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Optimization of cellulase production from a novel strain of Aspergillus Tubingensis IMMIS2 through response surface methodology
Biocatalysis and Agricultural Biotechnology 12 (2017) 191–198
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Optimization of cellulase production from a novel strain of Aspergillus Tubingensis IMMIS2 through response surface methodology
MARK
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Muhammad Imrana,b, , Zahid Anwarb, Muhammad Irshadb, Arshad Javidc, Ali Hussainc, Shahzad Alic a b c
Institute of Biochemistry and Biotechnology, University of Veterinary and Animal Sciences, Lahore, Pakistan Department of Biochemistry and Molecular Biology, University of Gujrat, Pakistan Department of Wildlife & Ecology, University of Veterinary and Animal Sciences, Lahore, Pakistan
A R T I C L E I N F O
A B S T R A C T
Keywords: Response surface methodology Cellulase Aspergillus tubingensis Saccharification Primers
Cellulose is basic structural part of cell wall of plant and cellulases digest cellulose into glucose and provide a useful utilization of this enzyme in food and feed industry. The purpose of this study was to investigate hyper production of cellulase complex from indigenous local strain of Aspergillus tubingensis IMMIS2 extracted from rotten tomato. Corn stover revealed maximum cellulase complex activity (81 ± 1.5 µg/mL/min) after screening six substrates. The conserve sequences of fungus were used to identify hyper cellulase producing fungus using primers. The optimum temperature, pH and incubation time were analyzed to be 40 °C, 4.8, 96 h with cellulase activity of 86.4 ± 2.1 µg/mL/min for Aspergillus tubingensis IMMIS2. Cellulase revealed maximum activity (112 µg/mL/min) with Response Surface Methodology (RSM) using 40 mesh size substrate, 8 g substrate, 80% moisture, 5 mL inoculum, 0.5 g urea, 0.1 g KCl, 0.1 g CaCl2 and 0.06 g MgSO4 using Aspergillus tubingensisIMMIS2 (LT732556). Most of the factors had significant impact on cellulase yield while some factors did not have good effect on cellulase yield. Cellulases have wide spread application in fruit saccharification and saccharification value of mango was 59.54%. This optimization study paves the way for the scientists to utilize RSM for enzymes production.
1. Introduction In the last few years, biofuel production from low cost substrate has been an important issue and lignocellulosic waste material is gaining popularity due its availability relative abundance and low cost. Cellulosic biomass generally composed of cellulose, lignin, hemicellulose and other complex carbohydrates (Baek and Kwon, 2007; Blumer-Schuette et al., 2008). Lignocellulosic waste material is composed of celluloses and hemicelluloses components that can be converted into bioethanol via fermentable sugars. Although, beta 1, 4 glycosidic linkages is present in cellulose but crystalline structure of cellulose makes cellulase action very tough. Microbial enzymes are used to convert cellulose and other cell wall material into fermentable sugars and these enzymes produce from microbes have synergic action. Among these, endoglucocanase (EC 3.2.1.4) has random cleavage pattern on soluble and insoluble chains of cellulose, exoglucanase (EC 3.2.1.91) produces cellobiose units from reducing and non-reducing ends of cellulose and β-gucosidase (EC 3.2.1.21) is used to generate glucose from cellobiose (Imran et al., 2017; Dashtban et al., 2010).
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Many fungal species including Aspergillus, Penicillium and Trichoderma are used to produce these cellulolytic enzymes. Various strains belonging to Trichoderma and Aspergillus are unable to secrete complete group of enzymes which convert lignocellulosic digestive enzymes but Aspergillus species are widely used for cellulase production (Hanif et al., 2004; Azin et al., 2007). Cellulases find a vast array of applications in many industries due to which they have attained the sturdy attention of many researchers throughout the world. Cellulases are the third most industrially made enzymes worldwide attributable to their applications, for example, in starch processing, plant product fermentation, malting, fruit and vegetable juices extraction, as animal food additives, paper recycling and textile industry (Gao et al., 2008; Onofre et al., 2013). Cellulase is also useful for understanding plant protoplast isolation, plant virology, metabolic and genetics in concerns with its employments in these areas of research (Suresh et al., 2005, Shah et al., 2007). It is also used to study single cell proteins (Alam et al., 2005). Moreover, it is also used in many pharmaceutical companies as a cure to phytobezons (bezoar is a polysaccharide within human stomach) textile business particularly in detergent formation
Corresponding author at: Institute of Biochemistry and Biotechnology, University of Veterinary and Animal Sciences, Lahore, Pakistan. E-mail addresses: [email protected], [email protected] (M. Imran).
http://dx.doi.org/10.1016/j.bcab.2017.10.005 Received 20 July 2017; Received in revised form 28 September 2017; Accepted 5 October 2017 Available online 07 October 2017 1878-8181/ © 2017 Elsevier Ltd. All rights reserved.
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(Ali and El-Dein, 2008). Many research areas, optimization methods have been used to determine possible methods for effective economic production of cellulases. In this way; various statistical methods such as Response Surface Methodology (RSM) and Taguchi methods are employed to optimize the conditions for various enzymes production through microbial sources. In RSM, a mutual interaction between variables can be established to estimate the interaction of these variables (Cavaco-Paulo, 1998). For instance, many endo-glucanases are used in textiles because of their thermophilic properties which make them to perform their functions even at 60 °C (Castro et al., 2010). Some of its industrial applications are also in wood processing and deinking method in utilization of printed papers (Oyeleke et al., 2012). In present study, various organic and inorganic factors were optimized through RSM and mutual relationships among these variables were studies. In the last step, RSM was applied to investigate three factorial levels and their effects on cellulase production.
Table 1 Coded value for RSM optimization for cellulase production from Aspergillus tubingensis IMMIS2. S. No.
Factors
Coded values
1 2 3 4 5 6 7 8
Substrate Particle size (nm) Substrate level (g) Inoculum size Moisture level (%) KCl Level (g) CaCl2 Level (g) MgSO4. 7H2O Level (g) Urea Level
-2 -2 −2 −2 −2 −2 −2 −2
-1 -1 −1 −1 −1 −1 −1 −1
0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2
Table 2 Un-Coded value for RSM optimization for cellulase production from Aspergillus tubingensis IMMIS2.
2. Materials and methods 2.1. Microorganism Aspergillus tubingensis IMMIS2 was isolated from tomoto waste and screened for hyper cellulolytic activity among 19 fungal species belong to three genera. A plate potato dextrose agar media (PDA) was used to maintain fungus for 3–7 days at 30 °C and stored at 4 °C until spore formation (Imran et al., 2017; Kim et al., 2011).
S. No.
Factors
Un-coded values
1 2 3 4 5 6 7 8
Substrate Particle size (nm) Substrate level (g) Inoculum size Moisture level (%) KCl Level (g) CaCl2 Level (g) MgSO4. 7H2O Level (g) Urea Level
40 3 3 40 0.1 0.1 0.02 0.1
50 4 4 50 0.2 0.2 0.03 0.2
60 5 5 60 0.3 0.3 0.04 0.3
70 6 6 70 0.4 0.4 0.05 0.4
80 7 7 80 0.5 0.5 0.06 0.5
2.2. Fungal identification Aspergillus tubingensis was identified using conserve sequence of 18S rRNA. The primers were designed for these conserve sequences using primer blast software. DNA of Aspergillus tubingensis was extracted through CTAB method and 18s rRNA conserve sequences were amplified using polymerase chain reactions (PCR).
Forward 5′ACGCAGCGAAATGCGATAAC3′ (Primer primer BLAST) Reverse primer 5′ACCAACTAGACTCCTCCGCT3′ These conserve sequences were submitted to BLAST and Pakistani sequences accompanied by closely-related recovered sequences from the GenBank, were aligned using Clustal W (MEGA 6) software (Tamura et al., 2013). The evolutionary antiquity of these sequences was derived using the Neighbor-Joining method (Saitou and Nei, 1987). The tree was drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed as described by Tamura and Nei (1993) and were in the units of the number of base substitutions per site. Consensus nucleotide arrangement of the identified fungal species was submitted to European Molecular Biology Laboratory (EMBL) database which was available under the accession number LT732556.1.
