Optimization of Physical Parameters for Phycobiliprotein Extracted from Oscillatoria Agardhii and Synechococcus Nidulans

Turkish Journal of Biochemistry – Türk Biyokimya Dergisi 2015; 40(6): 481–491

Bioingeniería Research Article – 33600

İrem Deniz, Esra İmamoğlu*, Meltem Conk Dalay Optimization of physical parameters for phycobiliprotein extracted from Oscillatoria agardhii and Synechococcus nidulans

Oscillatoria agardhii ve Synechococcus nidulans türlerinden fikobiliprotein ekstraksiyonu için fiziksel parametrelerin optimizasyonu

Abstract: Objective: Physical process parameters play a growth than increased the light intensity for the growths major role in the cultivation of cyanobacteria to provide of cyanobacterial strains. high yield. The aim of this study was to optimize physical parameters such as light intensity and agitation rate Keywords: Agitation, cyanobacteria, light, optimization, which might affect the phycobiliprotein formations for phycobiliprotein, bioprocess design cyanobacterial strains of Oscillatoria agardhii and Syn- echococcus nidulans using response surface methodology. Özet: Amaç: Siyanobakteri kültivasyonunda yüksek verime ulaşmak için fiziksel proses parametreleri ana rolü oyna- Methods: The cyanobacterial strains were cultured in 250 maktadır. Bu çalışmada amaç, yanıt yüzey metodu ile Oscil- mL flasks containing 100 mL of EM medium in orbital latoria agardhii ve Synechococcus nidulans türlerinin fiko- shaking incubator under the temperature of 22±2°C at dif- biliprotein oluşumunu etkileyen ışık şiddeti ve çalkalama ferent light intensities and agitation rates for 10 days. The hızı gibi fiziksel proses parametrelerini optimize etmektir. experimental design was carried out using 22 full-factorial experiments design with four axial points (α=1.414) and Metod: Siyanobakteri türleri 250 ml’lik erlenlerde 100 mL five replicates at the central point (65 μmol photons m-2s-1 EM ortamında 22±2°C sıcaklığında farklı ışık şiddetleri ve and 150 rpm), according to the central composite design. karıştırma hızlarında 10 günlük periyotlarda kültive edil- miştir. Deney tasarımı, 22 faktöriyel kullanılarak CCD ile Results: The optimization solution of O. agardhii (approx- gerçekleştirilmiştir. imately at 156 rpm under the light intensity of 65 μmol photons m-2s-1) was selected because it resulted in the Bulgular: O. agardhii için en yüksek tahmini cevap ile highest predicted response with the highest desirabil- üretimin 156 rpm’de 65 μmol photons m-2s-1 ışık şiddetinde ity. Furthermore, the optimization solution for S. nidu- olması gerektiği belirlenmiştir. Diğer taraftan, S. nidulans lans suggested the phycobiliprotein amount of 9.95 mg/L optimizasyon çözümü 9.95mg/L fikobiliprotein miktarına obtaining at the agitation rate of 185 rpm under the light ulaşmak için üretimin 185 rpm’de 46 μmol fotons m-2s-1 ışık intensity of 46 μmol photons m-2s-1. The optimized results şiddetinde gerçekleştirilebileceğini göstermiştir. were reliable and the regions studied were proven to be statistically adequate. Sonuç: Yüksek çalkalama hızı, yüksek ışık şiddetine kıyasla siyanobakteri türlerinin daha hızlı üremesini tetik- Conclusion: High agitation rate stimulated the faster lemiştir.

*Corresponding author: Esra İmamoğlu: Ege University, Faculty of Bioengineering, Izmir, Turkey, e-mail: [email protected] Engineering, Department of Bioengineering, 35100 Bornova, Izmir, Meltem Conk Dalay: Ege University, Faculty of Engineering, Turkey, e-mail: [email protected] Department of Bioengineering, Izmir, Turkey, İrem Deniz: Ege University, Faculty of Engineering, Department of e-mail: [email protected] 482 İrem Deniz et al.: Phycobiliprotein extraction from cyanobacteria

Anahtar Kelimeler: Çalkalama, siyanobakteri, ışık, opti- the response function within the range of investigation. It mizasyon, fikobiliprotein, biyoproses tasarımı is a useful statistical technique which used to study the complex variable processes, build models, evaluate the

