Antibiotic resistance of Pseudomonas aeruginosa strains—intracellular metabolomics analysis

Abstract

Objectives

Pseudomonas aeruginosa (Pa) stands as a dangerous multidrug-resistant human pathogen, and the associated antibiotic resistance mechanisms, governed by diverse molecular processes, present a persistent challenge in eradicating bacterial infections, especially in patients with cystic fibrosis. The search for novel antibiotic resistance markers has led researchers to explore the field of metabolomics. This study aims to discern alterations in metabolite levels resulting from varied antibiotic resistance profiles in Pa strains isolated from clinical patients.

Methods

In-depth analysis of intracellular metabolites from Pa strains was conducted. Following bacterial cultivation and extraction, LC-MS measurements were performed, and subsequent data analysis used a combination of statistical and chemometric methodologies.

Results

A total of 45 metabolites were identified under positive ionization mode. Statistically significant variations were discerned across all comparisons involving the 14 antibiotics investigated, primarily manifesting as alterations in amino acid pathways and their derivatives. The chemometric outcomes revealed a distinct clustering of samples corresponding to the specific antibiotics tested.

Conclusion

The presented findings show noteworthy shifts in bacterial metabolic pathways, laying the groundwork for further investigations. These initial insights promise to delineate avenues towards comprehensive explorations into antibiotic resistance in bacteria. This study provides a foundation for future investigations aimed at unravelling the intricate dynamics of antibiotic resistance in Pa.

Introduction

Pseudomonas aeruginosa (Pa), a ubiquitous Gram-negative bacterium, is a causative agent of nosocomial infections.^1,2 The escalating resistance observed in Pa strains against antibiotics is a challenge to effective therapeutic interventions and has an impact on paediatric patients afflicted by cystic fibrosis^3,4

Classified within the ESKAPE group by the Infectious Diseases Society of America alongside five other bacterial species (E. faecium, S. aureus, K. pneumoniae, A. baumannii and Enterobacter spp.), Pa has been prioritized by the WHO for antibiotic resistance research, emphasizing the imperative for innovative treatments to mitigate the risk of a healthcare crisis.^5–7 The antibiotic resistance mechanisms are extensively documented for diverse compounds. They encompass diminished expression of genes encoding porins,⁸ the presence of specific enzymes within bacterial cells facilitating the inactivation of antibiotic structures (e.g. β-lactams)⁹ and modifications to critical proteins such as penicillin-binding proteins (PBPs), in addition to the involvement of efflux pumps.^10,11 Biofilm formation, serving as a physical barrier to external agents, further exacerbates antibiotic resistance in Pa.¹²

Traditional therapeutic strategies against Pa are gradually losing efficacy, necessitating comprehensive scientific investigations across various metabolic levels.^7,13 In addition to genomics and proteomics, metabolomic methodologies provide an additional source for investigating this complex phenomenon.^14–16 Profiling low-molecular-weight compounds in microorganisms facilitates the identification of bacterial species, the influence of external factors’ impacts on microbial physiology, and exploration of novel adaptive pathways within the bacterial environment.^17,18

Metabolomics provides invaluable information about antibiotic resistance mechanisms in bacteria. A noteworthy example was conducted by Schelli et al., who used mass spectrometry to analyse methicillin-susceptible and resistant Staphylococcus aureus strains. Their findings unveiled a complex array of metabolic changes induced by various antibiotics, underscoring the potential of comparative metabolomics for understanding this process.¹⁹ Furthermore, investigating ciprofloxacin-resistant Escherichia coli strains has revealed that lamB gene knockout may augment bacterial antibiotic resistance by attenuating metabolic pathway activity levels.²⁰

Metabolomic investigations into antibiotic-resistant Pa strains aim to discern variations in bacterial phenotypes and identify potential antibiotic resistance biomarkers. In these studies, the LC-MS method was used to discover the differences between strains with varying sensitivities to 14 antibiotics. The alterations in intracellular metabolites were analysed through the application of statistical and chemometric methodologies.

