Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraintbased modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.

Di Filippo, M., Pescini, D., Galuzzi, B., Bonanomi, M., Gaglio, D., Mangano, E., et al. (2022). INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLOS COMPUTATIONAL BIOLOGY, 18(2) [10.1371/journal.pcbi.1009337].

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation

Di Filippo M.
Primo
;
Pescini D.;Galuzzi B. G.;Bonanomi M.;Gaglio D.;Alberghina L.;Vanoni M.;Damiani C.
Ultimo
2022

Abstract

Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraintbased modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.
Articolo in rivista - Articolo scientifico
metabolism, omics, data integration, regulation;
English
2022
18
2
e1009337
open
Di Filippo, M., Pescini, D., Galuzzi, B., Bonanomi, M., Gaglio, D., Mangano, E., et al. (2022). INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLOS COMPUTATIONAL BIOLOGY, 18(2) [10.1371/journal.pcbi.1009337].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/362631
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