Skip to main content
NCBI home page
ACS Omega logoLink to ACS Omega
. 2026 Sep 8;11(37):56406–56418. doi: 10.1021/acsomega.6c07419

Glutamate Potentiates Meropenem Killing of Carbapenem-Resistant Pseudomonas aeruginosa by Increasing Outer Membrane Permeability to Increase Uptake

Li-Fen Yang †,‡, Xu-Feng Li †, Hao-Feng Lai †, Yi-Yang Li †, Si-Chen Yuan ∥, Jiao Xiang †, Ying Liang †,*, Zhuang-Gui Chen †,‡,§,*
PMCID: PMC13613417  PMID: 42798984

Abstract

Carbapenem-resistant Pseudomonas aeruginosa (CR-PA) has been designated a “priority” pathogen by the World Health Organization, calling for the development of new control strategies. However, no such new drugs are currently available. In this study, we restored bacterial sensitivity to meropenem through a metabolic state-reprogramming approach. Glutamate was identified as the key reprogramming metabolite that converts an antibiotic-resistant metabolic state to an antibiotic-sensitive state in both lab-evolved and clinically isolated CR-PA. This reprogramming leads to increased meropenem uptake, which overcomes drug efflux and enzymatic hydrolysis, elevates intracellular drug concentrations, and restores meropenem killing efficacy in vitro and in vivo. Mechanistically, glutamate metabolic flux enters fatty acid biosynthesis via the pyruvate cycle, elevating lauric acid levels. Increased lauric acid enhances outer membrane permeability, thereby raising intracellular meropenem accumulation. Glutamate is a nutritional amino acid, and the dose used in this study is lower than that in routine clinical use. Collectively, these findings provide an effective, convenient, and economical approach to restore meropenem killing efficacy against CR-PA.

graphic file with name ao6c07419_0010.webp

graphic file with name ao6c07419_0008.webp

Introduction

Pseudomonas aeruginosa is a Gram-negative opportunistic pathogen responsible for a wide range of acute and chronic infections. In immunocompromised individuals or those with underlying chronic diseases, infection with this bacterium often leads to severe illness and high mortality. , The difficulty in treating such infections stems from the bacterium’s diverse antibiotic resistance mechanisms. In response, carbapenemslast-resort β-lactam antibioticsare frequently employed. However, their widespread use has driven the global emergence of carbapenem-resistant P. aeruginosa (CR-PA), a major healthcare-associated pathogen. In the United States, 10–30% of P. aeruginosa clinical isolates are carbapenem-resistant, with considerable international variation. In light of this threat, the World Health Organization has called for the development of novel anti-pseudomonal therapies.

CR-PA undermines conventional antibiotic treatment through the convergence of multiple resistance mechanisms. The most potent is the acquisition of carbapenem-hydrolyzing enzymes, including metallo-β-lactamases (e.g., IMP, VIM, NDM) and serine carbapenemases (e.g., KPC, GES). Equally critical is the loss or reduced expression of the outer membrane porin OprD, the primary entry channel for carbapenems, leading to impermeability. This defect is often compounded by overexpression of multidrug efflux pumps, notably MexAB-OprM and MexXY-OprM, which actively extrude antibiotics. These mechanisms rarely operate in isolation; rather, their co-occurrence within a single strainfor example, OprD deficiency combined with efflux pump upregulation and carbapenemase productionconfers high-level resistance, severely limiting treatment options and complicating clinical management.

Targeting bacterial metabolism has emerged as a promising strategy to reverse antibiotic resistance in bacterial pathogens. − The concept of “metabolic state reprogramming” involves modulating the nutritional microenvironment to disrupt the metabolic pathways that support resistance. − Specific metabolites can potentiate antibiotic activity by enhancing antibiotic uptake − and by stimulating central carbon metabolism, thereby increasing proton motive force and reactive oxygen species production. By shifting bacteria from a resistant to a sensitive metabolic state, this reprogramming approach circumvents traditional genetic resistance mechanisms , and offers a novel therapeutic avenue to restore carbapenem efficacy against multidrug-resistant (MDR) bacteria, potentially extending the clinical lifespan of existing antibiotics.

In this study, gas chromatography-mass spectrometry (GC-MS) was employed to compare the metabolic states of lab-evolved meropenem-sensitive and meropenem-resistant P. aeruginosa, identifying glutamate as the key biomarker. Glutamate potentiated meropenem-mediated killing not only in lab-evolved meropenem-resistant P. aeruginosa but also in clinically isolated CR-PA and MDR-PA (Table S1). Mechanistically, glutamate enhances outer membrane permeability to facilitate drug uptake by channeling flux toward unsaturated fatty acid biosynthesis.

Results

Differential Metabolic Profile of Meropenem-Resistant P. aeruginosa

To explore the mechanisms underlying meropenem resistance in P. aeruginosa and identify metabolic biomarkers that could reverse this resistance, we generated a lab-evolved meropenem-resistant strain (PA-RMEM) by sequentially propagating meropenem-sensitive P. aeruginosa (PA-S) in LB broth containing meropenem. PA-S cultured without meropenem served as the control. PA-RMEM exhibited a 16-fold increase in the minimum inhibitory concentration (MIC) compared to PA-S (Figure A).

1.

1

Open in a new tab

Differential metabolomics in PA-RMEM and PA-S strains. (A) MIC for meropenem of PA-RMEM compared with PA-S in Mueller–Hinton (MH) broth. (B) Heat map showing differential metabolites. The metabolites are organized top to bottom according to hierarchical clustering. Yellow and blue colors indicate increase and decrease of metabolites relative to the median, metabolite level, respectively. (C) Categorization of the identified differential metabolites in PA-RMEM. (D) Z-score plot of differential metabolites.

Bacterial antibiotic resistance is closely linked to metabolic state. We therefore investigated global metabolic changes in PA-RMEM using GC-MS-based metabolomics. Four biological replicates in PA-RMEM group and three biological replicates in PA-S group, each with two technical replicates, yielded a total of 14 datasets. Data reproducibility was confirmed by Pearson correlation coefficients ranging from 0.9912 to 0.9991 between technical replicates (Figure S1A). After removing the internal standard (ribitol) and merging identical compounds, 88 metabolites were identified (Figure S1B). Among these, 48 metabolites showed differential abundance (p < 0.05) in PA-RMEM compared to PA-S (Figure B). These altered metabolites were classified into carbohydrate (10%), amino acid (17%), nucleotide (13%), lipid (31%), and others (29%) (Figure C). Based on Z-score ranking relative to PA-S, 27 metabolites were upregulated and 21 downregulated. The most downregulated metabolites included oleic acid, methoxyacetic acid, glucose, glutamic acid, pentanoic acid, and lauric acid (Figure D). These results indicate that PA-RMEM possesses a distinct metabolome from PA-S, which may contribute to meropenem resistance.