The findings presented in this paper represent a logical extension of our earlier experimentation, wherein we compared two Pa strains exhibiting different antibiotic resistance profiles (with the ¹H NMR method).²¹ This progressive research project seeks to enrich our understanding of the dependencies between antibiotic resistance mechanisms and the metabolomic profile of Pa, thereby contributing valuable insights to the ongoing discourse on effective antimicrobial strategies.

Materials and methods

The procedure conducted in this experiment followed the typical metabolomics experiments (Figure 1). Detailed information about it is described in the following.

Figure 1.

Scheme of the conducted experiment.

Bacterial strains and culture conditions

In this experiment, 88 Pa strains isolated from patients suffering from cystic fibrosis were used. The strains were obtained at the Mother and Child Institute (Warsaw, Poland).

Before the final cultures, the growth curves for each strain were checked (the bacterial cells in the logarithmic growth phase were taken). For the growth curve measurement, the multidetection microplate reader (Spark, Tecan GmbH) was used. Optical density was checked in every 30 minutes during 24 h [cultures were incubated at 37°C with shaking (214 r.c.f.)].

After defrosting, the bacteria were grown on Miller’s LB agar (BioShop) plates at 37°C. After 24 h, the pre-culture were prepared: one single colony of each strain was inoculated in liquid LB medium and incubated in 37°C with shaking (257 r.c.f.). Subsequently, 100 mL of liquid LB medium was inoculated and incubated in the same conditions up to OD_600nm = 0.8. The initial OD_600nm for all cultures was 0.1.

Antibiotic resistance

The susceptibility of Pa strains to antibiotics was determined using the disc diffusion method. A bacterial suspension with a density equal to 0.5 McFarland was inoculated on MH Agar, and antibiotic discs were placed on the seeded medium (30 µg: amikacin, cefepime, piperacillin; 10 µg: gentamicin, netilmicin, tobramycin, ceftazidime, imipenem, meropenem, colistin; 5 µg: levofloxacin, ciprofloxacin; 30/6 µg: piperacillin/tazobactam; 75/10 µg: ticarcillin/clavulanic acid: all Emapol antibiotic discs). Cultivation was carried out for 18 ± 2 h at 35 ± 1°C under aerobic conditions. Interpretation was prepared following the current recommendations of the EUCAST.²² The results of antibiotic resistance tests are presented in Table S1 (available as Supplementary data at JAC Online).

Sample preparation

Following this, 8 mL of the bacterial culture in the logarithmic growth with OD_600nm = 0.8 was centrifuged (14 255 r.c.f., 10 min, 4°C) (Sigma). If the OD_600nm value after incubation (up to the middle of the logarithmic growth phase) was higher, the value of the centrifugated sample was proportionally higher/lower, to obtain the same amount of bacterial cell in the sample. The supernatant was discarded, and the samples were washed twice with a 0.9% NaCl solution. The extraction protocol for investigating intracellular metabolites was prepared based on the Depke workflow.²³

In the first step, a steel ball was added to each sample, along with 500 µL of the extraction mixture (methanol: water (4:1,v/v)). Subsequently, the samples were homogenized in the TissueLyser (Qiagen) for 10 min at a frequency of 30 1/s. Afterwards, the samples were centrifuged for 10 min at 19 956 r.c.f. at 4°C (Hettich Zentrifugen). Then 400 µL of the supernatant were transferred into new Eppendorf tubes and evaporated in a speedvac (Eppendorf) for 2.5 hours. The extracts were dissolved in 50 µL of the extraction mixture, mixed for 3 min, and centrifuged under the same conditions. The samples were then transferred into QuanRecovery plates (Waters) and stored at 4°C before measurements.

