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Branched-chain α-ketoacids are shown to aerobically activate HIF1α signalling, which induces a phenotypic switch in vascular smooth muscle cells that is potentially relevant in the context of pulmonary artery hypertension.

Understanding patient-specific pathobiological pathways is a critical step for advancing precision medicine. Here the authors show that individualized protein-protein interaction networks provide key insight on patient-level pathobiology and clinically relevant pathophenotypic characteristics in a complex disease.

Using well-calibrated statistical methods the authors jointly analyze Undiagnosed Diseases Network genomes, identifying known and novel disease genes. Software is publicly available to support future cross-cohort rare disease discovery efforts.

Environmental and dietary factors are known to play a role in the development of coronary heart disease. Here the authors apply a genomic-inspired methodology to Nurses’ Health Study data to explore comprehensively and agnostically the association of 257 nutrients and 117 foods with coronary heart disease risk.

Protein–protein interactomes incorporating structural data predict the functional consequences of disease mutations.

Human disease mutations affect protein–protein interfaces in a three-dimensional structurally resolved interaction network. Predicted oncoPPIs in cancer correlate with survival and drug sensitivity, and affect growth in vitro, supporting their relevance to disease pathogenesis.

Molecular interactions between polyphenol targets and proteins associated with disease are explored through a network medicine framework. The network proximity of polyphenol protein targets to disease proteins can predict therapeutic effects, highlighting more broadly the potential of network medicine as a tool for nutritional sciences.

One of the primary tools that researchers use to predict risk is the case-control study. Here, the authors identify a flaw, temporal bias, that is specific to and uniquely associated with these studies that occurs when the study period is not representative of the data that clinicians have during the diagnostic process, undermining the validity of predictions.

Robustness is a prominent feature of most biological systems, but most of the current efforts have been focused on studying homogeneous molecular networks. Here the authors propose a comprehensive framework for understanding how the interactions between genes, proteins, and metabolites contribute to the determinants of robustness.

Identification of disease modules in the human interactome can guide more efficacious therapeutic selections. Here, the authors introduce a network-based methodology to identify individualized disease modules by mapping patients’ DNA and RNA sequencing profiles to the interactome, enabling prediction of cancer type-specific drug responses.

Both the cardiovascular research and clinical communities are ideally positioned to develop and implement precision medicine to achieve more effective prevention and treatment. This Review highlights the advances in modern biomedicine that make possible the precision medicine era, provides current examples of the use of this approach in the cardiovascular field, and defines the goals and barriers to implementation of a precision medicine system.

Comprehensive understanding of the human protein-protein interaction network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Here the authors summarize the community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict protein-protein interactions.

In this Review, Narayan and colleagues discuss global disparities in access to digital health technologies, with a focus on cardiovascular medicine. The authors summarize the factors that affect access at various levels of society and present solutions that target each of these levels, culminating in a personalized framework to improve access to digital health technologies.

Advances such as machine learning may enable the full biochemical spectrum of food to be studied systematically. Uncovering the ‘dark matter’ of nutrition could open new avenues for a greater understanding of the composition of what we eat and how it relates to health and disease

Repurposing approved drugs could accelerate treatment options for various diseases. Here, the authors use network proximity of disease gene products and drug targets in the human protein interactome to identify drug-disease associations for cardiovascular disease, and validate these using longitudinal healthcare data.

Genetically encoded iNap sensors allow imaging of NADPH with high spatiotemporal resolution in living systems. The iNaps cover physiologically relevant NADPH concentrations and are demonstrated in mammalian cells and live zebrafish.

A series of bright fluorescent RNAs with a broad emission spectra enable live cell imaging of RNA dynamics.

Diseases are increasingly being found to reflect the perturbations of complex molecular networks. The principles of network medicine are being used to identify new disease genes, determine the functional significance of disease-associated mutations, and identify new drug targets and biomarkers.

During starvation, glyceraldehyde 3-phosphate dehydrogenase (GAPDH) targets GTPase-activating proteins to inhibit multiple intracellular transport pathways, thereby promoting energy homeostasis.

Commercializing innovations in academic environments is notoriously challenging. Here, we describe the progress of the NIH Centers for Accelerated Innovations program — initiated in 2013 to address these challenges — which we believe could help set a new standard for the early-stage commercialization of biomedical innovations in academic environments.

In this Review, the authors describe biological networks, discuss the properties that make these networks ideal for understanding how diseases arise from complex interactions of molecular and cellular systems, and explore how network medicine can be used to improve understanding of kidney and renovascular diseases.

FiLa biosensors allow real-time subcellular analysis of lactate metabolism. This protocol describes their preparation and characterization, and their use in in vitro and in vivo assays, under various nutritional and pharmacological conditions.

A diagnostic tool based on blood RNA-seq is shown to identify causal genes and variants linked to clinical phenotypes in individuals with rare diseases for which whole-exome genetic sequencing was uninformative.

This protocol describes how to combine up to four genetically encoded fluorescent sensors to image redox landscapes. The procedure describes applications in live imaging and flow cytometry of cultured cells, and in vivo imaging in zebrafish larvae.

Network Medicine is now an established scientific discipline, having generated key insights on integrated mechanisms underlying complex human diseases. The next discovery phase will emphasize innovating analytics that optimize networks for advancing precision medicine. Accomplishing this goal will require organizing a scientifically diverse team studying big datasets shared globally.