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Perseus plugin "Metis" for metabolic-pathway-centered quantitative multi-omics data analysis for static and
Hamid Hamzeiy1, Daniela Ferretti1, Maria S Robles2
1Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, Martinsried, Germany.
Cell Reports Methods
|May 2, 2022
Summary
Metis is a new plugin for analyzing multi-omics data through metabolic pathways. It reveals how phosphorylation cycles in phosphoproteome and metabolome regulate enzyme activity, with time lags indicating significant modulation.
Area of Science:
- Systems Biology
- Metabolomics
- Proteomics
- Bioinformatics
Background:
- Quantitative multi-omics data analysis requires integrated approaches to understand complex biological systems.
- Metabolic pathways provide a framework for connecting diverse omics data types.
- Understanding enzyme activity regulation is crucial for deciphering cellular processes.
Purpose of the Study:
- To introduce Metis, a novel plugin for Perseus software, designed for analyzing quantitative multi-omics data integrated with metabolic pathways.
- To demonstrate Metis's capability in handling both static comparative and time-series omics data.
- To investigate the regulatory roles of phosphoproteome and metabolome cycles in enzyme activity using Metis.
Main Methods:
- Development of the Metis plugin for Perseus software.
- Integration of multi-omics data (transcriptomics, proteomics, phosphoproteomics, metabolomics) with genome-scale metabolic pathway reconstructions.
- Application of Metis to analyze circadian mouse liver multi-omics data, focusing on time-series analysis.
- Correlation analysis of cycling phosphosites, metabolites, and enzyme-catalyzed reactions.
Main Results:
- Metis successfully integrates diverse omics data types by linking them through metabolic reactions and enzymes.
- Analysis of circadian mouse liver data identified 52 pairs of cycling phosphosites and metabolites connected via metabolic reactions.
- Observed non-uniform time lags between phosphorylation peaks and metabolite peaks.
Conclusions:
- Metis provides a powerful platform for integrated multi-omics data analysis within a metabolic pathway context.
- Phosphorylation plays a significant role in modulating enzyme activity, as evidenced by the non-uniform time lags observed between phosphosites and metabolites.
- The Metis plugin facilitates novel insights into dynamic regulatory mechanisms in biological systems.
