Explainable machine learning identifies multi-omics signatures of muscle response to spaceflight in mice

Kevin Li; Riya Desai; Ryan T Scott; Joel Ricky Steele; Meera Machado; Samuel Demharter; Adrienne Hoarfrost; Jessica L Braun; Val A Fajardo; Lauren M Sanders; Sylvain V Costes

doi:10.1038/s41526-023-00337-5

Explainable machine learning identifies multi-omics signatures of muscle response to spaceflight in mice

NPJ Microgravity. 2023 Dec 13;9(1):90. doi: 10.1038/s41526-023-00337-5.

Authors

Kevin Li^#^{1

2}, Riya Desai^#³, Ryan T Scott^{1

4}, Joel Ricky Steele^{4

5

6}, Meera Machado⁷, Samuel Demharter⁷, Adrienne Hoarfrost⁸, Jessica L Braun⁹, Val A Fajardo⁹, Lauren M Sanders^{10

11}, Sylvain V Costes¹²

Affiliations

¹ KBR, Moffett Field, CA, USA.
² NASA Space Life Sciences Training Program, Moffett Field, CA, USA.
³ College of Letters and Science, University of California at Davis, Davis, CA, USA.
⁴ Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, USA.
⁵ Monash Proteomics and Metabolomics Platform, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia.
⁶ Blue Marble Space, Seattle, WA, USA.
⁷ Abzu ApS, Copenhagen, Denmark.
⁸ Department of Marine Sciences, University of Georgia, Athens, GA, USA.
⁹ Department of Kinesiology, Centre for Bone and Muscle Health, Brock University, St. Catharines, Canada.
¹⁰ Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, USA. lauren.m.sanders@nasa.gov.
¹¹ Blue Marble Space, Seattle, WA, USA. lauren.m.sanders@nasa.gov.
¹² Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, USA. sylvain.v.costes@nasa.gov.

^# Contributed equally.

Abstract

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.