Optimization for improved cellulase production of Penicillium funiculosum NCIM 1228 in submerged fermentation

doi:10.21203/rs.3.rs-1721851/v1

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Optimization for improved cellulase production of Penicillium funiculosum NCIM 1228 in submerged fermentation

https://doi.org/10.21203/rs.3.rs-1721851/v1

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The cellulase enzyme is currently the world's third-largest enzyme. Because of their demands in various industries like textiles, food, waste management, pharmaceuticals, agriculture, pulp paper, biofuels, etc., their demand curve shifts upward sharply. The response surface methodology (RSM) approach was employed to optimize media components and process parameters in the current investigation, which successfully increased cellulase production from Penicillium funiculosum NCIM 1228 ( P. funiculosum NCIM 1228). Statistical optimization for the hyperproduction of cellulases has been conducted using RSM. The Plackett-Burman design (PBD) approach was used to investigate the critical factors of the cellulase production medium, followed by the Box-Behnken design (BBD) method, which was used to statistically estimate optimum values and conditions that had a substantial impact on cellulase production. The estimated optimal combinations of parameters for cellulase production were urea (0.2, %), CaCl 2 (0.2, %), MgSO 4 (0.05, %), peptone (1.5, %), microcrystalline cellulose (5.0, %), wheat bran (2.5, %), CSL (2.5, %), KH 2 PO 4 (0.15, %), inoculum (10.65%), agitation (157 rpm), pH (5.88), and temperature (29.84°C). Conclusively, experimental validation under optimal conditions detected an increased production of 3.82-fold and 3.61-fold in filter paper activity (FPase) and β-glucosidase respectively while 1.66-fold and 1.57-fold improvement in FPase and β-glucosidase specific activity were observed. The xylanase activity was enhanced by 3.29-fold and its specific activity improved by 69.62% after optimization. P. funiculosum NCIM 1228 offers the benefit of producing cellulase with an entire cellulolytic system of enzymes that can be excreted extracellularly, which provides a promising biocatalyst for biofuel industries.

Cellulase

Plackett-Burman design

Penicillium funiculosum

Response Surface Methodology

Submerged Fermentation

Xylanase.

The requirement for energy and fuel day by day increases. The primary source is fossil fuel, and there is a limit to how much can be consumed by humans. Therefore, scientists are focusing on alternative renewable sources of energy. Lignocellulosic biomass (LCB) is the most abundant compound on this planet, formed by the product of glucose units obtained through photosynthesis [1]. The building blocks of plant cell walls are hemicellulose, cellulose, and lignin, with cellulose being the major component. On average, LCB contains 23-28% lignin, 40–46% cellulose, and 33–38% hemicellulose[2]. Cellulose is one of the cheap raw materials for biochemicals, biofuel, and bioenergy production [2]. Cellulases, which hydrolyze the cellulose, and played an important role in the production of uncommon energy resources by utilizing LCB for bioethanol and biogas production. Exoglucanase, carboxymethyl cellulase (CMCase), β-glucosidase, and xylanases are the major enzymes essential for efficient LCB breakdown[3]. The combined action of these enzymes is crucial for the efficient hydrolysis of cellulose into accessible sugars [4, 5]. Cellulase is used in various industrial processes like textile wet processing, softening of garments, biostoning and biopolishing of textile fibers are some of the major applications of this enzyme in the industry. Because of these diverse applications of cellulase enzymes in different industries, the demand for cellulase has consistently increased. Very few companies produce cellulases for biomass conversion. "Novozyme" and "Genencor" have significant roles in the production of cellulases and are continuously working on cost reduction for cellulase enzyme production [6].

Production of cellulases in a cost-effective medium might facilitate industrial-scale cellulase production and their application in the second generation (2G) of ethanol will reduce the cost of LCB to ethanol. Very few industries have succeeded in producing enzyme cellulases commercially. The production cost of cellulases mostly relies on media components, process parameters, and enzyme yield. Currently, very few fungal strains like Trichoderma and Aspergillus, are used for the production of commercial cellulases. The filamentous fungi have the ability to grow on a wide range of substrates and produce a complete combination of extracellular cellulases enzymes. Trichoderma sp., Aspergillus sp., Schizophyllum sp. and Penicillium species have been communicated to produce cellulases. Cellulases secreted by P. funiculosum NCIM 1228 have excellent properties it contains exo-cellulases, endo cellulases, and β-glucosidase along with xylanases [7, 8]. This is the promising characteristics for future industrial enzymes. Meanwhile, this work would help to commercialize the second-generation biofuel production technology for biofuel industries of biomass conversion.

This study reveals enhancement of the fungal strain P. funiculosum NCIM 1228 for cellulase production. Submerged fermentation is a more relevant process for cellulase enzyme production over the solid-state production method as it is scalable, handy, and required comparatively less space. Improvements in cellulase production need to optimize media composition and process parameters. The traditional method for optimization of the media components is one factor at a time (OFAT) approach is inefficient, incapable of identifying variable interactions, and a time-consuming method for optimizing media components and process parameters. OFAT, is inaccurate and requires comparatively more manpower [9]. On the other hand, the statistical approach for media and process parameters optimization is more suitable, accurate, very quick, universally accepted, and requires less manpower. Achieving a higher titer by using the statistical approach for media optimization is generally obtained through full factorial design such as PBD and the use of RSM. The significant factors for cellulase production were identified by PBD and the factors' combined interactions and optimization were done by BBD [10].

In the present work, we found 200 to 300 % of improvement in FPase and xylanase activity and up to 50% increase in FPase and CMCase specific activity by applying screening design and PDB to select critical parameters and critical media variables for cellulase production. And then BBD was applied to optimize the level of selected parameters. The obtained optimum media components and parameters are wheat bran, avicel, peptone, corn steep liquor (CSL), urea CaCl₂, KH₂PO₄, pH, Inoculum percent, agitation, and temperature. After optimizing this parameter and media components the cellulase production increased by 3.82-fold. In this way, the use of a statistical tool like Minitab is a very effective tool, and the RSM model in optimization of media variables and cellulase process production was significant as the P-value of the model is < 0.05 [11]. Cellulases enzyme-producing P. funiculosum NCIM 1228 provides economically viable cellulase with a high yield and improvement in enzymatic hydrolysis suitable for producing industrial enzymes in the future.

