Biology subjects

Alabed, H. B. R.

Publications and source records attributed to Alabed, H. B. R..

3 recordsLinked to original sources

bioRxiv2026

From molecular lipidomics to interpretable food lipid profiles: the Lipid Food Profile module in LipidOne

LC/MS-based food lipidomics provides detailed information on intact lipid species, but the resulting datasets are often difficult to translate into concepts directly useful for food quality, processing, nutritional profiling and authenticity assessment. Here, we present Lipid Food Profile (LFP), a module of the LipidOne platform designed to convert annotated LC/MS lipidomics data into interpretable food-relevant lipid indices. LFP applies an in silico hydrolysis strategy to reconstruct acyl, alkyl and alkenyl chains from intact lipid species while preserving their lipid-class origin. The reconstructed information is then summarized into index categories related to food lipid quality, compositional balance, omega balance, oxidative stability, chain remodelling and ether-linked chain contribution. The interpretative value of LFP was evaluated using three published food lipidomics datasets addressing different analytical questions: X-ray-induced lipid remodelling in Chlorella vulgaris, spatial lipid heterogeneity in Mugil cephalus bottarga, and geographical-origin assessment of camel milk. Across these case studies, LFP recovered the main conclusions of the original lipidomics investigations, including treatment-associated lipid remodelling, inner-outer layer differences in bottarga and regional variation in camel milk. Importantly, LFP reorganized these findings into a smaller number of food-oriented indices, providing additional information on saturation balance, oxidative susceptibility, chain architecture and classification potential. Overall, LFP provides an interpretative layer for LC/MS food lipidomics that complement conventional fatty-acid analysis and molecular-species-based interpretation. By translating complex lipidomic tables into structured lipid index profiles, the module may support more accessible and chemically meaningful analysis of food composition, processing effects, lipid quality and exploratory traceability applications. LFP is freely accessible through the LipidOne web platform (LipidOne.eu). HighlightsO_LILipid Food Profile translates LC/MS food lipidomics into interpretable lipid indices. C_LIO_LIThe workflow preserves chain and lipid-class information without chemical hydrolysis. C_LIO_LIPublished case studies show that LFP recovers and extends previous interpretations. C_LIO_LILFP supports food quality, processing and exploratory origin/authenticity assessment. C_LIO_LIThe module complements conventional fatty-acid analysis and molecular lipidomics. C_LI

biochemistry↗

bioRxiv2026

Nutritional-Metabolic Lipid Profiling with LipidOne for plasma lipidomics interpretation in metabolic health

Background/ObjectivesHuman plasma lipidomics provides valuable information on dietary and metabolic phenotypes, but the interpretation of high-dimensional lipid datasets remains challenging. We developed the Nutritional-Metabolic Lipid Profile (NMLP) module within LipidOne to translate plasma lipidomics data into interpretable nutritional-metabolic indices, functional categories, visual outputs, and biological statements. Subjects/MethodsNMLP calculates lipid indices reflecting cardiometabolic lipid status, fatty acid remodelling, overall lipid quality, oxidative protection, and omega-3/essential fatty acid status. The module was applied to three human plasma lipidomics public datasets: a randomized crossover glycemic-load feeding study, a eucaloric high-fat diet intervention in normal-weight women, and a large public dataset stratified by insulin sensitivity. ResultsAcross datasets, NMLP converted complex lipidomic matrices into coherent nutritional-metabolic profiles. In the glycemic-load study, the module highlighted metabolic lipid shifts not captured by standard clinical lipid panels, mainly involving cardiometabolic lipid status, oxidative protection, and fatty acid remodelling. In the high-fat diet intervention, NMLP tracked temporal lipid remodelling across pre-diet, on-diet, and post-diet states, consistent with metabolic adaptation to increased dietary fat exposure. In the insulin-sensitivity dataset, insulin-resistant subjects showed a storage-oriented lipid phenotype characterized by increased neutral lipid storage indices and altered lipid quality and oxidative-protection features. Category-level clustering further revealed heterogeneous nutritional-metabolic states within insulin-resistant subjects. ConclusionsNMLP provides a deeper and clearer interpretative framework for human plasma lipidomics in nutrition and metabolic health research. By translating lipid species into functional indices and category-level readouts, the module may facilitate the use of lipidomics in clinical nutrition, metabolic phenotyping, and precision nutrition studies. NMLP is freely accessible as part of the online LipidOne platform.

bioinformatics↗

bioRxiv2025

Functional Lipid Analysis via Index-Based Lipidomics Profile: A New Computational Module in LipidOne

Understanding the functional roles of lipids is essential for interpreting metabolic phenotypes in health, disease, and dietary interventions. Here, we present a major update to LipidOne, a user-friendly web-based platform for lipidomic data interpretation (lipidone.eu), introducing the novel analytical module: Functional Lipid Analysis (FLA). This component enables the assessment of the quantitative features of lipidomic datasets through a biologically structured, index-based approach. The FLA module computes 42 indices representing specific lipid function-- including membrane structure, energy storage, and signaling. These indices are derived from lipid classes, molecular species, and fatty acyl-, alkyl-, and alkenyl-chain composition. Each index is statistically compared across experimental groups and analyzed and visualized through dedicated tools, including bar plots, volcano plots, PCA, PLS-DA, heatmaps, and functional radar charts. Every index is semantically annotated with biologically meaningful phrases, allowing users to move beyond numerical variations and toward mechanistic insights of lipid function. In the FLA module, index variations are further linked to predicted protein mediators, bridging lipid alterations to enzymatic pathways and enabling network-based interpretations. This integrative strategy lays the foundation for a systems biology perspective, connecting lipidomics to proteomics and or transcriptomics yielding functional pathway analysis. We demonstrate the utility of this framework using datasets from two published works. In both cases, FLA confirmed the authors conclusions and yielded additional, biologically coherent functional readouts not originally emphasized. By shifting the focus from individual lipid species to interpretable biochemical indices, LipidOne 2.3 offers a reproducible, scalable, and biologically informed platform for systems-level lipid biology and hypothesis generation.

bioinformatics↗