Cyclodextrin glycosyltransferase biosynthesis improvement by recombinantLactococcus lactisNZ:NSP:CGT: medium formulation and culture condition optimization

Challenges in developing cell culture media using machine learning

Microbial and mammalian cells are widely used in the food, pharmaceutical, and medical industries. Developing or optimizing culture media is essential to improve cell culture performance as a critical technology in cell culture engineering. Methodologies for media optimization have been developed to a great extent, such as the approaches of one-factor-at-a-time (OFAT) and response surface methodology (RSM). The present review introduces the emerging machine learning (ML) technology in cell culture engineering by combining high-throughput experimental technologies to develop highly efficient and effective culture media. The commonly used ML algorithms and the successful applications of employing ML in medium optimization are summarized. This review highlights the benefits of ML-assisted medium development and guides the selection of the media optimization method appropriate for various cell culture purposes.

Efficient Bioconversion of Stevioside and Rebaudioside A to Glucosylated Steviol Glycosides Using an Alkalihalobacillus oshimesis-Derived Cyclodextrin Glucanotransferase

The enzymatic transglycosylation of steviol glycosides can improve the edulcorant quality of steviol glycosides. Cyclodextrin glucanotransferase (CGTase) is one of the most popular glucanotransferases applied in this reaction. Herein, the CGTase-producing strain Alkalihalobacillus oshimensis CGMCC 23164 was isolated from Stevia planting soil. Using mass spectrometry-based secretome profiling, a high-efficiency CGTase that converted steviol glycosides to glucosylated steviol glycosides was identified and termed CGTase-13. CGTase-13 demonstrated optimal transglycosylation activity with 10 g/L steviol glycoside and 50 g/L soluble starch as substrates at <40 °C. Under the above conditions, the conversion rate of stevioside and rebaudioside A, two main components of steviol glycosides, reached 86.1% and 90.8%, respectively. To the best of our knowledge, this is the highest conversion rate reported to date. Compared with Toruzyme^® 3.0 L, the commonly used commercial enzyme blends, glucosylated steviol glycosides produced using CGTase-13 exhibited weaker astringency and unpleasant taste, faster sweetness onset, and stronger sweetness intensity. Thus, CGTase provides a novel option for producing high-quality glucosylated steviol glycoside products and has great potential for industrial applications.

Statistical Design of Experiments for Synthetic Biology

The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.

Transglycosylation of Steviol Glycosides and Rebaudioside A: Synthesis Optimization, Structural Analysis and Sensory Profiles

To improve flavor profiles, three cyclodextrin glucosyltransferases (CGTases) from different bacteriological sources, Paenibacillus macerans, Geobacillus sp. and Thermoanaerobacter sp., were used with an extract of steviol glycosides (SVglys) and rebaudioside A (RebA) as acceptor substrates in two parallel sets of reactions. A central composite experimental design was employed to maximize the concentration of glucosylated species synthesized, considering temperature, pH, time of reaction, enzymatic activity, maltodextrin concentration and SVglys/RebA concentration as experimental factors, together with their interactions. Liquid chromatography coupled to a diode-array detector (LC-DAD), liquid chromatography-mass spectrometry (LC-ESI-MS) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) were used to characterize and identify the chemical structures obtained along the optimization. To assess the impact on the sensory properties, a sensory analysis was carried out with a group of panelists that evaluated up to 16 sensorial attributes. CGTase transglucosylation of the C-13 and/or C-19 led to the addition of up to 11 glucose units to the steviol aglycone, which meant the achievement of enhanced sensory profiles due to a diminution of bitterness and licorice appreciations. The outcome herein obtained supposes the development of new potential alternatives to replace free sugars with low-calorie sweeteners with added health benefits.

Prediction of Human Induced Pluripotent Stem Cell Cardiac Differentiation Outcome by Multifactorial Process Modeling

Human cardiomyocytes (CMs) have potential for use in therapeutic cell therapy and high-throughput drug screening. Because of the inability to expand adult CMs, their large-scale production from human pluripotent stem cells (hPSC) has been suggested. Significant improvements have been made in understanding directed differentiation processes of CMs from hPSCs and their suspension culture-based production at chemically defined conditions. However, optimization experiments are costly, time-consuming, and highly variable, leading to challenges in developing reliable and consistent protocols for the generation of large CM numbers at high purity. This study examined the ability of data-driven modeling with machine learning for identifying key experimental conditions and predicting final CM content using data collected during hPSC-cardiac differentiation in advanced stirred tank bioreactors (STBRs). Through feature selection, we identified process conditions, features, and patterns that are the most influential on and predictive of the CM content at the process endpoint, on differentiation day 10 (dd10). Process-related features were extracted from experimental data collected from 58 differentiation experiments by feature engineering. These features included data continuously collected online by the bioreactor system, such as dissolved oxygen concentration and pH patterns, as well as offline determined data, including the cell density, cell aggregate size, and nutrient concentrations. The selected features were used as inputs to construct models to classify the resulting CM content as being "sufficient" or "insufficient" regarding pre-defined thresholds. The models built using random forests and Gaussian process modeling predicted insufficient CM content for a differentiation process with 90% accuracy and precision on dd7 of the protocol and with 85% accuracy and 82% precision at a substantially earlier stage: dd5. These models provide insight into potential key factors affecting hPSC cardiac differentiation to aid in selecting future experimental conditions and can predict the final CM content at earlier process timepoints, providing cost and time savings. This study suggests that data-driven models and machine learning techniques can be employed using existing data for understanding and improving production of a specific cell type, which is potentially applicable to other lineages and critical for realization of their therapeutic applications.

