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Revealing disease-associated pathways by network integration of untargeted metabolomics


Uncovering the molecular context of dysregulated metabolites is crucial to understand pathogenic pathways. However, their system-level analysis has been limited owing to challenges in global metabolite identification. Most metabolite features detected by untargeted metabolomics carried out by liquid-chromatography-mass spectrometry cannot be uniquely identified without additional, time-consuming experiments. We report a network-based approach, prize-collecting Steiner forest algorithm for integrative analysis of untargeted metabolomics (PIUMet), that infers molecular pathways and components via integrative analysis of metabolite features, without requiring their identification. We demonstrated PIUMet by analyzing changes in metabolism of sphingolipids, fatty acids and steroids in a Huntington's disease model. Additionally, PIUMet enabled us to elucidate putative identities of altered metabolite features in diseased cells, and infer experimentally undetected, disease-associated metabolites and dysregulated proteins. Finally, we established PIUMet's ability for integrative analysis of untargeted metabolomics data with proteomics data, demonstrating that this approach elicits disease-associated metabolites and proteins that cannot be inferred by individual analysis of these data.

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Figure 1: PIUMet identifies disease-associated pathways and hidden components from untargeted metabolomic data.
Figure 2: PIUMet.
Figure 3: The PIUMet subnetwork showing altered sphingolipid metabolism in the STHdh cell line model of HD.
Figure 4: Altered fatty acid and steroid metabolic processes identified by PIUMet.
Figure 5
Figure 6: Regions of the resulting network obtained from integrative analysis of lipidomics and phosphoproteomics, displaying the dysregulation of high-scoring, hidden components.


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We thank A. Soltis and S. Dalin. This work was supported by grants from US National Institute of Health R01-GM089903, U54-NS091046 and U01-CA184898 (E.F.), and National Cancer Institute U54 CA112967 (E.F. and F.M.W.) and P30 CA014051 (F.M.W.) as well as Searle Scholars Program (A.S.).

Author information

Authors and Affiliations



L.P. and E.F. designed the approach. L.P. implemented the algorithm and performed the computational analyses. P.M. prepared cell cultures and performed western blot and cell viability experiments. M.L. and A.S. performed the untargeted lipidomic experiments. T.C. and F.M.W. measured global levels of phosphoproteins. J.A.-P. and C.B.C. performed targeted lipidomic experiments. L.P. and E.F. wrote the manuscript, and all the authors approved the final version.

Corresponding author

Correspondence to Ernest Fraenkel.

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Competing interests

L.P. and E.F. are co-founders of ReviveMed, Inc., and have filed a provisional patent based on the work described here (application number US 62/203,292).

Integrated supplementary information

Supplementary Figure 1 A flow chart describing different steps of the PIUMet algorithm.

Supplementary Figure 2 The comparison of the disease-specific score distributions from real and random results.

The figure shows that the scores for the real results are significantly higher than random results (P value = 4.238E-37).

Supplementary Figure 3 The frequency of resulting hidden components from real data in the networks inferred from randomly selected metabolite features.

Supplementary Figure 4 A Venn diagram comparing the potential metabolites matching disease features based on mass compared to the putative ones inferred by PIUMet.

The metabolites inferred by PIUMet are significantly enriched for the metabolites detected by a targeted metabolomic platform (hypergeometric test P value= 6.00×10− 4). All of the metabolites identified by the targeted platform were dysregulated in diseased cells.

Supplementary Figure 5 Altered sphingolipids in STHdh Q111 cells compared to STHdh Q7 cells. These sphingolipids were measured in nine biological replicates.

Two-sided student t-test analysis showed significant changes in C24:0 Ceramide (d18:1) with P value =1.2×10−11 (a), C16:1 Ceramide (d18:1) with P value =7.74×10−5 (b), C24:1 Ceramide (d18:1) with P value =0.02 (c), and C24:1 sphingomyelin (SM) with P value =2.07×10−8 (d). The height of the bar plot shows the average of each sphingolipid levels, while the error bar shows their standard deviation.

Supplementary Figure 6 PIUMet inferred Sphingosine-1-phosphate (S1P) as a disease-modifying hidden component of dysregulated sphingolipid pathway.

(a) S1P was significantly downregulated (P value = 1.99×10−4, two-sided student t-test) in STHdh Q111 cells compared to STHdh Q7 cells. The bar plot shows the average levels of S1P that were measured in nine biological replicates. The error bars show the standard deviation of S1P levels (b). The treatment of diseased cells with an analogue of S1P (FTY720-P) significantly decreased apoptosis (P value = 7.98×10−5, two-sided student t-test). The bar plot shows the average percentage of cell death, and the error bars show the standard deviation from two independent experiments with twenty replicates each. (c) Calcein (green) detects viable cells and monitors changes in cell shape and morphology, while propidium iodide (PI – red) stains late apoptotic cells with damaged membranes.

Supplementary Figure 7 Western blot results showed that the DHCR7-encoded protein was significantly downregulated (P value = 0.025, two-sided student t-test) in the STHdh Q111 cells compared to STHdh Q7 cells.

The boxplot shows the measured protein levels in six biological replicates (black dots).

Supplementary Figure 8 Altered fatty acids in STHdh Q111 cells compared to STHdh Q7 cells.

These fatty acids were measured in nine biological replicates. Two-sided student t-test analysis showed significant changes in eicosapentaenoic acid (EPA, P value=7.8×10−6, a) and dihomo-gamma-linolenic acid (DHGLA, P value=0.01, b). The height of the bar plot shows the average of each fatty acid levels, while the error bars show their standard deviation.

Supplementary Figure 9 Western blot results showed that fatty acid synthase enzyme, encoded by the FASN gene, was significantly upregulated (P value = 0.025, two-sided student t-test) in the STHdh Q111 cells compared to STHdh Q7 cells.

The boxplot shows the measured protein levels in six biological replicates (black dots).

Supplementary Figure 10 Western blot results of measuring RASA1 and ERCC6 protein levels.

(a) Western blot results showed a significant increase (P value = 0.008, two-sided student t-test) in the RASA1 protein levels. The boxplot shows the measured protein levels in three biological replicates (black dots). (b) The plot shows Western blot results indicating a significant increase (P value =0.028, two-sided student t-test) in the ERCC6 protein levels. The boxplot shows the measured protein levels in six biological replicates (black dots).

Supplementary Figure 11 The comparison of degree distributions of features from terminal and detectable metabolite feature (DMF) sets.

The degree of a feature shows the number of metabolites corresponding to the feature.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11 and Supplementary Table 1 (PDF 4541 kb)

Supplementary Table 2

The summary of untargeted lipidomic data fromSTHdh cell lines. (XLSX 26 kb)

Supplementary Table 3

The summary of global phospho-proteomic data from STHdh cell lines. (XLSX 11 kb)

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Pirhaji, L., Milani, P., Leidl, M. et al. Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat Methods 13, 770–776 (2016).

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