Pan-cancer analysis of the interplay between mutational signatures and cellular signaling

Cancer is a multi-faceted disease with intricate relationships between mutagenic processes, alterations in cellular signaling, and the tissue microenvironment. To date, these processes have been largely studied in isolation. A systematic understanding of how they interact and influence each other is lacking. Here, we present a framework for systematically characterizing the interaction between pairs of mutational signatures and between signatures and signaling pathway alterations. We applied this framework to large-scale data from TCGA and PCAWG and identified multiple positive and negative interactions, both cross֊tissue and tissue֊specific, that provide new insights into the molecular routes observed in tumorigenesis and their respective drivers. This framework allows for a more fine-grained dissection of common and distinct etiology of mutational signatures. We further identified several interactions with both positive and negative impacts on patient survival, demonstrating their clinical relevance and potential for improving personalized cancer care.

Coordinated ARP2/3 and glycolytic activities regulate the morphological and functional fitness of human CD8+ T cells

Actin cytoskeleton remodeling sustains the ability of cytotoxic T cells to search for target cells and eliminate them. We here investigated the relationship between energetic status, actin remodeling, and functional fitness in human CD8+ effector T cells. Cell spreading during migration or immunological synapse assembly mirrored cytotoxic activity. Morphological and functional fitness were boosted by interleukin-2 (IL-2), which also stimulated the transcription of glycolytic enzymes, actin isoforms, and actin-related protein (ARP)2/3 complex subunits. This molecular program scaled with F-actin content and cell spreading. Inhibiting glycolysis impaired F-actin remodeling at the lamellipodium, chemokine-driven motility, and adhesion, while mitochondrial oxidative phosphorylation blockade impacted cell elongation during confined migration. The severe morphological and functional defects of ARPC1B-deficient T cells were only partially corrected by IL-2, emphasizing ARP2/3-mediated actin polymerization as a crucial energy state integrator. The study therefore underscores the tight coordination between metabolic and actin remodeling programs to sustain the cytotoxic activity of CD8+ T cells.

Low-dose Bisphenol A exposure alters the functionality and cellular environment in a human cardiomyocyte model

Early embryonic development represents a sensitive time-window during which the foetus might be vulnerable to the exposure of environmental contaminants, potentially leading to heart diseases also later in life. Bisphenol A (BPA), a synthetic chemical widely used in plastics manufacturing, has been associated with heart developmental defects, even in low concentrations. This study aims to investigate the effects of environmentally relevant doses of BPA on developing cardiomyocytes using a human induced pluripotent stem cell (hiPSC)-derived model. Firstly, a 2D in vitro differentiation system to obtain cardiomyocytes from hiPSCs (hiPSC-CMs) have been established and characterised to provide a suitable model for the early stages of cardiac development. Then, the effects of a repeated BPA exposure, starting from the undifferentiated stage throughout the differentiation process, were evaluated. The chemical significantly decreased the beat rate of hiPSC-CMs, extending the contraction and relaxation time in a dose-dependent manner. Quantitative proteomics analysis revealed a high abundance of basement membrane (BM) components (e.g., COL4A1, COL4A2, LAMC1, NID2) and a significant increase in TNNC1 and SERBP1 proteins in hiPSC-CMs treated with BPA. Network analysis of proteomics data supported altered extracellular matrix remodelling and provided a disease-gene association with well-known pathological conditions of the heart. Furthermore, upon hypoxia-reoxygenation challenge, hiPSC-CMs treated with BPA showed higher rate of apoptotic events. Taken together, our results revealed that a long-term treatment, even with low doses of BPA, interferes with hiPSC-CMs functionality and alters the surrounding cellular environment, providing new insights about diseases that might arise upon the toxin exposure. Our study contributes to the current understanding of BPA effects on developing human foetal cardiomyocytes, in correlation with human clinical observations and animal studies, and it provides a suitable model for New Approach Methodologies (NAMs) for environmental chemical hazard and risk assessment.

AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation

Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.

Mutational landscape of intestinal crypt cells after long-term in vivo exposure to high fat diet

Obesity is a modifiable risk factor in cancer development, especially for gastrointestinal cancer. While the etiology of colorectal cancer is well characterized by the adenoma-carcinoma sequence, it remains unclear how obesity influences colorectal cancer development. Dietary components of a high fat diet along with obesity have been shown to modulate the cancer risk by perturbing the homeostasis of intestinal stem cells, yet how adiposity impacts the development of genomic instability has not been studied. Mutational signatures are a powerful way to understand how a complex biological response impacts genomic stability. We utilized a mouse model of diet-induced obesity to study the mutational landscape of intestinal crypt cells after a 48-week exposure to an experimental high fat diet in vivo. By clonally enriching single crypt derived cells in organoid culture and obtaining whole genome sequences, we analyzed and compared the mutational landscape of intestinal epithelial cells from normal diet and high fat diet mice. Single nucleotide substitution signatures and indel signatures present in our cohort are found equally active in both diet groups and reflect biological processes of normal aging, cellular replication, and oxidative stress induced during organoid culturing. Thus, we demonstrate that in the absence of activating mutations or chemical exposure, high fat diet alone is not sufficient to increase genomic instability.

