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

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.

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.

Autophagy and Mitophagy-Related Pathways at the Crossroads of Genetic Pathways Involved in Familial Sarcoidosis and Host-Pathogen Interactions Induced by Coronaviruses

Sarcoidosis is a multisystem disease characterized by the development and accumulation of granulomas, the hallmark of an inflammatory process induced by environmental and/or infectious and or genetic factors. This auto-inflammatory disease mainly affects the lungs, the gateway to environmental aggressions and viral infections. We have shown previously that genetic predisposition to sarcoidosis occurring in familial cases is related to a large spectrum of pathogenic variants with, however, a clustering around mTOR (mammalian Target Of Rapamycin)-related pathways and autophagy regulation. The context of the COVID-19 pandemic led us to evaluate whether such genetic defects may increase the risk of a severe course of SARS-CoV2 infection in patients with sarcoidosis. We extended a whole exome screening to 13 families predisposed to sarcoidosis and crossed the genes sharing mutations with the list of genes involved in the SARS-CoV2 host-pathogen protein-protein interactome. A similar analysis protocol was applied to a series of 100 healthy individuals. Using ENRICH.R, a comprehensive gene set enrichment web server, we identified the functional pathways represented in the set of genes carrying deleterious mutations and confirmed the overrepresentation of autophagy- and mitophagy-related functions in familial cases of sarcoidosis. The same protocol was applied to the set of genes common to sarcoidosis and the SARS-CoV2-host interactome and found a significant enrichment of genes related to mitochondrial factors involved in autophagy, mitophagy, and RIG-I-like (Retinoic Acid Inducible Gene 1) Receptor antiviral response signaling. From these results, we discuss the hypothesis according to which sarcoidosis is a model for studying genetic abnormalities associated with host response to viral infections as a consequence of defects in autophagy and mitophagy processes.