A total number of 35 strains were obtained on a solid medium composed of chicken feather meal, mainly based on the morphological features of actinobacteria and also the appearance of hydrolysis zones around the colonies. After the selection of pure colonies, a second selection in a submerged medium was carried out by quantitative estimation of keratinase production. Through this selection, five strains were preserved for subsequent studies, particularly for their ability to produce keratinase in a liquid medium.
3.3. Screening of Critical Factors Affecting Keratinase Production via the Plackett-Burman Design
The production of keratinase from bacterial genus/species has often been optimized using a single-step statistical experimental design and response surface methodology. Among these investigations were enzymatic bioconversion of feather waste with keratinases of Bacillus cereus PCM 2849 [1], statistical optimization of keratinase production by Bacillus cereus GJBBR [62], enhanced production, purification, and characterization of alkaline keratinase from Streptomyces minutiscleroticus DNA38 [63], keratinase production by Bacillus pumilus GHD in solid-state fermentation using sugar cane bagasse [64], plus response surface methodology optimization of keratinase production from alkali-treated feather waste and horn meal using Bacillus sp. MG-MASC-BT [65]. However, few studies have been reported using a complete statistical experimental process, including statistical methods for screening the various influencing factors and optimizing their variation levels, as reported in this study.
In the present study, the determination of keratinase production influencing factors by isolating Streptomyces sp. strain DZ 06 (ES41) was carried out by practical testing of the PBD plan generated by the statistical software Minitab 19 (trial version). The results of the obtained PBD experiments are presented in Table 2 and Figure 7 and Figure 8. Table 2 shows the PBD design matrix provided for screening ten two-level independent variables representing the initial production conditions and corresponding responses. The collected response data showed variable keratinase activities ranging from 43.99 U/mL (minimum) to 180.13 U/mL (maximum). The different combinations of high and low levels of the various influencing factors are at the origin of the variations in response [66]. This wide variation reflected the eminence of optimizing production conditions in order to reach high levels in terms of enzyme production. The maximum production of keratinase was obtained in the 1st series (180.13 U/mL), with inoculum size (X2), initial pH (X4), K2HPO4(X5), orbital agitation (X6), chicken feather meal (X7), and MgSO4·7H2O (X10) present at high levels. While NaCl (X1), incubation time (X3), CaCO3 (X8), and incubation temperature (X9) were present at low levels (Table 2).
The adequacy of the statistical model was verified by highlighting the effects of the variables examined via Fisher’s F test and analysis of variance. For p-values < 0.05, factors were considered to have a significant effect on response [67]. The ANOVA and the p-value of the model and for each parameter are presented in Table 5.
Among the variables studied and in decreasing order of influence, the factors identified, chicken feather meal (p = 0.000), initial pH (p = 0.000), incubation temperature (p = 0.001), inoculum size (p = 0.003), and incubation time (p = 0.009), showed highly significant effects on keratinase production with p values < 0.01. The remaining variables showing p values > 0.05 are considered non-influential factors. This suggests that low concentrations of these factors are sufficient for keratinase production within the strain studied [66].
At the same time, the Pareto chart of the effects of the independent variables revealed the factors influencing the response represented by horizontal bluebars exceeding the red line representing the significance level, thus confirming the results obtained from the analysis of the statistical model.(Figure 7) [68].
Figure 8 shows the main plots of the effect of the significant factors on the response and confirms the results reported in Table 5 and Figure 7. Increases in chicken feather meal concentrations, initial pH values, and inoculum size greatly influenced keratinase production, while increases in incubation temperature and incubation time values hurt keratinase activity detected [69].
The regression model “F” value (52.45) was found to be significant. The coefficient of determination R2 provides an explanation of the model fit goodness, which is used to assess the explanatory power of regression models and reflects the variation in response in the proposed statistical model. [70].