Proteomics and disease network associations evaluation of environmentally relevant Bisphenol A concentrations in a human 3D neural stem cell model

Bisphenol A (BPA) exposure is associated with a plethora of neurodevelopmental abnormalities and brain disorders. Previous studies have demonstrated BPA-induced perturbations to critical neural stem cell (NSC) characteristics, such as proliferation and differentiation, although the underlying molecular mechanisms remain under debate. The present study evaluated the effects of a repeated-dose exposure of environmentally relevant BPA concentrations during the in vitro 3D neural induction of human induced pluripotent stem cells (hiPSCs), emulating a chronic exposure scenario. Firstly, we demonstrated that our model is suitable for NSC differentiation during the early stages of embryonic brain development. Our morphological image analysis showed that BPA exposure at 0.01, 0.1 and 1 µM decreased the average spheroid size by day 21 (D21) of the neural induction, while no effect on cell viability was detected. No alteration to the rate of the neural induction was observed based on the expression of key neural lineage and neuroectodermal transcripts. Quantitative proteomics at D21 revealed several differentially abundant proteins across all BPA-treated groups with important functions in NSC proliferation and maintenance (e.g., FABP7, GPC4, GAP43, Wnt-8B, TPPP3). Additionally, a network analysis demonstrated alterations to the glycolytic pathway, potentially implicating BPA-induced changes to glycolytic signalling in NSC proliferation impairments, as well as the pathophysiology of brain disorders including intellectual disability, autism spectrum disorders, and amyotrophic lateral sclerosis (ALS). This study enhances the current understanding of BPA-related NSC aberrations based mostly on acute, often high dose exposures of rodent in vivo and in vitro models and human GWAS data in a novel human 3D cell-based model with real-life scenario relevant prolonged and low-level exposures, offering further mechanistic insights into the ramifications of BPA exposure on the developing human brain and consequently, later life neurological disorders.

Systemic Inflammation and Normocytic Anemia in DOCK11 Deficiency

BACKGROUND

Increasing evidence links genetic defects affecting actin-regulatory proteins to diseases with severe autoimmunity and autoinflammation, yet the underlying molecular mechanisms are poorly understood. Dedicator of cytokinesis 11 (DOCK11) activates the small Rho guanosine triphosphatase (GTPase) cell division cycle 42 (CDC42), a central regulator of actin cytoskeleton dynamics. The role of DOCK11 in human immune-cell function and disease remains unknown.

METHODS

We conducted genetic, immunologic, and molecular assays in four patients from four unrelated families who presented with infections, early-onset severe immune dysregulation, normocytic anemia of variable severity associated with anisopoikilocytosis, and developmental delay. Functional assays were performed in patient-derived cells, as well as in mouse and zebrafish models.

RESULTS

We identified rare, X-linked germline mutations in DOCK11 in the patients, leading to a loss of protein expression in two patients and impaired CDC42 activation in all four patients. Patient-derived T cells did not form filopodia and showed abnormal migration. In addition, the patient-derived T cells, as well as the T cells from Dock11-knockout mice, showed overt activation and production of proinflammatory cytokines that were associated with an increased degree of nuclear translocation of nuclear factor of activated T cell 1 (NFATc1). Anemia and aberrant erythrocyte morphologic features were recapitulated in a newly generated dock11-knockout zebrafish model, and anemia was amenable to rescue on ectopic expression of constitutively active CDC42.

CONCLUSIONS

Germline hemizygous loss-of-function mutations affecting the actin regulator DOCK11 were shown to cause a previously unknown inborn error of hematopoiesis and immunity characterized by severe immune dysregulation and systemic inflammation, recurrent infections, and anemia. (Funded by the European Research Council and others.).

Maternal hyperglycemia induces alterations in hepatic amino acid, glucose and lipid metabolism of neonatal offspring: Multi-omics insights from a diabetic pig model

OBJECTIVE

To gain mechanistic insights into adverse effects of maternal hyperglycemia on the liver of neonates, we performed a multi-omics analysis of liver tissue from piglets developed in genetically diabetic (mutant INS gene induced diabetes of youth; MIDY) or wild-type (WT) pigs.

METHODS

Proteome, metabolome and lipidome profiles of liver and clinical parameters of serum samples from 3-day-old WT piglets (n=9) born to MIDY mothers (PHG) were compared with those of WT piglets (n=10) born to normoglycemic mothers (PNG). Furthermore, protein-protein interaction network analysis was used to reveal highly interacting proteins that participate in the same molecular mechanisms and to relate these mechanisms with human pathology.

RESULTS

Hepatocytes of PHG displayed pronounced lipid droplet accumulation, although the abundances of central lipogenic enzymes such as fatty acid-synthase (FASN) were decreased. Additionally, circulating triglyceride (TG) levels were reduced as a trend. Serum levels of non-esterified free fatty acids (NEFA) were elevated in PHG, potentially stimulating hepatic gluconeogenesis. This is supported by elevated hepatic phosphoenolpyruvate carboxykinase (PCK1) and circulating alanine transaminase (ALT) levels. Even though targeted metabolomics showed strongly elevated phosphatidylcholine (PC) levels, the abundances of multiple key enzymes involved in major PC synthesis pathways - most prominently those from the Kennedy pathway - were paradoxically reduced in PHG liver. Conversely, enzymes involved in PC excretion and breakdown such as PC-specific translocase ATP-binding cassette 4 (ABCB4) and phospholipase A2 were increased in abundance.

CONCLUSIONS

Our study indicates that maternal hyperglycemia without confounding obesity induces profound molecular changes in the liver of neonatal offspring. In particular, we found evidence for stimulated gluconeogenesis and hepatic lipid accumulation independent of de novo lipogenesis. Reduced levels of PC biosynthesis enzymes and increased levels of proteins involved in PC translocation or breakdown may represent counter-regulatory mechanisms to maternally elevated PC levels. Our comprehensive multi-omics dataset provides a valuable resource for future meta-analysis studies focusing on liver metabolism in newborns from diabetic mothers.

