B-G.C.15: An extension of over-representation methods based on metabolic distance: application to the interpretation of metabolic perturbation in relation to key events
Over-representation methods are widely used to interpret omics analyses and identify enriched gene sets or pathways. These approaches assume that all relevant entities are included in predefined gene or pathway sets. To tackle this limitation, we investigated scenarios in which elements of interest do not belong to any existing set. We leveraged the topology of the human metabolic network to compute metrics based on the metabolic distance between entities and a predefined set.
To illustrate our purpose, we considered sets of metabolic reactions identified as modulated under chemical exposure using a modelling pipeline. To better capture the mechanism of action of a chemical, modulated reactions were linked to key events (KE), defined as measurable biological changes leading to adverse outcomes. Relevant metabolic genes were identified combining knowledge graph resources and expert curation. These genes were mapped to metabolic reactions using metabolic network annotations, yielding KE-associated reaction sets. To compute proposed metrics, relying on statistical and graph-neighborhood exploration methods, a refined reaction distance matrix of the human metabolic network was constructed using the Met4j library. This metrics quantifies both proximity and specificity of modulated reactions relative to cluster of KE-associated reactions.
This approach enables the identification of reactions that are highly specific to particular KE clusters. More broadly, it extends traditional enrichment analyses by leveraging network topology, moving beyond strict list-based methods. As a result, it provides a more flexible and functional interpretation of omics data, adaptable to different biological questions.
Co-authors: Fabien Jourdan, Louison Fresnais, Kahina Abed, Mickael Le Balch, Romain Grall, Nathalie Poupin, Gladys Ouedraogo
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