Curation of causal interactions mediated by genes associated with autism accelerates the understanding of gene-phenotype relationships underlying neurodevelopmental disorders.

Autism spectrum disorder (ASD) comprises a large group of neurodevelopmental conditions featuring, over a wide range of severity and combinations, a core set of manifestations (restricted sociality, stereotyped behavior and language impairment) alongside various comorbidities. Common and rare variants in several hundreds of genes and regulatory regions have been implicated in the molecular pathogenesis of ASD along a range of causation evidence strength. Despite significant progress in elucidating the impact of few paradigmatic individual loci, such sheer complexity in the genetic architecture underlying ASD as a whole has hampered the identification of convergent actionable hubs hypothesized to relay between the vastness of risk alleles and the core phenotypes. In turn this has limited the development of strategies that can revert or ameliorate this condition, calling for a systems-level approach to probe the cross-talk of cooperating genes in terms of causal interaction networks in order to make convergences experimentally tractable and reveal their clinical actionability. As a first step in this direction, we have captured from the scientific literature information on the causal links between the genes whose variants have been associated with ASD and the whole human proteome. This information has been annotated in a computer readable format in the SIGNOR database and is made freely available in the resource website. To link this information to cell functions and phenotypes, we have developed graph algorithms that estimate the functional distance of any protein in the SIGNOR causal interactome to phenotypes and pathways. The main novelty of our approach resides in the possibility to explore the mechanistic links connecting the suggested gene-phenotype relations.

SIGNOR 3.0, the SIGnaling network open resource 3.0: 2022 update.

The SIGnaling Network Open Resource (SIGNOR 3.0, https://signor.uniroma2.it) is a public repository that captures causal information and represents it according to an 'activity-flow' model. SIGNOR provides freely-accessible static maps of causal interactions that can be tailored, pruned and refined to build dynamic and predictive models. Each signaling relationship is annotated with an effect (up/down-regulation) and with the mechanism (e.g. binding, phosphorylation, transcriptional activation, etc.) causing the regulation of the target entity. Since its latest release, SIGNOR has undergone a significant upgrade including: (i) a new website that offers an improved user experience and novel advanced search and graph tools; (ii) a significant content growth adding up to a total of approx. 33,000 manually-annotated causal relationships between more than 8900 biological entities; (iii) an increase in the number of manually annotated pathways, currently including pathways deregulated by SARS-CoV-2 infection or involved in neurodevelopment synaptic transmission and metabolism, among others; (iv) additional features such as new model to represent metabolic reactions and a new confidence score assigned to each interaction.

Inter and intra-tumor heterogeneity of paediatric type diffuse high-grade gliomas revealed by single-cell mass cytometry.

Paediatric-type diffuse high-grade gliomas (PDHGG) are aggressive tumors affecting children and young adults, with no effective treatment. These highly heterogeneous malignancies arise in different sites of the Central Nervous System (CNS), carrying distinctive molecular alterations and clinical outcomes (inter-tumor heterogeneity). Moreover, deep cellular and molecular profiling studies highlighted the coexistence of genetically and phenotypically different subpopulations within the same tumor mass (intra-tumor heterogeneity). Despite the recent advances made in the field, the marked heterogeneity of PDHGGs still impedes the development of effective targeted therapies and the identification of suitable biomarkers. In order to fill the existing gap, we used mass cytometry to dissect PDHGG inter- and intra-heterogeneity. This is one of the most advanced technologies of the "-omics" era that, using antibodies conjugated to heavy metals, allows the simultaneous measurement of more than 40 markers at single-cell level. To this end, we analyzed eight PDHGG patient-derived cell lines from different locational and molecular subgroups. By using a panel of 15 antibodies, directly conjugated to metals or specifically customized to detect important histone variants, significant differences were highlighted in the expression of the considered antigens. The single-cell multiparametric approach realized has deepened our understanding of PDHGG, confirming a high degree of intra- and inter-tumoral heterogeneity and identifying some antigens that could represent useful biomarkers for the specific PDHGG locational or molecular subgroups.

A Resource to Infer Molecular Paths Linking Cancer Mutations to Perturbation of Cell Metabolism.

