Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Abstract

Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identi ed, much less is known about how di erent combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting di erent classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the speci c interplay between drug interaction and cross resistance. For example, selection in a dual -lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance pro les are nearly identical. On the other hand, populations evolved in an aminoglycoside and -lactam combination exhibit decreased growth adaptation and resistant pro les that depend on the speci c drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance pro les selected by drug combinations correspond to (nearly) optimized linear combinations of those selected by the component drugs. Our results highlight tradeo s between drug interactions and collateral e ects during the evolution of multi-drug resistance and emphasize evolutionary bene ts and disadvantages of particular drug pairs targeting enterococci.

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

“…On the other hand, modulating the drug interaction in the absence of collateral effects will also significantly impact adaptation, with synergistic interactions leading to accelerated adaptation relative to both 1) other types of drug interaction (Figure 4, middle row; compare green curves across row) and 2) single drug adaptation (Figure 4, middle row, left panel). Similar interactiondriven adaptation has been observed in multiple bacterial species (Chait et al, 2007;Michel et al, 2008;Hegreness et al, 2008;Dean et al, 2020).…”

“…When both collateral effects and drug interactions vary, the dynamics can be considerably more complex, and the dominant driver of adaptation can be drug interaction, collateral effects, or a combination of both. Previous studies support this picture, as adaptation has been observed to be driven primarily by drug interactions (Chait et al, 2007;Michel et al, 2008;Hegreness et al, 2008), primarily by collateral effects (Munck et al, 2014;Barbosa et al, 2018), or by combinations of both (Baym et al, 2016b;Barbosa et al, 2018;Dean et al, 2020). Figure 4 shows schematic examples of growth rate adaptation for different types of collateral effects (rows, ranging from cross resistance (top) to collateral sensitivity (bottom)) and drug interactions (columns, ranging from synergy (left) to antagonism (right)).…”

“…Different estimates of initial resistant fraction lead to similar qualitative dynamics. A. Experimentally measured growth response surface for tigecycline (TGC) and ciprofloxacin (CIP) from (Dean et al, 2020). Circles represent 11 different adaptation conditions, each corresponding to a specific dosage combination ( 0 , 0 ).…”

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

“…Estimated growth surfaces for ancestral cells as well as all estimated growth rates and IC 50 values are available in ref [66]. rate time series (circles) and both linear and saturating fits to determine mean adaptation rate (lines) for populations grown in conditions A (top 3 rows, red), B (magenta), C (cyan), and D (last 3 rows, blue) for combinations of ceftriaxone (CRO) and ampicillin (AMP).…”

CombiANT: Antibiotic interaction testing made easy

“…Moreover, antibiotic combinations often reduce the rate of resistance evolution during treatment, especially for chronic infections, as highlighted by P . aeruginosa in cystic fibrosis and other bacterial pathogens [ 10 , 17 – 19 ]. The efficacy of these multitarget treatments is likely explained by a requirement for the co-occurrence of multiple resistance factors for treatment escape.…”