stay-green), flat pods and abnormal seeds of soybean, has become the most serious disease of soybean in China. However, the direct cause of SGS is highly debated, and little is known about how SGS affect soybean microbiome dynamics, particularly the seed microbiome. We studied the bacterial, fungal, and viral communities associated with different soybean tissues with and without SGS using a multi-omics approach, and investigated the possible pathogenic agents associated with SGS and how SGS affects the assembly and functions of plant-associated microbiomes. We obtained a comprehensive view of the composition, function, loads, diversity, and dynamics of soybean microbiomes in the rhizosphere, root, stem, leaf, pod, and seed compartments, and discovered that soybean SGS was associated with dramatically increased microbial loads and dysbiosis of the bacterial microbiota in seeds. Furthermore, we identified a novel geminivirus that was strongly associated with soybean SGS, regardless of plant cultivar, sampling location, or harvest year. This whole-plant microbiome profiling of soybean provides the first demonstration of geminivirus infection associated with microbiota dysbiosis, which might represent a general microbiological symptom of plant diseases.

Integrated proteomic and metabolomic profile analyses of cardiac valves revealed molecular mechanisms and targets in calcific aortic valve disease

Background

This study aimed to define changes in the metabolic and protein profiles of patients with calcific aortic valve disease (CAVD).

Methods and results

We analyzed cardiac valve samples of patients with and without (control) CAVD (n = 24 per group) using untargeted metabolomics and tandem mass tag-based quantitative proteomics. Significantly different metabolites and proteins between the CAVD and control groups were screened; then, functional enrichment was analyzed. We analyzed co-expressed differential metabolites and proteins, and constructed a metabolite-protein-pathway network. The expression of key proteins was validated using western blotting. Differential analysis identified 229 metabolites in CAVD among which, 2-aminophenol, hydroxykynurenine, erythritol, carnosine, and choline were the top five. Proteomic analysis identified 549 differentially expressed proteins in CAVD, most of which were localized in the nuclear, cytoplasmic, extracellular, and plasma membranes. Levels of selenium binding protein 1 (SELENBP1) positively correlated with multiple metabolites. Adenosine triphosphate-binding cassette transporters, starch and sucrose metabolism, hypoxia-inducible factor 1 (HIF-1) signaling, and purine metabolism were key pathways in the network. Ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), calcium²⁺/calmodulin-dependent protein kinase II delta (CAMK2D), and ATP binding cassette subfamily a member 8 (ABCA8) were identified as hub proteins in the metabolite-protein-pathway network as they interacted with ADP, glucose 6-phosphate, choline, and other proteins. Western blotting confirmed that ENPP1 was upregulated, whereas ABCA8 and CAMK2D were downregulated in CAVD samples.

Conclusion

The metabolic and protein profiles of cardiac valves from patients with CAVD significantly changed. The present findings provide a holistic view of the molecular mechanisms underlying CAVD that may lead to the development of novel diagnostic biomarkers and therapeutic targets to treat CAVD.

Insights into functional connectivity in mammalian signal transduction pathways by pairwise comparison of protein interaction partners of critical signaling hubs

Growth factors and cytokines activate signal transduction pathways and regulate gene expression in eukaryotes. Intracellular domains of activated receptors recruit several protein kinases as well as transcription factors that serve as platforms or hubs for the assembly of multi-protein complexes. The signaling hubs involved in a related biologic function often share common interaction proteins and target genes. This functional connectivity suggests that a pairwise comparison of protein interaction partners of signaling hubs and network analysis of common partners and their expression analysis might lead to the identification of critical nodes in cellular signaling. A pairwise comparison of signaling hubs across several related pathways might reveal novel signaling modules. Analysis of protein interaction connectome by Venn (PIC-Venn) of transcription factors STAT1, STAT3, NFKB1, RELA, FOS, and JUN, and their common interaction network suggested that BRCA1 and TSC22D3 function as critical nodes in immune responses by connecting the signaling hubs into signaling modules. Transcriptional regulation of critical hubs may play a major role in the lung epithelial cells in response to SARS-CoV-2 and in COVID-19 patients. Mutations and differential expression levels of these critical nodes and modules in pathological conditions might deregulate signaling pathways and their target genes involved in inflammation. Biological connectivity emerges from the structural connectivity of interaction networks across several signaling hubs in related pathways. The main objectives of this study are to identify critical hubs, critical nodes, and modules involved in the signal transduction pathways of innate and adaptive immunity. Application of PIC-Venn to several signaling hubs might reveal novel nodes and modules that can be targeted by small regulatory molecules to simultaneously activate or inhibit cell signaling in health and disease.

Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial

Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, 681 (71%) had obstructive CAD. The algorithm dataset was 73 biochemical parameters and metabolic biomarkers as well as anthropometric and medical history variables. The performance of the XGBoost algorithm had an AUC value of 0.725 (95% CI: 0.691−0.759). Thus, a ML model incorporating clinical features in addition to certain metabolic features can estimate the pre-test likelihood of obstructive CAD.

N, Adigal S, Lukose J, Kartha VB, Chidangil S. Cardiovascular biomarkers in body fluids: progress and prospects in optical sensors

Cardiovascular diseases (CVD) are the major causative factors for high mortality and morbidity in developing and developed nations. The biomarker detection plays a crucial role in the early diagnosis of several non-infectious and life-threatening diseases like CVD and many cancers, which in turn will help in more successful therapy, reducing the mortality rate. Biomarkers have diagnostic, prognostic and therapeutic significances. The search for novel biomarkers using proteomics, bio-sensing, micro-fluidics, and spectroscopic techniques with good sensitivity and specificity for CVD is progressing rapidly at present, in addition to the use of gold standard biomarkers like troponin. This review is dealing with the current progress and prospects in biomarker research for the diagnosis of cardiovascular diseases.

Expert opinion.

Fast diagnosis of cardiovascular diseases (CVDs) can help to provide rapid medical intervention, which can affect the patient’s short and long-term health. Identification and detection of proper biomarkers for early diagnosis are crucial for successful therapy and prognosis of CVDs. The present review discusses the analysis of clinical samples such as whole blood, blood serum, and other body fluids using techniques like high-performance liquid chromatography-LASER/LED-induced fluorescence, Raman spectroscopy, mainly, optical methods, combined with nanotechnology and micro-fluidic technologies, to probe patterns of multiple markers (marker signatures) as compared to conventional techniques.

Advances in application of single-cell RNA sequencing in cardiovascular research

Single-cell RNA sequencing (scRNA-seq) provides high-resolution information on transcriptomic changes at the single-cell level, which is of great significance for distinguishing cell subtypes, identifying stem cell differentiation processes, and identifying targets for disease treatment. In recent years, emerging single-cell RNA sequencing technologies have been used to make breakthroughs regarding decoding developmental trajectories, phenotypic transitions, and cellular interactions in the cardiovascular system, providing new insights into cardiovascular disease. This paper reviews the technical processes of single-cell RNA sequencing and the latest progress based on single-cell RNA sequencing in the field of cardiovascular system research, compares single-cell RNA sequencing with other single-cell technologies, and summarizes the extended applications and advantages and disadvantages of single-cell RNA sequencing. Finally, the prospects for applying single-cell RNA sequencing in the field of cardiovascular research are discussed.

The Analysis of the Human Megakaryocyte and Platelet Coding Transcriptome in Healthy and Diseased Subjects