Some inherited or somatically-acquired gene variants are observed significantly more frequently in the genome of cancer cells. Although many of these cannot be confidently classified as driver mutations, they may contribute to shaping a cell environment that favours cancer onset and development. Understanding how these gene variants causally affect cancer phenotypes may help developing strategies for reverting the disease phenotype. Here we focus on variants of genes whose products have the potential to modulate metabolism to support uncontrolled cell growth. Over recent months our team of expert curators has undertaken an effort to annotate in the database SIGNOR 1) metabolic pathways that are deregulated in cancer and 2) interactions connecting oncogenes and tumour suppressors to metabolic enzymes. In addition, we refined a recently developed graph analysis tool that permits users to infer causal paths leading from any human gene to modulation of metabolic pathways. The tool grounds on a human signed and directed network that connects ∼8400 biological entities such as proteins and protein complexes via causal relationships. The network, which is based on more than 30,000 published causal links, can be downloaded from the SIGNOR website. In addition, as SIGNOR stores information on drugs or other chemicals targeting the activity of many of the genes in the network, the identification of likely functional paths offers a rational framework for exploring new therapeutic strategies that revert the disease phenotype.

Transcription Factor Activation Profiles (TFAP) identify compounds promoting differentiation of Acute Myeloid Leukemia cell lines.

Repurposing of drugs for new therapeutic use has received considerable attention for its potential to limit time and cost of drug development. Here we present a new strategy to identify chemicals that are likely to promote a desired phenotype. We used data from the Connectivity Map (CMap) to produce a ranked list of drugs according to their potential to activate transcription factors that mediate myeloid differentiation of leukemic progenitor cells. To validate our strategy, we tested the in vitro differentiation potential of candidate compounds using the HL-60 human cell line as a myeloid differentiation model. Ten out of 22 compounds, which were ranked high in the inferred list, were confirmed to promote significant differentiation of HL-60. These compounds may be considered candidate for differentiation therapy. The method that we have developed is versatile and it can be adapted to different drug repurposing projects.

The IntAct database: efficient access to fine-grained molecular interaction data.

The IntAct molecular interaction database (https://www.ebi.ac.uk/intact) is a curated resource of molecular interactions, derived from the scientific literature and from direct data depositions. As of August 2021, IntAct provides more than one million binary interactions, curated by twelve global partners of the International Molecular Exchange consortium, for which the IntAct database provides a shared curation and dissemination platform. The IMEx curation policy has always emphasised a fine-grained data and curation model, aiming to capture the relevant experimental detail essential for the interpretation of the provided molecular interaction data. Here, we present recent curation focus and progress, as well as a completely redeveloped website which presents IntAct data in a much more user-friendly and detailed way.

SIGNORApp: a Cytoscape 3 application to access SIGNOR data.

SUMMARY: SIGNORApp is a Cytoscape 3 (3.8 and later) application that provides access to causal interactions annotated in the SIGNOR resource. The application builds networks that can be represented as weighted, signed, directed graphs, where nodes are interacting biological entities and edges represent causal interactions captured by expert curators from experiments reported in peer reviewed journals. Users can query the SIGNOR dataset with (i) single or multiple entity name(s) or identifier(s) and optionally they may require to include in the output network their interacting partners, (ii) browse pathways that are annotated in the SIGNOR resource and (iii) extract the entire causal interactome. The app offers two visualizations modes: one only displaying entity interactions and a second emphasizing the post-translational modifications occurring as a consequence of the interaction. In addition, users can click on nodes and edges to access entity and interaction annotations. Causal information is available for three model organisms: Homo sapiens, Mus musculus and Rattus norvegicus. AVAILABILITY AND IMPLEMENTATION: SIGNORApp has been developed for Cytoscape 3 (3.8 and later) in the Java programming language. The latest source code and the plugin can be found at: https://github.com/SIGNORcysAPP/signor-app and https://apps.cytoscape.org/apps/signorapp, respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterization of the Skeletal Muscle Secretome Reveals a Role for Extracellular Vesicles and IL1α/IL1ß in Restricting Fibro/Adipogenic Progenitor Adipogenesis.

