The systemic complexity of a monogenic disease: how interactomes help to understand molecular disease networks

Models of monogenic diseases are paradigms for the causal analyses of molecular alterations. Spinal Muscular Atrophy (SMA) is a monogenic disease caused by mutations or deletions of the Survival of Motoneuron 1 ( SMN1) gene (Lefebvre et al., 1995). Current available drugs for SMA increase SMN protein levels resulting in a prolonged survival of patients. However, individual pathomechanisms remain altered (Butterfield, 2021; Hensel et al., 2020). Known pathomechanisms include splicing, cytoskeletal regulation or translation (Hensel & Claus, 2018; Lauria et al., 2020; Pellizzoni et al., 1998). Single mechanisms have been well studied but represent specific aspects of SMA only. The connectivity of these single pathways within a network remains largely unknown. We aimed to analyze the systemic character of SMA by combining proteome, phosphoproteome and translatome data from two SMA mouse models. This network biology-based analyses revealed subnetworks and proteins overlapping between models, but also protein communities defining separate entities. Different disease severities known in patients, here represented by the different animal models, hold common upstream regulators and kinases. Such regulators can affect several changes simultaneously, an important characteristic for the development of one drug for all types of SMA. In addition, the identified regulators are known genes involved in neurodegenerative diseases. Additional inclusion of SMN-interactome data with the proteome network elucidated disease hubs and bottlenecks between the disease-causing protein and altered proteins, which created a comprehensive representation of SMA. We could translate the identification of a common dysregulation in purine metabolism between models to treatment naïve patient samples which showed an increase in associated urine metabolites before and after neuromuscular symptom onset. We highlight that SMA is a system disease only representable by different experimental models and omics levels. This study combines different molecular levels and methods to describe SMA on a systems level and enables the interpretation of single protein changes in the disease context.