Drug resistance mutations and the cellular immune response: a valuable synergy for the development of novel immune therapies

Strong Environment-Genotype Interactions Determine the Fitness Costs of Antibiotic Resistance In Vitro and in an Insect Model of Infection

The acquisition of antibiotic resistance commonly imposes fitness costs, a reduction in the fitness of bacteria in the absence of drugs. These costs have been quantified primarily using in vitro experiments and a small number of in vivo studies in mice, and it is commonly assumed that these diverse methods are consistent. Here, we used an insect model of infection to compare the fitness costs of antibiotic resistance in vivo to those in vitro Experiments explored diverse mechanisms of resistance in a Gram-positive pathogen, Bacillus thuringiensis, and a Gram-negative intestinal symbiont, Enterobacter cloacae Rifampin resistance in B. thuringiensis showed fitness costs that were typically elevated in vivo, although these were modulated by genotype-environment interactions. In contrast, resistance to cefotaxime via derepression of AmpC β-lactamase in E. cloacae resulted in no detectable costs in vivo or in vitro, while spontaneous resistance to nalidixic acid, and carriage of the IncP plasmid RP4, imposed costs that increased in vivo Overall, fitness costs in vitro were a poor predictor of fitness costs in vivo because of strong genotype-environment interactions throughout this study. Insect infections provide a cheap and accessible means of assessing the fitness consequences of resistance mutations, data that are important for understanding the evolution and spread of resistance. This study emphasizes that the fitness costs imposed by particular mutations or different modes of resistance are extremely variable and that only a subset of these mutations is likely to be prevalent outside the laboratory.

Predicting the efficacy of cancer vaccines by evaluating T-cell responses

In a preclinical experimental model of human papilloma virus (HPV)-induced cervical carcinoma, we have shown that the efficacy of cancer vaccines can be predicted by the evaluation of vaccine-induced T-cell responses in healthy subjects. We argue that such knowledge can be used to screen candidates for vaccination, which in turn may accelerate the development and increase the overall efficacy of cancer vaccines.