Repeated mechanical stress causes injuries in the adult skeletal muscle that need to be repaired. Although muscle regeneration is a highly efficient process, it fails in some pathological conditions, compromising tissue functionality. This may be caused by aberrant cell-cell communication, resulting in the deposition of fibrotic and adipose infiltrates. Here, we investigate in vivo changes in the profile of skeletal muscle secretome during the regeneration process to suggest new targetable regulatory circuits whose failure may lead to tissue degeneration in pathological conditions. We describe the kinetic variation of expression levels of 76 secreted proteins during the regeneration process. In addition, we profile the gene expression of immune cells, endothelial cells, satellite cells, and fibro-adipogenic progenitors. This analysis allowed us to annotate each cell-type with the cytokines and receptors they have the potential to synthetize, thus making it possible to draw a cell-cell interaction map. We next selected 12 cytokines whose receptors are expressed in FAPs and tested their ability to modulate FAP adipogenesis and proliferation. We observed that IL1α and IL1ß potently inhibit FAP adipogenesis, while EGF and BTC notably promote FAP proliferation. In addition, we characterized the cross-talk mediated by extracellular vesicles (EVs). We first monitored the modulation of muscle EV cargo during tissue regeneration. Using a single-vesicle flow cytometry approach, we observed that EVs differentially affect the uptake of RNA and proteins into their lumen. We also investigated the EV capability to interact with SCs and FAPs and to modulate their proliferation and differentiation. We conclude that both cytokines and EVs secreted during muscle regeneration have the potential to modulate adipogenic differentiation of FAPs. The results of our approach provide a system-wide picture of mechanisms that control cell fate during the regeneration process in the muscle niche.

Assembling Disease Networks From Causal Interaction Resources.

The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.

A Resource for the Network Representation of Cell Perturbations Caused by SARS-CoV-2 Infection.

The coronavirus disease 2019 (COVID-19) pandemic has caused more than 2.3 million casualties worldwide and the lack of effective treatments is a major health concern. The development of targeted drugs is held back due to a limited understanding of the molecular mechanisms underlying the perturbation of cell physiology observed after viral infection. Recently, several approaches, aimed at identifying cellular proteins that may contribute to COVID-19 pathology, have been reported. Albeit valuable, this information offers limited mechanistic insight as these efforts have produced long lists of cellular proteins, the majority of which are not annotated to any cellular pathway. We have embarked in a project aimed at bridging this mechanistic gap by developing a new bioinformatic approach to estimate the functional distance between a subset of proteins and a list of pathways. A comprehensive literature search allowed us to annotate, in the SIGNOR 2.0 resource, causal information underlying the main molecular mechanisms through which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and related coronaviruses affect the host-cell physiology. Next, we developed a new strategy that enabled us to link SARS-CoV-2 interacting proteins to cellular phenotypes via paths of causal relationships. Remarkably, the extensive information about inhibitors of signaling proteins annotated in SIGNOR 2.0 makes it possible to formulate new potential therapeutic strategies. The proposed approach, which is generally applicable, generated a literature-based causal network that can be used as a framework to formulate informed mechanistic hypotheses on COVID-19 etiology and pathology.

Skeletal Muscle Subpopulation Rearrangements upon Rhabdomyosarcoma Development through Single-Cell Mass Cytometry.

The embryonal rhabdomyosarcoma (eRMS) is a soft tissue sarcoma commonly affecting the head and neck, the extremities and the genitourinary tract. To contribute to revealing the cell types that may originate this tumor, we exploited mass cytometry, a single-cell technique that, by using heavy-metal-tagged antibodies, allows the accurate monitoring of the changes occurring in the mononuclear cell composition of skeletal muscle tissue during tumor development. To this end, we compared cell populations of healthy muscles with those from spatiotemporal-induced eRMS tumors in a mouse model (LSL-KrasG12D/+;Tp53Fl/Fl) that can be used to develop rhabdomyosarcoma by means of infection with an adenovirus vector expressing Cre (Ad-Cre) recombinase. By monitoring different time points after tumor induction, we were able to analyze tumor progression and composition, identifying fibro/adipogenic progenitors (FAPs) as the cell type that, in this model system, had a pivotal role in tumor development. In vitro studies highlighted that both FAPs and satellite cells (SCs), upon infection with the Ad-Cre, acquired the potential to develop rhabdomyosarcomas when transplanted into immunocompromised mice. However, only infected FAPs had an antigen profile that was similar to embryonal rhabdomyosarcoma cells. Overall, our analysis supports the involvement of FAPs in eRMS development.

Biofabricating murine and human myo-substitutes for rapid volumetric muscle loss restoration.

The importance of skeletal muscle tissue is undoubted being the controller of several vital functions including respiration and all voluntary locomotion activities. However, its regenerative capability is limited and significant tissue loss often leads to a chronic pathologic condition known as volumetric muscle loss. Here, we propose a biofabrication approach to rapidly restore skeletal muscle mass, 3D histoarchitecture, and functionality. By recapitulating muscle anisotropic organization at the microscale level, we demonstrate to efficiently guide cell differentiation and myobundle formation both in vitro and in vivo. Of note, upon implantation, the biofabricated myo-substitutes support the formation of new blood vessels and neuromuscular junctions-pivotal aspects for cell survival and muscle contractile functionalities-together with an advanced muscle mass and force recovery. Altogether, these data represent a solid base for further testing the myo-substitutes in large animal size and a promising platform to be eventually translated into clinical scenarios.

