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. 2023 Dec 12;17(1):e14379. doi: 10.1111/1751-7915.14379

Elevated proton motive force is a tetracycline resistance mechanism that leads to the sensitivity to gentamicin in Edwardsiella tarda

Shao‐hua Li 1, Jiao Xiang 1, Ying‐yue Zeng 1, Xuan‐xian Peng 1,2,3, Hui Li 1,2,✉
PMCID: PMC10832521  PMID: 38085112

Abstract

Tetracycline is a commonly used human and veterinary antibiotic that is mostly discharged into environment and thereby tetracycline‐resistant bacteria are widely isolated. To combat these resistant bacteria, further understanding for tetracycline resistance mechanisms is needed. Here, GC–MS based untargeted metabolomics with biochemistry and molecular biology techniques was used to explore tetracycline resistance mechanisms of Edwardsiella tarda. Tetracycline‐resistant E. tarda (LTB4‐RTET) exhibited a globally repressed metabolism against elevated proton motive force (PMF) as the most characteristic feature. The elevated PMF contributed to the resistance, which was supported by the three results: (i) viability was decreased with increasing PMF inhibitor carbonylcyanide‐3‐chlorophenylhydrazone; (ii) survival is related to PMF regulated by pH; (iii) LTB4‐RTET were sensitive to gentamicin, an antibiotic that is dependent upon PMF to kill bacteria. Meanwhile, gentamicin‐resistant E. tarda with low PMF are sensitive to tetracycline is also demonstrated. These results together indicate that the combination of tetracycline with gentamycin will effectively kill both gentamycin and tetracycline resistant bacteria. Therefore, the present study reveals a PMF‐enhanced tetracycline resistance mechanism in LTB4‐RTET and provides an effective approach to combat resistant bacteria.

Elevated proton motive force is revealed to be a tetracycline resistance mechanism that leads to the sensitivity to gentamicin in Edwardsiella tarda. Meanwhile, gentamicin‐resistant E. tarda with low PMF are sensitive to tetracycline is also demonstrated. Therefore, the combination of tetracycline with gentamycin will effectively kill both gentamycin and tetracycline resistant bacteria.

graphic file with name MBT2-17-e14379-g001.jpg

INTRODUCTION

Edwardsella tarda is a significant fish pathogen that has global implications for aquaculture, leading to economic losses (Kamiyama et al., 2019). Additionally, it is a human pathogen linked to gastrointestinal and parenteral diseases (Bakirova et al., 2020; Kamiyama et al., 2019). This pathogen exhibits a rapid ability to invade host epithelial cells or macrophages, proliferating within the host's cytoplasm or phagocytic body. Consequently, the host may experience symptoms such as ascites and septicemia. Like other enteric pathogens within the same family such as Escherichia coli, Shigella, and Salmonella species, E. tarda possesses a lot of virulence systems and genes including type III and type VI secretion systems, quorum sensing, two‐component systems, and exoenzymes to gain entry into and to survive within the host (Algammal, Mabrok, et al., 2022; Leung et al., 2011). With the overuse and inappropriate consumption and application of antibiotics as well as the routine application of the antimicrobial susceptibility testing, multidrug resistance has been increased all over the world that is considered a public health threat (Algammal et al., 2023; Algammal, Alfifi, et al., 2022; Algammal, Ibrahim, et al., 2022; Elayaraja et al., 2020). Among them, multidrug‐resistant E. tarda emerge with the other four multidrug‐resistant Edwardsiella species, E. piscicida, E. ictulari, E. anguillarum, and E. hoshinae in aquatic environments worldwide (Leung et al., 2022; Preena et al., 2021). These bacteria are resistant to almost 16 families of common antibiotics used in aquaculture/agriculture (Preena et al., 2021).

Among antibiotic resistances mounted by these multidrug‐resistant E. tarda, tetracycline resistance is a characteristic feature and thereby strains with resistance to tetracyclines are frequently identified (Elgendy et al., 2022; Preena et al., 2022; Roberts & Schwarz, 2016). For example, MT263022, a strain recovered from diseased goldfish, showed a multidrug resistance to tetracyclines, β‐Lactams, aminoglycosides, macrolides, rifampicins, peptides, and sulphonamides (Preena et al., 2022). This is because tetracycline is one of the most common antibiotics used in aquaculture/agriculture (Dos Santos et al., 2017). Therefore, a further understanding for tetracycline resistance mechanisms is needed to combat tetracycline‐resistant E. tarda.

Four molecular mechanisms of antibiotic resistance have been identified in bacteria that are resistant to antibiotics: permeability disturbances, efflux activation, enzymatic inactivation, and changes in the target site (Du et al., 2018; Peng et al., 2019). Recent evidence has also demonstrated that metabolic modulation is an additional mechanism of antibiotic resistance (Lopatkin et al., 2021; Zhao et al., 2021). This metabolic modulation can be observed globally through a metabolomics approach (Peng, Li, & Peng, 2015; Peng, Su, et al., 2015). Elucidating the metabolic mechanism based on this global metabolic modulation has led to the development of metabolite‐enabled killing of antibiotic‐resistant bacteria by antibiotics (Chen et al., 2023; Li et al., 2020; Peng et al., 2023; Tang et al., 2022; Zhang et al., 2019, 2020; Zhao et al., 2021). The approach uncovers E. tarda kanamycin‐resistant metabolome and then uses alanine, glucose, glutamate, and fructose to reprogram the resistance metabolome into a kanamycin‐sensitive metabolome, leading to high efficiency to eliminate multidrug‐resistant E. tarda (Peng, Su, et al., 2015; Su et al., 2015, 2018). It is also found that global transcriptional regulator FNR regulates the pyruvate cycle and proton motive force (PMF) to play a role in aminoglycosides resistance of E. tarda (Griffith et al., 2019; Mao et al., 2022). Therefore, an understanding for metabolic mechanisms of tetracycline resistance is needed to explore effective measures to control tetracycline‐resistant E. tarda.

