Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers in Biology

ISSN 1674-7984

ISSN 1674-7992(Online)

CN 11-5892/Q

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Biol.    2018, Vol. 13 Issue (1) : 70-77    https://doi.org/10.1007/s11515-017-1476-9
RESEARCH ARTICLE
Optimization and modeling studies on the production of a new fibrinolytic protease using Streptomyces radiopugnans_VITSD8
Dhamodharan Duraikannu, Subathra Devi Chandrasekaran()
School of Biosciences and Technology, VIT University, Vellore, Tamil Nadu, India
 Download: PDF(686 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

BACKGROUND: The current study demonstrated the possibility of statistical design tools combination with computational tools for optimization of fermentation conditions for enhanced fibrinolytic protease production.

METHODS: The effects of using different carbon and nitrogen sources for protease production by Streptomyces radiopugnans_VITSD8 were examined by a full factorial design method. The incubation time, temperature, pH of the medium, and RPM were assessed by the predictable one factor at a time (OFAT) method. Optimization was carried out using starch and oat meal as carbon source, nitrogen source as peptic and malt extract using Fractional Factorial Design (FFD). The analysis was further continued for medium volume, temperature, initial medium pH, inoculum concentration, high determination co-efficient as (R’-0.965), and lower determination co-efficient of variation (CV-8.19%), which defines a reliable and accurate experimental value.

RESULTS: Analysis of variance by the fixed slope effect by temperature and starch; temperature and L-aspargine, temperature and oat meal, temperature and peptic extracts, temperature and pH, temperature and duration of incubation were more vital for protease production at an interactive level. Response surface plots revealed that temperature, starch, and peptic extracts affix critical concerning in temperature. Programming estimated a 28% increase in protease production. Incubation temperature and medium volume portrayed extreme impact among all factor. Starch, peptic and temperature play an important regulatory role in protease production. Optimium temperature for protease production was 33°C. The ratio of carbon and nitrogen sources and pH were the major regulatory factors in protease production by Streptomyces radiopugnans_VITSD8. It demonstrated a 4% noteworthy change in condition.

CONCLUSION: Among all the selected parameters, temperature was the most intuitive factor, demonstrating a notable connection with the type of media and pH, while inoculum fixation had a direct impact on protein production.
Keywords Protease      Streptomyces radiopugnans_VITSD8      Full Factorial design (FFD)      fermentation      optimization      RSM     
Corresponding Author(s): Subathra Devi Chandrasekaran   
Online First Date: 02 February 2018    Issue Date: 26 March 2018
 Cite this article:   
Dhamodharan Duraikannu,Subathra Devi Chandrasekaran. Optimization and modeling studies on the production of a new fibrinolytic protease using Streptomyces radiopugnans_VITSD8[J]. Front. Biol., 2018, 13(1): 70-77.
 URL:  
https://academic.hep.com.cn/fib/EN/10.1007/s11515-017-1476-9
https://academic.hep.com.cn/fib/EN/Y2018/V13/I1/70
Source of variation Df
Replicates r-1
Whole- plot analysis
WP effect
WP error		 2p1-1
(r-1)(2p-1)
Subplot analysis
