The systemic complexity of a monogenic disease: the molecular network of spinal muscular atrophy.

Monogenic diseases are well-suited paradigms for the causal analysis of disease-driving molecular patterns. Spinal Muscular Atrophy (SMA) is one such monogenic model caused by mutation or deletion of the Survival of motor neuron 1 (SMN1) gene. Although several functions of the SMN protein have been studied, single functions and pathways alone do not allow to identify critical disease-driving molecules. Here, we analyzed the systemic characteristics of SMA employing proteomics, phosphoproteomics, translatomics and interactomics from two mouse models with different disease-severities and genetics. This systems approach revealed sub-networks and proteins characterizing commonalities and differences of both models. To link the identified molecular networks with the disease-causing SMN protein, we combined SMN-interactome data with both proteomes creating a comprehensive representation of SMA. By this approach, disease hubs and bottlenecks between SMN and downstream pathways could be identified. Linking a disease-causing molecule with widespread molecular dysregulations via multiomics is a concept for analyses of monogenic diseases.

The SMN-ribosome interplay: a new opportunity for Spinal Muscular Atrophy therapies.

The underlying cause of Spinal Muscular Atrophy (SMA) is in the reduction of survival motor neuron (SMN) protein levels due to mutations in the SMN1 gene. The specific effects of SMN protein loss and the resulting pathological alterations are not fully understood. Given the crucial roles of the SMN protein in snRNP biogenesis and its interactions with ribosomes and translation-related proteins and mRNAs, a decrease in SMN levels below a specific threshold in SMA is expected to affect translational control of gene expression. This review covers both direct and indirect SMN interactions across various translation-related cellular compartments and processes, spanning from ribosome biogenesis to local translation and beyond. Additionally, it aims to outline deficiencies and alterations in translation observed in SMA models and patients, while also discussing the implications of the relationship between SMN protein and the translation machinery within the context of current and future therapies.

Visualizing gene expression changes in time, space, and single cells with expressyouRcell.

The last decade has witnessed massive advancements in high-throughput techniques capable of producing increasingly complex gene expression datasets across time and space and at the resolution of single cells. Yet, the large volume of big data available and the complexity of experimental designs hamper an easy understanding and effective communication of the results. We present expressyouRcell, an easy-to-use R package to map the multi-dimensional variations of transcript and protein levels in dynamic cell pictographs. expressyouRcell visualizes gene expression variations as pictographic representations of cell-type thematic maps. expressyouRcell visually reduces the complexity of displaying gene expression and protein level changes across multiple measurements (time points or single-cell trajectories) by generating dynamic representations of cellular pictographs. We applied expressyouRcell to single cell, bulk RNA sequencing (RNA-seq), and proteomics datasets, demonstrating its flexibility and usability in the visualization of complex variations in gene expression. Our approach improves the standard quantitative interpretation and communication of relevant results.