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Full Text: 1 Introduction Proteases (EC 3.4), hydrolyzing proteins to short peptides or free amino acids (Barrett & McDonald, 1986), account for nearly 60% of the industrially important enzyme market. These are used in many industries like detergents, food processing, animal nutrition, pharmaceuticals, textiles and paper (Kamath et al., 2010). Recently, researchers developed interest in fungal proteases due to high diversity, broad substrate specificity and stability under extreme conditions, besides producing acidic, neutral and alkaline proteases (Tremacoldi et al., 2004). Moreover fungi can be grown on cheaper substrates over a wide range of pH (4.0-11.0) (Murthy & Naidu, 2010) and produce extracellular enzymes, that can be easily recovered from fermenting broth by simple filtration (Chandrasekaran et al., 2015). Poultry industry waste consists of mainly a mixture of poultry droppings, bedding material (e.g. wood shavings or straw), dead birds, waste feed, broken eggs and feathers removed from poultry houses (Kelleher et al., 2002). Poultry litter has high pollutant load so, its direct application on land may lead to nutrient leaching, spread of pathogens, production of phytotoxic substances, eutrophication of water bodies, and other air pollutants (Costa et al., 2012), No doubt, its disposal is of great concern but  being  organic in  nature, the poultry droppings can be utilized for biogas generation. Poultry droppings had been found to generate more biogas than piggery and cattle wastes (Rao et al., 2011). Biogas production from poultry droppings helps us to meet our energy sources. The anaerobic digestion of poultry droppings yields approximately 60% methane, 38% carbon dioxide, and mixture of water vapors, ammonia and hydrogen sulphide (Bolan et al., 2010). The use of agro-industrial waste, as cultivation media, is a matter of great interest, as this may help to decrease the costs of enzyme production (Singh et al., 2009).  After biogas production, safe disposal of slurry is a big problem as it is very difficult to transport viscous material to the fields. Also, its direct application in the soil, forms a slow degradable layer on the soil surface, which is not desirable. Poultry dropping based biodigested slurry contains large amount of proteins, nitrates and amino acids, that can be utilized as substrate for protease production.     Thermodynamic studies can provide information about the thermostability of enzymes at the operating temperature. The enzyme undergoes a first order kinetics reaction, which is responsible for its irreversible denaturation and is expressed in terms of its half life (t1/2). The activation energy and change in Gibbs free energy, enthalpy and entropy between the folded and unfolded states of enzyme are to describe denaturation thermodynamics (Saqib et al., 2010). The activity and thermostability of enzymes are important parameters to determine the economic feasibility in industrial processes. High stability is generally considered an economic advantage because of its reduced enzyme turnover (Vielle & Zeikus, 2001). Various factors like temperature, pH, slurry concentration and inoculum concentration can be optimized by Response Surface Methodology using Statgraphics Centurian XVI.I software. The main advantage of response surface methodolgy is the reduced number of experimental runs needed to provide sufficient information for statistically acceptable results. No reports are available on protease production from Humicola fuscoatra MTCC 1409 using poultry dropping based biodigested slurry. Thus, the study was designed to optimize the cultural conditions for protease production by H. fuscoatra MTCC 1409 using poultry dropping based biodigested slurry which will be a cheaper substrate for decreasing the cost of enzyme production. 2 Materials and Methods 2.1 Procurement of substrate and culture The poultry droppings, collected from the Guru Angad Dev Veterinary and Animal Sciences, University (GADVASU), Ludhiana (India) were used in lab scale 10 liter biogas digester set up at Biogas Laboratory, Punjab Agricultural University (PAU), Ludhiana (India). Standard culture of H. fuscoatra MTCC 1409 was procured from Microbial Type Culture Collection (MTCC), Institute of Microbial Technology, Chandigarh (India) and was maintained on potato dextrose agar slants at 40±2ºC by monthly transfers. The culture was stored at 4ºC after sub-culturing. 2.2 Qualitative enzyme estimation The fungal culture was studied qualitatively for protease enzyme production by clearance zone method (Saran et al., 2007). H. fuscoatra MTCC 1409 was point inoculated on the Petri plates containing skimmed milk medium and incubated at 40°C. Plates were flooded with tannic acid (10%) after luxuriant growth of fungi and were observed for the formation of clear zone around the colony which represents the production of proteases by the fungus. Potency index was calculated as given below: Potency index =  Area of clearance zone (cm2) Area of colony (cm2) 2.3 Protease production from biodigested slurry through fermentation   Table 1 Independent variables with their coded and actual values    
