l-Threonine production from whey and fish hydrolysate by E. coli ATCC® 21277TM.

In this work production of l-threonine by Escherichia coli ATCC® 21277™ has been studied using a mixture of alternative low-cost substrates, which are recognized to be a major pollution problem. Whey was used as the primary carbon source, whereas Red Tilapia (Oreochromis sp.) viscera hydrolysates constituted the nitrogen source. A Box-Behnken Design was used for optimizing l-threonine and biomass production, using temperature and glucose, whey, and Red Tilapia (Oreochromis sp.) viscera hydrolysate contents as factors. Results indicate that biomass production is affected by the concentration of hydrolysate and temperature. On the other hand, l-threonine production is affected by concentration of whey, hydrolysate, and temperature. In this context, it was possible to maximize l-threonine production, but with a detriment on biomass production. The optimal conditions for biomass and l-threonine maximization (after 24 h) were identified and validated experimentally, resulting in biomass and l-threonine production of 0.767 g/L and 0.406 g/L, respectively. This work has shown the technical feasibility of using whey and Red Tilapia (Oreochromis sp.) viscera hydrolysates for the production of l-threonine by E. coli ATCC® 21277TM. Finally, the complications associated to the use of these low-cost complex substrates for the production of l-threonine by E. coli, suggest that more in detail studies (i.e. at the metabolic level) are required in order to propose strategies to increase the process productivity, before its scale up. This is a first step in our long-term goal of developing a production process for i) dealing with the pollution problems caused by those wastes, and ii) strengthen the milk and fish industries which are important poles of the Colombian economy.

Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis.

Flux balance analysis (FBA) is currently the standard method to compute metabolic fluxes in genome-scale networks. Several FBA extensions employing diverse objective functions and/or constraints have been published. Here we propose a hybrid semi-parametric FBA extension that combines mechanistic-level constraints (parametric) with empirical constraints (non-parametric) in the same linear program. A CHO dataset with 27 measured exchange fluxes obtained from 21 reactor experiments served to evaluate the method. The mechanistic constraints were deduced from a reduced CHO-K1 genome-scale network with 686 metabolites, 788 reactions and 210 degrees of freedom. The non-parametric constraints were obtained by principal component analysis of the flux dataset. The two types of constraints were integrated in the same linear program showing comparable computational cost to standard FBA. The hybrid FBA is shown to significantly improve the specific growth rate prediction under different constraints scenarios. A metabolically efficient cell growth feed targeting minimal byproducts accumulation was designed by hybrid FBA. It is concluded that integrating parametric and nonparametric constraints in the same linear program may be an efficient approach to reduce the solution space and to improve the predictive power of FBA methods when critical mechanistic information is missing.

One-Time Optimization of Advanced T Cell Culture Media Using a Machine Learning Pipeline.

The growing application of cell and gene therapies in humans leads to a need for cell type-optimized culture media. Design of Experiments (DoE) is a successful and well known tool for the development and optimization of cell culture media for bioprocessing. When optimizing culture media for primary cells used in cell and gene therapy, traditional DoE approaches that depend on interpretable models will not always provide reliable predictions due to high donor variability. Here we present the implementation of a machine learning pipeline into the DoE-based design of cell culture media to optimize T cell cultures in one experimental step (one-time optimization). We applied a definitive screening design from the DoE toolbox to screen 12 major media components, resulting in 25 (2k + 1) media formulations. T cells purified from a set of four human donors were cultured for 6 days and cell viability on day 3 and cell expansion on day 6 were recorded as response variables. These data were used as a training set in the machine learning pipeline. In the first step, individual models were created for each donor, evaluated and selected for each response variable, resulting in eight final statistical models (R 2 > 0.92, RMSE < 1.5). These statistical models were used to predict T cell viability and expansion for 105 random in silico-generated media formulations for each donor in a grid search approach. With the aim of identifying similar formulations in all donors, the 40 best performing media formulations of each response variable were pooled from all donors (n = 320) and subjected to unsupervised clustering using the k-means algorithm. The median of each media component in each cluster was defined as the cluster media formulation. When these formulations were tested in a new set of donor cells, they not only showed a higher T cell expansion than the reference medium, but also precisely matched the average expansion predicted from the donor models of the training set. In summary, we have shown that the introduction of a machine learning pipeline resulted in a one-time optimized T cell culture medium and is advantageous when working with heterogeneous biological material.

The insertion of bioactive peptides at the C-terminal end of an 11S globulin changes the structural stability and improves the antihypertensive activity

BACKGROUND: The 11S globulin from amaranth is the most abundant storage protein in mature seeds and is well recognized for its nutritional value. We used this globulin to engineer a new protein by adding a four valinetyrosine antihypertensive peptide at its C-terminal end to improve its functionality. The new protein was named AMR5 and expressed in the Escherichia coli BL21-CodonPlus(DE3)-RIL strain using a custom medium (F8PW) designed for this work. RESULTS: The alternative medium allowed for the production of 652 mg/L expressed protein at the flask level, mostly in an insoluble form, and this protein was subjected to in vitro refolding. The spectrometric analysis suggests that the protein adopts a ß/α structure with a small increment of α-helix conformation relative to the native amaranth 11S globulin. Thermal and urea denaturation experiments determined apparent Tm and C1/2 values of 50.4°C and 3.04 M, respectively, thus indicating that the antihypertensive peptide insertion destabilized the modified protein relative to the native one. AMR5 hydrolyzed by trypsin and chymotrypsin showed 14- and 1.3-fold stronger inhibitory activity against angiotensin I-converting enzyme (IC50 of 0.034 mg/mL) than the unmodified protein and the previously reported amaranth acidic subunit modified with antihypertensive peptides, respectively. CONCLUSION: The inserted peptide decreases the structural stability of amaranth 11S globulin and improves its antihypertensive activity.

Analysis of culture media screening data by projection to latent pathways: The case of Pichia pastoris X-33.

Cell culture media formulations contain hundreds of individual components in water solutions which have complex interactions with metabolic pathways. The currently used statistical design methods are empirical and very limited to explore such a large design space. In a previous work we developed a computational method called projection to latent pathways (PLP), which was conceived to maximize covariance between envirome and fluxome data under the constraint of metabolic network elementary flux modes (EFM). More specifically, PLP identifies a minimal set of EFMs (i.e., pathways) with the highest possible correlation with envirome and fluxome measurements. In this paper we extend the concept for the analysis of culture media screening data to investigate how culture medium components up-regulate or down-regulate key metabolic pathways. A Pichia pastoris X-33 strain was cultivated in 26 shake flask experiments with variations in trace elements concentrations and basal medium dilution, based on the standard BSM+PTM1 medium. PLP identified 3 EFMs (growth, maintenance and by-product formation) describing 98.8% of the variance in observed fluxes. Furthermore, PLP presented an overall predictive power comparable to that of PLS regression. Our results show iron and manganese at concentrations close to the PTM1 standard inhibit overall metabolic activity, while the main salts concentration (BSM) affected mainly energy expenditures for cellular maintenance.