Abstract
Spaceflight presents a spectrum of stresses very different from those associated with terrestrial conditions. Our previous study (BMC Genom. 15: 659, 2014) integrated the expressions of mRNAs, microRNAs, and proteins and results indicated that microgravity induces an immunosuppressive state that can facilitate opportunistic pathogenic attack. However, the existing data are not sufficient for elucidating the molecular drivers of the given immunosuppressed state. To meet this knowledge gap, we focused on the metabolite profile of spaceflown human cells. Independent studies have attributed cellular energy deficiency as a major cause of compromised immunity of the host, and metabolites that are closely associated with energy production could be a robust signature of atypical energy fluctuation. Our protocol involved inoculation of human endothelial cells in cell culture modules in spaceflight and on the ground concurrently. Ten days later, the cells in space and on the ground were exposed to lipopolysaccharide (LPS), a ubiquitous membrane endotoxin of Gram-negative bacteria. Nucleic acids, proteins, and metabolites were collected 4 and 8 h post-LPS exposure. Untargeted profiling of metabolites was followed by targeted identification of amino acids and knowledge integration with gene expression profiles. Consistent with the past reports associating microgravity with increased energy expenditure, we identified several markers linked to energy deficiency, including various amino acids such as tryptophan, creatinine, dopamine, and glycine, and cofactors such as lactate and pyruvate. The present study revealed a molecular architecture linking energy metabolism and immunodeficiency in microgravity. The energy-deficient condition potentially cascaded into dysregulation of protein metabolism and impairment of host immunity. This project is limited by a small sample size. Although a strict statistical screening was carefully implemented, the present results further emphasize the need for additional studies with larger sample sizes. Validating this hypothesis using an in vivo model is essential to extend the knowledge towards identifying markers of diagnostic and therapeutic value.