Transcriptomic Remodeling and Survival Strategies of Extensively Drug-Resistant Klebsiella pneumoniae Under Meropenem Pressure

Transcriptomic Remodeling and Survival Strategies of Extensively Drug-Resistant Klebsiella pneumoniae Under Meropenem Pressure | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 May 2025 V1 Latest version Share on Transcriptomic Remodeling and Survival Strategies of Extensively Drug-Resistant Klebsiella pneumoniae Under Meropenem Pressure Authors : Xhaulla Maria Quariguasi Cunha Fonseca , Marco A.F. Clementino 0000-0002-0628-8047 [email protected] , Rafhaella Nogueira Della Guardia Gondim , Luciana França da Silva , Maria Gleiciane da Rocha , Francisco Cleber Silva Ferreira , Ana Karolina Silva dos Santos , … Show All … , Lyvia M.V.C. Magalhães , Jose Q.S. Filho , Ila F.N. Lima , Glairta de Souza Costa , Fábio Miyajima , Luzia Gabrielle Zeferino de Castro , Caroline Rebouças Damasceno , Alexandre Havt Bindá , and Aldo A.M. Lima Show Fewer Authors Info & Affiliations https://doi.org/10.22541/au.174714408.80680893/v1 175 views 111 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Klebsiella pneumoniae is an opportunistic pathogen of great medical relevance due to its high virulence and resistance to antimicrobials. Carbapenem resistance is mediated by carbapenemases and represents a global clinical challenge due to the limited therapeutic arsenal. Transcriptomic assays can elucidate antimicrobial resistance mechanisms and enables the identification of pharmacological targets in extensively drug-resistant (XDR) Klebsiella pneumoniae . This study evaluated the transcriptomic profile of carbapenemase-producing K. pneumoniae after exposure to meropenem and assessed its morpho-functional and metabolic pathway modifications in response to antibiotic treatment. The sensitivity of carbapenemase-producing K. pneumoniae, isolated from the ICU of the University Hospital Walter Cantídio in Fortaleza, CE, was determined by disk diffusion, broth microdilution, and the automated VITEK-2® method. The strain was exposed to subinhibitory concentrations of meropenem and their bacterial growth curve was determined. Total RNA was extracted from bacteria with and without antibiotic treatment at their growth curve log phase. RNA sequencing (RNA-Seq) was performed on a high-throughput MiSeq platform, and differential expression analysis identified both upregulated and downregulated transcripts. Following exposure to meropenem, K. pneumoniae showed increased expression of genes related to the plasma membrane, efflux pumps, and cell wall modifications, as well as genes involved in resistance to polymyxins and changes in energy metabolism, such as glycolysis and fermentation. Furthermore, genes associated with antioxidant systems and oxidative stress response were expressed at elevated levels, indicating a survival mechanism under selective pressure. On the other hand, transcripts associated with protein metabolism and DNA synthesis were downregulated, suggesting a global metabolic reprogramming aimed at cell maintenance under hostile conditions. Thus, exposure to meropenem induced complex adaptations in K. pneumoniae . The results provide valuable insights into the resistance and adaptation mechanisms of this bacterium, highlighting potential targets for new therapeutic strategies. Introduction Klebsiella pneumoniae is an encapsulated, opportunistic bacterium of high medical relevance, frequently associated with severe nosocomial infections, including septicemia (Paczosa; Mecsas, 2016; Rodriguez-Medina et al., 2024). Approximately 62% of isolates in intensive care units (ICUs) are Gram-negative, with K. pneumoniae being one of the main multidrug-resistant organisms responsible for hospital outbreaks. Its ability to acquire antimicrobial resistance mechanisms makes its control a major clinical challenge. Among the most concerning resistance mechanisms is the production of carbapenemases, which confers resistance to carbapenems—one of the main classes of antimicrobials used to treat infections caused by multidrug-resistant bacteria (Navon-Venezia et al., 2017; Hawkey et al., 2022). Mortality associated with carbapenemase-producing strains can exceed 50% in hospitals due to the limited therapeutic options and failures in conventional treatments (Prestinaci et al., 2015; Kerneis et al., 2021; Adams et al., 2018). Among the carbapenems, meropenem stands out as one of the main antimicrobials used in clinical practice, acting by inhibiting bacterial cell wall synthesis through binding to penicillin-binding proteins (PBPs), leading to cell lysis and bacterial death. However, the effectiveness of this drug has been compromised due to the emergence of extensively drug-resistant ( XDR ) K. pneumoniae strains bacteria resistant to almost all classes of antimicrobials, except for one or two last-line therapeutic options (Yang et al., 2021; Dai; Hu, 2022). Understanding the molecular mechanisms of antibiotic resistance is essential for developing new therapeutic strategies. Transcriptomic studies are valuable tools for elucidating these mechanisms, as they allow a global analysis of gene expression under specific antibacterial stress conditions (Brennan-Krohn et al., 2022; Long et al., 2019), including modifications in metabolic pathways, which play a crucial role in the survival and adaptation of Klebsiella pneumoniae , particularly under environmental stress and antimicrobial exposure. Furthermore, morpho-functional changes, such as alterations in cell membrane composition and energy metabolism, can confer antimicrobial resistance by modulating cell permeability and the activity of efflux pumps (Russo; Marr, 2019; Liu et al., 2024a). Therefore, this study aimed to evaluate the transcriptome of K. pneumoniae XDR