Statistical Optimization of Extracellular Thermo-Alkaline Lipase Production from Aeromonas caviae LipT51 with Response Surface Methodology

Yıl 2021, , 1770 - 1780, 01.09.2021

Sümeyra Gürkök

https://doi.org/10.21597/jist.872699

Öz

Extracellular thermo-alkaline lipase production from Aeromonas caviae LipT51 was statistically optimized by response surface methodology (RSM). First, the one factor at a time approach was implemented to screen the sources of carbon (olive oil, tributyrin, sunflower oil, waste frying oil, glycerol, Tween 80, Tween 20, palm oil, and Triton X100) and nitrogen (peptone, yeast extract, tryptone, whey, urea, NaNO2, NH4NO3) for the highest lipase production. Then, optimum values for waste frying oil selected as carbon source, tryptone selected as nitrogen source and initial pH of the medium were determined by RSM using Box-Behnken design (BBD). The quadratic model of BBD for lipase production was statistically significant and reliable (p < 0.0001, R2 = 0.9881). The validated optimal conditions for maximum lipase production (1.6 U mL-1) were determined as 1.13% waste frying oil, 1.5% tryptone and pH 7.9. For the first time in this study, optimization of lipase production from an A. caviae strain was carried out and under optimized culture conditions using cheap waste material. The production efficiency of lipase enzyme, which is known to be valuable with its detergent activity, increased 2.7 times compared to non-optimized conditions.

Anahtar Kelimeler

Aeromonas caviae, lipase, optimization, response surface methodology, Box-Behnken Design

Destekleyen Kurum

Atatürk Üniversitesi

Proje Numarası

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Teşekkür

The author thanks Murat Özdal for accompanying in the isolation of A. caviae strain and his advises.

Tepki Yüzey Metodu ile Aeromonas caviae LipT51'den Ekstrasellüler, Termo-Alkali Lipaz Üretiminin İstatistiksel Optimizasyonu

Öz

Aeromonas caviae LipT51'den ekstrasellüler termo-alkali lipaz üretimi, tepki yüzey metodu (response surface methodology: RSM) ile istatistiksel olarak optimize edilmiştir. İlk olarak, en yüksek lipaz üretimi için farklı karbon (zeytinyağı, tributirin, ayçiçek yağı, atık kızartma yağı, gliserol, Tween 80, Tween 20, palmiye yağı ve Triton X100) ve azot (pepton, maya özütü, tripton, peynir altı suyu, üre, NaNO2, NH4NO3) kaynaklarını taramak üzere tek seferde bir faktör yöntemi uygulandı. Ardından, karbon kaynağı olarak seçilen atık kızartma yağı, azot kaynağı olarak seçilen tripton ve başlangıç pH’sı için optimum değerler Box-Behnken tasarımı (BBD) kullanılarak RSM ile belirlenmiştir. Lipaz üretimi için BBD'nin ikinci dereceden modeli istatistiksel olarak anlamlı ve güvenilir bulundu (p <0.0001, R2 = 0.9881). Maksimum lipaz üretimi (1.6 U mL-1) için doğrulanmış optimum koşullar, % 1.13 atık kızartma yağı, % 1.5 tripton ve pH 7.9 olarak belirlenmiştir. İlk kez bu çalışmada, bir A. caviae suşundan lipaz üretiminin optimizasyonu, ucuz atık malzeme kullanılarak optimize edilmiş kültür koşulları altında gerçekleştirildi. Deterjan aktivitesi ile değerli olduğu bilinen lipaz enziminin üretim verimliliği, optimize edilmeyen koşullara göre 2.7 kat arttı.

Anahtar Kelimeler

Aeromonas caviae, lipaz, atık kızartma yağı, optimizasyon, tepki yüzey metodu, BoxBehnken tasarımı