Integrating Patient-Specific Information into Logic Models of Complex Diseases: Application to Acute Myeloid Leukemia.

High throughput technologies such as deep sequencing and proteomics are increasingly becoming mainstream in clinical practice and support diagnosis and patient stratification. Developing computational models that recapitulate cell physiology and its perturbations in disease is a required step to help with the interpretation of results of high content experiments and to devise personalized treatments. As complete cell-models are difficult to achieve, given limited experimental information and insurmountable computational problems, approximate approaches should be considered. We present here a general approach to modeling complex diseases by embedding patient-specific genomics data into actionable logic models that take into account prior knowledge. We apply the strategy to acute myeloid leukemia (AML) and assemble a network of logical relationships linking most of the genes that are found frequently mutated in AML patients. We derive Boolean models from this network and we show that by priming the model with genomic data we can infer relevant patient-specific clinical features. Here we propose that the integration of literature-derived causal networks with patient-specific data should be explored to help bedside decisions.

SCA-1 micro-heterogeneity in the fate decision of dystrophic fibro/adipogenic progenitors.

The term micro-heterogeneity refers to non-genetic cell to cell variability observed in a bell-shaped distribution of the expression of a trait within a population. The contribution of micro-heterogeneity to physiology and pathology remains largely uncharacterised. To address such an issue, we investigated the impact of heterogeneity in skeletal muscle fibro/adipogenic progenitors (FAPs) isolated from an animal model of Duchenne muscular dystrophy (DMD), the mdx mouse. FAPs play an essential role in muscle homoeostasis. However, in pathological conditions or ageing, they are the source of intramuscular infiltrations of fibrotic or adipose tissue. By applying a multiplex flow cytometry assay, we characterised and purified from mdx muscles two FAP cell states expressing different levels of SCA-1. The two cell states are morphologically identical and repopulate each other after several growth cycles. However, they differ in their in vitro behaviour. Cells expressing higher levels of SCA-1 (SCA1-High-FAPs) differentiate more readily into adipocytes while, when exposed to a fibrogenic stimulation, increase the expression of Col1a1 and Timp1 mRNA. A transcriptomic analysis confirmed the adipogenic propensity of SCA1-High-FAPs. In addition, SCA1-High-FAPs proliferate more extensively ex vivo and display more proliferating cells in dystrophic muscles in comparison to SCA1-Low-FAPs. Adipogenesis of both FAP cell states is inhibited in vitro by leucocytes from young dystrophic mice, while leucocytes isolated from aged dystrophic mice are less effective in limiting the adipogenesis of SCA1-High-FAPs suggesting a differential regulatory effect of the microenvironment on micro-heterogeneity. Our data suggest that FAP micro-heterogeneity is modulated in pathological conditions and that this heterogeneity in turn may impact on the behaviour of interstitial mesenchymal cells in genetic diseases.

Towards a unified open access dataset of molecular interactions.

The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources.

High-Dimensional Single-Cell Quantitative Profiling of Skeletal Muscle Cell Population Dynamics during Regeneration.

The interstitial space surrounding the skeletal muscle fibers is populated by a variety of mononuclear cell types. Upon acute or chronic insult, these cell populations become activated and initiate finely-orchestrated crosstalk that promotes myofiber repair and regeneration. Mass cytometry is a powerful and highly multiplexed technique for profiling single-cells. Herein, it was used to dissect the dynamics of cell populations in the skeletal muscle in physiological and pathological conditions. Here, we characterized an antibody panel that could be used to identify most of the cell populations in the muscle interstitial space. By exploiting the mass cytometry resolution, we provided a comprehensive picture of the dynamics of the major cell populations that sensed and responded to acute damage in wild type mice and in a mouse model of Duchenne muscular dystrophy. In addition, we revealed the intrinsic heterogeneity of many of these cell populations.

Adipogenesis of skeletal muscle fibro/adipogenic progenitors is affected by the WNT5a/GSK3/ß-catenin axis.

