Adaptive transcriptional remodeling of Streptococcus mutans under simulated microgravity and silver stress reveals...

Understanding how microorganisms adapt to novel physical and chemical environments requires integrating evolutionary, regulatory, and phenotypic perspectives. Here, we examined Streptococcus mutans populations previously evolved for 100 days under simulated microgravity (sMG) or combined microgravity and silver nitrate (sMGAg), generating new transcriptomic and phenotypic datasets and integrating them with prior whole-genome sequencing. These environments model key pressures encountered in enclosed spaceflight habitats, including altered fluid shear, oxidative challenges, and exposure to disinfectants. Populations maintained under normal gravity (NG) largely preserved ancestral metabolic and redox characteristics. In contrast, sMG populations exhibited divergent physiological and transcriptional outcomes that were not predictable from genomic variants alone, including multiple ROS response patterns, broad reductions in carbohydrate metabolism, and consistent retention of trehalose utilization. Populations evolved under sMGAg showed more convergent patterns, characterized by broad activation of oxidoreductase and metal-handling pathways, elevated basal ROS relative to the ancestral strain with reduced inducibility, and a consistent gain in nitrate-reduction capability. These outcomes reflect condition-associated physiological states resolved only through combined genomic, transcriptomic, and phenotype-level data, as no single data type was sufficient to capture the full structure of adaptive responses. Together, these findings illustrate how distinct physical and chemical stress regimes reshape the landscape of accessible evolutionary responses, with microgravity alone permitting a wider range of adaptive trajectories and microgravity combined with silver favoring more uniform physiological states. More broadly, this work demonstrates that integrated multi-level datasets are essential for accurately characterizing adaptive outcomes in extreme or non-terrestrial environments.