Cardiovascular Consequences of Uremic Metabolites: an Overview of the Involved Signaling Pathways.

The crosstalk of the heart with distant organs such as the lung, liver, gut, and kidney has been intensively approached lately. The kidney is involved in (1) the production of systemic relevant products, such as renin, as part of the most essential vasoregulatory system of the human body, and (2) in the clearance of metabolites with systemic and organ effects. Metabolic residue accumulation during kidney dysfunction is known to determine cardiovascular pathologies such as endothelial activation/dysfunction, atherosclerosis, cardiomyocyte apoptosis, cardiac fibrosis, and vascular and valvular calcification, leading to hypertension, arrhythmias, myocardial infarction, and cardiomyopathies. However, this review offers an overview of the uremic metabolites and details their signaling pathways involved in cardiorenal syndrome and the development of heart failure. A holistic view of the metabolites, but more importantly, an exhaustive crosstalk of their known signaling pathways, is important for depicting new therapeutic strategies in the cardiovascular field.

Reversal of Pulmonary Hypertension in a Human-Like Model: Therapeutic Targeting of Endothelial DHFR.

BACKGROUND: Pulmonary hypertension (PH) is a progressive disorder characterized by remodeling of the pulmonary vasculature and elevated mean pulmonary arterial pressure, resulting in right heart failure. METHODS: Here, we show that direct targeting of the endothelium to uncouple eNOS (endothelial nitric oxide synthase) with DAHP (2,4-diamino 6-hydroxypyrimidine; an inhibitor of GTP cyclohydrolase 1, the rate-limiting synthetic enzyme for the critical eNOS cofactor tetrahydrobiopterin) induces human-like, time-dependent progression of PH phenotypes in mice. RESULTS: Critical phenotypic features include progressive elevation in mean pulmonary arterial pressure, right ventricular systolic blood pressure, and right ventricle (RV)/left ventricle plus septum (LV+S) weight ratio; extensive vascular remodeling of pulmonary arterioles with increased medial thickness/perivascular collagen deposition and increased expression of PCNA (proliferative cell nuclear antigen) and alpha-actin; markedly increased total and mitochondrial superoxide production, substantially reduced tetrahydrobiopterin and nitric oxide bioavailabilities; and formation of an array of human-like vascular lesions. Intriguingly, novel in-house generated endothelial-specific dihydrofolate reductase (DHFR) transgenic mice (tg-EC-DHFR) were completely protected from the pathophysiological and molecular features of PH upon DAHP treatment or hypoxia exposure. Furthermore, DHFR overexpression with a pCMV-DHFR plasmid transfection in mice after initiation of DAHP treatment completely reversed PH phenotypes. DHFR knockout mice spontaneously developed PH at baseline and had no additional deterioration in response to hypoxia, indicating an intrinsic role of DHFR deficiency in causing PH. RNA-sequencing experiments indicated great similarity in gene regulation profiles between the DAHP model and human patients with PH. CONCLUSIONS: Taken together, these results establish a novel human-like murine model of PH that has long been lacking in the field, which can be broadly used for future mechanistic and translational studies. These data also indicate that targeting endothelial DHFR deficiency represents a novel and robust therapeutic strategy for the treatment of PH.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Disease gene prioritization with quantum walks.

MOTIVATION: Disease gene prioritization methods assign scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. This scoring can be used to find new biologically relevant genes or proteins for many diseases. Although methods based on classical random walks have proven to yield competitive results, quantum walk methods have not been explored to this end. RESULTS: We propose a new algorithm for disease gene prioritization based on continuous-time quantum walks using the adjacency matrix of a protein-protein interaction (PPI) network. We demonstrate the success of our proposed quantum walk method by comparing it to several well-known gene prioritization methods on three disease sets, across seven different PPI networks. In order to compare these methods, we use cross-validation and examine the mean reciprocal ranks of recall and average precision values. We further validate our method by performing an enrichment analysis of the predicted genes for coronary artery disease. AVAILABILITY AND IMPLEMENTATION: The data and code for the methods can be accessed at https://github.com/markgolds/qdgp.

Decoding the Foodome: Molecular Networks Connecting Diet and Health.

