Relationships between enzymatic flux capacities and metabolic flux rates: Nonequilibrium reactions in muscle glycolysis

In changing environments, cells modulate resource budgeting through distinct metabolic routes to control growth. Accordingly, the TORC1 and SNF1/AMPK pathways operate contrastingly in nutrient replete or limited environments to maintain homeostasis. The functions of TORC1 under glucose and amino acid limitation are relatively unknown. We identified a modified form of the yeast TORC1 component Kog1/Raptor, which exhibits delayed growth exclusively during glucose and amino acid limitations. Using this, we found a necessary function for Kog1 in these conditions where TORC1 kinase activity is undetectable. Metabolic flux and transcriptome analysis revealed that Kog1 controls SNF1-dependent carbon flux apportioning between glutamate/amino acid biosynthesis and gluconeogenesis. Kog1 regulates SNF1/AMPK activity and outputs and mediates a rapamycin-independent activation of the SNF1 targets Mig1 and Cat8. This enables effective glucose derepression, gluconeogenesis activation, and carbon allocation through different pathways. Therefore, Kog1 centrally regulates metabolic homeostasis and carbon utilization during nutrient limitation by managing SNF1 activity.

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Fine tuning the glycolytic flux ratio of EP-bifido pathway for mevalonate production by enhancing glucose-6-phosphate dehydrogenase (Zwf) and CRISPRi suppressing 6-phosphofructose kinase (PfkA) in Escherichia coli

Microbial Cell Factories ◽

10.1186/s12934-021-01526-1 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Ying Li ◽

He Xian ◽

Ya Xu ◽

Yuan Zhu ◽

Zhijie Sun ◽

...

Keyword(s):

Pentose Phosphate Pathway ◽

Conversion Rate ◽

Metabolic Flux ◽

Flux Ratio ◽

Reducing Power ◽

Pentose Phosphate ◽

Fine Tuning ◽

High Yield ◽

Glycolytic Flux ◽

Acetyl Coa

Abstract Background Natural glycolysis encounters the decarboxylation of glucose partial oxidation product pyruvate into acetyl-CoA, where one-third of the carbon is lost at CO2. We previously constructed a carbon saving pathway, EP-bifido pathway by combining Embden-Meyerhof-Parnas Pathway, Pentose Phosphate Pathway and “bifid shunt”, to generate high yield acetyl-CoA from glucose. However, the carbon conversion rate and reducing power of this pathway was not optimal, the flux ratio of EMP pathway and pentose phosphate pathway (PPP) needs to be precisely and dynamically adjusted to improve the production of mevalonate (MVA). Result Here, we finely tuned the glycolytic flux ratio in two ways. First, we enhanced PPP flux for NADPH supply by replacing the promoter of zwf on the genome with a set of different strength promoters. Compared with the previous EP-bifido strains, the zwf-modified strains showed obvious differences in NADPH, NADH, and ATP synthesis levels. Among them, strain BP10BF accumulated 11.2 g/L of MVA after 72 h of fermentation and the molar conversion rate from glucose reached 62.2%. Second, pfkA was finely down-regulated by the clustered regularly interspaced short palindromic repeats interference (CRISPRi) system. The MVA yield of the regulated strain BiB1F was 8.53 g/L, and the conversion rate from glucose reached 68.7%. Conclusion This is the highest MVA conversion rate reported in shaken flask fermentation. The CRISPRi and promoter fine-tuning provided an effective strategy for metabolic flux redistribution in many metabolic pathways and promotes the chemicals production.

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Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Nature Communications ◽

10.1038/s41467-021-22989-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Joshua E. Lewis ◽

Melissa L. Kemp

Keyword(s):

Machine Learning ◽

Metabolic Flux ◽

Metabolic Modeling ◽

The Cancer Genome Atlas ◽

Radiation Response ◽

Metabolomics Data ◽

Machine Learning Classifiers ◽

Learning Classifiers ◽

Metabolic Biomarkers ◽

Genome Scale

AbstractResistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

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Rhythmic glucose metabolism regulates the redox circadian clockwork in human red blood cells

Nature Communications ◽

10.1038/s41467-020-20479-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ratnasekhar Ch ◽

Guillaume Rey ◽

Sandipan Ray ◽

Pawan K. Jha ◽

Paul C. Driscoll ◽

...

Keyword(s):

Glucose Metabolism ◽

Red Blood Cells ◽

Blood Cells ◽

Mammalian Cells ◽

Metabolic Flux ◽

Pentose Phosphate ◽

Human Red Blood Cells ◽

Integral Process ◽

Metabolic Oscillations ◽

Human Rbcs

AbstractCircadian clocks coordinate mammalian behavior and physiology enabling organisms to anticipate 24-hour cycles. Transcription-translation feedback loops are thought to drive these clocks in most of mammalian cells. However, red blood cells (RBCs), which do not contain a nucleus, and cannot perform transcription or translation, nonetheless exhibit circadian redox rhythms. Here we show human RBCs display circadian regulation of glucose metabolism, which is required to sustain daily redox oscillations. We found daily rhythms of metabolite levels and flux through glycolysis and the pentose phosphate pathway (PPP). We show that inhibition of critical enzymes in either pathway abolished 24-hour rhythms in metabolic flux and redox oscillations, and determined that metabolic oscillations are necessary for redox rhythmicity. Furthermore, metabolic flux rhythms also occur in nucleated cells, and persist when the core transcriptional circadian clockwork is absent in Bmal1 knockouts. Thus, we propose that rhythmic glucose metabolism is an integral process in circadian rhythms.

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