exposed to meropenem. The analysis detailed differential gene expression, identifying genes that are positively and negatively regulated in response to the antimicrobial treatment. Materials and methods Bacterial strains The Klebsiella pneumoniae strain was isolated from an ICU patient (strain code 202). A preview project collected 258 Gram-negative bacterial (GNB) strains, revealing a high prevalence of resistance to penicillins, cephalosporins, and carbapenems among isolates from patients admitted to the Intensive Care Unit (ICU) of the Hospital Universitário Walter Cantídio (HUWC), Fortaleza, Brazil (Approval No. 3.276.113 from the ethics committee). Klebsiella pneumoniae strain isolates were identified using the VITEK® 2 Compact automated system (BioMérieux, France), following the manufacturer’s recommendations. Carbapenemase production was confirmed by the Ng-Test Carba 5 test®, following the manufacturer’s recommendations. For this study, a Klebsiella pneumoniae isolate resistant to at least one antimicrobial agent from β-lactams, fluoroquinolones, aminoglycosides, polymyxins, and/or glycylcyclines was used. This isolate was classified as extensively drug-resistant (XDR), where XDR isolates were resistant to at least one agent in all tested classes, except for a maximum of two. It was collected from a tracheal aspirate. Antibacterial agents Ciprofloxacin (Medley Farmacêutica, São Paulo, Brazil) was diluted in 0.1M hydrochloric acid, while gentamicin (Medley Farmacêutica, São Paulo, Brazil) and meropenem (Medley Farmacêutica, São Paulo, Brazil) were diluted in sterile deionized water. The stock solutions of the antibacterial agents were prepared at the following concentrations: 5,000 µg.ml -1 for ciprofloxacin, 50,000 µg.ml -1 for gentamicin, and 250,000 µg.ml -1 for meropenem. All drugs were prepared according to the CLSI M100 document (CLSI, 2017). The stock solutions of ciprofloxacin, gentamicin, and meropenem were stored at –20ºC until use. Antibacterial susceptibility testing The K. pneumoniae strain was stored in the Biobank of the Federal University of Ceará. It was cultured for 18 hours in LB medium (Sigma-Aldrich, USA) at 37°C and stored at –80°C in 50% glycerol LB (v/v: 500µL/500µL) and was then used to perform antimicrobial susceptibility testing (AST) using three different techniques: disk diffusion, broth microdilution, and the automated VITEK-2® method. For the disk diffusion method, the Klebsiella pneumoniae strain was diluted in 5 mL of LB medium (Sigma-Aldrich, USA), then 100 µL of the bacterial suspension was transferred to Mueller-Hinton agar plates (KASVI, Brazil) using a Drigalski loop for uniform spreading. Antibiotic-impregnated disks containing ciprofloxacin 5 µg, gentamicin 10 µg, and meropenem 10 µg (Sensidisc DME – Diagnósticos Microbiológicos Especializados) were placed on the inoculum. After 24 hours of incubation at 37°C, inhibition zones formed around the disks were measured, and the results were interpreted according to the manufacturer’s guidelines and the CLSI (2017) and BRCAST standards. In addition to the disk diffusion method, susceptibility testing was performed in 96-well microplates using the broth microdilution technique, following the CLSI M100 guidelines (CLSI, 2017). The concentration range tested was 4 to 2048 µg.mL -1 for meropenem, 2 to 1024 µg.mL -1 for ciprofloxacin, and 2 to 1024 µg.mL -1 for gentamicin. A control drug was tested using an E. coli ATCC 25922 strain for quality control of the experiment, the meropenem at concentrations ranging from 4 to 0.008 µg.mL -1 . The inocula were prepared in 0.9% saline solution and then adjusted to a final concentration of 5 x 10⁵ CFU/mL in cation-adjusted Mueller-Hinton broth. The plate was then incubated at 37ºC for 16 to 20 hours. The Minimum Inhibitory Concentration (MIC) reading was performed visually, where the MIC was determined as the concentration capable of inhibiting 100% of growth compared to the control. For the VITEK 2® susceptibility test, a bacterial suspension was prepared in 0.45% sterile saline solution (NaCl), adjusting the optical density to 0.5 on the McFarland scale. After preparing the suspension, the specific VITEK 2® cards were inoculated. Growth curve of carbapenemase-producing Klebsiella pneumoniae The growth curve was plotted to determine the incubation time required to obtain biomass for rna extraction, which was defined within the exponential growth phase. The exponential growth phase represents the period of highest metabolic activity of the cell and is, therefore, the ideal stage for using cells in assays. The method used to define the growth curve was liquid culture, with sampling of aliquots over time. To define the growth curve, the assay was repeated three times. First, a pre-inoculum was prepared: five isolated colonies of Klebsiella pneumoniae were cultivated on Mueller-Hinton agar at 37ºC for 24 hours, then inoculated into test tubes containing 10 mL of previously autoclaved Mueller-Hinton broth. The pre-inoculum was maintained at 37ºC, 180 RPM for 24 hours. After this period, a 100 µL aliquot of 0.5 McFarland inoculum (~10⁸ cells) from the pre-inoculum was aseptically inoculated into eight test tubes containing 10 mL of autoclaved Mueller-Hinton broth. At predetermined time intervals of every 2 hours, from 0 to 12 hours, 1 mL aliquots were analyzed using an absorption spectrophotometer to determine the optical density (OD) at 600 nm. Growth curve of carbapenemase-producing Klebsiella pneumoniae exposed to different concentrations of antibacterials To evaluate the response of carbapenemase-producing Klebsiella pneumoniae under different antibiotic concentrations, bacterial cultures were prepared in Mueller-Hinton (MH) medium. Initially, 10 mL of MH was added to experimental