Inhibitory Concentrations of Ciprofloxacin Induce an Adaptive Response Promoting the Intracellular Survival of Salmonella enterica Serovar Typhimurium

Tetracycline and quinolone contamination mediate microbial and antibiotic resistant gene composition in epiphytic biofilms of mesocosmic wetlands

The fate and ecological impact of antibiotics on aquatic ecosystems have not been properly elucidated in mesocosm wetlands scale. This study explored how tetracyclines (TCs, including tetracycline TC and oxytetracycline) and fluoroquinolones (QNs, including ciprofloxacin CIP and levofloxacin) affect mesocosm wetlands vegetated by V. spiralis, focusing on their impact on epiphytic biofilm microbial communities and antibiotic resistance genes (ARGs). Results showed that submerged plants absorbed more antibiotics than sediment. Both TCs and QNs disrupted microbial communities in different ways and increased eukaryotic community diversity in a concentration-dependent manner (2-4 mg/L for CIP, 4-8 mg/L for TC). TCs mainly inhibited epiphytic bacteria, while CIP increased bacterial phyla abundance. TC reduced Cyanobacteriota, Acidobacteriota, and Patescibacteria but increased Bacillota, Bacteroidota, and Armatimonadota. In contrast, CIP reduced Bacteroidota, Cyanobacteriota, and Gemmatimonadota but increased Bacillota, Planctomycetota, and Acidobacteriota. Significant differences in ARG profiles were observed between QNs and TCs, with TCs having a more substantial effect on ARGs due to their stronger impact on bacterial communities. Both antibiotics raised ARG levels with higher concentrations, particularly for multidrug resistance, tetracyclines, trimethoprim, sulfonamides, aminoglycosides, and fosfomycin, emphasizing their role in antimicrobial resistance. The study suggests that antibiotics can either stimulate or inhibit ARGs depending on their effects on bacterial communities. This study provides key evidence on the ecological mechanisms underlying the impact of TCs and QNs on epiphytic microbes of mesocosm wetlands.

Antibiotic concentrations induce morphological changes and increase biofilm formation in multi-antibiotic and heavy metal resistant Kluyvera cryocrescens and Serratia fonticola

Water pollution is the biggest challenge that has rendered existing water resources unusable due to contamination with antibiotics and heavy metals. Antibiotics are often used to treat bacterial diseases. Heavy metals, on the other hand, are micro-pollutants that pose a threat to aquatic systems, especially when they accumulate in nature. Increasing pollution and the uncontrolled use of antibiotics have exposed bacteria to non-lethal concentrations (sub-MIC), potentially leading to resistance. In this study, Kluyvera cryocrescens and Serratia fonticola were isolated from a freshwater source and characterised. The resistance profiles of the isolates to 16 antibiotics and 8 heavy metals were determined, revealing that they are multidrug-resistant. The effects of sub-MICs (MIC/2 and MIC/4) of antibiotics on biofilm formation, siderophore production, and cell morphology of bacteria were analysed. It was found that at some sub-MIC values of kanamycin, tetracycline, meropenem, erythromycin, and clarithromycin, biofilm formation by K. cryocrescens increased. An increase in biofilm production was also observed in S. fonticola at sub-MIC values of imipenem, meropenem, ceftazidime, ciprofloxacin, and clarithromycin. Moreover, significant morphological changes were observed in both isolates following treatment with meropenem, ciprofloxacin, and ceftazidime. After treatment with meropenem, the typical rod-shaped (bacillary) morphology of the isolates shifted to a round (coccoid) form. In contrast, the bacteria developed into long filaments after treatment with ciprofloxacin and ceftazidime. These changes in the bacteria may favour the development of resistance and pose challenges for the prevention and treatment of diseases. Therefore, it is crucial to understand how sub-MIC levels of antimicrobial agents alter the virulence properties of bacteria.