The regression equation acquired showed an R2 value of 0.9882, indicating that 98.82% of the total variation detected for the responses could be interpreted by the model, demonstrating that the design is highly significant in predicting the factors effects on keratinase activity by the studied strain. An R2 value > 0.75 indicates fit to the biological models [71]. The correlation of the predicted obtained responses in the present study is explained by the closeness of the R2, adjusted R2, and predicted R2 values: 98.82%, 96.94%, and 91.12%, respectively (Table 5). The design performance was valuated by a first-order model analysis, showing its fitting to the experimental data using the following Equation (5):
The PBD results showed that the factors, chicken feather meal (% w/v), initial pH, incubation temperature (°C), incubation time (days), and inoculum size (spores/v), exert significant effects on keratinase production, with chicken feather meal concentration forming a major contributor. This result is identical to those obtained by Abdul Gafar et al. and Laba et al. when PBD was utilized in the screening procedure.It was shown that the concentration of chicken feather meal was the most important influencing factor for keratinase synthesis by Bacillus sp. UPM-AAG1 and the actinobacterium strain Kocuriarhizophila p3-3, respectively [67,72]. In work undertaken by Manivasagan et al. and Demir et al., the same result was obtained where the yield in keratinase production was observed when chicken feather meal was employed as culture substrate during submerged fermentation using Actinoalloteichus sp. MA-32 and Streptomyces sp. 2M21, respectively [73,74]. The use of chicken feather meal as an organic material source shows the ability of the strain Streptomyces sp. DZ 06 (ES41) to grow and to obtain its carbon and nitrogen requirements directly from this substrate. Furthermore, variations in the nutritional needs of each feather-degrading microorganism as well as the type of the keratinolytic proteases generated by the producer microorganisms may be the primary causes of the disparities in the role of the substrate in keratinase production among various feather-degrading bacteria, e.g., which carbon and/or nitrogen sources would act as inducers [75,76].
Similar outcomes to those obtained in the present research showing the significant effect of the initial culture pH during optimization work appeared in the literature. Among these works are those carried out by Fakhfakh–Zouari et al. using Bacillus pumilus A1 and Abd El-Aziz et al. on Streptomyces swerraensis KN23 [75,77]. The pH of the medium culture affects the microbial growth, effectiveness of feather degradation, and keratinase synthesis via influencing the reaction environment, enzymatic process, and movement of nutrients across the cell membrane of bacteria [1,20].
Apart from this, the important role of incubation temperature in the production of keratinases is revealed during this work and in those conducted by Demir et al. on Streptomyces sp. 2M21 and by Matikevičienė et al. using Actinomyces fradiae 119 [74,78]. The importance of incubation temperature is crucial for best enzyme production due to modifications in the structure and characteristics of microbial proteins with changes in temperature. Metabolic activities are reduced at temperatures below or above the optimal temperature, resulting in inhibition of growth and enzyme synthesis [79].
The role of incubation time as a determinant factor for keratinase production obtained during this work was similar to studies on keratinase production by Streptomyces sp. 2M21 and Streptomyces swerraensis KN23 [74,77]. Incubation time is an important parameter for keratinase production. It varies according to the microorganism, nature of the substrate used, and the production medium conditions [80].
Similarly, it was reported that the inoculum size used had a significant effect on keratinase production by Amycolatopsis sp. strain MBRL 40, A. fradiae 119, and B. licheniformis ALW1 [40,78,81]. The inoculum size is the initial bacterial mass required to carry out a fermentation, hence the need to test the selection of this factor and determining its variation levels. Keratinolytic activity is often increased with increasing inoculum size. No discernible increase or even decrease in activity is seen above the necessary initial rate [82].The selected parameters influencing keratinase production in Streptomyces sp. DZ 06 (ES41) are factors usually considered to influence most microorganisms when testing the production of different types of bioactive substances, in particular keratinases; this suggests that the strain studied has no particular requirements and that testing these factors for optimization via response surface methodology using the Box–Behnken design would enable keratinase production to be better controlled in terms of both quantitative and qualitative yields.
