  The adverse effects of microgravity exposure on mammalian physiology during
spaceflight necessitate a deep understanding of the underlying mechanisms to
develop effective countermeasures. One such concern is muscle atrophy, which is
partly attributed to the dysregulation of calcium levels due to abnormalities
in SERCA pump functioning. To identify potential biomarkers for this condition,
multi-omics data and physiological data available on the NASA Open Science Data
Repository (osdr.nasa.gov) were used, and machine learning methods were
employed. Specifically, we used multi-omics (transcriptomic, proteomic, and DNA
methylation) data and calcium reuptake data collected from C57BL/6J mouse
soleus and tibialis anterior tissues during several 30+ day-long missions on
the international space station. The QLattice symbolic regression algorithm was
introduced to generate highly explainable models that predict either
experimental conditions or calcium reuptake levels based on multi-omics
features. The list of candidate models established by QLattice was used to
identify key features contributing to the predictive capability of these
models, with Acyp1 and Rps7 proteins found to be the most predictive biomarkers
related to the resilience of the tibialis anterior muscle in space. These
findings could serve as targets for future interventions aiming to reduce the
extent of muscle atrophy during space travel.