Platelets are generated and released into the bloodstream from their precursor cells, megakaryocytes that reside in the bone marrow. Though platelets have no nucleus or DNA, they contain a full transcriptome that, during platelet formation, is transported from the megakaryocyte to the platelet. It has been described that transcripts in platelets can be translated into proteins that influence platelet response. The platelet transcriptome is highly dynamic and has been extensively studied using microarrays and, more recently, RNA sequencing (RNA-seq) in relation to diverse conditions (inflammation, obesity, cancer, pathogens and others). In this review, we focus on bulk and single-cell RNA-seq studies that have aimed to characterize the coding transcriptome of healthy megakaryocytes and platelets in humans. It has been noted that bulk RNA-seq has limitations when studying in vitro-generated megakaryocyte cultures that are highly heterogeneous, while single-cell RNA-seq has not yet been applied to platelets due to their very limited RNA content. Next, we illustrate how these methods can be applied in the field of inherited platelet disorders for gene discovery and for unraveling novel disease mechanisms using RNA from platelets and megakaryocytes and rare disease bioinformatics. Next, future perspectives are discussed on how this field of coding transcriptomics can be integrated with other next-generation technologies to decipher unexplained inherited platelet disorders in a multiomics approach.

Galectin-4 levels in hospitalized versus non-hospitalized subjects with obesity: the Malmö Preventive Project

BACKGROUND

Obesity is strongly associated with the development of cardiovascular disease (CVD). However, the heterogenous nature of obesity in CVD-risk is still poorly understood. We aimed to explore novel CVD biomarkers and their possible association with presumed unhealthy obesity, defined as hospitalized subjects with obesity (HO).

METHODS

Ninety-two proteins associated with CVD were analyzed in 517 (mean age 67 ± 6 years; 33.7% women) individuals with obesity (BMI ≥30 kg/m²) from the Malmö Preventive Project cohort, using a proximity extension array technique from the Olink CVD III panel. Individuals with at least one recorded hospitalization for somatic disease prior to study baseline were defined as HO phenotypes. Associations between proteins and HO (n = 407) versus non-hospitalized subjects with obesity (NHO, n = 110), were analyzed using multivariable binary logistic regression, adjusted for traditional risk factors.

RESULTS

Of 92 analyzed unadjusted associations between biomarkers and HO, increased levels of two proteins were significant at a false discovery rate < 0.05: Galectin-4 (Gal-4) and insulin-like growth factor-binding protein 1 (IGFBP-1). When these two proteins were included in logistic regression analyses adjusted for age and sex, Gal-4 remained significant. Gal-4 was independently associated with the HO phenotype in multivariable logistic regression analysis (OR 1.72; CI95% 1.16-2.54). Post-hoc analysis revealed that this association was only present in the subpopulation with diabetes (OR 2.26; CI95% 1.25-4.07). However, an interaction analysis was performed, showing no significant interaction between Gal-4 and prevalent diabetes (p = 0.16).

CONCLUSIONS

In middle-aged and older individuals with obesity, increased Gal-4 levels were associated with a higher probability of HO. This association was only significant in subjects with diabetes only, further implying a role for Gal-4 in diabetes and its complications.

Skin microbiome reconstruction and lipid metabolism profile alteration reveal the treatment mechanism of Cryptotanshinone in the acne rat

BACKGROUND

Acne has become one of the most prevalent skin disorders, affecting mostly young people's physical and mental health globally. Cryptotanshinone (CPT) is a potential drug for acne, but its mechanism of acne treatment has not been thoroughly studied on the microbiota. Till date, only a few studies are directed to the impact of acne therapy on skin microbiota and lipid metabolites.

PURPOSE

The action mechanism of CPT treatment of acne was investigated by the strategy of microbiome integration with lipidomics.

METHODS

The 16Sr DNA sequencing was used to detect skin microbiota composition, and absolute quantitative lipidomics was utilized to identify lipid metabolites profiles levels. Four key proteins of the glycolysis pathway were detected with the immunochemistry method. Antibacterial analysis was used to evaluate CPT treatment of acne.

RESULTS

CPT significantly inhibited Staphylococcus epidermidis and Staphylococcus aureus. Combination of the skin microbiome and lipidomics analysis, 29 types of differentially expressed flora (DEFs) and 782 differentially expressed lipid metabolites (DELMs) were significantly altered, especially Staphylococcus, Corynebacterium, Ralstonia, Enhydrobacter, Burkholderia, and Streptococcus. Cer was mainly regulated by Staphylococcus and Corynebacterium, whereas TG and DG were mainly regulated by Ralstonia, Enhydrobacter, Burkholderia, and Streptococcus. The glycolysis pathway was significantly regulated by Staphylococcus on CPT treatment of acne. The energy metabolism, lipid metabolism, immune system, glycan biosynthesis, and metabolism could be reversed by CPT.

