Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp

Improved production of Bacillus subtilis cholesterol oxidase by optimization of process parameters using response surface methodology

BACKGROUND

Cholesterol oxidase has numerous biomedical and industrial applications. In the current study, a new bacterial strain was isolated from sewage and was selected for its high potency for cholesterol degradation (%) and production of high cholesterol oxidase activity (U/OD600).

RESULTS

Based on the sequence of 16S rRNA gene, the bacterium was identified as Bacillus subtilis. The fermentation conditions affecting cholesterol degradation (%) and the activity of cholesterol oxidase (U/OD600) of B. subtilis were optimized through fractional factorial design (FFD) and response surface methodology (RSM). According to this sequential optimization approach, 80.152% cholesterol degradation was achieved by setting the concentrations of cholesterol, inoculum size, and magnesium sulphate at 0.05 g/l, 6%, and 0.05 g/l, respectively. Moreover, 85.461 U of cholesterol oxidase/OD600 were attained by adjusting the fermentation conditions at initial pH, 6; volume of the fermentation medium, 15 ml/flask; and concentration of cholesterol, 0.05 g/l. The optimization process improved cholesterol degradation (%) and the activity of cholesterol oxidase (U/OD600) by 139% and 154%, respectively. No cholesterol was detected in the spectroscopic analysis of the optimized fermented medium via gas chromatography-mass spectroscopy (GC-MS).

CONCLUSION

The current study provides principal information for the development of efficient production of cholesterol oxidase by B. subtilis that could be used in various applications.

A review of algorithmic approaches for cell culture media optimization

Cell culture media composition and culture conditions play a crucial role in product yield, quality and cost of production. Culture media optimization is the technique of improving media composition and culture conditions to achieve desired product outcomes. To achieve this, there have been many algorithmic methods proposed and used for culture media optimization in the literature. To help readers evaluate and decide on a method that best suits their specific application, we carried out a systematic review of the different methods from an algorithmic perspective that classifies, explains and compares the available methods. We also examine the trends and new developments in the area. This review provides recommendations to researchers regarding the suitable media optimization algorithm for their applications and we hope to also promote the development of new cell culture media optimization methods that are better suited to existing and upcoming challenges in this biotechnology field, which will be essential for more efficient production of various cell culture products.

Bioprospecting of novel peroxidase from Streptomyces coelicolor strain SPR7 for carcinogenic azo dyes decolorization

Peroxidase (POX) is a heme-containing oxidoreductase, its voluminous immuno-diagnostic and bioremediatory intuitions have incited optimization and large scale-generation from novel microbial repertoires. Azo dyes are the most detrimental classes of synthetic dyes and they are the common ecotoxic industrial pollutants in wastewater. In addition, azo dyes are refractory to degradation owing to their chemical nature, comprising of azoic linkages, amino moieties with recalcitrant traits. Moreover, they are major carcinogenic and mutagenic on humans and animals, whereby emphasizing the need for decolorization. In the present study, a novel POX from Streptomyces coelicolor strain SPR7 was investigated for the deterioration of ecotoxic dyestuffs. The initial medium component screening for POX production was achieved using, One Factor at a Time and Placket-Burman methodologies with starch, casein and temperature as essential parameters. In auxiliary, Response Surface Methodology (RSM) was recruited and followed by model validation using Back propagation algorithm (BPA). RSM-BPA composite approach prophesied that combination of starch, casein, and temperature at optimal values 2.5%, 0.035% and 35 °C respectively, has resulted in 7 folds enhancement of POX outturn (2.52 U/mL) compared to the unoptimized media (0.36 U/mL). The concentrated enzyme decolorized 75.4% and 90% of the two azo dyes with lignin (10 mM), respectively. Hence, this investigation confirms the potentiality of mangrove actinomycete derived POX for elimination of noxious azo dyes to overcome their carcinogenic, mutagenic and teratogenic effects on humans and aquatic organisms.

Enhanced Pharmaceutically Active Compounds Productivity from Streptomyces SUK 25: Optimization, Characterization, Mechanism and Techno-Economic Analysis

The present research aimed to enhance the pharmaceutically active compounds’ (PhACs’) productivity from Streptomyces SUK 25 in submerged fermentation using response surface methodology (RSM) as a tool for optimization. Besides, the characteristics and mechanism of PhACs against methicillin-resistant Staphylococcus aureus were determined. Further, the techno-economic analysis of PhACs production was estimated. The independent factors include the following: incubation time, pH, temperature, shaker rotation speed, the concentration of glucose, mannitol, and asparagine, although the responses were the dry weight of crude extracts, minimum inhibitory concentration, and inhibition zone and were determined by RSM. The PhACs were characterized using GC-MS and FTIR, while the mechanism of action was determined using gene ontology extracted from DNA microarray data. The results revealed that the best operating parameters for the dry mass crude extracts production were 8.20 mg/L, the minimum inhibitory concentrations (MIC) value was 8.00 µg/mL, and an inhibition zone of 17.60 mm was determined after 12 days, pH 7, temperature 28 °C, shaker rotation speed 120 rpm, 1 g glucose /L, 3 g mannitol/L, and 0.5 g asparagine/L with R² coefficient value of 0.70. The GC-MS and FTIR spectra confirmed the presence of 21 PhACs, and several functional groups were detected. The gene ontology revealed that 485 genes were upregulated and nine genes were downregulated. The specific and annual operation cost of the production of PhACs was U.S. Dollar (U.S.D) 48.61 per 100 mg compared to U.S.D 164.3/100 mg of the market price, indicating that it is economically cheaper than that at the market price.