In this study, GS‐MS based untargeted metabolomics with biochemistry and molecular biology techniques was employed to explore metabolic resistance mechanisms of tetracycline‐resistant E. tarda LTB4 (LTB4‐RTET). The analysis identified an elevated proton motive force (PMF) against a global depressed metabolism as the most distinctive characteristic. Further experiments showed that the elevated PMF contributes to tetracycline resistance. Moreover, differential PMF between tetracycline‐resistant and gentamicin‐resistant bacteria was utilized to establish therapeutic approaches involving gentamicin and tetracycline for the respective resistant strains.

EXPERIMENTAL PROCEDURES

Chemicals

Luria‐Bertani (LB) was purchased from Huankai Biotech Limited. All antibiotics tested, including tetracyclines (tetracycline), aminoglycosides (gentamicin, kanamycin and amikacin), penecillins (penecillin), cephalosporins (ceftazidime), quinolones (ofloxacin), macrolides (erythromycin), and polypeptide (polymyxin E) were purchased from Sangon Biotech Limited. Metabolome sample preparation and derivatization reagents including methanol, pyridine and methoxyamine hydrochloride were purchased from Thermo Fisher Scientific (American).

Bacterial strains and culture

LTB4 was a gift from Professor Xiaohua Zhang, Department of Marine Biology, Ocean University of China, and is kept in the collections of our laboratory (Han et al., 2010; Peng, Su, et al., 2015). LTB4 were sequentially propagated in LB medium with or without 1/2 minimum inhibitory concentration (MIC) of tetracycline and led to tetracycline‐resistant strain (LTB4‐RTET) and tetracycline‐sensitive strains (LTB4‐S). The MIC of LTB4‐RTET and LTB4‐S were 25 μg/mL (16 MIC) and 1.56 μg/mL (1 MIC), respectively. The MIC of LTB4‐S was equal to that of the LTB4. The tetracycline‐gentamicin‐resistant bacteria (LTB4‐RTET‐GEN) and gentamicin‐tetracycline‐resistant bacteria (LTB4‐RGEN‐TET) were obtained by cross‐passing the above obtained LTB4‐RTET and LTB4‐RGEN in 1/2 gentamicin and tetracycline LB medium, respectively. Strains were cultured in 5 mL LB in a constant temperature shaker, 30°C, 200 rpm overnight.

Genome sequencing

This experiment consists of three consecutive steps of DNA preparation, Illumina sequencing, and bioinformatics analysis (Zhang et al., 2019). For DNA preparation, the overnight LTB4‐RTET and LTB4‐S with three biological replicates each strain were collected for extraction of genomic DNA using the Gentra Puregene Yeast/Bact Kit (Qiagen). DNA concentrations were measured with the NanoDrop 2000 (Thermo Fisher Scientific). For Illumina sequencing, DNA concentrations of the resulting sequencing libraries were measured with the Qubit 2.0 fluorometer dsDNA HS Assay (Thermo Fisher Scientific). Quantities and sizes of the resulting sequencing libraries were analysed using Agilent BioAnalyzer 2100 (Agilent). The libraries were used in cluster formation on an Illumina cBOT cluster generation system with HiSeq XHD PE Cluster Kits (Illumina). Paired‐end sequencing is performed using an Illumina HiSeq X following Illumina‐provided protocols for 2 × 150 paired‐end sequencing. For bioinformatics analysis, raw reads were quality checked with FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and reads with low quality, adaptors or high proportion Ns were filtered out using in‐house Linux shell script. Qualified reads were fed to further analysis. We used LTB4‐S as a control group to identify whether the gene of LTB4‐RTET was mutated and the type of mutation. Mutation filter criterions are listed as follows: (i) filter the low‐quality sites and sites with the read depth exceeding a limit (QUAL < 20||DP > 40); (ii) each variant was set to be supported by at least 10 reads and the mutation frequency should be greater than 0.8 (Mao et al., 2022). To annotate those mutations, we extract the flanking 100 bp sequence of each mutation from mapped scaffolds and perform blast search against nt sequence library in NCBI website.

Gene sequencing for conformation of the mutations revealed by genome sequencing

LTB4‐RTET (six single colonies) and LTB4‐S (two single colonies) were cultured in 5 mL LB medium at 30°C for 24 h on a shaker. Aliquot of 3 mL of the bacterial liquid was taken to extract the genome, and the genes of ETAE_RS04175, ETAE_RS10215, and ETAE_RS15730 were amplified and sequenced according to the designed primers (Table S1). After sequencing, the obtained sequences were compared with the corresponding NCBI sequences (the same sequences were detected in LTB4‐S) of these three genes using snapgene software to find out the mutation points and amino acid substitutions.