LC-MS analysis of the bacterial metabolites

The samples were analysed using a Waters UPLC-Q-ToF-MS/MS system comprising an Acquity I-class UPLC coupled to a Synapt G2-Si Q-TOF-MS (Waters), which was operated in the data-independent acquisition mode. In the positive ion mode, the source temperature was set up to 120°C with a cone gas flow of 50 l/h, a desolvation temperature of 450°C, and a desolvation gas flow of 900 l/h. A scan time of 0.3 s for MS and 1 s for MS/MS with an inter-scan delay of 0.05 s was used for all the analyses. The nebulizer gas pressure was set at 6.5. The high-accuracy MS data were collected from m/z 50–1300 E centroid modes. Instrument average resolution was 20 000 full-width at half-maximum. Leucine enkephalin at a concentration of 50 pg/μL (in 50:50 acetonitrile:0.1% formic acid) was used as a lock mass in positive mode ([M+H] = 556.2771). The lock spray frequency was 15 s, and the lock mass data averaged over three scans for collection. Data acquisition was performed using Waters MassLynx software (Waters). UPLC-MS/MS raw data were imported, processed, normalized and reviewed using Progenesis QI v.3.0 (Nonlinear Dynamics).

Chromatographic separation was achieved using an Acquity BEH Amide UPLC column (100 mm × 2.1 mm, 1.7 µm, Waters). The mobile phase consisted of (A) 10 mM ammonium acetate in 5% water and 95% acetonitrile and (B) 10 mM ammonium acetate in 95% water and 5% acetonitrile. In all UPLC runs the elution gradient started at 1%B increasing to 11% B over 5 minutes, followed by 13 minutes increasing to 70% B and 5 min re-equilibration period. A sample volume of 3 μL was injected for each UPLC run. The run contained a sample, blanks and quality controls (QC) intercalated throughout the UPLC run to control for any acquisition-dependent variation. A linear gradient curve (type 6) was used for the analytical separation of analytes on the column. QC samples were analysed to check LC-MS performance and monitor column equilibrium. QC samples were prepared by mixing 10μL of each sample after extraction.

Metabolites identification, data processing and multivariate statistical data analysis

The multivariate and statistical data analysis were conducted on a set of 53 assigned metabolites in the positive ion mode. Metabolite identification was performed using Progenesis QI (v.3.0.3.0, Nonlinear Dynamics). The data were aligned and quantified, followed by peak picking using the algorithm that is an integral part of the Progenesis QI software, and compound review based on KEGG Pathways and PAMDB databases. Identification criteria included retention time, neutral mass, m/z, fragmentation scores (>35.0), mass error (<5.0 ppm), isotope similarity (>85.0), presence of adducts and peak symmetry.

The relative concentrations of metabolites were obtained using Progenesis QI, after the total ion current normalization method was used. The input for SIMCA-P software was a transformed data matrix (v.15.02, Umetrics). The datasets were unit variance scaled before chemometric analysis. PCA and OPLS analysis were conducted for bacterial strain analysis. The reliability of the OPLS-DA model was tested using CV-ANOVA at a significance level of α < 0.05.

For univariate analysis, MATLAB (v.14.0, The MathWorks) was used to perform Student’s t-test (equal/unequal variance) for data originating from a normal distribution and using Mann–Whitney–Wilcoxon test for data that did not meet this requirement. Normality of distribution was assessed by the Shapiro–Wilk test. The correction for multiple comparisons was preceded by the Benjamini–Hochberg procedure (false discovery rate). All univariate statistics were carried out at the level of significance of α < 0.05.

Results

Metabolite identification

A total of 45 metabolites were systematically identified, including essential compounds such as uridine 5′-monophosphate, uridine, uracil, thymidine 3′-monophosphate, S-adenosylmethionine, S-adenosylhomocysteine, pantothenic acid, nicotinic acid, niacinamide and NAD, and amino acids including valine, threonine, phenylalanine, methionine, lysine, histidine, glutamine, serine, proline, cysteine, glutamic acid, asparagine and aspartic acid. In addition, the dataset includes metabolites crucial for cellular processes, such as guanosine monophosphate, guanine, glycerol 3-phosphate, glutathione, glucosamine 6-phosphate, glucosamine and cofactors such as FAD.

