Transcriptomic Profiling of Klebsiella pneumoniae Reveals Metabolic Adaptations to Meropenem Exposure

Transcriptomic Profiling of Klebsiella pneumoniae Reveals Metabolic Adaptations to Meropenem Exposure | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 7 July 2025 V1 Latest version Share on Transcriptomic Profiling of Klebsiella pneumoniae Reveals Metabolic Adaptations to Meropenem Exposure Authors : Felipe Francisco Tuon [email protected] , Suzana Carstensen , Paula Hansen Suss , Gabriel Burato Ortis , Thalissa Colodiano Martins , Hirwigy Lee , Liziane Cristina dos Santos , Michelle Zibetti Tadra , Willian Klassen de Oliveira , Joao Paulo Telles , and Leticia Ramos Dantas Authors Info & Affiliations https://doi.org/10.22541/au.175189325.54376087/v1 307 views 216 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Klebsiella pneumoniae is a major multidrug-resistant pathogen associated with severe hospital-acquired infections. Although meropenem remains a key therapeutic option, little is known about the global transcriptional response of K. pneumoniae to carbapenem exposure, especially in clinical isolates. Methods: We performed RNA-sequencing (RNA-seq) on an extended-spectrum β-lactamase (ESBL)-producing K. pneumoniae strain exposed to meropenem at clinically relevant serum concentrations. Differentially expressed genes were identified and mapped onto central metabolic pathways. Results: Meropenem exposure triggered a profound transcriptional reprogramming. Glycolytic genes were significantly upregulated, including hexokinase , phosphoglycerate kinase , and pyruvate kinase , suggesting increased energy production via substrate-level phosphorylation. In contrast, genes in the oxidative branch of the tricarboxylic acid (TCA) cycle, such as succinate dehydrogenase and isocitrate dehydrogenase , were downregulated. Several amino acid biosynthetic pathways—including those for glutamate, serine, arginine, methionine, and branched-chain amino acids—were transcriptionally activated, indicating a shift toward anabolism and redox balance. Conclusion: Our findings reveal a coordinated metabolic adaptation in K. pneumoniae under meropenem stress, characterized by enhanced glycolysis and amino acid biosynthesis alongside partial TCA suppression. This Warburg-like phenotype may support bacterial survival, stress tolerance, and early persistence. These insights offer new perspectives on noncanonical antibiotic response pathways and potential metabolic targets for therapeutic intervention. TITLE: Transcriptomic Profiling of Klebsiella pneumoniae Reveals Metabolic Adaptations to Meropenem Exposure RUNNING TITLE: Meropenem and stress metabolism AUTHORS:C Felipe Francisco Tuon (1), Suzana Carstensen (1), Paula Hansen Suss (1), Gabriel Burato Ortis (1), Thalissa Colodiano Martins (1), Hirwigy Lee (2), Liziane Cristina dos Santos (2), Michelle Zibetti Tadra (2), Willian Klassen de Oliveira (2), Joao Paulo Telles (3), Leticia Ramos Dantas (1) AFFILIATIONS (1) Laboratory of Emerging Infectious Diseases, School of Medicine, Pontifícia Universidade Católica do Paraná, Curitiba, PR, 80215-901, Brazil (2) Gogenetic, Curitiba, PR, Brazil (3) AC Camargo Cancer Center, Infectious Diseases Department, São Paulo, São Paulo, Brazil CORRESPONDING AUTHOR: Felipe F. Tuon Phone/Fax: 55-41-3071 1133 and +55 41 988521893 Email: [email protected] Pontificia Universidade Catolica do Paraná Rua Imaculada Conceição, 1155 Curitiba, PR, Brazil ZIP Code 80215-901 Abstract Background: Klebsiella pneumoniae is a major multidrug-resistant pathogen associated with severe hospital-acquired infections. Although meropenem remains a key therapeutic option, little is known about the global transcriptional response of K. pneumoniae to carbapenem exposure, especially in clinical isolates. Methods: We performed RNA-sequencing (RNA-seq) on an extended-spectrum β-lactamase (ESBL)-producing K. pneumoniae strain exposed to meropenem at clinically relevant serum concentrations. Differentially expressed genes were identified and mapped onto central metabolic pathways. Results: Meropenem exposure triggered a profound transcriptional reprogramming. Glycolytic genes were significantly upregulated, including hexokinase , phosphoglycerate