Global transcriptomic analysis suggests carbon dioxide as an environmental stressor in spaceflight: A systems biology GeneLab case study

• Published in: Scientific reports 2018-03, Vol.8 (1), p.4191-10, Article 4191
• Main Authors: Beheshti, Afshin, Cekanaviciute, Egle, Smith, David J., Costes, Sylvain V.
• Format: Article
• Language: English
• Subjects: 38; 38/39; 38/61; 631/114/2407; 631/208/199; 631/553; 64/60; Carbon dioxide; Environmental stress; Experimental research; Humanities and Social Sciences; Hypoxia; Immune response; Investigations; Ionizing radiation; Microgravity; multidisciplinary; Phenotypes; Rodents; Science; Science (multidisciplinary); Space flight
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-22613-1

Spaceflight introduces a combination of environmental stressors, including microgravity, ionizing radiation, changes in diet and altered atmospheric gas composition. In order to understand the impact of each environmental component on astronauts it is important to investigate potential influences in isolation. Rodent spaceflight experiments involve both standard vivarium cages and animal enclosure modules (AEMs), which are cages used to house rodents in spaceflight. Ground control AEMs are engineered to match the spaceflight environment. There are limited studies examining the biological response invariably due to the configuration of AEM and vivarium housing. To investigate the innate global transcriptomic patterns of rodents housed in spaceflight-matched AEM compared to standard vivarium cages we utilized publicly available data from the NASA GeneLab repository. Using a systems biology approach, we observed that AEM housing was associated with significant transcriptomic differences, including reduced metabolism, altered immune responses, and activation of possible tumorigenic pathways. Although we did not perform any functional studies, our findings revealed a mild hypoxic phenotype in AEM, possibly due to atmospheric carbon dioxide that was increased to match conditions in spaceflight. Our investigation illustrates the process of generating new hypotheses and informing future experimental research by repurposing multiple space-flown datasets.