Carbon availability in Staphylococcus aureus infection: cellular access, metabolic fate and antibiotic response

Carbon availability during Staphylococcus aureus infection is often equated with extracellular substrate concentration, although this measurement reveals neither cellular partitioning nor the intracellular fate of acquired carbon. This distinction becomes treatment-relevant because conditions that permit bacterial survival need not be those that later favor population recovery or resistant genotypes. Here, we synthesize infection-relevant evidence across five inferential levels: local substrate pool, cellular access, intracellular carbon fate, physiological state and antibiotic response. Applying this separation across time distinguishes immediate metabolic effects on killing from subsequent population recovery and resistant-variant expansion. In a diabetic skin-infection model, hyperglycaemia increased recovery of heritable rifampicin resistance without genomic evidence of hypermutability, consistent with altered population opportunity rather than a direct carbon-to-mutation mechanism. Across the evidence reviewed, no study in S. aureus has linked the measured carbon fate of a bacterium surviving antibiotic exposure to the recovery and resistance phenotypes of its descendants. We therefore propose a two-stage, non-obligatory model in which restrictive carbon conditions can modify survival during treatment, whereas restoration or rerouting of metabolically usable carbon can restore killing, permit susceptible regrowth or favor expansion of resistant variants. The model yields a lineage-specific, falsifiable prediction: carbon-restricted survivors contribute disproportionately to the recovered population and, under continued selection, to its resistant fraction. Testing this prediction requires experiments that preserve substrate identity, cellular identity, intracellular carbon fate and ancestry through recovery. By defining this unconnected causal step, the framework makes cellular descent testable and provides a basis for evaluating metabolic interventions across antibiotic–niche combinations.