kinase , and pyruvate kinase , suggesting increased energy production via substrate-level phosphorylation. In contrast, genes in the oxidative branch of the tricarboxylic acid (TCA) cycle, such as succinate dehydrogenase and isocitrate dehydrogenase , were downregulated. Several amino acid biosynthetic pathways—including those for glutamate, serine, arginine, methionine, and branched-chain amino acids—were transcriptionally activated, indicating a shift toward anabolism and redox balance. Conclusion: Our findings reveal a coordinated metabolic adaptation in K. pneumoniae under meropenem stress, characterized by enhanced glycolysis and amino acid biosynthesis alongside partial TCA suppression. This Warburg-like phenotype may support bacterial survival, stress tolerance, and early persistence. These insights offer new perspectives on noncanonical antibiotic response pathways and potential metabolic targets for therapeutic intervention. Keywords : Klebsiella pneumoniae; Meropenem; Transcriptomics; Metabolic reprogramming; Amino acid biosynthesis 1. Introduction Antimicrobial resistance (AMR) poses an escalating global health threat, undermining the successful treatment of bacterial infections across various healthcare environments 1 . Klebsiella pneumoniae , a Gram-negative opportunistic pathogen, is among the most worrisome multidrug-resistant organisms. It is frequently implicated in hospital-acquired infections such as pneumonia, bloodstream infections, and urinary tract infections 2 . Its remarkable ability to rapidly acquire and spread resistance genes, especially those targeting β-lactam antibiotics, has led to increased treatment failures and mortality rates 3 . Carbapenems, including meropenem, have been considered critical agents in combating infections caused by extended-spectrum β-lactamase (ESBL)-producing K. pneumoniae strains 4 . However, the rise of carbapenem-resistant K. pneumoniae (CRKP) strains worldwide has severely limited this treatment option. Resistance mechanisms commonly involve carbapenemase production, alterations in porin channels, and upregulation of efflux pumps 5 . Still, the comprehensive bacterial response to carbapenem exposure, particularly at the transcriptional level, remains only partially understood 6 . Transcriptomic profiling offers a powerful approach to elucidate bacterial adaptive mechanisms in real time 7 . By analyzing gene expression dynamics upon antibiotic exposure, researchers can uncover pathways related to stress responses, metabolic reprogramming, virulence modulation, and resistance development. Such insights are pivotal not only for understanding bacterial survival strategies but also for identifying potential therapeutic targets or diagnostic markers. While previous studies have examined global transcriptional changes in K. pneumoniae under various stress conditions, few have focused specifically on the response to meropenem, particularly using clinically relevant strains 8 . Furthermore, most investigations have centered on resistance mechanisms in established resistant strains, with less attention given to the immediate transcriptional landscape following antibiotic challenge in susceptible or partially resistant backgrounds 9,10 . In this study, we performed RNA-sequencing (RNA-seq) to characterize the transcriptomic response of K. pneumoniae following exposure to meropenem concentration obtained from serum level. Particularly, we focused on the expression dynamics of genes associated with central metabolic processes, including amino acid biosynthesis and glycolysis. These pathways are essential for bacterial growth, stress adaptation, and energy production, and may play a critical role in facilitating persistence or resilience under antibiotic stress. Understanding how meropenem exposure reshapes metabolic gene expression can offer new insights into bacterial fitness strategies and reveal alternative targets for antimicrobial intervention. 