CONCLUSION

CPT might help acne rats rebuild their skin microbiota and alter lipid metabolism signatures. Furthermore, since skin microbes and skin lipid metabolites have a close correlation and are both regulated by CPT, the findings potentially provide a research foundation for the discovery of biomarkers of skin microbiome imbalance and targeted treatment of acne development mechanisms.

Gut Microbiota-Derived Metabolites and Cardiovascular Disease Risk: A Systematic Review of Prospective Cohort Studies

Gut microbiota-derived metabolites have recently attracted considerable attention due to their role in host-microbial crosstalk and their link with cardiovascular health. The MEDLINE-PubMed and Elsevier’s Scopus databases were searched up to June 2022 for studies evaluating the association of baseline circulating levels of trimethylamine N-oxide (TMAO), secondary bile acids, short-chain fatty acids (SCFAs), branched-chain amino acids (BCAAs), tryptophan and indole derivatives, with risk of cardiovascular disease (CVD). A total of twenty-one studies were included in the systematic review after evaluating 1210 non-duplicate records. There were nineteen of the twenty-one studies that were cohort studies and two studies had a nested case–control design. All of the included studies were of high quality according to the “Newcastle–Ottawa Scale”. TMAO was positively associated with adverse cardiovascular events and CVD/all-cause mortality in some, but not all of the included studies. Bile acids were associated with atrial fibrillation and CVD/all-cause mortality, but not with CVD. Positive associations were found between BCAAs and CVD, and between indole derivatives and major adverse cardiovascular events, while a negative association was reported between tryptophan and all-cause mortality. No studies examining the relationship between SCFAs and CVD risk were identified. Evidence from prospective studies included in the systematic review supports a role of microbial metabolites in CVD.

Alterations of Cardiac Protein Kinases in Cyclic Nucleotide-Dependent Signaling Pathways in Human Ischemic Heart Failure

Objectives

Impaired protein kinase signaling is a hallmark of ischemic heart disease (IHD). Inadequate understanding of the pathological mechanisms limits the development of therapeutic approaches. We aimed to identify the key cardiac kinases and signaling pathways in patients with IHD with an effort to discover potential therapeutic strategies.

Methods

Cardiac kinase activity in IHD left ventricle (LV) and the related signaling pathways were investigated by kinomics, transcriptomics, proteomics, and integrated multi-omics approach.

Results

Protein kinase A (PKA) and protein kinase G (PKG) ranked on top in the activity shift among the cardiac kinases. In the IHD LVs, PKA activity decreased markedly compared with that of controls (62% reduction, p = 0.0034), whereas PKG activity remained stable, although the amount of PKG protein increased remarkably (65%, p = 0.003). mRNA levels of adenylate cyclases (ADCY 1, 3, 5, 9) and cAMP-hydrolysing phosphodiesterases (PDE4A, PDE4D) decreased significantly, although no statistically significant alterations were observed in that of PKGs (PRKG1 and PRKG2) and guanylate cyclases (GUCYs). The gene expression of natriuretic peptide CNP decreased remarkably, whereas those of BNP, ANP, and neprilysin increased significantly in the IHD LVs. Proteomics analysis revealed a significant reduction in protein levels of “Energy metabolism” and “Muscle contraction” in the patients. Multi-omics integration highlighted intracellular signaling by second messengers as the top enriched Reactome pathway.

Conclusion

The deficiency in cAMP/PKA signaling pathway is strongly implicated in the pathogenesis of IHD. Natriuretic peptide CNP could be a potential therapeutic target for the modulation of cGMP/PKG signaling.