Assessment of community efforts to advance network-based prediction of protein-protein interactions

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

Large neutral amino acid levels tune perinatal neuronal excitability and survival

Little is known about the critical metabolic changes that neural cells have to undergo during development and how temporary shifts in this program can influence brain circuitries and behavior. Inspired by the discovery that mutations in SLC7A5, a transporter of metabolically essential large neutral amino acids (LNAAs), lead to autism, we employed metabolomic profiling to study the metabolic states of the cerebral cortex across different developmental stages. We found that the forebrain undergoes significant metabolic remodeling throughout development, with certain groups of metabolites showing stage-specific changes, but what are the consequences of perturbing this metabolic program? By manipulating Slc7a5 expression in neural cells, we found that the metabolism of LNAAs and lipids are interconnected in the cortex. Deletion of Slc7a5 in neurons affects the postnatal metabolic state, leading to a shift in lipid metabolism. Additionally, it causes stage- and cell-type-specific alterations in neuronal activity patterns, resulting in a long-term circuit dysfunction.

Proline and glucose metabolic reprogramming supports vascular endothelial and medial biomass in pulmonary arterial hypertension

In pulmonary arterial hypertension (PAH), inflammation promotes a fibroproliferative pulmonary vasculopathy. Reductionist studies emphasizing single biochemical reactions suggest a shift toward glycolytic metabolism in PAH; however, key questions remain regarding the metabolic profile of specific cell types within PAH vascular lesions in vivo. We used RNA-Seq to profile the transcriptome of pulmonary artery endothelial cells (PAECs) freshly isolated from an inflammatory vascular injury model of PAH ex vivo, and these data were integrated with information from human gene ontology pathways. Network medicine was then used to map all aa and glucose pathways to the consolidated human interactome, which includes data on 233,957 physical protein-protein interactions. Glucose and proline pathways were significantly close to the human PAH disease module, suggesting that these pathways are functionally relevant to PAH pathobiology. To test this observation in vivo, we used multi-isotope imaging mass spectrometry to map and quantify utilization of glucose and proline in the PAH pulmonary vasculature at subcellular resolution. Our findings suggest that elevated glucose and proline avidity underlie increased biomass in PAECs and the media of fibrosed PAH pulmonary arterioles. Overall, these data show that anabolic utilization of glucose and proline are fundamental to the vascular pathology of PAH.

BioProfiling.jl: profiling biological perturbations with high-content imaging in single cells and heterogeneous populations

MOTIVATION

High-content imaging screens provide a cost-effective and scalable way to assess cell states across diverse experimental conditions. The analysis of the acquired microscopy images involves assembling and curating raw cellular measurements into morphological profiles suitable for testing biological hypotheses. Despite being a critical step, general-purpose and adaptable tools for morphological profiling are lacking and no solution is available for the high-performance Julia programming language.

RESULTS

Here, we introduce BioProfiling.jl, an efficient end-to-end solution for compiling and filtering informative morphological profiles in Julia. The package contains all the necessary data structures to curate morphological measurements and helper functions to transform, normalize and visualize profiles. Robust statistical distances and permutation tests enable quantification of the significance of the observed changes despite the high fraction of outliers inherent to high-content screens. This package also simplifies visual artifact diagnostics, thus streamlining a bottleneck of morphological analyses. We showcase the features of the package by analyzing a chemical imaging screen, in which the morphological profiles prove to be informative about the compounds' mechanisms of action and can be conveniently integrated with the network localization of molecular targets.

AVAILABILITY AND IMPLEMENTATION

The Julia package is available on GitHub: https://github.com/menchelab/BioProfiling.jl. We also provide Jupyter notebooks reproducing our analyses: https://github.com/menchelab/BioProfilingNotebooks. The data underlying this article are available from FigShare, at https://doi.org/10.6084/m9.figshare.14784678.v2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Network cartographs for interpretable visualizations

Networks offer an intuitive visual representation of complex systems. Important network characteristics can often be recognized by eye and, in turn, patterns that stand out visually often have a meaningful interpretation. In conventional network layout algorithms, however, the precise determinants of a node's position within a layout are difficult to decipher and to control. Here we propose an approach for directly encoding arbitrary structural or functional network characteristics into node positions. We introduce a series of two- and three-dimensional layouts, benchmark their efficiency for model networks, and demonstrate their power for elucidating structure-to-function relationships in large-scale biological networks.

Network analysis reveals rare disease signatures across multiple levels of biological organization

Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype. A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration.

Hydrothermal polymerization of porous aromatic polyimide networks and machine learning-assisted computational morphology evolution interpretation

We report on the hydrothermal polymerization (HTP) of polyimide (PI) networks using the medium H2O and the comonomers 1,3,5-tris(4-aminophenyl)benzene (TAPB) and pyromellitic acid (PMA). Full condensation is obtained at minimal reaction times of only 2 h at 200 °C. The PI networks are obtained as monoliths and feature thermal stabilities of >500 °C, and in several cases even up to 595 °C. The monoliths are built up by networks of densely packed, near-monodisperse spherical particles and annealed microfibers, and show three types of porosity: (i) intrinsic inter-segment ultramicroporosity (<0.8 nm) of the PI networks composing the particles (∼3-5 μm), (ii) interstitial voids between the particles (0.1-2 μm), and (iii) monolith cell porosity (∽10-100 μm), as studied via low pressure gas physisorption and Hg intrusion porosimetry analyses. This unique hierarchical porosity generates an outstandingly high specific pore volume of 7250 mm3 g-1. A large-scale micromorphological study screening the reaction parameters time, temperature, and the absence/presence of the additive acetic acid was performed. Through expert interpretation of hundreds of scanning electron microscopy (SEM) images of the products of these experiments, we devise a hypothesis for morphology formation and evolution: a monomer salt is initially formed and subsequently transformed to overall eight different fiber, pearl chain, and spherical morphologies, composed of PI and, at long reaction times (>48 h), also PI/SiO2 hybrids that form through reaction with the reaction vessel. Moreover, we have developed a computational image analysis pipeline that deciphers the complex morphologies of these SEM images automatically and also allows for formulating a hypothesis of morphology development in HTP that is in good agreement with the manual morphology analysis. Finally, we upscaled the HTP of PI(TAPB-PMA) and processed the resulting powder into dense cylindrical specimen by green solvent-free warm-pressing, showing that one can follow the full route from the synthesis of these PI networks to a final material without employing harmful solvents.