tubes, while 5 mL was added to control tubes without drug addition. The bacterial inoculum was adjusted to 0.5 on the McFarland scale (~10⁸ cells/mL), and 100 µL of the suspension was transferred to each tube. Bacterial cultures were incubated at 37°C under constant agitation at 180 RPM until they reached an OD600 between 0.5 and 0.6, indicating the exponential growth phase. At this point, antibiotics were added to the experimental cultures at concentrations corresponding to MIC, 2× MIC, and 4× MIC. The cultures were then re-incubated under the same conditions. Aliquots of 1 mL were collected at 1, 3, and 6-hour intervals, and OD600 was measured to monitor bacterial growth. The remaining volume was immediately chilled on ice for 30 seconds, followed by centrifugation at 3220 g at 4°C for 15 minutes. The supernatant was discarded, and the cell pellets were stored at –80°C for further RNA extraction. RNA extraction and rRNA removal To isolate high-quality RNA, the bacterial pellet was initially homogenized in TRIzol™, ensuring the inhibition of RNase activity. Homogenization was performed using 1 mL of TRIzol™ per 50–100 mg of sample. After homogenization, the sample was incubated for 5 minutes at room temperature, followed by the addition of 0.2 mL of chloroform per mL of TRIzol™ used. The mixture was vigorously shaken and incubated for 2–3 minutes, allowing phase separation after centrifugation at 12,000 g at 4°C for 15 minutes, resulting in the formation of an upper aqueous phase (containing RNA), an interphase, and a lower organic phase. The aqueous phase was carefully transferred to a new tube, avoiding contamination from the other phases. To precipitate RNA, 0.5 mL of isopropanol per mL of TRIzol™ was added, followed by a 10-minute incubation and centrifugation at 12,000 g for 10 minutes at 4°C, forming an RNA pellet in a white gel-like form. The RNA pellet was washed with 1 mL of 75% ethanol, followed by centrifugation at 7,500 g for 5 minutes at 4°C. After ethanol removal, the pellet was air-dried for 5–10 minutes, then resuspended in RNase-free water, with heating at 55–60°C for 10–15 minutes to ensure complete solubilization. The RNA quality and concentration were evaluated using spectrophotometry or fluorometry, considering an A260/A280 ratio close to 2 as indicative of high purity. Sample quality assessment After RNA extraction for cDNA library preparation, the Agilent D1000 ScreenTape system, integrated with the 2200 TapeStation instrument, was used to analyze the quality of the extracted RNA. Following the manufacturer’s recommendations, Quality assessment was performed to ensure that all samples had an RNA Integrity Number (RIN) equal to or greater than 7. Initially, the equipment was configured using the Agilent TapeStation Controller software, and the necessary devices, including the ScreenTape and reagents, were inserted into the instrument after stabilization at room temperature for 30 minutes. For RNA sample preparation, 3 µL of Sample Buffer was mixed with 1 µL of previously purified RNA. Quality control of the assay was ensured by including a standard control, prepared by mixing 3 µL of Sample Buffer with 1 µL of the RNA Ladder provided in the kit. The mixtures were homogenized in a shaker at 2000 rpm for 1 minute, followed by brief centrifugation to position the contents at the bottom of the tubes. The prepared samples were then loaded into the D1000 ScreenTape device. The results were generated and analyzed using the Agilent TapeStation Analysis software. The system’s sensitivity allowed for RNA detection at concentrations as low as 0.1 ng/µL, while quantification accuracy was maintained within 10% variation in the specified concentration ranges. All reagents, including the ScreenTape device, were stored at 2–8°C, and strict precautions were followed during sample handling to prevent RNA degradation and avoid contamination. cDNA library construction and RNA-seq sequencing For each experimental condition mRNA enrichment was performed using the MICROBExpress Bacterial mRNA Purification Kit (Ambion), and rRNA removal was conducted using the Ribo-Zero Kit (Epicentre). From this purified and enriched sample, cDNA library was constructed for three biological replicates per condition, and sequencing was carried out using the Illumina HiSeq 2500 platform (Fasteris, Geneva, Switzerland). The transcriptomic analysis of Klebsiella pneumoniae and identify differentially expressed genes (DEGs) under specific conditions began with pre-processing of RNA-seq data, which involved the removal of low-quality sequences, adapters, and contaminants. Quality control was performed using tools such as FastQC to ensure that the RNA-seq reads were error-free, had good coverage, and high sequence quality. Additionally, sequence alignment was carried out against the reference genome of Klebsiella pneumoniae using the BV-BRC system (Bacterial and Viral Bioinformatics Resource Center) (Olson et al., 2023). After alignment, data normalization was applied to correct sequencing depth discrepancies and gene expression variations. Differential expression analysis was performed using the DESeq2 package. Through this analysis, it was possible to identify genes that exhibited significant expression variations, associating these changes with specific biological processes or phenotypic modifications in Klebsiella pneumoniae . Gene clustering Gene clustering was conducted based on functional classification, differential expression levels, and involvement in critical metabolic pathways. Genes were grouped into specific functional categories, including resistance mechanisms (efflux pumps, membrane permeability modifications, enzymatic inactivation of antimicrobials, oxidative stress responses, polysaccharide biosynthesis, biofilm