Self-replenishment cycles generate a threshold response

Kurata, Hiroyuki

doi:10.1038/s41598-019-53589-1

Cited by 6 publications

(6 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GTP, which is necessary for gluconeogenesis ( Figure 2 ), is also regenerated by the nucleotide diphosphate kinase reaction of ATP + GDP→ ADP + GTP. Those nucleotide cofactors conjugate multiple cofactor-coupled reactions to form global feedback loops, which is exemplified by glycolysis pathways with TCA cycle and oxidative phosphorylation ( Kurata, 2019 ; Teusink et al., 1998 ). Glycolysis requires ATP at the initial step (hexokinase [HK], glucokinase [GK], phosphofructokinase [PFK]) ( Figure 2 ).…”

Section: Discussionmentioning

confidence: 99%

“…An increase in the glycolysis flux enhances the TCA cycle with oxidative phosphorylation to further produce ATP, which activates the initial step of glycolysis in a positive feedback manner. This ATP amplification is called turbo-design or self-replenishment cycle ( Kurata, 2019 ; Teusink et al., 1998 ). It accelerates the glucose utilization (HK, GK, PFK) to reduce plasma glucose.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes

Kurata

2021

iScience

Self Cite

View full text Add to dashboard Cite

Summary A virtual metabolic human model is a valuable complement to experimental biology and clinical studies, because in vivo research involves serious ethical and technical problems. I have proposed a multi-organ and multi-scale kinetic model that formulates the reactions of enzymes and transporters with the regulation of hormonal actions at postprandial and postabsorptive states. The computational model consists of 202 ordinary differential equations for metabolites with 217 reaction rates and 1,140 kinetic parameter constants. It is the most comprehensive, largest, and highly predictive model of the whole-body metabolism. Use of the model revealed the mechanisms by which individual disorders, such as steatosis, β cell dysfunction, and insulin resistance, were combined to cause diabetes. The model predicted a glycerol kinase inhibitor to be an effective medicine for type 2 diabetes, which not only decreased hepatic triglyceride but also reduced plasma glucose. The model also enabled us to rationally design combination therapy.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes

Kurata

2021

iScience

Self Cite

View full text Add to dashboard Cite

show abstract

“…GTP, which is necessary for gluconeogenesis (Figure 2), is also regenerated by nucleotide diphosphate kinase reaction of ATP +GDP-> ADP + GTP. Those nucleotide cofactors conjugate multiple cofactor-coupled reactions to form global positive feedback loops, which is exemplified by glycolysis pathways with TCA cycle and oxidative phosphorylation (63,64). Glycolysis requires ATP at the initial step (HK, PFK) ( Figure 2).…”

Section: Robustnessmentioning

confidence: 99%

“…An increase in glycolysis flux enhances the TCA cycle with oxidative phosphorylation to further produce ATP, which activates the initial step of glycolysis in a positive feedback manner. This ATP amplification is called turbo-design or self-replenishment cycle (63,64). It accelerates the glucose utilization (HK, PFK) to reduce plasma glucose.…”

Section: Robustnessmentioning

confidence: 99%

Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes

Kurata

2020

Preprint

Self Cite

View full text Add to dashboard Cite

A virtual metabolic human model is a valuable complement to experimental biology and clinical studies, because in vivo research involves serious ethical and technical issues. A whole-body dynamic model is required not only to reproduce a variety of physiological and metabolic functions, but also to analyze pathology and design drugs. I first proposed a virtual metabolic human model, a multi-organ and multi-scale kinetic model of the whole-body metabolism that formulates the reactions of enzymes and transporters with the regulation of enzyme activities and hormonal actions under prandial and rest conditions. To accurately simulate metabolic changes and robustness, the model incorporates nucleotide cofactors that are critically responsible for global feedback regulations to adapt to metabolic changes. The model predicted a two-phase hepatic fatty acid production that consists of the synthesis of malonyl-CoA and the NADPH-dependent synthesis of fatty acid. The model performed pathological analysis of type 2 diabetes. I divided type 2 diabetes into specific disorders of steatosis, β cell dysfunction and insulin resistance for each organ, and analyzed the effect of the individual disorders on the dynamics of plasma glucose (hyperglycemia) and hepatic TG (steatosis). The model suggested that a chronic or irreversible change in hepatic TG accumulation plays a critical role in disease progression. The model predicted a glycerol kinase inhibitor to be a new medicine for type 2 diabetes, because it not only decreased hepatic TG but also reduced plasma glucose, unexpectedly. The model also enabled us to rationally design combination therapy.

show abstract

“…Inspired by these ideas, early theoretical studies have shown that metabolic systems featuring metabolite cycling together with allosteric regulation can introduce switch-like and bistable dynamics (18,19), and that metabolite cycling motifs introduce total co-substrate level as an additional control element in metabolic control analysis (20,21). Specific analyses of ATP cycling in the glycolysis pathway, sometimes referred to as a 'turbo-design', and metabolite cycling with autocatalysis, as seen for example in glyoxylate cycle, have shown that these features constrain pathway fluxes (22)(23)(24)(25)(26)(27). Taken together, these studies indicate that metabolite cycling, in general, and co-substrate cycling specifically, could provide a key 'design feature' in cell metabolism, imposing certain constraints or dynamical properties to it.…”

mentioning

confidence: 99%

Co-substrate pools can constrain and regulate pathway fluxes in cell metabolism

West

Delattre

Noor

et al. 2022

Preprint

View full text Add to dashboard Cite

Cycling of co-substrates, whereby a metabolite is converted among alternate forms via different reactions, is ubiquitous in metabolism. Several cycled co-substrates are well known as energy and electron carriers (e.g. ATP and NAD(P)H), but there are also other metabolites that act as cycled co-substrates in different parts of central metabolism. Here, we develop a mathematical framework to analyse the effect of co-substrate cycling on metabolic flux. In the cases of a single reaction and linear pathways, we find that co-substrate cycling imposes an additional flux limit on a reaction, distinct to the limit imposed by the kinetics of the primary enzyme catalysing that reaction. Using analytical methods, we show that this additional limit is a function of the total pool size and turnover rate of the cycled co-substrate. Expanding from this insight and using simulations, we show that regulation of co-substrate pool size can allow regulation of flux dynamics in branched and coupled pathways. To support theses theoretical insights, we analysed existing flux measurements and enzyme levels from the central carbon metabolism and identified several reactions that could be limited by co-substrate cycling. We discuss how the limitations imposed by co-substrate cycling provide experimentally testable hypotheses on specific metabolic phenotypes. We conclude that measuring and controlling co-substrate pools is crucial for understanding and engineering the dynamics of metabolism.

show abstract

Self-replenishment cycles generate a threshold response

Cited by 6 publications

References 49 publications

Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes

Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes

Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes

Co-substrate pools can constrain and regulate pathway fluxes in cell metabolism

Contact Info

Product

Resources

About