Notably, the identification of nucleotides such as cytidine, cystine and cyclic AMP, ADP ribose, 5-methylthioadenosine, adenosine triphosphate, ADP, adenine and adenosine alongside metabolites associated with cellular processes such as citrulline, biotin, dimethylglycine, β-D-glucose 6-phosphate and 2-phosphoglyceric acid underscores the comprehensive nature of the metabolomic profiling.

For all the strains, the same metabolites were identified. Table S2 offers detailed insights into the identified metabolites.

Multivariate data analysis

Multivariate data analysis was prepared to explain the distinctions among bacterial strain samples, encompassing a total of 14 comparisons (for each antibiotic separately). Each comparison analysed the differentiation between strains exhibiting resistance and susceptibility to individual antibiotics. The parameters characterizing the OPLS-DA models prepared in this investigation are delineated in Table 1.

Table 1.

Information about OPLS-DA models (in the grey background the statistically significant models are marked)

Antibiotic	P value (CV-ANOVA)	R 2 X	R 2 Y	Q 2	N
Amikacin	0.0342	0.315	0.383	0.122	84
Cefepime	0.0612a	0.308	0.379	0.102	88
Ceftazidime	0.0032	0.322	0.378	0.176	86
Ciprofloxacin	1.0000	—	—	—	84
Colistin	1.0000	—	—	—	88
Gentamicin	0.0012	0.136	0.477	0.145	86
Imipenem	0.9782	0.374	0.423	0.015	82
Levofloxacin	0.0517a	0.321	0.295	0.135	69
Meropenem	1.0000	—	—	—	86
Netilmicin	1.0000	—	—	—	87
Piperacillin	0.5576	0.315	0.322	0.036	86
Piperacillin + tazobactam	0.5606	0.323	0.276	0.035	88
Ticarcillin + clavulonic acid	1.0000	—	—	—	87
Tobramycin	1.0000	—	—	—	88

^aThe values are close to the value of statistical significance at level P = 0.05 and are therefore discussed later in the text.

Throughout all the chemometric analyses conducted, only five exhibited statistical significance (determined by CV-ANOVA results). Significantly distinguishable differentiations were observed in the models corresponding to amikacin, cefepime, ceftazidime, gentamicin and levofloxacin. The OPLS-DA score plots depicting these differentiations are presented in Figure 2. These findings showed the selective impact of the specified antibiotics on the metabolic profiles of the bacterial strains.

Figure 2.

OPLS-DA score plots for amikacin, gentamicin, cefepime, ceftazidime and levofloxacin (R, antibiotic resistance strains; S, antibiotic-susceptible strains). Detailed information about models is available in Table 1.

Statistical analysis

A comprehensive investigation was undertaken to assess the details of antibiotic susceptibility in Pa strains through statistical analyses. The outcomes revealed statistically significant distinctions across five antibiotics tested, with particular emphasis on the noteworthy results obtained from multivariate analyses, yielding statistically significant models except for the last two, which are on the verge of statistical significance (Figure 3).

Figure 3.

The differences in statistically significant metabolites (P value < 0.05) for amikacin, gentamicin and levofloxacin, cefepime and ceftazidime (R, antibiotic resistance strains, S, antibiotic-susceptible strains) (G6P, β-D-glucose-6-phosphate; URA, uracil; CYT, cytidine; GL6P, glucosamine-6-phosphate; 2PA, 2-phosphoglyceric acid; GSA, glucosamine; NIA, niacinamide).

The detailed data of the statistical analysis are available in the Supplementary Materials (Tables S3–S9).

In the case of amikacin, a focused examination unveiled seven metabolites of statistical significance: β-D-glucose-6-phosphate, glutamic acid, uracil, glucosamine 6-phosphate, 2-phosphoglyceric acid, valine and cAMP. Only two metabolites (β-D-glucose-6-phosphate and 2-phosphoglyceric acid), exhibited down-regulation in resistance strains.