formation, iron acquisition systems, and secondary metabolism). This manual clustering provided a detailed functional overview, highlighting the most relevant genes and pathways in antimicrobial resistance. The integration of this information allows for the prioritization of targets for therapeutic interventions and the development of strategies to control the spread of resistant Klebsiella pneumoniae strains. Statistical analysis Statistical analyses were conducted using GraphPad Prism 9 and R with RStudio (version 4.2.2). For the determination of antimicrobial susceptibility, MIC values were analyzed using one-way ANOVA, followed by Tukey’s test. The bacterial growth curve was fitted to a sigmoidal model and analyzed using repeated measures ANOVA with Greenhouse-Geisser correction. The growth of Klebsiella pneumoniae in the presence of meropenem, gentamicin, and ciprofloxacin was evaluated using two-way factorial ANOVA, followed by Sidak’s correction. Transcriptomic analysis was conducted using DESeq2, considering differentially expressed genes with log2 fold-change ≥1 or ≤-1, corrected by the Benjamini-Hochberg method. Images were statistically analyzed as follows: in growth curves, repeated measures ANOVA with Bonferroni correction was applied; in violin plots, the Mann-Whitney U test was used to compare gene expression between control and treated groups. In the volcano plot, gene dispersion analysis was performed using the Wald test, with Bonferroni correction for multiple comparisons. For all tests, a significance level of p < 0.05 was adopted. Antimicrobial susceptibility assay The minimum inhibitory concentrations (MICs) of meropenem, ciprofloxacin, and gentamicin against Klebsiella pneumoniae and the growth control Escherichia coli ATCC are described in Table 1. For ciprofloxacin and gentamicin, Klebsiella pneumoniae was tested within a concentration range of 2 to 1024 µg/mL, with MIC values of 64 µg/mL for ciprofloxacin and 256 µg/mL for gentamicin. In the case of meropenem, Klebsiella pneumoniae was tested at concentrations ranging from 4 to 2048 µg/mL, showing an MIC of 256 µg/mL. As a validation control, the strain E. coli ATCC 25922 was included, it was exposed to concentrations ranging from 0.008 to 4 µg/mL, and exhibited a sensitivity profile with an MIC of 0.06 µg/mL, a value consistent with the standards established by CLSI. The antimicrobial susceptibility results obtained through the VITEK® automated method are described in Table 2. This method was used to confirm strain identification and to assess susceptibility to clinically relevant antibiotics, both individually and in combination. The strain exhibited resistance to most tested antimicrobials, showing susceptibility only to the combination of Ceftazidime/Avibactam, with an MIC of 1 µg/mL. For meropenem, the strain showed resistance with an MIC of ≥16 µg/mL. Based on the susceptibility data obtained from the tested methods, the strain was classified as XDR (Extensively Drug-Resistant). Growth curve of carbapenemase-producing Klebsiella pneumoniae The growth curve of Klebsiella pneumoniae is presented in Figure 1. The curve follows a sigmoidal pattern, which can be divided into three distinct phases. The lag phase, between 0 and 1 hour, is characterized by the absence of significant growth, with OD600 values close to zero. At this stage, the cells were adapting to the culture medium, synthesizing enzymes, and adjusting to the conditions to initiate cell division. From 1 hour onward, the exponential phase begins, extending until approximately 5 to 6 hours. During this stage, a rapid increase in optical density is observed, reaching values above 0.4. Around 5-6 hours, the stationary phase begins and continues until approximately 12 hours. In this phase, growth progressively slows down and reaches a plateau, with OD600 values stabilizing around 0.6 to 0.65. Growth curve of carbapenemase-producing Klebsiella pneumoniae exposed to different concentrations of antibacterials The Growth curve of carbapenemase-producing Klebsiella pneumoniae exposed to different concentrations of antibacterials is presented in Figure 2. For meropenem, it was observed that over time, even at the highest concentrations (2×MIC and 4×MIC), bacterial growth was significantly stimulated (p < 0.05), increasing OD over 6 hours. However, at MIC concentration, bacterial growth exceeded control values after 6 hours of exposure. This behavior suggests that K. pneumoniae may adapt to concentrations equal to or higher than MIC, requiring concentrations above 4×MIC to achieve a bactericidal effect against K. pneumoniae (Figure 2A) . Figure 2B, related to gentamicin, reveals a different pattern. The MIC concentration has a greater impact on bacterial growth compared to 2×MIC and 4×MIC, which show a less pronounced reduction in optical density (OD600). However, even at these higher concentrations, bacterial growth is still visible, suggesting that the bacterium has some intrinsic or adaptive resistance to the antimicrobial, requiring higher concentrations for effective control. Finally, Figure 2C, which evaluates the exposure of K. pneumoniae to ciprofloxacin, shows a distinct behavior from the other two antimicrobials, with minimal stimulation of bacterial growth compared to the other tested antimicrobials. However, at the MIC concentration, there is a peak in optical density at 2 hours, followed by a sharp decline, indicating that the antimicrobial initially exerts a rapid stimulatory effect, but this effect is later reversed. The 2×MIC and 4×MIC concentrations keep bacterial optical densities consistently low over time, showing a significant inhibitory effect (p < 0.05) on growth. Transcripts of carbapenemase-producing Klebsiella pneumoniae after exposure to Meropenem Regarding