Cells are complex entities in which DNA, RNA, proteins and metabolites together orchestrate cellular function and complexity. Profiling multiple molecular layers together provides a holistic view of regulatory programs in the cell, but leveraging this information requires robust data integration strategies. Such strategies have already resulted in significant advances to our understanding of gene regulation, for example through inference of gene regulatory networks from omics data. Yet, although metabolites intrinsically represent a phenotypic readout of regulatory cascades in the cell and are involved in interactions with both DNA and proteins, there is currently a lack of data-driven, interpretable data integration strategies that facilitate the inclusion of metabolites into integrated gene regulatory networks. We therefore aim to adapt and extend the multi-omics module network inference method LemonTree to LemonIte (LemonTree for metabolites), which will identify putative regulatory metabolites and transcription factors for gene coexpression modules by integrating coupled transcriptomics and metabolomics data in an interpretable manner. We will apply this to glioblastoma and inflammatory bowel disease, for which coupled transcriptomics and metabolomics data are available for large cohorts. We will further look into prioritization of gene modules and their regulators by functional annotation enrichment analysis and including orthogonal information on protein-protein, protein-metabolite and metabolic pathways. Finally, we aim to experimentally validate the potential regulatory role of metabolites in in vitro experiments.