scholarly journals Streamlining the Analysis of Dynamic 13C-Labeling Patterns for the Metabolic Engineering of Corynebacterium glutamicum as l-Histidine Production Host

Metabolites ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 458
Author(s):  
André Feith ◽  
Andreas Schwentner ◽  
Attila Teleki ◽  
Lorenzo Favilli ◽  
Bastian Blombach ◽  
...  

Today’s possibilities of genome editing easily create plentitudes of strain mutants that need to be experimentally qualified for configuring the next steps of strain engineering. The application of design-build-test-learn cycles requires the identification of distinct metabolic engineering targets as design inputs for subsequent optimization rounds. Here, we present the pool influx kinetics (PIK) approach that identifies promising metabolic engineering targets by pairwise comparison of up- and downstream 13C labeling dynamics with respect to a metabolite of interest. Showcasing the complex l-histidine production with engineered Corynebacterium glutamicuml-histidine-on-glucose yields could be improved to 8.6 ± 0.1 mol% by PIK analysis, starting from a base strain. Amplification of purA, purB, purH, and formyl recycling was identified as key targets only analyzing the signal transduction kinetics mirrored in the PIK values.

2020 ◽  
Author(s):  
Sophia Tsouka ◽  
Meric Ataman ◽  
Tuure Hameri ◽  
Ljubisa Miskovic ◽  
Vassily Hatzimanikatis

AbstractThe advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolic concentrations, it fails to account for the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework based on MCA, Network Response Analysis (NRA), for the rational genetic strain design that incorporates biologically relevant constraints, as well as genome editing restrictions. The NRA core constraints being similar to the ones of Flux Balance Analysis, allow it to be used for a wide range of optimization criteria and with various physiological constraints. We show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.


Author(s):  
Christopher J. Robinson ◽  
Jonathan Tellechea-Luzardo ◽  
Pablo Carbonell ◽  
Adrian J. Jervis ◽  
Cunyu Yan ◽  
...  

Metabolic engineering technologies have been employed with increasing success over the last three decades for the engineering and optimization of industrial host strains to competitively produce high-value chemical targets. To this end, continued reductions in the time taken from concept, to development, to scale-up are essential. Design–Build–Test–Learn pipelines that are able to rapidly deliver diverse chemical targets through iterative optimization of microbial production strains have been established. Biofoundries are employing in silico tools for the design of genetic parts, alongside combinatorial design of experiments approaches to optimize selection from within the potential design space of biological circuits based on multi-criteria objectives. These genetic constructs can then be built and tested through automated laboratory workflows, with performance data analysed in the learn phase to inform further design. Successful examples of rapid prototyping processes for microbially produced compounds reveal the potential role of biofoundries in leading the sustainable production of next-generation bio-based chemicals.


2020 ◽  
Vol 54 (2) ◽  
pp. 137-146
Author(s):  
G. S. Andriiash ◽  
O. S. Sekan ◽  
O. O. Tigunova ◽  
Ya. B. Blume ◽  
S. M. Shulga

2020 ◽  
Vol 86 (8) ◽  
Author(s):  
Meijuan Xu ◽  
Mi Tang ◽  
Jiamin Chen ◽  
Taowei Yang ◽  
Xian Zhang ◽  
...  

ABSTRACT PII signal transduction proteins are ubiquitous and highly conserved in bacteria, archaea, and plants and play key roles in controlling nitrogen metabolism. However, research on biological functions and regulatory targets of PII proteins remains limited. Here, we illustrated experimentally that the PII protein Corynebacterium glutamicum GlnK (CgGlnK) increased l-arginine yield when glnK was overexpressed in Corynebacterium glutamicum. Data showed that CgGlnK regulated l-arginine biosynthesis by upregulating the expression of genes of the l-arginine metabolic pathway and interacting with N-acetyl-l-glutamate kinase (CgNAGK), the rate-limiting enzyme in l-arginine biosynthesis. Further assays indicated that CgGlnK contributed to alleviation of the feedback inhibition of CgNAGK caused by l-arginine. In silico analysis of the binding interface of CgGlnK-CgNAGK suggested that the B and T loops of CgGlnK mainly interacted with C and N domains of CgNAGK. Moreover, F11, R47, and K85 of CgGlnK were identified as crucial binding sites that interact with CgNAGK via hydrophobic interaction and H bonds, and these interactions probably had a positive effect on maintaining the stability of the complex. Collectively, this study reveals PII-NAGK interaction in nonphotosynthetic microorganisms and further provides insights into the regulatory mechanism of PII on amino acid biosynthesis in corynebacteria. IMPORTANCE Corynebacteria are safe industrial producers of diverse amino acids, including l-glutamic acid and l-arginine. In this study, we showed that PII protein GlnK played an important role in l-glutamic acid and l-arginine biosynthesis in C. glutamicum. Through clarifying the molecular mechanism of CgGlnK in l-arginine biosynthesis, the novel interaction between CgGlnK and CgNAGK was revealed. The alleviation of l-arginine inhibition of CgNAGK reached approximately 48.21% by CgGlnK addition, and the semi-inhibition constant of CgNAGK increased 1.4-fold. Furthermore, overexpression of glnK in a high-yield l-arginine-producing strain and fermentation of the recombinant strain in a 5-liter bioreactor led to a remarkably increased production of l-arginine, 49.978 g/liter, which was about 22.61% higher than that of the initial strain. In conclusion, this study provides a new strategy for modifying amino acid biosynthesis in C. glutamicum.


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