Functional lipid analysis via index-based lipidomics profile: a new computational module in LipidOne

Area of Science:

• Biochemistry
• Systems Biology
• Bioinformatics

Background:

• Understanding lipid functional roles is crucial for metabolic phenotype interpretation in health and disease.
• Current lipidomic analyses often lack systems-level insights due to a focus on individual lipid species.
• Translating quantitative lipidomic data into biologically structured, function-oriented interpretations remains a challenge.

Purpose of the Study:

• To introduce a novel analytical module, Functional Lipid Analysis (FLA), integrated into the LipidOne platform.
• To enable the translation of quantitative lipidomic data into biologically structured and function-oriented interpretations.
• To provide a tool for deeper mechanistic and systems-level insights into lipid metabolism.

Main Methods:

• The Functional Lipid Analysis (FLA) module computes 42 indices related to lipid functions (membrane structure, energy storage, signaling).
• Indices are derived from lipid class and fatty acyl-, alkyl-, and alkenyl-chain composition.
• Statistical comparisons across experimental groups and multivariate/visualization tools are employed for exploration.

Main Results:

• FLA computes 42 functional lipid indices from compositional data.
• Each index is semantically annotated and linked to predicted protein mediators for pathway/network interpretation.
• Application to published datasets confirmed prior findings and revealed novel, biologically coherent functional insights.

Conclusions:

• The new FLA module enhances LipidOne, providing a powerful tool for functional lipid analysis.
• FLA facilitates pathway- and network-based interpretation of lipidomic data.
• This approach advances the understanding of lipid roles in biological systems.