2.1 Penicillium funiculosum NCIM 1228

P. funiculosum NCIM 1228 was taken from the National Collection of Industrial Microorganisms (NCIM), CSIR-National Chemical Laboratory, Pune-411008, India, and used in the present study. The strain was streaked on potato dextrose agar (PDA), sporulation was observed after eight to ten days of incubation at 30°C. Short-term storage of the grown PDA slants was stored at 4°C for 6 months and for the long-term storage spores were stored at -80°C in 25% glycerol.

2.2 Culture, inoculum preparation, and media

Spore suspension has been prepared by harvesting spores of P. funiculosum NCIM 1228 from potato dextrose agar (PDA) slant surface aseptically by using sterile water and a glass rod [12]. Then, these spores are transferred into a sterile 50-mL falcon tube. After that, these spore suspensions were serially diluted to obtain a standard inoculum containing 1 X 10⁵ to 1 X 10⁶ spores per mL of inoculum. The spore count has been taken by using a haemocytometer [13]. Conical flasks (250 mL) carrying 100 mL of media for determining cellulose breakdown by the filamentous fungus P. funiculosum NCIM 1228. The medium contained 1.4 g (NH₄)₂SO₄ , 2 g KH₂PO₄, 0.3 g Urea, 0.3 g CaCl₂, 0.3 g MgSO₄·7H2O, 0.005 g FeSO₄, 0.0016 g MnSO₄ , 0.0017g ZnSO₄. 7H2O, 0.02 g CoCl₂, 0.75 g Peptone,0.5g Yeast extract (Y.E.),1g Tween80, per liter (L), and 5.0 g /L of the cellulose (Avicel) as a carbon source [14, 15].

2.3 Identification of most significant nutrients by PBD

In this study, statistical approaches like RSM were used to identify and optimize the most important media components and process parameters that have the greatest influence on cellulase production. The design of the experiment (DOE) is used to identify and reduce a large number of potential factors for further study. And the PBD experiment was used to detect the effective variable level. Screening design is used to identify the main effects of 6 to 15 factors and examine the significant variables that influence cellulase production [9].

2.3.1 Screening design

In this design, to find out the list of potential factors that have the greatest influence on the cellulase enzyme production, a screening design is used to reduce the statistically insignificant factors. We identified 18 different factors that have a significant influence on cellulase production. According to preliminary experiments and various references listed in Table 1.These 17 factors are wheat bran (%), Avicel (%), Lactose (%), CSL (%), Urea (%), KH₂PO₄ (%), Tween 80 (%), MgSO₄ (%), CaCl₂ (%), Y.E (%), Peptone (%), TMS (%), pH (%), Incubation time (days), Inoculum (%), Agitation (rpm) and Lactose (%), from which we choose 8 factors.

2.3.2 PBD for analysing potential media components

Based on the screening design study on different media components, Urea, CaCl₂, MgSO₄, Peptone, Avicel, Wheat bran, CSL and Lactose was found promising and selected for PBD with an objective to find out the potential components of the medium that have a critical role in cellulase production by P. funiculosum NCIM 1228 [16]. These 8 variables, which are components of cellulase production medium were examined while keeping other components constant. A total of 12 runs was designed including 12 combinations (Table 1). Lower and upper levels of the variables are indicated as -1 and +1 levels. To determine the effect of each factor the difference between the responses (+ and -) were considered.

The first-order polynomial equation is used in the PBD experimental design:

Y = β0 + ∑ βi Xi, ----------(1)

where Y denotes the FPU/mL, β0 is the model coefficient, βi is the linear coefficient, and Xi is the IV levels. The statistical significance of the responses was determined using the T-value and P-value. The more significant the associated coefficient is, the bigger the magnitude of the T-value and the smaller the P-value. Variables that were significantly above 95 percent level and at a confidence value of p < 0.05 in the regression analysis were determined to have a substantial impact on cellulase production and were further statistically optimized using BBD [17]. All of the experiments were conducted in triplicate. 100 mL of media in a 500 mL Erlenmeyer flask.

2.3.3 RSM of medium optimization using BBD

Minitab 19 software was used to obtain optimal concentrations of the media components and working conditions for the experiment, generate response surface graphs and perform statistical analysis of the data. The BBD design of RSM was used for the experiment. BBD was used to perform two sets of experiments [18]. In the first set, media components like Avicel, Peptone, Lactose, Wheat bran, CSL, Urea, MgSO₄, and CaCl₂ were taken as IV while in the second set, process controls like temperature, inoculum, agitation, and pH were taken as variable factors for the enhanced cellulase production. The first set is for optimization of media components and the second set is for optimization of process controls parameters.

First Set BBD: media components optimization

In the first set of experimental designs for the 9 variables, i.e., urea (0–0.5, %), CaCl₂(0-0.5, %), MgSO₄ (0-0.1,%), Peptone (0-1.5,%), Avicel (0-5,%), Wheat bran (0-5,%), CSL (0-5,%), Lactose (0-5,%), and KH₂PO₄ (0.1-0.25,%), were taken for maximum cellulase production. As indicated in Table 2, the design was used to select the range of each variable (Higher and Lower). For the first set of BBD tests, a total of 130 experiments in triplicate were optimized for media components for maximum cellulase production in terms of FPU U/mL.

Second Set BBD: Process parameters optimization

Temperature (25–35 °C), pH (3.5–7.5), inoculum (5–15%), and agitation (100–200 RPM) were optimized in this set experiment design [19]. These four variables were chosen for maximum cellulase production. As indicated in Table 3, the design was used to select the range of each factor (higher and lower). A total of 27 experimental runs in triplicates were designed for this set of BBD experiments to optimize the process control values.