Rapid Evaluation and Optimization of Medium Components Governing Tryptophan Production by Pediococcus acidilactici TP-6 Isolated from Malaysian Food via Statistical Approaches

Tryptophan is one of the most extensively used amino acids in livestock industry owing to its effectiveness in enhancing the growth performance of animals. Conventionally, the production of tryptophan relies heavily on genetically modified Escherichia coli but its pathogenicity is a great concern. Our recent study demonstrated that a lactic acid bacterium (LAB), Pediococcus acidilactici TP-6 that isolated from Malaysian food was a promising tryptophan producer. However, the tryptophan production must enhance further for viable industrial application. Hence, the current study evaluated the effects of medium components and optimized the medium composition for tryptophan production by P. acidilactici TP-6 statistically using Plackett-Burman Design, and Central Composite Design. The optimized medium containing molasses (14.06 g/L), meat extract (23.68 g/L), urea (5.56 g/L) and FeSO4 (0.024 g/L) significantly enhanced the tryptophan production by 150% as compared to the control de Man, Rogosa and Sharpe medium. The findings obtained in this study revealed that rapid evaluation and effective optimization of medium composition governing tryptophan production by P. acidilactici TP-6 were feasible via statistical approaches. Additionally, the current findings reveal the potential of utilizing LAB as a safer alternative tryptophan producer and provides insight for future exploitation of various amino acid productions by LAB.

Optimized medium via statistical approach enhanced threonine production by Pediococcus pentosaceus TL-3 isolated from Malaysian food

BACKGROUND

Threonine is an essential amino acid that is extensively used in livestock industry as feed supplement due to its pronounced effect in improving the growth performance of animals. Application of genetically engineered bacteria for amino acid production has its share of controversies after eosinophils myalgia syndrome outbreak in 1980s. This has urged for continuous search for a food grade producer as a safer alternative for industrial amino acid production. Lactic acid bacteria (LAB) appear as an exceptional candidate owing to their non-pathogenic nature and reputation of Generally Recognized as Safe (GRAS) status. Recently, we have identified a LAB, Pediococcus pentosaceus TL-3, isolated from Malaysian food as a potential threonine producer. Thus, the objective of this study was to enhance the threonine production by P. pentosaceus TL-3 via optimized medium developed by using Plackett-Burman design (PBD) and central composite design (CCD).

RESULTS

Molasses, meat extract, (NH4)2SO4, and MnSO4 were identified as the main medium components for threonine production by P. pentosaceus TL-3. The optimum concentration of molasses, meat extract, (NH4)2SO4 and MnSO4 were found to be 30.79 g/L, 25.30 g/L, 8.59 g/L, and 0.098 g/L respectively based on model obtained in CCD with a predicted net threonine production of 123.07 mg/L. The net threonine production by P. pentosaceus TL-3 in the optimized medium was enhanced approximately 2 folds compared to the control.

CONCLUSIONS

This study has revealed the potential of P. pentosaceus TL-3 as a safer alternative to produce threonine. Additionally, the current study has identified the key medium components affecting the production of threonine by P. pentosaceus TL-3, followed by optimization of their concentrations by means of statistical approach. The findings of this study could act as a guideline for the future exploration of amino acid production by LAB.

Purification and properties of beta-cyclomaltodextrin glucanotransferase from Bacillus flexus SV 1

Cyclomaltodextrin glucanotransferase is a unique enzyme that degrades starch into cyclic oligosaccharides called cyclodextrins, which have numerous applications in various industries such as pharmaceutical, textile, agricultural, cosmetics etc. Due to its wide applications, microorganism producing one type of cyclodextrin is of interest as it simplifies the down streaming process of separating mixture of cyclodextrins. In the present study, β-CGTase was isolated from Bacillus flexus SV 1 and biochemically characterized. Enzyme was purified by starch adsorption followed by DEAE cellulose column chromatography which resulted in a fold purification of 6.1, with a yield of 44.07%. Molecular weight of the purified enzyme was found to be 96.68 kDa, enzyme was monomeric in nature with a K_m and V_max of 0.08976 μmol mL^-1 and 585.1 μmol/ml/min, respectively. Optimum pH and temperature of the purified enzyme was found to be 8.0 and 60 °C. Ca²⁺ showed significant increase in enzyme activity. The inhibition of enzyme by EDTA indicates that CGTase is a metalloenzyme. CGTase produced majorly β-CD and was alkalotolarent and active at high temperatures which is a promising candidate for various industries such as textile, food, agriculture, and pharmaceuticals.