pH plays a role in the mode of action of trimethoprim on Escherichia coli

Metabolomics-based approaches were applied to understand interactions of trimethoprim with Escherichia coli K-12 at sub-minimum inhibitory concentrations (MIC≈0.2, 0.03 and 0.003 mg L-1). Trimethoprim inhibits dihydrofolate reductase and thereby is an indirect inhibitor of nucleic acid synthesis. Due to the basicity of trimethoprim, two pH levels (5 and 7) were selected which mimicked healthy urine pH. This also allowed investigation of the effect on bacterial metabolism when trimethoprim exists in different ionization states. UHPLC-MS was employed to detect trimethoprim molecules inside the bacterial cell and this showed that at pH 7 more of the drug was recovered compared to pH 5; this correlated with classical growth curve measurements. FT-IR spectroscopy was used to establish recovery of reproducible phenotypes under all 8 conditions (3 drug levels and control in 2 pH levels) and GC-MS was used to generate global metabolic profiles. In addition to finding direct mode-of-action effects where nucleotides were decreased at pH 7 with increasing trimethoprim levels, off-target pH-related effects were observed for many amino acids. Additionally, stress-related effects were observed where the osmoprotectant trehalose was higher at increased antibiotic levels at pH 7. This correlated with glucose and fructose consumption and increase in pyruvate-related products as well as lactate and alanine. Alanine is a known regulator of sugar metabolism and this increase may be to enhance sugar consumption and thus trehalose production. These results provide a wider view of the action of trimethoprim. Metabolomics indicated alternative metabolism areas to be investigated to further understand the off-target effects of trimethoprim.


Growth characteristics
E. coli was streaked on a NA plate to obtain axenic colonies. Biomass was collected from single colonies to prepare 1 mL 20% (v/v) glycerol working inoculum stocks which were stored at ─20 ºC. A 1 mL master stock of bacteria was also stored at ─80 ºC (S8 Fig.).
Gram staining was performed using a Sigma-Aldrich kit, the stained bacterial cells were observed with a Zeiss LSM 510 META confocal microscope using the ×100 objective (Carl Zeiss Ltd.).
Starting growth condition. At the start of each experiment, 49 mL of medium was inoculated with 1 mL of working stock and incubated at 37 ºC in a shaking incubator at 200 rpm for 24 h. The overnight cultures (1 mL) were diluted with 49 mL fresh media and further incubated at 37 ºC, 200 rpm for 1 h. These new cultures were diluted with physiological saline (0.9% (w/v) NaCl) to 0.5 McFarland standard optical density (OD) at 600 nm using a Biomate 5 spectrophotometer (Thermo) and used as experimental inoculate (S8 Fig.).

Estimation of bacterial biomass.
In order to standardise the size of the inocula for growth curve experiments, 40 mL of the culture was diluted and washed two times with physiological saline (0.9% (w/v) NaCl). The bacterial turbidity was adjusted to 0.5 McFarland standard (OD 0.1 ± 0.02) optical density (OD) at 600 nm using a Biomate 5 (S8 Fig.). 0.5 McFarland standard was prepared by adding 85 mL of 1% (w/v) H 2 SO 4 to 0.5 mL of 1.175% (w/v) barium chloride dihydrate (BaCl 2 .2H 2 O), and made up to 100 mL with deionized water and mixed well.

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The bacterial growth curves were measured using an OD 600 nm in a Bioscreen spectrophotometer (Labsystems). This Bioscreen was run at the following settings: 10 min preheating, incubation temperature 37 ºC, continuous medium shake, measurement interval 10 min and 24 h for the total experiment.  succinic acid-d 4 , glycine-d 5 and leucine-d 4 were purchased from Sigma-Aldrich, as were methanol and water, which were of analytical grade or higher purity.
Sample preparation. Samples for FT-IR spectroscopy and GC-MS were collected from the same respective cultures for each sample. For GC-MS, samples inoculated at pH 9 were excluded from the analysis due to the extremely strong effect of the drug at this pH level that prevents the collection of adequate biomass for analysis.
For the remaining conditions, 15 mL from each flask was collected and applied for further experiments (S8 Fig.).
Metabolic Quenching. 15 mL from each flask of the overnight culture was collected and added to a double volume of 60% cold methanol (─48 ºC) and mixed quickly [5].
The quenched culture was centrifuged for 10 min at 4800 g and ─8 ºC. 2 mL of the supernatant was collected to assess the leakage of internal metabolites and the remainder was removed rapidly. Further centrifugation was applied on the pellet for 2 min to remove further residual supernatant. The cell pellets and collected supernatants were stored at ─80 ºC until metabolite extraction and further analysis (see S8 Fig.).

