Astaxanthin biosynthesis: A two-step optimization approach and model construction with Response Surface Methodology and Artificial Neural Network

Year 2019, Volume: 3 Issue: 3, 171 - 181, 27.09.2019

Derya Dursun Saydam , Ali Coşkun Dalgıç

https://doi.org/10.31015/jaefs.2019.3.9

Abstract

The
first part of this research is investigating and comparing yield of a synthetic
medium submerged three sugars (glucose, fructose and sucrose) at four different
concentrations and solid fermentation systems with wheat bran and lentil waste
for biosynthesis of astaxanthin (ASX) pigment by Xanthophyllomyces dendrorhous ATCC 24202 and Sporidiobolus salmonicolor ATCC 24259 microorganisms. The second
part is modeling and optimizing the most efficient biosynthesis depending on
waste, yeast and production variables consisted of moisture content, pH and
temperature using a design matrix. The yields produced by X. dendrorhous were 51.88 µg of ASX/g glucose for the submerged
medium with the least glucose, and 210.49 µg of ASX/g glucose for the wheat bran
fermentation system. It was understood that the yield values of the
submerged systems were
lower and there was no requirement for the addition of any
supplement to the waste systems. It was found that R²=0.9869 was
the highest value with the maximum predicted ASX amount of 109.23 µg of ASX/g
wheat bran with X. dendrorhous
using Artificial Neural Network modeling and the moisture content was the most
significant production parameter. 

Keywords

Astaxanthin biosynthesis, Neural network, Optimization, Response Surface Methodology, Submerged system

