Metabolite concentrations, fluxes and free energies imply efficient enzyme usage

Park, Junyoung O.; Rubin, Sara A.; Xu, Yi; Amador‐Noguez, Daniel; Fan, Jing; Shlomi, Tomer; Rabinowitz, Joshua D.

doi:10.1038/nchembio.2077

Cited by 377 publications

(576 citation statements)

References 58 publications

Supporting

Mentioning

536

Contrasting

Unclassified

Order By: Relevance

“…Based on the substantial decrease in ATP and other triphosphates in starving autophagy-null cells, we hypothesized that total nucleotide pools were depleted. Consistent with prior literature (Park et al 2016), we found that the total pools of adenosine and uridine nucleotides were substantially larger than the pools of cytidine and guanosine nucleotides (Fig. 5A).…”

Section: Autophagy Prevents Fatal Total Nucleotide Pool Depletion In supporting

confidence: 81%

Autophagy provides metabolic substrates to maintain energy charge and nucleotide pools in Ras-driven lung cancer cells

et al. 2016

View full text Add to dashboard Cite

Autophagy degrades and is thought to recycle proteins, other macromolecules, and organelles. In genetically engineered mouse models (GEMMs) for Kras-driven lung cancer, autophagy prevents the accumulation of defective mitochondria and promotes malignancy. Autophagy-deficient tumor-derived cell lines are respiration-impaired and starvation-sensitive. However, to what extent their sensitivity to starvation arises from defective mitochondria or an impaired supply of metabolic substrates remains unclear. Here, we sequenced the mitochondrial genomes of wild-type or autophagy-deficient (Atg7) Kras-driven lung tumors. Although Atg7 deletion resulted in increased mitochondrial mutations, there were too few nonsynonymous mutations to cause generalized mitochondrial dysfunction. In contrast, pulse-chase studies with isotope-labeled nutrients revealed impaired mitochondrial substrate supply during starvation of the autophagy-deficient cells. This was associated with increased reactive oxygen species (ROS), lower energy charge, and a dramatic drop in total nucleotide pools. While starvation survival of the autophagy-deficient cells was not rescued by the general antioxidant N-acetyl-cysteine, it was fully rescued by glutamine or glutamate (both amino acids that feed the TCA cycle and nucleotide synthesis) or nucleosides. Thus, maintenance of nucleotide pools is a critical challenge for starving Kras-driven tumor cells. By providing bioenergetic and biosynthetic substrates, autophagy supports nucleotide pools and thereby starvation survival.

show abstract

Section: Autophagy Prevents Fatal Total Nucleotide Pool Depletion In supporting

confidence: 81%

Autophagy provides metabolic substrates to maintain energy charge and nucleotide pools in Ras-driven lung cancer cells

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Metabolism is subject to thermodynamic constraints, with downstream metabolites in a pathway lower in free energy (chemical potential) than their upstream precursors. In practice, systems-level thermodynamic analysis can be used to identify mismeasured metabolites, to quantitate unmeasured metabolites, and to more precisely measure many species (7, 139, 140). Such analyses are most powerful when combined with flux measurements that further constrain metabolite concentrations.…”

Section: Discussionmentioning

confidence: 99%

“…Detailed methods for this purpose are provided in Reference 5. Using this approach, we have measured absolute concentrations for ~100 of the most important metabolites in Escherichia coli , yeast, and mammalian cells (6, 7). Once such absolute concentration measurements have been made, absolute concentration measurements in related specimens can be made by relative quantitation, multiplying the known absolute concentration in the previously measured sample by the experimentally observed fold change in the new condition.…”

Section: Absolute Versus Relative Quantitationmentioning

confidence: 99%

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Klein

et al. 2017

Annu. Rev. Biochem.

370

341

View full text Add to dashboard Cite

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites’ reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

show abstract

“…20 versus ref. 46 or the different effect of pfkA and pfkB knockouts on glycolytic activity in ref. 20 versus ref.…”

Section: Discussionmentioning

confidence: 99%

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

2016

View full text Add to dashboard Cite

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). The Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47 (k-ecoli457 is available for download at http://www.maranasgroup.com).

show abstract

Metabolite concentrations, fluxes and free energies imply efficient enzyme usage

Cited by 377 publications

References 58 publications

Autophagy provides metabolic substrates to maintain energy charge and nucleotide pools in Ras-driven lung cancer cells

Autophagy provides metabolic substrates to maintain energy charge and nucleotide pools in Ras-driven lung cancer cells

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

Contact Info

Product

Resources

About