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de Martino, D.

Publications and source records attributed to de Martino, D..

3 recordsLinked to original sources

Reconstructing heterogeneous metabolic trajectories of E. coli diauxie via a dynamical Maximum Entropy Principle

The glucose-acetate diauxic shift in E. coli is classically described as an abrupt, population-wide switch from glucose to acetate consumption. Recent experiments challenge this view, revealing a robust intermediate regime of co-consumption whose single-cell basis remains unresolved: does it reflect coexisting specialized subpopulations, or genuine mixed metabolic states within individual cells? We first develop a two-state consumer-resource model in which cells optimally grow on either glucose or acetate, and show that observed co-consumption trajectories cannot be decomposed into convex combinations of the two subpopulations -- ruling out discrete metabolic states as a sufficient explanation. Physico-chemical constraints of the metabolic network instead enforce genuine single-cell co-consumption across a continuous spectrum of phenotypes. To resolve this, we apply a dynamical maximum entropy (maximum caliber) framework constrained by batch and chemostat experiments, inferring time-resolved distributions of metabolic fluxes that naturally predict a continuum of single-cell phenotypes spanning glucose overflow, mixed substrate utilization, and acetate consumption -- revealing co-consumption as a dominant, persistent feature of single-cell metabolism around the switch rather than an artifact of population averaging. Finally, we formulate a continuous consumer-resource model over metabolic state space, in which selection, phenotypic diffusion, and moving metabolic boundaries driven by environmental feedback reproduce single-cell co-consumption trajectories and complex dynamical trends inaccessible to discrete models. Together, our results recast diauxic adaptation as a continuous redistribution of single-cell metabolic states rather than a discrete switch.

systems biology↗

Probing single cell fermentation flux and intercellular exchange networks via pH-microenvironment sensing and inverse modeling

The homeostatic control of their environment is an essential task of living cells. It has been hypothesized that when microenvironmental pH inhomogeneities are induced by high cellular metabolic activity, diffusing protons act as signaling molecules, driving the establishment of cross-feeding networks sustained by the cell-to-cell shuttling of overflow products such as lactate. Despite their fundamental role, the extent and dynamics of such networks is largely unknown due to the lack of methods in single cell flux analysis. In this study we provide direct experimental characterization of such exchange networks. We devise a method to quantify single cell fermentation fluxes over time by integrating high-resolution pH microenvironment sensing via ratiometric nanofibers with constraint-based inverse modeling. We apply our method to cell cultures with mixed populations of cancer cells and fibroblasts. We find that the proton trafficking underlying bulk acidification is strongly heterogeneous, with maximal single cell fluxes exceeding typical values by up to 3 orders of magnitude. In addition, a crossover in time from a networked phase sustained by densely connected "hubs" (corresponding to cells with high activity) to a sparse phase dominated by isolated dipolar motifs (i.e. by pair-wise cell-to-cell exchanges) is uncovered, which parallels the time course of bulk acidification. Our method promises to shed light on issues ranging from the homeostatic function of proton exchange to the metabolic coupling of cells with different energetic demands, and paves the way for real-time non-invasive single cell metabolic flux analysis.

biophysics↗

Unbiased metabolic flux inference through combined thermodynamic and 13C flux analysis

ABSTRACTQuantification of cellular metabolic fluxes, for instance with 13C-metabolic flux analysis, is highly important for applied and fundamental metabolic research. A current challenge in 13C-flux analysis is that the available experimental data are usually insufficient to resolve metabolic fluxes in large metabolic networks without making assumptions on flux directions and reversibility. To infer metabolic fluxes in a more unbiased manner, we devised an approach that does not require such assumptions. The developed three-step approach integrates thermodynamics, metabolome, physiological data, and 13C labelling data, and involves a novel method to comprehensively sample the complex thermodynamically-constrained metabolic flux space. Applying our approach to budding yeast with its compartmentalised metabolism and parallel pathways, we could resolve metabolic fluxes in an unbiased manner, we obtained an uncertainty estimate for each flux, and we found novel flux patterns that until now had remained unknown, likely due to assumptions made in previous 13C flux analysis studies. We expect that our approach will be an important step forward to determine metabolic fluxes with improved accuracy in microorganisms and possibly also in more complex organisms.View Full Text

systems biology↗