Proje Numarası

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Kaynakça

• Abdel Aziz MM, Elgammal EW, Ghitas RG, 2020. Comparative study on modeling by neural networks and response surface methodology for better prediction and optimization of fermentation parameters: Application on thermo-alkaline lipase production by Nocardiopsis sp. strain NRC/WN5. Biocatalysis and Agricultural Biotechnology, 25: 101619.
• Ameri A, Shakibaie M, Soleimani-Kermani M, Faramarzi MA, Doostmohammadi M, Forootanfar H, 2019. Overproduction of thermoalkalophilic lipase secreted by Bacillus atrophaeus FSHM2 using UV-induced mutagenesis and statistical optimization of medium components. Preparative Biochemistry and Biotechnology, 49(2): 184–191.
• Amini Z, Ilham Z, Ong HC, Mazaheri H, Chen WH, 2017. State of the art and prospective of lipase-catalyzed transesterification reaction for biodiesel production. Energy Conversion and Management, 141: 339–53.
• Beisson F, Tiss A, Rivière C, Verger R, 2000. Methods for lipase detection and assay: a critical review. European Journal of Lipid Science and Technology, 102(2): 133–53.
• Bharathi D, Rajalakshmi G, 2019. Microbial lipases: an overview of screening, production and purification. Biocatalysis and Agricultural Biotechnology, 22: 101368.
• Bora L, Gohain D, Das R. 2013. Recent advances in production and biotechnological applications of thermostable and alkaline bacterial lipases. Journal of Chemical Technology and Biotechnology, 88: 1959–1970.
• Bora L, Kalita MC, 2008. Production of thermostable alkaline lipase on vegetable oils from a thermophilic Bacillus sp. DH4, characterization and its potential applications as detergent additive. Journal of Chemical Technology and Biotechnology, 83(5): 688–93.
• Chandra P, Enespa, Singh R, Arora PK, 2020. Microbial lipases and their industrial applications: a comprehensive review. Microbial Cell Factories 19: 169.
• Chauhan M, Chauhan RS, Garlapati VK, 2013. Modeling and optimization studies on a novel lipase production by Staphylococcus arlettae through submerged fermentation. Enzyme research, 1–8.
• Dinanta Utama Q, Sitanggang AB, Adawiyah DR, Hariyadi P, 2019. Lipase-catalyzed interesterification for the synthesis of medium-long-medium (MLM) structured lipids—a review. Food Technology and Biotechnology, 57(3): 305–18.
• Dutta S, Ray L, 2009. Production and characterization of an alkaline thermostable crude lipase from an isolated strain of Bacillus cereus C7. Applied Biochemistry and Biotechnology, 159: 142–154.
• Ebrahimipour G, Sadeghi H, Zarinviarsagh M, 2017. Statistical methodologies for the optimization of lipase and biosurfactant by Ochrobactrum intermedium strain MZV101 in an identical medium for detergent applications. Molecules (Basel, Switzerland), 22(9): 1460.
• Ebrahimpour A, Abd Rahman RN, Ean Ch’ng DH, Basri M, Salleh AB, 2008. A modeling study by response surface methodology and artificial neural network on culture parameters optimization for thermostable lipase production from a newly isolated thermophilic Geobacillus sp. strain ARM. BMC Biotechnology, 8: 96.
• Erdem B, Kariptasi E, Cil E, Isik K, 2011. Biochemical identification and numerical taxonomy of Aeromonas spp. isolated from food samples in Turkey. Turkish Journal of Biology, 35: 463–472.
• Ertan B, Gurkok S, Efe D, 2020. An alternative usage of Urtica dioica as adsorbent for malachite green: Optimization study. STUDIA UBB CHEMIA, LXV, 4: 109–123.
• Gupta R, Gupta N, Rathi P, 2004. Bacterial lipases: an overview of production, purification and biochemical properties. Applied microbiology and biotechnology, 64: 763–768.
• Gurkok S., Cekmecelioglu D, Ogel ZB, 2011. Optimization of culture conditions for Aspergillus sojae expressing an Aspergillus fumigatus alpha-galactosidase. Bioresource Technology, 102: 4925–4929.
• Gururaj P, Ramalingam S, Devi GN, Gautam P, 2016. Process optimization for production and purification of a thermostable, organic solvent tolerant lipase from Acinetobacter sp. AU07. Brazilian Journal of Microbiology. 47: 647–657.
• Ilesanmi OI, Adekunle AE, Omolaiye JA, Olorode EM, Ogunkanmi AL, 2020. Isolation, optimization and molecular characterization of lipase producing bacteria from contaminated soil. Scientific African, 8: e00279.
• Javed S, Azeem F, Hussain S, Rasul I, Siddique MH, Riaz M, Afzal M, Kouser A, Nadeem H, 2018. Bacterial lipases: a review on purification and characterization. Progress in Biophysics and Molecular Biology, 132: 23–34.
• Lima VM, Krieger N, Sarquis MIM, Mitchell DA, Ramos LP, Fontana JD, 2003. Effect of nitrogen and carbon sources on lipase production by Penicillium aurantiogriseum. Food Technology and Biotechnology, 41(2): 105–110.
• Miñana-Galbis D, Farfán M, Lorén JG, Fusté MC, 2002. Biochemical identification and numerical taxonomy of Aeromonas spp. isolated from environmental and clinical samples in Spain. Journal of Applied Microbiology 93: 420–430.
• Ozdal M, Gurkok S, Ozdal OG, 2017b. Optimization of rhamnolipid production by Pseudomonas aeruginosa OG1 using waste frying oil and chicken feather peptone. 3 Biotech, 7(2): 117.
• Ozdal M, Ozdal OG, Gurkok S, 2017a. Statistical optimization of beta-carotene production by Arthrobacter agilis A17 using response surface methodology and Box-Behnken design. AIP Conference Proceedings, 1833: 020101. doi: 10.1063/1.4981749.
• Patel R, Prajapati V, Trivedi U, Patel K, 2020. Optimization of organic solvent-tolerant lipase production by Acinetobacter sp. UBT1 using deoiled castor seed cake. 3 Biotech, 10: 508.
• Putri DN, Khootama A, Perdani MS, Utami TS, Hermansyah H, 2020. Optimization of Aspergillus niger lipase production by solid state fermentation of agro-industrial waste. Energy Reports, 6331–335.
• Ramachandran S, Singh SK, Larroche C, Soccol CR, Pandey A, 2000. Oil cakes and their biotechnological applications--a review. Bioresource Technology, 98(10): 2000–2009.
• Sahoo RK, Kumar M, Mohanty S, Sawyer M, Rahman PKSM, Sukla LB, Subudhi E, 2018. Statistical optimization for lipase production from solid waste of vegetable oil industry. Preparative Biochemistry and Biotechnology, 48(4): 321–326.
• Soleymani S, Alizadeh H, Mohammadian H, Rabbani E, Moazen F, MirMohammad Sadeghi H, Shariat ZS, Etemadifar Z, Rabbani M, 2017. Efficient media for high lipase production: one variable at a time approach. Avicenna Journal of Medical Biotechnology, 9(2): 82–86.
• Vasiee A, Behbahani BA, Yazdi FT, Moradi S, 2016. Optimization of the production conditions of the lipase produced by Bacillus cereus from rice flour through Plackett-Burman Design (PBD) and response surface methodology (RSM). Microbial Pathogenesis, 101: 36–43.