Network-Based Metabolomic Analysis for Pathway Prediction
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Solution Overview
Problem
Current approaches for analyzing metabolomic data are limited by the difficulty in global measurements of metabolites and deciphering their relations to other molecular data, with existing methods like LC-MS and MS/MS being inefficient for high-throughput screening and requiring additional experiments for metabolic identification.
Innovation Solution
A novel systems biology approach that integrates untargeted metabolomic data with other biological data using network-based methods to predict high-probability biological response pathways, reducing the need for additional experiments and enabling the identification of proteins, genotypes, and drugs that alter metabolite levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If LC-MS is used for untargeted metabolomics, then global metabolite measurements can be obtained, but the method is not conducive for high-throughput screening and requires additional MS/MS experiments for metabolic identification
Solution Approach 1:
The patent segments the analysis process by first performing untargeted metabolomics to detect all metabolite features, then applying network-based analysis to prioritize and select specific features for further investigation. This segmentation allows global detection without requiring follow-up MS/MS experiments for every feature, thereby maintaining high throughput while still enabling metabolic identification for relevant metabolites.
2Measurement precision
If MS/MS experiments are performed to characterize differential metabolite peaks, then metabolic identification can be achieved, but the process becomes expensive and time-consuming
Solution Approach 1:
The patent performs preliminary network-based analysis on the untargeted metabolomics data before conducting MS/MS experiments. By using network context and pathway information to pre-filter and prioritize metabolite features, the system identifies which metabolites are most likely to be biologically relevant. This preliminary action reduces the number of metabolites requiring MS/MS confirmation, thereby maintaining identification accuracy while significantly reducing time and cost.
3Quantity of substance
If untargeted metabolomics measures relative levels of metabolite features, then global metabolite profiling is achieved, but the majority of features remain unknown despite high mass accuracy
Solution Approach 1:
The patent introduces network-based analysis as an intermediary between untargeted metabolomics data and metabolite identification. Instead of directly attempting to identify all metabolite features through MS/MS, the system uses network context (metabolic pathways, enzyme-substrate relationships, and pathway enrichment analysis) to infer the identities and functional relevance of metabolites. This intermediary approach allows the majority of features to remain as quantifiable data points while providing biological interpretation through network context, thereby reducing information loss without requiring exhaustive MS/MS characterization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach facilitates more efficient analysis and prediction of cellular mechanisms, allowing for targeted drug discovery and repurposing of existing drugs by integrating metabolomic data with protein-protein and protein-metabolite interaction networks, thereby overcoming the limitations of existing methods.
Implementation Method 1
liquid chromatography-mass spectrometry (LC-MS), an analytical chemistry technique that combines the physical separation capabilities of liquid chromatography (LC)
Implementation Method 2
mass spectrometry (MS), an analytical chemistry technique that combines the physical separation capabilities of liquid chromatography (LC) with the mass analysis capabilities of mass spectrometry (MS)
Data Source
AI summary
Integrative analysis of metabolites is essential to obtain a comprehensive view of dysregulated biological pathways leading to a disease. Despite the great potential of metabolites their system level analysis has been limited. Global measurements of the metabolites by liquid chromatography-mass spectrometry (MS) detects metabolites features changing in a disease. However, identification of each feature is a bottleneck in metabolomics, in which a fraction of them are identified via tandem MS. Consequently, the scarcity of these data add additional barriers to decipher their biological meaning, especially in relation to other 'omic data such as proteomics. To address these challenges, a novel network-based approach called PIUMet is described. PIUMet infers dysregulated pathways and components from the differential metabolite features between control and disease systems without the need for the prior identification. The application of PIUMet is demonstrated by integrative analysis of untargeted lipid profiling data of a cell line model of Huntington's disease. The results show that PIUMet inferred dysregulation of sphingolipid metabolism in the disease cells. Additionally, PIUMet identified disease-modifying metabolite in the pathway that remained undetected experimentally. Furthermore, the lipidomic data of these cell lines was integrated with global phospho-proteomic ones. Integrative analysis of these data using PIUMet was shown to systematically lead to identifying dysregulated proteins in the disease cells that cannot be distinguished with individual analysis of each dataset.