Diet, a modifiable risk factor, plays a pivotal role in most diseases, from cardiovascular disease to type 2 diabetes mellitus, cancer, and obesity. However, our understanding of the mechanistic role of the chemical compounds found in food remains incomplete. In this review, we explore the "dark matter" of nutrition, going beyond the macro- and micronutrients documented by national databases to unveil the exceptional chemical diversity of food composition. We also discuss the need to explore the impact of each compound in the presence of associated chemicals and relevant food sources and describe the tools that will allow us to do so. Finally, we discuss the role of network medicine in understanding the mechanism of action of each food molecule. Overall, we illustrate the important role of network science and artificial intelligence in our ability to reveal nutrition's multifaceted role in health and disease.

Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations.

COVID-19 is an infectious disease caused by SARS-CoV-2 leading to the ongoing global pandemic. Infected patients developed a range of respiratory symptoms, including respiratory failure, as well as other extrapulmonary complications. Multiple comorbidities, including hypertension, diabetes, cardiovascular diseases, and chronic kidney diseases, are associated with the severity and increased mortality of COVID-19. SARS-CoV-2 infection also causes a range of cardiovascular complications, including myocarditis, myocardial injury, heart failure, arrhythmias, acute coronary syndrome, and venous thromboembolism. Although a variety of methods have been developed and many clinical trials have been launched for drug repositioning for COVID-19, treatments that consider cardiovascular manifestations and cardiovascular disease comorbidities specifically are limited. In this review, we summarize recent advances in drug repositioning for COVID-19, including experimental drug repositioning, high-throughput drug screening, omics data-based, and network medicine-based computational drug repositioning, with particular attention on those drug treatments that consider cardiovascular manifestations of COVID-19. We discuss prospective opportunities and potential methods for repurposing drugs to treat cardiovascular complications of COVID-19.

Transcript and protein signatures derived from shared molecular interactions across cancers are associated with mortality.

BACKGROUND: Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell-cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality. METHODS: CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers. These CCIs were used to construct a shared multi-cellular tumor model (shared-MCTM) representing common CCIs across cancers. A gene signature was identified from the shared-MCTM and tested on the mRNA and protein level in two large independent cohorts: The Cancer Genome Atlas (TCGA, 9185 tumor samples and 727 controls across 22 cancers) and UK biobank (UKBB, 10,384 cancer patients and 5063 controls with proteomics data across 17 cancers). Cox proportional hazards models were used to evaluate the association of the signature with 10-year all-cause mortality, including sex-specific analysis. RESULTS: A shared-MCTM was derived from five individual cancers. A shared gene signature was extracted from this shared-MCTM and the most prominent regulatory cell type, matrix cancer-associated fibroblast (mCAF). The signature exhibited significant expression changes in multiple cancers compared to controls at both mRNA and protein levels in two independent cohorts. Importantly, it was significantly associated with mortality in cancer patients in both cohorts. The highest hazard ratios were observed for brain cancer in TCGA (HR [95%CI] = 6.90[4.64-10.25]) and ovarian cancer in UKBB (5.53[2.08-8.80]). Sex-specific analysis revealed distinct risks, with a higher mortality risk associated with the protein signature score in males (2.41[1.97-2.96]) compared to females (1.84[1.44-2.37]). CONCLUSION: We identified a gene signature from a comprehensive shared-MCTM representing common CCIs across different cancers and revealed the regulatory role of mCAF in the tumor microenvironment. The pathogenic relevance of the gene signature was supported by differential expression and association with mortality on both mRNA and protein levels in two independent cohorts.

Broadcasters, receivers, functional groups of metabolites, and the link to heart failure by revealing metabolomic network connectivity.

BACKGROUND AND OBJECTIVE: Blood-based small molecule metabolites offer easy accessibility and hold significant potential for insights into health processes, the impact of lifestyle, and genetic variation on disease, enabling precise risk prevention. In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. METHODS: We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. RESULTS: We identified metabolites associated with higher and lower risk of HF incidence, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. These associations were not confounded by the other metabolites due to uncovering the connectivity among metabolites and adjusting each association for the confounding metabolites. Examples of our findings include the direct influence of asparagine on glycine, both of which were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids, which are not synthesized in the human body and are obtained directly from the diet. CONCLUSION: Metabolites may play a critical role in linking genetic background and lifestyle factors to HF incidence. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates studying complex conditions like HF.