Separation of HCl/water mixture using air gap membrane distillation, Taguchi optimization and artificial neural network

The aim of this paper is to analyze the performance of the air gap membrane distillation (AGMD) process for the separation of HCl/Water mixture first by applying Taguchi optimization approach and second by developing an artificial neural network (ANN) model. The experimental data which are fed as input to the above approaches are collected from the fabricated AGMD lab-scale setup using poly-tetra-fluoro-ethylene membrane of 0.22 µm pore size. The process input variables considered are bulk feed temperature, feed flow rate, air gap thickness, cooling water temperature and cooing water flow rate and AGMD performance index is the total permeate flux. The optimum operating condition is found to be at feed temperature 50 °C, air gap thickness 7 mm, cooling water temperature 5 °C and feed flow rate 10 lpm. Analysis of variance test is carried out for both Taguchi and ANN models. Regression model has also been developed for the comparison between experimental and model predicted data. The developed ANN model has been found well fitted with experimental data having R 2 value of 0.998. Based on the calculated percentage of contribution of each input parameter on the AGMD permeate flux, it can be concluded that feed temperature and air gap thickness have highest weightage whereas feed flow rate and cooling water temperature have moderate effects. Predictive ability of the developed ANN model is further checked with 2D contour plot. The distinctive feature of the paper is the development of the Taguchi experimental design and ANN model and then consequently integration of both Taguchi and ANN has been carried out to optimized the developed ANN model parameters.

Ameliorating process parameters for zeaxanthin yield in Arthrobacter gandavensis MTCC 25325

The present study aims to escalate the production of prophylactic agent zeaxanthin using a screened potential bacterial isolate. For this purpose, a freshwater bacterium capable of producing zeaxanthin was isolated from Bor Talav, Bhavnagar. The 16S rRNA sequence confirmed the isolate as Arthrobacter gandavensis. The bacterium was also submitted to Microbial Type Culture Collection, CSIR-Institute of Microbial Technology, Chandigarh, India, with the accession number MTCC 25325. The chemo-metric tools were employed to optimise the influencing factors such as pH, temperature, inoculum size, agitation speed, carbon source and harvest time on zeaxanthin yield. Thereafter, six parameters were narrowed down to three factors and were optimised using the central composite design (CCD) matrix. Maximum zeaxanthin (1.51 mg/g) was derived when A. gandavensis MTCC 25325 was grown under pH 6.0, 1.5% (w/v) glucose and 10% (v/v) inoculum size. A high regression coefficient (R²= 0.92) of the developed model indicated the accurateness of the tested parameters. To the best of our knowledge, this is the first report on tailoring the process parameters using chemo-metric optimisation for escalating the zeaxanthin production by A. gandavensis MTCC 25325.

Preparation and Evaluation of the ZnO NP-Ampicillin/Sulbactam Nanoantibiotic: Optimization of Formulation Variables Using RSM Coupled GA Method and Antibacterial Activities

Nanoparticles (NPs) possessing antibacterial activity represent an effective way of overcoming bacterial resistance. In the present work, we report a novel formulation of a nanoantibiotic formed using Ampicillin/sulbactam (Ams) and a zinc oxide nanoparticle (ZnO NP). 'ZnO NP-Ams' nanoantibiotic formulation is optimized using response surface methodology coupled genetic algorithm approach. The optimized formulation of nanoantibiotic (ZnO NP: 49.9 μg/mL; Ams: 33.6 μg/mL; incubation time: 27 h) demonstrated 15% enhanced activity compared to the unoptimized formulation against K. pneumoniae. The reactive oxygen species (ROS) generation was directly proportional to the interaction time of nanoantibiotic and K. pneumoniae after the initial lag phase of ~18 h as evident from 2'-7'-Dichlorodihydrofluorescein diacetate assay. A low minimum inhibitory concentration (6.25 μg/mL) of nanoantibiotic formulation reveals that even a low concentration of nanoantibiotic can prove to be effective against K. pneumoniae. The importance of nanoantibiotic formulation is also evident by the fact that the 100 μg/mL of Ams and 25 µg of ZnO NP was required individually to inhibit the growth of K. pneumonia, whereas only 6.25 μg/mL of optimized nanoantibiotic formulation (ZnO NP and Ams in the ratio of 49.9: 33.6 in μg/mL and conjugation time of 27 h) was needed for the same.