Enriched Pathways in PA-RMEM

We next investigated the metabolic pathways affected in PA-RMEM. Eight pathways were significantly enriched. Ranked by impact (weight), they were: alanine, aspartate and glutamate metabolism; pyruvate metabolism; citrate cycle (TCA cycle); arginine biosynthesis; other carbon fixation pathways; tyrosine metabolism; glyoxylate and dicarboxylate metabolism; and taurine and hypotaurine metabolism (Figure A). An integrative analysis of metabolites within these enriched pathways is shown in Figure B, with orange and blue indicating increased and decreased metabolites, respectively. Notably, all or almost all metabolites in these pathways were downregulated. Moreover, the top two enriched pathways (alanine, aspartate and glutamate metabolism; pyruvate metabolism) contain all metabolites detected in the TCA cycle. Given that the bacterial pyruvate cyclecomprising the TCA cycle plus the phosphoenolpyruvate-pyruvate-AcCoA pathwayprovides respiratory energy, we also included pyruvic acid (detected in the first two pathways). All five TCA cycle metabolites plus pyruvic acid exhibited reduced abundance. These findings indicate that inactivation of the pyruvate cycle is a characteristic metabolic feature of PA-RMEM.

2.

2

Open in a new tab

Pathway enrichment analysis for lab-evolved PA-RMEM strains. (A) Enriched metabolic pathways based on differential metabolites. (B) Integrative analysis of metabolites in significantly enriched pathways. Orange and blue colors indicate increased and decreased metabolites (P < 0.05), respectively.

Pattern Recognition for Biomarkers in PA-RMEM

Pattern recognition is an effective tool for identifying biomarkers in metabolomics. Orthogonal partial least squares discriminant analysis (OPLS-DA) was performed to distinguish sample patterns (Figure A). Component t[1] clearly separated PA-RMEM from PA-S. Discriminating variables were visualized using an S-plot, where the predictive correlation between p[1] and p­(corr)[1] is shown. Red triangles indicate differential metabolites with larger weights (absolute value >0.05) and higher relevance (absolute value >0.5). This analysis identified 11 biomarkers (Figure B), of which 7 were downregulated and 4 upregulated (Figure C). The 7 downregulated metabolites were glutamic acid, proline, oleic acid, glucose, oxalic acid, adenosine, and tyrosine, with glutamic acid ranking as the top biomarker. Glutamic acid is a downstream metabolite of the pyruvate cycle and subsequently of adenosine. These results suggest that the most downregulated metabolite, glutamic acid, can be selected as a reprogramming metabolite to reverse meropenem resistance in PA-RMEM.

3.

3

Open in a new tab

Identification of crucial biomarkers. (A) OPLS-DA of metabolomes between PA-RMEM and PA-S. Each dot represents the biological and technical replicates analysis of samples in the plot. (B) S-plot from the OPLS-DA model. Potential biomarkers with |p­(corr)| ≥ 0.5 and |p[1]| ≥ 0.05 are shown as red triangles. (C) Abundance profiles of the identified crucial biomarkers. *P < 0.05 and **P < 0.01.

Glutamate Potentiates Meropenem Killing of PA-RMEM, CR-PA, MDR-PA and Other CR- and MDR-Pathogens

To investigate whether glutamate potentiates meropenem-mediated killing of lab-evolved meropenem-resistant P. aeruginosa and clinically isolated CR-PA, we performed checkerboard microdilution assays using PA-RMEM and a clinical CR-PA strain, P53. The combination showed increased killing efficiency with higher concentrations of both meropenem and glutamate in both strains. Bliss independence analysis revealed strong synergistic effects, with synergy scores of 10.692 for PA-RMEM and 45.956 for P53 (Figure A,B). To further confirm this interaction using a more widely accepted standard in antimicrobial chemotherapyparticularly for adjuvants lacking direct antibacterial activitywe performed checkerboard minimum inhibitory concentration (MIC) and minimum killing concentration (MKC) assays. , The fractional inhibitory concentration index (FICI) for both strains was ≤0.5 in MIC and MKC (Figure C,D), also indicating synergy. Collectively, these results demonstrate that glutamate potentiates meropenem killing of both lab-evolved PA-RMEM and clinical CR-PA.

4.

4

Open in a new tab

Glutamate potentiates the efficacy of MEM against CR-PA and MDR-PA strains. (A and B) Bliss analysis based on the data on bactericidal fold of PA-RMEM (A) and P53 (B) at specified concentrations of meropenem (MEM) without/with glutamate. (C and D) Checkerboard MIC and MKC assays of meropenem in combination with glutamate against PA-RMEM and P53, and the FICI calculated based on the MIC (C) and MKC (D) values, respectively. (E) Time–kill curves of P53 in response to MEM alone or in combination with glutamate. (F and G) Validation of the combinatory effect: viability of ten additional clinical CR-PA strains and ten additional clinical MDR-PA strains treated with MEM plus glutamate. (H) Percent survival of mice infected with P53 and then treated with saline, glutamate, meropenem, or the combination of glutamate and meropenem. *P < 0.05 and **P < 0.01.

Time-kill assays using strain P53 revealed that meropenem alone and the combination of meropenem with glutamate exhibited time-dependent killing, whereas glutamate alone showed no killing. However, the combination achieved significantly greater killing than meropenem alone, with maximal efficacy reached at 8 h (Figure E). When tested against 10 additional CR-PA isolates and 10 MDR-PA isolates, glutamate-potentiated killing was observed in all strains tested, with higher killing fold in 16 μg/mL than 8 μg/mL meropenem (Figure F,G). This finding was further validated in a mouse systemic infection model. Mice infected with P53 and treated with meropenem alone showed 7-day survival rate of 10%, whereas those treated with the meropenem-glutamate combination showed 70%. All mice died in the blank control and glutamate-alone groups (Figure H). The potentiating effect of glutamate on meropenem was further confirmed in CR and MDR strains of Escherichia coli, Klebsiella pneumoniae, and Acinetobacter baumannii (Figure S3).