GC–MS sample preparing and analysis

GC–MS analysis was performed as previously described (Zhao et al., 2021). LTB4‐S and LTB4‐RTET were incubated overnight at 30°C and 200 rpm to reach an OD600 nm of 1.0. Bacterial cells were collected, immediately quenched with precooled methanol (HPLC grade) and sonicated for 10 min (200 W total power with 35% output, 2 s pulse, 3 s pause over ice). To normalize variations across samples, the samples were added into 10 μL of ribose (0.1 mg/mL, Sigma) as an internal standard. Supernatants were isolated by centrifugation at 4°C and 12,000 rpm for 10 min and dried by a Labconco vacuum centrifuge dryer at a temperature of 37°C. The dryer extracts were incubated with 80 μL of methoxime‐pyridine hydrochloride (20 mg/mL, Sigma‐Aldrich) in pyridine (Sigma–Aldrich) for 3 h at 37°C, and then metabolite derivatization was done with 80 μL of n‐methyl‐n ‐trimethylsilyl trifluoroacetamide (MSTFA, Sigma‐Aldrich) for another 45 min. Samples were centrifuged at 12,000 rpm for 10 min, and the supernatant was transferred into new tubes to untargeted gas chromatography–mass spectrometry (GC–MS) analysis (Agilent).

A modified two‐stage technique was utilized to perform GC–MS analysis combined Agilent 7890A GC with the Agilent 5975C VL MSD detector (Agilent Technologies; Zhao et al., 2021). Approximately 1 μL of samples was infused into a 30‐m‐long and 250‐mm‐wide (i.d. with 0.25 mm DBS‐MS column) cylinder. The GC oven's initial temperature was maintained at 85°C for 5 min, then increased to 270°C at a rate of 15°C/min and maintained for 5 min. Helium was utilized as the carrier gas, the flow of which was 1 mL/min. The MS could operate between 50 and 600 m/z. Three biological repeats and two technical repeats were used. Initial peak detection and mass spectral deconvolution were analysed using XCalibur software (Thermo fisher version 2.1). The metabolites were identified from the National Institute of Standards and Technology (NIST) database and the NIST MS Search 2.0 program. The peak intensities of metabolites were normalized according to the internal standard (ribitol). Then these metabolites were analysed as follows: identification of metabolites that had a statistically significant difference (p‐value < 0.05) using IBM SPSS Statistics 19 software; Cluster analysis using the R software (R × 64 3.6.1); principal component analysis and S‐plot analysis using SIMCA‐P + 12.0 software (version 12; Umetrics); metabolic pathway analysis using MetaboAnalyst 4.0 enrichment; iInteractive Pathways (iPath) analysis wusing iPath3.0 (https://pathways.embl.de/).

Real‐time quantitative PCR

Total RNA samples were prepared from bacterial strains using TRIzol reagent (Invitrogen). Double‐stranded cDNA was synthesized from total RNA using the SYBR Perfect real‐time series kits (Takara). cDNA was analysed by real‐time RT‐PCR using Roche's LightCycler 480 real‐time PCR system. Quantitative PCR was performed on each cDNA sample in triplicate. 16 s rRNA was used as an internal control to normalize the expression level. The expression level was calculated using the comparative 2−ΔΔCt method. The primers used for amplification are listed in (Table S2).

Membrane potential measurement

Membrane potential (PMF) was measured as previously described using the BacLight bacterial membrane potential kit (Invitrogen; Kuang et al., 2022). Bacteria were collected by centrifugation and re‐suspended in 1 mL of saline. The cells were labelled with 10 μL of 3 mM DiOC2 and incubated for 30 min at 37°C. Aliquots with 1 mL of samples were analysed using a FACSCalibur flow cytometer (Becton Dickinson) under a 488 nm excitation wavelength. Forward versus side scatter and gated of samples were observed before the acquisition of data. The membrane potential was calculated by formula of membrane potential: log (103/2 × [red fluorescence/green fluorescence]). Triplicate repeats were carried out.

ATP determination

Bacterial suspensions (OD600 nm = 0.6) were collected and resuspended in saline solution. The samples were diluted into colonies of 107 CFU/mL and dispensed in 50 μL in a 96‐well plate. The samples were then mixed with 50 μL of pre‐equilibrated BacTiter‐Glo™ reagent (Promega) for 5 min. The luminescence was measured using a Victor X5 multimode plate reader and immediately record the luminous unit (RLU) of firefly luciferase (Wen et al., 2018).

Antibiotic bactericidal assay

Antibiotic bactericidal assay was carried out as previously described (Kuang et al., 2022). In brief, a single bacterial colony was grown in LB broth for 16 h at 30°C. The culture was re‐suspended in M9(17.7 g/L of Na2HPO4·12H2O2, 3 g/L of KH2HPO4, 1 g/L of NH4Cl, 0.5 g/L of NaCl, and 3.125% LB) with or without carbonylcyanide‐3‐chlorophenylhydrazone (CCCP) and/or antibiotics to 0.2 at OD600 and incubated at 30°C with shaking at 200 rpm for 6 h. For pH experiment, pH was adjusted by Na2HPO4·12H2O2 / KH2HPO4 (pH 6.6 = 10/90, pH 7.1 = 30/70, pH 7.4 = 55/45, pH 7.9 = 80/20, pH 8.4 = 98/2). To determine CFU/mL, 100 μL samples were 10‐fold serially diluted and an aliquot of 5 μL of each dilution was spotted onto the LB agar plates and cultured at 30°C for 12 h. The percent survival was determined by dividing the colony forming units (CFU) obtained from the treated sample by the CFU obtained from the control.