References

• Aber, A.B., Damtew, W., Emire, S.A. (2012). Evaluation of growth kinetics and biomass yield efficiency of industrial yeast strains. Archives of Applied Science Research, 4 (5), 1938−1948. http://scholarsresearchlibrary.com/archive.html.
• Amorim-Carrilho, K.T., Cepeda, A., Fente, C., Regal, P. (2014). Review of methods for analysis of carotenoids. TrAC Trends in Analytical Chemistry,56, 49−73. https://doi.org/10.1016/j.trac.2013.12.011.
• An, J., Gao, F., Ma, Q., Xiang, Y., Ren, D., Lu, J. (2017). Screening for enhanced astaxanthin accumulation among Spirulina platensis mutants generated by atmospheric and room temperature plasmas. Algal Research, 25, 464–472. https://doi.org/10.1016/j.algal.2017.06.006.
• Ananda, N., Vadlani, P.V. (2011). Carotenoid value addition of cereal products by monoculture and mixed-culture fermentation of Phaffia rhodozyma and Sporobolomyces roseus. Cereal Chemistry, 88, 467–472. https://doi.org/10.1094/CCHEM-04-11-0053.
• Arroyo-López, F.N., Orlić, S., Querol, A., Barrio, E. (2009). Effects of temperature, pH and sugar concentration on the growth parameters of Saccharomyces cerevisiae, S. kudriavzevii and their interspecific hybrid. International Journal of Food Microbiology, 131 (2-3), 120–127.https://doi.org/10.1016/j.ijfoodmicro.2009.01.035.
• Babitha, S., Soccol, C.R., Pandey, A. (2007). Solid-state fermentation for the production of Monascus pigments from jackfruit seed. Bioresource Technology, 98 (8), 1554−1560.https://doi.org/10.1016/j.biortech.2006.06.005.
• Bailey, J.E., Ollis, D.F. (1986). Biochemical Engineering Fundamentals. 2nd ed. McGraw-Hill, Singapore, 984 pages.
• Basri, M., Rahman, R.N.Z.R.A., Ebrahimpour, A., Salleh, A.B., Gunawan, E.R., Rahman, M.B.A. (2007). Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester. BMC Biotechnology, 7 (53), 1–14. http://www.biomedcentral.com/1472-6750/7/53.
• Baş, D., Boyacı, I.H. (2007). Modeling and optimization I: Usability of response surface methodology. Journal of Food Engineering, 78 (3), 836−845. https://doi.org/10.1016/j.jfoodeng.2005.11.024.
• Carlson, M. (1987). Regulation of sugar utilization in Saccharomyces species. Journal of Bacteriology, 169 (11), 4873−4877. doi:10.1128/jb.169.11.4873-4877.1987, PMCID: PMC213879.
• del Rio-Chanona, E.A., Manirafasha, E., Zhang, D., Yue, Q., Jing, K. (2016). Dynamic modeling and optimization of cyanobacterial C-phycocyanin production process by artificial neural network. Algal Research, 13, 7–15. https://doi.org/10.1016/j.algal.2015.11.004.
• Desai, K.M., Survase, S.A., Saudagar, P.S., Lele, S.S., Singhal, R.S. (2008). Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan. Biochemical Engineering Journal, 41 (39), 266–273. https://doi.org/10.1016/j.bej.2008.05.009.
• Dikshit, R., Tallapragada, P. (2015). Screening and optimization of γ-aminobutyric acid production from Monascus sanguineus under solid-state fermentation. Frontiers in Life Sciences, 8 (2), 172–181. https://doi.org/10.1080/21553769.2015.1028654.
• Dong, H., Li, X., Xue, C., Mao, X. (2016). Astaxanthin preparation by fermentation of esters from Haematococcus pluvialis algal extracts with Stenotrophomonas species. Biotechnology Progress, 32 (3), 649–656. https://doi.org/10.1002/btpr.2258.
• Dufossé, L., Galaup, P., Yaron, A., Arad, S.M., Blanc, P., Murthy, K.N.C., Ravishankar, G.A. (2005). Microorganisms and microalgae as sources of pigments for food use: a scientific oddity or an industrial reality? Trends in Food Science & Technology, 16 (9), 389−406.https://doi.org/10.1016/j.tifs.2005.02.006.
• Guo, X., Li, X., Xiao, D. (2010). Optimization of culture conditions for production of astaxanthin by Phaffia rhodozyma, Proceedings of the 4th Bioinformatics and Biomedical Engineering International Conference, IEEE, 18-20 June, Chengdu, China, 1-4, DOI: 10.1109/ICBBE.2010.5516101.
• Gupta, C., Garg, A.P., Prakash, D., Goyal, S., Gupta, S. (2011). Microbes as potential source of biocolours. Pharmacology, 2, 1309−1318.https://pharmacologyonline.silae.it/files/newsletter/2011/vol2/120.gupta.pdf
• Haard, N.F. (1988). Astaxanthin formation by the yeast Phaffia rhodozyma on molasses. Biotechnol Lettetrs, 10 (9), 609−614. https://link.springer.com/article/10.1007/BF01024710.