Factors Units Coded Levels -1 0 +1 Inoculum Concentration % 5 10 15 Slurry Concentration % 25 50 75 pH - 5 6 7 Temperature °C 35 40 45
For protease production, 50 ml poultry dropping based biodigested slurry was taken in Erlenmeyer flasks (250 ml) and diluted by using 50 ml distilled water. Flasks were inoculated with the 106 spore/ml (measured by haemocytometer) of H. fuscoatra MTCC 1409, incubated for 4 days at 40°C. The enzyme was extracted by centrifuging the incubated slurry at 10,000 rpm for 20 minutes at 4ºC and supernatant was used for estimation of protease activity by spectrophotometric method. The experiment was performed in triplicates. The required spore concentration was obtained by using the formula given below:   X=Required Spore Concentration × Final Volume NeededHemocytometer count Where,         X  = volume of spores suspension to be added 2.4 Quantitative estimation of protease Protease estimation was carried out according to the method described by Enyard (2008). Enzyme extract (0.1 ml) was taken in triplicate test tubes and mixed with 0.9 ml of distilled water. Then, 5 ml of 0.65% casein solution was added. The mixture was incubated at 37°C for 10 min. After incubation, 5 ml of tri-chloroacetic acid (TCA) was added and the mixture was incubated at 37°C for 30 min. To measure protease produced during this reaction, 5 ml of Na2CO3 solution (500mM) was added followed by immediate addition of 1 ml Folin phenol reagent. Mixture was kept at 37ºC for 30 min. Control experiment devoid of substrate, was run simultaneously. The % light absorbance was recorded at 660 nm in a UV-VIS spectrophotometer (Hitachi UV-VIS U-2800). A standard curve for protease activity was prepared under same conditions as described above using standard solution of L-tyrosine in the concentration range of 0.01 to 1.0 g. The corresponding enzyme activity was calculated from the standard curve.  2.5 Optimization of enzyme production by ‘one variable at a time approach’ The protease enzyme production by H. fuscoatra MTCC 1409 was first optimized by ‘one variable at a time approach’ to find the most important factors that affect its production. The effect of each variable like incubation temperature (35°C, 40°C and 45°C), pH (5, 6 and 7), slurry concentration (25%, 50% and 75%) and inoculum concentration (5%, 10% and 15%) on enzyme production was studied. Enzyme activity of all the triplicates sets was measured on 4th day. 2.6 Statistical optimization of protease production using RSM Statistical optimization via multilevel factorial design using a response surface methodology (RSM) approach was used for protease production. Four factors selected by ‘one variable at a time’ approach, were used in designing the experiment: slurry concentration (25-75%), inoculum concentration (5-15%), pH   (5-7) and temperature (35-45°C). The triplicate flasks with biodigested slurry were inoculated with inoculum of H. fuscoatra MTCC 1409, incubated at required temperature as per the sets formed by response surface methodology software. Table 1 illustrates independent variables with their coded and actual values. The following second order polynomial equation was used to determine the  relationship between the response and the independent variables   Where, y is the predicted response, is the interception co-efficient, βi is the linear coefficient, βii is the quadratic coefficient, βij is the interaction coefficient. For optimization, the analysis of variance (ANOVA) for the overall effect of four factor variables on the response variable according to fitted model was done using software Statgraphics Centurian XVI.I and the least significant factor affecting the response variable was selected. Response surface methodology (Multilevel Factorial Design) was used for multiple regression analysis of the experiment and F test was employed to evaluate the statistical significance of quadratic polynomial equation.             The performance of regression equation was evaluated by determination coefficient of correlation (R2). The contour                  plots were generated to study the interaction of various                 factors taken for protease production. The various combinations obtained, under which experiments were performed are given in Table 2. 2.7 Validation of model The protease production experiment from H. fuscoatra MTCC 1409 was conducted under optimized conditions i.e. biodigested slurry of 25% concentration, inoculum concentration of 10% and pH of 5 obtained through RSM and all flasks were incubated at optimized temperature i.e. 40ºC for 4 days. The observed value from validation experiment was compared with the value obtained through RSM and the predicted one. The crude enzyme was analyzed for protein content and protease activity. 2.8 Estimation of kinetic parameters   Table 2 Protease activity of experimental runs by Response Surface Methodology (RSM)  
S. No. Inoculum Concentration (%) Slurry Concentration (%) Temperature (?C) pH   Protease Activity (Ug-1)   Desirability Observed Predicted Observed Predicted 1 -1 +1 -1 -1 150±1.98 160.77 0.61  
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