Antibiotic content determination

The ELISA tetracycline detection kit was used to measure bacterial intracellular tetracycline (Shanghai JingKang bioengineering CO., LTD., E102002). Bacteria were cultured and re‐suspended as described in antibiotic bactericidal assay. The cells were incubated with different concentrations of CCCP plus tetracycline, or at different pH plus tetracycline. After being collected, the cells were washed three times in 1 × PBS (pH 7.4). The cells were then resuspended in 1 × PBS with an OD600 nm adjustment of 0.2. Aliquots of 10 mL of bacterial cells were centrifuged and re‐suspended in 1 mL of saline, then transfered into a 1.5 mL centrifuge tube after being resuspended in 0.4 mL of methanol‐hydrochloric acid extract. Sonic oscillation was used to crush the cells (1 min, 650 W total power, 35% output, 2 s pulse, 3 s pause over ice). Following the instructions on ELISA tetracycline detection kit, the resultant supernatant was gathered for tetracycline detection.

Determination of minimum inhibitory concentration (MIC)

A microdilution broth susceptibility assay for bacteria was used, as recommended by The Clinical & Laboratory Standards Institute (CLSI) for the determination of the minimum inhibitory concentration. Serial twofold dilutions of tetracycline (from 32 to 0.25 μg/mL) were prepared in a 96‐well plate (100 μL per well). Wells with no tetracycline were used as a positive growth control. A diluted bacterial suspension in LB was added to each well to give a final concentration of 5 × 104 colony‐forming units (CFU)/mL, confirmed by viable counts. Wells without bacteria were used as a negative growth control. The plate was incubated for 16–20 h at 30°C and growth was visually assessed. The MIC was defined as the lowest tetracycline concentration without visible growth. At least three independent determinations were done.

Survival capability assay

Bacterial survival capability assay was carried out as described previously with a modification (Li et al., 2008). To test the survival capability of bacteria in indicated antibiotic concentration, the inoculums of LTB4‐RTET and LTB4‐S were separately cultured in 5 mL LB medium at 30°C overnight, and then the bacteria were diluted into a 5 mL fresh LB medium at a ratio of 1:1000. The antibiotic concentrations were 0–12.5 μg/mL, respectively. These tubes were incubated at 30°C for 8 h. Bacterial growth was determined by measurement of OD600. The ability for survival was characterized by comparison between experimental and control groups and was termed as percent survival. At least three biologic replicates were performed.

Checkerboard assay

The checkerboard assay described by (Duarte et al., 2012) was followed with modifications. 96‐well plate was prepared: the first line was used to serial two‐fold dilutions of CCCP in horizontal orientation and the second line was used to make two‐fold of the tetracycline. Both dilutions were made in LB (100 μL per well). Aliquot of 100 μL of the antibiotic was transferred to the first line, and 10 μL of bacterial suspension (5 × 104) was added to each well and incubated for 16–20 h at 30°C. Wells with no CCCP were used as a positive growth control and without bacteria as negative control. The used concentrations of CCCP and tetracycline were selected on the basis of MIC values previously determined. The results of the combined effects of the tetracycline and CCCP were calculated and expressed in terms of a fractional inhibitory concentration (FIC) index, equal to the sum of the FICs for each drug. The FIC is defined as the MIC of the drug in combination divided by the MIC of the drug used alone. The results were considered as a synergy if the FIC index of the combination is ≤0.5, additive when it was 0.5 < FIC index < 1, subtractive when 1 < FIC index > 4 or antagonism for FIC index > 4. Experiments were performed in three independent assays.

RESULTS

Physiological and resistance phenotypes and mutations in LTB4‐RTET

LTB4 were cultured in LB medium with and without 1/2 minimum inhibitory concentration (MIC) of tetracycline to produce LTB4‐RTET and LTB4‐S, respectively. LTB4‐RTET had 16 MIC to tetracycline, while LTB4‐S had 1 MIC to this drug (Figure 1A), which was the same as LTB4 did. Survival capability test also showed that higher survival was detected in LTB4‐RTET than LTB4‐S in the presence of tetracycline (Figure 1B). These results indicate that LTB4‐RTET is a tetracycline resistance strain. The resistance caused a change in bacterial growth curve. Specifically, slower growth was detected from 4 to 18 h in LTB4‐RTET than LTB4‐S (Figure 1C). Bacterial swimming ability was slower in LTB4‐RTET than LTB4‐S (Figure 1D). These data suggest that the resistance affects bacterial physiology and resistance phenotypes. Moreover, genome sequencing was performed in LTB4‐RTET and LTB4‐S. Compared with LTB4‐S, LTB4‐RTET exhibited 14 mutations in seven genes. Specifically, ETAE_RS04175, ETAE_RS10215, and ETAE_RS15730 displayed four mutations, one mutation, and one mutation, respectively, while purD, ETAE_RS06525, hyaB, and ETAE_RS14830 had two, four, one, and one nonsense mutations (Figure 1E; Table S3). ETAE_RS04175, ETAE_RS10215, and ETAE_RS15730 encode hypothetical protein, DNA translocase FtsK, and HamP domain‐containing protein, respectively. Furthermore, the mutations of three genes were confirmed by PCR and gene sequencing (Figure 1F). Except for the hypothetical protein, which function is unknown, FtsK is an ATP‐dependent DNA translocase that links chromosome segregation and cell division in E. coli (Donachie, 2002; Mishra et al., 2022). HamP domain generally transmits an intramolecular signal between an extracellular sensory domain and an intracellular signalling domain (Elliott et al., 2009).

FIGURE 1.

