Statistical Optimization of Process Parameters and Purification of Uricase Using Isolate Pseudomonas mosselii DSS002.

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Learn more: PMC Disclaimer | PMC Copyright Notice Indian J Microbiol . 2025 Mar 19;65(2):1089–1104. doi: 10.1007/s12088-025-01467-y Statistical Optimization of Process Parameters and Purification of Uricase Using Isolate Pseudomonas mosselii DSS002 Sai Sushma Dudala 1 , T C Venkateswarulu 1 , A Venkata Narayana 1 , D John Babu 1, ✉ Author information Article notes Copyright and License information 1 Department of Biotechnology, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh 522213 India ✉ Corresponding author. Received 2024 Jul 19; Accepted 2025 Feb 19; Issue date 2025 Jun. © Association of Microbiologists of India 2025. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. PMC Copyright notice PMCID: PMC12246332  PMID: 40655368 Abstract Uricase holds significant pharmaceutical applications, particularly in treating diseases associated with elevated uric acid levels and serving as a diagnostic enzyme to detect uric acid in biological fluids. Enhancing uricase production is crucial to meet the demands of large-scale applications. This study focuses on optimizing process parameters for uricase production using advanced statistical methods, namely Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). Seven key process parameters were investigated: temperature, pH, medium volume, incubation time, inoculum size, inoculum age, and rpm. Conformational experimental studies at the ANN-GA predicted optimal conditions revealed a significant uricase activity of 63.92 ± 0.06 U/mL. The purified uricase exhibited specific activity of 92.48 U/mg and a molecular weight of approximately 32 kDa. Itdemonstrated remarkable stability, withstanding a wide pH range (6.0 to 10.0) and temperatures up to 50 °C, with an optimum pH of 9.0 and temperature of 30 °C. This broad pH and temperature tolerance of the purified uricase from Pseudomonas mosselii DSS002 underscores its potential as a valuable source for industrial-scale production, catering to various pharmaceutical applications. This study’s findings pave the way for efficient and scalable uricase production, offering promising implications for the pharmaceutical industry. Keywords: Artificial neural network, Box-Behnken design, Genetic algorithm, Pseudomonas , Uricase, Uric acid Introduction Uricase is used as a therapeutic enzyme to treat diseases in humans caused by the increase of uric acid levels in body. The uric acid is end product formed in humans as a result of purine metabolism and is generally excreted out of the body by kidneys. In some cases, the kidneys fail to excrete the excess uric acid formed in the body which can cause various complications such as hyperuricemia leading to gout, uric acid nephrolithiasis, diabetes, cardiovascular disease [ 1 , 2 ] and tumor lysis syndrome [ 3 ]. The diseases caused due to accumulation of uric acid can be treated with the help of uricase enzyme which will convert uric acid into a more soluble allantoin along with hydrogen peroxide, and carbon dioxide. Further, it is used as a diagnostic enzyme to quantify the uric acid levels in the urine and serum samples. Additionally, uricase is also used as an additive in formulating hair colouring agents [ 4 ]. Hence, production of uricase is having potential commercial significance. Despite its importance, the production of uricase faces challenges, particularly in terms of yield and cost-efficiency. Selection of suitable organism having the ability to produce more product cost effectively is essential factor taken into consideration for production of industrially useful products. Though` uricase is isolated from various microorganisms that include Bacillus cereus [ 5 ], Comamonas sp. [ 6 ], Delftia tsuruhatensis [ 37 ], Alkalihalobacillus clausii [ 38 ], Stenotrophomonas maltophilia [ 39 ], Streptomyces griseorubens [ 41 ] and Sphingobacterium thalpophilum [ 7 ] the yield and production cost are still challenging as the prevalence of gout is increasing across the world reaching 54 million people [ 8 ]. The global market value for the gout therapeutics is USD 2.49 billion in 2022 [ 9 ], highlighting the commercial potential of optimized uricase production. Optimizing process parameters is essential to enhance enzyme production while reducing production costs [ 10 ]. Key factors like temperature, pH, medium volume, RPM, inoculum size, inoculum age, and incubation time significantly influence enzyme yields and vary depending on the organism. Temperature affects extracellular enzyme production by altering cell membrane properties [ 18 ], while pH impacts enzyme activity and gene expression, with extreme pH levels potentially reducing uricase yields. Inoculum size and age determine cell viability and enzyme productivity, whereas RPM ensures proper nutrient mixing and oxygen transfer, avoiding shear stress. Medium volume optimization balances nutrient distribution and gas exchange, ensuring efficient growth and enzyme production. Incubation time affects cell viability and contamination risks, with prolonged periods potentially reducing yields. Optimizing the process parameters will focus on improving specific aspects of the overall system, leading to more efficient and effective results. Optimization of process parameters using Response Surface Methodology (RSM) is known to be most efficient and effective not only for process optimization but also to study the interaction effects of different process parameters on response [ 11 ]. The BBD of RSM is an apt design for statistical optimization of bioprocess with less number of experiments and also in situations where extreme conditions need to be avoided [ 12 ]. Artificial Neural Networks-Genetic Algorithm (ANN-GA) are the two machine learning techniques developed based on intelligence and evolution of biological systems and have been effectively used in data analysis. The ANN is a highly flexible and effective nonlinear systems dynamic modelling technique. The ANNs ability to generalization of the behavior of complicated systems makes it a viable tool for optimization [ 13 ]. In GA a population of individuals is created in each iteration and the population’s best point will be attained as an ideal solution. Therefore, ANN integrated with GA was used as an effective tool for optimizing the process parameters as stated in previous reports that includes production of chitinase, L-asparaginase, lipase and glutaminase from Thermomyces lanuginosus , Aspergillus terreus , Penicillium roqueforti and Bacillus subtilis respectively [ 14 – 17 ]. The current study hypothesizes that the production of uricase from Pseudomonas sp. can be significantly enhanced through the optimization of process parameters where Box-Behnken Design (BBD) was used for initial optimization, and was followed by ANN-GA. The objectives of this study are to optimize the process parameters using RSM and ANN-GA and to partially purify and characterize the purified uricase to evaluate its stability and activity. This study represents a significant advancement in enzyme biotechnology, particularly in the commercial production of uricase. The use of advanced statistical optimization techniques and through characterization, represents a significant advancement in enzyme biotechnology with broad implications for various fields. Methods and materials Culture Conditions and Uricase Production Pseudomonas mosselii DSS002 that was formerly isolated from canal water was used for the uricase production. This culture bearing accession number NCIM-5800 was maintained on agar slants containing uric acid and stored at 4 °C. The uricase production experiments were performed using optimized medium containing substrate uric acid, peptone and sucrose of 3.9 g/L; 22.5 g/L and 30.0 g/L respectively. The 100 mL medium was taken in a 250 mL Erlenmeyer flasks, the pH was adjusted to 7.8 and was inoculated with 3% culture and then incubated at 37 °C for 24 h in a shaking incubator (100 rpm). The uricase activity of the obtained cell-free supernatant (100 µL) was calculated as described by Pustake et al. [ 10 ]. The uricase activity of the strain was 59.91 U/mL previously reported [ 19 ]. Optimization of Process Parameters Using BBD Optimization of process variables was done using BBD which reduces the number of experimental runs and at the same time provides the optimum conditions required to enhance the yield. So, BBD of RSM is used to determine the impact of individuals and interactions of process parameters on the response and also to predict the optimum values of process parameters for optimizing response. The three-levels of process factors considered for optimization were denoted as − 1, 0, and + 1 in coded design (The values are taken based on initial one factor at a time (OFAT) results [ 19 ]). Sixty-two experimental runs were carried out using the data of BBD of Design Expert version 12 for the selected seven process parameters that are temperature (°C), pH, medium volume (mL/250 mL flask), incubation time (hrs), inoculum size (%), inoculum age (hrs) and the results were fitted to the second order polynomial model. By visualising the experimental model produced by the analysis of variance (ANOVA), the relationship between the independent variables and the observed responses was established. The Model validation of RSM was performed to confirm the accuracy and reliability. Optimization Using ANN-GA ANN was executed using neural fitting app and GA was executed using optimization tool box present in the MATLAB R2022b software online ( https://matlab.mathworks.com/ ). The ANN is utilised to forecast the non-linear and linear relationships between various inputs and outputs for complex processes. It is considered to be more accurate due to the presence of neurons which are highly interconnected elements. Its special parameters namely bias (b) and weights (w) serve as a representation for the linkage between the neurons [ 33 ]. In the current study, the neural network fitting app was used to build, visualise, and train a two-layer feed-forward network, and to address data fitting issues. It uses sigmoid hidden neurons and linear output neurons which are suitable for regression tasks. It constitutes an input layer with 7 neurons [temperature (x 1 ), pH (x 2 ), medium volume (x 3 ), incubation time (x 4 ), inoculum size (x 5 ), inoculum age (x 6 ) and rpm (x 7 )], an output layer with 1 neuron [uricase activity(Y)], and the neurons in the hidden layers which were optimised from 1 to 10 based on least mean squared error (MSE) and regression R-value close to 1. MSE is the mean squared difference between outputs and responses. R is the correlation between outputs and responses. Training was done using levenberg–marquardt backpropagation algorithm (trainlm) because it is recommended as the fastest backpropagation algorithm. By modifying the weights and biases, the back propagation training process reduces the function error. The data is divided into three subsets that are training, validation, and testing with a composition of 70%, 15%, and 15%. The data obtained from RSM study was given as input and was trained to map predictors to continuous responses. The executed model performance was estimated statistically by using R value and MSE [ 20 ]. GA is used to solve optimization problems that have any kind of constraint, including integer constraints. It is a stochastic method which searches randomly among population members through mutation and crossover to reach the best one. The GA was performed using the live