bioRxivBioengineeringPreprint
Genetic Diversity Modulates The Physical And Transcriptomic Response Of Skeletal Muscle To Simulated Microgravity
Y. Zeineddine, M. A. Friedman, E. G. Buettmann, L. B. Abraham, G. A. Hoppock, H. J. Donahue
Abstract
Developments in long-term space exploration necessitate advancements in countermeasures against microgravity-induced skeletal muscle loss. Astronaut data shows considerable variation in muscle loss in response to microgravity. Previous experiments suggest that genetic background influences the skeletal muscle response to unloading, but no in-depth analysis of genetic expression was performed. Here, we placed eight inbred founder strains of the diversity outbred mice (129S1/SvImJ, A/J, C57BL/6J, CAST/EiJ, NOD/ShiLtJ, NZO/HILtJ, PWK/PhJ, and WSB/EiJ) in simulated microgravity (SM) via hindlimb unloading for three weeks. Body weight, muscle morphology, muscle strength, protein synthesis marker expression, and RNA expression were collected. A/J and CAST/EiJ mice were most susceptible to SM-induced muscle loss, whereas NOD/ShiLtJ mice were the most protected. In response to SM, A/J and CAST/EiJ mice experienced reductions in body weight, muscle mass, muscle volume, and muscle cross-sectional area. A/J mice had the highest number of differentially expressed genes (68) and associated gene ontologies (328). Downregulation of immunological gene ontologies and genes encoding anabolic immune factors suggest that immune dysregulation contributes to the response of A/J mice to SM. Several muscle properties showed significant interactions between SM and mouse strain and a high degree of heritability. These data imply that genetic background plays a role in the degree of muscle loss in SM and that more individualized programs should be developed for astronauts to protect their skeletal muscles against microgravity on long term missions.
- organism: mouse
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
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