2. Materials and Methods 2.1 Bacterial strain and growth conditions One K. pneumoniae isolate was selected for transcriptomic analysis. This isolate was chosen from a prospective study evaluating serum levels of β-lactam antibiotics in patients with microbiologically confirmed infections. Given the objective of understanding gene expression in an ESBL-producing Gram-negative microorganism under meropenem-induced stress, this particular isolate was deemed suitable for the study. The bacterium was isolated from a blood culture sample collected prior to the initiation of antibiotic therapy. Species identification was confirmed using MALDI-TOF (Bruker), and antimicrobial susceptibility testing was performed by broth microdilution. The susceptible profile, including minimal inhibitory concentration (MIC) of the ESBL-producing K. pneumoniae was amicacin (R, MIC = 16 mg/L), amoxicillin/clavulanate (R, MIC>32 mg/L), cefepime (R, MIC>8 mg/L), cefotaxime (R, MIC>32 mg/L), ceftazidime (R, MIC>32 mg/L), ciprofloxacin (R, MIC>1 mg/L), colistin (S, MIC≤2 mg/L), gentamicin (S, MIC≤2 mg/L), meropenem (S, MIC≤0.125 mg/L), sulfamethoxazole/trimethoprim (R, MIC>4/76 mg/L). The phenotype of K. pneumoniae was compatible with ESBL-producing, and was confirmed by next generation sequencing (NGS). The genes are demonstrated in table 1. The MIC of meropenem was determined using broth microdilution in 96-well plates including more dilution, with concentrations ranging from 256 mg/L to 0.0078125 mg/L. The MIC for the K. pneumoniae isolate was 0.125 mg/L and was further validated using a macrodilution assay, including two concentrations above and below the initially observed MIC. This study was approved by the local ethics committee at Pontifícia Universidade Católica do Paraná (PUCPR) under protocol number CAAE: 67816923.0.0000.0020. 2.2 Next-generation bacterial genome sequencing Bacterial isolates were grown on tryptic soy agar (TSA) plates using the streaking technique to ensure colony separation. Three morphologically similar, well-isolated colonies were picked for genomic DNA extraction using the QIAamp DNA Mini Kit (Qiagen), following the protocol provided by the manufacturer. Genomic DNA was then fragmented and tagged with sequencing adapters using the Illumina Nextera XT DNA Library Prep Kit. Library quality was evaluated through fragment size analysis with a Bioanalyzer (Agilent Technologies), and DNA concentration was measured using a Qubit® fluorometer (Thermo Fisher Scientific). After passing quality control, the prepared libraries were subjected to high-throughput sequencing on the Illumina NextSeq 1000/2000 platform. Sequencing reads undergo adapter trimming and removal of low-quality reads using Trimmomatic 11 , followed by genome assembly with SPAdes v3.15.4 12 . Genome annotation was performed using Prokka v1.14.6 13 , a tool that enables automated bacterial genome annotation and provides information about the organism’s genetic composition.Genome identification and similarity analysis was conducted using the FastANI software 14 , which estimates the average nucleotide identity (ANI) between genomes. Genomes with ANI values above 80% are considered to belong to the same species, and those with ANI values greater than 95% are classified as the same species. To identify antimicrobial resistance genes in the bacterial genome, the ABRicate software (available on GitHub) was used. The Comprehensive Antibiotic Resistance Database (CARD) served as the reference for identifying antibiotic resistance genes. In parallel, the presence of virulence genes was assessed using the Virulence Factors Database (VFDB) 15,16 . 2.3 Meropenem serum level for stress test The dose of meropenem for bacterial stress test was the same obtained from the patient serum level with K. pneumoniae bacteremia. Protein precipitation was employed for sample extraction. Microtubes containing 100 µL of each sample were thawed, followed by the addition of 20 µL of internal standard solution (1000 µg/mL). The mixture was vortexed for 10 seconds. Subsequently, 300 µL of acetonitrile was added to each tube. The samples were vortexed again for 15 seconds and centrifuged at 14,000 rpm for 5 minutes. The resulting supernatant was transferred to glass tubes and evaporated to dryness using a SpeedVac® concentrator at room temperature for 1 hour and 30 minutes. The dried residues were stored at freezing temperature until reconstitution in 200 µL of water. A 40 µL aliquot of the reconstituted sample was then injected into