In both the above sets, that is, set 1: media components optimization and Set 2: Process parameters optimization the suitability of the model was examined by analysis of variance (ANOVA) and the coefficient of R² [20]. The statistical significance of all terms in the model was determined by calculating the F-value at a default P < 0.05. The 2D contour plots were created by maintaining two factors fixed at 0 levels and varying the other factors within the experimental range, using Minitab 19.0 software for the regression of the observational results and the production of the 2D contour plots [16]. The trials were done in triplicate, with the average response in cellulase production being used for analysis.

2.4 Enzymes production under submerged fermentation (SmF)

Spores of P. funiculosum NCIM 1228 has inoculated into Czapek dox broth and kept it at 30°C for 48 hrs for pre-seed inoculum generation. Then, for seed inoculum generation, 10% of grown pre-seed was inoculated in to modified Mandels and Reese medium consists of (g/L) of KH₂PO₄, 2.0 ; Urea, 0.3 ; CaCl₂.2H₂O, 0.3 ; MgSO₄.7H₂O, 0.3 ; (NH₄)2SO₄, 1.4 ; Yeast extract 0.1 ; Peptone, 0.25 ; Glucose, 2.0 ; Tween 80, 1.0 ; FeSO₄.7H₂O, 0.0005 ; ZnSO₄.7H₂O, 0.0014 ; MnSO₄, 0.0016 ; CoCl₂, 0.002 ; and Cellulose Acetate, 5.0 ; pH 5.5. After 48 h incubation at 30°C and 150 rpm.

The grown seed of P. funiculosum NCIM 1228 was inoculated in to optimized formulated by traditional method OFAT for cellulase production media consisted (g/L) of KH₂PO₄, 2.0 ; Urea, 5.0 ; CaCl₂.2H₂O, 1.0 ; MgSO₄.7H₂O, 0.3 ; Yeast extract 0.5 ; Peptone, 16.0 ; Tween 80, 1.0 ; FeSO₄.7H₂O, 0.0005 ; ZnSO₄.7H₂O, 0.0014 ; MnSO₄, 0.0016 ; CoCl₂, 0.002 ; Corn steep liquor (CSL), 10.0 ; Citric acid,5.0 ; Na₂HPO₄, 7.2 ; Lactose, 25.0 ; Wheat Bran,10.0 and Avicel, 30.0 (de Castro et al. 2010). In place of Avicel, we used microcrystalline cellulose 101 (MCC101). MCC101 was obtained from Blanver Farmoquimica Ltda. (made in Brazil) and was used as substrate in the fermentation medium throughout this study. Peptone, Yeast extract, CSL, and Urea is used as nitrogen source, while Wheat bran, Lactose, and MCC101 were used as carbon sources [13, 17, 21]. Wheat bran obtained from the local market was used as a carbon source, whereas, CSL was obtained from Rasayan Trading Co. Ambawadi, Ahmedabad, Gujarat, India. which is complex, viscous, and a by-product of corn wet milling, containing minerals, amino acids, vitamins, and other essential constituents of growth media. All of these media constituents were dispensed in 100mL in 500mL Erlenmeyer flasks and then autoclaved at 121°C for 15 minutes.

After seed inoculations, all these flasks were kept in an incubating shaker at 30°C and 150 rpm. The fermentation was continued for 12 days and samples were taken at every 24.00 hrs intervals for enzyme assay of cellulases, that is, FPase, CMCase, β-glucosidase, xylanases, and protein estimation. The pH was maintained at 5.5 ± 0.3 throughout the batch.

2.5 Sampling and data analysis

Experiments were performed in triplicate. 5mL of fermented broth were taken at 24-hour intervals for up to 12 days. The pH of the broth was checked. Then, samples were centrifuged at 11,000 rpm for 10 to 15 minutes at 10°C. The supernatant was taken for enzyme assay and protein estimation in duplicates and recorded mean values. The activities of CMCase, FPase, and Xylanase were calculated by using the 3,5-dinitrosalicylic acid (DNSA) method [22]. β – glucosidase activity was calculated as per T.K Ghose [23] and Protein in the broth was estimated based on the Bradford method [25].

2.6 Cellulase activity assay

Accurately determining cellulase activity is always a difficulty. The FPase, which was developed by T.K. Ghose in 1987 and is widely used to quantify cellulase activity, is the most widely used total cellulase activity assay. One enzyme unit (U) is defined as the quantity of enzyme required to release reducing sugar at micromoles (μ moles) per minute rate. [23].

2.6.1 FPase activity assay

FPase activity was determined as per the T.K. Ghose method [23] as reported earlier. It is also called the filter paper degrading assay, and it is a useful, simple method, and does not require sophisticated equipment’s. Saccharification of cellulose in filter paper needs the combined action of endo- exo cellulase and β-glucosidase. Therefore, it is referred to as "total cellulase activity." FPase activity was measured by adding 500 µL of diluted broth containing the enzyme to a 50 mM sodium citrate buffer with a pH of 4.8. Then the enzyme-substrate mixture was incubated at 50°C for 1 h. To stop the reaction, DNSA was used to measure the concentration of glucose as the reducing sugar. A total of 3 mL of DNSA was added to the enzyme mixture and then incubate at 98 ± 2°C for 5 minutes. Then add 20 mL of deionized water. The test tube was then left in cold water for 15 minutes to settle well. Next, the absorbance was recorded against the enzyme blank at 540 nm in a spectrophotometer (UV-2450, UV-VIS spectrophotometer, Shimadzu) to determine the amount of glucose present.