Fig. 1. Evolutionary Tree of Aspergillus niger IMMIS1 using Neighbor-Joining method.
2.4. Enzyme assay
2.3. Production of cellulase
For cellulase assay, culture medium was centrifuged at 7000 rpm for 20 min for separation and supernatant was obtained and diluted with distilled water. By taking glucose as standard in Dinitro-salicylic acid (DNS) method, the quantity of reducing sugars were determined by the action of dialyzed cellulase using carboxymethyl cellulose (CMC). The reaction mixture contained 0.5 mL of diluted enzyme solution, 1 mL of citrate buffer (pH 4.8). To estimate the reaction, 3 mL of DNS reagent was added and sample tubes were placed in water bath for five minutes and then cooled down in cold water to stop the activity. Finally, absorbance was taken at 540 nm using spectrophotometer. The release of 1 µg glucose per minute from carboxymethyl cellulose (CMC) was measured for cellulase activity (Imran et al., 2017).
The inoculum of Aspergillus tubingensis IMMIS2 (1 × 106 spores/mL) was prepared (Imran et al., 2017) and 5 mL of inoculum was poured into six substrate for few days for the production of cellulase from Aspergillus tubingensis (Ex # 732556.1) IMMIS2. Aspergillus tubingensis IMMIS2 appeared in these substrate medium showed different range of carbon sources consumption (cellulose) for 1–5 days after inoculum addition. Six variety of biomass waste material (sugar cane bagasse, wheat straw, corn stover, rice straw, rice bran and corn cobs) were studied as a substrate of fermentation medium. Pre-optimize conditions for temperature, pH, and incubation period were used in this study (Imran et al., 2017). 192
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Fig. 2. Contour and 3D Surface graph for cellulase produced from Aspergillus tubingensis IMMIS2 revealing interaction of substrate level (g) with substrate particle mesh size (mm).
2.5. Statistical optimization of medium
3. Results
For carboxymethyl cellulase production, appropriate variables were devised using Plackett-Burnman design (PDB). Among forty variables, the coded and un-coded values of 8 factors were selected for operating conditions (Tables 1, 2) for cellulase optimization produced through Aspergillus tubingensis IMMIS2.
3.1. Fungal screening Three cellulase producing Aspergillus species were reconfirmed by gel electrophoresis (Imran et al., 2017), and phylogenetic tree of Aspergillus tubingensis IMMIS2 and Aspergillus niger IMMIS1 were given (Fig. 1). There were a total of 573 positions in the final dataset. Evolutionary analyses were conducted in MEGA6 and results were drafted in Fig. 1.
2.6. Saccharification of fruits Culture filtrate of cellulase (25 mL) after gel filtration was taken in 500 mL flasks and added 100 mL of pre-treated fruit pulp of mango with 1% H2SO4 and NAOH in flask. Then, flasks were placed in incubator shaker at 140 rpm, 50 °C for 8 h. After enzymatic hydrolysis, the juice material was centrifuged at 10, 000 rpm for 10 min. Sugar contents were checked from supernatant. Saccharification of fruits was checked by the following formula (Imran, . et al., 2017)
Saccharification(%) =
3.2. Cellulase production Cellulase complex produced by Aspergillus tubingensis IMMIS2 was acted on 1% carboxy methyl cellulose (CMC), 1% filter paper, and 1% cellulose powder. Corn stover revealed maximum cellulase (β glucosidase) activity (81 ± 1.5 µg/mL/min) as compared to other agricultural wastes and selected for further study (Imran et al., 2017). Aspergillus tubingensis IMMIS2 growth was quite high and increased continuously up to 4 days (83.7 ± 1.6 µg/mL/min) and then decreased continuously.
Reducingsugar released (mg / mL) × 100 Substrateused (mg / mL) 193
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Fig. 3. Contour and 3D Surface graph for cellulase produced from Aspergillus tubingensis IMMIS2 revealing interaction of inoculum level (mL) with substrate level (g).
investigated for better yields of cellulase. Among these substrates, rice straw revealed lowest yield while corn stover revealed maximum production (Imran et al., 2017). Substrate complexity and composition are the limiting factors of cellulase activities and fungal growth either increasing or decreasing the cellulase production (Kim et al., 2011). Therefore, corn stover proved excellent carbon substrate or fermentation (Imran et al., 2017). The ANOVA used to calculate the results of regression equation and value of coefficient of determination (R2) was determined (0.918) (Table 4). The value of coefficient of determination was less than 0.75 made this model suitable for cellulase optimization. The model was equally adequately suitable to represent the interaction among different factors. The correlation coefficient (R) and determination of coefficient (R2) can also be used to check the aptness of RSM. The R2 value revealed the better response when it became close to 1, as the value of R2 was always between 0 and 1. The interaction of substrate with substrate particle size (mm) by keeping other factors constant (moisture 60%, inoculum 3 mL, urea 0.3 g, KCl 0.3 g, CaCl2 0.3 g, MgSO4 0.03 g) revealed that maximum cellulase activity (β glucosidase) 92.19 µg/mL/min (Fig. 2) was achieved during these set parameters. The interaction of urea with CaCl2 by keeping other factors