the transcripts of carbapenemase-producing Klebsiella pneumoniae after meropenem exposure, Figure 3 presents a violin plot displaying gene expression across various modified metabolic categories. Overall, the data indicate that most of the bacterium’s fundamental signaling processes remain stable, suggesting metabolic robustness under meropenem pressure. This stability is evident in categories such as carbohydrate, amino acid, lipid, and energy metabolism, which are essential for cell survival. The graphs presented in Figure 4 illustrated the gene expression analysis across various functional categories, comparing the standard strain condition and meropenem exposure. Genes related to metabolism (801 genes analyzed) exhibited a broad and consistent distribution across conditions, indicating that meropenem exposure sustained general metabolic pathways. Similarly, categories such as cellular processes (91 genes), protein processing (195 genes), and membrane transport (147 genes) maintained similar expression patterns, suggesting that these fundamental functions were not directly affected by antimicrobial exposure. The stress response, defense, and virulence category (177 genes) showed a significant difference (p < 0.05), indicating a possible induction of genes related to defense mechanisms or cellular adaptation in response to meropenem exposure. Thus, the data suggest that although most cellular functions remain unchanged between control and meropenem exposure, there is an activation of genes associated with defense and virulence, which may indicate an adaptive response to the antimicrobial. These observations highlight an overall stability of cellular processes, with localized responses to external stimuli in the stress and defense category. Genes upregulated by Meropenem exposure Two LysR‑type regulators—KPHS_00530 and KPHS_27080—were among the most strongly induced, pointing to a coordinated reprogramming of metabolic pathways. KPHS_45670, encoding the QseB response regulator of a two‑component system, was likewise up‑regulated, consistent with the need for rapid signal transduction under antibiotic pressure. Multiple transporters essential for survival in hostile conditions showed higher expression. KPHS_05350 encodes a glycerol‑3‑phosphate permease, whereas KPHS_05330 specifies an ABC transporter that imports maltose, nitrate, and iron. KPHS_16010, a molybdenum transporter, was also induced—reflecting the demand for this cofactor by molybdo‑enzymes activated during stress. Genes involved in anaerobic respiration were also up regulated. KPHS_28530 is part of the nitrate‑reduction chain, and KPHS_41170 encodes nitric‑oxide reductase, completing denitrification when oxygen is scarce. Furthermore, genes responsible for cell‑wall remodeling and genome plasticity were also upregulated. KPHS_39210, encoding a penicillin‑insensitive transglycosylase (Fig. 5), and KPHS_51150, a rhodanese‑related sulfotransferase, were up‑regulated, suggesting adjustments to peptidoglycan synthesis and redox balance. The transposase genes KPHS_03530 and KPHS_50590 showed increased expression, hinting at mobilization of genetic elements that could disseminate resistance determinants. KPHS_00070 (D‑ribose pyranase; Fig. 5) rose significantly (p < 0.05), indicating enhanced carbohydrate catabolism. KPHS_46990 (UPF0306 protein YhbP) and KPHS_47870 (septum‑formation protein Maf) were also induced, implying roles in structural maintenance and cell division under stress. Several hypothetical genes—e.g., KPHS_21930, KPHS_30290, KPHS_39420—were significantly up‑regulated (p < 0.05). Although their functions remain unknown, they may constitute novel components of the meropenem stress response. Collectively, these data reveal a multilayered transcriptional programme encompassing signal transduction, nutrient acquisition, energy metabolism, cell‑wall biogenesis, and genome dynamics, underscoring the capacity of Klebsiella pneumoniae to adapt rapidly to meropenem challenge. Genes downregulated by Meropenem exposure Most of the downregulated transcripts belong to pathways that become dispensable—or even detrimental—under drug pressure, namely secretion‑system biogenesis, central metabolism, and aerobic respiration. Virulence and gene transfer transcripts were down regulated. KPHS_34850, encoding the VirB11 ATPase that energizes type IV secretion, was markedly repressed. A diminished VirB11 pool is expected to curtail conjugative DNA transfer, thereby restraining the dissemination of resistance and virulence determinants—a strategic trade‑off under stress. KPHS_31130 (4‑hydroxyphenylpyruvate dioxygenase, tyrosine catabolism) and KPHS_30580 (cytochrome c biogenesis) both declined, consistent with a metabolic realignment that favours energy conservation over biosynthetic breadth. In the same vein, KPHS_18050, coding for anaerobic dimethyl‑sulfoxide reductase, was silenced, suggesting that alternative electron‑acceptor pathways are down‑prioritized once meropenem stalls growth. Furthermore, KPHS_30970 (multicopper polyphenol oxidase) and KPHS_45390 (short‑chain dehydrogenase/reductase) showed reduced expression, implying deliberate suppression of redox‑active enzymes that might otherwise amplify antibiotic‑induced oxidative damage. Several hypothetical or poorly annotated genes—KPHS_13360, KPHS_02850, KPHS_31530 and KPHS_36920 (UPF0118 family membrane protein)—were also down‑regulated (p < 0.05; Fig. 5). Although their functions remain undefined, their concerted repression hints at roles in membrane architecture or metabolite transport that become non‑essential—or energetically costly—during meropenem challenge. Taken together, the down‑regulated gene set paints a picture of K. pneumoniae throttling back energy‑intensive functions, horizontal gene