Establishing mRNA and microRNA interactions driving disease heterogeneity in amyotrophic lateral sclerosis patient survival.

Amyotrophic lateral sclerosis is a fatal neurodegenerative disease, associated with the degeneration of both upper and lower motor neurons of the motor cortex, brainstem and spinal cord. Death in most patients results from respiratory failure within 3-4 years from symptom onset. However, due to disease heterogeneity some individuals survive only months from symptom onset while others live for several years. Identifying specific biomarkers that aid in establishing disease prognosis, particularly in terms of predicting disease progression, will help our understanding of amyotrophic lateral sclerosis pathophysiology and could be used to monitor a patient's response to drugs and therapeutic agents. Transcriptomic profiling technologies are continually evolving, enabling us to identify key gene changes in biological processes associated with disease. MicroRNAs are small non-coding RNAs typically associated with regulating gene expression, by degrading mRNA or reducing levels of gene expression. Being able to associate gene expression changes with corresponding microRNA changes would help to distinguish a more complex biomarker signature enabling us to address key challenges associated with complex diseases such as amyotrophic lateral sclerosis. The present study aimed to investigate the transcriptomic profile (mRNA and microRNA) of lymphoblastoid cell lines from amyotrophic lateral sclerosis patients to identify key signatures that are distinguishable in those patients who suffered a short disease duration (<12 months) (n = 22) compared with those that had a longer disease duration (>6 years) (n = 20). Transcriptional profiling of microRNA-mRNA interactions from lymphoblastoid cell lines in amyotrophic lateral sclerosis patients revealed differential expression of genes involved in cell cycle, DNA damage and RNA processing in patients with longer survival from disease onset compared with those with short survival. Understanding these particular microRNA-mRNA interactions and the pathways in which they are involved may help to distinguish potential therapeutic targets that could exert neuroprotective effects to prolong the life expectancy of amyotrophic lateral sclerosis patients.

Mutations in the Glycosyltransferase Domain of GLT8D1 Are Associated with Familial Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disorder without effective neuroprotective therapy. Known genetic variants impair pathways, including RNA processing, axonal transport, and protein homeostasis. We report ALS-causing mutations within the gene encoding the glycosyltransferase GLT8D1. Exome sequencing in an autosomal-dominant ALS pedigree identified p.R92C mutations in GLT8D1, which co-segregate with disease. Sequencing of local and international cohorts demonstrated significant ALS association in the same exon, including additional rare deleterious mutations in conserved amino acids. Mutations are associated with the substrate binding site, and both R92C and G78W changes impair GLT8D1 enzyme activity. Mutated GLT8D1 exhibits in vitro cytotoxicity and induces motor deficits in zebrafish consistent with ALS. Relative toxicity of mutations in model systems mirrors clinical severity. In conclusion, we have linked ALS pathophysiology to inherited mutations that diminish the activity of a glycosyltransferase enzyme.

Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction model.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive, fatal motor neuron disease with a variable natural history. There are no accurate models that predict the disease course and outcomes, which complicates risk assessment and counselling for individual patients, stratification of patients for trials, and timing of interventions. We therefore aimed to develop and validate a model for predicting a composite survival endpoint for individual patients with ALS. METHODS: We obtained data for patients from 14 specialised ALS centres (each one designated as a cohort) in Belgium, France, the Netherlands, Germany, Ireland, Italy, Portugal, Switzerland, and the UK. All patients were diagnosed in the centres after excluding other diagnoses and classified according to revised El Escorial criteria. We assessed 16 patient characteristics as potential predictors of a composite survival outcome (time between onset of symptoms and non-invasive ventilation for more than 23 h per day, tracheostomy, or death) and applied backward elimination with bootstrapping in the largest population-based dataset for predictor selection. Data were gathered on the day of diagnosis or as soon as possible thereafter. Predictors that were selected in more than 70% of the bootstrap resamples were used to develop a multivariable Royston-Parmar model for predicting the composite survival outcome in individual patients. We assessed the generalisability of the model by estimating heterogeneity of predictive accuracy across external populations (ie, populations not used to develop the model) using internal-external cross-validation, and quantified the discrimination using the concordance (c) statistic (area under the receiver operator characteristic curve) and calibration using a calibration slope. FINDINGS: Data were collected between Jan 1, 1992, and Sept 22, 2016 (the largest data-set included data from 1936 patients). The median follow-up time was 97·5 months (IQR 52·9-168·5). Eight candidate predictors entered the prediction model: bulbar versus non-bulbar onset (univariable hazard ratio [HR] 1·71, 95% CI 1·63-1·79), age at onset (1·03, 1·03-1·03), definite versus probable or possible ALS (1·47, 1·39-1·55), diagnostic delay (0·52, 0·51-0·53), forced vital capacity (HR 0·99, 0·99-0·99), progression rate (6·33, 5·92-6·76), frontotemporal dementia (1·34, 1·20-1·50), and presence of a C9orf72 repeat expansion (1·45, 1·31-1·61), all p<0·0001. The c statistic for external predictive accuracy of the model was 0·78 (95% CI 0·77-0·80; 95% prediction interval [PI] 0·74-0·82) and the calibration slope was 1·01 (95% CI 0·95-1·07; 95% PI 0·83-1·18). The model was used to define five groups with distinct median predicted (SE) and observed (SE) times in months from symptom onset to the composite survival outcome: very short 17·7 (0·20), 16·5 (0·23); short 25·3 (0·06), 25·2 (0·35); intermediate 32·2 (0·09), 32·8 (0·46); long 43·7 (0·21), 44·6 (0·74); and very long 91·0 (1·84), 85·6 (1·96). INTERPRETATION: We have developed an externally validated model to predict survival without tracheostomy and non-invasive ventilation for more than 23 h per day in European patients with ALS. This model could be applied to individualised patient management, counselling, and future trial design, but to maximise the benefit and prevent harm it is intended to be used by medical doctors only. FUNDING: Netherlands ALS Foundation.