doi 10.1515/tjb-2015-0039 effects of factors and search the optimum conditions for Received May 21, 2015; accepted August 18, 2015 desirable responses function [13]. It can reduce the number of experimental runs and supply sufficient information for a statistically acceptable result. RSM has been success- fully applied in many researches, which has become more Introduction and more attractive in process optimization [14]. Light plays a major role in microalgal cultivation. The importance of microalgal biotechnology has under- Growth rate of microalgae increase directly proportionally gone a huge leap in recent years. Among the 30.000 with increasing light intensity at optimal intensities, up species of microalgae on Earth, many of them are known till saturation levels. Further, increases in light intensity to contain a variety of high-value bio-products that can cause inhibition of cellular growth [15,16]. It is import- be commercially harnessed, such as biodiesel-convertible ant to know the effect of the agitation rate on microal- neutral lipids, different isomers of carotenoids, polysac- gae. At high agitation rate, it could not only be impairing charides, polyunsaturated fatty acids, and phycobilipro- cell growth but it could be damaging in other ways as by teins [1,2]. Cyanobacteria or blue-green algae occur world- causing the leakage of important chemicals from within wide especially in calm, nutrient-rich waters. There are the cell [17]. more than 150 different types of cyanobacteria. Oscillato- The main target of this study was to optimize physi- ria is a cyanobacteria which is named for the oscillation cal parameters such as light intensity and agitation rate in its movement. Oscillatoria spp. are the most commonly which might affect the phycobiliprotein formations for found cyanobacteria saltwater bodies [3]. Synechococcus cyanobacterial strains of Oscillatoria agardhii and Syn- sp. is a microalga that belongs to the cyanophyceae or echococcus nidulans by central composite design (CCD) blue-green algae group. This is a photosynthetic prokary- using response surface methodology (RSM). Furthermore, otic organism with chlorophyll-a and is closer in charac- the biomass concentrations and chlorophyll-a amounts ter to other photosynthetic bacteria than eukaryotic algae were evaluated for the cultivations of O. agardhii and S. and, as such, is classified as cyanobacteria [4]. Synechoc- nidulans. To our knowledge, this is the first report describ- occus spp. are important components of the marine micro- ing the correlations of light intensity and agitation rate bial food web [5]. and their effect on the phycobiliprotein production for O. Blue green algae have photosynthetic reaction centers agardhii and S. nidulans. that are structurally and functionally similar to those found in eukaryotic chloroplasts, but their light-harvesting pigments are composed of chlorophyll-a (Chl-a) and the phycobiliproteins (PBP)s [6,7]. Phycobiliproteins are Materials and Methods a family of protein with covalently attached linear tetra- pyrrole prosthetic groups. The main application of phyco- Isolation and maintenance of cyanobacterial biliproteins is as fluorescent markers of cells and macro- strains molecules in biomedical research and in highly sensitive fluorescent techniques [3,8]. More recent studies revealed The cyanobacterial strains of O. agardhii and S. nidulans that some of these growth promoting substances in the were isolated from Acigol Lake, Denizli, Turkey located dialyzate from Synechococcus are phycobiliproteins: phy- geographically between 37°48’39” North latitude and cocyanin and allophycocyanin [9]. The cyanobacterial 29°42’25” East longitude. The taken sample (1 mL) was phycocyanin (CPC) is a blue colour red fluorescing bilip- inoculated into 9 mL sterilized Erdschreiber’s medium rotein and it was first reported in 1928 by Lemberg [10]. (EM) in 15 mL tube. The tube was incubated for 7 days at The cost of food grade phycocyanin (purity higher than 25°C at the light intensity of 30 µmol photons m-2s-1. The 0.7) is around 0.13 US$ mg-1, whereas the cost of analytical isolation was accomplished by streaking the natural grade (purity higher than 4.0) can be as high as 15 US$ sample across the agar surface. The isolated colonies were mg-1 [11,12]. picked up from the agar plate with disposable loop and Response surface methodology (RSM) was usually then both re-streaked on a new agar plate and rinsed in used to explore the effect of independent variables on liquid appropriate medium to free the cells. The isolates İrem Deniz et al.: Phycobiliprotein extraction from cyanobacteria 483

Table 1: Experimental range and levels of the independent variables.