Optimum pH and temperature for Aspergillus tubingensis IMMIS2 revealed pH 4.5 − 4.8 (85.4 ± 2.1 µg/mL/min) and temperature 40 °C (83.9 ± 1.4 µg/mL/min) were optimum for its growth and enzyme production. Aspergillus tubingensis IMMIS2 revealed maximum production of cellulase in all optimized conditions (86.4 ± 2.1 µg/mL/min) and selected for further studies (Imran et al., 2017). 3.3. RSM Implementation for optimization of culture media Aspergillus tubingensis IMMIS2 (LT 732556.1) produced crude cellulase which revealed maximum activity (112 µg/mL/min) through Response Surface Methodology (RSM) using 40 mm mesh size of corn stover, 8 g substrate of corn stover, 80% moisture, 5 mL inoculum, 0.5 g urea, 0.1 g KCl, 0.1 g CaCl2 and 0.06 g MgSO4 (Table 3; Fig. 4). Determination of suitable substrate, various lignocellulosic substrates including rice straw, wheat straw, corn stover and sugar cane bagasse were used as carbon source for fermentation process. Lignocellulosic hydrolysate conversion in to value added products, cellulase, is a cost effective fermentation problem and cellulase is produced from various microbes (Imran et al., 2017). The appropriate carbon source was 194
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Table 3 RSM parameters optimization for cellulase production. Substrate particle size (mm)
Substrate level (g)
Moisture level (%)
Inoculum size (mL)
Urea level (g)
KCl (g)
CaCl2 (g)
MgSO4 (g)
Cellulase activity μg/mL/ min
80 60 40 80 40 40 80 80 40 40 40 80 80 40 80 60 40 80 116.5 60 60 3.43 60 60 60 60 60 60 60 60 60 60 60 60 60 60 40 80 80 40 40 40 80 40 40 80 80 60 80 40 80 40 60 80 80 40 40 40 40 80 60 80 60 80 40 80 80 80 40 40 80 40 80
2 5 8 2 2 8 8 8 2 8 8 8 8 2 2 5 2 2 5 5 5 5 5 5 3.48 5 5 5 5 5 5 5 5 13.5 5 5 2 8 8 8 2 2 8 8 2 2 2 5 2 8 2 8 5 8 2 8 8 2 2 2 5 2 5 8 2 8 2 8 8 8 8 2 2
80 60 40 40 80 40 80 40 80 80 80 40 80 40 40 60 40 80 60 60 60 60 60 3.43 60 60 60 116.5 60 60 60 60 60 60 60 60 80 80 80 80 80 40 40 80 40 40 80 60 40 40 80 40 60 40 80 80 40 40 80 40 60 40 60 80 80 40 80 40 40 80 80 40 80
5 3 5 1 5 1 5 5 1 5 1 1 1 1 5 3 5 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 8.65 3 3 2.65 5 5 1 5 1 1 1 1 5 1 1 3 5 5 5 1 3 5 5 5 1 1 1 1 3 5 3 1 5 5 1 1 5 1 5 5 1
0.5 0.3 0.5 0.1 0.1 0.5 0.5 0.1 0.1 0.1 0.1 0.1 0.5 0.5 0.1 0.3 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.26 0.3 0.3 0.3 0.3 0.3 0.86 0.3 0.5 0.1 0.1 0.5 0.5 0.1 0.5 0.5 0.1 0.5 0.1 0.3 0.5 0.1 0.1 0.1 0.3 0.5 0.1 0.5 0.1 0.1 0.5 0.5 0.3 0.5 0.3 0.1 0.5 0.5 0.1 0.5 0.1 0.5 0.1 0.1 0.5
0.1 0.3 0.5 0.5 0.1 0.1 0.5 0.5 0.5 0.5 0.1 0.1 0.1 0.5 0.1 0.3 0.1 0.5 0.3 0.3 0.3 0.3 0.86 0.3 0.3 0.3 0.26 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.1 0.5 0.1 0.1 0.1 0.5 0.5 0.5 0.1 0.1 0.3 0.5 0.1 0.5 0.5 0.3 0.1 0.1 0.5 0.1 0.5 0.5 0.5 0.3 0.1 0.3 0.1 0.1 0.5 0.5 0.1 0.5 0.1 0.5 0.1 0.1
0.1 0.3 0.5 0.1 0.5 0.1 0.5 0.1 0.1 0.1 0.5 0.5 0.1 0.5 0.5 0.3 0.1 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.86 0.3 0.26 0.3 0.3 0.3 0.3 0.5 0.5 0.1 0.1 0.1 0.5 0.5 0.5 0.1 0.1 0.5 0.3 0.5 0.5 0.1 0.1 0.3 0.1 0.1 0.1 0.1 0.5 0.1 0.1 0.3 0.5 0.3 0.1 0.5 0.1 0.5 0.5 0.5 0.5 0.5 0.1 0.5
0.06 0.04 0.02 0.06 0.06 0.06 0.06 0.02 0.02 0.06 0.02 0.06 0.02 0.06 0.02 0.04 0.02 0.02 0.04 0.096 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.016 0.04 0.04 0.04 0.04 0.04 0.06 0.06 0.02 0.06 0.02 0.06 0.06 0.02 0.02 0.06 0.02 0.04 0.02 0.02 0.06 0.06 0.04 0.02 0.02 0.02 0.02 0.02 0.06 0.02 0.04 0.06 0.04 0.06 0.02 0.06 0.06 0.02 0.06 0.06 0.02 0.06 0.06
75 83 96 61 68 63 56 91 56 85 77 75 63 39 84 99 69 35 66 46 87 78 69 73 89 79 86 56 86 81 98 87 69 87 81 79 78 91 47 112 78 31 53 61 67 61 56 81 79 87 94 76 81 94 88 93 81 73 81 74 89 78 83 69 87 87 54 57 87 57 57 45 39 (continued on next page)
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Table 3 (continued) Substrate particle size (mm)
Substrate level (g)
Moisture level (%)
Inoculum size (mL)
Urea level (g)
KCl (g)
CaCl2 (g)
MgSO4 (g)
Cellulase activity μg/mL/ min
80 40 40 60 60 80 40 80 80 40 40 80 80 80 40 40 40
8 8 2 5 5 8 2 2 8 2 2 2 2 8 8 8 8
80 40 80 60 60 80 40 80 40 40 80 40 40 40 40 80 80
5 1 1 3 3 1 1 5 5 5 5 5 1 1 5 5 1
0.5 0.5 0.1 0.3 0.3 0.5 0.5 0.5 0.1 0.5 0.1 0.1 0.1 0.1 0.5 0.1 0.1
0.1 0.5 0.1 0.3 0.3 0.5 0.1 0.5 0.1 0.5 0.5 0.5 0.1 0.5 0.1 0.1 0.5
0.5 0.1 0.1 0.3 0.3 0.1 0.5 0.1 0.1 0.1 0.5 0.5 0.1 0.5 0.5 0.1 0.5
0.02 0.02 0.06 0.04 0.04 0.06 0.02 0.02 0.06 0.06 0.02 0.06 0.02 0.02 0.06 0.02 0.06
74 78 44 85 75 48 46 78 109 86 78 87 63 67 91 104 41
Table 4 Statistical model of RSM. Model summary S 7.26584 Term
R-sq 91.04%
R-sq(adj) 82.27%
Constant Substrate particle size with particle size (mm) Substrate level (g)*Substrate level (g) Moisture level (%)*Moisture level (%) Inoculum size (mL)*Inoculum size (mL) Urea level (g)*Urea level (g) KCl (g)*KCl (g) CaCl2 (g)*CaCl2 (g) MgSO4 (g)*MgSO4 (g) Substrate particle size (mm)*Substrate level (g) Substrate particle size (mm)*Moisture level (%) Substrate particle size (mm)*Inoculum size (mL) Substrate particle size (mm)*Urea level (g) Substrate particle size (mm)*KCl (g) Substrate particle size (mm)*CaCl2 (g) Substrate particle size (mm)*MgSO4 (g) Substrate level (g)*Moisture level (%) Substrate level (g)*Inoculum size (mL) Substrate level (g)*Urea level (g) Substrate level (g)*KCl (g) Substrate level (g)*CaCl2 (g) Substrate level (g)*MgSO4 (g) Moisture level (%)*Inoculum size (mL) Moisture level (%)*Urea level (g) Moisture level (%)*KCl (g) Moisture level (%)*CaCl2 (g) Moisture level (%)*MgSO4 (g) Inoculum size (mL)*Urea level (g) Inoculum size (mL)*KCl (g) Inoculum size (mL)*CaCl2 (g) Inoculum size (mL)*MgSO4 (g) Urea level (g)*KCl (g) Urea level (g)*CaCl2 (g) Urea level (g)*MgSO4 (g) KCl (g)*CaCl2 (g) KCl (g)*MgSO4 (g) CaCl2 (g)*MgSO4 (g)
Fig. 4. Surface plot for cellulase activity (μg/mL/min) = 112.
constant (substrate particle mesh size 60 mm, substrate 5 g, moisture 60%, inoculum 3 mL, KCl 0.3 g, MgSO4 0.03 g) revealed that maximum cellulase activity (β glucosidase) 92.19 µg/mL/min (Fig. 3) was accomplished during these set parameters. According to model equation, the value of R2 was close to 1 (0.91) established a close relationship between theoretical values and experimental results. The signal to noise was actually the precision value and ratio which was more than 4 was allowable (Emtiazi and Nahvi, 2000; Kim et al., 2011).