Relationship between Uptake, Hydrolysis, and Efflux in the Presence of Exogenous Glutamate

Increased antibiotic uptake is a key mechanism of metabolic reprogramming. To test whether glutamate enhances meropenem uptake to overcome hydrolysis and efflux, we performed dynamic concentration-dependent assays using P53. Strain P53 was sensitive to polymyxin B (PB), gentamicin (GEN), ceftazidime (CAZ), and aztreonam (AZT), but resistant to meropenem (MEM), levofloxacin (LEV), and piperacillin-tazobactam (TZP) (Figure A). Intracellular meropenem concentration increased in an extracellular meropenem dose-dependent manner, with higher uptake in the presence of glutamate than without (Figure B). Under the same conditions, efflux did not increase, and hydrolysis increased only weakly (Figure C,D), indicating that the absolute intracellular drug level was elevated. Furthermore, intracellular meropenem increased in a glutamate dose-dependent manner when a fixed concentration of meropenem was added (Figure E), whereas efflux remained unaffected and hydrolysis showed only a modest increase (Figure F,G). At the MIC concentration, quantitative analysis revealed that glutamate increased net uptake (Vin ≈ 1.72), favoring intracellular accumulation (Figure H).

5.

5

Open in a new tab

Dynamics of MEM uptake, efflux, and hydrolysis with or without glutamate. (A) MIC profile of isolate (μg/mL). Bluesensitive, whiteintermediate, orangeresistant. polymyxin B (PB), gentamicin (GEN), ceftazidime (CAZ), aztreonam (AZT), meropenem (MEM), levofloxacin (LEV), piperacillin tazobactam (TZP). Resistant (R), susceptible (S), intermediate (I). (B–D) Effect of increasing extracellular MEM concentrations on influx (B), efflux (C), and hydrolysis (D) with or without 20 mM glutamate in P53. (E–G) Effect of extracellular glutamate concentrations on influx (E), efflux (F), and hydrolysis (G) with 16 μg/mL MEM in the presence or absence of glutamate. (H) Calculated kinetic parameters (nmol/mg per second) for influx (Vin), efflux (Ve), and hydrolysis (Vh) in P53 at Co = MIC. Data are displayed as the mean ± standard deviation (SD), and statistically significant differences were determined with a Student’s t test. *p < 0.05 and **p < 0.01. Experiments were repeated independently at least three times.

Glutamate Reprograms the Metabolic State of CR-PA P53

To understand the metabolic state in which glutamate reprograms P53 to increase meropenem uptake, we treated P53 with or without glutamate and analyzed the metabolome by GC-MS. A total of 88 metabolites were identified (Figure S4A). Among them, 59 showed differential abundance in the glutamate-treated group compared to untreated controls, with 42 upregulated and 17 downregulated (Figure A). These differential metabolites were classified as carbohydrates (12%), lipids (32%), amino acids (20%), nucleic acids (12%), and others (24%) (Figures S4B, S4C). A Z-score plot visualized the distribution of differential metabolites; lauric acid showed the greatest increase in abundance (Figure B). The differential metabolites were enriched in 12 pathways (Figures S5A). In the glutamate-treated group, most metabolites were upregulated in pathways including alanine, aspartate and glutamate metabolism; glycine, serine and threonine metabolism; pyruvate metabolism; the TCA cycle; arginine biosynthesis; methane metabolism; pyrimidine metabolism; valine, leucine and isoleucine biosynthesis; other carbon fixation pathways; glyoxylate and dicarboxylate metabolism; taurine and hypotaurine metabolism; and d-amino acid metabolism (Figure S5B). Pattern recognition analysis of the metabolomics data using principal component analysis (PCA) showed that component t1 clearly separated the P53 and P53+glutamate groups (Figure C). S-plot analysis identified a total of 8 biomarkers, including 6 with increased abundance and 2 with decreased abundance (Figure D). The increased metabolites were glutamic acid, proline, lauric acid, oleic acid, tyrosine, and threonine (Figure E). Among these, lauric acid and oleic acid belong to the fatty acid biosynthesis pathway, with lauric acid being the most upregulated. Collectively, these results suggest that glutamate metabolic flux is directed toward unsaturated fatty acid biosynthesis.

6.

6

Open in a new tab

Reprogramming metabolomics of P53 in response to glutamate. (A) Heatmap showing the abundance of 59 differential metabolites in the control group vs. glutamate groups. Yellow indicates high abundance and blue indicates low abundance. (B) Z-score plot of the identified differential metabolites, corresponding to the data presented in (A). (C) OPLS-DA score plot comparing the metabolomes of control group vs. glutamate groups. Each dot represents a biological or technical replicate. (D) S-plot derived from OPLS-DA. Each triangle represents an individual metabolite. Potential biomarkers (highlighted in red) are defined as those with p­(cov)≥ 0.05 and p­(corr) ≥ 0.5; other metabolites are shown in black. (E) Abundance profiles of the identified crucial biomarkers. *P < 0.05 and **P < 0.01.

Mechanisms by Which Glutamate Increases Meropenem Uptake

The above findings suggest that glutamate metabolic flux enters unsaturated fatty acid biosynthesis. To validate this, we measured the activity of pyruvate cycle enzymes, as glutamate enters this pathway en route to fatty acid biosynthesis. The activities of pyruvate dehydrogenase (PDH), α-ketoglutarate dehydrogenase (α-KGDH), succinate dehydrogenase (SDH), and malate dehydrogenase (MDH) were lower in CR-PA than in carbapenem-sensitive P. aeruginosa (S-PA). Upon glutamate supplementation, all of the activity of pyruvate cycle enzymes returned to levels comparable to or higher than those in S-PA (Figure A). We next used qRT-PCR to measure the expression of 14 fatty acid biosynthesis genes in S-PA, MDR-PA, and CR-PA cultured with or without glutamate. Expression of these genes was lower in MDR-PA and CR-PA than in S-PA, but was significantly upregulated by exogenous glutamate (Figure B). Given that fatty acids modulate outer membrane permeability, , we also assessed membrane permeability in the presence of glutamate or lauric acid. Outer membrane permeability was lower in CR-PA than in S-PA, but increased upon exogenous glutamate or lauric acid addition (Figure C,D). Accordingly, intracellular meropenem levels increased following incubation with meropenem plus glutamate, or meropenem plus lauric acid (Figure E). Exogenous lauric acid enhanced meropenem killing in a dose-dependent manner for both lauric acid and meropenem (Figure F,G). These results indicate that glutamate potentiates meropenem killing via lauric acid. To further validate that glutamate permeabilizes the outer membrane to sensitize P. aeruginosa to killing, we tested a broad panel of antibiotics for synergistic activity against strain P53 in the presence of glutamate. The results demonstrated that glutamate exhibited synergy with amoxicillin (AMO), piperacillin/tazobactam (TZP), cefoperazone/sulbactam (SCF), ceftazidime (CAZ), amikacin (AMK), gentamicin (GEN), levofloxacin (LEV), ciprofloxacin (CIP), rifampicin (RFP), and vancomycin (VAN) against PA-RMEM and P53 (Figure H).