3.4. Box-Behnken Examination of Keratinase Synthesis-Based on RSM Design
Plackett–Burman Design, as indicated above, made it possible to select the main parameters influencing keratinase production by Streptomyces sp. strain DZ 06 (ES41). These independent variables, namely chicken feather meal, initial pH, incubation temperature, incubation time, and inoculum size, were evaluated at three levels (−1, 0, +1) to investigate their interaction and their effects on keratinase production applying a Box–Behnken design of experiment. For growth and keratinase production by Streptomyces sp. strain DZ 06 (ES41), the insignificant factors, including mineral salts and orbital agitation, were used at low levels during the enzyme production process [74].
The matrix experiments and results of the different trials performed by the Box–Behnken design are shown in Table 4. The highest keratinase production of 458 U/mL was obtained with 5 g/L of chicken feather meal (A), after 4 days of incubation time (B), at initial pH 7 (C), at incubation temperature of 40 °C (D), and an inoculum size of 1.00 × 106 spores/mL (run 44). In addition, that corresponding to the central points of the factor values tests (run: 2, 4, 11, 22, 24, and 28) showed the closeness and the repeatability of the responses obtained (448.23 ± 1.7 to 450.32 ± 0.4) (U/mL) and consequently the relevance of the statistical model used (Table 4). The performance and adequacy of the quadratic model were verified through variance analysis (ANOVA) using Design Expert 13® software (Table 6).
In the present study, the larger F-value of 10.68 and the low p-value < 0.001 indicate greater significance of model terms [83]. For p-values less than 0.05, the significant terms of the model are as follows: A (chicken feather meal), B (incubation time), E (inoculum size), AD (chicken feather meal vs. incubation temperature), BC (incubation time vs. initial pH), BD (incubation time vs. incubation temperature), A2 (chicken feather meal2), B2 (incubation time2), C2 (pH2), D2 (incubation temperature2), and E2 (inoculum size2) (Table 6). On the other hand, the p-values were inferior to 5% for the variables C (initial pH), D (incubation temperature), and the interactions AB (chicken feather meal vs. incubation time), AC (chicken feather meal vs. initial pH), AE (chicken feather meal vs. inoculum size), BE (incubation time vs. inoculum size), CD (initial pH vs. incubation temperature), CE (initial pH vs. inoculum size), and DE (incubation temperature vs. inoculum size), showing that these factors and interactions are not significant and therefore may be excluded from the regression model. The lack of fit of a regression model of 4.57 compared to the random pure error with a value of 99.38 shows that the regression model lack-of-fit is notably lower than a random pure error (p-value > 0.05) (Table 6), indicating that the regression model is pertinent [67]. According to Bezerra et al., in this particular context, a model that exhibits both a substantial regression and a non-significant lack of fit would be well matched to the experimental data [84].
In the present study, the coefficient of determination R2 value of 0.8952 reported in Table 6, reveals that the model is acceptable for predicting response values from experimental data. The R2 value greater than 0.75 is sufficient for the model to explain the majority of variations in responses [85]. The model accuracy can be estimated by the signal-to-noise ratio (adequate precision), which should be superior to 4. The value of the adequate precision calculated is 11.2756 (Table 6), indicating the highest precision and an adequate response ratio. This adequate precision value and that of the coefficient of variation (CV %; 13.15%) implies that the statistical model is valid and reproducible and can be used to provide navigation in the conception data space. [86,87].
Figure 9 clearly shows that the predicted and actual values for keratinase production are evenly distributed close to the straight line, indicating an ideal match between these values. The model is well adjusted and quite realistic; even a small difference between certain real and predicted values should be noted. Consequently, this indicates that the response variables for the experimental data can be fully expected to be sufficiently predicted by the quadratic model chosen. [83]. It may be concluded from its statistical properties that this model is suitable for determining the primary, quadratic, and interaction effects of implied factors in keratinase production by Streptomyces sp. strain DZ 06 (ES41).
Based on the coded factors, the following is the second-order polynomial quadratic regression Equation (6) generated for keratinase production:
where A: chicken feather meal (g/L), B: incubation time (days), C: initial pH, D: incubation temperature (°C), and E: inoculum size (spores/mL).