Regarding gentamicin, the relative concentration of four metabolites was found to be higher in susceptible strains (2-phosphoglyceric acid, β-D-glucose-6-phosphate, D-asparagine and FAD), while four metabolites demonstrated lower concentrations in susceptible strains (uracil, glutamic acid, cytidine and glucosamine 6-phosphate).

The analysis of levofloxacin identified six down-regulated metabolites in susceptible strains (glutamic acid, glucosamine, glucosamine 6-phosphate, uracil, methionine and niacinamide), while three metabolites were up-regulated in susceptible strains (asparagine and β-D-glucose-6-phosphate).

For the β-lactam cefepime, nine metabolites were identified as influential in discerning differences between resistance and susceptible strains. Notably, four metabolites were found to be down-regulated (glutamic acid, uracil and methionine), while four were up-regulated (β-D-glucose-6-phosphate, cytidine, ATP, cAMP) in susceptible Pa strains.

Analysing another β-lactam, ceftazidime, revealed 11 statistically significant metabolites. Only two were up-regulated in antibiotic-susceptible Pa strains (β-D-glucose-6-phosphate and asparagine), while the remaining metabolites were up-regulated in antibiotic resistance strains (glutamic acid, glucosamine 6-phosphate, cytidine, uracil, methionine, cAMP, threonine and valine). These intricate insights contribute substantively to our understanding of the complex metabolic dynamics associated with antibiotic susceptibility in Pa strains.

In-depth statistical analyses were conducted for additional antibiotics, revealing varying quantities of statistically significant metabolites. For piperacillin, nine metabolites were identified as noteworthy, with only three demonstrating down-regulation in resistance strains. Conversely, netilmicin exhibited statistical significance for three metabolites, and tobramycin showcased three compounds as significant. Notably, ciprofloxacin and meropenem analyses yielded two statistically significant metabolites each, while colistin analysis highlighted two crucial metabolites. Imipenem analysis, similarly, showcased the statistical significance of two compounds. In the case of piperacillin + tazobactam, eight metabolites emerged as statistically significant, while ticarcillin with clavulonic acid analysis revealed a notable 12 statistically significant compounds (Figure S1).

Comprehensive details regarding the statistically significant metabolites for each antibiotic are available in Table S10 within the Supplementary Materials. These findings further contribute to the intricate understanding of antibiotic-specific metabolic responses in Pa strains and provide valuable insights for future investigations in antibiotic resistance mechanisms.

Discussion

Over time, bacterial strains have demonstrated an escalating resistance to pharmaceutical interventions, giving rise to multifaceted challenges in eradicating bacterial infections.^24,25 This issue is particularly pronounced in individuals afflicted with cystic fibrosis, where patients experience complications in the pulmonary tract (accumulation of mucus and diminished mucociliary clearance, ultimately leading to inflammatory responses). The convergence of these factors renders the lungs more susceptible to bacterial infections.^26,27

In the treatment, diverse classes of antibiotics are employed, with the choice depending on the specific bacterium responsible for the lung infections.²⁸ Among the most widely used antibiotics for bacterial eradication are β-lactams, aminoglycosides, quinolones and peptides. The molecular mechanisms underlying the action of these antibiotics are well-documented.²⁴ The mode of action of β-lactam antibiotics revolves around the inhibition of bacterial enzymes known as transpeptidases or PBPs. These enzymes play a crucial role in the synthesis of the peptidoglycan within the bacterial cell wall.^29,30 Aminoglycosides disrupt the synthesis of bacterial proteins, including those integral to the composition of the cell membrane.³¹ The antibacterial activity of quinolones hinges on the inhibition of DNA gyrase, a bacterial topoisomerase.³²

One scientific discipline with significant potential for identifying potential biomarkers is metabolomics. In this context, the bacterial phenotype is analysed, revealing differences that may occur relatively rapidly over time (as observed in the prompt acquisition of antibiotic resistance by bacterial strains).^33,34 Consequently, we decided to compare Pa strains exhibiting different levels of antibiotic resistance, examining potential alterations among these strains depending on the specific antibiotics used. In all instances, discernible differences were observed: some metabolites were up-regulated and others down-regulated in drug-resistant strains (Figure 4).

