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Updated: Sep 29, 2025
Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform
Published on: January 13, 2019
Detection of Target Genes for Drug Repurposing to Treat Skeletal Muscle Atrophy in Mice Flown in Spaceflight
Vidya Manian1,2, Jairo Orozco-Sandoval1, Victor Diaz-Martinez1
1Department of Electrical & Computer Engineering, University of Puerto Rico, Mayaguez, PR 00681-9000, USA.
Abstract:
Skeletal muscle atrophy is a common condition in aging, diabetes, and in long duration spaceflights due to microgravity. This article investigates multi-modal gene disease and disease drug networks via link prediction algorithms to select drugs for repurposing to treat skeletal muscle atrophy. Key target genes that cause muscle atrophy in the left and right extensor digitorum longus muscle tissue, gastrocnemius, quadriceps, and the left and right soleus muscles are detected using graph theoretic network analysis, by mining the transcriptomic datasets collected from mice flown in spaceflight made available by GeneLab. We identified the top muscle atrophy gene regulators by the Pearson correlation and Bayesian Markov blanket method. The gene disease knowledge graph was constructed using the scalable precision medicine knowledge engine. We computed node embeddings, random walk measures from the networks. Graph convolutional networks, graph neural networks, random forest, and gradient boosting methods were trained using the embeddings, network features for predicting links and ranking top gene-disease associations for skeletal muscle atrophy. Drugs were selected and a disease drug knowledge graph was constructed. Link prediction methods were applied to the disease drug networks to identify top ranked drugs for therapeutic treatment of skeletal muscle atrophy. The graph convolution network performs best in link prediction based on receiver operating characteristic curves and prediction accuracies. The key genes involved in skeletal muscle atrophy are associated with metabolic and neurodegenerative diseases. The drugs selected for repurposing using the graph convolution network method were nutrients, corticosteroids, anti-inflammatory medications, and others related to insulin.
Insights
This study used network analysis and machine learning to identify key genes and repurpose drugs for skeletal muscle atrophy, a condition linked to aging and spaceflight. Graph convolutional networks effectively predicted top drug candidates for therapeutic treatment.
Area of Science:
- Biomedical Informatics
- Systems Biology
- Genomics
Background:
- Skeletal muscle atrophy is a significant condition associated with aging, diabetes, and microgravity exposure during long-duration spaceflights.
- Identifying key molecular regulators and effective therapeutic interventions for muscle atrophy remains a critical challenge.
Purpose of the Study:
- To investigate multi-modal gene-disease and disease-drug networks using link prediction algorithms for skeletal muscle atrophy drug repurposing.
- To identify key gene regulators and rank potential therapeutic drugs for skeletal muscle atrophy.
Main Methods:
- Graph theoretic network analysis of transcriptomic data from mice exposed to spaceflight to detect key muscle atrophy genes.
- Construction of gene-disease and disease-drug knowledge graphs using a precision medicine knowledge engine.
- Application of machine learning models, including graph convolutional networks (GCNs), for link prediction to identify gene-disease associations and rank drugs for repurposing.
Main Results:
- Identification of top muscle atrophy gene regulators using Pearson correlation and Bayesian Markov blanket methods.
- The graph convolutional network demonstrated superior performance in link prediction for identifying gene-disease associations and ranking drugs.
- Key genes implicated in skeletal muscle atrophy are linked to metabolic and neurodegenerative diseases.
Conclusions:
- The study successfully identified key genes and repurposed drugs for skeletal muscle atrophy using advanced network analysis and machine learning.
- Nutrients, corticosteroids, anti-inflammatory medications, and insulin-related drugs were identified as promising candidates for therapeutic intervention.
- GCNs provide a powerful approach for drug repurposing in complex diseases like skeletal muscle atrophy.
