A Metabolic Widget Adjusts the Phosphoenolpyruvate-Dependent Fructose Influx in Pseudomonas putida

Chavarría, Max; Goñi-Moreno, Ángel; Lorenzo, Vı́ctor de; Nikel, Pablo I.

doi:10.1128/msystems.00154-16

Cited by 30 publications

(32 citation statements)

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“…This makes the whole of the Cra regulon in this bacterium gravitate around fructose entry. In contrast to what is known for E. coli , no Cra‐related regulation has been observed in P. putida for genes belonging Entner Doudoroff pathway, Krebs cycle or other functions related to central metabolism (Chavarría et al, ). It thus seems that depending on the host, Cra may behave either as a virtually global regulator (e.g.…”

Section: Introductionmentioning

confidence: 90%

“…This member of GalR‐LacI family is also known as FruR because it was first identified in Escherichia coli and Salmonella typhimurium as a repressor that inhibited the genes encoding the fructose‐specific phosphotransferase (PTS Fru ) system when the sugar is not available (Chin et al, ; Jahreis et al, ; Ramseier et al, ). Subsequently, control of FruR over PTS Fru has been reported in other bacteria such as Lactococcus lactis (Barrière et al, ), Streptococcus gordonii (Loo et al, ) and Pseudomonas putida (Chavarría et al, ; ; ). Later work, however, revealed that the protein of E. coli downregulates expression of many enzymes of the central metabolism ( pfkA , pykA , pykF , acnB , edd , eda , mtlADR and gapB ; Bledig et al, ; Saier and Ramseier, ; Ow et al, ; Sarkar et al, ).…”

Section: Introductionmentioning

confidence: 96%

“…The regulatory breadth of Cra is much more limited in other species. For instance the tasks of Cra in P. putida are exclusively focused on the fructose transport system (Chavarría et al, ; ; ; ). Specifically, Cra controls the fruBKA operon and one of the four glyceraldehyde 3‐phosphate dehydrogenases (GAPDH) found in this species (Chavarría et al, ; see Fig.…”

Section: Introductionmentioning

confidence: 99%

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The imbroglio of the physiological Cra effector clarified at last

Chavarría

Lorenzo

2018

Molecular Microbiology

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Owing to its role in controlling carbon and energy metabolism, the catabolite repressor/activator protein Cra has been one of the most studied prokaryotic regulators of the last 30 years. Yet, a key mechanistic detail of its biological function - i.e. the nature of the metabolic effector that rules its DNA-binding ability - has remained controversial. Despite the high affinity of Cra for fructose-1-phosphate (F1P), the prevailing view claimed that fructose-1,6-biphosphate (FBP) was the key physiological effector. Building on such responsiveness to FBP, Cra was proposed to act as a glycolytic flux sensor and central regulator of critical metabolic transactions. At the same time, data raised on the Cra protein of Pseudomonas putida ruled out that FBP could be an effector - but instead suggested that it was the unintentional carrier of a small contamination by F1P, the actual signal molecule. While these data on the P. putida Cra were received with skepticism - if not dismissal - by the community of the time, the paper by (Bley-Folly et al, 2018) now demonstrates beyond any reasonable doubt that the one and only effector of E. coli Cra is F1P and that every action of FBP on this regulator can be traced to its systematic mix with the authentic binder.

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Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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The imbroglio of the physiological Cra effector clarified at last

Chavarría

Lorenzo

2018

Molecular Microbiology

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“…More recently, Sudarsan et al in 2016 developed a kinetic model of the β-ketoadipate pathway in P. putida KT2440 that contained mass balance equations for both extracellular and intracellular metabolites described by mechanistic rate expressions based on in vitro investigation of the participating enzymes [30]. Chavarria et al in 2016 modeled the dynamics of fructose uptake in P. putida KT2440 while taking into account the dynamics of gene expression, protein stability, enzymatic activity and the concentrations of intracellular and extracellular metabolites [31].…”

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confidence: 99%

Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies

Tokic

Hatzimanikatis

Mišković

2020

Biotechnol Biofuels

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Background: Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models. Results: In this work, we developed kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamicsbased flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand. Conclusions: The study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals. The curated genome-scale model of P. putida together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.

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“…More recently, Sudarsan et al developed a kinetic model of the b-ketoadipate pathway that contained mass balance equations for both extracellular and intracellular metabolites described by mechanistic rate expressions based on in vitro investigation of the participating enzymes [30]. Chavarria et al modeled the dynamics of fructose uptake while taking into account the dynamics of gene expression, protein stability, enzymatic activity and the concentrations of intracellular and extracellular metabolites [31].…”

mentioning

confidence: 99%

Large-scale kinetic metabolic models ofPseudomonas putidafor a consistent design of metabolic engineering strategies

Tokic

Mišković

Hatzimanikatis

2019

Preprint

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A high tolerance of Pseudomonas putida to toxic compounds and its ability to grow on a wide variety of substrates makes it a promising candidate for the industrial production of biofuels and biochemicals. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models. In this work, we developed large-scale kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites.We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then applied the redGEM and lumpGEM algorithms to reduce the curated iJN1411 model systematically, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we employed the ORACLE framework to generate populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand. Overall, we demonstrated the potential and predictive capabilities of developed kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals.

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A Metabolic Widget Adjusts the Phosphoenolpyruvate-Dependent Fructose Influx in Pseudomonas putida

Cited by 30 publications

References 59 publications

The imbroglio of the physiological Cra effector clarified at last

The imbroglio of the physiological Cra effector clarified at last

Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies

Large-scale kinetic metabolic models ofPseudomonas putidafor a consistent design of metabolic engineering strategies

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