Systematic analysis of drug combinations against Gram-positive bacteria

Abstract

Drug combinations can expand options for antibacterial therapies but have not been systematically tested in Gram-positive species. We profiled ~8,000 combinations of 65 antibacterial drugs against the model species Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulated previously known drug interactions, but also identified ten times more novel interactions in the pathogen S. aureus, including 150 synergies. We showed that two synergies were equally effective against multidrug-resistant S. aureus clinical isolates in vitro and in vivo. Interactions were largely species-specific and synergies were distinct from those of Gram-negative species, owing to cell surface and drug uptake differences. We also tested 2,728 combinations of 44 commonly prescribed non-antibiotic drugs with 62 drugs with antibacterial activity against S. aureus and identified numerous antagonisms that might compromise the efficacy of antimicrobial therapies. We identified even more synergies and showed that the anti-aggregant ticagrelor synergized with cationic antibiotics by modifying the surface charge of S. aureus. All data can be browsed in an interactive interface (https://apps.embl.de/combact/).

Within-species variability of antibiotic interactions in Gram-negative bacteria

“…Second, although not exhaustive by any means, the interaction pattern for a given antibiotic combination can vary between different Gram-negative species (see for example, tgc + gen in E. cloacae and K. pneumoniae ), precluding any general extrapolations between species. However, such conservation studies have been reported previously ( 11 , 22 , 37 ). Third, additive and antagonistic interactions are by far the most common patterns observed across species and antibiotics in which 99.7% (694/696) of all tested isolates and antibiotic combinations belonged to either of these two types, with as many as 12.6% of the isolates showing antagonistic interactions.…”

“…Thus, existing studies show diverse and conflicting results, often without any clear correlation between improved clinical outcome and use of antibiotic combinations ( 5 , 6 , 16 – 21 ). There are many potential explanations for this lack of correlation ( 9 ), but of relevance here is the occurrence of a general conservation of antibiotic interactions for a given species ( 11 , 22 ) and any isolate-specific variation in the conserved antibiotic interaction patterns, which can obscure potential correlations to clinical outcome. A few previous studies have suggested that there indeed exists isolate variation ( 11 , 22 – 24 ), but such studies are rare because existing testing technology, such as checkerboard and time-kill assays ( 25 ), is very resource demanding, effectively preventing the analysis and stratification of high numbers of clinical isolates.…”

Stability-based approaches in chemoproteomics

“…30) and proteoforms (Ref. 31), protein function and cellular processes' (Refs 32, 33) in various sample types (Refs 34, 35) and biological systems (Refs 29, 36, 37).…”

Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG)

“…A key reason to study ExE pertains to drug resistance (Foucquier & Guedj, 2015; Madani Tonekaboni et al, 2018; Russ & Kishony, 2018), though it is termed “drug interaction” (Gjini & Wood, 2021; Yeh et al, 2009) or occasionally “drug epistasis” (Michel et al, 2008) rather than “ExE”. There is practical interest in finding pairs of drugs that interact ‘synergistically’, i.e., the combination of both drugs is deadlier than one would predict based on either single drug ( Figure 1B ) (Cacace et al, 2023; Liu et al, 2021; Meyer et al, 2020; Mikhail et al, 2019; Roell et al, 2017). But just as genotype-phenotype mapping studies focus less on environment interactions, drug synergy studies focus less on genetic interactions.…”