Transcriptomic datasets from space-flown mice from GeneLab were extensively mined to extract the key genes that cause muscle atrophy in organ muscle tissues such as the thymus, liver, and spleen.
Top muscle atrophy gene regulators were identified, and a gene-disease knowledge graph was constructed using the scalable precision medicine knowledge engine. The top-ranked diseases were identified, and potential drug treatments were selected for repurposing using drug bank resources. Study authors generated a disease drug knowledge graph, and the graph neural network was then trained for predicting new drugs. The results are compared with machine learning methods.

Interestingly, in this study, several key target genes identified by the graph neural network are associated with cancer, diabetes, and neural disorders. The novel link prediction approach applied to the disease drug knowledge graph identifies the Monoclonal Antibodies drug therapy as a suitable candidate for drug repurposing for spaceflight induced microgravity. There are a total of 21 drugs identified as possible candidates for treating muscle atrophy. Graph neural network is a promising deep learning architecture for link prediction from gene-disease and disease-drug networks.

Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight. This study mined spaceflight data from the GeneLab data system to identify key target genes involved in response to spaceflight and then used that data to train a predictive model for potential drugs that can be used to treat spaceflight associated disuse atrophy.

Data from GeneLab were used in this study.

An Integrative Network Science and Artificial Intelligence Drug Repurposing Approach for Muscle Atrophy in Spaceflight Microgravity

Genomics, Space Biology, Astrobiology,