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.

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.

Metabolic Responses to Redox Stress in Vascular Cells.

Significance: Redox stress underlies numerous vascular disease mechanisms. Metabolic adaptability is essential for vascular cells to preserve energy and redox homeostasis. Recent Advances: Single-cell technologies and multiomic studies demonstrate significant metabolic heterogeneity among vascular cells in health and disease. Increasing evidence shows that reductive or oxidative stress can induce metabolic reprogramming of vascular cells. A recent example is intracellular L-2-hydroxyglutarate accumulation in response to hypoxic reductive stress, which attenuates the glucose flux through glycolysis and mitochondrial respiration in pulmonary vascular cells and provides protection against further reductive stress. Critical Issues: Regulation of cellular redox homeostasis is highly compartmentalized and complex. Vascular cells rely on multiple metabolic pathways, but the precise connectivity among these pathways and their regulatory mechanisms is only partially defined. There is also a critical need to understand better the cross-regulatory mechanisms between the redox system and metabolic pathways as perturbations in either systems or their cross talk can be detrimental. Future Directions: Future studies are needed to define further how multiple metabolic pathways are wired in vascular cells individually and as a network of closely intertwined processes given that a perturbation in one metabolic compartment often affects others. There also needs to be a comprehensive understanding of how different types of redox perturbations are sensed by and regulate different cellular metabolic pathways with specific attention to subcellular compartmentalization. Lastly, integration of dynamic changes occurring in multiple metabolic pathways and their cross talk with the redox system is an important goal in this multiomics era.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1,453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis.

Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. This study introduces DREAMER, a novel network-based method for exploring the mechanisms underlying ADRs and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes. Applying DREAMER to 649 ADRs, we identified proteins associated with the mechanism of action for 67 ADRs across multiple organ systems. In particular, DREAMER highlights the importance of GABAergic signaling and proteins of the coagulation pathways for personality disorders and intracranial hemorrhage, respectively. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes.

Single Cell Atlas: a single-cell multi-omics human cell encyclopedia.

Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.

Comprehensive multiscale analysis of lactate metabolic dynamics in vitro and in vivo using highly responsive biosensors.

As a key glycolytic metabolite, lactate has a central role in diverse physiological and pathological processes. However, comprehensive multiscale analysis of lactate metabolic dynamics in vitro and in vivo has remained an unsolved problem until now owing to the lack of a high-performance tool. We recently developed a series of genetically encoded fluorescent sensors for lactate, named FiLa, which illuminate lactate metabolism in cells, subcellular organelles, animals, and human serum and urine. In this protocol, we first describe the FiLa sensor-based strategies for real-time subcellular bioenergetic flux analysis by profiling the lactate metabolic response to different nutritional and pharmacological conditions, which provides a systematic-level view of cellular metabolic function at the subcellular scale for the first time. We also report detailed procedures for imaging lactate dynamics in live mice through a cell microcapsule system or recombinant adeno-associated virus and for the rapid and simple assay of lactate in human body fluids. This comprehensive multiscale metabolic analysis strategy may also be applied to other metabolite biosensors using various analytic platforms, further expanding its usability. The protocol is suited for users with expertise in biochemistry, molecular biology and cell biology. Typically, the preparation of FiLa-expressing cells or mice takes 2 days to 4 weeks, and live-cell and in vivo imaging can be performed within 1-2 hours. For the FiLa-based assay of body fluids, the whole measuring procedure generally takes ~1 min for one sample in a manual assay or ~3 min for 96 samples in an automatic microplate assay.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.

BACKGROUND: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. METHODS: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. RESULTS: scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. CONCLUSIONS: We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Branched chain α-ketoacids aerobically activate HIF1α signaling in vascular cells.

Hypoxia-inducible factor 1α (HIF1α) is a master regulator of numerous biological processes under low oxygen tensions. Yet, the mechanisms and biological consequences of aerobic HIF1α activation by intrinsic factors, particularly in primary cells remain elusive. Here, we show that HIF1α signaling is activated in several human primary vascular cells under ambient oxygen tensions, and in vascular smooth muscle cells (VSMCs) of normal human lung tissue, which contributed to a relative resistance to further enhancement of glycolytic activity in hypoxia. Mechanistically, aerobic HIFα activation is mediated by paracrine secretion of three branched chain α-ketoacids (BCKAs), which suppress prolyl hydroxylase domain-containing protein 2 (PHD2) activity via direct inhibition and via lactate dehydrogenase A (LDHA)-mediated generation of L-2-hydroxyglutarate (L2HG). Metabolic dysfunction induced by BCKAs was observed in the lungs of rats with pulmonary arterial hypertension (PAH) and in pulmonary artery smooth muscle cells (PASMCs) from idiopathic PAH patients. BCKA supplementation stimulated glycolytic activity and promoted a phenotypic switch to the synthetic phenotype in PASMCs of normal and PAH subjects. In summary, we identify BCKAs as novel signaling metabolites that activate HIF1α signaling in normoxia and that the BCKA-HIF1α pathway modulates VSMC function and may be relevant to pulmonary vascular pathobiology.

Harrison's principles of internal medicine / Harrison's principles of internal medicine

The seventeenth edition of Harrison’s has a full-color format that draws from and extends the excellent appearance of the sixteenth edition to make the content more accessible and pleasant to read. The placement of color illustrations within the chapters rather than in the separate atlas was very favorably received by our sixteenth edition readers and has been continued in the current edition. Many changes to the design of this edition have been made in order to speed the reader’s navigation through the textual and visual materials. For example, tables have been shaded for ease of reading, citations in tables and illustrations are now more instantly notable and in color, and our Treatment sections in each chapter have been redesigned to allow even faster access. The new global icons call greater attention to key epidemiologic and clinical differences in the practice of medicine throughout the world. The seventeenth edition has been enriched by the addition of a new editor, Joseph Loscalzo, MD, PhD, who joined with our most senior editor Eugene Braunwald, MD, in contributing to and/or editing of chapters in the Parts on Disorders of the Cardiovascular System, Disorders of the Respiratory System, and Disorders of the Kidney and Urinary Tract. The addition of Dr. Loscalzo provides a smooth editorial transition in preparation for the upcoming retirement from Harrison’s of Dr. Braunwald who has served as an esteemed editor for 12 editions