Critical role of growth medium for detecting drug interactions in Gram-negative bacteria that model in vivo responses

Abstract

The rise in infections caused by multidrug-resistant (MDR) bacteria has necessitated a variety of clinical approaches, including the use of antibiotic combinations. Here, we tested the hypothesis that drug-drug interactions vary in different media, and determined which in vitro models best predict drug interactions in the lungs. We systematically studied pair-wise antibiotic interactions in three different media, CAMHB, (a rich lab medium standard for antibiotic susceptibility testing), a urine mimetic medium (UMM), and a minimal medium of M9 salts supplemented with glucose and iron (M9Glu) with three Gram-negative ESKAPE pathogens, Acinetobacter baumannii (Ab), Klebsiella pneumoniae (Kp), and Pseudomonas aeruginosa (Pa). There were pronounced differences in responses to antibiotic combinations between the three bacterial species grown in the same medium. However, within species, PaO1 responded to drug combinations similarly when grown in all three different media, whereas Ab17978 and other Ab clinical isolates responded similarly when grown in CAMHB and M9Glu medium. By contrast, drug interactions in Kp43816, and other Kp clinical isolates poorly correlated across different media. To assess whether any of these media were predictive of antibiotic interactions against Kp in the lungs of mice, we tested three antibiotic combination pairs. In vitro measurements in M9Glu, but not rich medium or UMM, predicted in vivo outcomes. This work demonstrates that antibiotic interactions are highly variable across three Gram-negative pathogens and highlights the importance of growth medium by showing a superior correlation between in vitro interactions in a minimal growth medium and in vivo outcomes. IMPORTANCE Drug-resistant bacterial infections are a growing concern and have only continued to increase during the SARS-CoV-2 pandemic. Though not routinely used for Gram-negative bacteria, drug combinations are sometimes used for serious infections and may become more widely used as the prevalence of extremely drug-resistant organisms increases. To date, reliable methods are not available for identifying beneficial drug combinations for a particular infection. Our study shows variability across strains in how drug interactions are impacted by growth conditions. It also demonstrates that testing drug combinations in tissue-relevant growth conditions for some strains better models what happens during infection and may better inform combination therapy selection.

Preserving the efficacy of antibiotics to tackle antibiotic resistance

“…Additionally, synergistic or antagonistic interactions between different antibiotics depend on the composition of the environment. Understanding these interactions in vivo can inform the selection of combination therapies for difficult‐to‐treat infections (Davis et al., 2024 ).…”

Wiring Between Close Nodes in Molecular Networks Evolves More Quickly Than Between Distant Nodes

“…Only a few studies have explored interspecific variation of DDI scores ( Spitzer et al 2011 ; Robbins et al 2015 ; Brochado et al 2018 ; Davis et al 2024 ), but they have demonstrated that DC experiments (used to obtain DDI scores) could be scalable in the number of species and strains. A high-throughput study of DDIs in gram-negative bacteria has shown that synergies are more conserved across species than antagonisms and additive combinations ( Brochado et al 2018 ).…”