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Identifying candidate therapeutic targets in amyotrophic lateral sclerosis through a transcriptome-wide
Enrique J de Andrés-Galiana1, Juan Luis Fernández-Martínez2, Germán Morís3
1Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, Oviedo, Spain; Department of Computer Science, University of Oviedo, Oviedo, Spain.
This study identifies gene signatures and potential drug candidates for Amyotrophic Lateral Sclerosis (ALS) using machine learning on transcriptomic data. Findings highlight pathways involved in glial and immune regulation, offering new therapeutic hypotheses for ALS.
Area of Science:
- Neuroscience
- Genomics
- Pharmacology
Background:
- Amyotrophic lateral sclerosis (ALS) is a complex neurodegenerative disorder with limited effective treatments.
- Transcriptomic analysis offers insights into disease mechanisms and potential therapeutic targets.
Purpose of the Study:
- To identify recurrent ALS-associated transcriptional signatures from motor cortex and blood data.
- To generate drug-repositioning hypotheses using a machine learning framework and pathway analysis.
Main Methods:
- Utilized a consensus machine learning framework with feature selection on two transcriptomic datasets (motor cortex and blood).
- Performed 100 repetitions of 4-fold cross-validation to identify robust gene signatures.
- Applied COGENA pathway enrichment and Connectivity Map analysis for drug-signature correlation.
Main Results:
- Identified distinct transcriptional signatures in motor cortex and blood, with shared pathway-level themes including glial/immune regulation, proteostasis, and stress signaling.
- Motor cortex signatures emphasized astroglial support and glutamate handling, while blood signatures highlighted immune and metabolic signals.
- Several drugs, including deferoxamine, disulfiram, yohimbine, and atovaquone, showed potential for ALS treatment based on reversal or concordant connectivity profiles.
Conclusions:
- The study provides prioritized gene, pathway, and compound targets for ALS research.
- Findings suggest complementary roles of motor cortex and blood transcriptomics in understanding ALS.
- Further validation in independent cohorts and experimental models is required to confirm therapeutic efficacy and clinical suitability.