Figure 4.

The summary of up-graded (up arrow) and down-graded (down arrow) intracellular metabolites in statistically significant tested antibiotics.

Within the category of β-lactams, the most significant metabolite variations were noted in the presence of cefepime and ceftazidime. Notably, in the case of cefepime, most metabolites exhibited increasing in antibiotic-resistant strains. This increasing included metabolites associated with transcription and amino acid pathways, directly tied to the molecular mechanism of the antibiotic in question.²⁹ Similar patterns were observed for ceftazidime, wherein the perturbation of amino acid synthesis, linked to the antibiotics’ mode of action, was evident.³⁵ A similar trend emerged with aminoglycosides and quinolones, both exhibiting mechanisms of action intricately tied to protein turnover. As a result, our findings consistently revealed disruptions in amino acid synthesis, a phenomenon well-supported by literature.³⁶ Antibiotic-resistant Pa strains exhibited discernible alterations in pathways associated with amino acids, the tricarboxylic acid cycle and nucleotides.^37,38 These observed metabolic shifts seamlessly align with existing research on chronic Pa lung infections.³⁹ The convergence of evidence derived from our study and previous investigations enhances the value of our findings in the description of the metabolic changes that underlie antibiotic resistance in Pa strains, particularly within the context of chronic lung infections.

Peptidoglycan synthesis and processing may undergo modification in strains exhibiting resistance to multiple antibiotics. Specifically, two metabolites, glutamic acid and glucosamine 6-phosphate, consistently exhibited increasing in strains resistant to ceftazidime, gentamicin, cefepime, amikacin and levofloxacin, Both serve pivotal roles as substrates in peptidoglycan synthesis within Pa.⁴⁰ Furthermore, an increased concentration of uracil was observed in these instances, with uridine nucleotide playing a crucial role in the synthesis of muramino-N-acylo-pentapeptide: an essential component of peptidoglycan. Moreover, a decreased level of asparagine was noted in strains resistant to the aforementioned antibiotics (excluding cefepime and amikacin). This alteration in peptidoglycan composition and the activity of cell wall rearrangement mechanisms are pivotal factors influencing susceptibility to beta-lactam antibiotics.⁴¹ Previous studies have demonstrated that antibiotics can affect the activity of mechanisms responsible for cell wall synthesis, even when their mechanisms of action are not directly linked to peptidoglycan synthesis (e.g. polymyxin-B).^42,43 Intriguingly, Pa strains resistant to polymyxin-B did not exhibit changes in cell wall-associated metabolite levels on antibiotic treatment, prompting further inquiry into the specific response of cell wall synthesis to polymyxin treatment.

In a living cell, peptidoglycan constitutes a dynamic structure undergoing constant remodelling and partial degradation by specialized bacterial enzymes.⁴¹ The products of peptidoglycan biodegradation play a signalling role, conveying information about the cell wall condition and potentially triggering antibiotic defence mechanisms. For instance, the synthesis of beta-lactamase, regulated by the activity of the PBP4 protein encoded by dacB, is influenced by the degradation products of peptidoglycan.⁴⁴ The reduced activity of this enzyme leads to an increased synthesis of AmpC protein in Pa, enhancing resistance to β-lactam antibiotics. Thus, the modulation of cell wall degradation may serve as a contributory factor to antibiotic resistance, influencing the levels of metabolites associated with peptidoglycan biosynthesis.

