The genetic basis of amyotrophic lateral sclerosis: recent breakthroughs

Abstract

Deciphering the genetic architecture of amyotrophic lateral sclerosis (ALS), an adult-onset neurodegenerative disorder of the motor neuron system, is important to understand the etiology of this fatal disease as well as to develop customized ALS therapies based on the patient's genetic fingerprint. In this review, we discuss the genetic basis of ALS, and attempt to link the causal genes to three highly interrelated pathogenic mechanisms: dysproteostasis, RNA dysregulation, and axon dysfunction. In addition, we address the clinical and biological implications of these genetic findings. Furthermore, we explore to what extent genetic knowledge can be converted into targeted and personalized treatments.

Genome-Wide Analyses Identify KIF5A as a Novel ALS Gene

“…In addition to the insights provided by each novel ALS gene, the collective knowledge gained from genetic factors provides a more comprehensive understanding of the interacting pathways underlying motor neuron degeneration. For example, the identification of ALS genes has revealed at least three pathways believed to contribute to the development of ALS: (1) RNA metabolism (based on the observation of mutations in C9orf72, FUS,HNRNPA1,and MATR3); (2) protein homeostasis (UBQLN2,VCP,OPTN,VAPB); (3) cytoskeletal dynamics (PFN1, TUBA4A, DCTN1) (Chia et al, 2018;Robberecht and Eykens, 2015;Taylor et al, 2016). Understanding the mechanisms leading to disease pathogenesis again provides targets for therapeutic intervention that may be applicable to all forms of ALS.…”

Gene co-expression network analysis in human spinal cord highlights mechanisms underlying amyotrophic lateral sclerosis susceptibility

“…First, we identified gene co-expression modules and assigned each module a biological, as well as cell-type identity 19,20 . Next, we leveraged multiple datasets identifying ALS genetic risk factors [13][14][15][21][22][23] to comprehensively assess which pathways and cell-types are susceptible to ALS genetic risk. Finally, we compared these predictions with human ALS and animal model datasets [16][17][18] to develop a mechanistic understanding of ALS etiology and progression ( Fig.…”

“…To address how these modules fit within the context of known ALS susceptibility factors, we assessed whether a literature-curated set of well-known, large effect size, familial ALS risk genes 13,14 were enriched within our co-expression modules (Fig. 3a).…”

“…We analyzed a collection of gene sets generated by previous genetic studies of ALS. For large effect size ALS risk genes, we obtained 50 ALS literature curated ALS risk genes from two studies 13,14 . For the determination of cell-type specificity, we obtained the top 50 differentially Logistic regression was performed to calculate an odds ratio and p-value for enrichment of each co-expression module with a test gene set.…”

“…We characterized these co-expression modules through gene ontology (GO) enrichment analysis, which reveals that the modules represent a diverse range of biological processes. To identify modules enriched with ALS genetic risk factors, we queried enrichment of rare, large effect risk mutations previously described in the literature, as well as common genetic variation 13,14 . The second approach is genome-wide and therefore unbiased, which focuses on the enrichment of small effect common risk variants identified from a Genome Wide Association Study (GWAS) of ALS 15 .…”