Presentation Information
[4GteX-16]ΔΔG method for identification of rate-limiting steps in microbial cell factories from metabolome data
○Fumio Matsuda1 (1. The University of Osaka (Japan))
Keywords:
rate-limiting steps,Gibbs free energy change (ΔG),metabolic engineering,Saccharomyces cerevisiae,methylotrophic yeasts
Purpose
The rational engineering of microbial cell factories for bioproduction requires precise identification of rate-limiting steps in complex metabolic networks. Although metabolomics provides a snapshot of intracellular metabolite levels, pinpointing specific bottlenecks remains challenging due to the non-linear relationship between metabolite concentrations and reaction fluxes. To address this issue, we developed the ΔΔG method, which quantifies the difference in Gibbs free energy change (ΔG) between two metabolic steady states [1]. By focusing on changes in the thermodynamic driving force, this approach enables estimation of alterations in enzymatic activity and facilitates identification of key regulatory points.
Methods
The ΔΔGB/A value indicates the shift in the ΔG levels between metabolic steady states B and A. For a given metabolic reaction S -> P, the difference is derived as follows:ΔΔGB/A = ΔGB − ΔGA = ΔG0 + RT ln([P]B /([S]B)) − ΔG0 − RT ln([P] A/([S] A)) = RT ln(([P]B/[P]A) /([S]B/ [S]A)))In this equation, R is the gas constant, and T is the absolute temperature. The terms [P]B/[P]A and [S]B/[S]A represent the relative concentration ratios (fold changes) of the product and substrate, respectively, between the two conditions. These values are obtained directly from comparative metabolome datasets, making the ΔΔG method a robust tool for analyzing experimental data from different strains or culture conditions.
Results
The efficacy of the ΔΔG method was demonstrated through two distinct case studies. First, we analyzed a metabolomic dataset from wild-type and various single-gene deletion mutant strains of Saccharomyces cerevisiae. We calculated the ΔΔG values for individual glycolytic reactions across these strains. A significant positive correlation was observed between the ΔΔG values of phosphofructokinase (PFK) and the overall glycolytic flux levels. These findings provide quantitative support for the long-standing hypothesis that PFK is a primary regulator of glycolytic flux in yeast.
Second, we investigated metabolic bottlenecks in three methylotrophic yeasts, including Komagataella phaffii, Ogataea polymorpha, and Candida boidinii, during methanol assimilation. By comparing metabolome profiles under glucose- and methanol-grown conditions, ΔΔG analysis suggested that reduced activities of pyruvate kinase and entry into the TCA cycle are potential rate-limiting steps during methanol utilization [2].
The ΔΔG method provides critical insights into metabolic regulation that are not apparent from metabolite concentrations alone. This approach serves as a valuable tool for metabolic engineering, enabling the rational design of strains by pinpointing the exact reactions that limit productivity.
[1] Anal. Chem. 2025, 97, 12, 6391–6398
[2] J. Biosci. Bioeng. 2026, in press
The rational engineering of microbial cell factories for bioproduction requires precise identification of rate-limiting steps in complex metabolic networks. Although metabolomics provides a snapshot of intracellular metabolite levels, pinpointing specific bottlenecks remains challenging due to the non-linear relationship between metabolite concentrations and reaction fluxes. To address this issue, we developed the ΔΔG method, which quantifies the difference in Gibbs free energy change (ΔG) between two metabolic steady states [1]. By focusing on changes in the thermodynamic driving force, this approach enables estimation of alterations in enzymatic activity and facilitates identification of key regulatory points.
Methods
The ΔΔGB/A value indicates the shift in the ΔG levels between metabolic steady states B and A. For a given metabolic reaction S -> P, the difference is derived as follows:ΔΔGB/A = ΔGB − ΔGA = ΔG0 + RT ln([P]B /([S]B)) − ΔG0 − RT ln([P] A/([S] A)) = RT ln(([P]B/[P]A) /([S]B/ [S]A)))In this equation, R is the gas constant, and T is the absolute temperature. The terms [P]B/[P]A and [S]B/[S]A represent the relative concentration ratios (fold changes) of the product and substrate, respectively, between the two conditions. These values are obtained directly from comparative metabolome datasets, making the ΔΔG method a robust tool for analyzing experimental data from different strains or culture conditions.
Results
The efficacy of the ΔΔG method was demonstrated through two distinct case studies. First, we analyzed a metabolomic dataset from wild-type and various single-gene deletion mutant strains of Saccharomyces cerevisiae. We calculated the ΔΔG values for individual glycolytic reactions across these strains. A significant positive correlation was observed between the ΔΔG values of phosphofructokinase (PFK) and the overall glycolytic flux levels. These findings provide quantitative support for the long-standing hypothesis that PFK is a primary regulator of glycolytic flux in yeast.
Second, we investigated metabolic bottlenecks in three methylotrophic yeasts, including Komagataella phaffii, Ogataea polymorpha, and Candida boidinii, during methanol assimilation. By comparing metabolome profiles under glucose- and methanol-grown conditions, ΔΔG analysis suggested that reduced activities of pyruvate kinase and entry into the TCA cycle are potential rate-limiting steps during methanol utilization [2].
The ΔΔG method provides critical insights into metabolic regulation that are not apparent from metabolite concentrations alone. This approach serves as a valuable tool for metabolic engineering, enabling the rational design of strains by pinpointing the exact reactions that limit productivity.
[1] Anal. Chem. 2025, 97, 12, 6391–6398
[2] J. Biosci. Bioeng. 2026, in press
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