Molecular interaction networks and drug development: Novel approach to drug target identification and drug repositioning.

Conventional drug discovery requires identifying a protein target believed to be important for disease mechanism and screening compounds for those that beneficially alter the target's function. While this approach has been an effective one for decades, recent data suggest that its continued success is limited largely owing to the highly prevalent irreducibility of biologically complex systems that govern disease phenotype to a single primary disease driver. Network medicine, a new discipline that applies network science and systems biology to the analysis of complex biological systems and disease, offers a novel approach to overcoming these limitations of conventional drug discovery. Using the comprehensive protein-protein interaction network (interactome) as the template through which subnetworks that govern specific diseases are identified, potential disease drivers are unveiled and the effect of novel or repurposed drugs, used alone or in combination, is studied. This approach to drug discovery offers new and exciting unbiased possibilities for advancing our knowledge of disease mechanisms and precision therapeutics.

L-2-Hydroxyglutarate Protects Against Cardiac Injury via Metabolic Remodeling.

BACKGROUND: L-2-hydroxyglutarate (L2HG) couples mitochondrial and cytoplasmic energy metabolism to support cellular redox homeostasis. Under oxygen-limiting conditions, mammalian cells generate L2HG to counteract the adverse effects of reductive stress induced by hypoxia. Very little is known, however, about whether and how L2HG provides tissue protection from redox stress during low-flow ischemia (LFI) and ischemia-reperfusion injury. We examined the cardioprotective effects of L2HG accumulation against LFI and ischemia-reperfusion injury and its underlying mechanism using genetic mouse models. METHODS AND RESULTS: L2HG accumulation was induced by homozygous (L2HGDH [L-2-hydroxyglutarate dehydrogenase]-/-) or heterozygous (L2HGDH+/-) deletion of the L2HGDH gene in mice. Hearts isolated from these mice and their wild-type littermates (L2HGDH+/+) were subjected to baseline perfusion and 90-minute LFI or 30-minute no-flow ischemia followed by 60- or 120-minute reperfusion. Using [13C]- and [31P]-NMR (nuclear magnetic resonance) spectroscopy, high-performance liquid chromatography, reverse transcription quantitative reverse transcription polymerase chain reaction, ELISA, triphenyltetrazolium staining, colorimetric/fluorometric spectroscopy, and echocardiography, we found that L2HGDH deletion induces L2HG accumulation at baseline and under stress conditions with significant functional consequences. In response to LFI or ischemia-reperfusion, L2HG accumulation shifts glucose flux from glycolysis towards the pentose phosphate pathway. These key metabolic changes were accompanied by enhanced cellular reducing potential, increased elimination of reactive oxygen species, attenuated oxidative injury and myocardial infarction, preserved cellular energy state, and improved cardiac function in both L2HGDH-/- and L2HGDH+/- hearts compared with L2HGDH+/+ hearts under ischemic stress conditions. CONCLUSION: L2HGDH deletion-induced L2HG accumulation protects against myocardial injury during LFI and ischemia-reperfusion through a metabolic shift of glucose flux from glycolysis towards the pentose phosphate pathway. L2HG offers a novel mechanism for eliminating reactive oxygen species from myocardial tissue, mitigating redox stress, reducing myocardial infarct size, and preserving high-energy phosphates and cardiac function. Targeting L2HG levels through L2HGDH activity may serve as a new therapeutic strategy for cardiovascular diseases related to oxidative injury.

Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping.

The complex landscape of cardiovascular diseases encompasses a wide range of related pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous phenotypes. This variety of manifestations poses significant challenges in the development of treatment strategies. The increasing availability of precise phenotypic and multiomics data of cardiovascular disease patient populations has spurred the development of a variety of computational disease subtyping techniques to identify distinct subgroups with unique underlying pathogeneses. In this review, we outline the essential components of computational approaches to select, integrate, and cluster omics and clinical data in the context of cardiovascular disease research. We delve into the challenges faced during different stages of the analysis, including feature selection and extraction, data integration, and clustering algorithms. Next, we highlight representative applications of subtyping pipelines in heart failure and coronary artery disease. Finally, we discuss the current challenges and future directions in the development of robust subtyping approaches that can be implemented in clinical workflows, ultimately contributing to the ongoing evolution of precision medicine in health care.

Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases.

Cardiovascular diseases (CVD) are the leading cause of death worldwide and display complex phenotypic heterogeneity caused by many convergent processes, including interactions between genetic variation and environmental factors. Despite the identification of a large number of associated genes and genetic loci, the precise mechanisms by which these genes systematically influence the phenotypic heterogeneity of CVD are not well understood. In addition to DNA sequence, understanding the molecular mechanisms of CVD requires data from other omics levels, including the epigenome, the transcriptome, the proteome, as well as the metabolome. Recent advances in multiomics technologies have opened new precision medicine opportunities beyond genomics that can guide precise diagnosis and personalized treatment. At the same time, network medicine has emerged as an interdisciplinary field that integrates systems biology and network science to focus on the interactions among biological components in health and disease, providing an unbiased framework through which to integrate systematically these multiomics data. In this review, we briefly present such multiomics technologies, including bulk omics and single-cell omics technologies, and discuss how they can contribute to precision medicine. We then highlight network medicine-based integration of multiomics data for precision medicine and therapeutics in CVD. We also include a discussion of current challenges, potential limitations, and future directions in the study of CVD using multiomics network medicine approaches.

GAPDH inhibits intracellular pathways during starvation for cellular energy homeostasis.

Starvation poses a fundamental challenge to cell survival. Whereas the role of autophagy in promoting energy homeostasis in this setting has been extensively characterized1, other mechanisms are less well understood. Here we reveal that glyceraldehyde 3-phosphate dehydrogenase (GAPDH) inhibits coat protein I (COPI) transport by targeting a GTPase-activating protein (GAP) towards ADP-ribosylation factor 1 (ARF1) to suppress COPI vesicle fission. GAPDH inhibits multiple other transport pathways, also by targeting ARF GAPs. Further characterization suggests that this broad inhibition is activated by the cell during starvation to reduce energy consumption. These findings reveal a remarkable level of coordination among the intracellular transport pathways that underlies a critical mechanism of cellular energy homeostasis.

Network medicine framework for identifying drug-repurposing opportunities for COVID-19.

The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.

Pursuing functional biomarkers in complex disease: Focus on pulmonary arterial hypertension.

A major gap in diagnosis, classification, risk stratification, and prediction of therapeutic response exists in pulmonary arterial hypertension (PAH), driven in part by a lack of functional biomarkers that are also disease-specific. In this regard, leveraging big data-omics analyses using innovative approaches that integrate network medicine and machine learning correlated with clinically useful indices or risk stratification scores is an approach well-positioned to advance PAH precision medicine. For example, machine learning applied to a panel of 48 cytokines, chemokines, and growth factors could prognosticate PAH patients with immune-dominant subphenotypes at elevated or low-risk for mortality. Here, we discuss strengths and weaknesses of the most current studies evaluating omics-derived biomarkers in PAH. Progress in this field is offset by studies with small sample size, pervasive limitations in bioinformatics, and lack of standardized methods for data processing and interpretation. Future success in this field, in turn, is likely to hinge on mechanistic validation of data outputs in order to couple functional biomarker data with target-specific therapeutics in clinical practice.

Immunometabolic Endothelial Phenotypes: Integrating Inflammation and Glucose Metabolism.

[Figure: see text].

COVID-19 and Cardiovascular Disease: From Bench to Bedside.

A pandemic of historic impact, coronavirus disease 2019 (COVID-19) has potential consequences on the cardiovascular health of millions of people who survive infection worldwide. Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), the etiologic agent of COVID-19, can infect the heart, vascular tissues, and circulating cells through ACE2 (angiotensin-converting enzyme 2), the host cell receptor for the viral spike protein. Acute cardiac injury is a common extrapulmonary manifestation of COVID-19 with potential chronic consequences. This update provides a review of the clinical manifestations of cardiovascular involvement, potential direct SARS-CoV-2 and indirect immune response mechanisms impacting the cardiovascular system, and implications for the management of patients after recovery from acute COVID-19 infection.

Expression of CD70 Modulates Nitric Oxide and Redox Status in Endothelial Cells.

