Sequencing data were prepared and analyzed using the online tool Qiita (https://qiita.ucsd.edu/). The data were processed with the Deblur algorithm to identify unique taxa in the 16S rDNA amplicons (43). The taxonomic information was collapsed to the genus level for all statistical analyses except regressions between metabolites and bacterial abundance that were analyzed at the deblurred OTU level.

The Bruker LC-MS/MS.d data files were converted to .mzXML format and uploaded to the Global Natural Products Social Molecular Networking database for molecular networking and spectral annotation [GNPS; (http://gnps.ucsd.edu) (44)]. Molecular network were generated using the following parameters: parent and fragment tolerance of 0.2 Da (fragment tolerance of 0.5 Da for oxygen experiment), four minimum matched fragment ions to create a cluster node, minimum cosine score of 0.7, and a minimum cluster size of 2. The GNPS libraries were searched using a minimum cosine of 0.7 and minimum matched fragment ions of 4. Molecular networks were visualized using the Cytoscape software v3.3.0. Annotations from GNPS were shaped as arrowheads in the network to distinguish them from other nodes. The sizes of the nodes were scaled to the total ion count of the cluster. Edges in the network were created using the cosine score between two nodes, and the thickness of the edge was scaled to that value. Links to the pH network and oxygen networks are found here: pH (http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=1974c72bc4bf414faa1f5a3330e648ab) and oxygen (http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=34d825dbf4e9466e81d809faf814995b). The MS1-based feature detection for metabolite abundance estimation in the pH experiments was performed using the Knime OpenMS workflow using Optimus software (https://github.com/MolecularCartography/Optimus) (see Supplementary Methods for details). Metabolite annotations based on MS/MS library matches GNPS were linked to the MS1 features using the mass/charge ratio and retention time. Column blank samples and internal standard blanks were included in the Optimus run to remove background contaminant peaks from the data.

The GC-MS data were analyzed using the Thermo Scientific TraceFinder software (Thermo Fisher Scientific). The samples with the most diverse peak count were screened for metabolites of interest. Peaks of these metabolites were found and integrated using ICIS integration algorithm with peak threshold of 1% of largest peak. The National Institute of Standards and Technology (NIST) 2014 EI library was used to perform spectral matches with forward and reverse search. Known background contaminants (such as siloxanes and phthalates) were removed from targeted list of molecules, and 37 compounds of interest were selected. Batch mode with the developed method was used to find and integrate all targeted compounds. In addition, the abundances of specific metabolites of interest (acetic acid, propionic acid, and butyric acid) were manually calculated using the area under the curve feature of Thermo Xcalibur QualBrowser software (Thermo Fisher Scientific) to validate quantification of these targeted metabolites.

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