Molecular dynamics analysis of Superoxide Dismutase 1 mutations suggests decoupling between mechanisms underlying ALS onset and progression

Abstract

Mutations in the superoxide dismutase 1 (SOD1) gene are the second most common known cause of ALS. SOD1 variants express high phenotypic variability and over 200 have been reported in people with ALS. Investigating how different SOD1 variants affect the protein dynamics might help in understanding their pathogenic mechanism and explaining their heterogeneous clinical presentation. It was previously proposed that variants can be broadly classified in two groups, "wild-type like" (WTL) and "metal binding region" (MBR) variants, based on their structural location and biophysical properties. MBR variants are associated with a loss of SOD1 enzymatic activity. In this study we used molecular dynamics and large clinical datasets to characterise the differences in the structural and dynamic behaviour of WTL and MBR variants with respect to the wild-type SOD1, and how such differences influence the ALS clinical phenotype. Our study identified marked structural differences, some of which are observed in both variant groups, while others are group specific. Moreover, applying graph theory to a network representation of the proteins, we identified differences in the intramolecular contacts of the two classes of variants. Finally, collecting clinical data of approximately 500 SOD1 ALS patients carrying variants from both classes, we showed that the survival time of patients carrying an MBR variant is generally longer (~6 years median difference, p < 0.001) with respect to patients with a WTL variant. In conclusion, our study highlights key differences in the dynamic behaviour of the WTL and MBR SOD1 variants, and wild-type SOD1 at an atomic and molecular level. We identified interesting structural features that could be further investigated to explain the associated phenotypic variability. Our results support the hypothesis of a decoupling between mechanisms of onset and progression of SOD1 ALS, and an involvement of loss-of-function of SOD1 with the disease progression.

Unsupervised machine-learning identifies clinically distinct subtypes of ALS that reflect different genetic architectures and biological mechanisms

“…Secondly, Class 2 was uniquely enriched for genes involved in Parkinson's disease and schizophrenia (ErbB signalling (72,73)), which supports our finding that Class 2 displays higher polygenic risk scores of these diseases with respect to both controls and other classes. Finding differences in oxidative stress and apoptotic processes between Classes 1 and 2 aligns well with our finding that variants in the SOD1 gene (which codes for an antioxidant enzyme that has been proposed to affect ALS via both gain and loss of function mechanisms (47,74)) were more frequent in Class 2. The variability in biological trends observed across these clinical subgroups supports the perspective that patient stratification may be important for identifying biological disease mechanisms (27).…”

Computational evidences of a misfolding event in an aggregation‐prone light chain preceding the formation of the non‐native pathogenic dimer

“…In the majority of these diseases, the aggregative molecular species can be identified in a small set of proteins whose features are well‐defined. This is the case, for instance, of the amyloid peptide in AD, 8 Superoxide Dismutase 1 , TAR DNA‐binding protein 43 and RNA‐binding protein FUS in ALS 9–11 and Transthyretin in ATTR amyloidosis 12 …”

Unsupervised machine learning identifies distinct molecular and phenotypic ALS subtypes in post-mortem motor cortex and blood expression data

“…This design would not reflect motor neuron-specific ALS pathogenesis. Other studies adopted a case-control framework 8,9,11 , which could lead to reduced power given the potential decoupling between mechanisms underlying risk and clinical presentation [13][14][15] . Furthermore, previous work has not been validated in independent datasets or in different populations and did not investigate whether molecular subtypes identified in post-mortem brains are reflected in other tissues available pre-mortem.…”