Salmonella Typhimurium alters galactitol metabolism under ciprofloxacin treatment to balance resistance and virulence

Ciprofloxacin-resistant Salmonella Typhimurium (S. Typhimurium) causes a significant health burden worldwide. A wealth of studies has been published on the contributions of different mechanisms to ciprofloxacin resistance in Salmonella spp. But we still lack a deep understanding of the physiological responses and genetic changes that underlie ciprofloxacin exposure. This study aims to know how phenotypic and genotypic characteristics are impacted by ciprofloxacin exposure, from ciprofloxacin-susceptible to ciprofloxacin-resistant strains in vitro. Here, we investigated the multistep evolution of resistance in replicate populations of S. Typhimurium during 24 days of continuously increasing ciprofloxacin exposure and assessed how ciprofloxacin impacts physiology and genetics. Numerous studies have demonstrated that RamA is a global transcriptional regulator that prominently perturbs the transcriptional landscape of S. Typhimurium, resulting in a ciprofloxacin-resistant phenotype appearing first; the quinolone resistance-determining region mutation site can only be detected later. Comparing the microbial physiological changes and RNA sequencing (RNA-Seq) results of ancestral and selectable mutant strains, the selectable mutant strains had some fitness costs, such as decreased virulence, an increase of biofilm-forming ability, a change of "collateral" sensitivity to other drugs, and inability to utilize galactitol. Importantly, in the ciprofloxacin induced, RamA directly binds and activates the gatR gene responsible for the utilization of galactitol, but RamA deletion strains could not activate gatR. The elevated levels of RamA, which inhibit the galactitol metabolic pathway through the activation of gatR, can lead to a reduction in the growth rate, adhesion, and colonization resistance of S. Typhimurium. This finding is supported by studies conducted in M9 medium as well as in vivo infection models.

IMPORTANCE

Treatment of antibiotic resistance can significantly benefit from a deeper understanding of the interactions between drugs and genetics. The physiological responses and genetic mechanisms in antibiotic-exposed bacteria are not well understood. Traditional resistance studies, often retrospective, fail to capture the entire resistance development process and typically exhibit unpredictable dynamics. To explore how clinical isolates of S. Typhimurium respond to ciprofloxacin, we analyzed their adaptive responses. We found that S. Typhimurium RamA-mediated regulation disrupts microbial metabolism under ciprofloxacin exposure, affecting genes in the galactitol metabolic pathways. This disruption facilitates adaptive responses to drug therapy and enhances the efficiency of intracellular survival. A more comprehensive and integrated understanding of these physiological and genetic changes is crucial for improving treatment outcomes.

Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of Salmonella Typhimurium

Antimicrobial resistance (AMR) is a growing public health crisis that requires innovative solutions. Current susceptibility testing approaches limit our ability to rapidly distinguish between antimicrobial-susceptible and -resistant organisms. Salmonella Typhimurium (S. Typhimurium) is an enteric pathogen responsible for severe gastrointestinal illness and invasive disease. Despite widespread resistance, ciprofloxacin remains a common treatment for Salmonella infections, particularly in lower-resource settings, where the drug is given empirically. Here, we exploit high-content imaging to generate deep phenotyping of S. Typhimurium isolates longitudinally exposed to increasing concentrations of ciprofloxacin. We apply machine learning algorithms to the imaging data and demonstrate that individual isolates display distinct growth and morphological characteristics that cluster by time point and susceptibility to ciprofloxacin, which occur independently of ciprofloxacin exposure. Using a further set of S. Typhimurium clinical isolates, we find that machine learning classifiers can accurately predict ciprofloxacin susceptibility without exposure to it or any prior knowledge of resistance phenotype. These results demonstrate the principle of using high-content imaging with machine learning algorithms to predict drug susceptibility of clinical bacterial isolates. This technique may be an important tool in understanding the morphological impact of antimicrobials on the bacterial cell to identify drugs with new modes of action.

Evolutionary barriers to horizontal gene transfer in macrophage-associated Salmonella

Horizontal gene transfer (HGT) is a powerful evolutionary force facilitating bacterial adaptation and emergence of novel phenotypes. Several factors, including environmental ones, are predicted to restrict HGT, but we lack systematic and experimental data supporting these predictions. Here, we address this gap by measuring the relative fitness of 44 genes horizontally transferred from Escherichia coli to Salmonella enterica in infection-relevant environments. We estimated the distribution of fitness effects in each environment and identified that dosage-dependent effects across different environments are a significant barrier to HGT. The majority of genes were found to be deleterious. We also found longer genes had stronger negative fitness consequences than shorter ones, showing that gene length was negatively associated with HGT. Furthermore, fitness effects of transferred genes were found to be environmentally dependent. In summary, a substantial fraction of transferred genes had a significant fitness cost on the recipient, with both gene characteristics and the environment acting as evolutionary barriers to HGT.