the high-performance liquid chromatography system. The concentration obtained was 17.7 mg/L (141 times higher than MIC) 2.4 Time-Kill Curve (TKC) Assay Time-kill curve (TKC) assays were performed in accordance with the guidelines outlined in the CLSI document M26-A 17 . The isolate was exposed to meropenem at a concentration reflecting the serum level observed in the respective patient after 48 hours of antimicrobial therapy 18 . To prepare the bacterial suspension, individual colonies were adjusted to a 0.5 McFarland turbidity standard and then further diluted to reach a final concentration of approximately 10⁵ CFU per tube. Each bacterial strain was evaluated in triplicate. For each assay, a control sample containing the bacterial suspension without antibiotic exposure was included. Bacterial growth was assessed over a 24-hour period at specific intervals: 0, 10 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 8 hours, 12 hours, and 24 hours. At each time point, samples were serially diluted in duplicate, plated on agar, and colony-forming units (CFUs) were counted. Cell viability was determined by comparing CFU counts to those of the untreated control group, and the percentage of cell death was calculated accordingly. Data were expressed as log₁₀ CFU per milliliter over time. A bactericidal effect was defined as a reduction of at least 3 log₁₀ CFU/mL from the initial bacterial load. (Figure 1) 2.5 Transcriptome Sampling and Analysis Sample collection for transcriptomic analysis was carried out during the TKC experiment, with the preparation of the antimicrobial agent and bacterial isolate as previously described. After bacterial incubation in the presence of meropenem and in the antibiotic-free control condition, 1 mL of culture was collected at 0 and 1 hour from each tube (in triplicate). Samples were centrifuged at 6000 RPM for 10 minutes, the supernatant was discarded, and the pellet was resuspended in 1 mL of RNA-later solution and refrigerated storage. Total RNA was extracted using the TRIzol™ reagent (Invitrogen) from the three biological replicates. Ribosomal RNA was depleted using the Ribo-Zero Plus™ kit (Illumina). RNA fragmentation, reverse transcription, and adapter ligation were performed using the Stranded mRNA Prep Kit (Illumina). Following the library preparation protocol, fragment size distribution was evaluated using the Bioanalyzer (Agilent), and quantification was carried out using the Qubit® fluorometer (Thermo Fisher). After quality control, libraries were sequenced on the NextSeq 1000 next-generation sequencing (NGS) platform (Illumina), generating a minimum average of 1 million reads per sample. To assess differentially expressed genes (DEGs) between the tested conditions, RNA-seq data were processed using a systematic approach. First, raw reads were trimmed to remove adapters and low-quality sequences using TrimGalore ( https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ ) . Ribosomal RNA sequences were filtered out with SortMeRNA 19 . Alignment to the reference genome and transcript quantification were performed using STAR v2.7.11b and Salmon v1.2.0 20,21 , respectively. Aligned files were sorted and indexed using SAMtools 22 , and duplicate reads were marked using Picard MarkDuplicates (http://broadinstitute.github.io/picard/). 2.6 Genes of interest The transcriptome analysis generates a large volume of information. Therefore, this study focused on known genes related to amino acids biosynthesis and genes related to glycolysis and Krebs cycle. All evaluated genes are listed in Supplementary Material S1, with those showing a significant increase or decrease compared to time zero highlighted in green. Genes annotated as “hypothetical protein” were not included in the analysis. 