FIGURE 1

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Resistance, growth, motility, and gene mutations in LTB4‐RTET growth. (A) MIC of LTB4‐S and LTB4‐RTET to tetracycline. (B) Survival capability of LTB4‐S and LTB4‐RTET in LB medium with different concentrations of tetracycline. (C) Growth curves of LTB4‐S and LTB4‐RTET. (D) Motility of LTB4‐S and LTB4‐RTET. (E) Genome sequencing analysis for gene mutations in three LTB4‐RTET strains marked different colours. (F) PCR for validating the gene mutations detected in data (E). Results are displayed as mean ± SEM of three biological replicas and significant differences are identified by two‐tailed student's t‐test (*p < 0.05 and **p < 0.01).

Metabolomic profiling of LTB4‐RTET

To explore changes in the metabolic state of LTB4‐RTET, gas chromatography–mass spectrometry (GC–MS) metabolomics analysis was performed in LTB4‐S and LTB4‐RTET. Three biological samples with two technical replicates were examined in each group, yielding a total of 12 data sets. After subtracting the internal standard ribitol and deleting any recognized fake peaks and integrating identical chemicals, 58 metabolites with reliable signals were identified in each sample. The correlation coefficient between technical replicates varied between 0.9969 and 0.9999, demonstrating the reproducibility of the data (Figure S1A). According to the Kyoto Encyclopedia of Genes and Genomes (KEGG), biological functions of these metabolites were classified as carbohydrate (15%), amino acid (31%), lipid (31%), nucleotide (7%), and others (16%; Figure S1B). These metabolites between LTB4‐S and LTB4‐RTET were separately clustered as shown in (Figure S1C). These results indicate that the metabolomic profiling of LTB4‐RTET is different from that of LTB4‐S.

Identification of differential abundances of metabolites in LTB4‐RTET

A Kruskal–Wallis test (p < 0.05) was used to identify differential abundance metabolites between LTB4‐RTET and LTB4 by using SPSS 19.0. Heat maps were created to highlight how the 36 metabolites of LTB4‐RTET differed from those of LTB4‐S (Figure 2A). Z values for LTB4‐RTET were determined using the control group and varied from −14.89 to 15.00. Particularly, 16 metabolites were elevated and 20 metabolites were decreased in LTB4‐RTET (Figure 2B). We subsequently examined the metabolic subcategories of these differential abundances of metabolites, ranking amino acid > lipid > carbohydrate > nucleotide (Figure S2A). Number of these differential abundances of metabolites was similar between decreased and increased amino acid, lipid, and nucleotide, but more in decreased carbohydrate and others than elevated ones (Figure S2B). The aforementioned findings therefore imply a metabolic shift in LTB4‐RTET.

FIGURE 2.

FIGURE 2

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Differential metabolites and biomarkers in LTB4‐RTET. (A) LTB4‐S and LTB4‐RTET were collected for GC–MS analysis. Abundance of differential metabolites was quantified and presented as a heatmap. Yellow and blue correspond to high to low abundance, respectively. (B) Z‐score plots corresponding to the data in panel (A). The data from LTB4‐RTET are separately scaled to the mean and SD of LTB4‐S. Each point represents one metabolite in one technical repeat and is coloured by sample types (orange, LTB4‐RTET; grey, LTB4‐S). (C) Principal component analysis of LTB4‐S and LTB4‐RTET. Each dot represents the technologic replicate analysis of samples in the plot. (D) S‐plot generates from orthogonal partial least‐square discriminant analysis. Predictive component p[1] and correlation p(corr)[1] differentiate LTB4‐S and LTB4‐RTET. Each dot represents metabolites and candidate biomarkers are highlighted in red dot. (E) Candidate biomarkers by predictive component p[1], p(corr)[1]. Results are displayed as mean ± SEM of four biological replicas and significant differences are identified by Kruskal–Wallis test (**p < 0.01).

Identification of key metabolites

To identify biomarkers associated with the resistance to tetracycline, orthogonal partial least squares discriminant analysis (OPLS‐DA) was used for sample pattern identification. Principal component analysis revealed that component t [1] separated LTB4‐S from LTB4‐RTET, while component t [2] distinguished the two sets of variables (Figure 2C). The discriminant variables were represented as s‐plots, where the absolute values of covariance p and correlation p(corr) were set at critical values ≤0.05 and ≥0.5, respectively. This analysis identified a total of 17 biomarkers (Figure 2D). Among them, maltose, butanoic acid, isoleucine, glucose, citrulline, glutamic acid, uracil, proline, octanoic acid, octadecanoic acid, ribose and palmitic acid were downregulated, while ethanimidic acid, norvaline, tetradecanoic acid, pentose and asparagine were upregulated (Figure 2E).

Enrichment of metabolic pathways associated with tetracycline resistance

We further enriched metabolic pathways of these differential abundances of metabolites in LTB4‐RTET. To do this, MetaboAnalyst 4.0 was used. This led to the enrichment of 8 metabolic pathways (p < 0.05). According to the highest to lowest impact values, they were ranked as follows: alanine, aspartate, and glutamate metabolism; arginine biosynthesis; glycine, serine, and threonine metabolism; lysine biosynthesis; arginine and proline metabolism; glutathione metabolism; aminoacyl‐tRNA biosynthesis; biosynthesis of unsaturated fatty acids (Figure S3A). Among them, more metabolic pathways working for amino acid metabolism and biosynthesis of unsaturated fatty acids are involved. Except for alanine, aspartate, and glutamate metabolism and lysine biosynthesis, where almost or all metabolites were upregulated, almost or all metabolites in the other metabolic pathways were downregulated (Figure S3B). These results suggest that tetracycline resistance causes the alteration of metabolic pathways.