editor option by selecting the optimization task. For maximum uricase production, the concentrations of the input parameters were further optimized by GA using ANN as its fitness function. The parameters selected in solver options for population data type, creation function, mutation function, crossover function, fitness scaling function, selection function, and crossover fraction are double vector, feasible, feasible, scattered, rank, roulette, and 0.8 respectively and results of scores, stopping criteria, best fitness and best individual were obtained after execution. Uricase Purification and Characterization DEAE-Cellulose Chromatography The cell-free supernatant of the crude enzyme was collected and proteins were precipitated using ammonium sulphate precipitation (70% saturation) and incubated overnight at 4 °C. The precipitated proteins were centrifuged at 7860 rpm for 10 min at 4 °C and the obtained pellet was dissolved in 20 mM sodium borate buffer (pH 9.0) till the pellet was dissolved completely and was dialysed at 4 °C temperature for 12 h on a portable magnetic stirrer by changing buffer for every 2 h. After completion of dialysis the enzyme assay and protein content was determined. The dialysed sample was run on DEAE-Cellulose loaded flex column primarily equilibrated with 20 mM sodium borate buffer (pH 9.0). After loading the sample, the column was washed with same buffer to remove all the unbound proteins. The proteins bounded to the column were eluted by increasing the ionic strength using 0.1 to 0.5 M NaCl dissolved in 20 mM borate buffer (pH 9.0). The absorbance readings of the collected fractions were measured at 280 nm and the enzyme activity assay at 293 nm was performed. The fraction showing positive enzyme activity was pooled and concentrated was further analysed on SDS-PAGE to test the homogeneity of the sample [ 21 ]. pH Effect on Uricase Activity and Stability The role of pH in the stability of uricase, an enzyme that catalyzes the conversion of uric acid to allantoin, is a critical factor. Enzymes generally exhibit optimal activity and stability under specific pH conditions. Understanding and controlling the pH conditions in enzymatic reactions are essential as deviations from the optimal pH can lead to changes in enzyme structure, conformational stability, and overall function. The optimum pH for the enzyme was determined by performing the uricase enzyme assay using various buffers of different pH (5 to 10) as determined by Kai et al. [ 30 ]. The effect of pH on the stability of the uricase enzyme was investigated by incubating the enzyme alone at different pH conditions for duration of 30 min. After incubating the enzyme, the uricase assay was performed and the residual activity (%) was calculated. Temperature Effect on Uricase Activity and stability Keeping the temperature at the right level is essential to maximize uricase activity and stability. While temperatures within the ideal range sustain the catalytic activity of the enzyme, extreme temperatures can cause denaturation and loss of function. The ideal temperature varies from one enzyme to another. The optimum temperature for the uricase enzyme was determined by performing the enzyme assay by varying the temperatures of the assay mixtures from 25 to 60 °C. Further, the temperature sensitivity of uricase was determined by pre incubating the enzyme at various temperatures for 30 min and performing the enzyme assay under standard conditions and residual activity percent of the uricase was determined. Effect of Mineral Salts on Uricase Activity The catalytic effectiveness of uricase can be impacted by specific metal ions found in mineral salts, which can bind to particular residues or interfere with the active site of the enzyme. The uricase was incubated in various mineral salts of concentration 1 mM for 30 min and the activity was determined under standard assay conditions [ 7 ]. The mineral salts taken in the study are HgCl 2 , MnSO 4 , K 3 [Fe(CN) 6 ], CH 4 N 2 O, FeCl 3 , CaCl 2 , KCl, MgSO 4 ·7H 2 O, NaCl, FeSO 4 , NaH 2 PO 4 ·H 2 O, and Na 3 C 6 H 5 O 7 ·2H 2 O. Results and Discussion Optimization Using BBD The process factors that influence uricase production were written in actual terms and coded terms along with the actual and predicted values of the experiments and were presented in Table 1 . The best fit model as suggested by the software is the quadratic model. The high R 2 value (R-squared/ multiple correlation coefficient) of 0.9715 for the model says that 97.15% of the variability can be explained and only 2.85% of the variability is not explained by the model. The Predicted R 2 of 0.8089 is in reasonable agreement with the Adjusted R 2 of 0.9332 where the difference is less than 0.2 confirming the model is valid. Further, the reliability of the model was best explained by its high signal-to-noise ratio of 18.606 indicates an adequate signal. Table 1. BBD matrix for the seven factors with the experimental and predicted response for uricase activity (coded values in the parenthesis) Run A:Temperature B:pH C:Medium volume D:Incubation time E:Inoculum size F:Inoculum age G:RPM Enzyme activity Predicted value Predicted value °C pH mL/250 mL Hrs % hrs rpm U/mL RSM ANN 1 37 (0) 7.8 (0) 100 (0) 24 (0) 5 (0) 24 (0) 50 (0) 61.8 60.61 60.93 2 40 (1) 6.8 (− 1) 100 (0) 20 (− 1) 5 (0) 24 (0) 50 (0) 40.24 40.66 46.06 3 40 (1) 7.8 (0) 150 (1) 24 (0) 3 (− 1) 24 (0) 50 (0) 42.54 43.06 42.54 4 34 (− 1) 7.8 (0) 100 (0) 24 (0) 5 (0) 36 (1) 0 (− 1) 48.39 48.63 48.39 5 34 (− 1) 7.8 (0) 150 (1) 24 (0) 7 (1) 24 (0) 50 (0) 56.42 55.11 56.42 6 37 (0) 7.8 (0) 50 (− 1) 28 (1) 5 (0) 24 (0) 100 (1) 50.17 48.01 50.17 7 37 (0) 8.8 (1) 50 (− 1) 24 (0) 5 (0) 36 (1) 50 (0) 44.02 45.06 44.02 8 37 (0) 8.8 (1) 50 (− 1) 24 (0) 5 (0) 12 (− 1) 50 (0) 47.02 46.96 49.12 9 37 (0) 7.8 (0) 50 (− 1) 20 (− 1) 5 (0) 24 (0) 100 (1) 54.23 54.46 54.23 10 37 (0) 7.8 (0) 100 (0) 28 (1) 7 (1) 36 (1) 50 (0) 48.42 49.6 48.42 11 37 (0) 8.8 (1) 100 (0) 24 (0) 7 (1) 24 (0) 100 (1) 49.82 48.96 49.82 12 37 (0) 6.8 (− 1) 100 (0) 24 (0) 7 (1) 24 (0) 0 (− 1) 49.52 49.77 49.52 13 40 (1) 7.8 (0) 100 (0) 24 (0) 5 (0) 12 (− 1) 100 (1) 43.2 42.96 43.2 14 37 (0) 7.8 (0) 100 (0) 20 (− 1) 7 (1) 36 (1) 50 (0) 56.12 55.35 56.12 15 37 (0) 6.8 (− 1) 150 (1) 24 (0) 5 (0) 12 (− 1) 50 (0) 51.52 50.48 51.52 16 34 (− 1) 7.8 (0) 50 (− 1) 24 (0) 7 (1) 24 (0) 50 (0) 55.91 55.39 55.91 17 37 (0) 8.8 (1) 150 (1) 24 (0) 5 (0) 12 (− 1) 50 (0) 49.72 50.53 49.72 18 40 (1) 7.8 (0) 50 (− 1) 24 (0) 7 (1) 24 (0) 50 (0) 43.14 43.26 40.84 19 34 (− 1) 6.8 (− 1) 100 (0) 28 (1) 5 (0) 24 (0) 50 (0) 54.04 52.02 55.54 20 37 (0) 6.8 (− 1) 50 (− 1) 24 (0) 5 (0) 36 (1) 50 (0) 56.36 55.55 52.62 21 40 (1) 8.8 (1) 100 (0) 28 (1) 5 (0) 24 (0) 50 (0) 36.57 35.22 36.57 22 37 (0) 8.8 (1) 100 (0) 24 (0) 7 (1) 24 (0) 0 (− 1) 39.59 38.82 39.59 23 37 (0) 8.8 (1) 150 (1) 24 (0) 5 (0) 36 (1) 50 (0) 44.62 43.95 44.62 24 37 (0) 7.8 (0) 150 (1) 20 (− 1) 5 (0) 24 (0) 0 (− 1) 42.22 44.38 46.16 25 37 (0) 7.8 (0) 100 (0) 24 (0) 5 (0) 24 (0) 50 (0) 59.03 60.61 60.93 26 37 (0) 7.8 (0) 100 (0) 20 (− 1) 7 (1) 12 (− 1) 50 (0) 57.22 55.24 57.22 27 40 (1) 7.8 (0) 100 (0) 24 (0) 5 (0) 12 (− 1) 0 (− 1) 39.31 38.1 39.31 28 37 (0) 7.8 (0) 150 (1) 28 (1) 5 (0) 24 (0) 0 (− 1) 44.61 44.38 44.61 29 34 (− 1) 7.8 (0) 100 (0) 24 (0) 5 (0) 12 (− 1) 0 (− 1) 50.39 50.19 50.39 30 37 (0) 7.8 (0) 150 (1) 20 (− 1) 5 (0) 24 (0) 100 (1) 60.32 58.85 60.32 31 37 (0) 8.8 (1) 100 (0) 24 (0) 3 (− 1) 24 (0) 100 (1) 52.06 51.81 52.06 32 37 (0) 7.8 (0) 150 (1) 28 (1) 5 (0) 24 (0) 100 (1) 53.02 53.11 55.76 33 40 (1) 7.8 (0) 100 (0) 24 (0) 5 (0) 36 (1) 0 (− 1) 38.21 36.88 37.67 34 37 (0) 7.8 (0) 100 (0) 28 (1) 7 (1) 12 (− 1) 50 (0) 49.13 51.47 49.13 35 37 (0) 7.8 (0) 100 (0) 24 (0) 5 (0) 24 (0) 50 (0) 60.06 60.61 60.93 36 40 (1) 7.8 (0) 100 (0) 24 (0) 5 (0) 36 (1) 100 (1) 42.26 42.46 32.41 37 37 (0) 7.8 (0) 100 (0) 28 (1) 3 (− 1) 36 (1) 50 (0) 50.24 52.22 50.24 38 34 (− 1) 7.8 (0) 50 (− 1) 24 (0) 3 (− 1) 24 (0) 50 (0) 55.71 54.52 55.71 39 40 (1) 7.8 (0) 50 (− 1) 24 (0) 3 (− 1) 24 (0) 50 (0) 43.14 44.45 44.11 40 37 (0) 7.8 (0) 50 (− 1) 28 (1) 5 (0) 24 (0) 0 (− 1) 48.97 50.44 48.97 41 34 (− 1) 8.8 (1) 100 (0) 28 (1) 5 (0) 24 (0) 50 (0) 45.37 44.95 50.87 42 37 (0) 6.8 (− 1) 100 (0) 24 (0) 3 (− 1) 24 (0) 0 (− 1) 48.52 49.38 48.52 43 40 (1) 7.8 (0) 150 (1) 24 (0) 7 (1) 24 (0) 50 (0) 38.54 39.73 35.96 44 34 (− 1) 7.8 (0) 100 (0) 24 (0) 5 (0) 36 (1) 100 (1) 54.61 55.82 55.01 45 37 (0) 7.8 (0) 100 (0) 20 (− 1) 3 (− 1) 12 (− 1) 50 (0) 56.26 55.08 56.26 46 37 (0) 7.8 (0) 100 (0) 24 (0) 5 (0) 24 (0) 50 (0) 61.76 60.61 60.93 47 34 (− 1) 7.8 (0) 100 (0) 24 (0) 5 (0) 12 (− 1) 100 (1) 55.34 56.67 55.34 48 37 (0) 7.8 (0) 100 (0) 28 (1) 3 (− 1) 12 (− 1) 50 (0) 53.62 54.39 53.62 49 37 (0) 7.8 (0) 100 (0) 24 (0) 5 (0) 24 (0) 50 (0) 60.93 60.61 60.93 50 37 (0) 7.8 (0) 50 (− 1) 20 (− 1) 5 (0) 24 (0) 0 (− 1) 51.23 51.14 52.28 51 34 (− 1) 7.8 (0) 150 (1) 24 (0) 3 (− 1) 24 (0) 50 (0) 56.51 56.39 56.51 52 34 (− 1) 8.8 (1) 100 (0) 20 (− 1) 5 (0) 24 (0) 50 (0) 45.67 47.32 45.67 53 37 (0) 6.8 (− 1) 50 (− 1) 24 (0) 5 (0) 12 (− 1) 50 (0) 50.36 51.03 50.36 54 37 (0) 6.8 (− 1) 100 (0) 24 (0) 3 (− 1) 24 (0) 100 (1) 50.52 51.29 50.52 55 37 (0) 6.8 (− 1) 150 (1) 24 (0) 5 (0) 36 (1) 50 (0) 50.26 50.32 50.26 56 40 (1) 8.8 (1) 100 (0) 20 (− 1) 5 (0) 24 (0) 50 (0) 35.27 37.29 35.27 57 37 (0) 7.8 (0) 100 (0) 20 (− 1) 3 (− 1) 36 (1) 50 (0) 57.23 54.89 51.79 58 37 (0) 7.8 (0) 100 (0) 24 (0) 5 (0) 24 (0) 50 (0) 60.06 60.61 60.93 59 37 (0) 8.8 (1) 100 (0) 24 (0) 3 (− 1) 24 (0) 0 (− 1) 44.82 43.67 44.82 60 34 (− 1) 6.8 (− 1) 100 (0) 20 (− 1) 5 (0) 24 (0) 50 (0) 55.04 56.39 55.04 61 40 (1) 6.8 (− 1) 100 (0) 28 (1) 5 (0) 24 (0) 50 (0) 38.23 36.58 38.23 62 37 (0) 6.8 (− 1) 100 (0) 24 (0) 7 (1) 24 (0) 100 (1) 52.52 53.67 52.52 Open in a new tab The results of the ANOVA were presented in Table 2 . The variables showing lower level of significance ( p -value) of < 0.05 indicates significant model terms. The Model Fishers’s function (F-value) of 25.33 point toward that there is only a 0.01% chance that large F-value possibly will take place due to noise. The Lack of Fit F-value of 3.10 indicates it is not significant relative to the pure error. There is a 10.62% chance that this large a Lack of Fit F-value will take place due to noise. Non-significant lack of fit is good. The standard deviation (SD), mean and coefficient of variation (CV%) are 1.79, 49.87 and 3.59 respectively. The linear terms A, B, D, and G; the interaction terms AB, BE, BF, BG, CG, and DG; and quadratic terms A 2 , B 2 , C 2 , D 2 , E 2 , F 2 , G 2 are significant model terms with p -values less than 0.05. Table 2. ANOVA for the uricase optimization using BBD (before elimination) Source Sum of squares Df Mean square F-value p -value Model 2842.06 35 81.2 25.33 < 0.0001 A-temperature 972.19 1 972.19 303.26 < 0.0001 B-pH 163.18 1 163.18 50.9 < 0.0001 C-medium volume 4.13 1 4.13 1.29 0.2665 D-incubation time 62.27 1 62.27 19.43 0.0002 E-inoculum size 9.15 1 9.15 2.85 0.1031 F-inoculum age 6.36 1 6.36 1.98 0.171 G-RPM 217.74 1 217.74 67.92 < 0.0001 AB 16.27 1 16.27 5.08 0.0329 AC 5.3 1 5.3 1.65 0.21 AD 0.0435 1 0.0435 0.0136 0.9081 AE 2.11 1 2.11 0.6587 0.4244 AF 0.0595 1 0.0595 0.0186 0.8927 AG 1.3 1 1.3 0.4068 0.5292 BC 8.49 1 8.49 2.65 0.1158 BD 2.01 1 2.01 0.627 0.4356 BE 13.7 1 13.7 4.27 0.0488 BF 20.61 1 20.61 6.43 0.0176 BG 19.44 1 19.44 6.06 0.0207 CD 0.2485 1 0.2485 0.0775 0.7829 CE 2.3 1 2.3 0.7176 0.4047 CF 10.95 1 10.95 3.42 0.076 CG 62.22 1 62.22 19.41 0.0002 DE 4.74 1 4.74 1.48 0.2348 DF 1.96 1 1.96 0.6115 0.4413 DG 16.5 1 16.5 5.15 0.0318 EF 0.045 1 0.045 0.014 0.9066 EG 1.99 1 1.99 0.6208 0.4379 FG 0.2556 1 0.2556 