Metabolomics Data Supplementary to: Bismuth subsalicylate profoundly alters gut microbiome and immunity with increased susceptibility to infection

Stool Sample PreparationFor all LCMS methods LCMS grade solvents were used. All samples were immersed in 0.4 mL of ice-cold methanol. To each sample 0.4 mL of water and 0.4 mL of chloroform were added. Samples were shaken for 30 minutes under refrigeration and centrifuged at 16k xg for 20 min. 400 µL each of the top (aqueous) layer and bottom (organic) were collected separately. A subaliquot of the aqueous layer was taken for O-benzylhydroxylamine derivatization of carboxylic acids and short chain fatty acid analysis. The remaining aqueous layer was diluted 5x in 50 % methanol in water for LCMS analysis of central polar metabolites. The organic layer was dried down under vacuum and resuspended in an equivalent volume of 5 µg/mL butylated hydroxytoluene in 6:1 isopropanol:methanol for bile acid analysis. Short Chain Fatty Acid Derivatization Samples were derivatized with O-benzylhydroxylamine (O-BHA) according to previously established protocols. 52,53 Reaction buffer was prepared fresh consisting of 1M pyridine and 0.5 M hydrochloric acid in water. A 35 µL aliquot of the aqueous extract was taken and to the sample was added 10 µL of 1M O-BHA in reaction buffer and 10 µL of 1M 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide in reaction buffer. Samples were shaken at room temperature for 2 hrs. Each sample was quenched with 50 µL of 0.1 % formic acid for 10 min. Derivatized carboxylic acid compounds were extracted with the addition of 400 µL ethyl acetate. Samples were centrifuged at 16k xg for 5 min at 4 oC to induce layering and the upper (organic) layer was collected. The extract was dried under vacuum and each sample was resuspended in 300 µL of water for LCMS injection. Liquid Chromatography Mass Spectrometry (LC-MS/MS) Tributylamine and all synthetic molecular references were purchased from Millipore Sigma. LCMS grade water, methanol, isopropanol and acetic acid were purchased through Fisher Scientific. Aqueous metabolites were analyzed using a combination of two analytical methods with opposing ionization polarities 54,55. Both methodologies utilized a LD40 XR UHPLC (Shimadzu Co.) system for separation and a 6500+ QTrap mass spectrometer (AB Sciex Pte. Ltd.) for detection. Negative mode samples were separated on a Waters Atlantis T3 column (100Å, 3 µm, 3 mm X 100 mm) and eluted using a binary gradient from 5 mM tributylamine, 5 mM acetic acid in 2% isopropanol, 5% methanol, 93% water (v/v) to 100% isopropanol over 5 minutes. Two distinct MRM pairs in negative mode were used for each metabolite. Positive mode method samples were separated across a Phenomenex Kinetex F5 column (100 Å, 2.6 µm, 100 x 2.1 mm) and eluted with a gradient from 0.1 % formic acid in water to 0.1 % formic acid in acetonitrile over 5 minutes. Derivatized short chain fatty acid samples were analyzed using a LD40 XR UHPLC (Shimadzu Co.) system for separation and a 6500+ QTrap mass spectrometer (AB Sciex Pte. Ltd.) for detection. Samples were separated with a Waters™ Atlantis dC18 column (100Å, 3 µm, 3 mm X 100 mm) using a 6 min gradient from 5-80 % B with buffer A consisting of 0.1 % formic acid in water and B consisting of 0.1 % formic acid in methanol. Short chain fatty acids and central metabolic carboxylic acids were detected using positive mode MRMs from previously established methods and identity was confirmed by comparison to derivatized standards. Metabolome analysis All signals were integrated using SciexOS 3.1 (AB Sciex Pte. Ltd.). Signals with greater than 50% missing values for a specific tissue set were discarded and remaining missing values were replaced with the lowest registered signal value. Where appropriate, signals with a QC coefficient of variance greater than 30 % were discarded. Metabolites with multiple MRMs were quantified with the higher signal to noise MRM. Filtered datasets of the negative mode aqueous metabolites were total sum normalized after initial filtering. The SCFA dataset and the positive mode aqueous metabolomics dataset were scaled and combined with the negative mode aqueous metabolite dataset using common signal for malate and tyrosine respectively.The data provided was is the total sum normalized version with samples in columns and metabolites in rows. The first row contains time point identifications.