Inactivated global metabolism in LTB4‐RTET

To understand a global metabolism in response to tetracycline resistance, iPath was used to characterize the altered metabolism in LTB4‐RTET compared to that of LTB4‐S. iPath is an open‐access online tool (http://pathways.embl.de) to visualize and analyse metabolic pathways (Letunic et al., 2008). This comparative analysis showed a global overview map that provides a better insight into the effects of tetracycline resistance, where yellow line represents increased pathways and blue line represents decreased pathways in LTB4‐RTET. Tetracycline resistance inactivated almost of metabolic pathways including carbohydrate metabolism and energy metabolism, while a few pathways are activated (Figure 3A). Since the pyruvate cycle (The P cycle) provides respiratory energy in bacteria (18), activity of pyruvate dehydrogenase (PDH), α‐ketoglutarate dehydrogenase (α‐KGDH), succinate dehydrogenase (SDH), and malate dehydrogenase (MDH) of the P cycle was measured. Activity of PDH and α‐KGDH was lower and that of SDH and MDH was higher in LTB4‐RTET than LTB4‐S (Figure 3B). Furthermore, expression of 15 genes in the P cycle was measured. Among them, 13 and 2 genes exhibited lower and stable expression, respectively, in LTB4‐RTET than LTB4‐S (Figure 3C). These results in gene expression and enzyme activity suggest that the P cycle was broken. PMF and ATP, which are two downstream products of the P cycle, were further measured. Surprisingly, PMF was higher in LTB4‐RTET than LTB4‐S (Figure 3D). Consistently, the same effect was detected in ATP level (Figure 3E). These results indicate that that the downregulation of global metabolism accompanied by increased local PMF is an important feature of LTB4‐RTET.

FIGURE 3.

FIGURE 3

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Metabolic flux analysis and ATP and PMF measurement. (A) Interactive Pathways Explorer (iPath) analysis. Metabolic network pathways in LTB4‐RTET were analysed with an open‐access online tool (http://pathways.embl.de) compared with LTB4‐S. iPath analysis of LTB4‐RTET intergroup comparisons. Yellow and blue lines indicate downregulation and upregulation of metabolic pathways, respectively. (B) Activities of PDH, KGDH, SDH, and MDH in the P cycle. (C) qRT‐PCR for expression of genes in the P cycle between LTB4‐S and LTB4‐RTET. (D) PMF level between LTB4‐S and LTB4‐RTET. Percentage increased PMF = (PMF value of the experimental group/average PMF value of the control group – average PMF value of the control group) × 100%. (E) ATP levels between LTB4‐S and LTB4‐RTET. Results are displayed as mean ± SEM of three biological replicas and significant differences are identified by two‐tailed student's t‐test (*p < 0.05 and **p < 0.01).

The elevated PMF contributes to tetracycline resistance

The above results motivated us to suppose that the elevated PMF contributes to tetracycline resistance and thereby the regulation of PMF may change LTB4‐RTET sensitivity to tetracycline. To do this, carbonylcyanide‐3‐chlorophenylhydrazone (CCCP), a PMF inhibitor, was used to inhibit PMF of LTB4‐RTET (Figure 4A). Then, a titration analysis was performed in counting viability in both double increasing tetracycline concentrations and CCCP doses, generating a two‐dimensional heat map (Figure 4B). A strong synergy to the viability was detected between the antibiotic and CCCP (Figure 4B,C). The drug concentration was increased with increasing CCCP dose, when 50 μg/mL tetracycline with 0–5 μm CCCP was added (Figure 4D). This was further demonstrated by measurement of intracellular tetracycline (Figure 4E). Fractional inhibitory concentration (FIC) indices showed that the CCCP combined with tetracycline was synergistic (Figure 4F). These results indicate that that the downregulation of global metabolism accompanied by increased local PMF is an important feature of LTB4‐RTET.

FIGURE 4.

FIGURE 4

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PMF‐dependent tetracycline resistance. (A) Membrane potential of LTB4‐RTET in the presence of the indicated concentrations of CCCP. (B) Survival of LTB4‐RTET in the presence of the indicated concentrations of tetracycline and the indicated concentrations of CCCP. (C, D) Bliss analysis for the synergistic effect of CCCP with tetracycline. (E) Effect of CCCP on intracellular tetracycline LTB4‐RTET in LTB4‐RTET. (F) Fractional inhibitory concentration (FIC) and FIC indices (FICI) of CCCP combined with conventional antibiotics against LTB4‐RTET using checkerboard assay. Results are displayed as mean ± SEM of three biological replicas and significant differences are identified by two‐tailed student's t‐test (*p < 0.05 and **p < 0.01).

Tetracycline killing is associated with internal pH

The association of tetracycline resistance with PMF suggests that external pH may regulate the ability of tetracycline killing since external pH influences PMF. To do this, PMF was measured in medium with different pH ranking from 6.6 to 8.4 using LTB4‐RTET as a model. PMF was increased with elevating pH (Figure 5A). Notably, tetracycline did not influence pH (Figure 5B). When 20 μg/mL tetracycline was added in this system, viability was reduced in the elevating pH (Figure 5C). Consistently, extracellular tetracycline was reduced in an elevating pH‐dependent manner (Figure 5D). Therefore, PMF is a mechanism of tetracycline resistance.

FIGURE 5.

FIGURE 5

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pH influences tetracycline killing. (A) Membrane potential of LTB4‐RTET in M9 medium with different pH. (B) Effect of tetracycline on pH of culture medium. (C) Survival of LTB4‐RTET in M9 medium with different pH and in the presence of 20 μg/mL tetracycline or plus 1 μM CCCP. (D) Intracellular tetracycline of LTB4‐RTET in M9 medium with different pH in the presence of 100 μg/mL tetracycline. Results are displayed as mean ± SEM of three biological replicas and significant differences are identified by two‐tailed student's t‐test (*p < 0.05 and **p < 0.01).

