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.

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.

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.

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.

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.

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.

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.).

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.

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.

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.

Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease.

The early developmental phase is of critical importance for human health and disease later in life. To decipher the molecular mechanisms at play, current biomedical research is increasingly relying on large quantities of diverse omics data. The integration and interpretation of the different datasets pose a critical challenge towards the holistic understanding of the complex biological processes that are involved in early development. In this review, we outline the major transcriptomic and epigenetic processes and the respective datasets that are most relevant for studying the periconceptional period. We cover both basic data processing and analysis steps, as well as more advanced data integration methods. A particular focus is given to network-based methods. Finally, we review the medical applications of such integrative analyses.

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.

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.

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.