Studies on process optimization methods for rapamycin production using Streptomyces hygroscopicus ATCC 29253

Insights into the roles of exogenous phenylalanine and tyrosine in improving rapamycin production of Streptomyces rapamycinicus with transcriptome analysis

Rapamycin is an important natural macrolide antibiotic with antifungal, immunosuppressive and antitumor activities produced by Streptomyces rapamycinicus. However, their prospective applications are limited by low fermentation units. In this study, we found that the exogenous aromatic amino acids phenylalanine and tyrosine could effectively increase the yield of rapamycin in industrial microbial fermentation. To gain insight into the mechanism of rapamycin overproduction, comparative transcriptomic profiling was performed between media with and without phenylalanine and tyrosine addition. The results showed that the addition of phenylalanine and tyrosine upregulated the transcription levels of genes involved in rapamycin biosynthesis, precursor production, and transporters. In addition, the transcription levels of many carbohydrate metabolism-related genes were down-regulated, leading to a decrease in growth, suggesting that balancing cell growth and rapamycin biosynthesis may be important to promote efficient biosynthesis of rapamycin in Streptomyces rapamycinicus. These results provide a basis for understanding physiological roles of phenylalanine and tyrosine, and a new way to increase rapamycin production in Streptomyces cultures.

Optimization of medium components for protein production by Escherichia coli with a high-throughput pipeline that uses a deep neural network

To optimize rapidly the medium for green fluorescent protein expression by Escherichia coli with an introduced plasmid, pRSET/emGFP, a single-cycle optimization pipeline was applied. The pipeline included a deep neural network (DNN) and mathematical optimization algorithms with simultaneous optimization of 18 medium components. To evaluate the DNN data sampling method, two methods, orthogonal array (OA) and Latin hypercube sampling (LHS), were used to design 64 initial media for each sampling method. The OA- and LHS-based data sampling resulted in green fluorescent protein fluorescence intensities of 0.088 × 103-1.85 × 104 and 3.30 × 103-1.50 × 104, respectively. Fifty DNN models were built using the OA and LHS datasets. Hold-out validation was performed using 15 % test of OA and LHS data. Mean square errors of the DNN models were 0.015-0.64, indicating the estimation accuracies were sufficient. However, the sensitivities of components in the DNN models varied and were grouped into six major classes by the index of k-means clustering. A representative model was selected for each class. Mathematical optimization algorithms using Bayesian optimization and genetic algorithm were applied to the representative models, and representative optimized medium (OM) compositions were selected by k-means clustering from the proposed OMs. A total of 54 OMs were obtained from the OA and LHS datasets. In the validating cultivation, the best OMs of OA and LHS were 2.12-fold and 2.13-fold higher, respectively, than those of the learning data.

Formulation of cost-effective medium and optimization studies for enhanced production of rapamycin

BACKGROUND

Enhancing rapamycin production using a cost-effective medium is crucial for wider accessibility, reduced manufacturing costs, sustainable pharmaceutical practices, and advancements in therapeutic applications. It promotes global health, biotechnological innovation, research collaboration, and societal well-being through affordable and effective treatments. This study focuses on the development of a novel cost-effective production medium for the synthesis of rapamycin from Streptomyces hygroscopicus.

RESULTS

In the initial screening, more rapamycin production was observed in medium A. Initially, the organism produced 10 µg/mL rapamycin. Based on the OFT results, a novel cost-effective medium composition was designed, incorporating soyabean, sugarcane juice, and dried tomato components. Using RSM, soyabean and tomato was found to be more significant in rapamycin production than sugarcane. In the optimized medium, the production of rapamycin increased significantly to 24 µg/mL. Furthermore, a comparative analysis of the growth kinetics between the production normal medium (referred to as production medium A) and the newly optimized cost-effective production medium revealed that the optimized cost-effective production medium significantly enhanced the production of rapamycin.

CONCLUSION

Overall, this study demonstrates the successful development of a cost-effective production medium for rapamycin synthesis from S. hygroscopicus. The findings highlight the potential of using a cost-effective medium to enhance the production of a valuable secondary metabolite, rapamycin, while reducing production costs.

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.