Metabolite extraction.
Methanol was applied as the extraction solvent. The biomass pellets were suspended in 1 mL of 80% methanol (─48 ºC), transferred to 2 mL tubes, flash frozen in liquid nitrogen and placed on wet ice, once semi-defrosted the samples were vortexed thoroughly for approximately 30 s.
The freeze-thaw and vortex cycle was repeated further two times to maximise extraction of intracellular metabolites from within the cells. The suspensions were centrifuged for 5 min at 13000 g and ─9 ºC. The supernatants were retrieved to clean 2 mL tubes and kept on dry ice. 500 µL of 80% methanol (─48 ºC) was added to the pellet and the whole procedure was repeated and the second extraction aliquot was 5 combined with the first (on dry ice) and vortexed thoroughly (S8 Fig.). For GC-MS samples, 825 µL of each extract (normalised to OD and made up with 80% methanol) was spiked with 100 µL of internal slandered (0.3 mg L -1 succinic-d 4  GC-TOF/MS data pre-processing and analysis. Raw GC-TOF/MS data was processed using the exact method of Begley et al. [6] and Wedge et al. [7], which was based on the 'Compare' capability of LECO's ChromaTOF v3.25 software (Leco Corp.). In this method, a set of reference spectra are compiled for a list of QC representative metabolites, including QC samples from within each analytical block.
All later samples are then searched against the particular reference table that has been created. For the reference table to be unbiased, all peaks evident in a representative QC sample that were within an appropriate range for signal/noise (S/N) ratio and chromatographic peak width could be included. Peak identities were assigned, whenever possible, based on similarity matching to mass spectral entries in the NIST (National Institute of Standards and Technology) library and GMD (Golm Metabolome Database) [8] for putative level identification or by comparing the mass spectral and retention index with an in-house generated metabolite library of authentic reference compounds. A peak width of 1.8 s and a minimum S/N ratio of 10 were applied as the peak detection parameters in ChromaTOF. Relative quantification for each metabolite was applied on the basis of internal standards, and retention indices were calculated using retention markers. The quantified peak areas for each metabolite within each sample, along with their metabolite identities and the sample information, were transferred to an XY matrix in Microsoft Excel. Prior to statistical analysis, QC samples were used as in the work of Wedge et al. [7] to provide data quality assurance by evaluating and eliminating mass features that showed high deviation within QC samples.

Trimethoprim quantification from E. coli using LC-MS
All materials were purchased from Sigma-Aldrich unless otherwise stated.  (Fig. 2). The response of the reference standard was compared between the reference standard in solution and also when spiked into 9 an intracellular sample matrix, no major changes in the measured peak area for trimethoprim (M+H + m/z 291.143) were observed. The intracellular samples and reference standards were run in a completely randomised order. Blank samples were interspersed throughout the analytical run to allow assessments of carry over to be made. The peak areas were calculated for the extracted ion chromatogram of trimethoprim (M+H + m/z 291.143) and imported into Microsoft Excel.
Data processing. Using Microsoft Excel 2007, the peak areas obtained for the trimethoprim reference standard were plotted against the known concentration level, thus producing a calibration curve. The extracted peak areas for trimethoprim within the intracellular extracts were then inferred against the calibration curve, thus revealing the concentration of trimethoprim within the intracellular extract. As a final step, the intracellular concentration levels were then normalised to the OD 600 reading taken from the original bacterial culture, thus providing a normalised nonbiased relative quantification value to compare between the intracellular extracts and experimental conditions.