Escherichia coli coculture for de novo production of esters derived of methyl-branched alcohols and multi-methyl branched fatty acids

Background A broad diversity of natural and non-natural esters have now been made in bacteria, and in other microorganisms, as a result of original metabolic engineering approaches. However, the fact that the properties of these molecules, and therefore their applications, are largely defined by the structural features of the fatty acid and alcohol moieties, has driven a persistent interest in generating novel structures of these chemicals. Results In this research, we engineered Escherichia coli to synthesize de novo esters composed of multi-methyl-branched-chain fatty acids and short branched-chain alcohols (BCA), from glucose and propionate. A coculture engineering strategy was developed to avoid metabolic burden generated by the reconstitution of long heterologous biosynthetic pathways. The cocultures were composed of two independently optimized E. coli strains, one dedicated to efficiently achieve the biosynthesis and release of the BCA, and the other to synthesize the multi methyl-branched fatty acid and the corresponding multi-methyl-branched esters (MBE) as the final products. Response surface methodology, a cost-efficient multivariate statistical technique, was used to empirical model the BCA-derived MBE production landscape of the coculture and to optimize its productivity. Compared with the monoculture strategy, the utilization of the designed coculture improved the BCA-derived MBE production in 45%. Finally, the coculture was scaled up in a high-cell density fed-batch fermentation in a 2 L bioreactor by fine-tuning the inoculation ratio between the two engineered E. coli strains. Conclusion Previous work revealed that esters containing multiple methyl branches in their molecule present favorable physicochemical properties which are superior to those of linear esters. Here, we have successfully engineered an E. coli strain to broaden the diversity of these molecules by incorporating methyl branches also in the alcohol moiety. The limited production of these esters by a monoculture was considerable improved by a design of a coculture system and its optimization using response surface methodology. The possibility to scale-up this process was confirmed in high-cell density fed-batch fermentations.

Effects of Caprolactam Wastewater on Algal Growth and Nutrients Removal by Arthrospira platensis

Caprolactam wastewater (WCP), which is generated during the production of caprolactam, contains high contents of NO3− and inorganic P and is considered to be difficult to treat. In this study, Arthrospira platensis was used to remove N and P from WCP. Culture conditions and wastewater addition were optimized to relieve the inhibition effects of WCP. The results show that A. platensis growth and photosynthetic activity were inhibited depending on WCP concentrations. The inhibition rates were enhanced as the culture time increased under batch mode. However, the fed-batch mode significantly minimized the negative impact on A. platensis, which is beneficial for removing N and P from WCP by Arthrospira. After 10 d of cultivation of A. platensis in a 25 L circular photobioreactor in fed-batch addition of WCP (1.25% mixed WCP (v/v) each day), the average biomass productivity reached 17.48 g/(m2·d), the maximum protein content was 69.93%, and the N and P removal ratios were 100%. The accumulation effect of WCP inhibition on algal growth was not observed under this culture condition. Fed-batch cultivation of A. platensis is a promising way for bioremediation of WCP with high N and P removal efficiencies and high value-added biomass production.

Hybrid Gaussian Process Models for continuous time series in bolus fed-batch cultures

Hybrid modeling, meaning the integration of data-driven and knowledge-based methods, is quickly gaining popularity among many research fields, including bioprocess engineering and development. Recently, the data-driven part of hybrid methods have been largely extended with machine learning algorithms (e.g., artificial neural network, support vector regression), while the mechanistic part is typically using differential equations to describe the dynamics of the process based on its current state. In this work we present an alternative hybrid model formulation that merges the advantages of Gaussian Process State Space Models and the numerical approximation of differential equation systems through full discretization. The use of Gaussian Process Models to describe complex bioprocesses in batch, fed-batch, has been reported in several applications. Nevertheless, handling the dynamics of the states of the system, known to have a continuous time-dependent evolution governed by implicit dynamics, has proven to be a major challenge. Discretization of the process on the sampling steps is a source of several complications, as are: 1) not being able to handle multi-rate date sets, 2) the step-size of the derivative approximation is defined by the sampling frequency, and 3) a high sensitivity to sampling and addition errors. We present a coupling of polynomial regression with Gaussian Process Models as representation of the right-hand side of the ordinary differential equation system and demonstrate the advantages in a typical fed-batch cultivation for monoclonal antibody production.

Process engineering of biopharmaceutical production in moss bioreactors via model-based description and evaluation of phytohormone impact

The moss Physcomitrella is an interesting production host for recombinant biopharmaceuticals. Here we produced MFHR1, a synthetic complement regulator which has been proposed for the treatment of diseases associated to the complement system as part of human innate immunity. We studied the impact of different operation modes for the production process in 5 L stirred-tank photobioreactors. The total amount of recombinant protein was doubled by using fed-batch or batch compared to semi-continuous operation, although the maximum specific productivity (mg MFHR1/g FW) increased just by 35%. We proposed an unstructured kinetic model which fits accurately with the experimental data in batch and semi-continuous operation under autotrophic conditions with 2% CO2 enrichment. The model is able to predict recombinant protein production, nitrate uptake and biomass growth, which is useful for process control and optimization. We investigated strategies to further increase MFHR1 production. While mixotrophic and heterotrophic conditions decreased the MFHR1-specific productivity compared to autotrophic conditions, addition of the phytohormone auxin (NAA, 10 μM) to the medium enhanced it by 470% in shaken flasks and up to 230% and 260%, in batch and fed-batch bioreactors, respectively. Supporting this finding, the auxin-synthesis inhibitor L-Kynurenine (100 μM) decreased MFHR1 production significantly by 110% and 580% at day 7 and 18, respectively. Expression analysis revealed that the MFHR1 transgene, driven by the Physcomitrella actin5 (PpAct5) promoter, was upregulated 16 hours after NAA addition and remained enhanced over the whole process, whereas the auxin-responsive gene PpIAA1A was upregulated within the first two hours, indicating that the effect of auxin on PpAct5 promoter-driven expression is indirect. Furthermore, the day of NAA supplementation was crucial, leading to an up to 8-fold increase of MFHR1-specific productivity (0.82 mg MFHR1/ g fresh weight, 150 mg accumulated over 7 days) compared to the productivity reported previously. Our findings are likely to be applicable to other plant-based expression systems to increase biopharmaceutical production and yields.

Towards closed carbon loop fermentations: co-feeding of Yarrowia lipolytica with glucose and formic acid

A novel fermentation process was developed in which renewable electricity is indirectly used as a fermentation substrate, synergistically decreasing both the consumption of sugar as a first generation carbon source and emission of the greenhouse gas CO2. To achieve this, a glucose-based process is co-fed with formic acid, which can be generated by capturing CO2 from fermentation offgas followed by electrochemical reduction with renewable electricity. This ‘closed carbon loop’ concept is demonstrated by a case study in which co-feeding formic acid is shown to significantly increase the yield of biomass on glucose of the industrially relevant yeast species Yarrowia lipolytica. First, the optimal feed ratio of formic acid to glucose is established using chemostat cultivations. Subsequently, guided by a dynamic fermentation process model, a fed-batch protocol is developed and demonstrated on laboratory scale. Finally, the developed fed-batch process is proven to be scalable to pilot scale. An extension of this proven concept to also recycle the O2 that is co-generated with the formic acid to the fermentation process for intensification purposes, and a potential further application of the concept to anaerobic fermentations are discussed.