Sampling and metabolite extraction of P.a isolates grown in SCFM2 were performed as indicated by Aros-Calt and colleagues, with slight modifications [33,61] (See Supplementary Methods). Bacterial culture, sample preparation, metabolomics analyses, and data processing were performed in biological triplicates.

Untargeted metabolomic profiling of the bacterial samples was done using ultra high-performance liquid chromatography (Ultimate 3000 UPLC, Thermo Fisher Scientific, Waltham, MA, USA) coupled with an Exactive Orbitrap mass spectrometer (Thermo Fisher Scientific Waltham, MA, USA). In order to enhance the chemical coverage of the analysis, we used two different but complementary chromatographic columns, consisting in reversed phase chromatography (C18 chromatographic column) and Hydrophilic Interaction Liquid Chromatography (HILIC) for the analysis of hydrophobic and polar metabolites, respectively.

The C18 chromatographic separation was carried out on a Hypersil GOLD C18 column (1.9 µm, 150 × 2.1 mm, Thermo Fisher Scientific) at 30 °C, with flow elution rate of 500 μL/min. The mobile phases consisted of A (100% water + 0.1% formic acid) and B (100% acetonitrile (ACN) + 0.1% formic acid). Elution started with an isocratic step of 2 min at 5% mobile phase B, followed by a linear gradient from 5% to 100% mobile phase B for the next 11 min. These proportions were kept constant for the next 12.5 min before returning to 5% B for 4.5 min. The HILIC chromatographic separation was carried out on a Sequant ZIC-pHILIC column (5 µm, 150 × 2.1 mm, Merck, Darmstadt, Germany) maintained at 15 °C under a elution gradient of mobile phases A and B at a flow elution rate of 200 μL/min. Mobile phase A was 10 mM ammonium carbonate pH 10.5 (adjusted with ammonium hydroxide), and mobile phase B was 100% ACN. Elution was initiated with 80% B phase for 2 min, followed by a linear gradient of 80–40% B from 2 to 12 min. The chromatographic system was then rinsed for 5 min at 0% B, before returning at 80% B and the and the run ended with an equilibration step of 25 min at 80% B.

The mass spectrometer was fitted with an electrospray source (ESI) operating in positive and negative ionization modes for C18 and ZIC-pHILIC, respectively. It was operated with capillary voltage at −3kV in the negative ionization mode and 5 kV in the positive ionization and a capillary temperature set at 280 °C. Temperature of the autosampler compartment was set at 4 °C, and the injection volume was 10 μL. Detection was carried out from m/z 75 to 1000 in both ionization modes at a resolution of 50,000 at m/z 200 as reported by Aros-Calt et al. [61] (each scan taking 0.5 s).

Raw LC-HRMS data were converted to m/z extensible markup language (.mzXML) in centroid mode using MSConvert ProteoWizard (release version 3.0.9393). Peak detection and integration were performed using R version 3.3.2 and XCMS package version 3.0.2 [62]. Briefly, features were detected using the centWave algorithm (step = 0.01, m/z deviation tolerance = 10 ppm, peak width = 10–40 s for C18, 20–120 s for HILIC, signal-to-noise ratio = 5). Peaks were grouped by density and retention times were nonlinearly smoothed (loess). Missing values (gap filling) were imputed by the chrom method. Annotation of adducts, fragments, and isotopes was achieved using the CAMERA package [63].

Features detected following XCMS-CAMERA analysis were then filtered and standardized using the Workflow4Metabolomics platform [64,65]. Data filtering was done according to the following criteria: (i) correlation coefficient between dilution factor and peak area in QC samples > 0.7, (ii) ratio of mean peak area in blanks over biological samples < 0.33, and (iii) variation coefficient of peak area in QC samples <30%. Peak intensities were then normalized using the Probabilistic Quotient Normalization (PQN) algorithm described by Dieterle et al. [66].

Feature annotation was performed by using our spectral database first according to accurate measured masses and chromatographic retention times [61,67,68] and then according to publicly available databases KEGG, PAMDB, HMDB, and METLIN [34,69,70,71] solely using accurate masses. This data-based analysis allowed putative annotation of 271 metabolites (Supplementary Table S2). Metabolite identification was further confirmed for discriminant metabolites LC-MS/MS experiments using a Dionex Ultimate chromatographic system combined with a Q-Exactive mass spectrometer (Thermo Fisher Scientific, San Jose, CA, USA) under non-resonant collision-induced dissociation conditions using higher-energy C-trap dissociation (HCD). To be identified, metabolites had to match at least two orthogonal criteria (among accurate measured mass, retention time, and MS/MS spectrum) to those of an authentic chemical standard analyzed under the same analytical conditions, as proposed by the Metabolomics Standards Initiative [72]. In the absence of an available authentic chemical standard, metabolites of interest were only considered as putatively annotated based on accurately measured masses and interpretation of the MS/MS spectra when available as described by Aros-Calt et al. 2015 [61]. Under these conditions, up to 51 discriminant metabolites were characterized: 30 had accurate masses, retention times and MS/MS matching those of an authentic standard, 3 were putatively annotated by matching their MS/MS spectra to those from the METLIN public database or showed MS/MS spectra consistent with both the proposed structures and the spectra of structural homologues, 1 shared accurate mass and retention time with an authentic standard, and 17 compounds were only annotated based on their accurate masses (Supplementary Table S3).

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