0.0797 0.7799 A 2 742.04 1 742.04 231.47 < 0.0001 B 2 537.86 1 537.86 167.78 < 0.0001 C 2 93.52 1 93.52 29.17 < 0.0001 D 2 127.82 1 127.82 39.87 < 0.0001 E 2 33.36 1 33.36 10.41 0.0034 F 2 79.56 1 79.56 24.82 < 0.0001 G 2 249.78 1 249.78 77.91 < 0.0001 Residual 83.35 26 3.21 Lack of fit 77.41 21 3.69 3.1 0.1062 Pure error 5.94 5 1.19 R 2 0.9715 Cor total 2925.41 61 Open in a new tab The Table 3 represents the ANOVA after backward elimination of insignificant terms [ 25 ]. The Model F-value obtained was 45.87 that imply the model is significant. The R 2 value, standard deviation, mean and CV% are 0.9572, 1.75, 49.87 and 3.50 respectively. The Predicted R 2 of 0.8967 is in reasonable agreement with the Adjusted R 2 of 0.9363 where the difference is less than 0.2 confirming the model is valid. Further, the reliability of the model was best explained by its high signal-to-noise ratio of 25.53 indicates an adequate signal. Table 3. ANOVA for the uricase optimization using BBD (after backward elimination) Source Sum of squares Df Mean square F-value p -value Model 2800.25 20 140.01 45.87 < 0.0001 A-temperature 972.19 1 972.19 318.48 < 0.0001 B-pH 163.18 1 163.18 53.45 < 0.0001 C-medium volume 4.13 1 4.13 1.35 0.2513 D-incubation time 62.27 1 62.27 20.4 < 0.0001 E-inoculum size 9.15 1 9.15 3 0.0909 F-inoculum age 6.36 1 6.36 2.08 0.1567 G-RPM 217.74 1 217.74 71.33 < 0.0001 AB 16.27 1 16.27 5.33 0.0261 BE 13.7 1 13.7 4.49 0.0402 BF 20.61 1 20.61 6.75 0.013 BG 19.44 1 19.44 6.37 0.0156 CG 62.22 1 62.22 20.38 < 0.0001 DG 16.5 1 16.5 5.41 0.0251 A 2 742.04 1 742.04 243.08 < 0.0001 B 2 537.86 1 537.86 176.2 < 0.0001 C 2 93.52 1 93.52 30.64 < 0.0001 D 2 127.82 1 127.82 41.87 < 0.0001 E 2 33.36 1 33.36 10.93 0.002 F 2 79.56 1 79.56 26.06 < 0.0001 G 2 249.78 1 249.78 81.82 < 0.0001 Residual 125.16 41 3.05 Lack of fit 119.22 36 3.31 2.79 0.1269 Pure error 5.94 5 1.19 R 2 0.9572 Cor total 2925.41 61 Open in a new tab The quadratic polynomial regression equation obtained after backward elimination to support the model hierarchy in terms of coded terms is used to make predictions about uricase production as shown in Eq. ( 1 ). R e s p o n s e U r i c a s e a c t i v i t y U mL = + 60.61 - 6.36 ∗ A - 2.61 ∗ B - 0.415 ∗ C - 1.61 ∗ D - 0.6175 ∗ E - 0.5146 ∗ F + 3.01 ∗ G + 1.43 ∗ A B - 1.31 ∗ B E - 1.60 ∗ B F + 1.56 ∗ B G + 2.79 ∗ C G - 1.44 ∗ D G - 7.41 ∗ A 2 - 6.31 ∗ B 2 - 2.63 ∗ ∗ C 2 - 3.08 ∗ D 2 - 1.57 ∗ E 2 - 2.43 ∗ F 2 - 4.30 ∗ G 2 1 The positive sign indicates that there is a synergistic effect between the variables, meaning that their combined impact on uricase production is greater than the sum of their individual impacts. On the other hand, the negative sign indicates an antagonistic effect, where the variables work against each other and diminish the overall response. The effect of variables, interaction and their association with uricase production is determined using response surface and contour plots. Uricase production by the bacterium is understood by keeping two variables at their zero level and observing the interaction effects of the other two variables. The 3D surface and 2D contour plots for the significant interaction variables are presented in Fig. 1 . The elliptical nature of the contour plots represents the significant interactions among the resultant variables. Fig. 1. Open in a new tab Response surface and counter plots showing interaction effects of process parameters predicted by BBD ( a1 , a2 Temperature and pH; b1 , b2 pH and inoculum size; c1 , c2 pH and inoculum age; d1 , d2 pH and rpm; e1 , e2 Medium volume and rpm; f1 , f2 Incubation time and rpm) Figure 1 a1 represents the surface plots determining the effects of temperature and pH (AB) on uricase production. The slight elliptical nature of the contour plot determines there is less significant interaction between the temperature and pH while keeping the other variables at their central points as shown in Fig. 1 a2. The F value of temperature is larger than that of pH which tells that the temperature has a larger influence on the uricase production than the pH. From the surface plots, it is determined that the increase in the temperature had a drastic effect on uricase production. Higher temperature above the central point leads to a reduction in uricase production. It can be due to the reason that higher temperatures can affect the enzyme activity and at the same time influence bacterial growth metabolism towards the production of the uricase. Figure 1 b1 shows the interaction effect between the pH and inoculum size (BE). The increase in pH above 7.8 and decrease in pH below 7.8 can lead to a decrease in the production of uricase. Figure 1 b2 shows the elliptical nature of the 2D contour plot which represents the significant interaction among the variables. The pH of the medium plays a key role in any bioprocess because it affects the final product. The F value of pH is more than that of inoculum size which indicates that the pH has more influence on the uricase production over the inoculum size. Moreover, the regression equation shows a negative value for pH (− 2.61B) and its interaction term with inoculum (− 1.31BE) indicating an antagonistic effect of both variables. The effect of pH and inoculum age (BF) is represented as surface plots and 2D contour plots in Fig. 1 c1 and c2. The mutual interaction for the effect of pH and inoculum age was assessed at fixed central points for other variables. The uricase production is gradually increased to a certain point whereas after which there is a reduction in uricase production. The negative coefficient of − 1.60 BF obtained in the regression equation represents the antagonistic interaction between the two variables. It indicates that higher production of uricase is obtained when each of the condition variables is inversed. The F value of