This readme.txt file was generated on 2024-01-26 by Thomas Niehaus. Recommended citation for the data: Niehaus, Thomas D; Hegeman, Adrian D. (2024). Metabolomics data from A universal metabolite repair enzyme removes a strong inhibitor of the TCA cycle. Retrieved from the Data Repository for the University of Minnesota, https://doi.org/10.13020/fjt8-ed44. ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset: Metabolomics data from A universal metabolite repair enzyme removes a strong inhibitor of the TCA cycle 2. Author Information Author Contact: Thomas Niehaus (tniehaus@umn.edu) Name: Thomas D Niehaus Institution: University of Minnesota Email: tniehaus@umn.edu ORCID: 0000-0002-3575-8001 Name: Adrian D Hegeman Institution: University of Minnesota Email: hegem007@umn.edu ORCID: 0000-0003-1008-6066 3. Date published or finalized for release: 2024-01-04 4. Date of data collection: 2022-01-12 to 2023-08-2023 5. Geographic location of data collection: University of Minnesota, Twin Cities campus 6. Information about funding sources that supported the collection of the data: Support was provided by startup funds from the University of Minnesota, Department of Plant andMicrobial Biology (Niehaus) 7. Overview of the data (abstract): Metabolomics was performed on wild-type and OAT1 knockout Escherichia coli. Here we provide all data collected as .mzXML files. Descriptions of this dataset can be found in a recently accepted Nature Communications article entitled "A universal metabolite repair enzyme removes a strong inhibitor of the TCA cycle". -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: CC0 1.0 Universal (http://creativecommons.org/publicdomain/zero/1.0/) 2. Links to publications that cite or use the data: https://doi.org/10.1038/s41467-024-45134-0 3. Was data derived from another source? No If yes, list source(s): 4. Terms of Use: Data Repository for the U of Minnesota (DRUM) By using these files, users agree to the Terms of Use. https://conservancy.umn.edu/pages/drum/policies/#terms-of-use --------------------- DATA & FILE OVERVIEW --------------------- File List Filename: mzXML files.zip Short description: Compressed folder with mzXML files Filename: Readme_Niehaus_2024 Short description: Description of data 2. Relationship between files: All files represent data derived from independent extractions of bacterial cells given different treatments. -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: Metabolomic data were obtained using an ultra-performance liquid chromatography-electrospray ionization-hybrid quadrupole-orbitrap mass spectrometer (Ultimate® 3000 HPLC, Q Exactive™, Thermo Scientific) with an autosampler and with a sample vial block maintained at 4°C. Chromatographic separations were carried out on an SeQuant® ZIC®-cHILIC 3µm, 100Å, 100 x 2.1 mm column (Merck, Darmstadt, Germany) with column temperature 40°C, flow rate 0.40 mL/min, and a 2 μL injection volume. Mobile phases A: 0.1% formic acid in water and B: 0.1% formic acid in acetonitrile were delivered over a 23 min. gradient according to the following profile: initial 98% B, 2 min 98% B, 20 min 40% B, 22 min 98% B, 23 min 98% B. The MS conditions used were full scan (mass range 50-750 m/z, and 115-1000 m/z in separate analyses), resolution 70,000, desolvation temperature 350°C, spray voltage 3800 V, auxiliary gas flow rate 20, sheath gas flow rate 50, sweep gas flow rate 1, S-Lens RF level 50, and auxiliary gas heater temperature 300°C. Xcalibur™ software version 2.1 (Thermo Scientific) was used for data collection. Tandem MS data were obtained using data dependent Top N acquisition (Full MS & dd-MS/MS). Precursor ions (top 5 most abundant ions per scan) were sequentially fragmented in the HCD collision cell with normalized collision energies (NCE) of 10, 20, 30, 40, 50, and 60 for six independent injections of each sample. MS/MS scans were acquired with 17,500 resolution, target value 1.0 × 105, 50 ms maximum injection time, and isolation window of 4.0 m/z. 2. Methods for processing the data: Data files were converted from .RAW to .mzXML and .mgf formats using the ProteoWizard tool MSConvertGUI. MZmine 2.53 was utilized for extracting exact-mass chromatographic data for isotope ratio calculations, for generating untargeted metabolite feature tables, and for tabulating peak heights from targeted exact masses for calculating relative signal intensity ratios. Targets for quantification were selected two ways, first MZmine was used to generate separate untargeted feature tables for unmixed unlabeled and 13C-labeled samples. Those lists consisted of the 12C- or 13C-monoisotopic mass, retention time, and intensity values for each subset of samples. The lists were compared to find features within ± 0.1 retention time on both lists with a 12C to 13C mass difference corresponding to an integer multiple of 1.003355 amu (the mass difference between 12C and 13C). These pairs of features were then sorted by difference in average intensity ratios between wt WT and ∆ycgM replicates at mid log and early stationary phase to identify metabolites that were changing with genotype. Elemental composition was calculated using both the 12C and the 13C monoisotopic masses considering only formulae that were present on both lists as a way to increase accuracy. Metabolite identities (table S1Supplementary Data file 1, tab1) were assigned to elemental compositions using prior identification in E. coli K12 (E. coli Metabolome DataBase, ECMDB) and MS/MS spectral matches (described below). The second target selection strategy sought to include metabolites in the TCA cycle and central metabolism as well in pathways that included metabolites identified using the first approach. From this list of metabolites elemental compositions were obtained, 12C and 13C monoisotopic masses were calculated, extracted ion chromatograms (EICs) were compared to confirm co-elution, and isomeric metabolites were identified using the ECMDB. These lists were