2.6.2 CMCase and Xylanases activity assay

Carboxymethyl and Xylanase activities were described by T.K. Ghose in 1987[24]. In short, the reaction system included 500 µL of diluted enzyme solution and 500 µL of 2% substrate (carboxymethyl cellulose sigma medium viscosity in citrate buffer (50 mM) for CMCase assay and xylan birch wood in citrate buffer (50 mM) for Xylanase activity ) of pH 4.8, and then the reaction mixture was kept at 50°C in a water bath for half-hour. Then, the reaction system was stopped by adding 3.0 mL of DNS reagent and incubating it in a water bath at 100 °C for 5 minutes. The OD was taken at 540 nm, and the reducing sugar concentration was measured [23, 24]

To calculate the activity of CMCase and xylanases, one unit (U) of CMCase or xylanase activity is defined as the amount of enzyme releasing 1 μmoL of reducing sugar of glucose for CMCase and xylose for xylanases per minute under the assay conditions. The CMCase and xylanase activities were measured based on the standard curves of glucose and xylose, respectively.

2.6.3 β-glucosidase activity assay

β-glucosidase or cellobiase activity was estimated based on the method by Ghose et al. in 1987.

2.7 Protein estimation

Total protein was calculated based on Bradford, bovine serum albumin (BSA) used as a standard. The Folin protein determination method was used to calculate the amount of protein present in supernatants of fermented samples based on the absorbance reading at a wavelength of 750 nm. The soluble protein content of the supernatants was estimated using the standard curve of BSA [25].

3.1 Evolution of significant nutrients for cellulase production

3.1.1. Screening of variables and their effect on cellulase production

The seventeen different variables were screened to find out potential factors that influence cellulase enzyme production. As shown in Fig. 1, out of seventeen variables, wheat bran, Avicel, CSL, Peptone, MgSO₄, Urea, CaCl₂, and KH₂PO₄ have been found to have a significant role in cellulase production in given conditions.

According to Fig. 1, the greatest influence variables Wheat bran, Avicel, CSL, and peptone are significantly affecting cellulase production. For further study, other unimportant factors were kept constant.

3.1.2. Optimization of potential components of the medium that have a critical role

The screening design observed that seventeen different factors had somehow influenced cellulase production. Urea, CaCl₂, MgSO₄, wheat bran, Avicel, CSL, peptone, and lactose are some of the variables. The PBD is aimed at selecting the potential factor in the system that affects cellulase production. The PBD is a full factorial design and it scrutinizes N-1 variables by the N number of runs (N must be a multiple of four) [26]. As shown in Table 4, every variable has two levels, namely, "high level indicated as (+1)" and "low level indicated as (-1)".

For each variable, the high (+) and low levels (-) were selected and mentioned in Table 4. For medium optimization, twelve different trials were made and all trials were performed in triplicate, the average of three values was considered for cellulase production. The PBD of the 8 variables was composed of 12 runs and the respective responses in the form of FPU/mL specified in Table 5.

Regression Equation in Uncoded Units

FPU U/mL = 0.023 + 0.053 Urea % + 0.021 CaCl₂ % + 0.335 MgSO₄ % + 0.0696 Peptone% + 0.0760 Avicel% + 0.0927 Wheat bran % + 0.0785 CSL % + 0.0622 Lactose %

When the P-values in the coded coefficients Table 7 are less than α, the effect of the IV is statistically significant. The following effects of the IV are significant at the default value of α = 0.05:

Wheat bran, CSL, Lactose, and Avicel.

The main effect plot for FPU (U/mL) as per Supplementary Fig. S1 also showed the effects of the listed variables on cellulase production.

Thus, a large contrast coefficient, either positive or negative, indicates that a factor has a large influence on enzyme yield. The analysis showed that among the media component variables tested, wheat bran, CSL, lactose, and Avicel concentrations had a significant impact on cellulase production, as shown in Supplementary Fig. S1. The order of influence of these variables on cellulase enzyme production was wheat bran > CSL > Lactose > Avicel > Peptone > MgSO₄ > Urea > CaCl₂.

PBD model displayed a Normal plot of the standardized effects as shown in Supplementary Fig. S2. shows that wheat bran% (F) has the most significant effect of about 90%, followed by CSL% (G) at about 70%, then lactose%(H), and Avicel% (E) has about 70% and 60%, respectively, at the default significant level (α) value is 0.05. Minitab 19 shows the absolute value of the standardized effects in the Pareto chart (Fig. 2). Any impacts that go beyond the reference line have a lot of weight. At the default of 0.05, wheat bran% (F), CSL% (G), lactose% (H), and Avicel% (E) are all significant. The residual plot (Supplementary Fig. S3) shows that the measurement deviation is randomly distributed around the mean.

3.2 Response surface experimental design using BBD

The concentration of these important variables was optimized using RSM after an investigation of significant factors for cellulase production. In this study, the BBD was employed to optimize various concentrations for increased cellulase formation by P. funiculosum NCIM 1228. Two sets of BBD experiments were carried out to analyse the interactions between different variables and optimize the factors [27]. In one set, media component variables were selected, and in the second set, process parameters variables were selected.

3.2.1 Statistical optimization- RSM of media components Set:1

In this set, nine different media components were selected. These selected factors are urea, CaCl₂, MgSO₄, peptone, Avicel, wheat bran, CSL, lactose, and KH₂PO₄ were designated as A, B, C, D, E, F, G, H, and J respectively. And each of the above-mentioned IVs was examined at three levels in a total of 130 runs (Table 2 ). 130 experiments were conducted and each experiment was performed in triplicate and an average of three were considered to evaluate the 54 coefficients for cellulase production. Based on BBD investigation, the final regression equation in terms of coded factors[10] :