Targeted Genetic Screen in Amyotrophic Lateral Sclerosis Reveals Novel Genetic Variants with Synergistic Effect on Clinical Phenotype.

Amyotrophic lateral sclerosis (ALS) is underpinned by an oligogenic rare variant architecture. Identified genetic variants of ALS include RNA-binding proteins containing prion-like domains (PrLDs). We hypothesized that screening genes encoding additional similar proteins will yield novel genetic causes of ALS. The most common genetic variant of ALS patients is a G4C2-repeat expansion within C9ORF72. We have shown that G4C2-repeat RNA sequesters RNA-binding proteins. A logical consequence of this is that loss-of-function mutations in G4C2-binding partners might contribute to ALS pathogenesis independently of and/or synergistically with C9ORF72 expansions. Targeted sequencing of genomic DNA encoding either RNA-binding proteins or known ALS genes (n = 274 genes) was performed in ALS patients to identify rare deleterious genetic variants and explore genotype-phenotype relationships. Genomic DNA was extracted from 103 ALS patients including 42 familial ALS patients and 61 young-onset (average age of onset 41 years) sporadic ALS patients; patients were chosen to maximize the probability of identifying genetic causes of ALS. Thirteen patients carried a G4C2-repeat expansion of C9ORF72. We identified 42 patients with rare deleterious variants; 6 patients carried more than one variant. Twelve mutations were discovered in known ALS genes which served as a validation of our strategy. Rare deleterious variants in RNA-binding proteins were significantly enriched in ALS patients compared to control frequencies (p = 5.31E-18). Nineteen patients featured at least one variant in a RNA-binding protein containing a PrLD. The number of variants per patient correlated with rate of disease progression (t-test, p = 0.033). We identified eighteen patients with a single variant in a G4C2-repeat binding protein. Patients with a G4C2-binding protein variant in combination with a C9ORF72 expansion had a significantly faster disease course (t-test, p = 0.025). Our data are consistent with an oligogenic model of ALS. We provide evidence for a number of entirely novel genetic variants of ALS caused by mutations in RNA-binding proteins. Moreover we show that these mutations act synergistically with each other and with C9ORF72 expansions to modify the clinical phenotype of ALS. A key finding is that this synergy is present only between functionally interacting variants. This work has significant implications for ALS therapy development.

Serum miRNAs miR-206, 143-3p and 374b-5p as potential biomarkers for amyotrophic lateral sclerosis (ALS).

Amyotrophic lateral sclerosis (ALS) is a fatal, neurodegenerative condition characterized by loss of motor neurones and progressive muscle wasting. There is no diagnostic test for ALS therefore robust biomarkers would not only be valuable for diagnosis, but also for the classification of disease subtypes, monitoring responses to drugs and tracking disease progression. As regulators of gene expression, microRNAs (miRNAs) are increasingly used for diagnostic and prognostic purposes in various disease states with increasing exploration in neurodegenerative disorders. We hypothesize that circulating blood-based miRNAs will serve as biomarkers and use miRNA profiling to determine miRNA signatures from the serum of sporadic ALS patients compared to healthy controls and patients with diseases that mimic ALS. A number of differentially expressed miRNAs were identified in each set of patient comparisons. Validation in an additional patient cohort showed that miR-206 and miR-143-3p were increased and miR-374b-5p was decreased compared to controls. A continued change in miRNA expression persisted during disease progression indicating the potential use of these particular miRNAs as longitudinal biomarkers in ALS.

Small RNA Sequencing of Sporadic Amyotrophic Lateral Sclerosis Cerebrospinal Fluid Reveals Differentially Expressed miRNAs Related to Neural and Glial Activity.

Amyotrophic lateral sclerosis (ALS) is a clinical subtype of motor neurone disease (MND), a fatal neurodegenerative disease involving the loss of both the upper and lower motor neurones from the motor cortex, brainstem, and spinal cord. Identifying specific disease biomarkers would help to not only improve diagnostic delay but also to classify disease subtypes, monitor response to therapeutic drugs and track disease progression. miRNAs are small non-coding RNA responsible for regulating gene expression and ultimately protein expression and have been used as biomarkers for many cancers and neurodegenerative disorders. Investigating the detection of miRNAs in cerebrospinal fluid (CSF), the fluid that bathes the central nervous system (CNS) is a prime target for identifying potential biomarkers for ALS. This is the first study to investigate the expression of miRNAs in the CSF of ALS patients using small RNA sequencing. We detected 11 differentially expressed miRNAs in the CSF of sporadic ALS (sALS) patients related to neural and glial activity. Additionally, miRNAs involved in glucose metabolism and the regulation of oxidative stress were also identified. Detecting the presence of potential CSF derived miRNA biomarkers in sALS could open up a whole new area of knowledge to help gain a better understanding of disease pathophysiology. Additionally, with further investigation, the tracking of CSF miRNA over the disease course could be used to follow the disease progression and monitor the effect of novel therapeutics that could be personalized to an individual disease phenotype.