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 the identification of crucial disease-driving molecules. Here, we analysed the systemic characteristics of SMA, using proteomics, phosphoproteomics, translatomics and interactomics, from two mouse models with different disease severities and genetics. This systems approach revealed subnetworks 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.

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Publication Details

Journal
Brain
Published
2024-08-26
DOI
https://doi.org/10.1093/brain/awae272
Citations
9
Primary Topic
Neurogenetic and Muscular Disorders Research
Type
article
Field-Weighted Citation Impact
3.94

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article

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

Christian M. Simon, Florian Gerstner, Gabriella Viero, Rashmi Kothary et al.
9 citations
Brain
Neurogenetic and Muscular Disorders Research
3.94
article

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

Christian M. Simon, Florian Gerstner, Gabriella Viero, Rashmi Kothary, Martina Paganin, Theresa Schweitzer, Andreas Pich, Peter Claus, Andreas Ziegler, Ines Tapken, Elia Di Schiavi, Nora Tula Detering, Thomas H. Gillingwater, Claire Cannet, D. Kühn, Moritz Niesert, Federica Cieri, Pamela Santonicola, Afshin Saffari, Niko Hensel, Kiterie M. E. Faller, Tobias Schüning, Amy Glynn, Gaurav Sharma, Yu-Ting Huang
article en
9 citations

Abstract

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 the identification of crucial disease-driving molecules. Here, we analysed the systemic characteristics of SMA, using proteomics, phosphoproteomics, translatomics and interactomics, from two mouse models with different disease severities and genetics. This systems approach revealed subnetworks 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.

BrainVol. 148(2)
Mott MacDonald (United Kingdom) (GB), University of Ottawa (CA), Bruker (United States) (US), Heidelberg University (DE), Ottawa Hospital (CA), Luther University (KR), University Hospital Heidelberg (DE), Medizinische Hochschule Hannover (DE), Institute of Biosciences and Bioresources (IT), Institute of Biophysics (BG), Biomedical Research Institute (US), Bruker (Germany) (DE), Martin Luther University Halle-Wittenberg (DE), University of Naples Federico II (IT), Heidelberg University (US), Leipzig University (DE), University of Edinburgh (GB)
Biogen, European Commission, Università degli Studi di Trento
Openalex Percentile: Top 6%
Neurogenetic and Muscular Disorders Research
3.94
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