Single-Cell Proteomics: The Critical Role of Nanotechnology

In single-cell analysis, biological variability can be attributed to individual cells, their specific state, and the ability to respond to external stimuli, which are determined by protein abundance and their relative alterations. Mass spectrometry (MS)-based proteomics (e.g., SCoPE-MS and SCoPE2) can be used as a non-targeted method to detect molecules across hundreds of individual cells. To achieve high-throughput investigation, novel approaches in Single-Cell Proteomics (SCP) are needed to identify and quantify proteins as accurately as possible. Controlling sample preparation prior to LC-MS analysis is critical, as it influences sensitivity, robustness, and reproducibility. Several nanotechnological approaches have been developed for the removal of cellular debris, salts, and detergents, and to facilitate systematic sample processing at the nano- and microfluidic scale. In addition, nanotechnology has enabled high-throughput proteomics analysis, which have required the improvement of software tools, such as DART-ID or DO-MS, which are also fundamental for addressing key biological questions. Single-cell proteomics has many applications in nanomedicine and biomedical research, including advanced cancer immunotherapies or biomarker characterization, among others; and novel methods allow the quantification of more than a thousand proteins while analyzing hundreds of single cells.

Integrating serum proteomics and metabolomics to compare the common and distinct features between acute aggressive ischemic stroke (APIS) and acute non-aggressive ischemic stroke (ANPIS)

Understanding common and distinct pathophysiological features between acute progressive ischemic stroke (APIS) and acute non-progressive ischemic stroke (ANPIS) is a prerequisite to making clear the mechanism to determine the prognosis of acute ischemic stroke (AIS). Here, we recruited three independent sets of subjects, all of which included the APIS, ANPIS, and control groups. They were used for serum proteomic and metabolomic analyses, and validation of the critical pathophysiological processes and potential biomarkers of APIS, respectively. Results showed that there were both common and distinct metabolome and proteome between APIS and ANPIS. APIS and ANPIS shared basic processes of AIS in inflammation and oxidative stress response. Coagulation and lipid metabolism disorder, activation of the complement system, and inflammation may enhance with each other in the symptom worsening of APIS. The contents of serum amyloid A1 (SAA1) and S100 calcium-binding protein A9 (S100-A9) in the validation set confirmed the key pathophysiological processes indicated by omics data; they also jointly conferred a moderate value to distinguish APIS from ANPIS. Collectively, disturbance in coagulation and lipid metabolism, complement activation, and inflammation may be synergistically involved in symptom deterioration in APIS. SAA1 and S100-A9 serve as a potential biomarker panel to distinguish APIS from ANPIS. THE SIGNIFICANCE: In this study, we integrated serum proteomics and metabolomics to explore the similarities and differences in pathophysiological processes between APIS and ANPIS. The global metabolic networks have been constructed, and the crucial common pathophysiological processes and the key distinct pathophysiological features between APIS and ANPIS were investigated based on the differentially expressed proteins and metabolites (DEPs/DEMs). Furthermore, pivotal serum proteins (SAA1 and S100A9) were detected in a dependent set to validate the key pathophysiological characteristics, as well as to assess the possibility of them being used as a biomarker panel. Taken together, the multi-omics integration strategy used in this clinical study shows potential to comprehensively interpret and compare the pathophysiological processes of AIS in various conditions, as well as to screen a reliable new biomarker panel.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

MOVIS: A multi-omics software solution for multi-modal time-series clustering, embedding, and visualizing tasks

Thanks to recent advances in sequencing and computational technologies, many researchers with biological and/or medical backgrounds are now producing multiple data sets with an embedded temporal dimension. Multi-modalities enable researchers to explore and investigate different biological and physico-chemical processes with various technologies. Motivated to explore multi-omics data and time-series multi-omics specifically, the exploration process has been hindered by the separation introduced by each omics-type. To effectively explore such temporal data sets, discover anomalies, find patterns, and better understand their intricacies, expertise in computer science and bioinformatics is required. Here we present MOVIS, a modular time-series multi-omics exploration tool with a user-friendly web interface that facilitates the data exploration of such data. It brings into equal participation each time-series omic-type for analysis and visualization. As of the time of writing, two time-series multi-omics data sets have been integrated and successfully reproduced. The resulting visualizations are task-specific, reproducible, and publication-ready. MOVIS is built on open-source software and is easily extendable to accommodate different analytical tasks. An online version of MOVIS is available under https://movis.mathematik.uni-marburg.de/ and on Docker Hub (https://hub.docker.com/r/aanzel/movis).