7.

7

Open in a new tab

Mechanisms by which glutamate promotes intracellular drug concentration. (A) Activity of enzymes of the pyruvate cycle in S-PA, MDR-PA, and CR-PA with and without glutamate. (B) Expression levels of genes related to fatty acid biosynthesis among sensitive bacteria, CR-PA, MDR-PA, glutamate-treated CR-PA and glutamate-treated MDR-PA. (C) Determination of outer membrane permeability of sensitive bacteria, CR-PA, 20mM glutamate-treated CR-PA and 2mM lauric acid-treated CR-PA. (D) Mean FITC-A value of outer membrane permeability. (E) Intracellular meropenem accumulation in P53 (OD600 = 1.0, 1 mL) incubated with 16 μg/mL meropenem alone, 16 μg/mL meropenem + 20 mM glutamate, or 16 μg/mL meropenem + 2 mM lauric acid. (F) Percent survival of P53 in the presence of the indicated concentrations of lauric acid plus 16 μg/mL meropenem. (G) Percent survival of P53 in the presence of the indicated concentrations of meropenem plus 2 mM lauric acid. (H) Viability of PA-RMEM and P53 treated with the indicated antibiotics plus 20 mM glutamate. AMO, amoxicillin, 40 μg/mL; TZP, piperacillin/tazobactam, 60 μg/mL; SCF, cefoperazone/sulbactam, 40 μg/mL; CAZ, ceftazidime, 10 μg/mL; AMK, amikacin, 8 μg/mL; GEN, gentamicin, 2 μg/mL; LEV, levofloxacin, 2 μg/mL; CIP, ciprofloxacin, 0.2 μg/mL; RFP, rifampicin, 2 μg/mL; VAN, vancomycin, 30 μg/mL. *P < 0.05 and **P < 0.01.

Discussion

To combat CR-PAa major healthcare-associated pathogen worldwidevarious strategies have been developed, including β-lactamase inhibitors and non-ribosomal tobramycin–cyclam conjugates that restore meropenem activity. , In this study, we adopted a metabolic reprogramming approach to compare the metabolic differences between PA-S and PA-RMEM, identifying glutamate as the most critical metabolite. Glutamate reprograms an antibiotic-resistant metabolic state into an antibiotic-sensitive one, rendering resistant bacteriaincluding clinical CR-PA isolatessusceptible to meropenem. While metabolic reprogramming has been explored as a strategy to resensitize bacteria to existing antibiotics, no study has yet applied this approach to restore meropenem killing against MDR-PA and CR-PA. Thus, our findings provide a previously unknown approach that restores the killing efficiency of meropenema last-line antibioticagainst MDR-PA and CR-PA.

Restoration of meropenem killing efficacy is particularly critical, as meropenem serves as a last-line antibiotic for MDR Gram-negative bacteria. Once resistance to carbapenems develops, nephrotoxic polymyxins are often the remaining therapeutic option for infections caused by carbapenem-resistant MDR pathogens, notably A. baumannii, P. aeruginosa, and Enterobacterales. Although cefiderocol offers an alternative, cefiderocol-resistant P. aeruginosa isolates have already emerged. To avoid the use of current polymyxins with their associated nephrotoxicity, two main options exist: restoring meropenem efficacy and developing less toxic next-generation polymyxins. While polymyxin analogues represent a potential strategy to limit renal accumulation and mitigate toxicity, and while ATP-potentiated meropenem has been reported to kill carbapenem-resistant A. baumannii with increasing 30% survival of mice, a metabolic reprogramming approach to eliminate CR-PA has remained unknown. The present study demonstrates that glutamate potentiates meropenem against CR-PA, increasing the 7-day survival rate of mice by 60%, and provides an effective reprogramming metabolite that restores meropenem killing efficacy.

The association between fatty acid metabolism and antibiotic resistance has been reported, albeit with controversy. For instance, inhibiting fatty acid synthesis restores antibiotic sensitivity in mcr-1-positive K. pneumoniae, colistin-resistant E. coli, and Vibrio alginolyticus, whereas fatty acid salts promote cefepime resistance in six Gram-negative and Gram-positive species. − In contrast, increased fatty acid biosynthesis has been shown to enhance antibiotic sensitivity, particularly when fatty acids are produced via biosynthesis promoted by exogenous metabolites. ,, Indeed, exogenous supplementation of unsaturated and saturated fatty acids lead to increased and decreased bacterial susceptibility to antibiotics, respectively. These seemingly contradictory findings may be attributable to differences in antibiotic classes, bacterial species, and fatty acid types, and the exact trend warrants further investigation. Consistent with earlier findings that exogenous fatty acids modulate the fatty acid profile linked to outer membrane permeability, , lauric acid may increase drug uptake by altering outer membrane permeability. Given that LPS is a key determinant of outer membrane permeability, we speculate that lauric acid acts indirectlyby interfering with LPS synthesis, assembly, or transportrather than through direct interaction with penicillin-binding proteins, the target of meropenem. This is consistent with lauric acid’s known ability to ameliorate LPS-induced inflammation. −

Our study demonstrates that glutamate restores meropenem killing both in vitro and in vivo, with efficacy against CR-PA. Glutamate is an amino acid. In standard 18-amino acid compound infusions (18AA-III), 6.5 g of glutamate is present per 1000 mL, corresponding to an adult daily dose. This amount translates to approximately 770–946.6 mg/kg in mice. The present study used 500 mg/kg in mice, which is well below the amount contained in 18AA-III. Meanwhile, the recommended adult dosage of meropenem is 1 g per use, which is equivalent to approximately 200 mg/kg for mice. Thus, the safety profile is favorable.

In summary, this study identify glutamate is a metabolic reprogramming metabolite that restores the efficacy of meropenemdespite existing resistanceagainst CR-PA both in vitro and in vivo. This is attributed to the increase of outer membrane permeability to increase meropenem uptake. However, a limitation of this study is the mechanisms how lauric acid regulates outer membrane permeability.

Materials and Methods

Ethics Statement

All mouse experiments were approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University (Approval No. SYSU IACUC 2020 B126716) and conducted in accordance with the NIH Guide for the Care and Use of Laboratory Animals.