WP*SP interaction effects
SP error		 2p-2p1
(r-1)(2p-2p1)
Total rap-1
Tab.1  Analysis of variation
Factors Independent variable Coded values
( + 1) (-1)
A Temperature (°C) 30 40
B pH 7 7.5
C Duration (h) 72 96
D Starch (g/100mL) 1.0 1.2
E L-asparagine (g/100mL) 0.1 0.2
F Peptic (g/100mL) 0.8 1.2
G Oat meal (g/100mL) 1.2 1.5
H Malt extract (g/100mL) 0.2 0.4
Tab.2  Fractional factorial design- range and coded values of the variables for fibrinolytic protease production
Factors Independent variable Coded values
-a (-1) 0 ( + 1) A
D Starch (g/100mL) 0.5 1.0 1.5 2.0 2.5
E L-asparagine (g/100mL) 1.5 0.1 0.15 0.2 2.0
F Peptic (g/100mL) 0.5 1.0 1.5 2.0 2.5
G Oat meal (g/100mL) 1.5 0.1 0.15 0.2 2.0
H Malt extract (g/100mL) 0.5 1.0 1.5 2.0 2.5
Tab.3  Central composite experimental design- independent variables and their levels
Std order Run order Centre pt Block Temperature pH Duration Starch L-aspargine Peptic Oat meal Malt extract Activity RESI1 RESI2
15 4 1 1 30 7.5 96 0.3 0.05 0.2 0.3 0.05 269.862 0.87452 1.39338
11 15 1 1 30 7.5 72 0.3 0.025 0.3 0.375 0.05 216.597 -1.14879 1.39337
5 11 1 1 30 7 96 0.25 0.05 0.3 0.375 0.05 178.496 -0.21807 1.39338
13 9 1 1 30 7 96 0.3 0.025 0.2 0.375 0.1 257.789 -0.87452 -1.39338
14 12 1 1 40 7 96 0.3 0.025 0.3 0.3 0.05 538.462 0.02698 -1.39337
10 3 1 1 40 7 72 0.3 0.05 0.2 0.375 0.05 329.286 -1.81647 -1.39337
8 7 1 1 40 7.5 96 0.25 0.025 0.2 0.375 0.05 260.517 -0.53955 -1.39337
2 1 1 1 40 7 72 0.25 0.025 0.3 0.375 0.1 397.218 1.24995 1.39337
6 5 1 1 40 7 96 0.25 0.05 0.2 0.3 0.1 343.716 0.53955 1.39338
3 8 1 1 30 7.5 72 0.25 0.05 0.2 0.375 0.1 186.419 0.0562 -1.39337
12 10 1 1 40 7.5 72 0.3 0.025 0.2 0.3 0.1 620.196 1.81647 1.393937
4 2 1 1 40 7.5 72 0.25 0.05 0.3 0.3 0.05 306.28 -1.24995 -1.39337
9 16 1 1 30 7 72 0.3 0.05 0.3 0.3 0.1 447.791 1.14879 -1.39337
16 14 1 1 40 7.5 96 0.3 0.05 0.3 0.375 0.1 483.056 -0.02698 1.39338
1 13 1 1 30 7 72 0.25 0.025 0.2 0.3 0.05 217.987 -0.0562 1.39338
7 6 1 1 30 7.5 96 0.25 0.025 0.3 0.3 0.1 378.29 0.21807 -1.39338
Tab.4  Design matrix of fractional factorial design
Analysis of variance
Source df Adj SS Adj MS F-value p-value
Model 14 253733 18123.8 583.44 0.032
Linear 8 230749 28843.6 928.53 0.025
Temperature 1 79172 79171.9 2548.68 0.013
pH 1 7 6.9 0.22 0.720
Duration 1 8 8.4 0.27 0.695
Starch 1 49965 49965.2 1608.47 0.016
L-Aspargine 1 7317 7316.7 235.54 0.041
Peptic 1 13249 13249.0 426.51 0.031
Oat meal 1 41331 41331.5 1330.53 0.017
Malt extract 1 39699 39699.4 1277.99 0.018
2-way interaction 6 22984 3830.7 123.32 0.069
Temperature*pH 1 788 787.7 25.36 0.125
Temperature*duration 1 115 114.9 3.70 0.305
Temperature*starch 1 11687 11686.8 376.22 0.033
Temperature*L-aspargine 1 8370 8370.4 269.46 0.039
Temperature*peptic 1 868 867.5 27.93 0.119
Temperature*oat meal 1 1157 1156.9 37.24 0.103
Error 1 31 31.1
Total 15 253764
Model summary
S R-Sq R-Sq(Adj) R-Sq(Pred)
5.5735 99.99% 99.82% 96.87%
Coded coefficients
Term Effect Coef SE Coef T-value F-Value VIF
Constant 140.69 339.50 1.39 243.65 0.003
Temperature 140.69 70.34 1.39 50.48 0.013 1.00
pH 1.31 0.65 1.39 0.47 0.720 1.00
Duration -1.45 -0.72 1.39 -0.52 0.695 1.00
Starch 111.76 55.88 1.39 40.11 0.016 1.00
Tab.5  Design matrix of fractional factorial design
Fig.1  Normal plot of the significant factors on fibrinolytic protease production by (A) Starch (B) Peptic (C) L-Aspargine (D) Oat meal (E) Malt Extract (F) Temperature (G) pH (H) Incubation time.