Application of omics in hypertension and resistant hypertension

Hypertension is a major modifiable risk factor that affects the global health burden. Despite the availability of multiple antihypertensive drugs, blood pressure is often not optimally controlled. The prevalence of true resistant hypertension in treated hypertensive patients is ~2-20%, and these patients are at higher risk for adverse events and poor clinical outcomes. Therefore, an in-depth dissection of the pathophysiological mechanisms of hypertension and resistant hypertension is needed to identify more effective targets for regulating blood pressure. Omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and microbiomics, can accurately present the characteristics of organisms at varying molecular levels. Integrative omics can further reveal the network of interactions between molecular levels and provide a complete dynamic view of the organism. In this review, we describe the applications, progress, and challenges of omics technologies in hypertension. Specifically, we discuss the application of omics in resistant hypertension. We believe that omics approaches will produce a better understanding of the pathogenesis of hypertension and resistant hypertension and improve diagnostic and therapeutic strategies, thus increasing rates of blood pressure control and reducing the public health burden of hypertension.

Actionable absolute risk prediction of atherosclerotic cardiovascular disease based on the UK Biobank

Cardiovascular diseases (CVDs) are the primary cause of all death globally. Timely and accurate identification of people at risk of developing an atherosclerotic CVD and its sequelae is a central pillar of preventive cardiology. One widely used approach is risk prediction models; however, currently available models consider only a limited set of risk factors and outcomes, yield no actionable advice to individuals based on their holistic medical state and lifestyle, are often not interpretable, were built with small cohort sizes or are based on lifestyle data from the 1960s, e.g. the Framingham model. The risk of developing atherosclerotic CVDs is heavily lifestyle dependent, potentially making many occurrences preventable. Providing actionable and accurate risk prediction tools to the public could assist in atherosclerotic CVD prevention. Accordingly, we developed a benchmarking pipeline to find the best set of data preprocessing and algorithms to predict absolute 10-year atherosclerotic CVD risk. Based on the data of 464,547 UK Biobank participants without atherosclerotic CVD at baseline, we used a comprehensive set of 203 consolidated risk factors associated with atherosclerosis and its sequelae (e.g. heart failure). Our two best performing absolute atherosclerotic risk prediction models provided higher performance, (AUROC: 0.7573, 95% CI: 0.755-0.7595) and (AUROC: 0.7544, 95% CI: 0.7522-0.7567), than Framingham (AUROC: 0.680, 95% CI: 0.6775-0.6824) and QRisk3 (AUROC: 0.725, 95% CI: 0.7226-0.7273). Using a subset of 25 risk factors identified with feature selection, our reduced model achieves similar performance (AUROC 0.7415, 95% CI: 0.7392-0.7438) while being less complex. Further, it is interpretable, actionable and highly generalizable. The model could be incorporated into clinical practice and might allow continuous personalized predictions with automated intervention suggestions.

Network Analyses Reveal Negative Link Between Changes in Adipose Tissue GDF15 and BMI During Dietary-induced Weight Loss

CONTEXT

Adipose tissue (AT) transcriptome studies provide holistic pictures of adaptation to weight and related bioclinical settings changes.

OBJECTIVE

To implement AT gene expression profiling and investigate the link between changes in bioclinical parameters and AT gene expression during 3 steps of a 2-phase dietary intervention (DI).

METHODS

AT transcriptome profiling was obtained from sequencing 1051 samples, corresponding to 556 distinct individuals enrolled in a weight loss intervention (8-week low-calorie diet (LCD) at 800 kcal/day) followed with a 6-month ad libitum randomized DI. Transcriptome profiles obtained with QuantSeq sequencing were benchmarked against Illumina RNAseq. Reverse transcription quantitative polymerase chain reaction was used to further confirm associations. Cell specificity was assessed using freshly isolated cells and THP-1 cell line.

