Escherichia coli and many other microorganisms, has long been considered a toxic waste compound that inhibits microbial growth. This counterproductive auto‐inhibition represents a major problem in biotechnology and has puzzled the scientific community for decades. Recent studies have however revealed that acetate is also a co‐substrate of glycolytic nutrients and a global regulator of E. coli metabolism and physiology. Here, we used a systems biology strategy to investigate the mutual regulation of glycolytic and acetate metabolism in E. coli. Computational and experimental analyses demonstrate that decreasing the glycolytic flux enhances co‐utilization of acetate with glucose. Acetate metabolism thus compensates for the reduction in glycolytic flux and eventually buffers carbon uptake so that acetate, rather than being toxic, actually enhances E. coli growth under these conditions. We validated this mechanism using three orthogonal strategies: chemical inhibition of glucose uptake, glycolytic mutant strains, and alternative substrates with a natively low glycolytic flux. In summary, acetate makes E. coli more robust to glycolytic perturbations and is a valuable nutrient, with a beneficial effect on microbial growth.Acetate, a major by‐product of glycolytic metabolism in  Systems biology analysis highlights an unexpected role for acetate as a compensatory carbon source under conditions of reduced glycolysis. The preferred nutrients of many organisms are glycolytic substrates, such as glucose, which are oxidized into pyruvate by glycolysis. In the presence of oxygen, pyruvate can then be fully oxidized into carbon dioxide and water via respiratory pathways involving the tricarboxylic acid (TCA) cycle and oxidative phosphorylation. When oxygen is limiting, pyruvate is incompletely oxidized through non‐respiratory pathways, and fermentation by‐products are released into the environment. Despite fermentation being less efficient than respiration, fermentation by‐products are still released in the presence of oxygen by most microorganisms, including bacteria Harden, , yeasts Escherichia coli and many other microorganisms  plus acetate (from 0.1 to 100 mM) at different concentrations of αMG  with acetate (0.1–100 mM) in the presence of 100 mM αMG to inhibit glycolysis . As predicted by the model therefore, acetate appears to be beneficial rather than toxic to E. coli when the glycolytic flux is reduced. The growth rate was slightly lower at 100 mM acetate (0.30 ± 0.03 h−1) than at 60 mM acetate, but the similar growth rates observed across the concentration range demonstrate that acetate is not toxic when the glycolytic flux is low, even at high concentrations.To test this prediction, we measured the growth rate of ysis Fig . The datE. coli K‐12 BW25113 Δpgi and ΔpfkA strains, where the glycolytic flux is 24 and 29% that of the wild‐type strain, respectively  but increased its growth in the presence of 100 mM αMG . These results confirm that the beneficial effect of acetate is not due to the presence of αMG but is a consequence of the decrease in glycolytic flux.As an orthogonal, alternative strategy to the use of a glycolytic inhibitor, we tested the impact of acetate on the growth of mutant strains with low glycolytic flux, namely, the ains Fig , whose gE. coli growth when the glycolytic flux is reduced by either chemical or genetic perturbations.Overall, all results are in line with the predictions of the model and demonstrate that acetate enhances et al, E. coli grown on glucose (15 mM) plus acetate  in the presence of αMG (100 mM), with those measured in the absence of αMG at the same acetate concentrations , acetate did not trigger a stress response at low glycolytic flux . Finally, expression of genes encoding ribosomal proteins  was increased by 57% on average at 50 mM acetate and 48% on average at 100 mM acetate  and growth machinery  was not observed at low glycolytic flux Fig . In fact Dataset .These results indicate that the stress response and lower metabolic activity triggered by acetate at high glycolytic flux do not occur at low glycolytic flux. In this situation, acetate enhances the expression of ribosomal and central metabolic genes, in line with its positive effect on growth. This is the opposite of what is observed at high glycolytic flux, demonstrating that the regulatory role of acetate is largely determined by the glycolytic flux.E. coli growth, without the need for Acs (acetate consumption only). To confirm this hypothesis, we measured the growth rates of E. coli K‐12 BW25113 wild‐type and of Δpta and Δacs isogenic mutant strains cultivated on 15 mM glucose plus 100 mM αMG, with or without 10 mM acetate. The effect of acetate was quantified as the relative change in growth rate induced  and of the Δacs strain , but not for the Δpta strain, whose growth rate decreased slightly but significantly . These results confirm that the beneficial effect of acetate on E. coli growth at low glycolytic flux is mediated by the Pta‐AckA pathway, with no involvement of Acs in this phenomenon.According to the model Fig , the revuced Fig . AcetateE. coli growth stems from the tight interplay of glycolytic and acetate fluxes, this should be independent of the nature of the carbon source, implying that acetate should boost E. coli growth on glycolytic substrates with