Using the MINT Database to Search Protein Interactions.

The Molecular INTeractions Database (MINT) is a public database designed to store information about protein interactions. Protein interactions are extracted from scientific literature and annotated in the database by expert curators. Currently (October 2019), MINT contains information on more than 26,000 proteins and more than 131,600 interactions in over 30 model organisms. This article provides protocols for searching MINT over the Internet, using the new MINT Web Page. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Searching MINT over the internet Alternate Protocol: MINT visualizer Basic Protocol 2: Submitting interaction data.

SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update.

The SIGnaling Network Open Resource 2.0 (SIGNOR 2.0) is a public repository that stores signaling information as binary causal relationships between biological entities. The captured information is represented graphically as a signed directed graph. Each signaling relationship is associated to an effect (up/down-regulation) and to the mechanism (e.g. binding, phosphorylation, transcriptional activation, etc.) causing the up/down-regulation of the target entity. Since its first release, SIGNOR has undergone a significant content increase and the number of annotated causal interactions have almost doubled. SIGNOR 2.0 now stores almost 23 000 manually-annotated causal relationships between proteins and other biologically relevant entities: chemicals, phenotypes, complexes, etc. We describe here significant changes in curation policy and a new confidence score, which is assigned to each interaction. We have also improved the compliance to the FAIR data principles by providing (i) SIGNOR stable identifiers, (ii) programmatic access through REST APIs, (iii) bioschemas and (iv) downloadable data in standard-compliant formats, such as PSI-MI CausalTAB and GMT. The data are freely accessible and downloadable at https://signor.uniroma2.it/.

SPV: a JavaScript Signaling Pathway Visualizer.

Summary: The visualization of molecular interactions annotated in web resources is useful to offer to users such information in a clear intuitive layout. These interactions are frequently represented as binary interactions that are laid out in free space where, different entities, cellular compartments and interaction types are hardly distinguishable. Signaling Pathway Visualizer is a free open source JavaScript library, which offers a series of pre-defined elements, compartments and interaction types meant to facilitate the representation of signaling pathways consisting of causal interactions without neglecting simple protein-protein interaction networks. Availability and implementation: Freely available under Apache version 2 license; Source code: https://github.com/Sinnefa/SPV_Signaling_Pathway_Visualizer_v1.0. Language: JavaScript; Web technology: Scalable Vector Graphics; Libraries: D3.js.

DISNOR: a disease network open resource.

DISNOR is a new resource that aims at exploiting the explosion of data on the identification of disease-associated genes to assemble inferred disease pathways. This may help dissecting the signaling events whose disruption causes the pathological phenotypes and may contribute to build a platform for precision medicine. To this end we combine the gene-disease association (GDA) data annotated in the DisGeNET resource with a new curation effort aimed at populating the SIGNOR database with causal interactions related to disease genes with the highest possible coverage. DISNOR can be freely accessed at http://DISNOR.uniroma2.it/ where >3700 disease-networks, linking ∼2600 disease genes, can be explored. For each disease curated in DisGeNET, DISNOR links disease genes by manually annotated causal relationships and offers an intuitive visualization of the inferred 'patho-pathways' at different complexity levels. User-defined gene lists are also accepted in the query pipeline. In addition, for each list of query genes-either annotated in DisGeNET or user-defined-DISNOR performs a gene set enrichment analysis on KEGG-defined pathways or on the lists of proteins associated with the inferred disease pathways. This function offers additional information on disease-associated cellular pathways and disease similarity.

SIGNOR: A Database of Causal Relationships Between Biological Entities-A Short Guide to Searching and Browsing.