Characterization and overproduction of cell-associated cholesterol oxidase ChoD from Streptomyces lavendulae YAKB-15

Cholesterol oxidases are important enzymes with a wide range of applications from basic research to industry. In this study, we have discovered and described the first cell-associated cholesterol oxidase, ChoD, from Streptomyces lavendulae YAKB-15. This strain is a naturally high producer of ChoD, but only produces ChoD in a complex medium containing whole yeast cells. For characterization of ChoD, we acquired a draft genome sequence of S. lavendulae YAKB-15 and identified a gene product containing a flavin adenine dinucleotide binding motif, which could be responsible for the ChoD activity. The enzymatic activity was confirmed in vitro with histidine tagged ChoD produced in Escherichia coli TOP10, which lead to the determination of basic kinetic parameters with K_m 15.9 µM and k_cat 10.4/s. The optimum temperature and pH was 65 °C and 5, respectively. In order to increase the efficiency of production, we then expressed the cholesterol oxidase, choD, gene heterologously in Streptomyces lividans TK24 and Streptomyces albus J1074 using two different expression systems. In S. albus J1074, the ChoD activity was comparable to the wild type S. lavendulae YAKB-15, but importantly allowed production of ChoD without the presence of yeast cells.

Statistical versus artificial intelligence -based modeling for the optimization of antifungal activity against Fusarium oxysporum using Streptomyces sp. strain TN71

A Streptomyces sp. strain TN71 was isolated from Tunisian Saharan soil and selected for its antimicrobial activity against phytopathogenic fungi. In an attempt to increase its anti-Fusarium oxysporum activity, GYM+S (glucose, yeast extract, malt extract and starch) culture medium was selected out of five different production media. Plackett-Burman design (PBD) was used to select yeast extract, malt extract and calcium carbonate (CaCO₃) as parameters having significant effects on antifungal activity, and a Box-Behnken design was applied for further optimization. The analysis revealed that the optimum concentrations for the anti-F. oxysporum activity of the tested variables were yeast extract 5.03g/L, malt extract 8.05g/L and CaCO₃ 4.51g/L. Artificial Neural Networks (ANNs): the Multilayer perceptron (MLP) and the Radial basis function (RBF) were created to predict the anti-F. oxysporum activity. The comparison between experimental and predicted outputs from ANN and Response Surface Methodology (RSM) were studied. The ANN model presents an improvement of 14.73%. To our knowledge, this is the first work reporting the statistical versus artificial intelligence -based modeling for the optimization of bioactive molecules against mycotoxigenic and phytopathogenic fungi.

Response Surface Methodology-Genetic Algorithm Based Medium Optimization, Purification, and Characterization of Cholesterol Oxidase from Streptomyces rimosus

The applicability of the statistical tools coupled with artificial intelligence techniques was tested to optimize the critical medium components for the production of extracellular cholesterol oxidase (COD; an enzyme of commercial interest) from Streptomyces rimosus MTCC 10792. The initial medium component screening was performed using Placket-Burman design with yeast extract, dextrose, starch and ammonium carbonate as significant factors. Response surface methodology (RSM) was attempted to develop a statistical model with a significant coefficient of determination (R² = 0.89847), followed by model optimization using Genetic Algorithm (GA). RSM-GA based optimization approach predicted that the combination of yeast extract, dextrose, starch and ammonium carbonate at concentrations 0.99, 0.8, 0.1, and 0.05 g/100 ml respectively, has resulted in 3.6 folds increase in COD production (5.41 U/ml) in comparison with the un-optimized medium (1.5 U/ml). COD was purified 10.34 folds having specific activity of 12.37 U/mg with molecular mass of 54 kDa. The enzyme was stable at pH 7.0 and 40 °C temperature. The apparent Michaelis constant (K_m) and V_max values of COD were 0.043 mM and 2.21 μmol/min/mg, respectively. This is the first communication reporting RSM-GA based medium optimization, purification and characterization of COD by S. rimosus isolated from the forest soil of eastern India.

Harnessing QbD, Programming Languages, and Automation for Reproducible Biology

Building robust manufacturing processes from biological components is a task that is highly complex and requires sophisticated tools to describe processes, inputs, and measurements and administrate management of knowledge, data, and materials. We argue that for bioengineering to fully access biological potential, it will require application of statistically designed experiments to derive detailed empirical models of underlying systems. This requires execution of large-scale structured experimentation for which laboratory automation is necessary. This requires development of expressive, high-level languages that allow reusability of protocols, characterization of their reliability, and a change in focus from implementation details to functional properties. We review recent developments in these areas and identify what we believe is an exciting trend that promises to revolutionize biotechnology.