The evolution of resistance to synergistic multi-drug combinations is more complex than evolving resistance to each individual drug component.

Multidrug antibiotic resistance is an urgent public health concern. Multiple strategies have been suggested to alleviate this problem, including the use of antibiotic combinations and cyclic therapies. We examine how adaptation to (1) combinations of drugs affects resistance to individual drugs, and to (2) individual drugs alters responses to drug combinations. To evaluate this, we evolved multiple strains of drug resistant Staphylococcus epidermidis in the lab. We show that evolving resistance to four highly synergistic combinations does not result in cross-resistance to all of their components. Likewise, prior resistance to one antibiotic in a combination does not guarantee survival when exposed to the combination. We also identify four 3-step and four 2-step treatments that inhibit bacterial growth and confer collateral sensitivity with each step, impeding the development of multidrug resistance. This study highlights the importance of considering higher-order drug combinations in sequential therapies and how antibiotic interactions can influence the evolutionary trajectory of bacterial populations.

Meta-analysis of three-stressor combinations on population-level fitness reveal substantial higher-order interactions.

Although natural populations are typically subjected to multiple stressors, most past research has focused on single-stressor and two-stressor interactions, with little attention paid to higher-order interactions among three or more stressors. However, higher-order interactions increasingly appear to be widespread. Consequently, we used a recently introduced and improved framework to re-analyze higher-order ecological interactions. We conducted a literature review of the last 100 years (1920-2020) and reanalyzed 142 ecological three-stressor interactions on species' populations from 38 published papers; the vast majority of these studies were from the past 10 years. We found that 95.8 % (n = 136) of the three-stressor combinations had either not been categorized before or resulted in different interactions than previously reported. We also found substantial levels of emergent properties-interactions that are not due to strong pairwise interactions within the combination but rather uniquely due to all three stressors being combined. Calculating net interactions-the overall accounting for all possible interactions within a combination including the emergent and all pairwise interactions-we found that the most prevalent interaction type is antagonism, corresponding to a smaller than expected effect based on single stressor effects. In contrast, for emergent interactions, the most prevalent interaction type is synergistic, resulting in a larger than expected effect based on single stressor effects. Additionally, we found that hidden suppressive interactions-where a pairwise interaction is suppressed by a third stressor-are found in the majority of combinations (74 %). Collectively, understanding multiple stressor interactions through applying an appropriate framework is crucial for answering fundamental questions in ecology and has implications for conservation biology and population management. Crucially, identifying emergent properties can reveal hidden suppressive interactions that could be particularly important for the ecological management of at-risk populations.

Experimental Evolution of Copper Resistance in Escherichia coli Produces Evolutionary Trade-Offs in the Antibiotics Chloramphenicol, Bacitracin, and Sulfonamide.

The rise in antimicrobial resistant bacteria have prompted the need for antibiotic alternatives. To address this problem, significant attention has been given to the antimicrobial use and novel applications of copper. As novel applications of antimicrobial copper increase, it is important to investigate how bacteria may adapt to copper over time. Here, we used experimental evolution with re-sequencing (EER-seq) and RNA-sequencing to study the evolution of copper resistance in Escherichia coli. Subsequently, we tested whether copper resistance led to rifampicin, chloramphenicol, bacitracin, and/or sulfonamide resistance. Our results demonstrate that E. coli is capable of rapidly evolving resistance to CuSO4 after 37 days of selection. We also identified multiple de novo mutations and differential gene expression patterns associated with copper, most notably those mutations identified in the cpx gene. Furthermore, we found that the copper resistant bacteria had decreased sensitivity when compared to the ancestors in the presence of chloramphenicol, bacitracin, and sulfonamide. Our data suggest that the selection of copper resistance may inhibit growth in the antimicrobials tested, resulting in evolutionary trade-offs. The results of our study may have important implications as we consider the antimicrobial use of copper and how bacteria may respond to increased use over time.

Hidden suppressive interactions are common in higher-order drug combinations.