Bacterial Strains and Culture Conditions

P. aeruginosa ATCC 27853 and clinical strains were obtained from our laboratory collection. Strains were cultured on LB agar plates at 37 °C, and single colonies were used to inoculate LB broth for 16 h at 37 °C with shaking at 200 rpm. A meropenem-resistant strain (PA-RMEM) was sequentially generated from ATCC 27853 by culturing in LB broth containing 0.5× MIC (0.5 μg/mL) of meropenem until the MIC reached 16 μg/mL. A meropenem-sensitive control strain (PA-S) was generated identically without meropenem. Overnight cultures were harvested, washed three times with saline, and resuspended in M9 minimal medium (8 g/L K2HPO4, 2 g/L KH2PO4, 1 g/L (NH4)2SO4) supplemented with 830 μM MgSO4·7H2O and 2 mM sodium citrate, to an OD600 of 0.2. Bacteria were then incubated at 37 °C with shaking at 200 rpm for 8 h unless otherwise specified. After incubation, 100 μL of each culture was serially diluted 10-fold in saline and plated to determine CFU. Percent survival was calculated as (CFU experimental/CFU control) × 100%.

Minimum Inhibitory Concentration (MIC) Measurement

MICs were determined by broth microdilution following CLSI guidelines and methods described in previous studies. Stationary-phase cultures were diluted 1:100 in MH broth and incubated at 37 °C with shaking at 200 rpm until the OD600 reached 0.5, then further diluted to 5 × 106 CFU/mL. Two-fold serial dilutions of meropenem (horizontal) and various concentrations of glutamate (vertical) in MH broth were prepared in 96-well plates (100 μL per well). 10 μL of the bacterial suspension was added to each well (5 × 105 CFU/mL final), and plates were incubated at 37 °C for 16 h. The MIC was recorded as the lowest concentration of meropenem that inhibited visible growth.

Minimum Killing Concentration (MKC) Assay

MKC was determined following previously described methods. Overnight cultures in LB broth were harvested by centrifugation (8,000 rpm, 5 min), washed three times with sterile saline, and resuspended in M9 medium to an OD600 of 1.0. Two-fold serial dilutions of meropenem (horizontal) and various concentrations of glutamate (vertical) in M9 medium were prepared in 96-well plates (100 μL per well). Bacterial suspension (100 μL) was added to each well and incubated statically at 37 °C for 16 h. Bacterial survival was then assessed visually. The lowest antibiotic concentration showing no visible bacterial pellet was defined as the MKC value.

Sample Preparation for GC-MS Metabolomics

Sample preparation was performed as previously described. Bacterial strains were cultured overnight in LB broth (37 °C, 200 rpm, 16 h), then diluted 1:100 into 100 mL fresh LB and incubated to OD600 = 1.0. For each sample, two parallel cultures were prepared: one quenched with two volumes of pre-cooled methanol (−80 °C, HPLC grade) for metabolomics, and the other for OD600 measurement. Quenched cells were washed twice with saline, resuspended to OD600 = 1.0, and 10 mL were centrifuged (8,000 × g, 5 min, 4 °C). The pellet was resuspended in 1 mL saline, transferred to a microcentrifuge tube, and centrifuged again (12,000 × g, 10 min, 4 °C). The pellet was resuspended in 1 mL pre-cooled methanol containing 10 μL of 0.1 mg/mL ribitol (internal standard) and sonicated on ice (650 W total power, 35% amplitude, 2 s pulse, 3 s pause, 10 min). The lysate was centrifuged (12,000 × g, 10 min, 4 °C), and the supernatant was dried in a vacuum centrifuge at 37 °C. The residue was derivatized with 80 μL of 20 mg/mL methoxyamine hydrochloride in pyridine (37 °C, 200 rpm, 3 h), followed by 80 μL MSTFA (37 °C, 200 rpm, 30 min).

GC-MS Analysis

GC-MS was performed on an Agilent 7890A-5975C VL system with a DB-5MS column (30 m × 250 μm i.d., 0.25 μm film thickness). One microliter of derivatized sample was injected. Oven temperature: 85 °C for 5 min, then ramped at 15 °C/min to 270 °C and held for 5 min. Carrier gas: helium at 1 mL/min constant flow. MS operated in full scan mode (50–600 m/z). Each sample was analyzed in duplicate (two technical replicates).

Metabolomic Data Analysis

Metabolites were identified by matching mass spectra against the NIST library (version 2008). Raw data were normalized by total peak area. Differentially abundant metabolites were identified using SPSS (version 19) with a threshold of P < 0.05 (Student’s t-test or ANOVA as appropriate). Hierarchical clustering and Z-score heatmaps were generated with R (version 4.3.3) using the gplots package. PCA and S-plot analyses were performed using SIMCA P+ (version 12.0). Pathway enrichment analysis was conducted with MetaboAnalyst 4.0.

Antibiotic Bactericidal Assay

Antibiotic bactericidal assay was determined following previously described methods. Three single colonies were cultured in LB broth (37 °C, 200 rpm, 16 h). Cells were harvested, resuspended in M9 medium to OD600 = 0.2, and incubated with specified concentrations of antibiotics and/or glutamate at 37 °C, 200 rpm for 8 h. Bacterial counts were determined by serial dilution and spot plating on LB agar (5 μL per dilution, 37 °C, 18 h). Colonies (20–200 per plate) were counted, and CFU/mL was calculated. Three biological replicates were performed.

Intracellular Meropenem Quantification

Intracellular meropenem quantification was determined following previously described methods. Overnight cultures were harvested, washed, and resuspended in M9 medium to OD600 = 0.2. After incubation with meropenem alone, meropenem with glutamate, or meropenem with lauric acid (37 °C, 200 rpm, 8 h), 10 mL of bacterial suspension (OD600 = 1.0) was collected by centrifugation. Pellets were washed three times with saline, resuspended in 200 μL saline, and sonicated on ice (750 W total power, 35% amplitude, 2 s pulse, 3 s pause, 10 min). The lysate was centrifuged (12,000 × g, 10 min, 4 °C), and intracellular meropenem in the supernatant was quantified using a detection kit (Shanghai Yuduo Biotechnology Co., Ltd.).

Membrane Permeability Assay

Membrane permeability measurement was performed as previously described by Li et al. Bacteria resuspended in M9 medium (OD600 = 0.2) were treated with or without 20 mM glutamate and incubated at 37 °C, 200 rpm for 8 h. A 100-μL aliquot was mixed with 900 μL M9 medium, then 1 mL of this suspension was mixed with 2 μL of 2.5 mM SYTO-9 (Invitrogen) and incubated at 37 °C in the dark for 15 min. Green fluorescence was measured by flow cytometry; higher fluorescence indicates greater membrane permeability.

