Nurwono, G. | Search bioRxiv

bioRxiv2023

Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns

All life forms operate metabolism in constant flux. Metabolic fluxes offer a direct readout of cellular state, detailing the rates and driving forces of metabolic pathways. However, indirect, iterative solvers for mapping isotope patterns from tracing experiments onto metabolic fluxes leave much of cellular state uncharted. Here, we streamline metabolic flux quantitation by innovating a machine learning framework, ML-Flux, that deciphers complex isotope labeling patterns. We train neural networks using isotope pattern-flux pairs across central carbon metabolism from 26 key 13C-glucose, 2H-glucose, and 13C-glutamine tracers. ML-Flux takes variable-size isotope labeling patterns as input, imputes missing isotope patterns, and outputs mass-balanced metabolic fluxes. Computation of fluxes using ML-Flux is more accurate and faster than that of leading metabolic flux analysis software employing a least-squares method. Our biochemical networks and machine learning models constitute a curated and growing online knowledgebase of metabolic flux and free energy to democratize quantitative metabolic profiling.

systems biology↗

bioRxiv2022

A parallel glycolysis supports rapid adaptation in dynamic environments

Glycolysis is a universal metabolic process that breaks down glucose to produce cellular energy currency ATP and biomass precursors1. The Entner-Doudoroff pathway is a glycolytic pathway that parallels the textbook glycolysis but yields half as many ATP2. In organisms that possess both glycolytic pathways, such as Escherichia coli, inactivating the less energy-efficient Entner-Doudoroff pathway does not alter growth rates3. The benefit of the Entner-Doudoroff pathway has instead been hypothesized to be metabolic flexibility as an auxiliary enzyme-efficient catabolic route4. However, its raison detre remains incompletely understood. Here we identify the advantage of employing parallel glycolytic pathways under dynamic nutrient environments. Upon carbon and nitrogen upshifts, wild-type cells accelerate growth faster than those with the Entner-Doudoroff pathway knocked out. Using stable isotope tracers and mass spectrometry, we find that the Entner-Doudoroff pathway flux increases disproportionately faster than that of the textbook glycolysis during nutrient upshifts. We attribute the fast response time of the Entner-Doudoroff pathway to its strong thermodynamic driving force and concerted regulation facilitating glucose uptake. Intermittent supply of nutrients manifests this evolutionary advantage of the parallel glycolysis. Thus, the dynamic nature of an ostensibly redundant pathways role in promoting rapid adaptation constitutes a metabolic design principle.

systems biology↗