Fig.2  Pareto chart for the standardized effects of different media components on fibrinolytic protease production for various factors (UL1).
Fig.3  Effect of significant factors on fibrinolytic protease production.
Fig.4  Half-normal plot of the standardized effect.
Fig.5  Main effect plot for activity.
1 Abdeltif A (2000). Effect of inorganic phosphate on lactate production by Lactobacillus helviticus grown on supplemented whey permeate. J Chem Technol Biotechnol, 75(3): 223–238
https://doi.org/10.1002/(SICI)1097-4660(200003)75:3<223::AID-JCTB205>3.0.CO;2-5
2 Adinarayana K, Ellaiah P (2002). Response surface optimization of the critical medium components for the production of alkaline protease by a newly isolated Bacillus sp. J Pharm Pharm Sci, 5(3): 272–278
pmid: 12553896
3 Chen X C, Bai J X, Cao J M, Li Z J, Xiong J, Zhang L, Hong Y, Ying H J (2009). Medium optimization for the production of cyclic adenosine 3′,5′-monophosphate by Microbacterium sp. no. 205 using response surface methodology. Bioresour Technol, 100(2): 919–924
https://doi.org/10.1016/j.biortech.2008.07.062 pmid: 18778935
4 Conto S R, Isabel R, Sanroman A (2001). Design of different bioreactor configurations: application to ligninolytic enzyme production in semi-solid state cultivation. J Chem Technol Biotechnol, 76: 78–82
https://doi.org/10.1002/1097-4660(200101)76:1<78::AID-JCTB326>3.0.CO;2-K
5 Curdová E, Jechová V, Zima J, Vanĕk Z (1989). The effect of inorganic phosphate on the production of avermectin in Streptomyces avermitilis. J Basic Microbiol, 29(6): 341–346
https://doi.org/10.1002/jobm.3620290607 pmid: 2614673
6 Deepak V, Kalishwaralal K, Ramkumarpandian S, Babu S V, Senthilkumar S R, Sangiliyandi G (2008). Optimization of media composition for Nattokinase production by Bacillus subtilis using response surface methodology. Bioresour Technol, 99(17): 8170–8174
https://doi.org/10.1016/j.biortech.2008.03.018 pmid: 18430568
7 Dworkin M, Falkow S, Rosenberg E, Schleifer K H, Stackebrandt E (2006). The Prokaryotes. In: Ecophysiology and Biochemistry, vol 2. Springer, New York, pp. 124
8 Gao H, Liu M, Liu J, Dai H, Zhou X, Liu X, Zhuo Y, Zhang W, Zhang L (2009). Medium optimization for the production of avermectin B1a by Streptomyces avermitilis 14-12A using response surface methodology. Bioresour Technol, 100(17): 4012–4016
https://doi.org/10.1016/j.biortech.2009.03.013 pmid: 19356927
9 Garde A, Jonsson G, Schmidt A S, Ahring B K (2002). Lactic acid production from wheat straw hemicellulose hydrolysate by Lactobacillus pentosus and Lactobacillus brevis. Bioresour Technol, 81(3): 217–223
https://doi.org/10.1016/S0960-8524(01)00135-3 pmid: 11800488
10 Gheshlaghi R, Scharer J M, Moo-Young M, Douglas P L (2005). Medium optimization for hen egg white lysozyme production by recombinant Aspergillus niger using statistical methods. Biotechnol Bioeng, 90(6): 754–760
https://doi.org/10.1002/bit.20474 pmid: 15806549
11 Gill P K, Sharma A D, Harchand R K, Singh P (2003). Effect of media supplements and culture conditions on inulinase production by an actinomycete strain. Bioresour Technol, 87(3): 359–362
https://doi.org/10.1016/S0960-8524(02)00262-6 pmid: 12507880
12 Guo W Q, Ren N Q, Wang X J, Xiang W S, Ding J, You Y, Liu B F (2009). Optimization of culture conditions for hydrogen production by Ethanoligenens harbinense B49 using response surface methodology. Bioresour Technol, 100(3): 1192–1196
