A parallel glycolysis provides a selective advantage through rapid growth acceleration.

Glycolysis is a universal metabolic process that breaks down glucose to produce adenosine triphosphate (ATP) and biomass precursors. The Entner-Doudoroff (ED) pathway is a glycolytic pathway that parallels textbook glycolysis but yields half as much ATP. Accordingly, in organisms that possess both glycolytic pathways (for example, Escherichia coli), its raison d'être remains a mystery. In this study, we found that the ED pathway provides a selective advantage during growth acceleration. Upon carbon and nitrogen upshifts, E. coli accelerates growth faster with than without the ED pathway. Concurrent isotope tracing reveals that the ED pathway flux increases faster than that of textbook glycolysis. We attribute the fast response time of the ED pathway to its strong thermodynamic driving force and streamlining of glucose import. Intermittent nutrient supply manifests the evolutionary advantage of the parallel glycolysis; thus, the dynamic nature of an ostensibly redundant pathway's role in promoting rapid adaptation constitutes a metabolic design principle.

Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns.

Fluxomics offers a direct readout of metabolic state but relies on indirect measurement. Stable isotope tracers imprint flux-dependent isotope labeling patterns on metabolites we measure; however, the relationship between labeling patterns and fluxes remains elusive. Here we innovate a two-stage machine learning framework termed ML-Flux that streamlines metabolic flux quantitation from isotope tracing. We train machine learning models by simulating atom transitions across five universal metabolic models starting from 26 13C-glucose, 2H-glucose, and 13C-glutamine tracers within feasible flux space. ML-Flux employs deep-learning-based imputation to take variable measurements of labeling patterns as input and successive neural networks to convert the ensuing comprehensive labeling information into metabolic fluxes. Using ML-Flux with multi-isotope tracing, we obtain fluxes through central carbon metabolism that are comparable to those from a least-squares method but orders-of-magnitude faster. ML-Flux is deployed as a webtool to expand the accessibility of metabolic flux quantitation and afford actionable information on metabolism.

Sustainable metabolic engineering requires a perfect trifecta.

The versatility of cellular metabolism in converting various substrates to products inspires sustainable alternatives to conventional chemical processes. Metabolism can be engineered to maximize the yield, rate, and titer of product generation. However, the numerous combinations of substrate, product, and organism make metabolic engineering projects difficult to navigate. A perfect trifecta of substrate, product, and organism is prerequisite for an environmentally and economically sustainable metabolic engineering endeavor. As a step toward this endeavor, we propose a reverse engineering strategy that starts with product selection, followed by substrate and organism pairing. While a large bioproduct space has been explored, the top-ten compounds have been synthesized mainly using glucose and model organisms. Unconventional feedstocks (e.g. hemicellulosic sugars and CO2) and non-model organisms are increasingly gaining traction for advanced bioproduct synthesis due to their specialized metabolic modes. Judicious selection of the substrate-organism-product combination will illuminate the untapped territory of sustainable metabolic engineering.