Optimization of Celullase Production by Molecular Identification Bacterial Isolated, PUA 18 by Using Response Simulation Modelling

Feskaharny Alamsjah; Dwiky Rahmadi Aswan; Anthoni Agustien

doi:10.22146/jtbb.18807

Feskaharny Alamsjah Department of Biology, Faculty of Mathematics and Natural Science, Universitas Andalas, Padang, West Sumatra, Indonesia, 25175 https://orcid.org/0000-0003-3304-7991
Dwiky Rahmadi Aswan Department of Biology, Faculty of Mathematics and Natural Science, Universitas Andalas, Padang, West Sumatra, Indonesia, 25175 https://orcid.org/0009-0002-1177-2615
Anthoni Agustien Department of Biology, Faculty of Mathematics and Natural Science, Universitas Andalas, Padang, West Sumatra, Indonesia, 25175 https://orcid.org/0000-0002-2385-5920

DOI: https://doi.org/10.22146/jtbb.18807

Keywords: Bacillus subtilis, Cellulase, Screening design, Enzyme optimisation, Response surface methodology

Abstract

Cellulase enzymes are essential for converting cellulose into glucose, enabling sustainable industrial processes. Optimising cellulase production at an industrial scale requires refining microbial strains, growth medium components, and operating conditions. Traditional One-Factor-At-A-Time (OFAT) approaches are limited in efficiency, whereas Response Surface Methodology (RSM) provides a robust statistical tool for evaluating multiple variables simultaneously. This study improved cellulase production from Bacillus subtilis PUA-18, identified through 16S rRNA sequencing, using a combined Design of Experiment (DoE) approach. Screening and optimising were applied by using Plackett Burman Design (PBD) and Box-Behnken Design (BBD), respectively. Temperature was the most influential factor, followed by KNO₃, FeSO₄, and CaCl₂. The predicted maximum cellulase activity of 0.574 U mL⁻¹ was achieved at 40 °C, 1.45 g L⁻¹ KNO₃, 20 mg L⁻¹ FeSO₄, and 50 mg L⁻¹ CaCl₂. The DoE approach yielded a 293 % improvement in cellulase activity, demonstrating its superiority over traditional methods. These findings highlight Bacillus subtilis PUA-18 as a promising cellulase producer and emphasise the critical role of advanced optimisation strategies for enhancing enzyme production, paving the way for broader industrial applications.

References

Alamsjah, F., Ananta, Y. & Agustien, A., 2024a. Optimization of Temperature and pH for Protease Production from Some Bacterial Isolates of Mangrove Waters. OnLine Journal of Biological Sciences, 24(4), pp.911–918. doi: 10.3844/ojbsci.2024.911.918.

Alamsjah, F., Aswan, D.R. & Agustien, A., 2024b. pH and Temperature Optimization of Several Bacterial Isolates from Mangrove Waters in the Mandeh Area to Produce Cellulase Enzyme. OnLine Journal of Biological Sciences, 24(3), pp.367–373. doi: 10.3844/ojbsci.2024.367.373.

Annapure, U.S. & Pratisha, N., 2022. Psychrozymes: A novel and promising resource for industrial applications. In Microbial Extremozymes: Novel Sources and Industrial Applications. Academic Press, pp.185–195. doi: 10.1016/B978-0-12-822945-3.00018-X.

Basak, P. et al., 2021. Cellulases in paper and pulp, brewing and food industries: Principles associated with its diverse applications. In Current Status and Future Scope of Microbial Cellulases. Elsevier, pp.275–293. doi: 10.1016/B978-0-12-821882-2.00003-X.

Dewiyanti, I. et al., 2024. Analyzing cellulolytic bacteria diversity in mangrove ecosystem soil using 16 svedberg ribosomal ribonucleic acid gene. Global Journal of Environmental Science and Management, 10(1), pp.51–68. doi: 10.22034/gjesm.2024.01.05.

Djekrif, S.D. et al., 2024. Production Optimization, Partial Characterization, and Gluten-Digesting Ability of the Acidic Protease from Clavispora lusitaniae PC3. Fermentation, 10(3), 139. doi: 10.3390/fermentation10030139.