the pH is more than that of inoculum age suggesting that pH has a larger influence over inoculum age to enhance uricase yield. Figure 1 d1 and d2 shows the interaction effect between the pH and rpm (BG). The positive coefficient of + 1.56 BG obtained in the regression equation represents the synergistic interaction between the two variables. The elliptical nature of the 2D contour plot represents the significant interaction among the variables. The F-value of rpm is greater than that of pH which indicates that the rpm has more influence on the uricase production over the pH of the medium. Figure 1 e1 and e2 shows the interaction effect between medium volume and rpm (CG). The positive coefficient of + 2.79 CG obtained in the regression equation represents the synergistic interaction between the two variables. The elliptical nature of the 2D contour plot represents the significant interaction among the variables. The F-value of rpm is greater than that of medium volume which indicates that the rpm has more influence on the uricase production over the medium volume. Figure 1 f1 and f2 shows the interaction effect between incubation time and rpm (DG). The negative coefficient of − 1.44 DG obtained in the regression equation represents the antagonistic interaction between the two variables. The elliptical nature of the 2D contour plot represents the significant interaction among the variables. Model validation was done for the RSM proposed optimum conditions of temperature 35.2 °C, and 7.73 pH, medium volume 106.2 mL/250 mL flask, incubation time 22.5 h, inoculum size 3.86%, inoculum age 19.98 h and 65.36 rpm were evaluated experimentally. The uricase production was increased to 62.39 ± 0.12 U/mL which is close to the RSM predicted value of 62.223 U/mL. In view of the minor variation in actual and predicted values, the proposed model appears to be adequate and valid. Comparative analysis with previous studies underscores the effectiveness of this optimization approach. For instance, statistical optimization studies conducted on uricase production on Aspergillus welwitschiae increased uricase activity to 3.02 times. The optimum process parameters obtained are the temperature 35 °C, 5 days of incubation time taking inoculum size of 8% and medium volume 50 mL/250 mL. Pseudomonas aeruginosa improved production to 6.6 U/mL that is 15 folds more than the initial medium at the optimum pH and temperature of 6.0 and 30 °C [ 11 ]. Similarly uricase activity enhanced to 13.23 folds for Bacillus subtilis [ 10 ]. This statistical technique is implemented in various studies to enhance the production of enzymes such as cellulose, l-asparaginase, pectinase [ 22 – 24 ]. Based on the data obtained in the RSM it is further analysed using ANN-GA method to refine the process parameters and explore the potential for additional enhancements in enzyme production. Optimization Using Artificial Neural Network (ANN)-Genetic algorithm (GA) Upon training, it was found that setting the number of neurons in the hidden layer to 9 resulted in the best performance. The minimum MSE values obtained were 0.0412 for training, 5.4984 for validation, and 9.2761 for testing whereas the maximum R values were 0.9995 for training, 0.9401 for validation, and 0.9281 for testing. The overall R value, representing the correlation between predicted and actual responses, was determined to be 0.9767. These results indicate that the selected ANN model with 9 neurons in the hidden layer achieved high accuracy and strong correlation in predicting the desired responses. The ANN model used was illustrated in Fig. 2 . After the training process, the neural network model obtains optimized weights and bias values for the hidden layer and these values are presented in Table 4 . After executing the artificial neural network (ANN) model, a post-regression analysis was conducted to evaluate the performance and agreement between the predicted and actual values. Several metrics and visualizations were generated to assess the quality of the ANN predictions. The generated code is executed and the ANN Predicted values were represented in Table 1 . The key findings from the post-regression analysis were presented in Fig. 3 . Fig. 2. Open in a new tab ANN network adopted for optimization of uricase production Table 4. Trained ANN model weights and biases values IW = - 0.2343 - 0.0271 - 0.2475 1.9176 0.7647 - 0.2811 - 2.2134 - 0.1408 - 4.3913 - 0.4288 - 0.0427 0.3937 - 0.2062 0.7523 1.0547 - 0.3167 0.2136 0.0568 0.4096 0.0482 - 0.6353 1.3278 - 0.5491 0.1546 0.4983 - 1.3436 0.2683 - 2.3767 1.2454 - 0.0371 - 1.3053 - 0.1890 - 1.8239 - 0.7953 - 0.7318 - 0.0158 - 1.2623 0.9144 1.3207 - 0.6444 1.3674 - 0.3508 - 1.6598 - 1.7430 0.3149 - 2.0889 - 0.4032 0.2105 - 1.6176 - 3.3480 - 1.6317 - 0.5940 - 0.5402 - 0.0925 - 0.8993 0.8287 - 0.9068 3.2801 - 0.0134 - 0.3116 0.5749 0.5834 - 1.4646 bH = 1.9163 2.2033 - 0.5992 - 1.1851 - 0.0223 - 2.5729 1.2837 - 1.9220 - 2.4375 LW = 0.2509 - 0.0786 - 1.0173 - 0.1314 - 0.1116 - 0.2325 0.4039 - 0.4362 - 0.6260 bO = - 1.4348 Open in a new tab IW represents the input weights connecting the neurons in the input layer to those in the hidden layer LW represents the weights connecting the hidden layer to the output layer bH represents the bias values of the neurons in the hidden layer bO represents the bias value of the neurons in the output layer Fig. 3. Open in a new tab Post-training a best validation performance b error histogram and c regression analysis generated by ANN model Figure 3 c shows regression analysis plot in which the R-values obtained from the regression analysis indicate the correlation between the predicted and experimental values. From Fig. 3 it can be observed that the R-values are close to 1 in all four analyses: 0.99466 for training, 0.96748 for validation, 0.97078 for