low glycolytic flux, such as glycerol and galactose , but not for the Δpta strain  chemical inhibition of glucose uptake with αMG, (ii) mutant strains deleted for glycolytic genes, and (iii) glycolytic substrates with a naturally low glycolytic flux . Since the impact of acetate on growth is partly determined by the glycolytic flux, this metabolic by‐product is not toxic per se. Importantly, these results also weaken several hypotheses that have been proposed to explain the apparent “toxicity” of acetate for microbial growth. Indeed, perturbations of the proton gradient, of the anion composition of the cell, or of methionine biosynthesis, would have a monotonic impact on growth. The slight but global activating effect of acetate on the transcriptome at low glycolytic flux, which correlates with the growth response, also contrasts with its inhibitory effect at high glycolytic flux. The results of this study indicate that mechanisms that only explain growth inhibition at high glycolytic flux are inadequate. Likewise, the recent suggestion that gluconeogenic substrates such as acetate enhance E. coli growth on glycolytic substrates use acetate present in the feedstock, provided as a co‐substrate or produced by the Pta‐AckA pathway. Given that the Pta‐AckA pathway produces one ATP and Acs consumes two ATPs, strategies based on activating the Pta‐AckA‐Acs cycle are highly energy‐consuming  under accession number E‐MTAB‐11717. Transcriptomics data in the absence of glycolytic inhibition , as described previously  by 1D https://github.com/MetaSys-LISBP/PhysioFit; Peiro et al, Glucose uptake, acetate, and growth rates were calculated from glucose, acetate, and biomass concentration–time profiles using PhysioFit  in R . The R notebook used to perform the simulations and to generate the figures is provided at https://github.com/MetaSys-LISBP/glucose_acetate_interplay.We used a kinetic model of et al (We used a Monte‐Carlo approach (Saa & Nielsen, et al , 500 simt‐tests with two‐tailed distributions. The R notebook used to perform the calculations is provided at https://github.com/MetaSys-LISBP/glucose_acetate_interplay.Comparisons between strains or conditions were performed using Student's Pierre Millard: Conceptualization; resources; data curation; software; formal analysis; supervision; funding acquisition; validation; investigation; visualization; methodology; writing – original draft; project administration; writing – review and editing. Thomas Gosselin‐Monplaisir: Resources; data curation; formal analysis; validation; investigation; visualization; methodology; writing – review and editing. Sandrine Uttenweiler‐Joseph: Resources; validation; methodology; writing – review and editing. Brice Enjalbert: Resources; data curation; formal analysis; supervision; validation; investigation; visualization; methodology; writing – review and editing.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Dataset EV1Click here for additional data file.Source Data for Expanded ViewClick here for additional data file.PDF+Click here for additional data file.Source Data for Figure 1Click here for additional data file.Source Data for Figure 2Click here for additional data file.Source Data for Figure 3Click here for additional data file.Source Data for Figure 4Click here for additional data file.Source Data for Figure 6Click here for additional data file.Source Data for Figure 7Click here for additional data file.


We applied Pheflux to thousands of normal and tumor cell transcriptomes obtained from The Cancer Genome Atlas. Pheflux showed statistically significantly higher glucose yields on lactate in breast, kidney, and bronchus-lung tumoral cells than their normal counterparts. Results are consistent with the Warburg effect, a hallmark of cancer metabolism, suggesting that Pheflux can be efficiently used to study the metabolism of eukaryotic cells.A cell’s genome influences its metabolism via the expression of enzyme-related genes, but transcriptome and fluxome are not perfectly correlated as post-transcriptional mechanisms also regulate reaction’s kinetics. Here, we addressed the question: given a transcriptome, how unobserved mechanisms of reaction kinetics should be systematically accounted for when inferring the fluxome? To infer the most likely and least biased fluxome, we present Pheflux, a constraint-based model maximizing Shannon’s entropy of fluxes per mRNA. Benchmarked against   •A novel computational model estimates phenotype-specific fluxomes at the genome-scale•Results accurately estimate the carbon core metabolism in yeast and bacteria•Using RNA-seq data, the Warburg effect is predicted in various cancer types Cellular physiology; Complex system biology; Omics; Transcriptomics The principle of maximum entropy has been applied to infer genetic interaction networks,,,,,,To gain intuition about how Pheflux differs from current methods, we compared its predictions to the Simplified Pearson Correlation with Transcriptomic data (SPOT) model.As illustrated by the three study cases in ,,,,,,To assess the goodness of fit of Pheflux estimations, we used as benchmark reported flux values of the core carbon metabolism.,Escherichia coli study case, Bhadra-Lobo et al. (2020),In general, Pheflux yielded an average Pearson correlation value of 0.843 , outperfironment A, and liironment B. A