Fouda, A. et al., 2024. Synthesis, Optimization, and Characterization of Cellulase Enzyme Obtained from Thermotolerant Bacillus subtilis F3: An Insight into Cotton Fabric Polishing Activity. Journal of Microbiology and Biotechnology, 34(1), pp.207–223. doi: 10.4014/jmb.2309.09023.

Ghazanfar, M. et al., 2022. Cellulase Production Optimization by Bacillus Aerius Through Response Surface Methodology in Submerged Fermentation. Cellulose Chemistry and Technology, 56(3–4), pp.321–330.

Ghose, T.K., 1987. Measurement of cellulase activities. Pure and Applied Chemistry, 59(2), pp.257–268. doi: 10.1351/pac198759020257.

Jänsch, N. et al., 2019. Using design of experiment to optimize enzyme activity assays. ChemTexts, 5, 20. doi: 10.1007/s40828-019-0095-2.

Juturu, V. & Wu, J.C., 2014. Microbial cellulases: Engineering, production and applications. Renewable and Sustainable Energy Reviews, 33, pp.188–203. doi: 10.1016/j.rser.2014.01.077.

Liu, G. et al., 2023. Whole genome sequencing and the lignocellulose degradation potential of Bacillus subtilis RLI2019 isolated from the intestine of termites. Biotechnology for Biofuels and Bioproducts, 16(1), 130. doi: 10.1186/s13068-023-02375-3.

McKee, L.S., 2017. Measuring Enzyme Kinetics of Glycoside Hydrolases Using the 3,5-Dinitrosalicylic Acid Assay. Methods in Molecular Biology (Clifton, N.J.), 1588, pp.27–36. doi: 10.1007/978-1-4939-6899-2_3.

Naresh, S. et al., 2019. Isolation and Partial Characterisation of Thermophilic Cellulolytic Bacteria from North Malaysian Tropical Mangrove Soil. Tropical Life Sciences Research, 30(1), pp.123–147. doi: 10.21315/tlsr2019.30.1.8.

Neesa, L. et al., 2020. Optimization of Culture Conditions and Reaction Parameters of β-glucosidase From a New Isolate of Bacillus subtilis (B1). Journal of Applied Biotechnology Reports, 7(3), pp.152–158. doi: 10.30491/jabr.2020.109990.

Nwagala, P. et al., 2024. Evaluation of the probiotic ability of Cellulolytic Bacillus subtilis SBMP4 (FSP20) isolated from waste-dump site in FIIRO, Lagos Nigeria. ASFI Research Journal, 1(1), e13512. doi: 10.70040/asfirj-e5vc-e54k.

Onyeogaziri, F.C. & Papaneophytou, C., 2019. A General Guide for the Optimization of Enzyme Assay Conditions Using the Design of Experiments Approach. SLAS Discovery, 24(5), pp.587–596. doi: 10.1177/2472555219830084.

Shah, F. & Mishra, S., 2020. In vitro optimization for enhanced cellulose degrading enzyme from Bacillus licheniformis KY962963 associated with a microalgae Chlorococcum sp. using OVAT and statistical modeling. SN Applied Sciences, 2, 1923. doi: 10.1007/s42452-020-03697-9.

Shajahan, S. et al., 2017. Statistical modeling and optimization of cellulase production by Bacillus licheniformis NCIM 5556 isolated from the hot spring, Maharashtra, India. Journal of King Saud University - Science, 29(3), pp.302–310. doi: 10.1016/j.jksus.2016.08.001.

Singh, V. et al., 2017. Strategies for Fermentation Medium Optimization: An In-Depth Review. Frontiers in Microbiology, 7, 2087. doi: 10.3389/fmicb.2016.02087.

Yu, H. et al., 2019. Integration of Optimal Experimental Design and Process Optimisation to An Enzyme Reaction System. 2019 Chinese Automation Congress (CAC), pp.3615–3620. doi: 10.1109/CAC48633.2019.8997445.

Yu, H., Yue, H. & Halling, P., 2015. Optimal Experimental Design for an Enzymatic Biodiesel Production System. IFAC-PapersOnLine, 48(8), pp.1258–1263. doi: 10.1016/j.ifacol.2015.09.141.