Abstract
Metabolically versatile Pseudomonas species can assimilate various glycolytic and gluconeogenic substrates. Simultaneous assimilation is known to segregate carbons from each substrate type into different metabolic pathways. However, the mechanisms of this metabolic segregation remain unresolved. Here, we investigate Pseudomonas putida KT2440 during processing of the sugar glucose through glycolysis versus the phenolic acid ferulate through gluconeogenesis. Metabolome profiling reveals up to twofold less tricarboxylic acid cycle metabolites but up to 10-fold higher metabolites of upper glycolysis, pentose-phosphate, and Entner–Doudoroff pathways in glucose-grown cells compared to ferulate-grown cells. After 13C-substrate switching, kinetic isotopic profiling captures rapid assimilation of new substrate carbons into initial catabolic pathways, but incorporation into downstream pathways is absent or incomplete. Proteomics identifies a 22-fold higher abundance of one homolog of glyceraldehyde-3-phosphate dehydrogenase (GAPDH, GapA) in cells fed on glucose relative to ferulate, while abundance of another homolog (GapB) remains unchanged. Growth phenotypes and quantitative metabolomics for single and double knockout mutants of these GAPDH homologs indicate only GapA involvement in glycolytic flux, which can be compensated by the Entner–Doudoroff pathway, and distinct preference of GapB with minimal role of GapA for gluconeogenic flux. Accordingly, growth of triple knockout mutant with deletion of gapA, gapB, and edd is possible only when glycolytic and gluconeogenic substrates are provided together to meet metabolic demands in a segregated fashion, but metabolic tradeoffs lead to slow growth. A mathematical, experimentally constrained, model of the GAPDH node shows that tuning of GapA and GapB concentrations enables transition between flux regimes for nutritional adaptability.
| Original language | English |
|---|---|
| Article number | e2513479122 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Volume | 122 |
| Issue number | 47 |
| DOIs | |
| State | Published - Nov 25 2025 |
Funding
This work is supported by the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research, Genomic Science Program under Award Number DE-SC0022181. Funding for metabolomics studies was provided in part by a grant awarded from the US NSF (CBET-2022854). This work was authored, in part, by Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC, for the US DOE under contract DE-AC05-00OR22725. Funding was provided in part by the U.S. DOE Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office for the Agile BioFoundry. The views presented in this work do not necessarily reflect those of the DOE or the United States Government. By accepting this article, the publisher acknowledges that the U.S. Government retains a nonexclusive, royalty-free, irrevocable, worldwide license to publish or reproduce the final work, and to authorize others to do so, for U.S. Government purposes. ACKNOWLEDGMENTS. This work is supported by the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research, Genomic Science Program under Award Number DE-SC0022181. Funding for metabolomics studies was provided in part by a grant awarded from the US NSF (CBET-2022854). This work was authored, in part, by Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC, for the US DOE under contract DE-AC05-00OR22725. Funding was provided in part by the U.S. DOE Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office for the Agile BioFoundry. The views presented in this work do not necessarily reflect those of the DOE or the United States Government. By accepting this article, the publisher acknowledges that the U.S. Government retains a nonexclusive, royalty-free, irrevocable, worldwide license to publish or reproduce the final work, and to authorize others to do so, for U.S. Government purposes.
Keywords
- carbon switch
- gluconeogenesis
- glycolysis
- metabolomics
- proteomics