2.7 Statistical analysis Data of growth curves and viability assessment were expressed as the mean of the three independent experiments. Data obtained from the mRNA expression analysis were presented as means ± standard error of three independent assessments. Values obtained for the expression of each individual gene exposed to antibiotics and H 2 O 2 were compared using one-way analysis of variance (ANOVA) test followed by Tukey’s post-hoc test for multiple comparisons by Prism 8 (GraphPad Software, Inc.). Transcript assembly and quantification were conducted with StringTie 23 and Salmon v1.10.3 (https://salmon.readthedocs.io/en/latest/). Differential gene expression analysis was performed using the DESeq2 package in R 24 . The log₂ fold change ( log₂FC ) was used to measure the ratio of mean gene expression between groups. Genes with a log₂FC greater than 0.5 or less than -0.5 were considered differentially expressed. Positive values indicate higher expression in the experimental group compared to the control, while negative values indicate lower expression. 3.1 Regulation of Glycolytic and TCA Cycle Genes Under Meropenem Stress The transcriptomic profile of K. pneumoniae exposed to meropenem revealed notable changes in the expression of genes involved in central carbon metabolism, particularly glycolysis and the tricarboxylic acid (TCA) cycle (Figure 2). In the glycolytic pathway, there was a general upregulation of key enzymes, suggesting an enhancement in energy-generating processes. Early glycolytic genes such as hexokinase (+2.3 log), phosphoglucose isomerase (+1.2 log), and aldolase (+1.1 log) were upregulated, indicating increased flux through the upper glycolysis. Midstream enzymes including glyceraldehyde-3-phosphate dehydrogenase (+0.6 log), phosphoglycerate kinase (+4.1 log), and phosphoglyceromutase (+0.4 log) also showed increased expression. Additionally, downstream steps catalyzed by enolase (+1.4 log) and pyruvate kinase (+1.4 log) were upregulated, supporting the notion of enhanced ATP production during antibiotic stress. Conversely, the TCA cycle exhibited a mixed response. While malate dehydrogenase (+2.5 log) and fumarase (+1.4 log) were upregulated, suggesting increased conversion of malate to oxaloacetate, several enzymes showed repression. Notably, succinate dehydrogenase (–3.3 log), isocitrate dehydrogenase (–1.1 log), and citrate synthase (–0.5 log) were downregulated, indicating a possible bottleneck in the oxidative branch of the cycle. These findings point toward a partial inhibition of the TCA cycle, potentially redirecting metabolic flux toward gluconeogenesis or amino acid biosynthesis. 3.2 Transcriptomic response of amino acid biosynthesis pathways under meropenem exposure Transcriptomic analysis revealed distinct expression changes in K. pneumoniae genes associated with amino acid biosynthesis following exposure to meropenem. Overall, genes involved in multiple biosynthetic pathways were upregulated, suggesting a shift in metabolic activity as part of the bacterial response to antibiotic stress (Figure 3). Key enzymes within the glutamate and serine biosynthetic branches showed significant transcriptional activation. Glutamate dehydrogenase (log₂FC: +2.2) and aspartate aminotransferase (log₂FC: +0.7) were both upregulated, indicating increased nitrogen assimilation and transamination activity. Additionally, the serine biosynthesis pathway was strongly activated, as evidenced by increased expression of D-3-phosphoglycerate dehydrogenase (+2.0), phosphoserine phosphatase (+0.1), and phosphoserine aminotransferase (–0.8), although the latter showed a slight downregulation. The histidine biosynthetic pathway enzyme ATP phosphoribosyltransferase was upregulated (+2.1), along with key arginine biosynthesis enzymes such as argininosuccinate synthase (+1.6) and acetylglutamate kinase (+1.3), reinforcing the notion of heightened biosynthetic activity under stress. Several branches of branched-chain and aromatic amino acid synthesis were also transcriptionally enhanced. Acetolactate synthase , involved in valine and isoleucine biosynthesis, showed a strong increase in expression (+2.7), as did aspartokinase (+2.5), critical for lysine and threonine biosynthesis. Likewise, genes encoding homoserine dehydrogenase (+1.9) and methionine synthase (+2.5) were upregulated, supporting increased methionine production. In the aromatic amino acid pathway, upregulation was observed for chorismate mutase (+1.8), involved in phenylalanine biosynthesis, and tryptophan synthase (+1.0), indicating a broad transcriptional activation across aromatic amino acid branches. Additionally, upregulation of γ-glutamyl phosphate reductase (+2.0) in the proline pathway and threonine synthase (+1.2) further supports a generalized boost in amino acid biosynthetic capacity. The glycine cleavage system also showed a notable increase (+2.7), suggesting reconfiguration of one-carbon metabolism and redox balance under meropenem pressure. 