The positive sign preceding the equation terms indicates a synergistic effect, whereas the negative sign indicates an antagonistic effect. The primary, quadratic, and interaction impacts of factors on the production of keratinase enzyme are indicated by positive or negative values of linear, quadratic, and interaction coefficients, respectively [83]. The model based on the coded variable development is useful for identifying the most important factors having an impact on the response [88].
3.4.1. Interaction Analysis between Critical Parameters
Based on the regression analysis of the BBD, interaction plots and three-dimensional response surface plots (Figure 10a–f) were used to investigate the interplay of relevant factors and their impacts on response. The analysis of different interaction types obtained during this study was carried out by varying the values of each variable involved while maintaining the other factors at their optimal level.
The fluctuation in response (keratinase activity) and the significant p-value (0.0109) for the interaction between AD (chicken feather meal and incubation temperature) demonstrated the presence of positive interactions between these two variables (Figure 10a). Figure 10b depicts the effect of AD on keratinase production, which increased significantly (p < 0.05) when increasing the chicken feather meal quantity and the incubation temperature up to 5.6 g/L and 41 °C, respectively, then followed by a slight decrease with increasing levels of both parameters. On the other hand, keratinase production depended mainly on chicken feather meal quantity and incubation temperature as their quadratic effects, which were highly significant (p < 0.0001) and on the linear effect (p = 0.0009) of the first factor, confirming the single-factor experimental results (Table 6) and the influence in the metabolic rate of the interaction between these two parameters.
Previous investigations conducted by Siddharthan et al. and Ahmadpour [89,90], showed that the interaction of chicken feather meal and incubation temperature was significant in the optimization of keratinase production by Geobacillusthermodenitrificans PS41 and Bacillus cereus during submerged fermentation. However, Abdul Gafar et al. and Demir et al. [67,74] found that the effect of the interaction between chicken meal and incubation temperature was insignificant on keratinase activity when Bacillus sp. UPM-AAG1 and Streptomyces sp. 2M21 were cultivated in a chicken feather meal medium.
The positive interaction between chicken feather meal and incubation temperature (AD) was found to be evident in this study. In this context, Singh et al. [34] revealed that carbon is a critical component of the medium, as it provides energy to the microorganisms and is necessary for their development and the synthesis of primary and secondary metabolites. On the other hand, Revankar et al. [91] reported that incubation temperature is a critical feature in the production process, impacting in turn not only microbial development but also enzyme synthesis.
Figure 10c,d showsthe second interaction influencing the keratinase production yield. This interaction occurred between incubation time and initial pH (BC). Figure 10c clearly shows the interaction effects of the two factors on keratinase activity, as also shown in Table 6 (p = 0.0010). This combination maximizes keratinase activity detected with increasing incubation time and initial pH values of up to 7 days and 6, respectively. Beyond these values, a significant decrease in terms of enzymatic yield is recorded (Figure 10d). The positive influence of this interaction is consistent with the results presented in Table 6, showing a highly significant quadratic effect of initial pH (p < 0.0001) and significant quadratic and linear impacts of incubation time (p < 0.05) on the enzyme yield, confirming the single-factor trial results.
Khalil et al. andDhiva et al. [92,93] both reported that the interaction between incubation time and initial pH was significant for keratinase production by Pichia kudriavzevii and Bacillus sp. CBNRBT2. The first study was carried out on a medium consisting only of chicken feather meal, while in the second study, glucose was added. However, in work conducted by Abd El-Aziz et al. [77], a non-significant result for the incubation time-initial pH interaction was obtained when using the feather-degrading keratinase-producing Streptomyces swerraensis KN23 strain through submerged fermentation in cultivation medium with sucrose as an added carbon source.