SIGNOR (http://signor.uniroma2.it), the SIGnaling Network Open Resource, is a database designed to store experimentally validated causal interactions, i.e., interactions where a source entity has a regulatory effect (up-regulation, down-regulation, etc.) on a second target entity. SIGNOR acts both as a source of signaling information and a support for data analysis, modeling, and prediction. A user-friendly interface features the ability to search entries for any given protein or group of proteins and to display their interactions graphically in a network view. At the time of writing, SIGNOR stores approximately 16,000 manually curated interactions connecting more than 4,000 biological entities (proteins, chemicals, protein complexes, etc.) that play a role in signal transduction. SIGNOR also offers a collection of 37 signaling pathways. SIGNOR can be queried by three search tools: "single-entity" search, "multiple-entity" search, and "pathway" search. This manuscript describes two basic protocols detailing how to navigate and search the SIGNOR database and how to download the annotated dataset for local use. Finally, the support protocol reviews the utilities of the graphic visualizer. © 2017 by John Wiley & Sons, Inc.

The cell-autonomous mechanisms underlying the activity of metformin as an anticancer drug.

The biguanide drug metformin profoundly affects cell metabolism, causing an impairment of the cell energy balance and triggering a plethora of pleiotropic effects that vary depending on the cellular or environmental context. Interestingly, a decade ago, it was observed that metformin-treated diabetic patients have a significantly lower cancer risk. Although a variety of in vivo and in vitro observations emphasising the role of metformin as anticancer drug have been reported, the underlying mechanisms are still poorly understood. Here, we discuss our current understanding of the molecular mechanisms that are perturbed by metformin treatment and that might be relevant to understand its antitumour activities. We focus on the cell-autonomous mechanisms modulating growth and death of cancer cells.

Comparing Alzheimer's and Parkinson's diseases networks using graph communities structure.

BACKGROUND: Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of "omics" data to shed light on Alzheimer's and Parkinson's diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors. Classical reductionist approaches are focused on a few elements, providing a narrow overview of the etiopathogenic complexity of multifactorial diseases. On the other hand, high-throughput technologies allow the evaluation of many components of biological systems and their behaviors. Analysis of Parkinson's Disease (PD) and Alzheimer's Disease (AD) from a network perspective can highlight proteins or pathways common but differently represented that can be discriminating between the two pathological conditions, thus highlight similarities and differences. RESULTS: In this work we propose a strategy that exploits network community structure identified with a state-of-the-art network community discovery algorithm called InfoMap, which takes advantage of information theory principles. We used two similarity measurements to quantify functional and topological similarities between the two pathologies. We built a Similarity Matrix to highlight similar communities and we analyzed statistically significant GO terms found in clustered areas of the matrix and in network communities. Our strategy allowed us to identify common known and unknown processes including DNA repair, RNA metabolism and glucose metabolism not detected with simple GO enrichment analysis. In particular, we were able to capture the connection between mitochondrial dysfunction and metabolism (glucose and glutamate/glutamine). CONCLUSIONS: This approach allows the identification of communities present in both pathologies which highlight common biological processes. Conversely, the identification of communities without any counterpart can be used to investigate processes that are characteristic of only one of the two pathologies. In general, the same strategy can be applied to compare any pair of biological networks.

SIGNOR: a database of causal relationships between biological entities.

Assembly of large biochemical networks can be achieved by confronting new cell-specific experimental data with an interaction subspace constrained by prior literature evidence. The SIGnaling Network Open Resource, SIGNOR (available on line at http://signor.uniroma2.it), was developed to support such a strategy by providing a scaffold of prior experimental evidence of causal relationships between biological entities. The core of SIGNOR is a collection of approximately 12,000 manually-annotated causal relationships between over 2800 human proteins participating in signal transduction. Other entities annotated in SIGNOR are complexes, chemicals, phenotypes and stimuli. The information captured in SIGNOR can be represented as a signed directed graph illustrating the activation/inactivation relationships between signalling entities. Each entry is associated to the post-translational modifications that cause the activation/inactivation of the target proteins. More than 4900 modified residues causing a change in protein concentration or activity have been curated and linked to the modifying enzymes (about 351 human kinases and 94 phosphatases). Additional modifications such as ubiquitinations, sumoylations, acetylations and their effect on the modified target proteins are also annotated. This wealth of structured information can support experimental approaches based on multi-parametric analysis of cell systems after physiological or pathological perturbations and to assemble large logic models.