4. Discussion In this study, we investigated the transcriptional response of K. pneumoniae to clinically relevant concentrations of meropenem, focusing on genes involved in amino acid biosynthesis and central carbon metabolism. Our RNA-seq data reveal a coordinated metabolic reprogramming under antibiotic stress, characterized by upregulation of glycolytic enzymes and amino acid biosynthesis pathways, coupled with a partial suppression of the TCA cycle 25 . These findings suggest a metabolic shift that supports survival and adaptation during antibiotic exposure 26 . The upregulation of amino acid biosynthetic genes, including those involved in the synthesis of glutamate, serine, arginine, and branched-chain amino acids, highlights an active anabolic response 27 . Notably, enzymes such as glutamate dehydrogenase, D-3 - phosphoglycerate dehydrogenase, argininosuccinate synthase, and acetolactate synthase were strongly upregulated. These pathways are essential not only for biomass production but also for managing oxidative stress, generating metabolic precursors, and replenishing intermediates drained from central metabolism—functions that are likely critical during antibiotic-induced stress 28 . Janes et al. described a clone with overexpression of glutamate dehydrogenase, which was associated with growth inhibition, suggesting a latent status under metabolic stress 29 . The argininosuccinate synthase is associated with a complex cAMP-CAP which is linked with tRNA initiators, interfering with translation 30 . Similar upregulation of amino acid biosynthesis has been reported in E. coli and Acinetobacter baumannii exposed to β-lactams, supporting the notion that biosynthetic activation is a conserved adaptive strategy 31 . Biological nitrogen fixation is an energy-demanding process. It is therefore not surprising that cellular regulatory mechanisms prevent nitrogenase synthesis under conditions where its activity is unnecessary. Also ATP phosphoribosyltransferase apparently accumulated in cultures, suggesting that translational control of the ATP phosphoribosyltransferase gene occurs under unfavorable energetic conditions 32 . Acetolactate synthase is an essential enzyme involved in the biosynthesis of platform chemicals acetoin and 2,3-butanediol in several microorganisms 33 . In our model, the expression of acetolactate synthase was increased, which can be associated with less acetate with higher ATP yields 34 . This reaction is important considering the conversion of pyruvate with higher ATP yields. Our data also show robust induction of glycolytic genes across all steps of the pathway, including hexokinase , phosphoglycerate kinase, and pyruvate kinase, suggesting increased energy production via substrate-level phosphorylation 35 . This may be essential for coping with the energetic burden imposed by meropenem exposure, such as activation of stress responses, efflux systems, and membrane repair 36 . Interestingly, while glycolysis was upregulated, components of the oxidative TCA cycle—specifically succinate dehydrogenase and isocitrate dehydrogenase—were repressed. This mirrors the Warburg-like metabolic phenotype previously described in Staphylococcus aureus under daptomycin stress, where a shift toward fermentative or gluconeogenic metabolism supports survival 37 . Our observation of upregulated malate dehydrogenase and fumarase may reflect anaplerotic reactions that maintain redox balance or supply precursors for biosynthesis despite partial TCA cycle inhibition 38,39 . The combined analysis of glycolytic, tricarboxylic acid cycle, and amino acid biosynthetic pathways revealed a coordinated transcriptional upregulation that supports an enhanced anabolic state. Notably, most genes encoding glycolytic enzymes—including hexokinase, phosphofructokinase -1 , and pyruvate kinase—were significantly upregulated (up to +4.1 log), suggesting increased carbon flux toward pyruvate formation 40 . This