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.

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.

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.

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.

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.

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.

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.

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 ).

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.

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.

Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations.

Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. 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.

Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis.

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.

Cord blood DNA methylation signatures associated with preeclampsia are enriched for cardiovascular pathways: insights from the VDAART trial.

BACKGROUND: Preeclampsia has been associated with maternal epigenetic changes, in particular DNA methylation changes in the placenta. It has been suggested that preeclampsia could also cause DNA methylation changes in the neonate. We examined DNA methylation in relation to gene expression in the cord blood of offspring born to mothers with preeclampsia. METHODS: This study included 128 mother-child pairs who participated in the Vitamin D Antenatal Asthma Reduction Trial (VDAART), where assessment of preeclampsia served as secondary outcome. We performed an epigenome-wide association study of preeclampsia and cord blood DNA methylation (Illumina 450 K chip). We then examined gene expression of the same subjects for validation and replicated the gene signatures in independent DNA methylation datasets. Lastly, we applied functional enrichment and network analyses to identify biological pathways that could potentially be involved in preeclampsia. FINDINGS: In the cord blood samples (n = 128), 263 CpGs were differentially methylated (FDR <0.10) in preeclampsia (n = 16), of which 217 were annotated. Top pathways in the functional enrichment analysis included apelin signaling pathway and other endothelial and cardiovascular pathways. Of the 217 genes, 13 showed differential expression (p's < 0.001) in preeclampsia and 11 had been previously related to preeclampsia (p's < 0.0001). These genes were linked to apelin, cGMP and Notch signaling pathways, all having a role in angiogenic process and cardiovascular function. INTERPRETATION: Preeclampsia is related to differential cord blood DNA methylation signatures of cardiovascular pathways, including the apelin signaling pathway. The association of these cord blood DNA methylation signatures with offspring's long-term morbidities due to preeclampsia should be further investigated. FUNDING: VDAART is funded by National Heart, Lung, and Blood Institute grants of R01HL091528 and UH3OD023268. HMK is supported by Jane and Aatos Erkko Foundation, Paulo Foundation, and the Pediatric Research Foundation. HM is supported by K01 award from NHLBI (1K01HL146977-01A1). PK is supported by K99HL159234 from NIH/NHLBI.

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. Conclusion: 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).