Antibiotic pretreatment with ampicillin or streptomycin can differentially affect the course of Salmonella-induced intestinal inflammation
Salmonella enterica serovar Typhimurium (S. Tm) is a widely used model organism to study enteric pathogenesis. In natural infections, S. Tm competes with the resident gut microbiota, which confers colonization resistance. To overcome this barrier in mouse models, high-dose streptomycin pretreatment is commonly applied to facilitate gut colonization and reproducible infection kinetics. More recently, alternative pretreatment regimens using broad-spectrum antibiotics such as ampicillin have been employed, particularly for infections with type III secretion system 2 (TTSS-2)–deficient S. Tm (S. TmSPI2). However, the differential impact of streptomycin versus ampicillin pretreatment on key infection parameters such as inflammation dynamics and S. Tm colonization, remains poorly characterized. Here, we directly compared infection outcomes of S. TmSPI2 in streptomycin- versus ampicillin-pretreated mice across different genetic backgrounds. Ampicillin pretreatment elicited a markedly stronger intestinal inflammatory response and more severe histopathology than streptomycin, despite comparable S. TmSPI2 colonization levels. These differences correlated with distinct antibiotic-induced changes in microbiota composition. Moreover, the altered gut environment in ampicillin-pretreated mice was linked to enhanced activation of early TTSS-1 virulence programs in S. Tm, which mediate epithelial invasion. This comparative approach highlights how different antibiotic pretreatments shape the gut environment, thereby influencing infection kinetics, pathogen behavior, and host responses. It also provides valuable guidance for selecting appropriate pretreatments in experimental models. Together, these findings emphasize that the choice of antibiotic pretreatment is a major determinant of Salmonella pathogenesis in mice and should be carefully considered when designing infection studies to accurately capture host–pathogen–microbiota interactions.