shift likely facilitates the biosynthesis of pyruvate-derived amino acids such as alanine, valine, isoleucine, and phenylalanine. In parallel, selective modulation of TCA cycle genes was observed. While succinate dehydrogenase was markedly downregulated, malate dehydrogenase exhibited substantial upregulation, indicating a rerouting of intermediates toward oxaloacetate formation. This metabolic adjustment aligns with the observed upregulation of genes involved in aspartate and glutamate biosynthesis, key precursors for multiple amino acids including lysine, methionine, threonine, arginine, and proline 41 . Glycolytic intermediates such as 3-phosphoglycerate and phosphoenolpyruvate (PEP) also served as biosynthetic precursors. Enhanced expression of phosphoserine aminotransferase and phosphoserine phosphatase supports increased serine biosynthesis from 3-phosphoglycerate. Simultaneously, upregulation of chorismate mutase and tryptophan synthase suggests active aromatic amino acid synthesis from PEP. The convergence of glycolytic and TCA cycle intermediates with nitrogen assimilation pathways is further evidenced by the elevated transcription of glutamate dehydrogenase and argininosuccinate synthase, supporting the synthesis of glutamate- and arginine-family amino acids 42 . These findings reflect a global transcriptional program favoring amino acid anabolism, likely to meet increased demands for protein synthesis and cellular growth 43 . Together, these data demonstrate a tightly coordinated metabolic rewiring, with elevated carbon flux through glycolysis and targeted modulation of the TCA cycle to fuel the biosynthesis of multiple amino acid families. Transcriptomic studies in K. pneumoniae under various stress conditions—such as exposure to host serum, nitrogen excess, and biofilm formation—consistently show reprogramming of central carbon and amino acid metabolism 44 . These adaptations include enhanced expression of glycolytic and TCA cycle genes, as well as differential regulation of amino acid biosynthesis and transport pathways 45 . Clinical isolates often display increased catabolism of specific amino acids like valine, glutamine, and asparagine. Nitrogen stress also triggers upregulation of genes involved in carbon–nitrogen balance. Altogether, these findings highlight a coordinated metabolic response enabling K. pneumoniae to survive and adapt under hostile or nutrient-limited environments 46 . Transcriptomic analyses of K. pneumoniae under antibiotic-induced stress, particularly with colistin and polymyxin B, reveal significant reprogramming of central metabolic pathways 47 . Genes involved in glycolysis, the TCA cycle, and amino acid biosynthesis are differentially regulated, reflecting metabolic adaptation to membrane damage and oxidative stress. Exposure to ciprofloxacin also alters expression of stress response and metabolic genes 48 . These changes suggest that metabolic plasticity is a key component of antibiotic tolerance and resistance. Such responses are often heterogeneous across cell populations, highlighting phenotypic diversity during antibiotic challenge. This study, while comprehensive in transcriptional scope, has several limitations. First, RNA-seq captures changes at the transcript level, which may not directly correlate with protein abundance or enzymatic activity. Further proteomic or metabolomic validation is needed to confirm the functional impact of the observed expression changes. Second, only one strain background and one antibiotic condition were examined; broader strain comparisons would help generalize the findings. Additionally, the experimental design captured temporal dynamics at selected points, which may have missed transient or delayed responses. 