Y = 31 + β1 x1 + β2 x2 + β12 x1 x2 + β11 x1 2 + β22 x2 2 + ε
(1)
Y = 31−0.001A + 0.351B + 0.001C + 0.078D + 0.230E + 0.004F + 0.038G 0.039H + 0.000AB + 0.000AC + 0.404AD + 0.000AE + + 0.018AF + 0.506AG + 0.004AH + + 0.001BC + 0.385BD + 0.109BE + + 0.000BF 0.269BG + 0.284BH 0.216CD + 0.516CE + 0.001CF
T-Value
P-Value
VIF
23.23 −2.34 −1.16 −3.75 −6.35 −0.09 −1.44 1.42 −3.82 0.000 −3.97 −4.83 0.84 −4.42 −2.46 −0.67 3.05 −3.49 0.88 −1.63 −4.97 −1.12 1.08 1.26 0.67 −3.56 −2.25 0.64 0.84 −0.71 1.43 2.12 0.50 −2.53 0.02 −0.71 1.43 −1.05
0.000 0.024 0.253 0.001 0.000 0.930 0.157 0.163 3.21
1.01 3.85 1.01 3.84 3.86 3.86 3.86
0.000 0.000 0.404 0.000 0.018 0.506 0.004 0.001 0.385 0.109 0.000 0.269 0.284 0.216 0.506 0.001 0.029 0.528 0.404 0.484 0.160 0.040 0.620 0.015 0.986 0.484 0.160 0.300
2.05 1.00 2.04 2.01 2.01 2.01 1.80 2.05 2.88 2.86 2.86 2.86 2.68 2.04 2.01 2.01 2.01 1.80 2.84 2.84 2.84 2.67 2.82 2.82 2.64 2.82 2.64 2.64
+ 0.029CG + 0.528CH + 0.404DE + 0.484DF0.160DG + revealed factors and their probability values. The data revealed that most of the factors had significant impact on cellulase production while some factors showed non-significant effect on cellulase production Eqs. (1 and 2). Substrate level (g) and urea level (g) had non-significant relationship for cellulase production from Aspergillus tubingensis IMMIS2
0.040DH + 0.620EF + 0.015EG + 0.986EH + 0.484FG + 0.160FH + 0.300GH − 0.024A2− 0.253B2− 0.001C2− 0.000D2− 0.930E2− 0.157F2− 0.163G2− 0.000H2 (2) Whereas, Y represented the cellulase activity and English letters 196
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0.25 g. Another study showed good saccharification of fruits and increased juice volume after treating with β glucosidase (Vaillant et al., 2001). Improvement in the fruit saccharification of fruit juices has also been reported after enzymatic treatment (Barman et al., 2015).
while substrate particle mesh size (mm), moisture level (%), inoculum size (mL), KCl, CaCl2, and MgSO4 had significant impact on cellulase production using Response Surface Methodology (Table 4). The tenacity of RSM application in this study was to improve the yield of cellulase by saving time, energy, expenditures and mistakes in production. This practice had showed very effective as by changing the independent factors the dependent factors automatically fluctuated. Moreover, it also defined the correlation of factors by providing imaginings of 3-D graphs and contour graphs.
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Emtiazi, G., Nahvi, I., 2000. Multi-enzyme production by Cellulomonas sp. grown on wheat straw. Biomass Bioenergy 19, 31–37. Gao, J., Weng, H., Zhu, D., Yuan, M., Guan, F., Xi, Y., 2008. Production and characterization of Cellulolytic enzymes from the thermo-acidophilic fungal Aspergillus terreus M11 under solid-state cultivation of corn stover. Bioresour. Technol. 99, 7623–7629. Hanif, A., Yasmeen, A., Rajoka, M.I., 2004. Induction, production, repression, and derepression of exoglucanase synthesis in Aspergillus niger. Bioresour. Technol. 94, 311–319. Imran, M., Anwar, Z., Zafar, M., Irshad, M., Iqbal, T., 2017. Hyper-productivity, characterization, and exploitation of a cellulase complex from a novel isolate of aspergillus tubingensis s2 using lignocellulose-based material. BioResources 12 (3), 5649–5663. 〈http://152.1.0.246/index.php/BioRes/article/view/Biores_12_3_5649_ Imran_Hyper_Productivity_Cellulase_Complex_Aspergillus〉. Kim, H.J., Lee, Y.J., Gao, W., Chung, C.H., Son, C.W., Lee, J.W., 2011. Statistical optimization of fermentation conditions and comparison of their influences on production of cellulases by a psychrophilic marine bacterium, psychrobacter aquimaris lbh10 using orthogonal array method. Biotechnol. Bioprocess Eng. 16 (542–548), 2011. Latifian, M., Hamidi-Esfahani, Z., Barzegar, M., 2007. Evaluation of culture conditions for cellulase production by two Trichoderma reesei mutants under solid-state fermentation conditions. Bioresour. Technol. 98 (18), 3634–3637. Liang, X., Huang, Y., Hua, D., Zhang, J., Xu, H., Li, Y., Zhang, X., 2012. Cellulase production by Aspergillus sp. on rice grass (Spartina spp.) under solid-state fermentation. Afr. J. Microb. Res. 6, 6785–6792. Onofre, S.B., Mattiello, S.P., Silva, G.C.D., Groth, D., Ivair Malagi, I., 2013. Production of cellulases by the endophytic fungus fusarium oxysporum sideney becker. J. Microbiol. Res. 3 (4), 131–134. http://dx.doi.org/10.5923/j.microbiology. 20130304.01. Oyeleke, S.B., Oyewole, O.A., Egwim, E.C., Dauda, B.E.N., Ibeh, E.N., 2012. Cellulase and pectinase production potentials of aspergillus niger isolated from Corn Cob. Bayero. J. Pure Appl. Sci. 5 (1), 78–83. Saitou, N., Nei, M., 1987. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425. Singhania, R.R., Sukumaran, R.K., Pandey, A., 2007. Improved cellulase production by Trichoderma reesei RUT C30 under SSF through process optimization. Appl. Biochem. Biotechnol. 142, 60. Shajahan, S., Moorthy, I.G., Sivakumar, N., Selvakumar, G., 2017. Statistical modeling and optimization of cellulase production by Bacillus licheniformis NCIM 5556 isolated from the hot spring, Maharashtra, India. J. King Saud Univ. Sci. 29 (2017), 302–310. Soni, S.K., Batra, N., Bansal, N., Soni, R., 2010. Bioconversion of sugarcane bagasse into second generation bioethanol after enzymatic hydrolysis with in-house produced cellulase from Aspegillus sp. S4B2F. Bioresour. Technol. 5, 741–758. Sun, H., Ge, X., Hao, Z., Peng, M., 2010. Cellulase production by Trichoderma sp. on apple pomace under solid state fermentation. Afr. J. Biotechnol. 9 (2), 163–166. Suresh, C.K., Snishamol, C., Nagendra, P.G., 2005. Cellulase production by native bacteria using water hyacinth as substrate under solid state fermentation. Malays. J. Microbiol. 1 (2), 25–29. Tamura, K., Nei, M., 1993. Estimation of the number of nucleotide substitutions in the
3.4. Saccharification of fruits Fruit saccharification of mango with cellulase was increased up to 59.54% using 1% sulphuric acid which is much higher as compared to 1% base. The results revealed that acidic treatment showed better response to cellulase saccharification. 4. Discussions The tenacity of RSM application in this study was to improve the yield of cellulase by saving time, energy, expenditures and mistakes in production. This practice had showed very effective as by changing the independent factors the dependent factors automatically fluctuated (Shajahan et al., 2017). Moreover, it also defined the correlation of factors by providing imaginings of 3-D graphs and contour graphs. It eased the inferences using color discrimination techniques. Higher initial moisture content for SSF showed a negative or constraining effect on cellulase production, using lower moisture content for supreme yield. The maximum cellulase yield was elevated up to 6 folds by low level of moisture content and by regulating incubation period by CCD strategy (Singhania et al., 2007; Shajahan et al., 2017). Latifian et al. (2007) also highlighted the importance of moisture level for maximum cellulase yield by conducting a trial for cellulase production using two mutants of Trichoderma reesei under SSF and for assessment of cultural conditions of RSM methodology was employed. The moisture level of 40%, 55% and 70% was used to analyze the optimized level by RSM and the maximum activity was found at 70% of moisture level with 8.5 g of NaCl whereas in current findings the maximum cellulase activity was found at 50% moisture level and 0.3 g of urea. Sun et al. (2010) accomplished an experiment for cellulase production by apple pomace using Trichoderma sp. under SSF conditions. The results of this experiment suggested that initial moisture level, inoculum size and temperature influence cellulase production expressively. The optimum initial moisture level, incubation temperature and inoculum size were 70%, 32 and 2×108 spores/flask, respectively. Each fungal strain had a specific compatibility with a particular nitrogen source, and cellulase production by two fungal strains which were Melanocarpus sp. and Fusarium oxysporum had been affected largely by nitrogen source. When ammonium acetate was replaced by a beef extract (0.25%, w/v), it showed enhanced cellulases activities using Aspergillus fumigatus as a fermentative microorganism (Soni et al., 2010). The R2 value revealed the better response when it became close to 1, as the value of R2 was always between 0 and 1 (Yu et al., 1998; Alam et al., 2008; Kim et al., 2011). In current study, ammonium sulphate had been proved to be an efficient garnish of cellulase production by providing nitrogen source. Infect, it had a collective enhancing effect with other inorganic salts including potassium chloride, sodium chloride, calcium chloride, zinc sulphate and magnesium sulphate. Liang et al. (2012) completed experiment for cellulase production by utilizing rice grass as lignocellulosic biomass and Aspergillus sp. as a fermentative microbe. He used response surface methodology to get optimum conditions for highest cellulase production. By examining the results of RSM, the optimum conditions for highest cellulase yield were substrate concentration 5 g, moisture content 30%, inoculums size 5 mL, Ammonium Sulphate 0.35 g, Sodium Chloride 4 g, Potassium Chloride 0.05 g, Calcium Chloride 0.05 g, Zinc Sulphate 0.25 g and Magnesium Sulphate 197
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economical optimisation of the clarification of pulpy fruit juices using crossflow microfiltration. J. Food Eng. 48, 83–90. Yu, X.B., Nam, J.H., Yun, H.S., Koo, Y.M., 1998. Optimization of cellulose production in batch fermentation by Trichoderma reesei. Biotechnol. Bioprocess Eng. 3, 44–47.