BACKGROUND: Endothelial dysfunction is a critical component in the pathogenesis of cardiovascular diseases and is closely associated with nitric oxide (NO) levels and oxidative stress. Here, we report on novel findings linking endothelial expression of CD70 (also known as CD27 ligand) with alterations in NO and reactive oxygen species. METHODS: CD70 expression was genetically manipulated in human aortic and pulmonary artery endothelial cells. Intracellular NO and hydrogen peroxide (H2O2) were measured using genetically encoded biosensors, and cellular phenotypes were assessed. RESULTS: An unbiased phenome-wide association study demonstrated that polymorphisms in CD70 associate with vascular phenotypes. Endothelial cells treated with CD70-directed short-interfering RNA demonstrated impaired wound closure, decreased agonist-stimulated NO levels, and reduced eNOS (endothelial nitric oxide synthase) protein. These changes were accompanied by reduced NO bioactivity, increased 3-nitrotyrosine levels, and a decrease in the eNOS binding partner heat shock protein 90. Following treatment with the thioredoxin inhibitor auranofin or with agonist histamine, intracellular H2O2 levels increased up to 80% in the cytosol, plasmalemmal caveolae, and mitochondria. There was increased expression of NADPH oxidase 1 complex and gp91phox; expression of copper/zinc and manganese superoxide dismutases was also elevated. CD70 knockdown reduced levels of the H2O2 scavenger catalase; by contrast, glutathione peroxidase 1 expression and activity were increased. CD70 overexpression enhanced endothelial wound closure, increased NO levels, and attenuated the reduction in eNOS mRNA induced by TNFα. CONCLUSIONS: Taken together, these data establish CD70 as a novel regulatory protein in endothelial NO and reactive oxygen species homeostasis, with implications for human vascular disease.

Interferon-γ Impairs Human Coronary Artery Endothelial Glucose Metabolism by Tryptophan Catabolism and Activates Fatty Acid Oxidation.

BACKGROUND: Endothelial cells depend on glycolysis for much of their energy production. Impaired endothelial glycolysis has been associated with various vascular pathobiologies, including impaired angiogenesis and atherogenesis. IFN-γ (interferon-γ)-producing CD4+ and CD8+ T lymphocytes have been identified as the predominant pathological cell subsets in human atherosclerotic plaques. Although the immunologic consequences of these cells have been extensively evaluated, their IFN-γ-mediated metabolic effects on endothelial cells remain unknown. The purpose of this study was to determine the metabolic consequences of the T-lymphocyte cytokine, IFN-γ, on human coronary artery endothelial cells. METHODS: The metabolic effects of IFN-γ on primary human coronary artery endothelial cells were assessed by unbiased transcriptomic and metabolomic analyses combined with real-time extracellular flux analyses and molecular mechanistic studies. Cellular phenotypic correlations were made by measuring altered endothelial intracellular cGMP content, wound-healing capacity, and adhesion molecule expression. RESULTS: IFN-γ exposure inhibited basal glycolysis of quiescent primary human coronary artery endothelial cells by 20% through the global transcriptional suppression of glycolytic enzymes resulting from decreased basal HIF1α (hypoxia-inducible factor 1α) nuclear availability in normoxia. The decrease in HIF1α activity was a consequence of IFN-γ-induced tryptophan catabolism resulting in ARNT (aryl hydrocarbon receptor nuclear translocator)/HIF1ß sequestration by the kynurenine-activated AHR (aryl hydrocarbon receptor). In addition, IFN-γ resulted in a 23% depletion of intracellular nicotinamide adenine dinucleotide in human coronary artery endothelial cells. This altered glucose metabolism was met with concomitant activation of fatty acid oxidation, which augmented its contribution to intracellular ATP balance by >20%. These metabolic derangements were associated with adverse endothelial phenotypic changes, including decreased basal intracellular cGMP, impaired endothelial migration, and a switch to a proinflammatory state. CONCLUSIONS: IFN-γ impairs endothelial glucose metabolism by altered tryptophan catabolism destabilizing HIF1, depletes nicotinamide adenine dinucleotide, and results in a metabolic shift toward increased fatty acid oxidation. This work suggests a novel mechanistic basis for pathological T lymphocyte-endothelial interactions in atherosclerosis mediated by IFN-γ, linking endothelial glucose, tryptophan, and fatty acid metabolism with the nicotinamide adenine dinucleotide balance and ATP generation and their adverse endothelial functional consequences.