Beneficial Metabolic Effects of TREM2 in Obesity Are Uncoupled From Its Expression on Macrophages

Obesity-induced white adipose tissue (WAT) hypertrophy is associated with elevated adipose tissue macrophage (ATM) content. Overexpression of the triggering receptor expressed on myeloid cells 2 (TREM2) reportedly increases adiposity, worsening health. Paradoxically, using insulin resistance, elevated fat mass, and hypercholesterolemia as hallmarks of unhealthy obesity, a recent report demonstrated that ATM-expressed TREM2 promoted health. Here, we identified that in mice, TREM2 deficiency aggravated diet-induced insulin resistance and hepatic steatosis independently of fat and cholesterol levels. Metabolomics linked TREM2 deficiency with elevated obesity-instigated serum ceramides that correlated with impaired insulin sensitivity. Remarkably, while inhibiting ceramide synthesis exerted no influences on TREM2-dependent ATM remodeling, inflammation, or lipid load, it restored insulin tolerance, reversing adipose hypertrophy and secondary hepatic steatosis of TREM2-deficient animals. Bone marrow transplantation experiments revealed unremarkable influences of immune cell-expressed TREM2 on health, instead demonstrating that WAT-intrinsic mechanisms impinging on sphingolipid metabolism dominate in the systemic protective effects of TREM2 on metabolic health.

Morphological profiling of human T and NK lymphocytes by high-content cell imaging

The immunological synapse is a complex structure that decodes stimulatory signals into adapted lymphocyte responses. It is a unique window to monitor lymphocyte activity because of development of systematic quantitative approaches. Here we demonstrate the applicability of high-content imaging to human T and natural killer (NK) cells and develop a pipeline for unbiased analysis of high-definition morphological profiles. Our approach reveals how distinct facets of actin cytoskeleton remodeling shape immunological synapse architecture and affect lytic granule positioning. Morphological profiling of CD8⁺ T cells from immunodeficient individuals allows discrimination of the roles of the ARP2/3 subunit ARPC1B and the ARP2/3 activator Wiskott-Aldrich syndrome protein (WASP) in immunological synapse assembly. Single-cell analysis further identifies uncoupling of lytic granules and F-actin radial distribution in ARPC1B-deficient lymphocytes. Our study provides a foundation for development of morphological profiling as a scalable approach to monitor primary lymphocyte responsiveness and to identify complex aspects of lymphocyte micro-architecture.

The VRNetzer platform enables interactive network analysis in Virtual Reality

Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting in proverbial ‘hairballs’. Here, we introduce a Virtual Reality (VR) platform that overcomes these limitations by facilitating the thorough visual, and interactive, exploration of large networks. Our platform allows maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features. As a proof of concept, we show how our platform can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development. Our platform represents a general purpose, VR-based data exploration platform for large and diverse data types by providing an interface that facilitates the interaction between human intuition and state-of-the-art analysis methods.

Data-rich networks can be difficult to interpret beyond a certain size. Here, the authors introduce a platform that uses virtual reality to allow the visual exploration of large networks, while interfacing with data repositories and other analytical methods to improve the interpretation of big data.

Green Hydrothermal Synthesis of Fluorescent 2,3-Diarylquinoxalines and Large-Scale Computational Comparison to Existing Alternatives

Here, the hydrothermal synthesis (HTS) of 2,3-diarylquinoxalines from 1,2-diketones and o-phenylendiamines (o-PDAs) was achieved. The synthesis is simple, fast, and generates high yields, without requiring any organic solvents, strong acids or toxic catalysts. Reaction times down to <10 min without decrease in yield could be achieved through adding acetic acid as promoter, even for highly apolar biquinoxalines (yield >90 % in all cases). Moreover, it was shown that HTS has high compatibility: (i) hydrochlorides, a standard commercial form of amines, could be used directly as combined amine source and acidic catalyst, and (ii) Boc-diprotected o-PDA could be directly employed as substrate that underwent HT deprotection. A systematic large-scale computational comparison of all reported syntheses of the presented quinoxalines from the same starting compounds showed that this method is more environmentally friendly and less toxic than all existing methods and revealed generic synthetic routes for improving reaction yields. Finally, the application of the synthesized compounds as fluorescent dyes for cell staining was explored.

Green Hydrothermal Synthesis of Fluorescent 2,3-Diarylquinoxalines and Large-Scale Computational Comparison to Existing Alternatives

Invited for this month's cover is the group of Miriam Unterlass at the Technische Universität Wien and the CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences. The image illustrates the synthesis of quinoxalines in "hot water" and the large-scale computational comparison of all existing syntheses of these quinoxalines. The Full Paper itself is available at 10.1002/cssc.202100433.

Complex Networks in Health and Disease

From protein interactions to signal transduction, from metabolism to the nervous system: Virtually all processes in health and disease rely on the careful orchestration of a large number of diverse individual components ranging from molecules to cells and entire organs. Networks provide a powerful framework for describing and understanding these complex systems in a wholistic fashion. They offer a unique combination of a highly intuitive, qualitative description, and a plethora of analytical, quantitative tools. Here we provide a brief introduction to the emerging field of network medicine. After an overview of the core concepts for connecting network characteristics to biological functions, we review commonly used networks, ranging from the molecular interaction networks that form the basis of all biological processes in the cell to the global transportation networks that govern the spread of global epidemics. Lastly, we highlight current conceptual and practical challenges.