further constrained using MS/MS spectral assignments and were added to “table S1Supplementary Data file 1”. Relative quantification between wt WT and ∆ycgM samples was accomplished using two complementary strategies. The first strategy is summarized in detail in “table S3Supplementary Data file 2” for pyruvate, starting with the ‘Read Me’ tab. Briefly, one first determines the ratio of 12C to 13C monoisotopic peaks in samples spiked with an equal volume of pooled samples with the other label for use in subsequent calculations. This is accomplished by plotting the EIC for the 12C-monoisotopic mass vs. the EIC for the 13C-monoisotopic mass for each sample-pool-mix, performing a linear regression, and using the slope of the regression as the 12C to 13C ratio. The regression strategy works well because the two isotopomers will coelute exactly even if the peak shape is not ideal. With the ratios in hand one can derive the relative abundance of a specific compound by taking the ratio of two ratios [e.g. (12C-wtWT@mid log/13C-mid log pool) / (12C-∆ycgM@mid log/13C-mid log pool) = wtWT/∆ycgM@mid log]. This use of the internal isotope labeled metabolites provides an ideal internal control for ion suppression and other matrix effects that can introduce systematic error in complex metabolomics samples. A reciprocal labeling strategy is used throughout so that relative abundances are calculated using 12C-labeled samples mixed with 13C-pool in quadruplicate and then again using 13C-labeled samples mixed with 12C-pools. This labeling strategy makes it possible to eliminate the possibility of either very rare 13C-isotope effects or less rare perturbations derived from different contaminants in labeled vs. unlabeled reagents. The relative abundance by isotope ratio results and statistics are summarized in table S1Supplementary Data file 1, tab2. The second strategy derives the relative abundance by calculating the ratio of the average peak heights for each compound in all of the unmixed samples using the appropriate carbon isotope monoisotopic mass. These values were extracted using MZmine only if a peak was detectable with signal at least 5 times background. As 13C-enrichment in labeled samples was ~99%, the same as 12C-enrichment in natural abundance samples, no isotopic enrichment correction was needed. The relative abundance by peak height results and statistics are summarized in table S1Supplementary Data file 1, tab3 and while they are less accurate than the isotope ratio quantities they provide a reasonable abundance ratio approximation in cases where the isotope ratio regression or calculations failed either due to excessive noise or large abundance differences between treatments where the regression slope approaches 0 or infinity. ​​Molecular networking was used to cluster MS/MS from data collected at different collision energies using Global Natural Product Social Molecular Networking hosted at: gnps.ucsd.edu67. Raw data files were converted to the .mgf format, uploaded to the GNPS site, and filtered by removing all MS/MS fragment ions within +/- 17 Da of the precursor m/z. MS/MS spectra were window filtered by choosing only the top 6 fragment ions in the ±50Da window throughout the spectrum. The clustered spectra were then searched against GNPS spectral libraries and all of the matches kept between network spectra and library spectra were required to have a score >0.5 and at least 2 matched peaks. This atypical setting for the number of matching peaks was selected to avoid exclusion of small and phosphorylated metabolites that often have simple MS/MS spectra (see Supplementary Data file 1,table S1 tabs 5 and 6). 3. Instrument- or software-specific information needed to interpret the data: MZmine 2.53 was utilized for extracting exact-mass chromatographic data for isotope ratio calculations, for generating untargeted metabolite feature tables, and for tabulating peak heights from targeted exact masses for calculating relative signal intensity ratios. 4. Standards and calibration information, if appropriate: N/A 5. Environmental/experimental conditions: Freezer stocks of wt wild type (BW25113) and ΔycgM E. coli were streaked on LB plates and 4 single colonies from each strain were grown for 24 h in M9 minimal medium (0.2% glucose). Cells were washed with 1x M9 salts and used to inoculate 5 mL culture tubes containing M9 medium with either 0.2% glucose (natural isotopic abundance) or 0.2% 13C6-glucose (fully labeled) to an initial OD600 = 0.05. Four replicate cultures of each genotype were grown in each media formulation by shaking at 37°C for ~4 h until cells reached mid-log growth phase (OD600 ~0.6; determined with a Thermo Scientific Genesys 30 spectrophotometer that can measure OD inside culture tubes). To minimize metabolic disturbances, a rapid harvesting protocol was used as described previously. Briefly, an equivalent of 1 mL at OD600 = 1.0 was quickly transferred to 1.5 mL polypropylene tubes, cells were pelleted in a microcentrifuge at full speed for 30 s, the supernatant was quickly aspirated and collection tubes were immediately snap frozen in liquid nitrogen. Samples were stored at -80°C prior to extraction. After collecting the mid-log samples, cultures were shaken for an additional ~2 h until early stationary growth phase (OD600 ~1.2) and samples were harvested as above. Collection tubes were placed on dry ice, 0.2 mL of cold 90% methanol was added, and tubes were incubated at -80°C for 72 h. Samples were removed from the freezer, vortexed for 15 s, and incubated on ice for 3 h with vortexing every ~30 min. Afterwards samples were spun for 15 min in a microcentrifuge (16,000 g) at 4°C and supernatants were collected for analysis. 6. Describe any quality-assurance procedures performed on the data: N/A 7. People involved with sample collection, processing, analysis and/or submission: Thomas Niehaus prepared metabolomics samples and Adrian Hegeman performed extractions and LC-MS analysis.