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

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 constraint-based 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.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

“… 829 Moreover, multi-omics integrative analysis can uncover disease biomarkers and new pathological pathways, deepen understanding of mechanistic basis and therapeutic targets of metabolic diseases, and accelerate new drug development for better therapy, significantly enhanced translational capability. 830 – 833 The integration of multi-omics analysis may present precise metabolic biomarkers, a global metabolic snapshot and metabolic networks, which can deepen exploration of underlying mechanisms towards improving clinical management of disease. However, due to the complexity of metabolic pathway data and the interaction between metabolic network and other factors, the integration of multi omics data is a huge challenge.…”

Enzyme fluctuations data improve inference of metabolic interaction networks with an inverse differential Jacobian approach

“…The data underlying this article are available in its online supplementary material. And the original data of breast cancer case study can be accessed in the reference [46]. The matlab code is available in https://bitbucket.org/mosys-univie/covrecon/.…”

“…We evaluate this approach with several published models collected from the EBI BioModels database [21], and also apply the approach to a breast cancer dataset with two different conditions. We compared the inverse Jacobian results with different assumptions on the structure of the fluctuation matrix D [46]. We show that the inverse differential Jacobian approach is enhanced by incorporating this information.…”

How artificial intelligence enables modeling and simulation of biological networks to accelerate drug discovery

“…Using ML, metabolite concentrations have been even predicted from proteomic data (Zelezniak et al, 2018) or from network topologybased optimizations (Tepper et al, 2013;Küken et al, 2019). Protein abundance can be also estimated by integrating network models with transcriptomics (Li et al, 2022) or metabolomic (Di Filippo et al, 2022) data.…”