• Higuera-Ciapara, I., Félix-Valenzuela, L., Goycoolea, F.M. (2006). Astaxanthin: A review of its chemistry and applications. Critical Reviews in Food Science and Nutrition, 46 (2), 185−196. https://doi.org/10.1080/10408690590957188.
• Hu, Z., Zheng, Y., Wang, T.Z., Shen, Y. (2005). Effect of sugar-feeding strategies on astaxanthin production by Xanthophyllomyces dendrorhous. World Journal of Microbiology and Biotechnology, 21, 771–775. DOI10.1007/s11274-004-5566-x.
• Johnson, E.A., Lewis, M.J. (1979). Astaxanthin formation by the yeast Phaffia rhodozyma. Journal of General Microbiology, 115, 173−183. https://doi.org/10.1099/00221287-115-1-173.
• Joshi, V.K., Attri, D., Bala, A., Bhushan, S. (2003). Microbial pigments. Indian Journal of Biotechnology, 2 (3), 362−369. https://pdfs.semanticscholar.org/6d19/ddc53c2ca633f24f6417e392e2c7d0154928.pdf
• Kalil, S.J., Maugeri, F., Rodrigues, M.I. (2000). Response surface analysis and simulation as a tool for bioprocess design and optimization. Process Biochemistry, 35 (6), 539–550. DOI: 10.1016/S0032-9592(99)00101-6.
• Kashkouli, Y.S., Mogharei, A., Mousavian, S., Vahabzadeh, F. (2011). Performance of artificial neural network for predicting fermentation characteristics in biosurfactant production by Bacillus subtilis ATCC 6633 using sugar cane molasses. International Journal of Food Engineering, 7 (6), 1556–3758.https://doi.org/10.2202/1556-3758.1939.
• Lopes, C.A., Rodríguez, M.E., Sangorrín, M., Quero, A., Caballero, A.C. (2007). Patagonian wines: the selection of an indigenous yeast starter. Journal of Industrial Microbiology and Biotechnology, 34 (8), 539–546. DOI: 10.1007/s10295-007-0227-3.
• Maran, J.P., Priya, B. (2015). Modeling of ultrasound assisted intensification of biodiesel production from neem (Azadirachta indica) oil using response surface methodology and artificial neural network. Fuel, 143: 262–267. DOI: 10.1016/j.fuel.2014.11.058.
• Meyer, P.S., du Preez, J.C. (1994). Astaxanthin production by a Phaffia rhodozyma mutant on grape juice. World Journal of Microbiology and Biotechnology, 10 (2), 178−183. DOI: 10.1007/BF00360882.
• Mitchell, D.A., Meien, O.F., Kriger, N., Dalsenter, F.D.H. (2004). A review of recent developments in modeling of microbial growth kinetics and intraparticle phenomena in solid-state fermentation. Biochemical Engineering Journal, 17: 15−26.http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.604.2954&rep=rep1&type=pdf
• Naguib, Y.M.A. (2000). Antioxidant activities of astaxanthin and related carotenoids. Journal of Agricultural and Food Chemistry, 48: 1150−1154. DOI: 10.1021/jf991106k
• Nelofer, R., Ramanan, R.N., Rahman, R.N.Z.R.A., Basri, M., Ariff, A.B. (2012). Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21J. Industrial Microbiology and Biotechnology, 39 (2), 243–254. DOI: 10.1007/s10295-011-1019-3.
• Ni, H., Chen, Q., Ruan, H., Yang-Yuan, F., Li, L., Wu, G., Hu, Y., He, G. (2007). Studies on optimization of nitrogen sources for astaxanthin production by Phaffia rhodozyma. Journal of Zhejiang University Scıence B, 8 (5), 365−370. doi: 10.1631/jzus.2007.B0365
• Nigam PS, Pandey A (2009). Biotechnology for agro-industrial residues utilization. Springer Science+Business Media B.V. https://doi.org/10.1007/978-1-4020-9942-7_2.
• Niizawa, I., Espinaco, B.Y., Leonardi, J.R., Heinrich, J.M., Sihufe, G.A. (2018). Enhancement of astaxanthin production from Haematococcus pluvialis under autotrophic growth conditions by a sequential stress strategy. Preparative Biochemistry and Biotechnology, https://doi.org/10.1080/10826068.2018.1466159.
• Panesar, R., Kaur, S., Panesar, P.S. (2015). Production of microbial pigments utilizing agro-industrial waste: a review. Current Opinion in Food Science, 1, 70−76. DOI: 10.1016/j.cofs.2014.12.002.
• Panis, G., Rosales, Carreon, J. (2016). Commercial astaxanthin production derived by green alga Haematococcus pluvialis: A microalgae process model and a techno-economic assessment all through production line. Algal Research, 18, 175–190. https://doi.org/10.1016/j.algal.2016.06.007.