transfer, and certain redox processes to prioritize core survival pathways when confronted with meropenem. Discussion Antimicrobial Resistance and Growth Curve Analysis of Klebsiella pneumoniae Exposed to Meropenem, Ciprofloxacin, and Gentamicin showed that the antimicrobial resistance of Klebsiella pneumoniae represents a growing challenge in nosocomial environments, especially due to its ability to acquire resistance mechanisms that limit therapeutic options (Prestinaci et al., 2015). Among the antibiotics frequently used in the treatment of infections caused by this bacterium, gentamicin, ciprofloxacin, and meropenem stand out. In the present study, these three antimicrobials were selected to evaluate the resistance of the K. pneumoniae strain due to their widespread clinical use and the need to understand their effectiveness against multidrug-resistant strains. For this purpose, susceptibility assays were performed using three different methods. In the present study, the MIC values obtained were ≥16 µg/mL for meropenem, ≥4 µg/mL for ciprofloxacin, and ≥16 µg/mL for gentamicin, classifying the strain as resistant to all tested antibiotics. According to the Clinical and Laboratory Standards Institute (CLSI, 2017), the resistance breakpoints for K. pneumoniae are ≥4 µg/mL for meropenem, ≥1 µg/mL for ciprofloxacin, and ≥8 µg/mL for gentamicin. The Brazilian Committee on Antimicrobial Susceptibility Testing (BRcast, 2024) establishes resistance breakpoints of ≥8 µg/mL for meropenem, ≥1 µg/mL for ciprofloxacin, and ≥4 µg/mL for gentamicin. Comparing the values obtained with these standards, the MIC of the studied strain exceeds the defined breakpoints in both guidelines, confirming a highly resistant phenotype. The analyzed strain was classified as XDR (extensively drug-resistant), a concerning condition that significantly reduces the available therapeutic options. XDR gram-negative resistance is associated with high mortality rates, especially in immunocompromised patients or those in intensive care units (Li et al., 2024). The study by Elkady et al. (2024) identified MIC values for meropenem ranging between 8 and 32 µg/mL in clinical isolates of Klebsiella pneumoniae , confirming the dissemination of genetic resistance mechanisms among these strains. Comparing these results with those of the present study, in which the MIC for meropenem was ≥16 µg/mL, it is observed that the reported value aligns with the range described by Elkady et al. (2024), reinforcing the severity of carbapenem resistance in different clinical contexts. Based on the susceptibility data and MIC determination, the growth curve of carbapenemase-producing K. pneumoniae was analyzed to recognize the log phase and perform a comparative assessment of the strain’s growth curve after exposure to selected antibacterial agents. Meropenem significantly impacted the growth rate of carbapenemase-producing K. pneumoniae . In the conducted assays, the growth curve showed an extended lag phase, suggesting that the bacterium required a longer adaptation period when exposed to the antibiotic. This effect may be related to the induction of resistance mechanisms, such as the overproduction of efflux pumps or modulation of membrane porin expression, strategies previously described for carbapenem-resistant K. pneumoniae (Elkady et al., 2024; Yao et al., 2023). During the exponential phase, a reduced growth rate was observed, indicating that despite resistance, meropenem still exerts an inhibitory effect on the bacterium. This phenomenon may be associated with the metabolic cost of resistance, as the production of carbapenemases and the activation of adaptation mechanisms can compromise bacterial metabolic efficiency (Li et al., 2024; Li et al., 2024b; Marzouk et al., 2024). However, upon reaching the stationary phase, the cell density was comparable to that of cultures not exposed to the antibiotic, demonstrating that the bacterium can circumvent the effect of meropenem and maintain long-term viability. This behavior is concerning from a clinical perspective, as it suggests that even in the presence of a carbapenem, the bacterium can persist in the host and promote the development of chronic infections or relapses (Kerneis et al., 2021; Mehrota et al., 2023). Given this behavior, meropenem was chosen as the target drug for subsequent tests, where the transcriptome of this strain was evaluated after exposure to MIC concentrations of meropenem. Gene expression analysis demonstrated an increase in the expression of genes associated with energy metabolism and oxidative stress response after exposure to meropenem. Among the highly expressed genes, those related to amino acid biosynthesis and carbohydrate metabolism regulation stood out, a pattern also identified by Liu et al. (2024a) and Wright et al. (2014). Specifically, the soxS (KPHS_17670) gene was highly expressed, indicating activation of the SoxRS pathway, which is directly involved in protection against oxidative damage. This finding aligns with Cain et al. (2018), who demonstrated that adaptation to antibiotics often involves detoxification mechanisms to minimize DNA and membrane damage. The transcriptomic analysis revealed that exposure to meropenem induced the overexpression of genes associated with nutrient transport, particularly ABC-type transporters involved in the uptake of compounds such as maltose, nitrate, and iron, notably the KPHS_05330 gene. This finding indicates a metabolic reprogramming aimed at maintaining cellular viability under stress conditions, favoring bacterial adaptation through enhanced acquisition of essential micronutrients. The study by Polani et al. (2025) supports this observation by demonstrating that the presence of the plasmid-borne ferric citrate transport system ( fecABCDE ) in K. pneumoniae contributes to increased resistance to cefiderocol by repressing the expression of endogenous siderophore receptors and promoting an alternative iron uptake pathway. These data converge toward the understanding that modulation of iron acquisition, whether through endogenous or plasmid-mediated mechanisms, is a key determinant in the bacterial adaptive response. It is also noteworthy that therapeutic strategies combining carbapenems with iron chelators have been explored as alternatives against XDR strains, aiming to limit iron availability and impair bacterial metabolism. However, the data obtained in this study indicate that K. pneumoniae may activate compensatory iron transport pathways under antimicrobial stress, potentially undermining the efficacy of such therapeutic combinations. Therefore, a detailed understanding of iron uptake and regulatory mechanisms provides valuable insights for the development of new approaches against multidrug-resistant isolates. Additionally, the katG (KPHS_09820) gene, encoding catalase, also showed positive regulation, confirming the activation of antioxidant mechanisms to counteract meropenem-induced stress (Sundaresan et al., 2024). This integrative phenotypic‑transcriptomic approach exposes the molecular strategy that an XDR K. pneumoniae deploys to survive carbapenem assault. This study uncovered a shift toward (i) enhanced micronutrient acquisition (iron, molybdenum, nitrate), (ii) redox‑stress mitigation (SoxRS, KatG), and (iii) energy‑spar­ing down‑regulation of high‑cost functions such as type IV secretion. These pathways supply a shortlist of drug‑gable nodes—ABC transporters, alternative respiratory enzymes, and oxidative‑stress regulators—that can be probed in future screens for adjuvant compounds or targeted gene knock‑downs. Moreover, the growth‑phase–specific transcriptional signatures provide a roadmap for timing therapeutic interventions to catch the bacterium at its most vulnerable metabolic state. Taken together, our findings not only clarify why current carbapenem monotherapy falters but also chart a rational path toward combination regimens and novel inhibitors designed to disarm the very circuits that prop up extreme drug resistance. Conflicts of interest The author(s) declare that there are no conflicts of interest. Acknowledgement This work was supported by the National Council for Scientific and Technological Development (CNPq; Brazil; Grants 408549-2022.0) and CAPES (Coordination for the Improvement of Higher Education Personnel; Brazil). Declaration of interest statement We declare there is no conflict of interest and agree with the disclosure of the results. References ADAMS, S.; GAYOSO, A.; RILEY, L. W. 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Klebsiella pneumoniae Range (µg/mL) 2- 1024 2- 1024 4- 2048 MIC (µg/mL) 64 256 256 Escherichia coli ATCC 25922 Range (µg/mL) - - 0.008 - 4 MIC (µg/mL) - - 0.06 MIC: Minimum Inhibitory Concentration ; ATCC: American Type Culture Colection . Table 2. Minimum Inhibitory Concentration (µg/mL) of different antimicrobials by automated VITEK® method Amoxicilin/clavulanic acid Urine ≥32 R Other ≥32 R Piperacillin/Tazobactam ≥128 R Cefuroxime ≥64 R Cefuroxime Axetil ≥64 R Ceftazidime 32 R Ceftriaxone Meningitis ≥64 R Other ≥64 R Ceftazidime/Avibactam 1 S Ceftolozane/Tazobactam ≥32 R Cefepime ≥32 R Aztreonam ≥64 R Ertapenem ≥8 R Meropenem Meningitis ≥16 R Other ≥16 R Amikacin 32 R Gentamicin ≥16 R Ciprofloxacin ≥4 R Tigecycline 1 R MIC: Minimum Inhibitory Concentration ; R: Resistant; S: Sensitive Figure legends Figure 1. Growth curve of carbapenemase-producing Klebsiella pneumoniae over 12 hours. The optical density at 600 nm (OD600) was measured at different time points to assess bacterial growth dynamics. The curve represents the mean and standard deviation, showing an initial lag phase, followed by an exponential growth phase, and reaching a plateau after approximately 10 hours, indicating bacterial adaptation and stabilization in the culture medium. Figure 2. Growth curve of Klebsiella pneumoniae after exposure to different concentrations of meropenem, gentamicin, and ciprofloxacin. The optical density at 600 nm (OD600) was measured at different time points (0, 2, 4, and 6 hours) to evaluate bacterial growth under antimicrobial stress. A: OD600 bacterial exposed to meropenem (256 to 1024 µg/ml); B: OD600 bacterial exposed to gentamicin (256 to 1024 µg/ml); C: OD600 bacterial exposed to ciprofloxacin (64 to 256 µg/ml). MER: Meropenem. GEN: Gentamicin. CIP: Ciprofloxacin. * Represents statistically significant differences (P<0.05) between the absorbance values of cells exposed to drugs, when compared to the drug-free growth. Figure 3. Gene expression in different metabolic pathways in Klebsiella pneumoniae after exposure to Meropenem, represented by violin plots. 12 genes belonging to carbohydrate metabolism, amino acid metabolism, lipid metabolism, and energy metabolism showed significantly different expression levels between the conditions (p< 0.05). The plots illustrate the distribution of gene expression levels (TPM) across various metabolic categories, comparing control without exposition (red) and exposition to meropenem (blue) conditions. Each violin represents the density of genes at different expression levels, with the white box inside indicating the interquartile range and the median. Figure 4. Analysis of gene expression in Klebsiella pneumoniae under exposure to the MIC of Meropenem, represented by violin plots. 