The PI3K pathway preserves metabolic health through MARCO-dependent lipid uptake by adipose tissue macrophages

Adipose tissue macrophages (ATMs) display tremendous heterogeneity depending on signals in their local microenvironment and contribute to the pathogenesis of obesity. The phosphoinositide 3-kinase (PI3K) signalling pathway, antagonized by the phosphatase and tensin homologue (PTEN), is important for metabolic responses to obesity. We hypothesized that fluctuations in macrophage-intrinsic PI3K activity via PTEN could alter the trajectory of metabolic disease by driving distinct ATM populations. Using mice harbouring macrophage-specific PTEN deletion or bone marrow chimeras carrying additional PTEN copies, we demonstrate that sustained PI3K activity in macrophages preserves metabolic health in obesity by preventing lipotoxicity. Myeloid PI3K signalling promotes a beneficial ATM population characterized by lipid uptake, catabolism and high expression of the scavenger macrophage receptor with collagenous structure (MARCO). Dual MARCO and myeloid PTEN deficiencies prevent the generation of lipid-buffering ATMs, reversing the beneficial actions of elevated myeloid PI3K activity in metabolic disease. Thus, macrophage-intrinsic PI3K signalling boosts metabolic health by driving ATM programmes associated with MARCO-dependent lipid uptake.

The cytoskeletal regulator HEM1 governs B cell development and prevents autoimmunity

The WAVE regulatory complex (WRC) is crucial for assembly of the peripheral branched actin network constituting one of the main drivers of eukaryotic cell migration. Here, we uncover an essential role of the hematopoietic-specific WRC component HEM1 for immune cell development. Germline-encoded HEM1 deficiency underlies an inborn error of immunity with systemic autoimmunity, at cellular level marked by WRC destabilization, reduced filamentous actin, and failure to assemble lamellipodia. Hem1^-/- mice display systemic autoimmunity, phenocopying the human disease. In the absence of Hem1, B cells become deprived of extracellular stimuli necessary to maintain the strength of B cell receptor signaling at a level permissive for survival of non-autoreactive B cells. This shifts the balance of B cell fate choices toward autoreactive B cells and thus autoimmunity.

Mapping the Human Kinome in Response to DNA Damage

We provide a catalog for the effects of the human kinome on cell survival in response to DNA-damaging agents, covering all major DNA repair pathways. By treating 313 kinase-deficient cell lines with ten diverse DNA-damaging agents, including seven commonly used chemotherapeutics, we identified examples of vulnerability and resistance that are kinase specific. To investigate synthetic lethal interactions, we tested the response to carmustine for 25 cell lines by establishing a phenotypic fluorescence-activated cell sorting (FACS) assay designed to validate gene-drug interactions. We show apoptosis, cell cycle changes, and DNA damage and proliferation after alkylation- or crosslink-induced damage. In addition, we reconstitute the cellular sensitivity of DYRK4, EPHB6, MARK3, and PNCK as a proof of principle for our study. Furthermore, using global phosphoproteomics on cells lacking MARK3, we provide evidence for its role in the DNA damage response. Our data suggest that cancers with inactivating mutations in kinases, including MARK3, are particularly vulnerable to alkylating chemotherapeutic agents.

Plasticity of the Cullin-RING Ligase Repertoire Shapes Sensitivity to Ligand-Induced Protein Degradation

Inducing protein degradation via small molecules is a transformative therapeutic paradigm. Although structural requirements of target degradation are emerging, mechanisms determining the cellular response to small-molecule degraders remain poorly understood. To systematically delineate effectors required for targeted protein degradation, we applied genome-scale CRISPR/Cas9 screens for five drugs that hijack different substrate receptors (SRs) of cullin RING ligases (CRLs) to induce target proteolysis. We found that sensitivity to small-molecule degraders is dictated by shared and drug-specific modulator networks, including the COP9 signalosome and the SR exchange factor CAND1. Genetic or pharmacologic perturbation of these effectors impairs CRL plasticity and arrests a wide array of ligases in a constitutively active state. Resulting defects in CRL decommissioning prompt widespread CRL auto-degradation that confers resistance to multiple degraders. Collectively, our study informs on regulation and architecture of CRLs amenable for targeted protein degradation and outlines biomarkers and putative resistance mechanisms for upcoming clinical investigation.

Environmental arginine controls multinuclear giant cell metabolism and formation

Multinucleated giant cells (MGCs) are implicated in many diseases including schistosomiasis, sarcoidosis and arthritis. MGC generation is energy intensive to enforce membrane fusion and cytoplasmic expansion. Using receptor activator of nuclear factor kappa-Β ligand (RANKL) induced osteoclastogenesis to model MGC formation, here we report RANKL cellular programming requires extracellular arginine. Systemic arginine restriction improves outcome in multiple murine arthritis models and its removal induces preosteoclast metabolic quiescence, associated with impaired tricarboxylic acid (TCA) cycle function and metabolite induction. Effects of arginine deprivation on osteoclastogenesis are independent of mTORC1 activity or global transcriptional and translational inhibition. Arginine scarcity also dampens generation of IL-4 induced MGCs. Strikingly, in extracellular arginine absence, both cell types display flexibility as their formation can be restored with select arginine precursors. These data establish how environmental amino acids control the metabolic fate of polykaryons and suggest metabolic ways to manipulate MGC-associated pathologies and bone remodelling.

