High-throughput identification of gut microbiome-dependent metabolites

Shuo Han, Emma R. Guiberson, Yuxin Li, Justin L. Sonnenburg

Published: 2024-05-13 DOI: 10.1038/s41596-024-00980-6

Extended

Extended Data Fig. 1 Example images of extracted ion chromatograms generated in the Agilent MassHunter Qualitative Data Analysis software.

a , Example of a single peak identified from extracting a chromatogram from a single m/z value. b , Example of multiple peaks identified from extracting a chromatogram based on a single m/z value. c , Example of no peak identified from extracting a chromatogram based on a single m/z value.

Extended Data Fig. 2 Pre- and post-filtering comparisons in peak areas.

Pairwise molecular feature comparisons before and after the mega media being passed through a 0.2 μm membrane filter. a , b , Two independent batches of mega media have been tested (n = 5 for each of pre- and post-filtered conditions for each batch) using the C18 positive method. 1882 unique molecular features are reported for both batches following the background noise elimination and peak quality checks. Linear regression and r 2 values of pairwise molecular feature comparisons, performed on log10 transformed peak area data: pre-filtered versus post-filtered in batch 1 (r 2 = 0.977, Post-filtered peak area = 1 × Pre-filtered peak area - 0.0109, a ) and batch 2 (r 2 = 0.994, Post-filtered peak area = 0.9974 × Pre-filtered peak area + 0.0206, b ).

Extended Data Fig. 3 Agilent qTOF mass calibration settings on Agilent MassHunter.

Example calibration shown is in the negative ionization mode (e.g., C18 negative method). For the positive ionization mode (C18 positive, HILIC positive), these calibration settings remain identical with the exception of shifting ion polarity from “negative” to “positive” in both “Ion polarity” and “qTOF” sections. a , Tune and calibration. b , Instrument state. Red boxes highlight each tab in the software that corresponds to each type of settings.

Extended Data Fig. 4 Project settings using the MS-DIAL software.

a , General project information. b , Project file information. Red boxes and associated arrows highlight explanations for specific input.

Extended Data Fig. 5 MS-DIAL analysis parameters and alignment result export settings.

a , Data collection. b , Peak detection. c , MS2 deconvolution (not used in this protocol). d , Identification. e , Adduct (not modified here, but information is provided in the reference library input file in “Identification.”) f , Alignment. g , Isotope tracking (not used in this protocol). h , Alignment result export. Red boxes and associated arrows highlight explanations for specific input.

Extended Data Fig. 6 Residual plots for RT, precursor ion m/z , and peak area of a panel of internal standards detected in individual biological samples from a representative mouse serum metabolomic study.

a , b , Residual plot for linear regression model constructed using RTs ( a ) or precursor ion m/z values ( b ). Residuals are calculated based on values from individual biological samples vs. reference library. c , Residual plot for linear regression model constructed on peak areas of extracted ion chromatograms. Residuals are calculated based on detected values from individual biological samples (log 10 -transformed) vs. QC samples (log 10 -transformed). A single dot represents individual biological replicate per condition (e.g., germ-free or mono-association). Overlapping dots, which represent identical values, are shown in darker shades than the non-overlapping dots.

Extended Data Fig. 7 MS-DIAL user interface for checking the quality of annotated peaks.

a , Example of visualizing an analyzed dataset on MS-DIAL. b , Example of a low-quality peak to be eliminated from the output result file. Orange boxes highlight specific items to select for visualization. Red arrows highlight specific explanations or instructions.

Detect-seq, a chemical labeling and biotin pull-down approach for the unbiased and genome-wide off-target evaluation of programmable cytosine base editors

A systems-level mass spectrometry-based technique for accurate and sensitive quantification of the RNA cap epitranscriptome

References

Introduction
Materials
Procedure
Troubleshooting
Timing
Anticipated results
- Reporting summary
Acknowledgements
Extended Data Fig. 1 Example images of extracted ion chromatograms generated in the Agilent MassHunter Qualitative Data Analysis software.
Extended Data Fig. 2 Pre- and post-filtering comparisons in peak areas.
Extended Data Fig. 3 Agilent qTOF mass calibration settings on Agilent MassHunter.
Extended Data Fig. 4 Project settings using the MS-DIAL software.
Extended Data Fig. 5 MS-DIAL analysis parameters and alignment result export settings.
Extended Data Fig. 6 Residual plots for RT, precursor ion m/z, and peak area of a panel of internal standards detected in individual biological samples from a representative mouse serum metabolomic study.
Extended Data Fig. 7 MS-DIAL user interface for checking the quality of annotated peaks.

