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Hypoxia-inducible factor 1α (HIF1α) is a master regulator of biological processes in hypoxia. Yet, the mechanisms and biological consequences of aerobic HIF1α activation by intrinsic factors, particularly in normal (primary) cells, remain elusive. Here we show that HIF1α signalling is activated in several human primary vascular cells in normoxia and in vascular smooth muscle cells of normal human lungs. Mechanistically, aerobic HIF1α activation is mediated by paracrine secretion of three branched-chain α-ketoacids (BCKAs), which suppress PHD2 activity via direct inhibition and via LDHA-mediated generation of L-2-hydroxyglutarate. BCKA-mediated HIF1α signalling activation stimulated glycolytic activity and governed a phenotypic switch of pulmonary artery smooth muscle cells, which correlated with BCKA metabolic dysregulation and pathophenotypic changes in pulmonary arterial hypertension patients and male rat models. We thus identify BCKAs as previously unrecognized signalling metabolites that aerobically activate HIF1α and that the BCKA-HIF1α pathway modulates vascular smooth muscle cell function, an effect that may be relevant to pulmonary vascular pathobiology., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Arginine is one of the most metabolically versatile amino acids and plays pivotal roles in diverse biological and pathological processes; however, sensitive tracking of arginine dynamics in situ remains technically challenging. Here, we engineer high-performance fluorescent biosensors, denoted sensitive to arginine (STAR), to illuminate arginine metabolism in cells, mice, and clinical samples. Utilizing STAR, we demonstrate the effects of different amino acids in regulating intra- and extracellular arginine levels. STAR enabled live-cell monitoring of arginine fluctuations during macrophage activation, phagocytosis, efferocytosis, and senescence and revealed cellular senescence depending on arginine availability. Moreover, a simple and fast assay based on STAR revealed that serum arginine levels tended to increase with age, and the elevated serum arginine level is a potential indicator for discriminating the progression and severity of vitiligo. Collectively, our study provides important insights into the metabolic and functional roles of arginine, as well as its potential in diagnostic and therapeutic applications., Competing Interests: Declaration of interests Y.Y., Y. Zhao, R.L., T.L., Y.L., K.J., L.Z., Y.S., Y. Zou, and X. Li have filed three related patents through the East China University of Science and Technology., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Endothelial dysfunction is an underlying mechanism for the development of pulmonary arterial hypertension (PAH). Vascular endothelial growth factor (VEGF) and stromal cell-derived factor-1α (SDF) may help repair the dysfunctional endothelium and provide treatment for PAH. To examine this possibility, nanoparticles carrying human recombinant VEGF and SDF (VEGFNP and SDFNP) were aerosolized into the lungs of nude rats at Day 14 after monocrotaline (MCT) injection and analyses were performed at Day 28. The data show that the VEGFNP/SDFNP delivery led to a lower pulmonary arterial pressure and prevented right ventricular hypertrophy in the MCT rats: the right ventricular systolic pressure of the control, MCT, and MCT + VEGFNP/SDFNP treatment groups were 29±2, 70±9, and 44±5 (mean±SD) mmHg, respectively; the pulmonary vascular resistance indices of the groups were 0.6±0.3, 3.2±0.7, and 1.7±0.5, respectively; and the Fulton indices [-RV/(LV + Septum)] were 0.22±0.01, 0.44±0.07, and 0.23±0.02, respectively. The VEGFNP/SDFNP delivery delayed the thickening of distal pulmonary vessels: the number of nearly occluded vessels in a whole lung section from the MCT and MCT + VEGFNP/SDFNP groups were 46±12 and 2±3, respectively. Gene expression analysis of the endothelial cell markers, VE-cadherin, KDR, BMPR2, and eNOS, and smooth cell markers, SM-MHC and α-SMA, indicated significant loss of distal pulmonary vessels in the MCT- treated rats. VEGFNP/SDFNP delivery did not recover the loss, but significantly increased eNOS and decreased α-SMA expression in the MCT-treated lungs. Thus, the therapeutic effect of VEGFNP/SDFNP may be mediated by improving/repairing endothelial function in the PAH lungs., Competing Interests: The authors declare no conflicts of interest., (© 2024 The Author(s). Pulmonary Circulation published by John Wiley & Sons Ltd on behalf of Pulmonary Vascular Research Institute.)

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.

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., (© The Author(s) 2024. Published by Oxford University Press.)

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.

Background: Determining spatial relationships between disease and the exposome is limited by available methodologies. aPEER (algorithm for Projection of Exposome and Epidemiological Relationships) uses machine learning (ML) and network analysis to find spatial relationships between diseases and the exposome in the United States., Methods: Using aPEER we examined the relationship between 12 chronic diseases and 186 pollutants. PCA, K-means clustering, and map projection produced clusters of counties derived from pollutants, and the Jaccard correlation of these clusters with counties with high rates of disease was calculated. Pollution correlation matrices were used together with network analysis to identify the strongest disease-pollution relationships. Results were compared to LISA, Moran's I, univariate, elastic net, and random forest regression., Findings: aPEER produced 68,820 maps with human interpretable, distinct pollution-derived regions. Diseases with the strongest pollution associations were hypertension (J=0.5316, p=3.89x10-208), COPD (J=0.4545, p=8.27x10-131), stroke (J=0.4517, p=1.15x10-127), stroke mortality (J=0.4445, p=4.28x10-125), and diabetes mellitus (J=0.4425, p=2.34x10-127). Methanol, acetaldehyde, and formaldehyde were identified as strongly associated with stroke, COPD, stroke mortality, hypertension, and diabetes mellitus in the southeast United States (which correlated with both the Stroke and Diabetes Belt). Pollutants were strongly predictive of chronic disease geography and outperformed conventional prediction models based on preventive services and social determinants of health (using elastic net and random forest regression)., Interpretation: aPEER used machine learning to identify disease and air pollutant relationships with similar or superior AUCs compared to social determinants of health (SDOH) and healthcare preventive service models. These findings highlight the utility of aPEER in epidemiological and geospatial analysis as well as the emerging role of exposomics in understanding chronic disease pathology., Funding: Boston Public Health Commission, NHLBI (R03 HL157890) and the CDC.

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.

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., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

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., (© 2024. Springer Nature Limited.)

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., (© 2024. The Author(s).)

Competing Interests: Disclosures None.

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 )., (© 2024. The Author(s).)

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., Competing Interests: Disclosures None.

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., Competing Interests: Disclosures None.

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks., (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)