testing, and 0.9868 overall. These high R-values suggest a strong agreement between the predicted and actual values, indicating the accuracy and reliability of the ANN predictions. The best validation performance plot as shown in Fig. 3 a illustrates the performance of the ANN model during the validation process. It shows that the best performance is achieved at 59 epochs, with an MSE value of 1.9477. This indicates that the model’s predictions closely match the actual values, resulting in a low mean squared error. Figure 3 b explains the error Histogram plot. This histogram provides an overview of the distribution of errors between the predicted and actual values. It shows that the range of errors lies between − 3.378 and 2.194. This information helps to assess the accuracy and consistency of the ANN predictions. Overall, the post-regression analysis of the ANN model demonstrates a strong correlation between the predicted and experimental values. The high R-values, low MSE, and the distribution of errors within a specific range indicate the reliability and accuracy of the ANN predictions. To optimize the process parameters which would enhance the uricase production, ANN model was further optimized using GA. The fitness function obtained in the ANN model was used for GA. The results showed that GA reached the optimal point after 58 generations further than that there was no improvement of the fitness function. Figure 4 illustrates iterations plots obtained after executing GA. The optimal conditions obtained after performing the ANN-GA were: temperature 35.75 °C, 7.65 pH, medium volume 108.57 mL/250 mL flask, incubation time 23.13 h, inoculum size 4.5%, inoculum age 20 h and 59 rpm and at this optimal conditions the predicted enzyme activity was 63.859 U/mL. The predicted enzyme activity was validated by conducting fresh experiments in triplicates and the average value obtained was 63.92 ± 0.06 U/mL which is acceptable with a deviation of 0.095% which is less than 1%. The increase in fold was 1.07. Several studies have reported the successful implementation of ANN-GA for optimizing various processes, These similar reports includes the production of chitinase, human interferon gamma, L-methioninase from Thermomyces sp. [ 26 ], Kluyveromyces lactis and Trichoderma harzianum respectively [ 14 , 27 ]. Additionally, ANN-GA has been implemented in various extraction processes for improving the efficiency of the enzymes that includes tannase and cellulose [ 28 , 29 ]. Fig. 4. Open in a new tab Fitness value of each individual, stopping criteria, Best fitness value for uricase activity, and best individual for seven independent variables obtained by GA The comparison of optimization results of ANN-GA and RSM models for enhanced production of uricase was presented in Table 5 . However, in both the studies the predictions are close to the experimental values with acceptable and low percent deviations, The RSM prior to ANN increased the model predictive capability of ANN-GA reducing its complexity, demonstrating the complementary nature of these methodologies. Table 5. Comparison of RSM and ANN-GA models Model Temperature (°C) pH Medium volume (mL/250mL flask) Incubation time (hrs) Inoculum size (%) Inoculum age (hrs) RPM Predicted value Experimental value Deviation (%) RSM 35.20 7.73 106.20 22.50 3.86 19.98 65.36 62.22 62.39 0.274 ANN-GA 35.75 7.65 108.57 23.13 4.50 20.00 59.00 63.86 63.92 0.095 Open in a new tab Purification Using DEAE-Cellulose Chromatography The crude enzyme having enzyme activity of 63.92 U/mL was purified using ammonium sulphate precipitation method followed by ion exchange chromatography. Maximum uricase was precipitated at 80% ammonium sulphate precipitation which was determined by uricase assay. The dialysed sample was further purified using ion exchange chromatography (DEAE-cellulose). By pooling the active fractions and subsequently concentrating them using ammonium sulfate precipitation, we aimed to increase the enzyme yield while maintaining its homogeneity, as evidenced by the consistent band observed on the SDS-PAGE. Similar study was conducted by Pustake et al. [ 21 ] using Bacillus subtilis . The Fig. 5 is showing the SDS-PAGE results for crude sample, and purified sample. The single band shows the homogeneity of the purified enzyme. The molecular weight of the purified enzyme was found to be ~ 32 kDa. Similar Studies on uricase from various organisms report that for Bacillus subtilis , Microbacterium sp., Priestia flexa and S. thalpophilum had molecular weight of ~ 44 kDa, 34 kDa, 58.87 kDa [ 40 ] and 48 kDa respectively. Fig. 5. Open in a new tab SDS PAGE results showing the approximate molecular weight (kDa) of bands obtained 1: Marker; 2, 3: purified fraction; 4, 5: crude enzyme; 6: Dialysed sample after precipitation The purification profile for crude uricase was presented in Table 6 . The specific activity of the enzyme was found to be 92.48 U/mg with purification fold of 4.16. In contrast the specific activity of uricase obtained after purification for Bacillus subtilis was 117.44 U/mg with purification fold and yield % of 8.65 and 26.35 respectively. Similarly, The specific activity of the uricase obtained from Microbacterium sp. was 5.32 U/mg with 19.70 purification fold and 31% yield [ 30 ]. Table 6. Purification profile for isolation uricase enzyme Purification step Total uricase activity Total protein (mg) Specific activity (U/mg) Purification fold Crude enzyme 6392 287.30 22.25 1.00 Ammonium sulphate and dialysis 2560.8 36.08 70.98 3.19 DEAE cellulose 264.12 2.86 92.48 4.16 Open in a new tab Characterization of Uricase pH, temperature, and salts are often critical factors influencing the stability and activity of biological molecules, especially in enzymatic processes. These factors are commonly tested because