Pyruvate Cycle Enzyme Activity Assays

Activities of pyruvate dehydrogenase (PDH), α-ketoglutarate dehydrogenase (KGDH), succinate dehydrogenase (SDH), and malate dehydrogenase (MDH) were measured as previously described by Xiang et al. Bacteria grown in M9 with or without glutamate (37 °C, 200 rpm, 8 h) were harvested, frozen in liquid nitrogen, and stored at −80 °C. Pellets were resuspended in PBS and sonicated on ice (200 W, 35% amplitude, 2 s pulse, 3 s pause, 10 min). Protein concentration was determined by Bicinchoninic Acid (BCA) assay (Beyotime), and samples were normalized. Reaction mixtures contained 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium Bromide (MTT), Magnesium Chloride (MgCl2), Phenazine Methosulfate (PMS), Thiamine Pyrophosphate (TPP), and the appropriate substrate (pyruvate for PDH; α-ketoglutarate for KGDH; succinate for SDH; malate for MDH). After incubation (37 °C, 30 min for MDH/PDH/KGDH; 5 min for SDH), absorbance was measured at 562 nm. Three biological replicates were performed.

Quantitative Real-Time PCR (qRT-PCR)

Bacteria were incubated in M9 with or without glutamate (37 °C, 200 rpm, 6 h), and cells were adjusted to OD600 = 1.0. Total RNA was extracted using TRIzol (Ambion). cDNA was synthesized from 1 μg total RNA using a PrimeScript RT kit with gDNA Eraser (IGE Biotechnology). qRT-PCR was performed in 384-well plates with SYBR Green Premix Pro Taq HS (IGE Biotechnology) on a LightCycler 480 system (Roche). Cycling conditions: 95 °C for 30 s; 40 cycles of 95 °C for 5 s, 58 °C for 30 s; fluorescence read at 75 °C for 1 s. Relative mRNA expression was normalized to 16S rRNA.

Mouse Model of CR-PA Infection

BALB/c mice were intraperitoneally injected with 100 μL bacterial suspension (OD600 = 1.0, ∼1.47 × 107 CFU/mouse). One hour post-infection, mice were divided into four groups (n = 10 per group) and treated with saline, glutamate (500 mg/kg), meropenem (300 mg/kg), or meropenem + glutamate. Survival was monitored for 7 days. For bacterial load quantification, liver, kidney, and spleen were collected 6 h post-treatment, homogenized in saline, serially diluted, and plated for CFU counting.

Statistical Analysis

Data are presented as mean ± SEM. Comparisons between two groups were analyzed by unpaired t-test (or Welch’s correction for unequal variance, or Mann-Whitney U test for non-normal distributions). Comparisons among more than two groups were analyzed by one-way ANOVA with Dunnett’s post hoc test (or Kruskal-Wallis with Dunn’s post hoc test for non-normal distributions). Normality was assessed by D’Agostino-Pearson and Kolmogorov-Smirnov tests. Homogeneity of variance was assessed by Brown-Forsythe test. Statistical details are provided in figure legends. Significance: *P < 0.05, **P < 0.01.

Supplementary Material

ao6c07419_si_001.pdf (1.2MB, pdf)

Acknowledgments

We gratefully acknowledge financial support from the National Natural Science Foundation of China (Grant No. 32370191), the Basic and Applied Basic Research Foundation of Guangdong Province (Grant No. 2024A1515010700), and the Guangzhou Municipal Science and Technology Project (Grant No. 2024A03J1057).

The data underlying this study are available in the published article and its Supporting Information.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.6c07419.

  • Additional MIC profiles of isolate (Table S1); metabolic profiles of lab-evolved PA-RMEM and PA-S (Figure S1); bactericidal fold of PA-RMEM (Figure S2); combinatory effect: viability of clinical CR and MDR strains of Escherichia coli (EC), Klebsiella pneumoniae (KP), and Acinetobacter baumannii (AB) treated with meropenem alone or in combination with glutamate (Figure S3); metabolic profiles of P53 in the control group vs. glutamate groups (Figure S4); pathway enrichment analysis of P53 in the control group vs. glutamate groups (Figure S5) (PDF)

#.

L.-f.Y., X.-f.L., and H.-f.L. contributed equally to this article. L.-f.Y.: formal analysis, methodology, visualization, writingoriginal draft. X.-f.L.: methodology, visualization, writingreview and editing. H.-f.L.: methodology, visualization, writingreview and editing. Y.-y.L.: investigation, validation. S.-c.Y.: investigation, validation. J.X.: methodology, visualization. Y.L.: conceptualization, methodology, visualization, writingreview and editing. Z.-g.C.: conceptualization, funding acquisition, methodology, visualization, writingreview and editing.

The authors declare no competing financial interest.