Type I Interferon Signaling Disrupts the Hepatic Urea Cycle and Alters Systemic Metabolism to Suppress T Cell Function

Infections induce complex host responses linked to antiviral defense, inflammation, and tissue damage and repair. We hypothesized that the liver, as a central metabolic hub, may orchestrate systemic metabolic changes during infection. We infected mice with chronic lymphocytic choriomeningitis virus (LCMV), performed RNA sequencing and proteomics of liver tissue, and integrated these data with serum metabolomics at different infection phases. Widespread reprogramming of liver metabolism occurred early after infection, correlating with type I interferon (IFN-I) responses. Viral infection induced metabolic alterations of the liver that depended on the interferon alpha/beta receptor (IFNAR1). Hepatocyte-intrinsic IFNAR1 repressed the transcription of metabolic genes, including Otc and Ass1, which encode urea cycle enzymes. This led to decreased arginine and increased ornithine concentrations in the circulation, resulting in suppressed virus-specific CD8+ T cell responses and ameliorated liver pathology. These findings establish IFN-I-induced modulation of hepatic metabolism and the urea cycle as an endogenous mechanism of immunoregulation. VIDEO ABSTRACT.

HACE1 deficiency leads to structural and functional neurodevelopmental defects

Objective

We aim to characterize the causality and molecular and functional underpinnings of HACE1 deficiency in a mouse model of a recessive neurodevelopmental syndrome called spastic paraplegia and psychomotor retardation with or without seizures (SPPRS).

Methods

By exome sequencing, we identified 2 novel homozygous truncating mutations in HACE1 in 3 patients from 2 families, p.Q209* and p.R332*. Furthermore, we performed detailed molecular and phenotypic analyses of Hace1 knock-out (KO) mice and SPPRS patient fibroblasts.

Results

We show that Hace1 KO mice display many clinical features of SPPRS including enlarged ventricles, hypoplastic corpus callosum, as well as locomotion and learning deficiencies. Mechanistically, loss of HACE1 results in altered levels and activity of the small guanosine triphosphate (GTP)ase, RAC1. In addition, HACE1 deficiency results in reduction in synaptic puncta number and long-term potentiation in the hippocampus. Similarly, in SPPRS patient-derived fibroblasts, carrying a disruptive HACE1 mutation resembling loss of HACE1 in KO mice, we observed marked upregulation of the total and active, GTP-bound, form of RAC1, along with an induction of RAC1-regulated downstream pathways.

Conclusions

Our results provide a first animal model to dissect this complex human disease syndrome, establishing the first causal proof that a HACE1 deficiency results in decreased synapse number and structural and behavioral neuropathologic features that resemble SPPRS patients.

B Cell-Activating Factor Neutralization Aggravates Atherosclerosis

BACKGROUND

Atherosclerotic cardiovascular disease (heart attacks and strokes) is the major cause of death globally and is caused by the buildup of a plaque in the arterial wall. Genomic data showed that the B cell-activating factor (BAFF) receptor pathway, which is specifically essential for the survival of conventional B lymphocytes (B-2 cells), is a key driver of coronary heart disease. Deletion or antibody-mediated blockade of BAFF receptor ablates B-2 cells and decreases experimental atherosclerosis. Anti-BAFF immunotherapy is approved for treatment of autoimmune systemic lupus erythematosus, and can therefore be expected to limit their associated cardiovascular risk. However, direct effects of anti-BAFF immunotherapy on atherosclerosis remain unknown.

METHODS

To investigate the effect of BAFF neutralization in atherosclerosis, the authors treated Apoe^-/- and Ldlr^-/- mice with a well-characterized blocking anti-BAFF antibody. Moreover, to investigate the mechanism by which BAFF impacts atherosclerosis, the authors studied atherosclerosis-prone mice that lack the alternative receptor for BAFF: transmembrane activator and calcium modulator and cyclophilin ligand interactor.

RESULTS

The authors demonstrate here that anti-BAFF antibody treatment increased atherosclerosis in mice, despite efficient depletion of mature B-2 cells, suggesting a unique mechanism of action. Indeed, myeloid cell-specific deletion of transmembrane activator and calcium modulator and cyclophilin ligand interactor also results in increased atherosclerosis, while B cell-specific transmembrane activator and calcium modulator and cyclophilin ligand interactor deletion had no effect. Mechanistically, BAFF-transmembrane activator and calcium modulator and cyclophilin ligand interactor signaling represses macrophage IRF7-dependent (but not NF-κB-dependent) Toll-like receptor 9 responses including proatherogenic CXCL10 production.

CONCLUSIONS

These data identify a novel B cell-independent anti-inflammatory role for BAFF in atherosclerosis and may have important clinical implications.

Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module

The polygenic nature of complex diseases offers potential opportunities to utilize network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease network module. We identified an initial disease network neighborhood by applying a random-walk method. Next, we developed a network-based closeness approach (C_AB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the initial network neighborhood. Moreover, compared to a similar method (local radiality), the C_AB approach predicts low-degree genes as potential candidates. The candidates identified by the network-based closeness approach were combined with the initial network neighborhood to build a comprehensive disease network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related network components using new laboratory data to overcome incompleteness of the current interactome.

Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes

Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium, and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of four putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways' relationship to T2D.

Non-classical monocytes as mediators of tissue destruction in arthritis

Objectives

Bone destruction in rheumatoid arthritis is mediated by osteoclasts (OC), which are derived from precursor cells of the myeloid lineage. The role of the two monocyte subsets, classical monocytes (expressing CD115, Ly6C and CCR2) and non-classical monocytes (which are CD115 positive, but low in Ly6C and CCR2), in serving as precursors for OC in arthritis is still elusive.

Methods

We investigated CCR2^−/− mice, which lack circulating classical monocytes, crossed into hTNFtg mice for the extent of joint damage. We analysed monocyte subsets in hTNFtg and K/BxN serum transfer arthritis by flow cytometry. We sorted monocyte subsets and analysed their potential to differentiate into OC and their transcriptional response in response to RANKL by RNA sequencing. With these data, we performed a gene ontology enrichment analysis and gene set enrichment analysis.