FPU (U/mL) = -0.925 - 1.488 A - 0.073 B - 8.07 C + 0.837 D+ 0.4165 E + 0.3449 F + 0.2714 G+ 0.2354 H+ 4.78 J+ 0.048 A² + 0.367 B² + 4.5 C²- 0.3782 D² - 0.02354 E² - 0.01909 F² - 0.03089 G² - 0.01169 H² - 6.65 J²+ 0.06 A*B + 21.50 A*C + 0.097 A*D - 0.014 A*E - 0.046 A*F - 0.1090 A*G + 0.058 A*H+ 2.20 A*J+ 13.30 B*C + 0.007 B*D- 0.0158 B*E - 0.180 B*F - 0.092 B*G+ 0.0503 B*H - 0.07 B*J- 0.87 C*D+ 0.320 C*E + 0.125 C*F + 0.475 C*G+ 0.020 C*H - 14.7 C*J+ 0.0627 D*E+ 0.0107 D*F + 0.0057 D*G - 0.0813 D*H+ 0.16 D*J+ 0.0124 E*F + 0.0184 E*G - 0.01062 E*H - 0.823 E*J- 0.0108 F*G- 0.0018 F*H + 0.030 F*J- 0.0097 G*H+ 0.157 G*J- 0.637 H*J

For the validation of the model's summary equation and statistical output, ANOVA was done as mentioned in Table 8. The F value is calculated as a proportion of mean square factors to mean square error. Most of the time, the F value is higher than the tabulated value.

The P-value is an indicator for examining the importance of every coefficient. If P values are < 0.05, it designates that model terms are significant[10] (Fig. 5), and if the R-square value is greater than 75%, it indicates that the model is suitable[10]. For a good statistical model, R-square should be close to 100%[10]. The model's F value in this study was 21.43, indicating that it is significant. The R-squared (predicted) is 80.51% in reasonable agreement with the R-squared value (adjusted) of 89.53% [28]. The R squared value for cellulase in terms of FPU U/mL was 93.91%, This guarantees that the model is adjusted to the experimental data in an acceptable manner. The overall model P-value is less than 0.050, which is 0.000 means the model is highly significant[9].

The main effects of the IV are statistically significant when their P-values are < 0.050. The following effects of the IV are significant:

Avicel, Wheat bran, CSL, and Peptone

The main effect plot for FPU (U/mL) as per Fig. 3 also showed the effects of the listed variables on cellulase production.

Thus, a large contrast coefficient, either positive or negative, indicates that an independent variable has a large influence on enzyme yield [29]. The analysis showed that among the media component factors examined, wheat bran, CSL, lactose, and Avicel concentrations had a significant impact on cellulase production, as shown in Supplementary Fig. S4. Based on BBD, the order of influence of the significant IV on cellulase production was Avicel > wheat bran > CSL > Peptone

Interaction between media components and their combined effect on cellulase production:

By using response surface plots, the factors' interactions and their influence on enhanced cellulase production were understood. These plots were generated by holding other media components stable at a time (preferably at their optimum value) and plotting the response obtained for varying levels of the other two media components. The total two-way interaction P-value is 0.03, which is less than 0.050 and indicates that the two-way interactions are significant. Out of a total of 36 two-way interactions and 9 square interactions, eight of those interactions are significant, as shown in Fig. 4. The two-way interactions between Urea and MgSO₄, CaCl₂ and MgSO₄, Peptone and Lactose, Avicel and KH₂PO₄ are significant, as shown in Fig. 4a, b, c, and d, respectively. Esterbauer et al. and Marjamaa et al. used these components for cellulase production [30, 31]. Fig. 4a represents the impact of the interaction between urea and MgSO₄ on cellulase production. It was discovered that factor urea had a positive impact on cellulase production at low levels, and factor MgSO₄ had a positive impact at high levels. Fig. 4b depicts the significant interaction between MgSO₄ and CaCl₂.In Fig. 4b, a high level of CaCl₂ results in higher cellulase production, while a high level of MgSO₄results in a greater impact on the cellulase enzyme.

As per Fig. 4c, improving in cellulase production when the concentration of peptone is increased from 0 to 1.40%, while slightly decreasing in cellulase production when the concentration of lactose is increased.

In Fig. 4d, it was observed that when the concentration of both Avicel and KH₂PO₄ increased, there was a steadily increased in cellulase production (FPU U/mL) up to Avicel 4.5 % and KH₂PO₄ 0.15%. Increasing the value of both IV (Avicel above 4.5% and KH₂PO₄ below 0.15%) also showed a negative effect on cellulase production. These potential candidate media components have also been used in research by Hadi et al. (2014), Das et al. (2013), Adsul et al. (2007), and Ogunmolu et al. (2018) [8, 32–34].

Prediction of the model:

Table 9 : Prediction summary of the model is Fit= 2.59; SE Fit=0.13; 95% CI= (2.33, 2.86);95% PI=(2.15, 3.04)

The output of the above model 95% prediction interval for a variable of Set 2 that is Urea (0.2, %), CaCl₂(0.2, %), MgSO₄ (0.05,%), Peptone (1.5,%), Avicel (5.0,%), Wheat bran (2.5,%), CSL (2.5,%), Lactose (0,%), and KH₂PO₄ (0.15,%) . We can be 95% confident that the cellulase production in the form of PFU U/mL is in between 2.15 and 3.04 FPU U/mL.

3.2.2 Statistical optimization- RSM of process parameters Set:2

In this set, the BBD approach was used to improve the process parameters for the enhanced cellulase production. The IVs like temperature, pH, inoculum, and agitation were selected and designated as A, B, C, and D. The effects of all the above IVs were analysed at three levels in a total of 27 experiments (Table 3). Ten coefficients were generated from twenty-seven experiments for the production of cellulase. Based on BBD experimental analysis, the final regression equation in terms of coded factors is:

FPU/mL = -21.06 + 0.945 A + 1.972 B + 0.2888 C+ 0.03101 D- 0.01585 A² - 0.1493 B² - 0.00973 C²- 0.000055 D²- 0.001400 B*D- 0.000510 C*D

For the validation of the model's summary equation and statistical output, ANOVA was done as mentioned in Table 10. The F value is a proportion between mean square factors and means square error. Most of the time, the F value is higher than the tabulated value.

