Dynamic collateral sensitivity profiles highlight challenges and opportunities for optimizing antibiotic sequences

As traditional antimicrobial therapies fail at escalating rates, recent focus has shifted to evolution-based therapies to slow resistance. Collateral sensitivity-the increased susceptibility to one drug associated with evolved resistance to a different drug-offers a potentially exploitable evolutionary constraint, but the manner in which collateral effects emerge over time is not well understood. Here, we use laboratory evolution in the opportunistic pathogen E. faecalis to phenotypically characterize collateral profiles through evolutionary time. Specifically, we measure collateral profiles for 400 strain-antibiotic combinations over the course of 4 evolutionary time points as strains are selected in increasing concentrations of antibiotic. We find that collateral resistance dominates during early phases of adaptation, whereas a diverse set of collateral profiles are accessible with further selection. Using simple numerical simulations, we illustrate how these temporally dynamic profiles potentially impact sequential drug therapies. Finally, we show experimentally how dynamic collateral sensitivity relationships can create optimal dosing windows that depend on finely timed switching between drugs.

The population genetics of collateral resistance and sensitivity

Resistance mutations against one drug can elicit collateral sensitivity against other drugs. Multi-drug treatments exploiting such trade-offs can help slow down the evolution of resistance. However, if mutations with diverse collateral effects are available, a treated population may evolve either collateral sensitivity or collateral resistance. How to design treatments robust to such uncertainty is unclear. We show that many resistance mutations in Escherichia coli against various antibiotics indeed have diverse collateral effects. We propose to characterize such diversity with a joint distribution of fitness effects (JDFE) and develop a theory for describing and predicting collateral evolution based on simple statistics of the JDFE. We show how to robustly rank drug pairs to minimize the risk of collateral resistance and how to estimate JDFEs. In addition to practical applications, these results have implications for our understanding of evolution in variable environments.

Collateral Sensitivity Interactions between Antibiotics Depend on Local Abiotic Conditions

When bacteria become resistant to an antibiotic, the genetic changes involved sometimes increase (cross-resistance) or decrease (collateral sensitivity) their resistance to other antibiotics. Antibiotic combinations showing repeatable collateral sensitivity could be used in treatment to slow resistance evolution.

Evolutionary instability of collateral susceptibility networks in ciprofloxacin resistant clinical Escherichia coli strains

Collateral sensitivity and resistance occur when resistance development towards one antimicrobial either potentiates or deteriorates the effect of others, respectively. Previous reports on collateral effects on susceptibility focus on newly acquired resistance determinants and propose that novel treatment guidelines informed by collateral networks may reduce the evolution, selection and spread of antimicrobial resistance. In this study, we investigate the evolutionary stability of collateral networks in five ciprofloxacin resistant, clinical Escherichia coli strains. After 300 generations of experimental evolution without antimicrobials, we show complete fitness restoration in four of five genetic backgrounds and demonstrate evolutionary instability in collateral networks of newly acquired resistance determinants. We show that compensatory mutations reducing efflux expression is the main driver destabilizing initial collateral networks and identify rpoS as a putative target for compensatory evolution. Our results add another layer of complexity to future predictions and clinical application of collateral networks.

Genetic determinants of antibiotic resistance and the evolution of trade-offs during adaptation in a single patient

The processes by which pathogens evolve within single hosts dictate the efficacy of treatment strategies designed to slow antibiotic resistance evolution and influence the population-wide resistance levels. The aim of this study is to describe the underlying genetic and phenotypic changes leading to antibiotic resistance within a single patient who died as resistance evolved to available antibiotics. We assess whether robust patterns of collateral sensitivity and response to combinations exist that might have been leveraged to improve therapy. Whole-genome sequencing was completed for nine isolates taken from this patient over 279 days of chronic infection with Enterobacter hormaechei, along with systematic measurements of changes in resistance against five of the most relevant drugs considered for treatment. The entirety of the genetic change is consistent with de novo mutations and plasmid loss events, without the acquisition of foreign genetic material via horizontal gene transfer. The isolates formed three genetically distinct lineages, with early evolutionary trajectories being supplanted by previously unobserved multi-step evolutionary trajectories. Importantly, no single isolate evolved resistance to all of the antibiotics considered for treatment against E. hormaechei (i.e., none was pan-resistant). Patterns of collateral sensitivity and response to combination therapy revealed contrasting patterns across this diversifying population. Translating antibiotic resistance management strategies from theoretical and laboratory data to clinical situations, such as this, may require managing diverse populations with unpredictable resistance trajectories.

Design principles of collateral sensitivity-based dosing strategies

Collateral sensitivity (CS)-based antibiotic treatments, where increased resistance to one antibiotic leads to increased sensitivity to a second antibiotic, may have the potential to limit the emergence of antimicrobial resistance. However, it remains unclear how to best design CS-based treatment schedules. To address this problem, we use mathematical modelling to study the effects of pathogen- and drug-specific characteristics for different treatment designs on bacterial population dynamics and resistance evolution. We confirm that simultaneous and one-day cycling treatments could supress resistance in the presence of CS. We show that the efficacy of CS-based cycling therapies depends critically on the order of drug administration. Finally, we find that reciprocal CS is not essential to suppress resistance, a result that significantly broadens treatment options given the ubiquity of one-way CS in pathogens. Overall, our analyses identify key design principles of CS-based treatment strategies and provide guidance to develop treatment schedules to suppress resistance.

Chemoreversal Agents from Taiwanofungus Genus and Their More Potent Methyl Derivatives Targeting Signal Transducer and Activator of Transcription 3 (STAT3) Phosphorylation

Multidrug resistance (MDR), for which the mechanisms are not yet fully clear, is one of the major obstacles to cancer treatment. In recent years, signal transducer and activator of transcription 3 (STAT3) were found to be one of the important MDR mechanism pathways. Based on the previous research, zhankuic acid A, B, and C were found to have collateral sensitivity effects on MDR cancer cells, and MDR inhibitory activity of zhankuic acid methyl ester was found to be better than that of its acid. Therefore, we executed a systematic examination of the structure–activity relationship of zhankuic acid methyl ester derivatives to collateral sensitivity in MDR cancer cells. The results showed that compound 12 is the best in terms of chemoreversal activity, where the reversal fold was 692, and the IC50 value of paclitaxel combined with 10 μM compound 12 treatment was 1.69 nM in MDR KBvin cells. Among all the derivatives, methyl ester compounds were found to be better than their acids, and a detailed discussion of the structure–activity relationships of all of the derivatives is provided in this work. In addition, compounds 8, 12, and 26 were shown to influence the activation of STAT3 in KBvin cells, accounting for part of their chemoreversal effects. Our results may provide a new combined therapy with paclitaxel to treat multidrug-resistant cancers and provide a new therapy option for patients.

Exploring the Monoterpene Indole Alkaloid Scaffold for Reversing P-Glycoprotein-Mediated Multidrug Resistance in Cancer

Dregamine (1), a major monoterpene indole alkaloid isolated from Tabernaemontana elegans, was submitted to chemical transformation of the ketone function, yielding 19 azines (3–21) and 11 semicarbazones (22–32) bearing aliphatic or aromatic substituents. Their structures were assigned mainly by 1D and 2D NMR (COSY, HMQC, and HMBC) experiments. Compounds 3–32 were evaluated as multidrug resistance (MDR) reversers through functional and chemosensitivity assays in a human ABCB1-transfected mouse T-lymphoma cell model, overexpressing P-glycoprotein. A significant increase of P-gp inhibitory activity was observed for most derivatives, mainly those containing azine moieties with aromatic substituents. Compounds with trimethoxyphenyl (17) or naphthyl motifs (18, 19) were among the most active, exhibiting strong inhibition at 0.2 µM. Moreover, most of the derivatives showed selective antiproliferative effects toward resistant cells, having a collateral sensitivity effect. In drug combination assays, all compounds showed to interact synergistically with doxorubicin. Selected compounds (12, 17, 18, 20, and 29) were evaluated in the ATPase activity assay, in which all compounds but 12 behaved as inhibitors. To gather further insights on drug–receptor interactions, in silico studies were also addressed. A QSAR model allowed us to deduce that compounds bearing bulky and lipophilic substituents were stronger P-gp inhibitors.

The roles of history, chance, and natural selection in the evolution of antibiotic resistance

History, chance, and selection are the fundamental factors that drive and constrain evolution. We designed evolution experiments to disentangle and quantify effects of these forces on the evolution of antibiotic resistance. Previously we showed that selection of the pathogen Acinetobacter baumannii in both structured and unstructured environments containing the antibiotic ciprofloxacin produced distinct genotypes and phenotypes, with lower resistance in biofilms as well as collateral sensitivity to b-lactam drugs (Santos-Lopez et al. 2019). Here we study how this prior history influences subsequent evolution in new b-lactam antibiotics. Selection was imposed by increasing concentrations of ceftazidime and imipenem and chance differences arose as random mutations among replicate populations. The effects of history were reduced by increasingly strong selection in new drugs, but not erased, at times revealing important contingencies. A history of selection in structured environments constrained resistance to new drugs and led to frequent loss of resistance to the initial drug by genetic reversions and not compensatory mutations. This research demonstrates that despite strong selective pressures of antibiotics leading to genetic parallelism, history can etch potential vulnerabilities to orthogonal drugs.