Inferring Metabolic Networks from Transcriptomic Data: a Gateway to Understand Fate Control by Metabolism in the Epiblast

• Applicants: Privatdozentin Dr. Ines Hellmann; Antonio Scialdone, Ph.D.
• Subject Area: Developmental Biology ; Bioinformatics and Theoretical Biology ; Evolutionary Cell and Developmental Biology (Zoology) ; Cell Biology
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 568872478

Transcriptomics data have allowed unprecedented advances to our understanding of cell fate transitions during human development. A bottleneck in the analyses lies in our ability to infer biological functions from genomic and transcriptomic data. Here, we plan to develop a framework to leverage omics-data from human pluripotent stem cells in the primed, and naive states to infer metabolic networks. In a proof of concept study (manuscript on biorxiv), we highlight the value of weighting genes based on the metabolic network for the identification of key metabolic characteristics of stem cell populations. Here, we plan to integrate metabolic and gene regulatory networks (GRN) to link cell fate decisions and metabolism. To achieve this, we will work on three aims. The first aim is to generate transcriptomic and proteomic data from human and cynomolgus macaque primed and naive pluripotent stem cells (PSCs) to compare metabolic networks. We are going to use information about evolutionary conservation to infer the robustness of these metabolic networks. The second aim is to develop a novel framework that integrates GRNs into the inference of metabolic networks. This will involve generating and analyzing scRNA-seq data to identify key genes, followed by the inference of GRNs using a newly designed algorithm that incorporates prior knowledge of gene regulation. By leveraging these GRNs, we will identify active pathways influencing metabolic processes and predict how gene perturbations could impact metabolism. The third aim is to validate inferred differences by perturbing metabolic reactions in human and cynomolgus pluripotent stem cells. Altogether, our project capitalizes on expertise in stem cell biology, molecular evolution, metabolic network computing and scRNAseq inference of cell fate regulatory networks to advance developmental biology.

• DFG Programme: Research Grants
• International Connection: France
• Cooperation Partners: Laurent David; Damien Eveillard