Results

We show that in hTNFtg arthritis local bone erosion and OC generation are even enhanced in the absence of CCR2. We further show the numbers of non-classical monocytes in blood are elevated and are significantly correlated with histological signs of joint destruction. Sorted non-classical monocytes display an increased capacity to differentiate into OCs. This is associated with an increased expression of signal transduction components of RANK, most importantly TRAF6, leading to an increased responsiveness to RANKL.

Conclusion

Therefore, non-classical monocytes are pivotal cells in arthritis tissue damage and a possible target for therapeutically intervention for the prevention of inflammatory joint damage.

Proximal Pathway Enrichment Analysis for Targeting Comorbid Diseases via Network Endopharmacology

The past decades have witnessed a paradigm shift from the traditional drug discovery shaped around the idea of “one target, one disease” to polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is to target common biological pathways involved in diseases via endopharmacology (multiple targets, multiple diseases). In this study, we present proximal pathway enrichment analysis (PxEA) for pinpointing drugs that target common disease pathways towards network endopharmacology. PxEA uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs by their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. Using PxEA, we show that many drugs indicated for autoimmune disorders are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimer’s disease, two conditions that have recently gained attention due to the increased comorbidity among patients.

Map of synthetic rescue interactions for the Fanconi anemia DNA repair pathway identifies USP48

Defects in DNA repair can cause various genetic diseases with severe pathological phenotypes. Fanconi anemia (FA) is a rare disease characterized by bone marrow failure, developmental abnormalities, and increased cancer risk that is caused by defective repair of DNA interstrand crosslinks (ICLs). Here, we identify the deubiquitylating enzyme USP48 as synthetic viable for FA-gene deficiencies by performing genome-wide loss-of-function screens across a panel of human haploid isogenic FA-defective cells (FANCA, FANCC, FANCG, FANCI, FANCD2). Thus, as compared to FA-defective cells alone, FA-deficient cells additionally lacking USP48 are less sensitive to genotoxic stress induced by ICL agents and display enhanced, BRCA1-dependent, clearance of DNA damage. Consequently, USP48 inactivation reduces chromosomal instability of FA-defective cells. Our results highlight a role for USP48 in controlling DNA repair and suggest it as a potential target that could be therapeutically exploited for FA.

Characterization of host proteins interacting with the lymphocytic choriomeningitis virus L protein

RNA-dependent RNA polymerases (RdRps) play a key role in the life cycle of RNA viruses and impact their immunobiology. The arenavirus lymphocytic choriomeningitis virus (LCMV) strain Clone 13 provides a benchmark model for studying chronic infection. A major genetic determinant for its ability to persist maps to a single amino acid exchange in the viral L protein, which exhibits RdRp activity, yet its functional consequences remain elusive. To unravel the L protein interactions with the host proteome, we engineered infectious L protein-tagged LCMV virions by reverse genetics. A subsequent mass-spectrometric analysis of L protein pulldowns from infected human cells revealed a comprehensive network of interacting host proteins. The obtained LCMV L protein interactome was bioinformatically integrated with known host protein interactors of RdRps from other RNA viruses, emphasizing interconnected modules of human proteins. Functional characterization of selected interactors highlighted proviral (DDX3X) as well as antiviral (NKRF, TRIM21) host factors. To corroborate these findings, we infected Trim21^-/- mice with LCMV and found impaired virus control in chronic infection. These results provide insights into the complex interactions of the arenavirus LCMV and other viral RdRps with the host proteome and contribute to a better molecular understanding of how chronic viruses interact with their host.

RNA-dependent RNA-polymerases (RdRps) play a key role in the life cycle of RNA viruses. They interact with cellular proteins during replication and transcription processes and impact the immunobiology of viral infections. This study characterized the host protein interactome of the RdRp-containing L protein of the prototypic arenavirus lymphocytic choriomeningitis virus (LCMV). Several L protein interactors with proviral and antiviral effects were identified in vitro, and mice lacking the identified L protein interactor TRIM21 exhibited impaired control of chronic LCMV infection. Integration of the L protein interactomes with known RdRp interactomes from other RNA viruses highlighted common and virus-specific strategies to interact with the host proteome, which may indicate novel avenues for antiviral interventions.

Parallel genome-wide screens identify synthetic viable interactions between the BLM helicase complex and Fanconi anemia

Maintenance of genome integrity via repair of DNA damage is a key biological process required to suppress diseases, including Fanconi anemia (FA). We generated loss-of-function human haploid cells for FA complementation group C (FANCC), a gene encoding a component of the FA core complex, and used genome-wide CRISPR libraries as well as insertional mutagenesis to identify synthetic viable (genetic suppressor) interactions for FA. Here we show that loss of the BLM helicase complex suppresses FANCC phenotypes and we confirm this interaction in cells deficient for FA complementation group I and D2 (FANCI and FANCD2) that function as part of the FA I-D2 complex, indicating that this interaction is not limited to the FA core complex, hence demonstrating that systematic genome-wide screening approaches can be used to reveal genetic viable interactions for DNA repair defects.

From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration

Background

Deep mining of healthcare data has provided maps of comorbidity relationships between diseases. In parallel, integrative multi-omics investigations have generated high-resolution molecular maps of putative relevance for understanding disease initiation and progression. Yet, it is unclear how to advance an observation of comorbidity relations (one disease to others) to a molecular understanding of the driver processes and associated biomarkers.

Results

Since Chronic Obstructive Pulmonary disease (COPD) has emerged as a central hub in temporal comorbidity networks, we developed a systematic integrative data-driven framework to identify shared disease-associated genes and pathways, as a proxy for the underlying generative mechanisms inducing comorbidity. We integrated records from approximately 13 M patients from the Medicare database with disease-gene maps that we derived from several resources including a semantic-derived knowledge-base. Using rank-based statistics we not only recovered known comorbidities but also discovered a novel association between COPD and digestive diseases. Furthermore, our analysis provides the first set of COPD co-morbidity candidate biomarkers, including IL15, TNF and JUP, and characterizes their association to aging and life-style conditions, such as smoking and physical activity.