In this study, the interaction between incubation time and initial pH (BC) was critical for keratinase production. Bhari et al. indicated that incubation time is an important parameter for metabolite production. Its effects may be explained by the significant changes in the rheological properties of the cultivation broth during the fermentation period. Keratinase, as a primary metabolite, is generated by actively developing cells using chicken plumage as cultural substrate. A continued decrease in keratinase production is then recorded. This may be assigned to nutrient depletion, accumulation of inhibitory byproducts in the medium, and catabolic repression of enzyme production. All of these adjustments have the potential to alter the medium pH, which is crucial for determining the best possible physiological function for bacterial cells as well as the passage of different nutrients through the cell membrane for the highest possible enzymatic production [83].
The third and last interaction affecting keratinase activity levels was between incubation time and incubation temperature (BD), as seen in Figure 10e,f. This combination presented a significant p-value of 0.0006 (Table 6), proving the interaction influence of the two factors as shown by Figure 10e. Keratinase production was improved for low values of incubation time and high values of incubation temperatures. Whereas for inverse values, the keratinase activity fell sharply (Figure 10f). The combination effects of the incubation time-incubation temperature pair on enzymatic production yield wereobviously observed via the high quadratic impact of temperature (p < 0.0001), as well as the quadratic and linear effects of incubation time with p < 0.05 (Table 6).
During work conducted by Demir et al., Dhiva et al., and Ahmadpour [74,90,94], it was shown that the interaction between incubation time and incubation temperature significantly affects keratinase activity observed for Streptomyces sp. 2M21, B. cereus, and Pseudomonas aeruginosa SU-1 on chicken feather meal medium using statistical optimization methods. However, during a study reported by Dhiva et al. [93], using Bacillus sp. CBNRBT2, it was found that the combination of incubation timeand incubation temperature was insignificant towards keratinase production.
The combination of incubation time and incubation temperature presented a significant effect on keratinase activity in the present work. As shown by Bhari et al., the essential role of incubation time depends on the changes that take place in the production medium during the fermentation period [83]. Moreover, as mentioned above, production temperature is a crucial factor that affects both microbial growth and enzyme synthesis, according to Revankar et al. [91].
Microbial degradation of chicken feathers is a complex process. In the present work, highlighting the different main, quadratic, and interaction effects of the various factors influencing keratinase production and the hydrolytic capacities against an insoluble keratin substrate, in this case chicken feather meal in Streptomyces sp. DZ 06, make it a potential candidate for use in various industrial fields, in particular the bioconversion of poultry industry waste.
3.4.2. Approval of Proposed Model
The response surface equations were approved by numerical optimization using Design Expert 13 software, using the desirability function on the one hand, and analysis of the disparity between the observed values and the predicted values resulting from the response regression on the other hand. This approach involves a desirability function scale between d = 0, suggesting that the response is totally unacceptable, and d = 1, suggesting that the response corresponds exactly to the desired value. The value progresses from 0 to 1 as the desirability of the corresponding response increases [83].
The RSM as an analysis model and the regression equation were approved by testing two solutions of the most suitable operating conditions proposed by the model. In order to anticipate and confirm the correctness of the mathematical model, a new position of the experiment was computed using the ideal conditions that had been established and the response predicted. Table 7 displays the system’s most appropriate alternative optimization outcomes for each parameter based on their chosen optimal values and the desirability value.
Two tests were conducted under identical circumstances to verify the predicted ideal parameters experimentally. Among the two solutions suggested by the model, the optimal conditions turned out to be a quantity of chicken feather meal of 6.13 g/L, an incubation time of 4.11 days, an initial pH of 6.25, an incubation temperature of 40.65 °C, and an inoculum size of 3.98 × 107 spores/mL for an optimal value of 485.44 U/mL of keratinase activity as illustrated by the desirability ramp for numerical optimization of the five independent variables (Figure 11). The results showed a keratinase production of 489.24 U/mL with a desirability rate of 0.998. The great dependability and validation of the keratinase production model were strongly supported by the good agreement between the anticipated values and the repeated results.
Analysis of the quadratic model under optimal conditions and determination of the appropriate values for the parameters involved in keratinase production by Streptomyces sp. DZ 06 attests to the reproducibility, relevance, and validity of the statistical model used.
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