they directly impact the folding, structure, and catalytic efficiency of enzymes. pH and temperature directly impact the structure and function of enzymes. Salts can affect the ionic strength and stability of the enzymes by acting as substrate, co-substrate or co-factors of enzymes [ 36 ]. pH Role on Uricase Activity and Stability The relative enzyme activity at different pH was presented in Fig. 6 a and it is found that at optimum pH 9.0., the maximum uricase activity is seen. The uricase was found to be stable at pH ranging from 6 to 10 where it retained stability over 85%. The maximum stability was obtained at pH range of 8.0 to 9.0. Figure 6 b Similar reports stated that uricase obtained from Bacillus subtilis , Sphingobacterium thalpophilum , Microbacterium and Bacillus thermocatenulatus has shown optimum pH at 9.0, 8.0, 8.5 and 7.0 respectively. The uricase obtained from Microbacterium can withstand pH of 7.0 to 10.0 and lost its activity at pH below 5.5 and higher pH of 10.5 [ 30 ]. Similar Bacillus subtilis and Sphingobacterium thalpophilum has retained 80% of its uricase activity at pH 6.0 to 9.0 [ 7 ]. The stability of enzyme in the current study retained over 85% of its activity at wide pH range showcasing its potential for industrial applications. Unlike uricase which loses significant activity below pH 7.0, the uricase obtained in this study retains over 85% activity, making it more suitable for processes requiring moderate acidity. Fig. 6. Open in a new tab a Optimum pH for uricase activity b Effect of pH on uricase stability[means (n = 2) that do not share a letter are significantly different from each other by the ANOVA Tukey test ( p < 0.05)] Role of Temperature on Uricase Activity and Stability The optimum temperature for uricase activity was found to be 30 °C (Fig. 7 a). The uricase had found to lose its stability at temperature over 50 °C whereas it retained above 80% of its stability at temperatures up to 45 °C (Fig. 7 b). These results suggest its use in various applications that are performed at temperatures below 45 °C. In comparison, Bacillus thermocatenulatus is a thermostable which can withstand a temperature of 75 °C. While, another thermotolerant species H. polymorpha can withstand temperature up to 50 °C [ 31 ]. The uricase extract from H. polymorpha have retained 68% of activity at temperature of 50 °C [ 32 ]. Conversely, studies on Bacillus subtilis reported that the uricase can withstand temperature up to 37 °C, whereas at temperatures of 80 °C, it retained only 10% of its activity [ 21 ]. Fig. 7. Open in a new tab a Optimum temperature for uricase activity b Effect of temperature on uricase stability [Means (n = 2) that do not share a letter are significantly different from each other by the ANOVA Tukey test ( p < 0.05)] Effect of Mineral Salts on Uricase Activity The effect of salts on the uricase activity was calculated in terms of residual activity (%) and is presented in Table 7 . The results stated that the uricase activity is highly inhibited by the salts like HgCl 2 , K 3 [Fe(CN) 6 ] and CH 4 N 2 O as it loses less than 50% of its activity and in case of salts such as MnSO 4 , FeCl 3 , FeSO 4 retains its activity < 60%, on the other hand, in case of KCl, NaCl and NaH 2 PO 4 ·H 2 O it retains above 90% of activity. It can be due to the reason that these bind to the active site of enzyme there by reducing the catalytic property. Studies conducted on Bacillus firmus by Kotb [ 34 ] also reported the suppression of uricase activity by Hg 2+ . Another study has reported suppressed activity of approximately 25% for uricase isolated from Bacillus subtilis by K 3 [Fe(CN) 6 ], and this suppression may be attributed to the cyanide present in K 3 [Fe(CN) 6 ], which can inhibit enzymes belonging to oxidoreductases [ 35 ]. This information will enable researchers to formulate the enzyme for extended self-life by eliminating those compounds affecting the enzyme activity. Table 7. Effect of mineral salts on uricase activity Mineral salt [1 mM] Residual activity (%)[(n = 2) ± SD] Mineral salt [1 mM] Residual activity (%)[(n = 2) ± SD] HgCl 2 38.43 ± 1.19 ef MgSO 4 ·7H 2 O 87.49 ± 1.56 ab MnSO 4 50.04 ± 1.26 de KCl 97.18 ± 0.21 a K 3 [Fe(CN) 6 ] 32.46 ± 1.27 f FeSO 4 57.86 ± 1.34 d CH 4 N 2 O 49.63 ± 1.4 de NaCl 99.84 ± 0.29 a FeCl 3 58.83 ± 1.55 d NaH 2 PO 4 ·H 2 O 94.71 ± 0.25 a CaCl 2 79.39 ± 0.89 bc Na 3 C 6 H 5 O 7 ·2H 2 O 74.21 ± 0.49 c Open in a new tab Means (n = 2) that do not share a letter are significantly different from each other by the ANOVA Tukey test ( p < 0.05) Conclusion This study demonstrates the potential of Pseudomonas mosselii DSS002 as a valuable source for uricase production, achieving high enzyme activity (63.92 ± 0.06 U/mL) under ANN-GA-optimized conditions. The partially purified uricase displayed a specific activity of 92.48 U/mg, a purification fold of 4.13, and a molecular weight of 32 kDa. It maintained stability across a broad pH range (6.0–10.0) and up to 50 °C, with optimal activity at pH 9.0 and 30 °C. These findings underline the enzyme’s robustness and versatility, making it suitable for commercial production and diverse pharmaceutical applications. Future work could explore additional factors influencing uricase stability and biophysical characterization to extend its therapeutic and diagnostic utility. Acknowledgements The authors would like to acknowledge Centre of Excellence; Department of ECE at Vignan’s Foundation for Science, Technology and Research for providing necessary software facilities. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by DSS and DJB. The first draft of the manuscript was written by DSS. TCV and AVN commented on previous versions of the manuscript. All authors read and approved the final manuscript. 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