Large-Scale Modeling Approach Reveals Functional Metabolic Shifts during Hepatic Differentiation

Being able to explore
the metabolism of broad metabolizing cells
is of critical importance in many research fields. This article presents
an original modeling solution combining metabolic network and omics
data to identify modulated metabolic pathways and changes in metabolic
functions occurring during differentiation of a human hepatic cell
line (HepaRG). Our results confirm the activation of hepato-specific
functionalities and newly evidence modulation of other metabolic pathways,
which could not be evidenced from transcriptomic data alone. Our method
takes advantage of the network structure to detect changes in metabolic
pathways that do not have gene annotations and exploits flux analyses
techniques to identify activated metabolic functions. Compared to
the usual cell-specific metabolic network reconstruction approaches,
it limits false predictions by considering several possible network
configurations to represent one phenotype rather than one arbitrarily
selected network. Our approach significantly enhances the comprehensive
and functional assessment of cell metabolism, opening further perspectives
to investigate metabolic shifts occurring within various biological
contexts.