5. Conclusion Our results highlight a coordinated transcriptional reprogramming in K. pneumoniae under meropenem stress, characterized by the upregulation of amino acid biosynthesis and glycolytic genes, alongside partial downregulation of the TCA cycle. These metabolic adaptations likely contribute to bacterial survival, tolerance, or early stages of resistance development. Understanding these responses provides deeper insight into noncanonical antibiotic stress adaptations and may reveal novel metabolic vulnerabilities that can be exploited therapeutically. Acknowledgments We thank for Rita Estrela, Edlaine Rijo Costa, Fernanda L. Moreira for serum level of antibiotic. CONFLICT OF INTEREST/DISCLOSURES All authors declare no conflicts of interests. FUNDING This study was supported by CNPQ and CAPES AI-GENERATED CONTAIN AI was used for language adequation and some genes searching on transcriptome database. ETHICAL APPROVAL This study was approved by the local ethics committee at Pontifícia Universidade Católica do Paraná (PUCPR) under protocol number CAAE: 67816923.0.0000.0020. CONTRIBUTION • Conceptualization - Suzana Carstensen • Data curation - Paula Hansen Suss • Formal analysis - Hirwigy Lee, Liziane Cristina dos Santos, Michelle Zibetti Tadra, Willian Klassen de Oliveira • Funding acquisition - Felipe Francisco Tuon • Investigation - Suzana Carstensen • Methodology - Paula Hansen Suss, Gabriel Burato Ortis, Thalissa Colodiano Martins • Writing – original draft - Joao Paulo Telles, Felipe Francisco Tuon • Writing – review and editing - Suzana Carstensen, Felipe Francisco Tuon 6. References 1 Brink, A. J. Epidemiology of carbapenem-resistant Gram-negative infections globally. Curr Opin Infect Dis 32 , 609-616, doi:10.1097/QCO.0000000000000608 (2019).2 De Oliveira, D. M. P. et al. Antimicrobial Resistance in ESKAPE Pathogens. 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The diagram illustrates the central carbon metabolism, including glycolysis and the TCA cycle, annotated with gene expression log₂ fold changes based on transcriptomic analysis. Enzymes are numbered according to their order in the respective pathways: glycolytic enzymes (1–10), anaplerotic and TCA cycle enzymes (11–15). Red numbers indicate downregulation, while green numbers indicate upregulation. The observed transcriptional pattern suggests an increased glycolytic flux and partial inhibition of the TCA cycle, with potential anaplerotic rerouting toward biosynthesis and redox homeostasis. Figure 3. Differential gene expression in amino acid biosynthetic pathways in Klebsiella pneumoniae following meropenem exposure. Metabolic map depicting key enzymes and intermediates involved in the biosynthesis of amino acids derived from glycolytic and tricarboxylic acid (TCA) cycle precursors. Enzymes are annotated with their respective log₂ fold changes in gene expression (green: upregulated; red: downregulated) based on transcriptomic profiling. The changes suggest metabolic reprogramming to support biosynthesis and stress adaptation under antibiotic challenge. Supplementary Material File (tables.docx) Download 16.46 KB Information & Authors Information Version history V1 Version 1 07 July 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords amino acid biosynthesis klebsiella pneumoniae meropenem metabolic reprogramming transcriptomics Authors Affiliations Felipe Francisco Tuon [email protected] Pontificia Universidade Catolica do Parana Sistema Integrado de Bibliotecas View all articles by this author Suzana Carstensen Pontificia Universidade Catolica do Parana Sistema Integrado de Bibliotecas View all articles by this author Paula Hansen Suss Pontificia Universidade Catolica do Parana Sistema Integrado de Bibliotecas View all articles by this author Gabriel Burato Ortis Pontificia Universidade Catolica do Parana Sistema Integrado de Bibliotecas View all articles by this author Thalissa Colodiano Martins Pontificia Universidade Catolica do Parana Sistema Integrado de Bibliotecas View all articles by this author Hirwigy Lee Gogenetic View all articles by this author Liziane Cristina dos Santos Gogenetic View all articles by this author Michelle Zibetti Tadra Gogenetic View all articles by this author Willian Klassen de Oliveira Gogenetic View all articles by this author Joao Paulo Telles A C Camargo Cancer Center View all articles by this author Leticia Ramos Dantas Pontificia Universidade Catolica do Parana Sistema Integrado de Bibliotecas View all articles by this author Metrics & Citations Metrics Article Usage 307 views 216 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Felipe Francisco Tuon, Suzana Carstensen, Paula Hansen Suss, et al. 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