Fibro/Adipogenic Progenitors (FAPs) are muscle-interstitial progenitors mediating pro-myogenic signals that are critical for muscle homeostasis and regeneration. In myopathies, the autocrine/paracrine constraints controlling FAP adipogenesis are released causing fat infiltrates. Here, by combining pharmacological screening, high-dimensional mass cytometry and in silico network modeling with the integration of single-cell/bulk RNA sequencing data, we highlighted the canonical WNT/GSK/ß-catenin signaling as a crucial pathway modulating FAP adipogenesis triggered by insulin signaling. Consistently, pharmacological blockade of GSK3, by the LY2090314 inhibitor, stabilizes ß-catenin and represses PPARγ expression abrogating FAP adipogenesis ex vivo while limiting fatty degeneration in vivo. Furthermore, GSK3 inhibition improves the FAP pro-myogenic role by efficiently stimulating, via follistatin secretion, muscle satellite cell (MuSC) differentiation into mature myotubes. Combining, publicly available single-cell RNAseq datasets, we characterize FAPs as the main source of WNT ligands inferring their potential in mediating autocrine/paracrine responses in the muscle niche. Lastly, we identify WNT5a, whose expression is impaired in dystrophic FAPs, as a crucial WNT ligand able to restrain the detrimental adipogenic differentiation drift of these cells through the positive modulation of the ß-catenin signaling.

Toward High-Dimensional Single-Cell Analysis of Graphene Oxide Biological Impact: Tracking on Immune Cells by Single-Cell Mass Cytometry.

Considering the potential exposure to graphene, the most investigated nanomaterial, the assessment of the impact on human health has become an urgent need. The deep understanding of nanomaterial safety is today possible by high-throughput single-cell technologies. Single-cell mass cytometry (cytometry by time-of flight, CyTOF) shows an unparalleled ability to phenotypically and functionally profile complex cellular systems, in particular related to the immune system, as recently also proved for graphene impact. The next challenge is to track the graphene distribution at the single-cell level. Therefore, graphene oxide (GO) is functionalized with AgInS2 nanocrystals (GO-In), allowing to trace GO immune-cell interactions via the indium (115 In) channel. Indium is specifically chosen to avoid overlaps with the commercial panels (>30 immune markers). As a proof of concept, the GO-In CyTOF tracking is performed at the single-cell level on blood immune subpopulations, showing the GO interaction with monocytes and B cells, therefore guiding future immune studies. The proposed approach can be applied not only to the immune safety assessment of the multitude of graphene physical and chemical parameters, but also for graphene applications in neuroscience. Moreover, this approach can be translated to other 2D emerging materials and will likely advance the understanding of their toxicology.

Janus effect of glucocorticoids on differentiation of muscle fibro/adipogenic progenitors.

Muscle resident fibro-adipogenic progenitors (FAPs), support muscle regeneration by releasing cytokines that stimulate the differentiation of myogenic stem cells. However, in non-physiological contexts (myopathies, atrophy, aging) FAPs cause fibrotic and fat infiltrations that impair muscle function. We set out to perform a fluorescence microscopy-based screening to identify compounds that perturb the differentiation trajectories of these multipotent stem cells. From a primary screen of 1,120 FDA/EMA approved drugs, we identified 34 compounds as potential inhibitors of adipogenic differentiation of FAPs isolated from the murine model (mdx) of Duchenne muscular dystrophy (DMD). The hit list from this screen was surprisingly enriched with compounds from the glucocorticoid (GCs) chemical class, drugs that are known to promote adipogenesis in vitro and in vivo. To shed light on these data, three GCs identified in our screening efforts were characterized by different approaches. We found that like dexamethasone, budesonide inhibits adipogenesis induced by insulin in sub-confluent FAPs. However, both drugs have a pro-adipogenic impact when the adipogenic mix contains factors that increase the concentration of cAMP. Gene expression analysis demonstrated that treatment with glucocorticoids induces the transcription of Gilz/Tsc22d3, an inhibitor of the adipogenic master regulator PPARγ, only in anti-adipogenic conditions. Additionally, alongside their anti-adipogenic effect, GCs are shown to promote terminal differentiation of satellite cells. Both the anti-adipogenic and pro-myogenic effects are mediated by the glucocorticoid receptor and are not observed in the presence of receptor inhibitors. Steroid administration currently represents the standard treatment for DMD patients, the rationale being based on their anti-inflammatory effects. The findings presented here offer new insights on additional glucocorticoid effects on muscle stem cells that may affect muscle homeostasis and physiology.