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Systems biology guided by XCMS Online metabolomics

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Figure 1: Workflow for metabolomic data and pathway analysis using XCMS Online.

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Acknowledgements

The authors thank J. Nazroo, G. Tampubolon, N. Pendleton and F.C.W. Wu from the University of Manchester for constructive discussions alongside Medical Research Council grant MRC G1001375/1 (R.G.) for generous funding. The authors thank the following for funding assistance: Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA), a Scientific Focus Area Program at Lawrence Berkeley National Laboratory for the US Department of Energy, Office of Science, Office of Biological and Environmental Research under contract number DE-AC02-05CH11231 (G.S.); and National Institutes of Health grants R01 GMH4368 (G.S.) and PO1 A1043376-02S1 (G.S.).

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Correspondence to Gary Siuzdak.

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Integrated supplementary information

Supplementary Figure 1 Overview of the systems biology platform.

Raw LC-MS data is processed in XCMS Online. The XCMS output is directly used to identify dysregulated metabolic pathways with predictive pathway enrichment algorithm. Protein and gene data is then integrated to perform the systems-level analysis and generate the pathway cloud plot and systems biology results table (not shown in the figure).

Supplementary Figure 2 Example of predictive pathway analysis

Schematic display of predictive pathway analysis on G20 metabolomic data to decipher biological roles during the process of metal corrosion.

Supplementary Figure 3 Percent pathway coverage using multi-omic analysis of colon cancer data.

The bar graph presents gene, protein and metabolite overlap on dysregulated metabolic pathways identified using predictive pathway analysis. These pathways also have a previously known association with colon cancer.

Supplementary Figure 4 Integrated metabolomics and transcriptomics data analysis.

Schematic of multi-omic analysis of Ercc1-/Δ mouse model using the XCMS Online systems biology platform in a study of XFE progeroid syndrome showing overlapping dysregulated pathways.

Supplementary Figure 5 Pathway cloud plot for DvH nitrate stress with integrated omics.

Plot focuses on p-value < 0.05 illustrating 18 dysregulated pathways and three overlapping genes leuA (leucine biosynthesis), glmU and glmS (UDP-N-acetyl-D-glucosamine biosynthesis). Pathways are plotted as a function of FET pathway significance versus average metabolic pathway overlap, with the radius of the circle representing the size of the metabolic pathway. Significantly dysregulated pathways appear in the upper right-hand quadrant of the plot. Each circle presents overlapping gene, protein and metabolite data when cursor is hovered over, as demonstrated for UDP-N-acetyl-D-glucosamine biosynthesis pathway. Clicking on these table features gives additional specific pathway, gene, protein and metabolite information.

Supplementary Figure 6 Pathway cloud plot presenting all dysregulated metabolic pathways under the effects of different carbon sources.

Here a total overview of the pathway cloud plot is presented to illustrate the all the identified pathways ranging from 0.0055 < p-value < 1. Significance of the pathway overlap (-log(p-value)) versus the percent overlap of the metabolites found in each pathway shows dysregulated features of greater interest in the upper right-hand quadrant of the plot. The radius of the circles represents the overall size of the metabolic pathway.

Supplementary Figure 7 Scalability of XCMS analysis with large sample cohort.

Predictive pathway analysis generated from metabolomic data on the 1,600 human samples, two top pathways shown.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Tables 1 and 2, Supplementary Note, and Supplementary Methods (PDF 3986 kb)

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Huan, T., Forsberg, E., Rinehart, D. et al. Systems biology guided by XCMS Online metabolomics. Nat Methods 14, 461–462 (2017). https://doi.org/10.1038/nmeth.4260

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