control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 10, 512–526. Tamura, K., Stecher, G., Peterson, D., Filipski, A., Kumar, S., 2013. MEGA6: molecular evolutionary genetics analysis version 6. 0. Mol. Biol. Evol. 30, 2725–2729. Vaillant, F., Millan, A., Dornier, M., Decloux, M., Reynes, M., 2001. Strategy for
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Optimization of cellulase production from a novel strain of Aspergillus Tubingensis IMMIS2 through response surface methodology
MARK
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Muhammad Imrana,b, , Zahid Anwarb, Muhammad Irshadb, Arshad Javidc, Ali Hussainc, Shahzad Alic a b c
Institute of Biochemistry and Biotechnology, University of Veterinary and Animal Sciences, Lahore, Pakistan Department of Biochemistry and Molecular Biology, University of Gujrat, Pakistan Department of Wildlife & Ecology, University of Veterinary and Animal Sciences, Lahore, Pakistan
A R T I C L E I N F O
A B S T R A C T
Keywords: Response surface methodology Cellulase Aspergillus tubingensis Saccharification Primers
Cellulose is basic structural part of cell wall of plant and cellulases digest cellulose into glucose and provide a useful utilization of this enzyme in food and feed industry. The purpose of this study was to investigate hyper production of cellulase complex from indigenous local strain of Aspergillus tubingensis IMMIS2 extracted from rotten tomato. Corn stover revealed maximum cellulase complex activity (81 ± 1.5 µg/mL/min) after screening six substrates. The conserve sequences of fungus were used to identify hyper cellulase producing fungus using primers. The optimum temperature, pH and incubation time were analyzed to be 40 °C, 4.8, 96 h with cellulase activity of 86.4 ± 2.1 µg/mL/min for Aspergillus tubingensis IMMIS2. Cellulase revealed maximum activity (112 µg/mL/min) with Response Surface Methodology (RSM) using 40 mesh size substrate, 8 g substrate, 80% moisture, 5 mL inoculum, 0.5 g urea, 0.1 g KCl, 0.1 g CaCl2 and 0.06 g MgSO4 using Aspergillus tubingensisIMMIS2 (LT732556). Most of the factors had significant impact on cellulase yield while some factors did not have good effect on cellulase yield. Cellulases have wide spread application in fruit saccharification and saccharification value of mango was 59.54%. This optimization study paves the way for the scientists to utilize RSM for enzymes production.
1. Introduction In the last few years, biofuel production from low cost substrate has been an important issue and lignocellulosic waste material is gaining popularity due its availability relative abundance and low cost. Cellulosic biomass generally composed of cellulose, lignin, hemicellulose and other complex carbohydrates (Baek and Kwon, 2007; Blumer-Schuette et al., 2008). Lignocellulosic waste material is composed of celluloses and hemicelluloses components that can be converted into bioethanol via fermentable sugars. Although, beta 1, 4 glycosidic linkages is present in cellulose but crystalline structure of cellulose makes cellulase action very tough. Microbial enzymes are used to convert cellulose and other cell wall material into fermentable sugars and these enzymes produce from microbes have synergic action. Among these, endoglucocanase (EC 3.2.1.4) has random cleavage pattern on soluble and insoluble chains of cellulose, exoglucanase (EC 3.2.1.91) produces cellobiose units from reducing and non-reducing ends of cellulose and β-gucosidase (EC 3.2.1.21) is used to generate glucose from cellobiose (Imran et al., 2017; Dashtban et al., 2010).
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Many fungal species including Aspergillus, Penicillium and Trichoderma are used to produce these cellulolytic enzymes. Various strains belonging to Trichoderma and Aspergillus are unable to secrete complete group of enzymes which convert lignocellulosic digestive enzymes but Aspergillus species are widely used for cellulase production (Hanif et al., 2004; Azin et al., 2007). Cellulases find a vast array of applications in many industries due to which they have attained the sturdy attention of many researchers throughout the world. Cellulases are the third most industrially made enzymes worldwide attributable to their applications, for example, in starch processing, plant product fermentation, malting, fruit and vegetable juices extraction, as animal food additives, paper recycling and textile industry (Gao et al., 2008; Onofre et al., 2013). Cellulase is also useful for understanding plant protoplast isolation, plant virology, metabolic and genetics in concerns with its employments in these areas of research (Suresh et al., 2005, Shah et al., 2007). It is also used to study single cell proteins (Alam et al., 2005). Moreover, it is also used in many pharmaceutical companies as a cure to phytobezons (bezoar is a polysaccharide within human stomach) textile business particularly in detergent formation
Corresponding author at: Institute of Biochemistry and Biotechnology, University of Veterinary and Animal Sciences, Lahore, Pakistan. E-mail addresses: [email protected], [email protected] (M. Imran).
http://dx.doi.org/10.1016/j.bcab.2017.10.005 Received 20 July 2017; Received in revised form 28 September 2017; Accepted 5 October 2017 Available online 07 October 2017 1878-8181/ © 2017 Elsevier Ltd. All rights reserved.
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(Ali and El-Dein, 2008). Many research areas, optimization methods have been used to determine possible methods for effective economic production of cellulases. In this way; various statistical methods such as Response Surface Methodology (RSM) and Taguchi methods are employed to optimize the conditions for various enzymes production through microbial sources. In RSM, a mutual interaction between variables can be established to estimate the interaction of these variables (Cavaco-Paulo, 1998). For instance, many endo-glucanases are used in textiles because of their thermophilic properties which make them to perform their functions even at 60 °C (Castro et al., 2010). Some of its industrial applications are also in wood processing and deinking method in utilization of printed papers (Oyeleke et al., 2012). In present study, various organic and inorganic factors were optimized through RSM and mutual relationships among these variables were studies. In the last step, RSM was applied to investigate three factorial levels and their effects on cellulase production.
Table 1 Coded value for RSM optimization for cellulase production from Aspergillus tubingensis IMMIS2. S. No.