Model Summary: Standard deviation of the model is 0.122; R²= 0.93; R² (adjusted)= 0.88; and R² (predicted)= 0.80

The P-value is an indicator for checking the significance of every coefficient. As shown in table 10, if P values are < 0.05, model terms are significant; if R² values are greater than 0.75, the model is suitable for good statistical modeling.R² should be close to 1.0. The model's F value in this study was 21.67, indicating that it is significant. The R² (predicted) is 0.80, in practical agreement with the R² value (adjusted) of 0.88. The R² value for cellulase in terms of FPU U/mL was 0.93, which further ensures an acceptable adjustment of the model to the experimental data. The overall model P-value is less than 0.050, which means 0.000 means the model is highly significant [35].

The main effects of the IV are statistically significant when their P-values are < 0.050. The IV pH, inoculum, and agitation are significant. And the order of influence of these factors in the provided conditions on cellulase production was

pH >Inoculum>Agitation>Temperature

The main effect plot for FPU (U/mL) as per Fig. 6 also showed the effects of the listed factors on cellulase production. The main effect plot depicts the effect of temperature, pH, inoculum percentage, and agitation[19, 35]. To confirm model acceptability, residual plots analysis was done as shown in Supplementary Fig. S5 and Fig. 7. The residuals are normally distributed in the normal probability plot. This implies that the model is effective, suggesting that it can be used to optimize cellulase production. The residuals histograms revealed a bell-shaped pattern, and the residuals versus plot were used to verify the hypothesis. The fact that the residuals are randomly distributed in this plot implies that the model is appropriate.

The estimated impacts of factors on cellulase production are represented by a Pareto chart (Supplementary Fig. S6). In this chart, the red reference line differentiates which effects are statistically significant and non-significant. The variable bars that cross these reference lines are statistically significant. In this Pareto chart, the bars that represent factors BB, AA, B, CC, DD, C, D, and BD cross the reference line that is at 2.16 at the α = 0.05 in the current model terms.

Interaction between process parameters and their combined effect on cellulase production

The interaction of process variables and their cumulative impact on cellulase production the response surface plot was used to understand the interactive influence of IVs like temperature, pH, inoculum, and agitation on cellulase production. Understanding the relationship between the two parameters and determining the optimum value for cellulase enzyme production. Response surface graphs of Fig. 8A–F were plotted to show the combined effect of pH, temperature, inoculum, and agitation and the optimum level of each variable required for higher cellulase production. In Fig. 8a, the cumulative effect of temperature and pH on cellulase production at 10% inoculum and 150 rpm. Scientists like Cho et al, Michael Y. Cho and Tallahassee, Parekh et al. Gao et al., Meng et al., Zheng et al. used these conditions of pH, temperature agitation, and inoculum for cellulase production for bacterial as well as fungal cultures [28, 36–39]. Cellulase production was 2.78 FPU/mL when the temperature was kept constant at 29.75°C and the pH was kept constant at 5.8, whereas decreasing or increasing values of temperature and pH gradually reduced cellulase production (that is, the decreasing value of FPU/mL), confirming that pH and temperature have a significant impact on cellulase production [40]. This production is comparatively higher than unoptimized media.

Based on the plot Fig. 8b, the cellulase activity was optimum at an inoculum of 10.6% and a 29.7°C temperature. The combined effect of temperature and agitation (Fig. 8c) showed that the optimum cellulase production was observed at 29.75°C and agitation at 158 rpm. Similarly, Fig. 8d depicts the interaction between pH and inoculum, while Fig. 8e depicts the combined effect of pH and agitation [10].

The interaction of inoculum and agitation on cellulase production. Fig. 8f shows that both factors have a positive impact on cellulase production when the concentration of inoculum increased from 5% to 11% and the agitation speed increased from 100 rpm to 158 rpm [19]. Afterward, increasing the value of both the parameters (inoculum above 11% and agitation above 158 rpm) shows a negative impact on cellulase production. Results investigated from contour 2D plots have given the same values of cellulase production (FPU/mL = 2.79), with the pH of the medium being 5.88, the inoculum 10.65%, at 29.8°C, and agitation of 157 rpm (Fig. 9). Contour plots (Supplementary Fig. S7 a-f) are a method to show a 3D surface on a 2D plane and demonstrate the relationship between cellulase production and the effect of combined factors. After process parameters optimization by RSM, the best recipe of variables settings for attaining the highest cellulase production was found with a desirability score of D = 0.93, which is close to 1 (Fig. 9). Overall, predicted cellulase production with a higher desirability score was obtained from optimization plots. To get the highest cellulase production, you need to set all the variables at their optimum level.

Regression Equation in Uncoded Units

FPU/mL = -22.25 + 0.973 Temperature( °C) + 2.155 pH + 0.342 Inoculum (%) + 0.03101 Agitation RPM- 0.01585 Temperature(°C)*Temperature(°C)- 0.1493 pH*pH- 0.00973 Inoculum(%)* Inoculum (%)- 0.000055 Agitation RPM*Agitation RPM - 0.00400 Temperature(°C)* pH - 0.00060 Temperature (°C)* Inoculum (%) - 0.00631 pH*Inoculum (%)- 0.001400 pH*Agitation RPM - 0.000510 Inoculum (%)*Agitation RPM

The followings are the Predictions by model

According to table 11, the output of the above model has a 95% prediction interval for a variable of the temperature of 30°C, pH 5.5, inoculum 10%, and 150 rpm. We can be 95% confident that the PFU U/mL is between 2.43 and 3.07 FPU U/mL.

3.3 Interpretation of process optimization curves

RSM optimization is typically acclimated to identify variable settings that increase the essential response (i.e., FPU/mL). In this study, the target for maximization of cellulase production by P. funiculosum NCIM 1228 was to achieve an enzyme production level that is nearly equivalent to the statistically predicted concentration of 3.17 FPU/mL in the first set. The results showed that concentrations of urea, CaCl₂, MgSO₄, CSL, and KH₂PO₄ of less than 0.1%, 0.15%, 0.05%, 3.48%, and 0.1%, respectively, along with Avicel, Peptone, and wheat bran concentrations of less than 5.0%, 1.42%, and 5.0%, respectively, will not achieve the cellulase production of 3.17 FPU/mL.

