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

Abstract

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.

SignalingProfiler2.0: a network-based approach to bridge multi-omics data to phenotypic hallmarks

“…An important novelty of SignalingProfiler 2.0 is the implementation of the PhenoScore algorithm that infers from the model the regulation of hallmark phenotypes ( Figure 1D, Step 3) . Specifically, it incorporates and adapts our in-house ProxPath method (Iannuccelli et al , 2023) (see Supplementary Material), a graph-based algorithm designed to measure the functional proximity of a list of gene products to target pathways and phenotypes, using causal interactions annotated in SIGNOR. The PhenoScore algorithm averages the activity of phenotype upstream regulators in the model and uses this value as a proxy of the activation level of phenotypes.…”

“…Indeed, it is possible to estimate the activity of more than 3300 proteins, including, but not limited to, kinases, phosphatases, and transcription factors. Novel implementations of SignalingProfiler 2.0 include PKN browsing methods, optimization strategies, and PhenoScore inference with ProxPath (Iannuccelli et al , 2023). In particular, the PhenoScore makes it possible to map up to 200 cellular phenotypes onto the final model.…”

Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction

“…First, to functionally interpret the results of the simulations, for each network, we extracted key regulators of ‘apoptosis’ and ‘proliferation’ hallmarks from SIGNOR. To this aim, we applied our recently developed ProxPath algorithm, a graph-based method able to retrieve significant paths linking the nodes of our two optimized models to proliferation and apoptosis phenotypes ((Iannuccelli et al, 2023), see Methods) ( Table S1, Fig. 3E, left panels, Fig.…”

“…Moreover, to functionally interpret the results and assess the reliability of the model, we computed the activity of ‘apoptosis’ and ‘proliferation’ phenotypes upon FLT3 and other druggable nodes inhibition after the annotation of model proteins as pro- and anti-apoptotic (or proliferative). To obtain the table of protein annotations, with proximal phenotypes, we exploited a recently in-house developed method, dubbed ProxPath, (Iannuccelli et al, 2023) which computes significantly ‘close’ paths linking SIGNOR proteins and phenotypes. The distance table connecting the model nodes to the ‘Apoptosis’ and ‘Proliferation’ phenotypes is available in Table S1.…”

Convergent Transcriptomic Evidence Reveals the Dysfunctional Quantitative Mechanism of Synaptic Plasticity Control in ASD

“…It poses a critical question: Do transcriptomics variations from genetic and early environmental risk factors ultimately converge at the protein level? [61][62][63][64][65] In response to this challenge, we developed a framework to transform the signal transduction network concerning synaptic plasticity-related phenotypes into the mRNA Signaling-Regulatory Networks (mSiReN). This facilitates the analysis of signaling network dysregulation using transcriptome data.…”