https://doi.org/10.1016/j.biortech.2008.07.070 pmid: 18793840
13 Gupte T E, Naik S R (1998). Optimisation of nutritional requirements and process control parameters for the production of HA-2–91, a new tetraene polyene antibiotic. Hindustan Antibiot Bull, 40: 5–13
14 Ikeda H, Omura S (1995). Control of avermectin biosynthesis in Streptomyces avermitilis for the selective production of a useful component. J Antibiot (Tokyo), 48(7): 549–562
https://doi.org/10.7164/antibiotics.48.549 pmid: 7649850
15 Ikeda H, Omura S (1997). Avermectin Biosynthesis. Chem Rev, 97(7): 2591–2610
https://doi.org/10.1021/cr960023p pmid: 11851473
16 Jia B, Jin Z H, Mei L H (2008). Medium optimization based on statistical methodologies for pristinamycins production by Streptomyces pristinaespiralis. Appl Biochem Biotechnol, 144(2): 133–143
https://doi.org/10.1007/s12010-007-8012-3 pmid: 18456945
17 Jonsbu E, McIntyre M, Nielsen J (2002). The influence of carbon sources and morphology on nystatin production by Streptomyces noursei. J Biotechnol, 95(2): 133–144
https://doi.org/10.1016/S0168-1656(02)00003-2 pmid: 11911923
18 Kar S, Ray R C (2008). Statistical optimization of alpha-amylase production by Streptomyces erumpens MTCC 7317 cells in calcium alginate beads using response surface methodology. Pol J Microbiol, 57(1): 49–57
pmid: 18610656
19 Knight V, Sanglier J J, DiTullio D, Braccili S, Bonner P, Waters J, Hughes D, Zhang L (2003). Diversifying microbial natural products for drug discovery. Appl Microbiol Biotechnol, 62(5-6): 446–458
https://doi.org/10.1007/s00253-003-1381-9 pmid: 12838377
20 Krishnan S, Bhattacharya S, Karanth N G (1998). Media optimization for production of lactic acid by Lactobacillus plantarum NCIM 2084 using response surface methodology. Food Biotechnol, 12(1/2): 105–121
https://doi.org/10.1080/08905439809549946
21 Li W, Rasmussen H T (2003). Strategy for developing and optimizing liquid chromatography methods in pharmaceutical development using computer-assisted screening and Plackett-Burman experimental design. J Chromatogr A, 1016(2): 165–180
https://doi.org/10.1016/S0021-9673(03)01324-4 pmid: 14601837
22 Li Y, Jiang H, Xu Y, Zhang X (2008). Optimization of nutrient components for enhanced phenazine-1-carboxylic acid production by gacA-inactivated Pseudomonas sp. M18G using response surface method. Appl Microbiol Biotechnol, 77(6): 1207–1217
https://doi.org/10.1007/s00253-007-1213-4 pmid: 18064455
23 Liu G Q, Wang X L (2007). Optimization of critical medium components using response surface methodology for biomass and extracellular polysaccharide production by Agaricus blazei. Appl Microbiol Biotechnol, 74(1): 78–83
https://doi.org/10.1007/s00253-006-0661-6 pmid: 17086412
24 Liu S, Fang Y, Lv M, Wang S, Chen L (2010). Optimization of the production of organic solvent-stable protease by Bacillus sphaericus DS11 with response surface methodology. Bioresour Technol, 101(20): 7924–7929
https://doi.org/10.1016/j.biortech.2010.05.057 pmid: 20542687
25 Lotfy W A (2007). The utilization of beet molasses as a novel carbon source for cephalosporin C production by Acremonium chrysogenum: Optimization of process parameters through statistical experimental designs. Bioresour Technol, 98(18): 3491–3498
https://doi.org/10.1016/j.biortech.2006.11.035 pmid: 17222554
