A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations.

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.

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.

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.

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 ).

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.

MYC overrides HIF-1α to regulate proliferating primary cell metabolism in hypoxia.

Hypoxia requires metabolic adaptations to sustain energetically demanding cellular activities. While the metabolic consequences of hypoxia have been studied extensively in cancer cell models, comparatively little is known about how primary cell metabolism responds to hypoxia. Thus, we developed metabolic flux models for human lung fibroblast and pulmonary artery smooth muscle cells proliferating in hypoxia. Unexpectedly, we found that hypoxia decreased glycolysis despite activation of hypoxia-inducible factor 1α (HIF-1α) and increased glycolytic enzyme expression. While HIF-1α activation in normoxia by prolyl hydroxylase (PHD) inhibition did increase glycolysis, hypoxia blocked this effect. Multi-omic profiling revealed distinct molecular responses to hypoxia and PHD inhibition, and suggested a critical role for MYC in modulating HIF-1α responses to hypoxia. Consistent with this hypothesis, MYC knockdown in hypoxia increased glycolysis and MYC over-expression in normoxia decreased glycolysis stimulated by PHD inhibition. These data suggest that MYC signaling in hypoxia uncouples an increase in HIF-dependent glycolytic gene transcription from glycolytic flux.

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.

The role of glutathione peroxidase-1 in health and disease.

Glutathione peroxidase 1 (GPx1) is an important cellular antioxidant enzyme that is found in the cytoplasm and mitochondria of mammalian cells. Like most selenoenzymes, it has a single redox-sensitive selenocysteine amino acid that is important for the enzymatic reduction of hydrogen peroxide and soluble lipid hydroperoxides. Glutathione provides the source of reducing equivalents for its function. As an antioxidant enzyme, GPx1 modulates the balance between necessary and harmful levels of reactive oxygen species. In this review, we discuss how selenium availability and modifiers of selenocysteine incorporation alter GPx1 expression to promote disease states. We review the role of GPx1 in cardiovascular and metabolic health, provide examples of how GPx1 modulates stroke and provides neuroprotection, and consider how GPx1 may contribute to cancer risk. Overall, GPx1 is protective against the development and progression of many chronic diseases; however, there are some situations in which increased expression of GPx1 may promote cellular dysfunction and disease owing to its removal of essential reactive oxygen species.

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.

Immunometabolic Endothelial Phenotypes: Integrating Inflammation and Glucose Metabolism.

[Figure: see text].

Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside.

Despite impressive efforts invested in epigenetic research in the last 50 years, clinical applications are still lacking. Only a few university hospital centers currently use epigenetic biomarkers at the bedside. Moreover, the overall concept of precision medicine is not widely recognized in routine medical practice and the reductionist approach remains predominant in treating patients affected by major diseases such as cancer and cardiovascular diseases. By its' very nature, epigenetics is integrative of genetic networks. The study of epigenetic biomarkers has led to the identification of numerous drugs with an increasingly significant role in clinical therapy especially of cancer patients. Here, we provide an overview of clinical epigenetics within the context of network analysis. We illustrate achievements to date and discuss how we can move from traditional medicine into the era of network medicine (NM), where pathway-informed molecular diagnostics will allow treatment selection following the paradigm of precision medicine.

Comprehensive characterization of protein-protein interactions perturbed by disease mutations.

Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein-protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000 Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.

Associations of methyl donor and methylation inhibitor levels during anti-oxidant therapy in heart failure.

Redox balance and methylation are crucial to homeostasis and are linked by the methionine-homocysteine cycle. We examined whether differences in methylation potential, measured as plasma levels of S-adenosyl methionine (SAM) and S-adenosyl homocysteine (SAH), occur at baseline and during anti-oxidant therapy with the xanthine oxidase inhibitor allopurinol in patients with heart failure with reduced ejection fraction. We analyzed plasma samples collected at baseline and 24 weeks in the Xanthine Oxidase Inhibition for Hyperuricemic Heart Failure Patients (EXACT-HF) study, which randomized patients with heart failure with reduced ejection fraction to allopurinol or placebo. Associations between plasma levels of SAM, SAH, SAM/SAH ratio, and outcomes, including laboratory markers and clinical events, were assessed. Despite randomization, median SAM levels were significantly lower at baseline in the allopurinol group. SAH levels at 24 weeks, and change in SAM from baseline to week 24, were significantly higher in the group of patients randomized to allopurinol compared to the placebo group. A significant correlation was observed between change in SAH levels and change in plasma uric acid (baseline to 24-week changes) in the allopurinol group. There were no significant associations between levels of SAM, SAH, and SAM/SAH ratio and clinical outcomes. Our results demonstrate significant biological variability in SAM and SAH levels at baseline and during treatment with an anti-oxidant and suggest a potential mechanism for the lack of efficacy observed in trials of anti-oxidant therapy. These data also highlight the need to explore personalized therapy for heart failure.

Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols.

Polyphenols, natural products present in plant-based foods, play a protective role against several complex diseases through their antioxidant activity and by diverse molecular mechanisms. Here we develop a network medicine framework to uncover mechanisms for the effects of polyphenols on health by considering the molecular interactions between polyphenol protein targets and proteins associated with diseases. We find that the protein targets of polyphenols cluster in specific neighbourhoods of the human interactome, whose network proximity to disease proteins is predictive of the molecule's known therapeutic effects. The methodology recovers known associations, such as the effect of epigallocatechin-3-O-gallate on type 2 diabetes, and predicts that rosmarinic acid has a direct impact on platelet function, representing a novel mechanism through which it could affect cardiovascular health. We experimentally confirm that rosmarinic acid inhibits platelet aggregation and α-granule secretion through inhibition of protein tyrosine phosphorylation, offering direct support for the predicted molecular mechanism. Our framework represents a starting point for mechanistic interpretation of the health effects underlying food-related compounds, allowing us to integrate into a predictive framework knowledge on food metabolism, bioavailability and drug interaction.

A systematic comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease.

Environmental factors, and in particular diet, are known to play a key role in the development of Coronary Heart Disease. Many of these factors were unveiled by detailed nutritional epidemiology studies, focusing on the role of a single nutrient or food at a time. Here, we apply an Environment-Wide Association Study approach to Nurses' Health Study data to explore comprehensively and agnostically the association of 257 nutrients and 117 foods with coronary heart disease risk (acute myocardial infarction and fatal coronary heart disease). After accounting for multiple testing, we identify 16 food items and 37 nutrients that show statistically significant association - while adjusting for potential confounding and control variables such as physical activity, smoking, calorie intake, and medication use - among which 38 associations were validated in Nurses' Health Study II. Our implementation of Environment-Wide Association Study successfully reproduces prior knowledge of diet-coronary heart disease associations in the epidemiological literature, and helps us detect new associations that were only marginally studied, opening potential avenues for further extensive experimental validation. We also show that Environment-Wide Association Study allows us to identify a bipartite food-nutrient network, highlighting which foods drive the associations of specific nutrients with coronary heart disease risk.

Robustness and lethality in multilayer biological molecular networks.

Robustness is a prominent feature of most biological systems. Most previous related studies have been focused on homogeneous molecular networks. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, a protein-protein interaction layer, and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall system's robustness, and find that influential genes are enriched in essential and cancer genes. We show that the proposed mechanism predicts a higher vulnerability of the metabolic layer to perturbations applied to genes associated with metabolic diseases. Furthermore, we find that the real network is comparably or more robust than expected in multiple random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within and between layers. These results provide insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.