Biology subjects

Vandemoortele, B.

Publications and source records attributed to Vandemoortele, B..

4 recordsLinked to original sources

bioRxiv2026

Focus on the edges: a biomolecular network of histone PTMs, metabolites and proteins unveils functional entanglement in AML

The cell phenotype is not a direct manifestation of the genotype but rather a product of cellular history and the environmental context. However, individual biomolecules cannot change independently and show coordinated behavior. To study this in acute myeloid leukemia (AML), we built a unique multi-omics biomolecular network made from proteins, metabolites and histone posttranslational modifications (hPTMs) sequentially extracted from each cell pellet. Edges between the nodes are measured directly using 400 LC-MSMS runs that cover 18 AML cell lines. We provide a novel conceptual framework to illustrate the different classes of functional entanglement between and within omics layers and present the data in three interactive data browsers to allow full community access. To help navigate the network, we approach it from the perspective of two biomolecular targets, i.e. CD34 and the epigenetic mark Histone H3 lysine 27 trimethylation (H3K27me3). Now, this easily accessible biomolecular network serves as a starting point for building and testing hypotheses and streamlining drug development, in the process positioning biomolecular associations center stage in understanding phenotypic complexity.

cancer biology↗

bioRxiv2026

Lemonite: identification of regulatory metabolites through data-driven, interpretable integration of transcriptomics and metabolomics data

Current transcriptomics-metabolomics integration approaches are either limited by poor interpretability or constrained by incomplete prior knowledge, preventing the systematic identification of regulatory metabolites. Here, we present Lemonite, a data-driven and interpretable framework for integrating bulk transcriptomics and metabolomics data to uncover regulatory metabolites acting on gene modules. Lemonite extends module network inference to jointly associate transcription factors and metabolites with gene programs, without requiring prior differential analysis or complete metabolome annotation. To contextualize predictions, we constructed a comprehensive gene/protein-metabolite knowledge graph integrating over 370 000 metabolite-gene/protein and 2.1 million protein-protein interactions. Applied to glioblastoma (n=99) and inflammatory bowel disease (n=75) cohorts, Lemonite identified over 50 functionally coherent gene modules per disease, revealing established and previously uncharacterized metabolite-gene regulatory relationships. In glioblastoma, myo-inositol and phosphatidylcholines, together with IRF6, regulate mesenchymal-like immune programs, which upon integration with single-cell transcriptomics are primarily expressed in tumor-associated macrophages and monocytes. In inflammatory bowel disease, regulatory metabolites were prioritized that change the expression of their predicted target genes in colonic epithelial cells in vitro. Overall, Lemonite provides a principled framework to explore the genome-wide regulatory potential of the metabolome and to generate biologically interpretable, experimentally testable hypotheses from multi-omics data.

bioinformatics↗

bioRxiv2026

Comparison of different computational frameworks for metabolic modeling from single-cell transcriptomics data in glioblastoma

Metabolic reprogramming is a hallmark of glioblastoma, yet how distinct malignant and tumor microenvironment cell populations contribute to this metabolic heterogeneity remains poorly defined. Since direct single-cell metabolomics remains technically limited, transcriptomics-based computational inference offers a powerful alternative. Here we apply and systematically compare three complementary computational methods: (1) metabolic pathway activity scoring, (2) gene regulatory network inference focused on metabolic enzyme gene regulation, and (3) single-cell metabolic flux prediction. These methods were applied to snRNA-seq data from a set of GBM patient samples using the Human1 genome-scale metabolic model as a unified reaction and pathway annotation prior knowledge reference. Across all three methods, tumor-associated macrophages emerge as the metabolically dominant tumor microenvironment population. Tumor-associated macrophages in mesenchymal-like tumors show coordinated transcriptional control of lipid metabolism by five recurrently active transcription factors. They also exhibit consistent nucleotide biosynthesis flux and glutamate-to-glutamine conversion potentially supporting malignant cells. These findings demonstrate that multi-layered metabolic inference can resolve cell-type/state-specific dependencies in glioblastoma and highlight tumor-associated macrophage metabolism as a promising therapeutic target

systems biology↗

bioRxiv2023

Evaluation of single-sample network inference methods for precision oncology

A major challenge in precision oncology is to identify targetable cancer vulnerabilities in individual patients. Modelling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning and predicting gene interactions for a group of patients. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of the individual patient. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made. Moreover, many methods rely on normal tissue samples as reference point for the tumor samples, which is not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, iENA, CSN and SSPGI using expression profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed networks with distinct network topologies, as observed by edge weight distributions and other network characteristics. Further, hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by edge weight clustering, enrichment of known subtype-specific driver genes among hub gene sets, and differential node importance. Finally, we show that single-sample networks correlate better to other omics data from the same cell line as compared to aggregate networks. Our results point to the important role of single-sample network inference in precision medicine.

systems biology↗