Conclusions

The developed framework provides novel insights in COPD and especially COPD co-morbidity associated mechanisms. The methodology could be used to discover and decipher the molecular underpinning of other comorbidity relationships and furthermore, allow the identification of candidate co-morbidity biomarkers.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1291-3) contains supplementary material, which is available to authorized users.

Endophenotype Network Models: Common Core of Complex Diseases

Historically, human diseases have been differentiated and categorized based on the organ system in which they primarily manifest. Recently, an alternative view is emerging that emphasizes that different diseases often have common underlying mechanisms and shared intermediate pathophenotypes, or endo(pheno)types. Within this framework, a specific disease’s expression is a consequence of the interplay between the relevant endophenotypes and their local, organ-based environment. Important examples of such endophenotypes are inflammation, fibrosis, and thrombosis and their essential roles in many developing diseases. In this study, we construct endophenotype network models and explore their relation to different diseases in general and to cardiovascular diseases in particular. We identify the local neighborhoods (module) within the interconnected map of molecular components, i.e., the subnetworks of the human interactome that represent the inflammasome, thrombosome, and fibrosome. We find that these neighborhoods are highly overlapping and significantly enriched with disease-associated genes. In particular they are also enriched with differentially expressed genes linked to cardiovascular disease (risk). Finally, using proteomic data, we explore how macrophage activation contributes to our understanding of inflammatory processes and responses. The results of our analysis show that inflammatory responses initiate from within the cross-talk of the three identified endophenotypic modules.

An inter-species protein-protein interaction network across vast evolutionary distance

In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical-versus-functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an "inter-interactome" approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter-interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra-species networks. Although substantially reduced relative to intra-species networks, the levels of functional overlap in the yeast-human inter-interactome network uncover significant remnants of co-functionality widely preserved in the two proteomes beyond human-yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co-functionality. Such non-functional interactions, however, represent a reservoir from which nascent functional interactions may arise.

Canonical genetic signatures of the adult human brain

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.

A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome

The observation that disease associated proteins often interact with each other has fueled the development of network-based approaches to elucidate the molecular mechanisms of human disease. Such approaches build on the assumption that protein interaction networks can be viewed as maps in which diseases can be identified with localized perturbation within a certain neighborhood. The identification of these neighborhoods, or disease modules, is therefore a prerequisite of a detailed investigation of a particular pathophenotype. While numerous heuristic methods exist that successfully pinpoint disease associated modules, the basic underlying connectivity patterns remain largely unexplored. In this work we aim to fill this gap by analyzing the network properties of a comprehensive corpus of 70 complex diseases. We find that disease associated proteins do not reside within locally dense communities and instead identify connectivity significance as the most predictive quantity. This quantity inspires the design of a novel Disease Module Detection (DIAMOnD) algorithm to identify the full disease module around a set of known disease proteins. We study the performance of the algorithm using well-controlled synthetic data and systematically validate the identified neighborhoods for a large corpus of diseases.

Diseases are rarely the result of an abnormality in a single gene, but involve a whole cascade of interactions between several cellular processes. To disentangle these complex interactions it is necessary to study genotype-phenotype relationships in the context of protein-protein interaction networks. Our analysis of 70 diseases shows that disease proteins are not randomly scattered within these networks, but agglomerate in specific regions, suggesting the existence of specific disease modules for each disease. The identification of these modules is the first step towards elucidating the biological mechanisms of a disease or for a targeted search of drug targets. We present a systematic analysis of the connectivity patterns of disease proteins and determine the most predictive topological property for their identification. This allows us to rationally design a reliable and efficient Disease Module Detection algorithm (DIAMOnD).

Disease networks. Uncovering disease-disease relationships through the incomplete interactome

According to the disease module hypothesis, the cellular components associated with a disease segregate in the same neighborhood of the human interactome, the map of biologically relevant molecular interactions. Yet, given the incompleteness of the interactome and the limited knowledge of disease-associated genes, it is not obvious if the available data have sufficient coverage to map out modules associated with each disease. Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases. For example, diseases with overlapping network modules show significant coexpression patterns, symptom similarity, and comorbidity, whereas diseases residing in separated network neighborhoods are phenotypically distinct. These tools represent an interactome-based platform to predict molecular commonalities between phenotypically related diseases, even if they do not share primary disease genes.

A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma

Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. Still, the detailed understanding of the molecular and physiological mechanisms through which these genes affect disease phenotypes remains a major challenge. Here, we identify the asthma disease module, i.e. the local neighborhood of the interactome whose perturbation is associated with asthma, and validate it for functional and pathophysiological relevance, using both computational and experimental approaches. We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells treated with an asthma-specific drug. The asthma module also contains immune response mechanisms that are shared with other immune-related disease modules. Further, using diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling pathway as an important novel modulator in asthma. The wiring diagram of the uncovered asthma module suggests a relatively close link between GAB1 and glucocorticoids (GCs), which we experimentally validate, observing an increase in the level of GAB1 after GC treatment in BEAS-2B bronchial epithelial cells. The siRNA knockdown of GAB1 in the BEAS-2B cell line resulted in a decrease in the NFkB level, suggesting a novel regulatory path of the pro-inflammatory factor NFkB by GAB1 in asthma.

Systems Medicine: from molecular features and models to the clinic in COPD

Background and hypothesis

Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice.

Objective and method

Our overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework.

Results

In the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice.

Conclusions

The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.

A proteome-scale map of the human interactome network

Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.