A significant reduction in the levels of β-D-glucose-6-phosphate was observed in strains resistant to the following antibiotics: ceftazidime, gentamicin, cefepime, amikacin and levofloxacin. This metabolite is considered as the main form of glucose in the cell cytosol when glucose availability in the environment is low. Reduced level of this metabolite is observed in starving cells, and may reflect the metabolic activity of the cell.⁴⁵ Low concentration of this metabolite observed in antibiotic-resistant cells may be the result of permanent activation of cell defence mechanisms. Antibiotic-resistant cells usually carry mutations causing permanent activation of defence mechanisms,⁴⁶ such as reduced production of porins. It was shown that antibiotic resistance may be caused by a lower level of porins in outer membrane of Pa.⁴⁷ Decreased presence of specific porins may reduce antibiotic membrane permeability, as in the case of OprD that is required for carbapenem uptake. In such a case, intracellular antibiotic concentration may be too low to cause effect.⁸ Another effect of reduced presence of porins may be the repression of general metabolism intensity in bacterial cell by reduced flow of nutrients from the environment, resembling starvation conditions. Moreover, this effect might be intensified by increased demand for energy generated by additional defence mechanisms like membrane pumps.

Reduced metabolic activity of bacterial cells resulting from permanent activation of certain defence mechanism may also cause tolerance to antibiotics not associated with the mechanism itself. It may be the case of tobramycin, for which cell incorporation requires proton motive force.⁴⁸ This hypothesis requires further experiments to be confirmed, including analysis of antibiotic resistance and tolerance of strains with confined activity of selected defence mechanism and reduced metabolic activity.

Culture conditions applied in the laboratory often use nutrient-rich culture media such as Mueller–Hinton broth (MHB).⁴⁹ These conditions may differ significantly from the environment occupied by bacterial cells during infection. For instance, the availability of free amino acids in airway surface liquid is usually lower when compared with MHB or LB media. However, during exacerbation of infection in CF patients, the composition of the surface liquid changes and concentration of free amino acids rises up to 5.7 g/L.⁵⁰ This value becomes much closer to the concentration of protein hydrolyses in MHB (17.5 g/L casein hydrolysate) and LB (tryptone 10 g/L). Thus, laboratory culture media may simulate the conditions during acute infection better than it initially seems. Since information regarding antibiotic resistance obtained in laboratory conditions using culture media such as MHB is valuable for the treatment, it seems likely that research results collected from experiments using nutrient-rich media carry significant information.

Conclusion

The presented findings extend our prior investigations into antibiotic-resistant strains, initially conducted on two strains of Pa and analysed using the ¹H NMR technique. In this study, we employed the LC-MS method for metabolomic analysis of bacterial specimens, encompassing an extensive investigation involving nearly 90 Pa strains. The results substantiate the presence of discernible differences among Pa strains exhibiting distinct antibiotic resistance profiles, particularly emphasizing associations with amino acid pathways. Notably, amino acids, specifically glutamic and aspartic acids, emerge as crucial elements required for peptidoglycan synthesis in these strains. Another noteworthy observation underscores a prevalent strategy in antibiotic-resistant strains: a general reduction in metabolic activity, as evidenced by a reduced level of glucose-6-phosphate. The implications of our findings warrant further exploration in future investigations, suggesting the justification for an in-depth analysis of bacterial post-breeding mediums. This analysis could be enriched with complementary scientific disciplines, including proteomics and genomics, which promise a more nuanced understanding of the intricate molecular mechanisms underpinning antibiotic resistance. The comprehensive and multidisciplinary nature of our study positions it as a valuable and advancing knowledge, and refines strategies for addressing antibiotic resistance in Pa.

Funding

The research was funded by an Internal Grant from Faculty of Chemistry, Wroclaw University of Science and Technology (no. 7/2023).

Transparency declarations

None to declare.

Ethical approval

This article does not contain any studies with human and/or animal participants performed by any of the authors.

Data availability

Data described in the paper, code book and analytic code will be made available upon request pending approval by corresponding author.

Supplementary data

Figure S1 and Tables S1 to S10 are available as Supplementary data at JAC Online.