Factors
Coded values
1 2 3 4 5 6 7 8
Substrate Particle size (nm) Substrate level (g) Inoculum size Moisture level (%) KCl Level (g) CaCl2 Level (g) MgSO4. 7H2O Level (g) Urea Level
-2 -2 −2 −2 −2 −2 −2 −2
-1 -1 −1 −1 −1 −1 −1 −1
0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2
Table 2 Un-Coded value for RSM optimization for cellulase production from Aspergillus tubingensis IMMIS2.
2. Materials and methods 2.1. Microorganism Aspergillus tubingensis IMMIS2 was isolated from tomoto waste and screened for hyper cellulolytic activity among 19 fungal species belong to three genera. A plate potato dextrose agar media (PDA) was used to maintain fungus for 3–7 days at 30 °C and stored at 4 °C until spore formation (Imran et al., 2017; Kim et al., 2011).
S. No.
Factors
Un-coded values
1 2 3 4 5 6 7 8
Substrate Particle size (nm) Substrate level (g) Inoculum size Moisture level (%) KCl Level (g) CaCl2 Level (g) MgSO4. 7H2O Level (g) Urea Level
40 3 3 40 0.1 0.1 0.02 0.1
50 4 4 50 0.2 0.2 0.03 0.2
60 5 5 60 0.3 0.3 0.04 0.3
70 6 6 70 0.4 0.4 0.05 0.4
80 7 7 80 0.5 0.5 0.06 0.5
2.2. Fungal identification Aspergillus tubingensis was identified using conserve sequence of 18S rRNA. The primers were designed for these conserve sequences using primer blast software. DNA of Aspergillus tubingensis was extracted through CTAB method and 18s rRNA conserve sequences were amplified using polymerase chain reactions (PCR).
Forward 5′ACGCAGCGAAATGCGATAAC3′ (Primer primer BLAST) Reverse primer 5′ACCAACTAGACTCCTCCGCT3′ These conserve sequences were submitted to BLAST and Pakistani sequences accompanied by closely-related recovered sequences from the GenBank, were aligned using Clustal W (MEGA 6) software (Tamura et al., 2013). The evolutionary antiquity of these sequences was derived using the Neighbor-Joining method (Saitou and Nei, 1987). The tree was drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed as described by Tamura and Nei (1993) and were in the units of the number of base substitutions per site. Consensus nucleotide arrangement of the identified fungal species was submitted to European Molecular Biology Laboratory (EMBL) database which was available under the accession number LT732556.1.
Fig. 1. Evolutionary Tree of Aspergillus niger IMMIS1 using Neighbor-Joining method.
2.4. Enzyme assay
2.3. Production of cellulase
For cellulase assay, culture medium was centrifuged at 7000 rpm for 20 min for separation and supernatant was obtained and diluted with distilled water. By taking glucose as standard in Dinitro-salicylic acid (DNS) method, the quantity of reducing sugars were determined by the action of dialyzed cellulase using carboxymethyl cellulose (CMC). The reaction mixture contained 0.5 mL of diluted enzyme solution, 1 mL of citrate buffer (pH 4.8). To estimate the reaction, 3 mL of DNS reagent was added and sample tubes were placed in water bath for five minutes and then cooled down in cold water to stop the activity. Finally, absorbance was taken at 540 nm using spectrophotometer. The release of 1 µg glucose per minute from carboxymethyl cellulose (CMC) was measured for cellulase activity (Imran et al., 2017).
The inoculum of Aspergillus tubingensis IMMIS2 (1 × 106 spores/mL) was prepared (Imran et al., 2017) and 5 mL of inoculum was poured into six substrate for few days for the production of cellulase from Aspergillus tubingensis (Ex # 732556.1) IMMIS2. Aspergillus tubingensis IMMIS2 appeared in these substrate medium showed different range of carbon sources consumption (cellulose) for 1–5 days after inoculum addition. Six variety of biomass waste material (sugar cane bagasse, wheat straw, corn stover, rice straw, rice bran and corn cobs) were studied as a substrate of fermentation medium. Pre-optimize conditions for temperature, pH, and incubation period were used in this study (Imran et al., 2017). 192
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Fig. 2. Contour and 3D Surface graph for cellulase produced from Aspergillus tubingensis IMMIS2 revealing interaction of substrate level (g) with substrate particle mesh size (mm).
2.5. Statistical optimization of medium
3. Results
For carboxymethyl cellulase production, appropriate variables were devised using Plackett-Burnman design (PDB). Among forty variables, the coded and un-coded values of 8 factors were selected for operating conditions (Tables 1, 2) for cellulase optimization produced through Aspergillus tubingensis IMMIS2.
3.1. Fungal screening Three cellulase producing Aspergillus species were reconfirmed by gel electrophoresis (Imran et al., 2017), and phylogenetic tree of Aspergillus tubingensis IMMIS2 and Aspergillus niger IMMIS1 were given (Fig. 1). There were a total of 573 positions in the final dataset. Evolutionary analyses were conducted in MEGA6 and results were drafted in Fig. 1.
2.6. Saccharification of fruits Culture filtrate of cellulase (25 mL) after gel filtration was taken in 500 mL flasks and added 100 mL of pre-treated fruit pulp of mango with 1% H2SO4 and NAOH in flask. Then, flasks were placed in incubator shaker at 140 rpm, 50 °C for 8 h. After enzymatic hydrolysis, the juice material was centrifuged at 10, 000 rpm for 10 min. Sugar contents were checked from supernatant. Saccharification of fruits was checked by the following formula (Imran, . et al., 2017)
Saccharification(%) =
3.2. Cellulase production Cellulase complex produced by Aspergillus tubingensis IMMIS2 was acted on 1% carboxy methyl cellulose (CMC), 1% filter paper, and 1% cellulose powder. Corn stover revealed maximum cellulase (β glucosidase) activity (81 ± 1.5 µg/mL/min) as compared to other agricultural wastes and selected for further study (Imran et al., 2017). Aspergillus tubingensis IMMIS2 growth was quite high and increased continuously up to 4 days (83.7 ± 1.6 µg/mL/min) and then decreased continuously.
Reducingsugar released (mg / mL) × 100 Substrateused (mg / mL) 193
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Fig. 3. Contour and 3D Surface graph for cellulase produced from Aspergillus tubingensis IMMIS2 revealing interaction of inoculum level (mL) with substrate level (g).
