A Network-Based Approach to Analyze and Integrate Untargeted Metabolomics with Various High-Throughput Experimental Data

Technology #17394

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Inventors
Professor Ernest Fraenkel
Department of Biological Engineering, MIT
External Link (fraenkel.mit.edu)
Leila Pirhaji
Department of Biological Engineering, MIT
Managed By
Daniel Dardani
MIT Technology Licensing Officer
Patent Protection

Systems, apparatus, and methods for analyzing and predicting cellular pathways

PCT Patent Application WO 2017-027559

Systems, apparatus, and methods for analyzing and predicting cellular pathways

US Patent Pending

Systems, apparatus, and methods for analyzing and predicting cellular pathways

Provisional Patent Application Filed

Applications

  • Drug target identification
  • Biomarker discovery
  • Patient stratification for improved diagnostic and treatment

Problem Addressed

Metabolomics is the study of chemical processes involving metabolites, and integrative analysis of metabolites can provide a comprehensive view of dysregulated biological pathways leading to a disease.  Liquid chromatography mass spectrometry (LC/MS) can compare levels of metabolites between disease and control systems; however, identification of each LC/MS peak requires an additional experiment, such as tandem MS, to identify each LC/MS peak.  Moreover, only a fraction of LC/MS peak features can be identified using tandem MS, making the analysis low-throughput, costly and time consuming. 

Technology

This invention presents a novel systems biology approach for more efficiently analyzing untargeted metabolomics data to identify disease-modifying metabolites that remained undetected experimentally.  This novel network-based approach infers dysregulated pathways and components from the differential metabolite features between control and disease system.  This invention effectively eliminates a bottleneck problem in metabolomics studies with LC/MS by reducing the need for additional experiments for metabolic identification.

As a proof of concept, the inventors have applied this novel algorithm to analyze untargeted lipid profiling data from a Huntington’s disease model.  They identified and experimentally validated a suspect network with altered levels of metabolites and phosphoproteins whose components can be considered drug targets.

Advantages

  • Enables high-throughput metabolomics data analysis.
  • Able to predict the mechanisms causing metabolite dysregulation.
  • Allows integrative analysis of untargeted metabolomics with various high-throughput experimental data, such as proteomic sequencing data.
  • Reduces the need for additional experiments for metabolic identification.