12 genes belong to metabolism, membrane transport, energy production, and stress response showed significantly different expression levels between the conditions (p< 0.05). The plots illustrate the distribution of gene expression levels (TPM) across various metabolic categories, comparing control without exposition (red) and exposition to meropenem (blue) conditions. Each violin represents the density of genes at various expression levels within a category. The white box inside each violin indicates the interquartile range, with the median marked. Figure 5. Analysis of gene expression in Klebsiella pneumoniae under exposure to the MIC of Meropenem, expressed by a volcano plot. Eight genes significantly upregulated (p < 0.05) are shown on the right (red), while significantly four downregulated genes (p < 0.05) are on the left (blue). Non-significant genes are clustered near the center (gray). a: ribosome-associated inhibitor A; b: hypothetical protein; c: Putative transposase InsK for insertion sequence element IS150; d: D-ribose pyranase; e: penicillin-insensitive transglycosylase; f: Septum formation protein Maf; g: hypothetical protein; h: UPF0306 protein YhbP; i: hypothetical protein; j: Uncharacterized UPF0118 membrane protein; k: hypothetical protein; l: hypothetical protein. The plot displays the distribution of differentially expressed genes, with the x-axis representing the log2 fold change (upregulated and downregulated genes) and the y-axis showing the -log10 p-value, indicating statistical significance. Software: R® (4.2.2). Author Contributions (CRediT taxonomy): Xhaulla Maria Quariguasi Cunha Fonseca: Investigation, Data Curation, Writing – Original Draft, Methodology – Performed RNA extraction, antimicrobial assays, and transcriptomic experiments. Led data analysis and wrote the first draft of the manuscript. Marco Antonio de Freitas Clementino: Conceptualization, Supervision, Project Administration, Formal Analysis, Visualization, Writing – Review & Editing – Designed the study, guided experimental execution, coordinated data analysis and figure generation, reviewed the manuscript, and led the submission process. Rafhaella Nogueira Della Guardia Gondim: Methodology, Investigation – Supported RNA-seq preparation and initial sequencing data analysis. Luciana França da Silva: Resources, Investigation – Provided technical support for media preparation, bacterial maintenance, and culture handling. Maria Gleiciane da Rocha, Francisco Cleber Silva Ferreira, and Ana Karolina Silva dos Santos: Methodology, Validation, Formal Analysis – Provided critical input on experimental design, bacterial growth analysis, and antimicrobial susceptibility testing. José Quirino Filho, Lyvia Maria Vasconcelos Carneiro Magalhães: Project Administration, Resources – Managed administrative lab processes and contributed to technical support. Ila Fernandes Nunes Lima , Glairta de Souza Costa: Investigation – Contributed to bacterial strain collection and antimicrobial susceptibility testing in the hospital setting. Fábio Miyajima : Methodology, Resources – Supported RNA sequencing procedures. Luzia Gabrielle Zeferino de Castro, Caroline Rebouças Damasceno : Investigation – Contributed to RNA sequencing workflows, Participated in sequencing execution and quality control. Alexandre Havt (AH) and Aldo Ângelo Moreira Lima (AAML): Supervision, Writing – Review & Editing – Supervised the study’s development, contributed to final manuscript revisions. Graphical abstract: Abbreviated Summary: Klebsiella pneumoniae adapts to meropenem pressure through complex transcriptomic remodeling, activating efflux systems, oxidative stress defenses, and nutrient acquisition pathways. Simultaneously, energy-intensive and non-essential processes are downregulated to enhance survival. These findings reveal adaptive strategies of extensively drug-resistant strains and highlight potential targets for therapeutic intervention. Information & Authors Information Version history V1 Version 1 13 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Xhaulla Maria Quariguasi Cunha Fonseca Universidade Federal do Ceara Faculdade de Medicina View all articles by this author Marco A.F. Clementino 0000-0002-0628-8047 [email protected] Universidade Federal do Ceara Faculdade de Medicina View all articles by this author Rafhaella Nogueira Della Guardia Gondim Universidade Federal do Ceara View all articles by this author Luciana França da Silva Universidade Federal do Ceara View all articles by this author Maria Gleiciane da Rocha Empresa Brasileira de Servicos Hospitalares View all articles by this author Francisco Cleber Silva Ferreira Universidade Federal do Ceara View all articles by this author Ana Karolina Silva dos Santos Universidade Federal do Ceara Faculdade de Medicina View all articles by this author Lyvia M.V.C. Magalhães Universidade Federal do Ceara Faculdade de Medicina View all articles by this author Jose Q.S. Filho Universidade Federal do Ceara Faculdade de Medicina View all articles by this author Ila F.N. Lima Empresa Brasileira de Servicos Hospitalares View all articles by this author Glairta de Souza Costa Empresa Brasileira de Servicos Hospitalares View all articles by this author Fábio Miyajima Fiocruz Ceara View all articles by this author Luzia Gabrielle Zeferino de Castro Fiocruz Ceara View all articles by this author Caroline Rebouças Damasceno Fiocruz Ceara View all articles by this author Alexandre Havt Bindá Universidade Federal do Ceara View all articles by this author Aldo A.M. Lima Universidade Federal do Ceara Faculdade de Medicina View all articles by this author Metrics & Citations Metrics Article Usage 175 views 111 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xhaulla Maria Quariguasi Cunha Fonseca, Marco A.F. Clementino, Rafhaella Nogueira Della Guardia Gondim, et al. Transcriptomic Remodeling and Survival Strategies of Extensively Drug-Resistant Klebsiella pneumoniae Under Meropenem Pressure. Authorea . 13 May 2025. 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