The rapid increase of multi-drug resistant bacteria has led to a greater emphasis on multi-drug combination treatments. However, some combinations can be suppressive-that is, bacteria grow faster in some drug combinations than when treated with a single drug. Typically, when studying interactions, the overall effect of the combination is only compared with the single-drug effects. However, doing so could miss "hidden" cases of suppression, which occur when the highest order is suppressive compared with a lower-order combination but not to a single drug. We examined an extensive dataset of 5-drug combinations and all lower-order-single, 2-, 3-, and 4-drug-combinations. We found that a majority of all combinations-54%-contain hidden suppression. Examining hidden interactions is critical to understanding the architecture of higher-order interactions and can substantially affect our understanding and predictions of the evolution of antibiotic resistance under multi-drug treatments.

Experimental Evolution of Magnetite Nanoparticle Resistance in Escherichia coli.

Both ionic and nanoparticle iron have been proposed as materials to control multidrug-resistant (MDR) bacteria. However, the potential bacteria to evolve resistance to nanoparticle bacteria remains unexplored. To this end, experimental evolution was utilized to produce five magnetite nanoparticle-resistant (FeNP1-5) populations of Escherichia coli. The control populations were not exposed to magnetite nanoparticles. The 24-h growth of these replicates was evaluated in the presence of increasing concentrations magnetite NPs as well as other ionic metals (gallium III, iron II, iron III, and silver I) and antibiotics (ampicillin, chloramphenicol, rifampicin, sulfanilamide, and tetracycline). Scanning electron microscopy was utilized to determine cell size and shape in response to magnetite nanoparticle selection. Whole genome sequencing was carried out to determine if any genomic changes resulted from magnetite nanoparticle resistance. After 25 days of selection, magnetite resistance was evident in the FeNP treatment. The FeNP populations also showed a highly significantly (p < 0.0001) greater 24-h growth as measured by optical density in metals (Fe (II), Fe (III), Ga (III), Ag, and Cu II) as well as antibiotics (ampicillin, chloramphenicol, rifampicin, sulfanilamide, and tetracycline). The FeNP-resistant populations also showed a significantly greater cell length compared to controls (p < 0.001). Genomic analysis of FeNP identified both polymorphisms and hard selective sweeps in the RNA polymerase genes rpoA, rpoB, and rpoC. Collectively, our results show that E. coli can rapidly evolve resistance to magnetite nanoparticles and that this result is correlated resistances to other metals and antibiotics. There were also changes in cell morphology resulting from adaptation to magnetite NPs. Thus, the various applications of magnetite nanoparticles could result in unanticipated changes in resistance to both metal and antibiotics.

Too much of a good thing: Adaption to iron (II) intoxication in Escherichia coli.

BACKGROUND: There has been an increased usage of metallic antimicrobial materials to control pathogenic and multi-drug resistant bacteria. Yet, there is a corresponding need to know if this usage leads to genetic adaptations that could produce more harmful strains. METHODOLOGY: Experimental evolution was used to adapt Escherichia coli K-12 MG1655 to excess iron (II) with subsequent genomic analysis. Phenotypic assays and gene expression studies were conducted to demonstrate pleiotropic effects associated with this adaptation and to elucidate potential cellular responses. RESULTS: After 200 days of adaptation, populations cultured in excess iron (II), showed a significant increase in 24-h optical densities compared to controls. Furthermore, these populations showed increased resistance toward other metals [iron (III) and gallium (III)] and to traditional antibiotics (bacitracin, rifampin, chloramphenicol and sulfanilamide). Genomic analysis identified selective sweeps in three genes; fecA, ptsP and ilvG unique to the iron (II) resistant populations, and gene expression studies demonstrated that their cellular response may be to downregulate genes involved in iron transport (cirA and fecA) while increasing the oxidative stress response (oxyR, soxS and soxR) prior to FeSO4 exposure. CONCLUSIONS AND IMPLICATIONS: Together, this indicates that the selected populations can quickly adapt to stressful levels of iron (II). This study is unique in that it demonstrates that E. coli can adapt to environments that contain excess levels of an essential micronutrient while also demonstrating the genomic foundations of the response and the pleiotropic consequences. The fact that adaptation to excess iron also causes increases in general antibiotic resistance is a serious concern. Lay summary: The evolution of iron resistance in E. coli leads to multi-drug and general metal resistance through the acquisition of mutations in three genes (fecA, ptsP and ilvG) while also initiating cellular defenses as part of their normal growth process.