Abstract
To identify potential diagnostic and prognostic biomarkers for clinical management and clinical trials in amyotrophic lateral sclerosis. We analysed proteomics data of ALS patient-induced pluripotent stem cell-derived motor neurons available through the AnswerALS consortium. After stratifying patients using clinical ALSFRS-R and ALS-CBS scales, we identified differentially expressed proteins indicative of ALS disease severity and progression rate as candidate ALS-related and prognostic biomarkers. Pathway analysis for identified proteins was performed using STITCH. Protein sets were correlated with the effects of drugs using the Connectivity Map tool to identify compounds likely to affect similar pathways. RNAi screening was performed in a Drosophila TDP-43 ALS model to validate pathological relevance. A statistical classification machine learning model was constructed using ridge regression that uses proteomics data to differentiate ALS patients from controls. We identified 76, 21, 71 and 1 candidate ALS-related biomarkers and 22, 41, 27 and 64 candidate prognostic biomarkers from patients stratified by ALSFRS-R baseline, ALSFRS-R progression slope, ALS-CBS baseline and ALS-CBS progression slope, respectively. Nineteen proteins enhanced or suppressed pathogenic eye phenotypes in the ALS fly model. Nutraceuticals, dopamine pathway modulators, statins, anti-inflammatories and antimicrobials were predicted starting points for drug repurposing using the connectivity map tool. Ten diagnostic biomarker proteins were predicted by machine learning to identify ALS patients with high accuracy and sensitivity. This study showcases the powerful approach of iPSC-motor neuron proteomics combined with machine learning and biological confirmation in the prediction of novel mechanisms and diagnostic and predictive biomarkers in ALS.