The goal for optimization of cellulase production by P. funiculosum NCIM 1228 in the second batch was to achieve an enzyme product that is equivalent to the statistically predicted concentration of 2.79 FPU/mL. Results showed that in the second set, all the first set optimized media components along with temperature, 29.8 °C, pH 5.88, inoculum 10.65%, and agitation of 157 rpm will achieve 2.79 FPU/mL.

The desired combination of variable settings for achieving the best result was anticipated after RSM optimization studies (g/L): Urea, 1; CaCl₂, 1.5; MgSO₄, 0.5; Avicel, 5; Wheat bran, 5; Peptone, 14.2; KH₂PO₄, 1; and CSL, 34.8. With the desired score close to 1, these concentrations yielded the predicted response of 3.17 U/mL. (Fig. 9). Optimization plots are acclimated to acquire the expected response with a better acceptability score, to lower-cost factor settings with optimal traits, and to understand the sensibility of response factors to change the variable settings in general.

3.4 Validation of the model

Conclusively, to inspect the desirability of the model, enzyme production experiments were carried out to compare the cellulase production in unoptimized media and statistically optimized media components along with optimized process conditions. Attained results (Fig. 10 ) showed that cellulase production by P. funiculosum NCIM 1228 using the statistically optimized media composition resulted in the highest production of cellulase in the form of FPU was 3.36 U/mL. The predicted response (3.17 U/mL) was closely linked to cellulase formation. Furthermore, during the sixth day, medium optimization boosted peak cellulase production by around 3.82 times that of the original unoptimized medium (0.88 FPU/mL). As a result, experimental runs demonstrate that the model may be accepted. RSM optimization was used to examine the interactions between various media components in order to improve a variety of industrially significant microbial products. Furthermore, the RSM optimization findings obtained are consistent with prior RSM medium optimization results for cellulase enzyme production. For cellulase production by P. funiculosum NCIM 1228, we used RSM techniques to optimize medium composition. We were able to increase cellulase enzyme production by 3.82 times over the initial production media. The experimental model was validated by using the model's predicted values for pH (5.88), temperature (29.8°C), agitation (158 rpm), and inoculum (10.6%). The enzyme production was done in flasks with optimized variables from set 1 and set 2 inoculated against unoptimized variables, with the validation experiment performed in triplicates. Cellulase production was measured using FPU/mL activity.

3.5 Discussion

Nowadays, biofuel and bioenergy have great demand and there is much focus on the biofuel and bioenergy sectors [41]. Although LCB is a preferred and cheaper source for biofuel production, few companies produce cellulase for LCB degradation. So, researchers are searching for and discovering efficient novel cellulase-producing strains with an adequate composition of cellulase formulation [42]. Cellulase production is mainly done by strain improvement for overexpression of cellulase enzyme, to increase the production by optimizing media components and optimization process parameters to increase the yield of cellulase, and to increase the cellulose hydrolysis efficiency by designing cellulase bioprocesses and producing cellulases [3]. Recently, fungal enzymes have received increasing interest as a source of cellulase since they are known to produce all four of the four enzymes required for the complete hydrolysis of LCB. P. funiculosum, a filamentous fungus, secretes a complete set of enzymes (Exo and endo-glucanase, cellobiase, and xylanase) for the effective saccharification of LCB [43, 44]. Despite these promising characteristics, production levels are lower as compared to other filamentous fungi like Aspergillus sp., and Trichoderma sp., which are mostly reported for higher yields.

Recently, the focus has been on the optimization of medium components and process parameters optimization[45]. The production of cellulase by SmF has led to great progress in the biofuels and bioenergy sectors because it is easy to maintain optimum conditions like temperature, pH, agitation, aeration, and nutrients for culture growth to achieve improved cellulase production. The nitrogen source plays an important role in cellulase production [17]. Peptone, yeast extract, urea, and CSL are used as nitrogen sources, and nitrogen is an important element for the synthesis of amino acids, proteins, and other microbial enzymatic activities. Venkatachalam et al. used Talaromyces albobiverticillius 30548 for statistical optimization of the process parameters for pigment production in SmF by using the BBD and RSM, the numerical optimization approach yielded the ideal conditions for maximum pigment and biomass production, with an initial pH of 6.4, the temperature of 24 C, agitation speed of 164 rpm, and fermentation time of 149 hours, respectively., with 3.2 and 2.84 fold of increased pigment and biomass, respectively [19].

Aanchal et al. used factorial design PBD and CCD by Design-Expert software to screen 11 and 4 factors, respectively. 7-fold increased cellulase activity was found in optimized media in the SmF of Bacillus subtilis NA15 [17] Saravanan et al. (2012) applied BBD for optimization of 5 factors for cellulase production by strain Trichoderma reesei NCIM 1186 [46]. The activities of cellulase in optimized conditions were increased 1.243 times. Cellulase activities of Aspergillus nidulans were increased 8.05 times in optimized conditions [47].

P. funiculosum ATCC 11797 produced cellulase at 0.5 FPU/mL and 2.3 U/mL β-glucosidase in unoptimized media, according to Carvalho et al. (2014) [48]. Study found that the optimum temperature for cellulase production is in the range of 30 to 37°C while the optimum pH range is between 5.0 and 6.5. Bacterial strains secrete primarily cellobiase, whereas the fungus P. funiculosum secretes cellulase as well as a promising number of xylanases [6, 49]. Parent strain of P. funiculosum produces 2.0 FPU, and 16 u/ml CMCase while UV mutant 49 produces 3.3 FPU and 24 U/ml CMCase at 12th day. That is almost 50% improvement in both the activities was observed due to UV mutagenesis in P.funiculosum strain [53].