RESULTS

During LCD, 5 modules were found, of which 3 included at least 1 bioclinical variable. Change in body mass index (BMI) connected with changes in mRNA level of genes with inflammatory response signature. In this module, change in BMI was negatively associated with changes in expression of genes encoding secreted protein (GDF15, CCL3, and SPP1). Through all phases of the DI, change in GDF15 was connected to changes in SPP1, CCL3, LIPA and CD68. Further characterization showed that these genes were specific to macrophages (with LIPA, CD68 and GDF15 expressed in anti-inflammatory macrophages) and GDF15 also expressed in preadipocytes.

CONCLUSION

Network analyses identified a novel AT feature with GDF15 upregulated with calorie restriction induced weight loss, concomitantly to macrophage markers. In AT, GDF15 was expressed in preadipocytes and macrophages where it was a hallmark of anti-inflammatory cells.

Application of omics beyond the central dogma in coronary heart disease research: A bibliometric study and literature review

Despite remarkable progress in disease diagnosis and treatment, coronary heart disease (CHD) remains the number one leading cause of death worldwide. Many practical challenges still faced in clinical settings necessitates the pursuit of omics studies to identify alternative/orthogonal biomarkers, as well as to discover novel insights into disease mechanisms. Albeit relatively nascent as compared to the omics frontrunners (genomics, transcriptomics, and proteomics), omics beyond the central dogma (OBCD; e.g., metabolomics, lipidomics, glycomics, and metallomics) have undeniable contributions and prospects in CHD research. In this bibliometric study, we characterised the global trends in publication/citation outputs, collaborations, and research hotspots concerning OBCD-CHD, with a focus on the more prolific fields of metabolomics and lipidomics. As for glycomics and metallomics, there were insufficient publication records on their applications in CHD research for quantitative bibliometrics analysis. Thus, we reviewed their applications in health/disease research in general, discussed and justified their potential in CHD research, and suggested important/promising research avenues. By summarising evidence obtained both quantitatively and qualitatively, this study offers a first and comprehensive picture of OBCD applications in CHD, facilitating the establishment of future research directions.

Systems biology of angiogenesis signaling: Computational models and omics

Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases.

This article is categorized under:

Cardiovascular Diseases > Computational Models

Cancer > Computational Models

Interpretation of network-based integration from multi-omics longitudinal data

Multi-omics integration is key to fully understand complex biological processes in an holistic manner. Furthermore, multi-omics combined with new longitudinal experimental design can unreveal dynamic relationships between omics layers and identify key players or interactions in system development or complex phenotypes. However, integration methods have to address various experimental designs and do not guarantee interpretable biological results. The new challenge of multi-omics integration is to solve interpretation and unlock the hidden knowledge within the multi-omics data. In this paper, we go beyond integration and propose a generic approach to face the interpretation problem. From multi-omics longitudinal data, this approach builds and explores hybrid multi-omics networks composed of both inferred and known relationships within and between omics layers. With smart node labelling and propagation analysis, this approach predicts regulation mechanisms and multi-omics functional modules. We applied the method on 3 case studies with various multi-omics designs and identified new multi-layer interactions involved in key biological functions that could not be revealed with single omics analysis. Moreover, we highlighted interplay in the kinetics that could help identify novel biological mechanisms. This method is available as an R package netOmics to readily suit any application.

[Personalized medicine in non-communicable diseases: latest advances and future prospects]

Since the human genome was decoded more than 15 years ago, there has been a huge leap forward in the development of genomic and post-genomic technologies. Personalized medicine is engaged in implementing these technologies in clinical practice by developing new methods for risk assessment, diagnosis, and treatment of diseases taking into account individual features of the patient. Significant progress has been achieved in decoding genetic bases of chronic noninfectious diseases; new markers for the risk of complications and targets for effects of drugs are being searched for. This review highlights promising directions in the development of personalized medicine, the problems facing modern scientists, and possible ways to solve them.