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/6 J 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.

太空飞行期间微重力暴露对哺乳动物生理机能的不利影响需要深入了解潜在机制，以制定有效的对策。其中一个问题是肌肉萎缩，部分原因是 SERCA 泵功能异常导致钙水平失调。为了识别这种情况的潜在生物标志物，使用了 NASA 开放科学数据存储库 (osdr.nasa.gov) 上提供的多组学数据和生理数据，并采用了机器学习方法。具体来说，我们使用了在国际空间站上多次为期 30 多天的任务期间从 C57BL/6 J 小鼠比目鱼肌和胫骨前组织收集的多组学（转录组学、蛋白质组学和 DNA 甲基化）数据和钙再摄取数据。引入 QLattice 符号回归算法来生成高度可解释的模型，该模型可根据多组学特征预测实验条件或钙再摄取水平。 QLattice 建立的候选模型列表用于确定有助于这些模型预测能力的关键特征，其中 Acyp1 和 Rps7 蛋白被发现是与胫骨前肌空间弹性相关的最具预测性的生物标志物。这些发现可以作为未来干预措施的目标，旨在减少太空旅行期间肌肉萎缩的程度。