investigated for better yields of cellulase. Among these substrates, rice straw revealed lowest yield while corn stover revealed maximum production (Imran et al., 2017). Substrate complexity and composition are the limiting factors of cellulase activities and fungal growth either increasing or decreasing the cellulase production (Kim et al., 2011). Therefore, corn stover proved excellent carbon substrate or fermentation (Imran et al., 2017). The ANOVA used to calculate the results of regression equation and value of coefficient of determination (R2) was determined (0.918) (Table 4). The value of coefficient of determination was less than 0.75 made this model suitable for cellulase optimization. The model was equally adequately suitable to represent the interaction among different factors. The correlation coefficient (R) and determination of coefficient (R2) can also be used to check the aptness of RSM. The R2 value revealed the better response when it became close to 1, as the value of R2 was always between 0 and 1. The interaction of substrate with substrate particle size (mm) by keeping other factors constant (moisture 60%, inoculum 3 mL, urea 0.3 g, KCl 0.3 g, CaCl2 0.3 g, MgSO4 0.03 g) revealed that maximum cellulase activity (β glucosidase) 92.19 µg/mL/min (Fig. 2) was achieved during these set parameters. The interaction of urea with CaCl2 by keeping other factors
Optimum pH and temperature for Aspergillus tubingensis IMMIS2 revealed pH 4.5 − 4.8 (85.4 ± 2.1 µg/mL/min) and temperature 40 °C (83.9 ± 1.4 µg/mL/min) were optimum for its growth and enzyme production. Aspergillus tubingensis IMMIS2 revealed maximum production of cellulase in all optimized conditions (86.4 ± 2.1 µg/mL/min) and selected for further studies (Imran et al., 2017). 3.3. RSM Implementation for optimization of culture media Aspergillus tubingensis IMMIS2 (LT 732556.1) produced crude cellulase which revealed maximum activity (112 µg/mL/min) through Response Surface Methodology (RSM) using 40 mm mesh size of corn stover, 8 g substrate of corn stover, 80% moisture, 5 mL inoculum, 0.5 g urea, 0.1 g KCl, 0.1 g CaCl2 and 0.06 g MgSO4 (Table 3; Fig. 4). Determination of suitable substrate, various lignocellulosic substrates including rice straw, wheat straw, corn stover and sugar cane bagasse were used as carbon source for fermentation process. Lignocellulosic hydrolysate conversion in to value added products, cellulase, is a cost effective fermentation problem and cellulase is produced from various microbes (Imran et al., 2017). The appropriate carbon source was 194
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Table 3 RSM parameters optimization for cellulase production. Substrate particle size (mm)
Substrate level (g)
Moisture level (%)
Inoculum size (mL)
Urea level (g)
KCl (g)
CaCl2 (g)
MgSO4 (g)
Cellulase activity μg/mL/ min
80 60 40 80 40 40 80 80 40 40 40 80 80 40 80 60 40 80 116.5 60 60 3.43 60 60 60 60 60 60 60 60 60 60 60 60 60 60 40 80 80 40 40 40 80 40 40 80 80 60 80 40 80 40 60 80 80 40 40 40 40 80 60 80 60 80 40 80 80 80 40 40 80 40 80
2 5 8 2 2 8 8 8 2 8 8 8 8 2 2 5 2 2 5 5 5 5 5 5 3.48 5 5 5 5 5 5 5 5 13.5 5 5 2 8 8 8 2 2 8 8 2 2 2 5 2 8 2 8 5 8 2 8 8 2 2 2 5 2 5 8 2 8 2 8 8 8 8 2 2
80 60 40 40 80 40 80 40 80 80 80 40 80 40 40 60 40 80 60 60 60 60 60 3.43 60 60 60 116.5 60 60 60 60 60 60 60 60 80 80 80 80 80 40 40 80 40 40 80 60 40 40 80 40 60 40 80 80 40 40 80 40 60 40 60 80 80 40 80 40 40 80 80 40 80
5 3 5 1 5 1 5 5 1 5 1 1 1 1 5 3 5 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 8.65 3 3 2.65 5 5 1 5 1 1 1 1 5 1 1 3 5 5 5 1 3 5 5 5 1 1 1 1 3 5 3 1 5 5 1 1 5 1 5 5 1
0.5 0.3 0.5 0.1 0.1 0.5 0.5 0.1 0.1 0.1 0.1 0.1 0.5 0.5 0.1 0.3 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.26 0.3 0.3 0.3 0.3 0.3 0.86 0.3 0.5 0.1 0.1 0.5 0.5 0.1 0.5 0.5 0.1 0.5 0.1 0.3 0.5 0.1 0.1 0.1 0.3 0.5 0.1 0.5 0.1 0.1 0.5 0.5 0.3 0.5 0.3 0.1 0.5 0.5 0.1 0.5 0.1 0.5 0.1 0.1 0.5
0.1 0.3 0.5 0.5 0.1 0.1 0.5 0.5 0.5 0.5 0.1 0.1 0.1 0.5 0.1 0.3 0.1 0.5 0.3 0.3 0.3 0.3 0.86 0.3 0.3 0.3 0.26 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.1 0.5 0.1 0.1 0.1 0.5 0.5 0.5 0.1 0.1 0.3 0.5 0.1 0.5 0.5 0.3 0.1 0.1 0.5 0.1 0.5 0.5 0.5 0.3 0.1 0.3 0.1 0.1 0.5 0.5 0.1 0.5 0.1 0.5 0.1 0.1
0.1 0.3 0.5 0.1 0.5 0.1 0.5 0.1 0.1 0.1 0.5 0.5 0.1 0.5 0.5 0.3 0.1 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.86 0.3 0.26 0.3 0.3 0.3 0.3 0.5 0.5 0.1 0.1 0.1 0.5 0.5 0.5 0.1 0.1 0.5 0.3 0.5 0.5 0.1 0.1 0.3 0.1 0.1 0.1 0.1 0.5 0.1 0.1 0.3 0.5 0.3 0.1 0.5 0.1 0.5 0.5 0.5 0.5 0.5 0.1 0.5
0.06 0.04 0.02 0.06 0.06 0.06 0.06 0.02 0.02 0.06 0.02 0.06 0.02 0.06 0.02 0.04 0.02 0.02 0.04 0.096 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.016 0.04 0.04 0.04 0.04 0.04 0.06 0.06 0.02 0.06 0.02 0.06 0.06 0.02 0.02 0.06 0.02 0.04 0.02 0.02 0.06 0.06 0.04 0.02 0.02 0.02 0.02 0.02 0.06 0.02 0.04 0.06 0.04 0.06 0.02 0.06 0.06 0.02 0.06 0.06 0.02 0.06 0.06
75 83 96 61 68 63 56 91 56 85 77 75 63 39 84 99 69 35 66 46 87 78 69 73 89 79 86 56 86 81 98 87 69 87 81 79 78 91 47 112 78 31 53 61 67 61 56 81 79 87 94 76 81 94 88 93 81 73 81 74 89 78 83 69 87 87 54 57 87 57 57 45 39 (continued on next page)
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Table 3 (continued) Substrate particle size (mm)
Substrate level (g)
Moisture level (%)
Inoculum size (mL)
Urea level (g)
KCl (g)
CaCl2 (g)
MgSO4 (g)
Cellulase activity μg/mL/ min
80 40 40 60 60 80 40 80 80 40 40 80 80 80 40 40 40
8 8 2 5 5 8 2 2 8 2 2 2 2 8 8 8 8
80 40 80 60 60 80 40 80 40 40 80 40 40 40 40 80 80
5 1 1 3 3 1 1 5 5 5 5 5 1 1 5 5 1
0.5 0.5 0.1 0.3 0.3 0.5 0.5 0.5 0.1 0.5 0.1 0.1 0.1 0.1 0.5 0.1 0.1
0.1 0.5 0.1 0.3 0.3 0.5 0.1 0.5 0.1 0.5 0.5 0.5 0.1 0.5 0.1 0.1 0.5
0.5 0.1 0.1 0.3 0.3 0.1 0.5 0.1 0.1 0.1 0.5 0.5 0.1 0.5 0.5 0.1 0.5
0.02 0.02 0.06 0.04 0.04 0.06 0.02 0.02 0.06 0.06 0.02 0.06 0.02 0.02 0.06 0.02 0.06
74 78 44 85 75 48 46 78 109 86 78 87 63 67 91 104 41
Table 4 Statistical model of RSM. Model summary S 7.26584 Term
R-sq 91.04%
R-sq(adj) 82.27%
Constant Substrate particle size with particle size (mm) Substrate level (g)*Substrate level (g) Moisture level (%)*Moisture level (%) Inoculum size (mL)*Inoculum size (mL) Urea level (g)*Urea level (g) KCl (g)*KCl (g) CaCl2 (g)*CaCl2 (g) MgSO4 (g)*MgSO4 (g) Substrate particle size (mm)*Substrate level (g) Substrate particle size (mm)*Moisture level (%) Substrate particle size (mm)*Inoculum size (mL) Substrate particle size (mm)*Urea level (g) Substrate particle size (mm)*KCl (g) Substrate particle size (mm)*CaCl2 (g) Substrate particle size (mm)*MgSO4 (g) Substrate level (g)*Moisture level (%) Substrate level (g)*Inoculum size (mL) Substrate level (g)*Urea level (g) Substrate level (g)*KCl (g) Substrate level (g)*CaCl2 (g) Substrate level (g)*MgSO4 (g) Moisture level (%)*Inoculum size (mL) Moisture level (%)*Urea level (g) Moisture level (%)*KCl (g) Moisture level (%)*CaCl2 (g) Moisture level (%)*MgSO4 (g) Inoculum size (mL)*Urea level (g) Inoculum size (mL)*KCl (g) Inoculum size (mL)*CaCl2 (g) Inoculum size (mL)*MgSO4 (g) Urea level (g)*KCl (g) Urea level (g)*CaCl2 (g) Urea level (g)*MgSO4 (g) KCl (g)*CaCl2 (g) KCl (g)*MgSO4 (g) CaCl2 (g)*MgSO4 (g)
Fig. 4. Surface plot for cellulase activity (μg/mL/min) = 112.