Rooks, M. G. & Garrett, W. S. Gut microbiota, metabolites and host immunity. Nat. Rev. Immunol. 16, 341–352 (2016).
Cani, P. D. Microbiota and metabolites in metabolic diseases. Nat. Rev. Endocrinol. 15, 69–70 (2019).
Sonnenburg, J. L. & Backhed, F. Diet–microbiota interactions as moderators of human metabolism. Nature 535, 56–64 (2016).
Kasahara, K. & Rey, F. E. The emerging role of gut microbial metabolism on cardiovascular disease. Curr. Opin. Microbiol. 50, 64–70 (2019).
Lynch, J. B. & Hsiao, E. Y. Microbiomes as sources of emergent host phenotypes. Science 365, 1405–1409 (2019).
Koppel, N., Maini Rekdal, V. & Balskus, E.P. Chemical transformation of xenobiotics by the human gut microbiota. Science https://doi.org/10.1126/science.aag2770 (2017).
Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Backhed, F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345 (2016).
Donia, M. S. & Fischbach, M. A. Human microbiota. Small molecules from the human microbiota. Science 349, 1254766 (2015).
Husted, A. S., Trauelsen, M., Rudenko, O., Hjorth, S. A. & Schwartz, T. W. GPCR-mediated signaling of metabolites. Cell Metab. 25, 777–796 (2017).
Krautkramer, K. A., Fan, J. & Backhed, F. Gut microbial metabolites as multi-kingdom intermediates. Nat. Rev. Microbiol. 19, 77–94 (2021).
Bauermeister, A., Mannochio-Russo, H., Costa-Lotufo, L. V., Jarmusch, A. K. & Dorrestein, P. C. Mass spectrometry-based metabolomics in microbiome investigations. Nat. Rev. Microbiol. 20, 143–160 (2022).
Guijas, C. et al. METLIN: a technology platform for identifying knowns and unknowns. Anal. Chem. 90, 3156–3164 (2018).
Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).
Melnik, A. V. et al. Coupling targeted and untargeted mass spectrometry for metabolome–microbiome-wide association studies of human fecal samples. Anal. Chem. 89, 7549–7559 (2017).
Han, S. et al. A metabolomics pipeline for the mechanistic interrogation of the gut microbiome. Nature 595, 415–420 (2021).
Showalter, M. R. et al. Obesogenic diets alter metabolism in mice. PLoS ONE 13, e0190632 (2018).
Evans, A. M. et al. High resolution mass spectrometry improves data quantity and quality as compared to unit mass resolution mass spectrometry in high-throughput profiling metabolomics. Metabolomics https://doi.org/10.4172/2153-0769.1000132 (2014).
Mars, R. A. T. et al. Longitudinal multi-omics reveals subset-specific mechanisms underlying irritable bowel syndrome. Cell 183, 1137–1140 (2020).
Holmes, E., Wijeyesekera, A., Taylor-Robinson, S. D. & Nicholson, J. K. The promise of metabolic phenotyping in gastroenterology and hepatology. Nat. Rev. Gastroenterol. Hepatol. 12, 458–471 (2015).
Fiehn, O. Metabolomics by gas chromatography–mass spectrometry: combined targeted and untargeted profiling. Curr. Protoc. Mol. Biol. 114, 303431–303432 (2016).
Rinschen, M. M., Ivanisevic, J., Giera, M. & Siuzdak, G. Identification of bioactive metabolites using activity metabolomics. Nat. Rev. Mol. Cell Biol. 20, 353–367 (2019).
Liebisch, G. et al. Quantification of fecal short chain fatty acids by liquid chromatography tandem mass spectrometry-investigation of pre-analytic stability. Biomolecules https://doi.org/10.3390/biom9040121 (2019).
Boulet, L., Faure, P., Flore, P., Monteremal, J. & Ducros, V. Simultaneous determination of tryptophan and 8 metabolites in human plasma by liquid chromatography/tandem mass spectrometry. J. Chromatogr. B 1054, 36–43 (2017).
Wegner, K. et al. Rapid analysis of bile acids in different biological matrices using LC–ESI–MS/MS for the investigation of bile acid transformation by mammalian gut bacteria. Anal. Bioanal. Chem. 409, 1231–1245 (2017).
Ayon, N. J., Sharma, A. D. & Gutheil, W. G. LC–MS/MS-based separation and quantification of Marfey’s reagent derivatized proteinogenic amino acid dl-stereoisomers. J. Am. Soc. Mass Spectrom. 30, 448–458 (2019).
Pismennoi, D., Kiritsenko, V., Marhivka, J., Kutt, M.L. & Vilu, R. Development and Optimisation of HILIC–LC–MS method for determination of carbohydrates in fermentation samples. Molecules https://doi.org/10.3390/molecules26123669 (2021).
Kind, T. et al. Identification of small molecules using accurate mass MS/MS search. Mass Spectrom. Rev. 37, 513–532 (2018).
Schymanski, E. L. et al. Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environ. Sci. Technol. 48, 2097–2098 (2014).
Pandher, R., Naegele, E., Fischer, S.M. & Raynaud, F.I. in Metabolomics (eds Roessner, U.) 318–328 (InTech, 2012).
Sousa, M. B. & Ziegler, T. E. Diurnal variation on the excretion patterns of fecal steroids in common marmoset (Callithrix jacchus) females. Am. J. Primatol. 46, 105–117 (1998).
Tsugawa, H. et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 12, 523–526 (2015).
Cajka, T., Smilowitz, J. T. & Fiehn, O. Validating quantitative untargeted lipidomics across nine liquid chromatography–high-resolution mass spectrometry platforms. Anal. Chem. 89, 12360–12368 (2017).
Wastyk, H. C. et al. Gut-microbiota-targeted diets modulate human immune status. Cell 184, 4137–4153 e4114 (2021).
Kotagale, N. R., Taksande, B. G. & Inamdar, N. N. Neuroprotective offerings by agmatine. Neurotoxicology 73, 228–245 (2019).
Chin, R. M. et al. The metabolite alpha-ketoglutarate extends lifespan by inhibiting ATP synthase and TOR. Nature 510, 397–401 (2014).

High-throughput identification of gut microbiome-dependent metabolites

Extended

Supplementary information

推荐阅读