VirusMentha: a new resource for virus-host protein interactions.

Viral infections often cause diseases by perturbing several cellular processes in the infected host. Viral proteins target host proteins and either form new complexes or modulate the formation of functional host complexes. Describing and understanding the perturbation of the host interactome following viral infection is essential for basic virology and for the development of antiviral therapies. In order to provide a general overview of such interactions, a few years ago we developed VirusMINT. We have now extended the scope and coverage of VirusMINT and established VirusMentha, a new virus-virus and virus-host interaction resource build on the detailed curation protocols of the IMEx consortium and on the integration strategies developed for mentha. VirusMentha is regularly and automatically updated every week by capturing, via the PSICQUIC protocol, interactions curated by five different databases that are part of the IMEx consortium. VirusMentha can be freely browsed at http://virusmentha.uniroma2.it/ and its complete data set is available for download.

Combining affinity proteomics and network context to identify new phosphatase substrates and adapters in growth pathways.

Protein phosphorylation homoeostasis is tightly controlled and pathological conditions are caused by subtle alterations of the cell phosphorylation profile. Altered levels of kinase activities have already been associated to specific diseases. Less is known about the impact of phosphatases, the enzymes that down-regulate phosphorylation by removing the phosphate groups. This is partly due to our poor understanding of the phosphatase-substrate network. Much of phosphatase substrate specificity is not based on intrinsic enzyme specificity with the catalytic pocket recognizing the sequence/structure context of the phosphorylated residue. In addition many phosphatase catalytic subunits do not form a stable complex with their substrates. This makes the inference and validation of phosphatase substrates a non-trivial task. Here, we present a novel approach that builds on the observation that much of phosphatase substrate selection is based on the network of physical interactions linking the phosphatase to the substrate. We first used affinity proteomics coupled to quantitative mass spectrometry to saturate the interactome of eight phosphatases whose down regulations was shown to affect the activation of the RAS-PI3K pathway. By integrating information from functional siRNA with protein interaction information, we develop a strategy that aims at inferring phosphatase physiological substrates. Graph analysis is used to identify protein scaffolds that may link the catalytic subunits to their substrates. By this approach we rediscover several previously described phosphatase substrate interactions and characterize two new protein scaffolds that promote the dephosphorylation of PTPN11 and ERK by DUSP18 and DUSP26, respectively.

DBATE: database of alternative transcripts expression.

The use of high-throughput RNA sequencing technology (RNA-seq) allows whole transcriptome analysis, providing an unbiased and unabridged view of alternative transcript expression. Coupling splicing variant-specific expression with its functional inference is still an open and difficult issue for which we created the DataBase of Alternative Transcripts Expression (DBATE), a web-based repository storing expression values and functional annotation of alternative splicing variants. We processed 13 large RNA-seq panels from human healthy tissues and in disease conditions, reporting expression levels and functional annotations gathered and integrated from different sources for each splicing variant, using a variant-specific annotation transfer pipeline. The possibility to perform complex queries by cross-referencing different functional annotations permits the retrieval of desired subsets of splicing variant expression values that can be visualized in several ways, from simple to more informative. DBATE is intended as a novel tool to help appreciate how, and possibly why, the transcriptome expression is shaped. DATABASE URL: http://bioinformatica.uniroma2.it/DBATE/.

HuPho: the human phosphatase portal.

Phosphatases and kinases contribute to the regulation of protein phosphorylation homeostasis in the cell. Phosphorylation is a key post-translational modification underlying the regulation of many cellular processes. Thus, a comprehensive picture of phosphatase function and the identification of their target substrates would aid a systematic approach to a mechanistic description of cell signalling. Here we present a website designed to facilitate the retrieval of information about human protein phosphatases. To this end we developed a search engine to recover and integrate information annotated in several publicly available web resources. In addition we present a text-mining-assisted annotation effort aimed at extracting phosphatase related data reported in the scientific literature. The HuPho (human phosphatases) website can be accessed at http://hupho.uniroma2.it.