References

  1. Uberoi A., McCready-Vangi A., Grice E. A.. The Wound Microbiota: Microbial Mechanisms of Impaired Wound Healing and Infection. Nat. Rev. Microbiol. 2024;22:507–521. doi: 10.1038/s41579-024-01035-z. [DOI] [PubMed] [Google Scholar]
  2. Letizia M., Diggle S. P., Whiteley M.. Pseudomonas aeruginosa: Ecology, Evolution, Pathogenesis and Antimicrobial Susceptibility. Nat. Rev. Microbiol. 2025;23:701–717. doi: 10.1038/s41579-025-01193-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Tenover F. C., Nicolau D. P., Gill C. M.. Carbapenemase-Producing Pseudomonas aeruginosa - An Emerging Challenge. Emerg Microbes Infect. 2022;11:811–814. doi: 10.1080/22221751.2022.2048972. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Alshammari H. O., Albarrag A. M., Moussa I. M., Al-Tawfiq J. A., Aldakeel S. A., Somily A. M.. Molecular Analysis of Carbapenemase-Producing Pseudomonas aeruginosa Strains in a Tertiary Care Hospital in Riyadh. J. Infect. Public Health. 2026;19:103159. doi: 10.1016/j.jiph.2026.103159. [DOI] [PubMed] [Google Scholar]
  5. La Rosa R., Molin S., Johansen H. K.. Pseudomonas aeruginosa: Persistence beyond Antibiotic Resistance. Trends Microbiol. 2025;33:1076–1084. doi: 10.1016/j.tim.2025.05.004. [DOI] [PubMed] [Google Scholar]
  6. Peng B., Su Y. B., Li H., Han Y., Guo C., Tian Y. M., Peng X. X.. Exogenous Alanine and/or Glucose plus Kanamycin Kills Antibiotic-Resistant Bacteria. Cell Metab. 2015;21:249–262. doi: 10.1016/j.cmet.2015.01.008. [DOI] [PubMed] [Google Scholar]
  7. Peng B., Li H., Peng X. X.. Functional Metabolomics: From Biomarker Discovery to Metabolome Reprogramming. Protein Cell. 2015;6:628–637. doi: 10.1007/s13238-015-0185-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Kuang S. F., Xiang J., Li S. H., Su Y. B., Chen Z. G., Li H., Peng B., Peng X. X.. Metabolic Reprogramming Enhances the Susceptibility of Multidrug- and Carbapenem-Resistant Bacteria to Antibiotics. Nat. Microbiol. 2025;10:2257–2274. doi: 10.1038/s41564-025-02083-8. [DOI] [PubMed] [Google Scholar]
  9. Peng B., Li H., Peng X. X.. Metabolic State-Driven Nutrient-Based Approach to Combat Bacterial Antibiotic Resistance. npj Antimicrob. Resist. 2025;3:24. doi: 10.1038/s44259-025-00092-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Peng B., Li H., Peng X. X.. Call for Next-Generation Drugs That Remove the Uptake Barrier to Combat Antibiotic Resistance. Drug Discovery Today. 2023;28:103753. doi: 10.1016/j.drudis.2023.103753. [DOI] [PubMed] [Google Scholar]
  11. Jiang M., Su Y. B., Ye J. Z., Li H., Kuang S. F., Wu J. H., Li S. H., Peng X. X., Peng B.. Ampicillin-Controlled Glucose Metabolism Manipulates the Transition from Tolerance to Resistance in Bacteria. Sci. Adv. 2023;9:eade8582. doi: 10.1126/sciadv.ade8582. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Zhao X. L., Chen Z. G., Yang T. C., Jiang M., Wang J., Cheng Z. X., Yang M. J., Zhu J. X., Zhang T. T., Li H., Peng B., Peng X. X.. Glutamine Promotes Antibiotic Uptake to Kill Multidrug-Resistant Uropathogenic Bacteria. Sci. Transl. Med. 2021;13:eabj0716. doi: 10.1126/scitranslmed.abj0716. [DOI] [PubMed] [Google Scholar]
  13. Xiang J., Zhou Y. Q., Yuan S. C., Zhang X. L., Wang S. W., Chen Z. G., Lin L. R., Liu Z. Q., Li H., Peng B.. Compound Amino Acid Synergizes Ceftazidime-Avibactam to Eradicate Extracellular and Facultative Intracellular MDR Pathogens. Cell Rep. 2026;45:117008. doi: 10.1016/j.celrep.2026.117008. [DOI] [PubMed] [Google Scholar]
  14. Xiang J., Wang X., Lin H., Yang L., Huang X., Chen Y., Zeng Y., Li S., Zhao X., Wang S., Tao Y., Fu H., Shi Z., Wu K., Peng X. X., Li H., Tang J., Chen Z. G.. Glutamine Potentiates Cefoperazone-Sulbactam Activity against Pseudomonas aeruginosa by Increasing Membrane Permeability and Cellular Uptake. Front. Microbiol. 2025;16:1631646. doi: 10.3389/fmicb.2025.1631646. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Wang S. W., Shi Z. Q., Zhu J. X., Xiang J., Chen Y. T., Li S. H., Zhao X. L., Zeng Y. Y., Tao Y., Fu H. Z., Lin H. Y., Tang J., Huang X. X., Wang X., Peng X. X., Wu K. H., Zhang T. T., Li H.. Glutamine Potentiates Cefoperazone-Sulbactam Activity against Acinetobacter baumannii by Increasing Drug Uptake and ROS. ACS Infect. Dis. 2025;11:3222–3236. doi: 10.1021/acsinfecdis.5c00616. [DOI] [PubMed] [Google Scholar]
  16. Wu J. H., Chen X. W., Liu Y. L., Wu J. Y., Chen Z. G., Peng B.. Metabolism-Dependent Succinylation Governs Resource Allocation for Antibiotic Resistance. Sci. Adv. 2025;11:eadu2856. doi: 10.1126/sciadv.adu2856. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Su Y. B., Peng B., Li H., Cheng Z. X., Zhang T. T., Zhu J. X., Li D., Li M. Y., Ye J. Z., Du C. C.. et al. Pyruvate cycle increases aminoglycoside efficacy and provides respiratory energy in bacteria. Proc. Natl. Acad. Sci. U. S. A. 2018;115:E1578. doi: 10.1073/pnas.1714645115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Zhang A. H., Sun H., Han Y., Yan G. L., Yuan Y., Song G. C., Yuan X. X., Xie N., Wang X. J.. Ultraperformance Liquid Chromatography–Mass Spectrometry Based Comprehensive Metabolomics Combined with Pattern Recognition and Network Analysis Methods for Characterization of Metabolites and Metabolic Pathways from Biological Data Sets. Anal. Chem. 2013;85:7606–7612. doi: 10.1021/ac401793d. [DOI] [PubMed] [Google Scholar]
  19. Odds F. C.. Synergy, Antagonism, and What the Chequerboard Puts between Them. J. Antimicrob. Chemother. 2003;52:1. doi: 10.1093/jac/dkg301. [DOI] [PubMed] [Google Scholar]