26 Mu W, Chen C, Li X, Zhang T, Jiang B (2009). Optimization of culture medium for the production of phenyllactic acid by Lactobacillus sp. SK007. Bioresour Technol, 100(3): 1366–1370
https://doi.org/10.1016/j.biortech.2008.08.010 pmid: 18793844
27 Naveena B J, Altaf M, Bhadrayya K, Madhavendra S S, Reddy G (2005). Direct fermentation of starch to l(+) lactic acid in SSF by Lactobacillus amylophilus GV6 using wheat bran as support and substrate: medium optimization using RSM. Bioprocess Biochemistry, 40(2): 681–690
https://doi.org/10.1016/j.procbio.2004.01.045
28 Pillai P, Mandge S, Archana G (2011). Statistical optimization of production and tannery applications of a keratinolytic serine protease from Bacillus subtilis P13. Process Biochem, 46(5): 1110–1117
https://doi.org/10.1016/j.procbio.2011.01.030
29 Shabbiri K, Adnan A, Jamil S, Ahmad W, Noor B, Rafique H M (2012). Medium optimization of protease production by Brevibacterium linens DSM 20158, using statistical approach. Braz J Microbiol, 43(3): 1051–1061
https://doi.org/10.1590/S1517-83822012000300031 pmid: 24031928
30 Xavier S, Lonsane B K (1994). Sugarcane press mud as a novel and inexpensive substrate for production of lactic acid in a solid state fermentation system. Appl Microbiol Biotechnol, 41(3): 291–295
https://doi.org/10.1007/BF00221221
[1] Sanjeeb Kumar Mandal, Nupur Ojha, Nilanjana Das. Process optimization of benzo[ghi]perylene biodegradation by yeast consortium in presence of ZnO nanoparticles and produced biosurfactant using Box-Behnken design[J]. Front. Biol., 2018, 13(6): 418-424.
[2] Moni Philip Jacob Kizhakedathil, Subathra Devi Chandrasekaran. Media optimization for extracellular amylase production by Pseudomonas balearica vitps19 using response surface methodology[J]. Front. Biol., 2018, 13(2): 123-129.
[3] Teetam Ghosal, Nikita Augustine, Ashwini Siddapur, Vaishnavi Babu, Merlyn Keziah Samuel, Subathra Devi Chandrasekaran. Strain improvement, optimization and purification studies for enhanced production of streptokinase from Streptococcus uberis TNA-M1[J]. Front. Biol., 2017, 12(5): 376-384.
[4] Hansa Jain. Inhibition and attenuation of pathogenicity of Porphyromonas gingivalis by leupeptin: A review[J]. Front. Biol., 2017, 12(3): 192-198.
[5] Sampriya SHARMA,Jitender SHARMA,Rishi Pal MANDHAN. Lucrative pectinase production by novel strain Pseudozyma sp. SPJ with statistical optimization techniques using agro-industrial residues[J]. Front. Biol., 2014, 9(4): 317-323.
[6] Bingying YE, Ting XUE, Shichao YE, Shengyan XU, Weiyan LI, Jihua LU, Fang WEI, Wenjin HE, Youqiang CHEN. Optimization of fermentation medium for xylanase-producing strain Xw2[J]. Front Biol, 2013, 8(6): 611-617.
[7] R. FOSSION, D. A. HARTASáNCHEZ, O. RESENDIS-ANTONIO, A. FRANK. Criticality, adaptability and early-warning signals in time series in a discrete quasispecies model[J]. Front Biol, 2013, 8(2): 247-259.
[8] Benyang Wang, Fuhe Luo, Xuening Zhen, Shixiao Yu. Quantitative method for identifying networks of minimum priority sites for protection of rare and endangered plant species in Guangdong, China[J]. Front Biol Chin, 2009, 4(1): 117-123.
[9] Yu Naigong, Ruan Xiaogang. A cellular automata model for simulating fed-batch penicillin fermentation process[J]. Front. Biol., 2006, 1(2): 195-202.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed