Biology subjects

Liakopoulos, A.

Publications and source records attributed to Liakopoulos, A..

7 recordsLinked to original sources

bioRxiv2025

Characterization of genetically novel chimeric plasmids from Salmonella Heidelberg conferring multi-drug resistance and increased pathogenicity

Salmonella enterica serotype Heidelberg (S. Heidelberg) is a significant cause of human salmonellosis, with resistance to extended-spectrum cephalosporins posing challenges for clinical management. In this study, we genetically and functionally characterized six plasmids isolated from distinct PFGE-types of S. Heidelberg previously reported in The Netherlands, revealing a chimeric IncC-I1a plasmid that shares similarities with the epidemic pESI plasmid identified in emergent S. Infantis isolates. Our analysis showed the accumulation of genetic determinants conferring resistance to antibiotics (blaCMY-2, tetA and sul2) and heavy metals (mer operon), as well as enhanced pathogenicity (Yersinia high-pathogenicity island). All these elements are located on a stable non-conjugative but likely mobilizable plasmid backbone that imposes only a marginal fitness cost on its bacterial host. This convergence of multidrug resistance and pathogenicity likely enhances bacterial adaptability and virulence, undermining control strategies based solely on reducing selective pressure. The emergence and dissemination of such hybrid plasmids represent an increasing threat to both public and animal health, analogous to that posed by pESI-like plasmids, and underscore the urgent need for integrated genomic surveillance and risk assessment, as their continued expansion could complicate antimicrobial therapy and containment efforts during Salmonella outbreaks.

microbiology↗

bioRxiv2025

Divergent spontaneous antibiotic-resistance evolution confers reciprocal and exploitable collateral sensitivity effects

The global rise of antibiotic-resistant pathogens has outpaced the development of new antibiotics, prompting the urgent need for alternative treatment strategies. One such approach is to leverage collateral sensitivity (CS), where resistance to one antibiotic increases susceptibility to another. However, the clinical implementation of CS-based therapies depends on the consistency of these responses, which is challenged by variable resistance mutations and the dynamics of resistant strains during infection. Here, we combined experiments and mathematical models to assess the consistency and consequences of CS responses in the Gram-positive pathogen Streptococcus pneumoniae following the de novo acquisition of resistance to five commonly used antibiotics. We found that many collateral responses were unpredictable and inconsistent between different resistance mutations. However, for two antibiotic pairs, we identified consistent unidirectional (RIF [->] FUS) and bidirectional (LNZ {leftrightarrow} FUS) CS interactions, despite the divergent evolutionary trajectories of resistant strains, as revealed by whole-genome sequencing. To evaluate if CS for these combinations can be exploited to design dosing strategies to eradicate S. pneumoniae infections while suppressing resistance, we developed a mathematical stochastic pharmacokinetic-pharmacodynamic (PK-PD) model, which integrated our experimentally derived PD parameters with existing clinical PK models. Our model-based analyses confirmed the superiority of these antibiotic combinations over monotherapy and showed that their efficacy depends on the presence of CS interactions between the administered antibiotics. In summary, our study demonstrates how consistent CS interactions can be leveraged to inform treatment strategies, laying the groundwork for CS-guided therapies to preserve antibiotic efficacy.

microbiology↗

bioRxiv2024

Interspecies interactions alter the antibiotic sensitivity of Pseudomonas aeruginosa

Polymicrobial infections are infections that are caused by multiple pathogens, and are common in patients with cystic fibrosis (CF). Although polymicrobial infections are associated with poor treatment responses in CF, the effects of the ecological interactions between co-infecting pathogens on antibiotic sensitivity and treatment outcome are poorly characterized. To this end, we systematically quantified the impact of these effects on the antibiotic sensitivity of Pseudomonas aeruginosa for nine antibiotics in the presence of thirteen secondary cystic fibrosis-associated bacterial and fungal pathogens through time-kill assays. We fitted pharmacodynamic models to these kill curves for each antibiotic-species combination and found that interspecies interactions changing the antibiotic sensitivity of P. aeruginosa are abundant. Interactions that lower antibiotic sensitivity are more common than those that increase it, with generally more substantial reductions than increases in sensitivity. For a selection of co-infecting species, we performed pharmacokinetic-pharmacodynamic modelling of P. aeruginosa treatment. We predicted that interspecies interactions can either improve or reduce treatment response to the extent that treatment is rendered ineffective from a previously effective antibiotic dosing schedule and vice versa. In summary, we show that quantifying the ecological interaction effects as pharmacodynamic parameters is necessary to determine the abundance and the extent to which these interactions affect antibiotic sensitivity in polymicrobial infections. ImportanceIn cystic fibrosis (CF) patients, chronic respiratory tract infections are often polymicrobial, involving multiple pathogens simultaneously. Polymicrobial infections are difficult to treat as they often respond unexpectedly to antibiotic treatment, which might possibly be explained because co-infecting pathogens can influence each others antibiotic sensitivity, but it is unknown to what extent such effects occur. To investigate this, we systematically quantified the impact of co-infecting species on antibiotic sensitivity, focusing on P. aeruginosa, a common CF pathogen. We studied for a large set co-infecting species and antibiotics whether changes in antibiotic response occur. Based on these experiments, we used mathematical modeling to simulate P. aeruginosas response to colistin and tobramycin treatment in the presence of multiple pathogens. This study offers comprehensive data on altered antibiotic sensitivity of P. aeruginosa in polymicrobial infections, serves as a foundation for optimizing treatment of such infections, and consolidates the importance of considering co-infecting pathogens.

pharmacology and toxicology↗

bioRxiv2021

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.

pharmacology and toxicology↗

bioRxiv2020

Allele-specific collateral and fitness effects determine the dynamics of fluoroquinolone-resistance evolution

Collateral sensitivity (CS), which arises when resistance to one antibiotic increases sensitivity towards other antibiotics, offers novel treatment opportunities to constrain or reverse the evolution of antibiotic-resistance. The applicability of CS-informed treatments remains uncertain, in part because we lack an understanding of the generality of CS effects for different resistance mutations, singly or in combination. Here we address this issue in the Gram-positive pathogen Streptococcus pneumoniae by measuring collateral and fitness effects of clinically relevant gyrA and parC alleles, and their combinations, that confer resistance to fluoroquinolones. We integrated these results in a mathematical model which allowed us to evaluate how different in silico combination treatments impact the dynamics of resistance evolution. We identified common and conserved CS effects of different gyrA and parC alleles; however, the spectrum of collateral effects was unique for each allele or allelic pair. This indicated that allelic identity can impact the evolutionary dynamics of resistance evolution during monotreatment and combination treatment. Our model simulations, which included the experimentally derived antibiotic susceptibilities and fitness effects, and antibiotic specific pharmacodynamics, revealed that both collateral and fitness effects impact the population dynamics of resistance evolution. Overall, we provide evidence that allelic identity and interactions can have a pronounced impact on collateral effects to different antibiotics and suggest that these need to be considered in models examining CS-based therapies. SignificanceA promising strategy to overcome the evolution of antibiotic resistant bacteria is to use collateral sensitivity-informed antibiotic treatments that rely on cycling or mixing of antibiotics, such that that resistance towards one antibiotic confers increased sensitivity to the other. Here, focusing on multi-step fluoroquinolone resistance in Streptococcus pneumoniae, we show that antibiotic-resistance induces diverse collateral responses whose magnitude and direction are determined by allelic identity. Using mathematical simulations, we show that these effects can be exploited via combination treatment regimens to suppress the de novo emergence of resistance during treatment.

microbiology↗

bioRxiv2020

Manganese complex Br increases antibiotic sensitivity in multidrug resistant Streptococcus pneumoniae

The emergence of multidrug-resistance (MDR) in Streptococcus pneumoniae clones and non-vaccine serotypes is of increasing concern, necessitating the development of novel treatment strategies. Here, we determined the efficacy of the Mn complex [Mn(CO)3(tpa-{kappa}3N)]Br against MDR S. pneumoniae strains. Our data showed that [Mn(CO)3(tpa-{kappa}3N)]Br has in vitro and in vivo antibacterial activity and has the potential to be used in combination with currently available antibiotics to increase their effectiveness against MDR S. pneumoniae.

microbiology↗

bioRxiv2019

Antibiotic production is organized by a division of labour in Streptomyces

One of the hallmark behaviors of social groups is division of labour, where different group members become specialized to carry out complementary tasks. By dividing labour, cooperative groups of individuals increase their efficiency, thereby raising group fitness even if these specialized behaviors reduce the fitness of individual group members. Here we provide evidence that antibiotic production in colonies of the multicellular bacterium Streptomyces coelicolor is coordinated by a division of labour. We show that S. coelicolor colonies are genetically heterogenous due to massive amplifications and deletions to the chromosome. Cells with gross chromosomal changes produce an increased diversity of secondary metabolites and secrete significantly more antibiotics; however, these changes come at the cost of dramatically reduced individual fitness, providing direct evidence for a trade-off between secondary metabolite production and fitness. Finally, we show that colonies containing mixtures of mutant strains and their parents produce significantly more antibiotics, while colony-wide spore production remains unchanged. Our work demonstrates that by generating mutants that are specialized to hyper-produce antibiotics, streptomycetes reduce the colony-wide fitness costs of secreted secondary metabolites while maximizing the yield and diversity of these products.

evolutionary biology↗