The Maeda et al optimized nitrogen sources like Urea, Ammonium sulphate, peptone and Yeast extract for the production of cellulase by P.funiculosum ATCC 11797 , which resulted in values for FPase of 0.227 U/mL, for CMCase of 6.917 U/mL, and for β-glucosidase of 1.375 U/mL. These values corresponded to increases of 3.3-, 3.2-, and 6.7-folds, respectively [54]. In this study, we examined different carbon and nitrogen sources, different minerals, and process parameters. The optimal values of the models were then calculated by applying PBD and RSM to fit the data based on the experimental design to obtain the regression equation models. We can calculate the effect of each factor on cellulase production by applying the regression equation and prediction model. The PBD experimental data of this study suggested that carbon sources like wheat bran, lactose, and avicel (MCC101) have significantly influenced cellulase overproduction [16, 50]. While the BBD and RSM results showed interactions between urea and MgSO₄, CaCl₂ and MgSO₄, peptone and lactose, and CaCl₂ and KH₂PO₄ were significant. Also, as a process point of view, pH, temperature, inoculum percentage, and agitation had a great impact on cellulase production. In a previous study, pH around 5.5, the temperature at 30°C, agitation at 150 rpm, and 10% inoculum were used to produce cellulase with 0.5 FPU/mL from P. funiculosum [19]. Whereas in the present study, a statistical tool was used to optimize the variables such as pH 5.8, inoculum 10.65%, temperature 29.8°C, and agitation at 158 rpm to achieve increased production of cellulases of 3.36 FPU/mL. Interestingly, in these optimized conditions, the strain was able to produce 36.5 U/mL of xylanases.

The key finding of this investigation is that the RSM data indicated that the model is significant as the P-value of this model is more than 95% (P = < 0.05). The R-squared (predicted) is 80 % is reasonable agreement with the R- squared value (adjusted) 88 % [28] . The R squared value for cellulase in terms of FPU U/mL was 93 %, which further ensures an acceptable adjustment of the model to the experimental data. The overall model P-value is less than 0.050 (i e 0.000) means the model is highly significant[9].

The main effects of the IV are statistically significant when their P-values are less than 0.050. The pH, inoculum percentage, and agitation all play a significant role in cellulase production, according to the normal and main effect plots (Fig.10 and 11). The 3D surface interaction plots (Fig. 8) and 2D contour plots (Supplementary Fig. S7) indicated that the interactions between the pH and agitation had significantly influenced the increasing production of FPU [17].

Validation experiments of the model again proved that the model is significant. And the cellulase production increased by 3.82-fold FPU, 3.1-fold CMCase, 3.61-fold -glucosidase activity, 2.4-fold xylanase activity, and 2.3-fold overall protein production. More interestingly, on the sixth day the specific activities of FPase, CMCase, and β- glucosidase significantly improved to 1.66-fold, 1.35-fold, and 1.57-fold respectively while the specific activity of xylanases. All these activities are promising for the existing reported activities of non-genetically modified P. funiculosum NCIM 1228 [12, 17, 48]. Overall results obtained from this study were encouraging with Randhawa et al. achieving the maximum cellulase production of 2.0 FPU/mL in PfMig188 engineered P. funiculosum NCIM 1228 in the presence of a 4:1 ratio of glucose and Avicel in the media. It was 2.0-fold higher than the parent strain [12, 51]. However, in this study, with the help of RSM, we achieved maximum cellulase production of 3.36 FPU/mL without any genetic modification. Without a doubt, further improvements like genetic modification and cellulase gene overexpression of P. funiculosum NCIM 1228 will further increase the possibility of increasing the FPU activity up to 3-fold [51]. Finally, the higher cellulase producing strain P. funiculosum NCIM 1228 provides an economically viable cellulase enzyme that could significantly reduce the cost of conversion of LCB to ethanol [52].

Submerged fermentation is generally regarded as more suitable for higher-scale enzyme production from fungal systems. P. funiculosum NCIM 1228 is a very promising cellulase producing strain. This strain has the ability to produce all four of the four enzymes (Exo cellulase, Endo cellulase, cellobiase, and xylanases) in an adequate amount. The enzyme yield was further enhanced by RSM, and model validation with RSM indicated that the model was valid, and the results were repeatable. In addition, this study utilizes PBD and BBD matrices for the optimization of culture media and process parameters for increased enzyme production. We also understand the IV interactions and their influence by using response surface plots for enhanced cellulase production.

The present study demonstrated the enhancement in cellulases and xylanases production capabilities by optimizing media components and process parameters in the P. funiculosum NCIM 1228 strain. More interestingly significantly improvement in the specific activities of all four enzymes was found. The specific activity of FPU, CMCase, and β- glucosidase improved to 1.66-fold, 1.35-fold, and 1.57-fold on the sixth day while the specific activity of xylanase was enhanced by 1.56-fold on the twelfth day. Due to improved titers of cellulase containing enough amounts of FPU, CMCase, and -glucosidase, along with xylanases in crude form, would complement the improvement in saccharification of LCB. Furthermore, this concentrated crude broth can be directly used for LCB saccharifications for value-added chemicals.

Statements and Declarations

Funding: This research did not have any financial support.

Competing interest: The author declares that he/she has no competing interest.

Ethical Approval: This article does not contain any studies with human participants or vertebrates.

Consent to Participate: Not applicable.

Consent for Publication: Not applicable.

Authors’ contribution: Sambhaji B. Chavan: conceptualization, methodology, data curation, formal analysis, writing original draft preparation, visualization, and editing. Mahesh S. Dharne and Ashvini M. Shete: editing, reviewing, visualization, supervision.

Acknowledgment: S.C. is thankful to the Directors of CSIR-NCL, Pune., Academy of Scientific and Innovative Research (AcSIR), Ghaziabad- 201002, India, and Praj Matrix - R & D Center (a division of Praj Industries Limited), Pune 412115 for infrastructure and support. The manuscript has been checked for plagiarism using iThenticate software.

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Optimization for improved cellulase production of Penicillium funiculosum NCIM 1228 in submerged fermentation

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