• Pérez-Guerra, N., Torrado-Agrasar, A., López-Macias, C., Pastrana, L. (2003). Main characteristics and applications of solid substrate fermentation. Electronic Journal of Environmental, Agricultural and Food Chemistry, 2, 343−350. https://www.cabdirect.org/cabdirect/abstract/20053096966.
• Pilkington, J.L., Preston, C., Gomes, R.L. (2014). Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annua. Industrial Crops and Products, 58, 15–24. https://doi.org/10.1016/j.indcrop.2014.03.016.
• Ramírez, J., Nuñez, M.L., Valdivia, R. (2000). Increased astaxanthin production by a Phaffia rhodozyma mutant grown on date juice from Yucca fillifera. Journal of Industrial Microbiology and Biotechnology, 24 (3), 187–190. https://doi.org/10.1038/sj.jim.2900792
• Ramírez, J., Gutierrez, H., Gschaedler, A. (2001). Optimization of astaxanthin production by Phaffia rhodozyma through factorial design and response surface methodology. Journal of Biotechnology, 88 (3), 259−268. https://www.ncbi.nlm.nih.gov/pubmed/11434971.
• Schewe, H., Kreutzer, A., Schmidt, I., Schubert, C., Schrader, J. (2017). High concentrations of biotechnologically produced astaxanthin by lowering pH in a Phaffia rhodozyma bioprocess. Biotechnology and Bioprocess Engineering, 22 (3), 319–326. https://doi.org/10.1007/s12257-016-0349-4.
• Sehrawat, R., Panesar, P.S., Swer, T.L., Kumar, A. (2017). Response surface methodology (RSM) mediated interaction of media concentration and process parameters for the pigment production by Monascus purpureus MTCC 369 under solid state fermentation. Pigment and Resin Technology, 46 (1), 14–20. https://doi.org/10.1108/PRT-08-2015-0077.
• Singh, D., Gupta, A., Wilkens, S.L., Mathur, A.S., Tuli, D.K., Barrow, C.J., Puri, M. (2015). Understanding response surface optimization to the modeling of astaxanthin extraction from a novel strain Thraustochytrium sp. S7. Algal Research, 11, 113–120. doi: 10.1016/j.algal.2015.06.005.
• Singh, N., Goel, G., Singh, N., Pathak, B.K., Kaushik, D. (2015). Modeling the red pigment production by Monascus purpureus MTCC 369 by Artificial Neural Network using rice water based medium. Food Bioscience, 11, 17–22. https://doi.org/10.1016/j.fbio.2015.04.001.
• Shafi J, Sun Z, Ji M, Gu Z, Ahmad W (2018). ANN and RSM based modelling for optimization of cell dry mass of Bacillus sp. strain B67 and its antifungal activity against Botrytis cinerea. Biotechnology and Biotechnological Equipment, 32 (1), 58–68. https://doi.org/10.1080/13102818.2017.1379359.
• Sujarit, C., Rittirut, W., Amornlerdpison, D., Siripatana, C. (2017). Astaxanthin production from sewage of traditional Thai rice Vermicelli. doi:10.1088/1742-6596/820/1/012011.
• Stoklosa, R.J., Johnston, D.B., Nghiem, N.P. (2018). Utilization of sweet sorghum juice for the production of astaxanthin as a biorefinery co-product by Phaffia rhodozyma. ACS Sustainable Chemistry and Engineering, 6 (3), 3124−3134. https://doi.org/10.1021/acssuschemeng.7b03154.
• Valduga E, Valério A, Treichel H, Di Luccio M, Furigo AJ (2008). Study of the bio-production of carotenoids by Sporidiobolus salmonicolor (CBS 2636) using pre-treated agro-industrial substrates. Journal of Chemical Technology and Biotechnology, 83, 1267–1274. https://doi.org/10.1002/jctb.1940.
• Valduga, E., Valério, A., Treichel, H., Furigo Júnior, A., Di Luccio, M. (2009). Kinetic and stoichiometric parameters in the production of carotenoids by Sporidiobolus salmonicolor (CBS 2636) in synthetic and agroindustrial media. Applied Biochemistry and Biotechnology, 157, 61−69. DOI 10.1007/s12010-008-8383-0.
• Visser, H., Ooyen, A.J.J., Verdoes, J.C. (2003). Metabolic engineering of the astaxanthin-biosynthetic pathway of Xanthophyllomyces dendrorhous. FEMS Yeast Research, 4, 221−231. DOI: 10.1016/S1567-1356(03)00158-2.
• Wei, P., Si, Z., Lu, Y., Yu, Q., Huang, L., Xu, Z. (2017). Medium optimization for pyrroloquinoline quinine (PQQ) production by Methylobacillus sp. zju323 using response surface methodology and artificial neural network–genetic algorithm. Preparative Biochemical and Biotechnology, 47 (7), 709–719. DOI: 10.1080/10826068.2017.1315596.
• Zou, T.B., Jia, Q., Li, H.W., Wang, C.X., Wu, H.F. (2013). Response surface methodology for ultrasound-assisted extraction of astaxanthin from Haematococcus pluvialis. Marine Drugs, 11 (5), 1644–1655. DOI: 10.3390/md11051644.