constant (substrate particle mesh size 60 mm, substrate 5 g, moisture 60%, inoculum 3 mL, KCl 0.3 g, MgSO4 0.03 g) revealed that maximum cellulase activity (β glucosidase) 92.19 µg/mL/min (Fig. 3) was accomplished during these set parameters. According to model equation, the value of R2 was close to 1 (0.91) established a close relationship between theoretical values and experimental results. The signal to noise was actually the precision value and ratio which was more than 4 was allowable (Emtiazi and Nahvi, 2000; Kim et al., 2011).
Y = 31 + β1 x1 + β2 x2 + β12 x1 x2 + β11 x1 2 + β22 x2 2 + ε
(1)
Y = 31−0.001A + 0.351B + 0.001C + 0.078D + 0.230E + 0.004F + 0.038G 0.039H + 0.000AB + 0.000AC + 0.404AD + 0.000AE + + 0.018AF + 0.506AG + 0.004AH + + 0.001BC + 0.385BD + 0.109BE + + 0.000BF 0.269BG + 0.284BH 0.216CD + 0.516CE + 0.001CF
T-Value
P-Value
VIF
23.23 −2.34 −1.16 −3.75 −6.35 −0.09 −1.44 1.42 −3.82 0.000 −3.97 −4.83 0.84 −4.42 −2.46 −0.67 3.05 −3.49 0.88 −1.63 −4.97 −1.12 1.08 1.26 0.67 −3.56 −2.25 0.64 0.84 −0.71 1.43 2.12 0.50 −2.53 0.02 −0.71 1.43 −1.05
0.000 0.024 0.253 0.001 0.000 0.930 0.157 0.163 3.21
1.01 3.85 1.01 3.84 3.86 3.86 3.86
0.000 0.000 0.404 0.000 0.018 0.506 0.004 0.001 0.385 0.109 0.000 0.269 0.284 0.216 0.506 0.001 0.029 0.528 0.404 0.484 0.160 0.040 0.620 0.015 0.986 0.484 0.160 0.300
2.05 1.00 2.04 2.01 2.01 2.01 1.80 2.05 2.88 2.86 2.86 2.86 2.68 2.04 2.01 2.01 2.01 1.80 2.84 2.84 2.84 2.67 2.82 2.82 2.64 2.82 2.64 2.64
+ 0.029CG + 0.528CH + 0.404DE + 0.484DF0.160DG + revealed factors and their probability values. The data revealed that most of the factors had significant impact on cellulase production while some factors showed non-significant effect on cellulase production Eqs. (1 and 2). Substrate level (g) and urea level (g) had non-significant relationship for cellulase production from Aspergillus tubingensis IMMIS2
0.040DH + 0.620EF + 0.015EG + 0.986EH + 0.484FG + 0.160FH + 0.300GH − 0.024A2− 0.253B2− 0.001C2− 0.000D2− 0.930E2− 0.157F2− 0.163G2− 0.000H2 (2) Whereas, Y represented the cellulase activity and English letters 196
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0.25 g. Another study showed good saccharification of fruits and increased juice volume after treating with β glucosidase (Vaillant et al., 2001). Improvement in the fruit saccharification of fruit juices has also been reported after enzymatic treatment (Barman et al., 2015).
while substrate particle mesh size (mm), moisture level (%), inoculum size (mL), KCl, CaCl2, and MgSO4 had significant impact on cellulase production using Response Surface Methodology (Table 4). The tenacity of RSM application in this study was to improve the yield of cellulase by saving time, energy, expenditures and mistakes in production. This practice had showed very effective as by changing the independent factors the dependent factors automatically fluctuated. Moreover, it also defined the correlation of factors by providing imaginings of 3-D graphs and contour graphs.
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3.4. Saccharification of fruits Fruit saccharification of mango with cellulase was increased up to 59.54% using 1% sulphuric acid which is much higher as compared to 1% base. The results revealed that acidic treatment showed better response to cellulase saccharification. 4. Discussions The tenacity of RSM application in this study was to improve the yield of cellulase by saving time, energy, expenditures and mistakes in production. This practice had showed very effective as by changing the independent factors the dependent factors automatically fluctuated (Shajahan et al., 2017). Moreover, it also defined the correlation of factors by providing imaginings of 3-D graphs and contour graphs. It eased the inferences using color discrimination techniques. Higher initial moisture content for SSF showed a negative or constraining effect on cellulase production, using lower moisture content for supreme yield. The maximum cellulase yield was elevated up to 6 folds by low level of moisture content and by regulating incubation period by CCD strategy (Singhania et al., 2007; Shajahan et al., 2017). Latifian et al. (2007) also highlighted the importance of moisture level for maximum cellulase yield by conducting a trial for cellulase production using two mutants of Trichoderma reesei under SSF and for assessment of cultural conditions of RSM methodology was employed. The moisture level of 40%, 55% and 70% was used to analyze the optimized level by RSM and the maximum activity was found at 70% of moisture level with 8.5 g of NaCl whereas in current findings the maximum cellulase activity was found at 50% moisture level and 0.3 g of urea. Sun et al. (2010) accomplished an experiment for cellulase production by apple pomace using Trichoderma sp. under SSF conditions. The results of this experiment suggested that initial moisture level, inoculum size and temperature influence cellulase production expressively. The optimum initial moisture level, incubation temperature and inoculum size were 70%, 32 and 2×108 spores/flask, respectively. Each fungal strain had a specific compatibility with a particular nitrogen source, and cellulase production by two fungal strains which were Melanocarpus sp. and Fusarium oxysporum had been affected largely by nitrogen source. When ammonium acetate was replaced by a beef extract (0.25%, w/v), it showed enhanced cellulases activities using Aspergillus fumigatus as a fermentative microorganism (Soni et al., 2010). The R2 value revealed the better response when it became close to 1, as the value of R2 was always between 0 and 1 (Yu et al., 1998; Alam et al., 2008; Kim et al., 2011). In current study, ammonium sulphate had been proved to be an efficient garnish of cellulase production by providing nitrogen source. Infect, it had a collective enhancing effect with other inorganic salts including potassium chloride, sodium chloride, calcium chloride, zinc sulphate and magnesium sulphate. Liang et al. (2012) completed experiment for cellulase production by utilizing rice grass as lignocellulosic biomass and Aspergillus sp. as a fermentative microbe. He used response surface methodology to get optimum conditions for highest cellulase production. By examining the results of RSM, the optimum conditions for highest cellulase yield were substrate concentration 5 g, moisture content 30%, inoculums size 5 mL, Ammonium Sulphate 0.35 g, Sodium Chloride 4 g, Potassium Chloride 0.05 g, Calcium Chloride 0.05 g, Zinc Sulphate 0.25 g and Magnesium Sulphate 197
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control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 10, 512–526. Tamura, K., Stecher, G., Peterson, D., Filipski, A., Kumar, S., 2013. MEGA6: molecular evolutionary genetics analysis version 6. 0. Mol. Biol. Evol. 30, 2725–2729. Vaillant, F., Millan, A., Dornier, M., Decloux, M., Reynes, M., 2001. Strategy for
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