possTICs see B and 1C.optGpSampler algorithmE. coli iJO1366 metabolic network,Current experimental methods do not allow measurements of the fluxome at a genome-scale in a reliable and reproducible manner.,At the core carbon scale, FBA+min cenarios A, outpercenarios B. FBA+miHowever, FBA+min  λ level C. Pheflume state D. It can,To determine whether Pheflux can estimate phenotype-specific fluxome distributions, we evaluated its ability to replicate known metabolic differences between normal and cancer cells at stages I, II, III, and IV.,,As The gene expression patterns of various metabolic pathways have been reported to be affected by cancer,Bacillus subtilis, Scheffersomyces stipitis, Saccharomyces cerevisiae, and E. coli— the computing times were, on average, 19 s, whereas the large human metabolic network −14000 variables– needed around 240 s to solve. It should be noted that Pheflux was solved without using any specialized algorithms, so that CPU times could be further reduced if a custom implementation is considered.Pheflux CPU times ranged between 30 s and 240 s, depending on the number of variables associated with the size of the metabolic network . For exaTo infer the fluxome conditioned on a cell’s metabolic network and transcriptome, we developed Pheflux, a novel CBM. Pheflux estimates the fluxome by maximizing Shannon’s entropy of fluxes per enzyme. Such an approach can not completely eliminate the uncertainty of the missing information of reactions’ kinetics, but should, on average, outperform alternative CBMs conditioned on the same prior data because a fluxome inferred according to the principle of maximum entropy corresponds to the one that can happen in the greatest number of ways and with the least amount of unwarranted assumptions. We found support for this hypothesis in the superior performance of Pheflux compared to alternative methods for estimating the fluxomes of various bacteria and yeasts. We further studied Pheflux capacity to infer phenotype-specific fluxomes using thousands of transcriptomes obtained from the TCGA. We found that Pheflux correctly reported higher yields of glucose on lactate on tumor cells compared to their normal counterparts, being this coherent with the Warburg effect, a hallmark of cancer metabolism.,,a priori objective function, which can be useful in cases where such a function is appropriate and easily measurable, for instance, the biomass growth rate for bacteria and yeast. In these cases, measurements of such function can be used as extra constraints of the fluxome space to further improve Pheflux predictions.The principle of maximum entropy has been previously used in CBMs to estimate a cell’s fluxomebona fide fluxome space is established, experimentally observed fluxes –e.g., exchange rates of substrates and products– can be used as extra constraints to reduce the fluxome space. Pheflux’s underlying assumptions may also limit the accuracy of its inferences, as the inclusion of transcriptomic information does not yield extra constraints to reduce further the solution space but weights the selection of the most probable fluxome. Pheflux assumes that gene expression is a good proxy of enzyme abundance, but it has been shown that the abundance of enzymes and mRNAs are not perfectly correlated.,,The quality of Pheflux inferences is affected by the grade of the data upon which it is conditioned and by the validity of its underlying assumptions. As a CBM, Pheflux requires a fluxome space that faithfully matches the metabolic capacities of the organism under study. For this, it is critical that the genome-scale metabolic network encompasses the reactions, metabolites, and cellular compartments that actually pertain to the organism under study. Once a CPU times showed that Pheflux could be efficiently applied to large eukaryotic metabolic networks in a computationally efficient manner, even when Pheflux objective function is convex but non-linear. As a result, we expect Pheflux to have a wide range of applications in studies that rely on large metabolic networks, such as RECON3D. These include the study of diabetes,in vivo genome-wide protein concentration data, we used gene expression data as an approximation of enzyme concentrations. However, post-transcriptional regulatory processes may result in mRNA levels not always proportional to their corresponding protein concentrations. As a result, this approximation may limit the accuracy of Pheflux estimations.In the absence of marcelo.rivas@utem.cl).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Marcelo Rivas-Astroza ,i/(giV))  when vi For SPOT and Pheflux, we assigned gene expression values to each reaction according to their gene-protein-reaction associations.,,,,,We use previously published informationhttps://www.cancer.gov/tcga). We considered the set of uptake reactions as reported by Shen et al. (2019)For kidney, breast, and bronchus-lung normal and cancer human tissues, we use Recon3D metabolic networkIn this study, differences between the statistics of tumoral and normal cancer cells were assess using the Mann-Whitney U test. Statistical details can be found in the figure legends, https://github.com/mrivas/pheflux.Pheflux computational implementation and the code used to generate the results used in all figures can be accessed from the following GitHub repository: