Revealing disease-associated pathways by network integration of untargeted metabolomics

2. Metabolomics study • Metabolomics study • Identify and quantify metabolites/correlate their changes with pathological states or external influencing factors * Image from Davis VW et al. J Surg Oncol. (2011)

3. Metabolomics study • How to interpret metabolomics data? • Metabolic pathway analysis • network-based visualization • The multi-level integration is fundamental! • However, challenges in complexity and heterogeneity MetaboAnalyst (https://www.metaboanalyst.ca/)

4. Metabolomics study • Targeted metabolomics • Detection and precise quantification (in nM, or mg/mL) of a small set of known compounds • Need to clearly define “standards” • Untargeted metabolomics (metabolite fingerprinting) • “complete” metabolome comparison (many metabolites as possible) • Technical limitations, bias toward the most abundant molecules • How to handle unknown metabolites?

5. PIUMet

6. PIUMet 1. Compile a PPMI network Edge has a weight reflecting confidence of the interaction Interactome

7. PIUMet 2. Link disease features to metabolites with the same mass

8. PIUMet 3. Infer a subnetwork connecting disease features using Prize-collecting Steiner forest (PCSF) optimization • Maximize the sum of prizes (significance of dysregulation) from connected disease features • Minimize the edge costs (anti-correlated with edge confidence) • disease features ↑, low-confidence edges ↓ Node penalties Edge costs Minimize Input The possible PCSF solution

9. PIUMet 4. Eliminate bias for highly connected nodes by assigning a penalty 5. Merge inferred family of networks from many runs by adding a random noise to capture metabolic network complexity 6. Calculate disease-specific scores for each node and a network by generating networks from random data mimicking the experimental data

10. Experiments in HD 115 metabolite features P 31 proteins m/z M M M 37 metabolite peaks (disease feature) 296 metabolites PIUMet can detect many disease features compared to targeted ones

11. PIUMet identifies dysregulated pathways in HD Control Disease cells cells

12. PIUMet identifies dysregulated pathways in HD Steroid metabolic process Fatty acid metabolic process Novel features

13. Integrating with other omics Integrative analysis can detect many novel disease features Node scores increase with a joint analysis of multi-omics

14. Conclusion • Challenges in global metabolite identification in untargeted metabolomics • PIUMet, a network-based PCSF algorithm for integrative analysis of untargeted metabolomics • Identify novel disease-associated metabolites and proteins that cannot be found in individual data using PPMI network • No prior assumptions, it can be generally used in other disease data

15. PIUMet is available online! • http://fraenkel-nsf.csbi.mit.edu/piumet2/

Revealing disease-associated pathways by network integration of untargeted metabolomics

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Revealing disease-associated pathways by network integration of untargeted metabolomics