scholarly journals Genome-scale metabolic rewiring to achieve predictable titers rates and yield of a non-native product at scale

Author(s):  
Deepanwita Banerjee ◽  
Thomas Eng ◽  
Andrew K. Lau ◽  
Brenda Wang ◽  
Yusuke Sasaki ◽  
...  

AbstractAchieving high titer rates and yields (TRY) remains a bottleneck in the production of heterologous products through microbial systems, requiring elaborate engineering and many iterations. Reliable scaling of engineered strains is also rarely addressed in the first designs of the engineered strains. Both high TRY and scale are challenging metrics to achieve due to the inherent trade-off between cellular use of carbon towards growth vs. target metabolite production. We hypothesized that being able to strongly couple product formation with growth may lead to improvements across both metrics. In this study, we use elementary mode analysis to predict metabolic reactions that could be targeted to couple the production of indigoidine, a sustainable pigment, with the growth of the chosen host, Pseudomonas putida KT2440. We then filtered the set of 16 predicted reactions using -omics data. We implemented a total of 14 gene knockdowns using a CRISPRi method optimized for P. putida and show that the resulting engineered P. putida strain could achieve high TRY. The engineered pairing of product formation with carbon use also shifted production from stationary to exponential phase and the high TRY phenotype was maintained across scale. In one design cycle, we constructed an engineered P. putida strain that demonstrates close to 50% maximum theoretical yield (0.33 g indigoidine/g glucose consumed), reaching 25.6 g/L indigoidine and a rate of 0.22g/l/h in exponential phase. These desirable phenotypes were maintained from batch to fed-batch cultivation mode, and from 100ml shake flasks to 250 mL ambr® and 2 L bioreactors.

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Deepanwita Banerjee ◽  
Thomas Eng ◽  
Andrew K. Lau ◽  
Yusuke Sasaki ◽  
Brenda Wang ◽  
...  

Abstract High titer, rate, yield (TRY), and scalability are challenging metrics to achieve due to trade-offs between carbon use for growth and production. To achieve these metrics, we take the minimal cut set (MCS) approach that predicts metabolic reactions for elimination to couple metabolite production strongly with growth. We compute MCS solution-sets for a non-native product indigoidine, a sustainable pigment, in Pseudomonas putida KT2440, an emerging industrial microbe. From the 63 solution-sets, our omics guided process identifies one experimentally feasible solution requiring 14 simultaneous reaction interventions. We implement a total of 14 genes knockdowns using multiplex-CRISPRi. MCS-based solution shifts production from stationary to exponential phase. We achieve 25.6 g/L, 0.22 g/l/h, and ~50% maximum theoretical yield (0.33 g indigoidine/g glucose). These phenotypes are maintained from batch to fed-batch mode, and across scales (100-ml shake flasks, 250-ml ambr®, and 2-L bioreactors).


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Philipp Noll ◽  
Chantal Treinen ◽  
Sven Müller ◽  
Lars Lilge ◽  
Rudolf Hausmann ◽  
...  

AbstractA key challenge to advance the efficiency of bioprocesses is the uncoupling of biomass from product formation, as biomass represents a by-product that is in most cases difficult to recycle efficiently. Using the example of rhamnolipid biosurfactants, a temperature-sensitive heterologous production system under translation control of a fourU RNA thermometer from Salmonella was established to allow separating phases of preferred growth from product formation. Rhamnolipids as bulk chemicals represent a model system for future processes of industrial biotechnology and are therefore tied to the efficiency requirements in competition with the chemical industry. Experimental data confirms function of the RNA thermometer and suggests a major effect of temperature on specific rhamnolipid production rates with an increase of the average production rate by a factor of 11 between 25 and 38 °C, while the major part of this increase is attributable to the regulatory effect of the RNA thermometer rather than an unspecific overall increase in bacterial metabolism. The production capacity of the developed temperature sensitive-system was evaluated in a simple batch process driven by a temperature switch. Product formation was evaluated by efficiency parameters and yields, confirming increased product formation rates and product-per-biomass yields compared to a high titer heterologous rhamnolipid production process from literature.


2020 ◽  
Vol 45 ◽  
pp. 101767
Author(s):  
Lujing Ren ◽  
Xiaoman Sun ◽  
Lihui Zhang ◽  
Quanyu Zhao ◽  
He Huang

2004 ◽  
Vol 1 (1) ◽  
pp. 52-63
Author(s):  
M. Dünßer ◽  
R. Lampidis ◽  
S. Schmidt ◽  
D. Seipel ◽  
T. Dandekar

Summary Integration of data in pathogenomics is achieved here considering three different levels of cellular complexity: (i) genome and comparative genomics, (ii) enzyme cascades and pathway analysis, (iii) networks including metabolic network analysis.After direct sequence annotation exploiting tools for protein domain annotation (e.g. AnDOM) and analysis of regulatory elements (e.g. the RNA analyzer tool) the analysis results from extensive comparative genomics are integrated for the first level, pathway alignment adds data for the pathway level, elementary mode analysis and metabolite databanks add to the third level of cellular complexity. For efficient data integration of all data the XML based platform myBSMLStudio2003 is discussed and developed here. It integrates XQuery capabilities, automatic scripting updates for sequence annotation and a JESS expert system shell for functional annotation. In the context of genome annotation platforms in place (GenDB, PEDANT) these different tools and approaches presented here allow improved functional genome annotation as well as data integration in pathogenomics.


2017 ◽  
Author(s):  
Sahely Bhadra ◽  
Peter Blomberg ◽  
Sandra Castillo ◽  
Juho Rousu

AbstractMotivationIn the analysis of metabolism using omics data, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and Stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, produce results that are readily interpretable in terms of metabolic flux modes, however, they are not best suited for exploratory analysis on a large set of samples.ResultsWe propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and Stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a Stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology.AvailabilityMatlab software for PMFA and SPMFA is available in https://github.com/ aalto-ics-kepaco/[email protected], [email protected], [email protected], [email protected] informationDetailed results are in Supplementary files. Supplementary data are available at https://github.com/aalto-ics-kepaco/PMFA/blob/master/Results.zip.


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