Gentamicin and tetracycline kill tetracycline‐ and gentamicin‐resistant E. Tarda, respectively

Since gentamicin‐mediated killing is PMF‐dependent, it is logical to suppose that gentamicin might combat the LTB4‐RTET with the elevated PMF. Contrarily, tetracycline may be effective to kill bacteria with low PMF. To test this idea, MIC to aminoglycosides was measured in LTB4‐S and LTB4‐RTET. MIC to all measured aminoglycosides was higher in LTB4‐RTET than LTB4‐S (Figure 6A). Viability of both strains was elevated with increasing CCCP dose in the presence of gentamicin (Figure 6B). Then, three other antibiotic‐resistant E. tarda, LTB4‐RGEN, LTB4‐RTET‐GEN, and LTB4‐RGEN‐ETE, were obtained and their MIC was shown in (Figure 6C). LTB4‐RTET‐GEN came from LTB4‐RTET by using passaging in medium with gentamicin, while LTB4‐RGEN‐TET originated from LTB4‐RGEN by passaging in medium with tetracycline. The highest and lowest PMF was measured in LTB4‐RTET and LTB4‐RGEN, respectively. Similar PMF was detected between LTB4‐RTET‐GEN and LTB4‐RGEN‐TET, which was higher than LTB4‐S but lower than LTB4‐RGEN (Figure 6D). Consistently, viability ranked from high to low as LTB4‐RGEN > LTB4‐RTET‐GEN > LTB4‐S > LTB4‐RGEN‐TET and LTB4‐RTET in the presence of gentamicin and as LTB4‐RGEN‐TET > LTB4‐RTET > LTB4‐S, LTB4‐RTET‐GEN, and LTB4‐RGEN in the presence of tetracycline (Figure 6E). The two MICs of LTB4‐S were located in middle, suggesting that PMF is related to antibiotic resistance. Therefore, tetracycline‐ and gentamicin‐mediated killing is related to bacterial PMF.

FIGURE 6.

FIGURE 6

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Sensitivity of LTB4‐RTET and LTB4‐RGEN to aminoglycosides and tetracyclines. (A) MIC of LTB4‐S and LTB4‐RTET to aminoglycoside antibiotics. (B) Survival of LTB4‐S and LTB4‐RTET in the presence of the indicated concentrations of CCCP and 5 μg/mL gentamicin. (C) MIC to gentamicin and tetracycline for LTB4‐S, LTB4‐RTET, LTB4‐RTET‐GEN, LTB4‐RGEN, and LTB4‐RGEN‐TET. (D) PMFs of LTB4‐S, LTB4‐RTET, LTB4‐RTET‐GEN, LTB4‐RGEN, and LTB4‐RGEN‐TET. (E) Survival of LTB4‐S, LTB4‐RTET, LTB4‐RTET‐TET, LTB4‐RGEN and LTB4‐RGEN‐TET in the presence of 5 μg/mL gentamicin or 20 μg/mL tetracycline. (F–H) MIC to gentamicin and tetracycline (F), PMFs (G), survival in the presence of 30 μg/mL gentamicin or 100 μg/mL tetracycline (H), and MDR classification (I) in six clinically isolated antibiotic‐resistant E. tarda. Results are displayed as mean ± SEM of three biological replicas and significant differences are identified by two‐tailed student's t‐test (*p < 0.05 and **p < 0.01).

To explore the potential clinic application of this understanding, clinically isolated multidrug‐resistant E. tarda were classified into two groups. Comparatively, ET‐13, PPD125/68, and PPD453/87 had low and high MIC to tetracycline and gentamicin, respectively, while G06, G07, and A918 had high and low MIC to tetracycline and gentamicin, respectively (Figure 6F). ET‐13, PPD125/68, and PPD453/87 with low PMF and G06, G07, and A918 with high PMF were designated as low and high PMF groups, respectively (Figure 6F,G). Gentamicin and tetracycline were used to kill these strains of the two groups. In low PMF group, gentamicin‐mediated killing was weak but tetracycline‐mediated killing was high. On the contrary, no or weak tetracycline‐mediated killing was in sharp contrast to high gentamycin‐mediated killing in high PMF group (Figure 6H). These clinically isolated E. tarda included three multidrug‐resistant (MDR) strains (G07, G06, and A918) (Magiorakos et al., 2012; Simner et al., 2022) (Figure 6I). These results indicate that the approach is effective to the clinical isolated strains including MDR strains.

DISCUSSION

Reprogramming metabolomics approach has been used to investigate antibiotic‐resistant and anti‐infective metabolic profiles and characterize metabolic mechanisms of antibiotic resistance in bacteria and of anti‐infection in hosts (Chen et al., 2022; Jiang et al., 2022; Kou et al., 2022; Peng et al., 2022; Yang et al., 2021; Yin et al., 2022). Among them, the metabolic mechanisms and resistance reverting of kanamycin‐resistant E. tarda are reported. The kanamycin‐resistant metabolome has a reduced metabolic state, which can be reversed by exogenous alanine, glucose, glutamate, and fructose (Peng, Su, et al., 2015; Su et al., 2015, 2018). However, bacterial tetracycline‐resistant metabolome is absent and the metabolic mechanisms of tetracycline resistance are largely unknown. The present study uses GC–MS based metabolomics approach to explore the metabolic mechanisms of E. tarda to tetracycline and identifies the elevated PMF and ATP against a global metabolic depression as the most characteristic feature of metabolic resistance in LTB4‐RTET. This is different from the E. tarda kanamycin‐resistant metabolome, where the suppressed levels of alanine and glucose are companied with the reduced NADH (Peng, Su, et al., 2015). Further investigation is required for exploring the pathways that elevate PMF and ATP in LTB4‐RTET, although non‐the P cycle pathways can generate them, for example, glycolysis and acetate overflow (Chaudhry & Varacallo, 2018; Wan et al., 2017). Notably, non‐targeted GC–MS instead of LC–MS based metabolomics is carried out because GC–MS approach is easier to identify non‐polar metabolites that include amino acid, sugar, and fatty acid with high abundance (Alder et al., 2006). These amino acid, sugar, and fatty acid play a key role in the main metabolic flux.

The present study further explores whether the increased PMF is needed for tetracycline resistance. To do this, three experiments were carried out. First, a titration test shows that viability is reduced in an increasing CCCP dose‐dependent manner under different concentrations of tetracycline. Then, tetracycline killing was performed in medium with different pH, which changes PMF. Correlation between increased survival and decreased PMF was determined. At last, LTB4‐RTET with high PMF was sensitive to gentamicin, an antibiotic that is dependent upon PMF for killing. These results together indicate that the elevated PMF is a tetracycline resistance mechanism in E. tarda. Four conventional molecular mechanisms of antibiotic resistance (decreased permeability, increased efflux, elevated hydrolase activity, and changed target site) are reported in E. tarda (Jiang et al., 2023; Li et al., 2008; Lin et al., 2010; Liu et al., 2015; Lo et al., 2014; Reger et al., 1993). Among them, the multidrug efflux pump, AcrAB‐TolC, is a common mechanism of tetracycline resistance in tetracycline‐resistant bacteria (Nolivos et al., 2019; Reuter et al., 2020). Recent evidences have shown that metabolic modulation plays a role in the bacterium (Cheng et al., 2017; Mao et al., 2022; Su et al., 2021). Interestingly, it is found that the PMF powers many low‐level of MDR efflux pumps, which pump antibiotics out of cells, suggesting a negative feedback of PMF resistance mechanism. Together, the finding on the PMF‐mediated tetracycline resistance highlights the way in understanding complex antibiotic resistance mechanisms of E. tarda.

Recent evidences have showed that antibiotic combination therapy provides an effective solution to antibiotic resistance (Coates et al., 2020; Farhat and Khan, 2022; Scudeller et al., 2021). However, how these antibiotics work together and how their underlying mechanisms operate are being explored. The present study demonstrates that tetracycline‐resistant bacteria have high PMF and thereby supposes that the bacteria with low PMF are sensitive to tetracycline. Reports have indicated that gentamicin‐mediated killing is PMF‐dependent (Allison et al., 2011), suggesting that tetracycline‐resistant bacteria should be sensitive to gentamicin. Conversely, gentamicin resistance leads to the decrease of PMF, suggesting that gentamicin‐resistant bacteria are sensitive to tetracycline due to the high PMF with tetracycline resistance. Therefore, the present study provides a previously unknown approach that tetracycline‐resistant bacteria are killed by gentamicin, while gentamicin‐resistant pathogens are eliminated with tetracycline. This finding suggests a possible solution on the synergistic use of gentamicin and tetracycline to combat antibiotic‐resistant bacteria with low or high PMF. Notably, more clinically isolated strains need to be tested to prove the solution.

In summary, the present study identifies the elevation of PMF in sharply contrast to the globally depressed metabolic state as the most characteristic feature in LTB4‐RTET. Further experiments demonstrate that the elevated PMF is needed for tetracycline resistance. The finding leads to a previously unknown approach to combat tetracycline‐resistant and gentamicin‐resistant bacteria by using gentamicin and tetracycline, respectively. Therefore, the synergistic use of tetracyclines and aminoglycosides will provide a new solution to effectively kill antibiotic‐resistant bacteria with low and high PMF. Taken together, the present study not only reveals a PMF‐enhanced tetracycline resistance mechanism, but also develops a combination of gentamycin with tetracycline to combat antibiotic‐resistant bacteria.

AUTHOR CONTRIBUTIONS

Shao‐hua Li: Data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); writing – original draft (supporting). Jiao Xiang: Data curation (supporting); investigation (supporting). Ying‐yue Zeng: Data curation (supporting); investigation (supporting). Xuan‐xian Peng: Conceptualization (equal); funding acquisition (equal); writing – original draft (equal); writing – review and editing (equal). Hui Li: Conceptualization (equal); funding acquisition (equal); writing – original draft (equal); writing – review and editing (lead).

FUNDING IMFORMATION

This work was financially supported by grants from National Natural Science Foundation of China (No. 42276125), Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (No. 311020006).

CONFLICT OF INTEREST STATEMENT

The authors declare that they do not have any competing interests.

DATA AVAILABLITY STATEMENT

The metabolomics data were deposited (MTBLS8481).

Supporting information

Data S1.

Click here for additional data file. (641.9KB, docx)

Li, S.‐h. , Xiang, J. , Zeng, Y.‐y. , Peng, X.‐x. & Li, H. (2024) Elevated proton motive force is a tetracycline resistance mechanism that leads to the sensitivity to gentamicin in Edwardsiella tarda . Microbial Biotechnology, 17, e14379. Available from: 10.1111/1751-7915.14379

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