Enhancement of rapamycin production by metabolic engineering in Streptomyces hygroscopicus based on genome-scale metabolic model

Rapamycin, as a macrocyclic polyketide with immunosuppressive, antifungal, and anti-tumor activity produced by Streptomyces hygroscopicus, is receiving considerable attention for its significant contribution in medical field. However, the production capacity of the wild strain is very low. Hereby, a computational guided engineering approach was proposed to improve the capability of rapamycin production. First, a genome-scale metabolic model of Streptomyces hygroscopicus ATCC 29253 was constructed based on its annotated genome and biochemical information. The model consists of 1003 reactions, 711 metabolites after manual refinement. Subsequently, several potential genetic targets that likely guaranteed an improved yield of rapamycin were identified by flux balance analysis and minimization of metabolic adjustment algorithm. Furthermore, according to the results of model prediction, target gene pfk (encoding 6-phosphofructokinase) was knocked out, and target genes dahP (encoding 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase) and rapK (encoding chorismatase) were overexpressed in the parent strain ATCC 29253. The yield of rapamycin increased by 30.8% by knocking out gene pfk and increased by 36.2 and 44.8% by overexpression of rapK and dahP, respectively, compared with parent strain. Finally, the combined effect of the genetic modifications was evaluated. The titer of rapamycin reached 250.8 mg/l by knockout of pfk and co-expression of genes dahP and rapK, corresponding to a 142.3% increase relative to that of the parent strain. The relationship between model prediction and experimental results demonstrates the validity and rationality of this approach for target identification and rapamycin production improvement.

An overview of rapamycin: from discovery to future perspectives

Rapamycin is an immunosuppressive metabolite produced from several actinomycete species. Besides its immunosuppressive activity, rapamycin and its analogs have additional therapeutic potentials, including antifungal, antitumor, neuroprotective/neuroregenerative, and lifespan extension activities. The core structure of rapamycin is derived from (4R,5R)-4,5-dihydrocyclohex-1-ene-carboxylic acid that is extended by polyketide synthase. The resulting linear polyketide chain is cyclized by incorporating pipecolate and further decorated by post-PKS modification enzymes. Herein, we review the discovery and biological activities of rapamycin as well as its mechanism of action, mechanistic target, biosynthesis, and regulation. In addition, we introduce the many efforts directed at enhancing the production of rapamycin and generating diverse analogs and also explore future perspectives in rapamycin research. This review will also emphasize the remarkable pilot studies on the biosynthesis and production improvement of rapamycin by Dr. Demain, one of the world's distinguished scientists in industrial microbiology and biotechnology.

Comparative metabolic profiling reveals the key role of amino acids metabolism in the rapamycin overproduction by Streptomyces hygroscopicus

Rapamycin is an important natural macrolide antibiotic with antifungal, immunosuppressive and anticancer activity produced by Streptomyces hygroscopicus. In this study, a mutant strain obtained by ultraviolet mutagenesis displayed higher rapamycin production capacity compared to the wild-type S. hygroscopicus ATCC 29253. To gain insights into the mechanism of rapamycin overproduction, comparative metabolic profiling between the wild-type and mutant strain was performed. A total of 86 metabolites were identified by gas chromatography–mass spectrometry. Pattern recognition methods, including principal component analysis, partial least squares and partial least squares discriminant analysis, were employed to determine the key biomarkers. The results showed that 22 potential biomarkers were closely associated with the increase of rapamycin production and the tremendous metabolic difference was observed between the two strains. Furthermore, metabolic pathway analysis revealed that amino acids metabolism played an important role in the synthesis of rapamycin, especially lysine, valine, tryptophan, isoleucine, glutamate, arginine and ornithine. The inadequate supply of amino acids, or namely “nitrogen starvation” occurred in the mutant strain. Subsequently, the exogenous addition of amino acids into the fermentation medium of the mutant strain confirmed the above conclusion, and rapamycin production of the mutant strain increased to 426.7 mg/L after adding lysine, approximately 5.8-fold of that in the wild-type strain. Finally, the results of real-time PCR and enzyme activity assays demonstrated that dihydrodipicolinate synthase involved with lysine metabolism played vital role in the biosynthesis of rapamycin. These findings will provide a theoretical basis for further improving production of rapamycin.