  20. Tao J. J., Xiang J. J., Jiang M., Kuang S. F., Peng X. X., Li H.. A Microtitre Plate Dilution Method for Minimum Killing Concentration Is Developed to Evaluate Metabolites-Enabled Killing of Bacteria by β-Lactam Antibiotics. Front. Mol. Biosci. 2022;9:878651. doi: 10.3389/fmolb.2022.878651. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Lai H. F., Pan L., Song K. Y., Dai Z. Y., Liang Y., Yuan W., Chen Z. G., Yang L. F.. Exogenous Glutamate Potentiates Gentamicin to Kill Multidrug- and Carbapenem-Resistant Pseudomonas aeruginosa by Activating the Biosynthesis of Unsaturated Fatty Acids. mSystems. 2025;10:e0123425. doi: 10.1128/msystems.01234-25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Li H., Yang J., Kuang S. F., Fu H. Z., Lin H. Y., Peng B.. Magnesium Modulates Phospholipid Metabolism to Promote Bacterial Phenotypic Resistance to Antibiotics. Elife. 2025;13:RP100427. doi: 10.7554/eLife.100427. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Miftode I. L., Radu V. D., Jigoranu R. A., Leca D. A., Prepeliuc C. S., Pasare M. A., Miftode R. S., Grigoriu M. G., Parângǎ T. G., Miftode E. G.. Novel Insights into Carbapenem Resistance: Mechanisms, Diagnostics, and Future Directions. Antibiotics. 2026;15:270. doi: 10.3390/antibiotics15030270. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Idowu T., Ammeter D., Arthur G., Zhanel G. G., Schweizer F.. Potentiation of Beta-Lactam Antibiotics and Beta-Lactam/Beta-Lactamase Inhibitor Combinations against MDR and XDR Pseudomonas aeruginosa Using Non-Ribosomal Tobramycin-Cyclam Conjugates. J. Antimicrob. Chemother. 2019;74:2640–2648. doi: 10.1093/jac/dkz228. [DOI] [PubMed] [Google Scholar]
  25. Nang S. C., Azad M. A. K., Velkov T., Zhou Q. T., Li J.. Rescuing the Last-Line Polymyxins: Achievements and Challenges. Pharmacol. Rev. 2021;73:679–728. doi: 10.1124/pharmrev.120.000020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Zhanel G. G., Golden A. R., Zelenitsky S., Wiebe K., Lawrence C. K., Adam H. J., Idowu T., Domalaon R., Schweizer F., Zhanel M. A.. et al. Cefiderocol: A Siderophore Cephalosporin with Activity against Carbapenem-Resistant and Multidrug-Resistant Gram-Negative Bacilli. Drugs. 2019;79:271–289. doi: 10.1007/s40265-019-1055-2. [DOI] [PubMed] [Google Scholar]
  27. Oliver A., Arca-Suárez J., Gomis-Font M. A., González-Pinto L., López-Causapé C.. Emerging Resistance Mechanisms to Newer Beta-Lactams in Pseudomonas aeruginosa. Clin. Microbiol. Infect. 2025;31:1790–1796. doi: 10.1016/j.cmi.2025.03.013. [DOI] [PubMed] [Google Scholar]
  28. Smith C., Siow A., Kowalczyk R., Ferguson S. A., Smith M. J. B., Cook G. M., Sander V., Davidson A. J., Brimble M. A., Harris P. W. R. D.. Synthesis and Biological Activity of Potential Retrometabolic Polymyxins via Thiol-Ene Chemistry. Chem. Commun. 2026;62:3790–3793. doi: 10.1039/D5CC03871E. [DOI] [PubMed] [Google Scholar]
  29. Li X., Feng D., Zhou J., Wu W., Zheng W., Gan W., Jiang M., Li H., Peng X., Zhang T.. Metabolomics Method in Understanding and Sensitizing Carbapenem-Resistant Acinetobacter baumannii to Meropenem. ACS Infect. Dis. 2024;10:184–195. doi: 10.1021/acsinfecdis.3c00480. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Carfrae L. A., Rachwalski K., French S., Gordzevich R., Seidel L., Tsai C. N., Tu M. M., MacNair C. R., Ovchinnikova O. G., Clarke B. R., Whitfield C., Brown E. D.. Inhibiting Fatty Acid Synthesis Overcomes Colistin Resistance. Nat. Microbiol. 2023;8:1026–1038. doi: 10.1038/s41564-023-01369-z. [DOI] [PubMed] [Google Scholar]
  31. Yu J., Wang H., Lu H., Zhu L.. Fatty Acid Salts Promote Antibiotic Resistance Development by Modulating Bacterial Motility and Antibiotic Uptake. J. Hazard. Mater. 2026;506:141614. doi: 10.1016/j.jhazmat.2026.141614. [DOI] [PubMed] [Google Scholar]
  32. Yin Y., Yin Y., Yang H., Chen Z., Zheng J., Peng B.. Vibrio alginolyticus Survives from Ofloxacin Stress by Metabolic Adjustment. Front. Microbiol. 2022;13:818923. doi: 10.3389/fmicb.2022.818923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Ye G., Fan L., Zheng Y., Liao X., Huang Q., Su Y.. Upregulated Palmitoleate and Oleate Production in Escherichia coli Promotes Gentamicin Resistance. Molecules. 2024;29:2504. doi: 10.3390/molecules29112504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Li S. H., Tao Y., Yang Z. C., Fu H. Z., Lin H. Y., Peng X. X., Li H.. Valine Potentiates Cefoperazone-Sulbactam to Kill Methicillin-Resistant Staphylococcus aureus . mSystems. 2025;10:e0124424. doi: 10.1128/msystems.01244-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. May K. L., Akiyama T., Parker B. G., Kim M., Grabowicz M.. LPS O-Antigen Polysaccharide Length Impacts Outer Membrane Permeability of Enteric Gram-Negative Bacteria. mBio. 2025;16:e0251825. doi: 10.1128/mbio.02518-25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Khan H. U., Aamir K., Jusuf P. R., Sethi G., Sisinthy S. P., Ghildyal R., Arya A.. Lauric Acid Ameliorates Lipopolysaccharide (LPS)-Induced Liver Inflammation by Mediating TLR4/MyD88 Pathway in Sprague Dawley (SD) Rats. Life Sci. 2021;265:118750. doi: 10.1016/j.lfs.2020.118750. [DOI] [PubMed] [Google Scholar]
  37. Bertani B., Ruiz N.. Function and Biogenesis of Lipopolysaccharides. EcoSal Plus. 2018;8:8. doi: 10.1128/ecosalplus.esp-0001-2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Zhanel G. G., Wiebe R., Dilay L., Thomson K., Rubinstein E., Hoban D. J., Noreddin A. M., Karlowsky J. A.. Comparative Review of the Carbapenems. Drugs. 2007;67:1027–1052. doi: 10.2165/00003495-200767070-00006. [DOI] [PubMed] [Google Scholar]
  39. Clinical And Laboratory Standards Institute. Methods For Dilution Antimicrobial Susceptibility Tests For Bacteria That Grow Aerobically; CLSI Standard M07, 12th ed.Clinical and Laboratory Standards Institute: Wayne, PA, 2024. [Google Scholar]
  40. Li S. H., Chen Y. Y., Fu H. Z., Lin H. Y., Tao Y., Zhao X. L., Chen Y. T., Xiang J., Zeng Y. Y., Wang S. W., Wu K. H., Shi Z. Q., Tang J., Huang X. X., Wang X., Li H., Lin L. R., Peng X. X.. Glutamine Potentiates Cefoperazone-Sulbactam against Klebsiella pneumoniae by Enhancing Cellular Uptake and ROS Production. ACS Infect. Dis. 2026;12(5):1739–1750. doi: 10.1021/acsinfecdis.6c00129. [DOI] [PubMed] [Google Scholar]
  41. Xiang J., Tian S. Q., Wang S. W., Liu Y. L., Li H., Peng B.. Pyruvate Abundance Confounds Aminoglycoside Killing of Multidrug-Resistant Bacteria via Glutathione Metabolism. Research. 2024;7:0554. doi: 10.34133/research.0554. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ao6c07419_si_001.pdf (1.2MB, pdf)

Data Availability Statement

The data underlying this study are available in the published article and its Supporting Information.

Articles from ACS Omega are provided here courtesy of American Chemical Society

RESOURCES