Independent variables Symbol coded Coded levels

-α -1 0 +1 +α

Agitation rate (rpm) X1 100 115 150 185 200 -2 -1 Light intensity (μmol photons m s ) X2 30 40 65 90 100 were incubated at 25°C at the light intensity of 40 µmol was extracted with 100% (v/v) methanol. The amount of photons m-2s-1 in 250 mL flasks for 14 days. The isolated chlorophyll-a was determined spectrophotometrically by strains of O. agardhii and S. nidulans were joined to Ege measuring the light absorption at different wavelengths University Microalga Culture Collection (EGE MACC) and of 665 and 750 nm [19]. coded with EgeMacc-014 and EgeMacc-007 respectively. For phycobiliprotein extraction, 5-mL culture sample Stock cultures were monoalgal (non-axenic) and culti- was filtered through 0.45-μm acetate filters. The filtrate vated in Erdschreiber’s medium (EM) [18] at 22±2°C under was digested in the dark in 5 mL of 5 mM Na2-PO4 buffer continuous illumination (75 µmol photons m-2s-1) in 2-L (pH 7). During the process of extraction, cyanobacterial sterile bottle for 22 days. For the preparation of the inoc- cells were lysed by freezing at –20°C and thawing at room ulum, the cells from the stock culture were collected and temperature. Phycobiliproteins were released by disrupt- concentrated by centrifugation (3500 rpm, 2 min) and the ing the cell wall using a sonicator (Bandelin Electronic supernatant was removed. The collected cells were trans- UW 2070, Berlin, Germany) for 3 min at 0.8 sec intervals. ferred, incubated aseptically in 250 mL flasks containing The dismembrator had a maximum power output of 180 100 mL of EM medium under the light intensity of 40 µmol W and was operated at a constant frequency of 20 kHz. photons m-2s-1 with the agitation rate of 120 rpm at 22±2°C Sonication was carried out in an ice-water bath to avoid for four days. Four-day-old cultures were used as inocu- the increase of temperature that could affect the cell via- lum at 10% volume for all experiments. bility and the product quality. The liquid was then centri- fuged at 2500 rpm for 5 min. Finally, the supernatant was obtained as a crude extract of phycobiliproteins and the Growth conditions for cyanobacterial strains supernatant absorbances at 620 nm, 680 nm and 750 nm were measured by UV/VIS spectrophotometer (GE Health- The cyanobacterial strains were cultured in 250 mL flasks care Ultrospec 1100 pro, UK). Phycobiliprotein (PBP) and containing 100 mL of EM medium in orbital shaking incu- C-Phycocyanin (C-PC) quantifications were calculated as bator under the temperature of 22±2°C at different light reported by Boussiba and Richmond [20]. intensities and agitation rates for 10 days. Illumination The specific growth rate (µ) of the cells was calculated was provided by LED downlight lamp (Cata 10 W CT-5254) from the initial logarithmic phase of growth for at least 48 from the top of the orbital shaking incubator. Irradiance h, as µ=(lnC2-lnC1)/dt, where C2 is the final cell concentra- was measured in the center of the flask with a quantum tion, C1 is the initial cell concentration and dt is the time meter (Lambda L1-185). required for the increase in concentration from C1 to C2. Doubling time (DT) was also calculated as DT=ln 2/µ.

Analytical procedures Experimental design and data analysis Samples were taken at indicated times, and following growth parameters were measured immediately. Biomass The experimental design was carried out using 22 full-fac- concentration was determined by filtering the aliquots torial experiments design with four axial points (α=1.414) on pre-weighed GF/C filter paper (Whatman, UK). The and five replicates at the central point (65 μmol photons filtered cells were dried at 105°C until constant weight m-2s-1 and 150 rpm), according to the central composite was obtained and were cooled to room temperature in a design (CCD) by response surface methodology (RSM) desiccator before weighing. Biomass concentration was using the Design Expert software (version 7.0.0, Stat-Ease used as indicator for the growth rates of cyanobacterial Inc., Minneapolis, MN). The range and the levels of the strains. For the chlorophyll measurements, cells were process variables are given in Table 1. 5 different agitation harvested at 3500 rpm for 3 min. Chlorophyll in the cells rates; X1-rpm (100, 115, 150, 185, 200) and 5 different light 484 İrem Deniz et al.: Phycobiliprotein extraction from cyanobacteria

Table 2: Experimental design matrix and the experimental results of X1 and X2 are the coded levels of independent variables. cyanobacterial strains. 2 The terms X1 X2 and Xi (i=1 or 2) represent the interaction and quadratic terms, respectively. Equation 1 expresses Runs X X O. agardhii S. nidulans 1 2 the relationship between the predicted response and the Phycobiliprotein Phycobiliprotein independent variables in coded values. The quality of (mg/L) (mg/L) developed model was determined by the value of correla- 1 150 100 8.98 5.61 tion (R2) while analysis of variance (ANOVA) was used to 2 185 40 9.1 9.28 evaluate the statistical significance of the model by the 3 185 90 8.93 6.78 4 150 65 15.5 8.41 values of regression and mean square of residual error. 5 150 65 18.67 8.71 6 115 90 5.73 3.32 7 100 65 14.69 5.11 8 150 65 19.95 8.63 Results and Discussion 9 150 30 8.79 8.94 10 150 65 17.11 7.49 A set of experiments were designed by central composite 11 200 65 15.97 10.33 design using response surface methodology and evalu- 12 150 65 21.55 8.08 ated the influence of physical process variables (agitation 13 115 40 6.78 5.76 rate and light intensity) for phycobiliprotein amounts of O. agardhii and S. nidulans. The experimental design and -2 -1 intensities; X2-μmol photons m s (30, 40, 65, 90, 100) the results obtained in the experiments for cyanobacterial were tested as physical variables. These variables have strains are given in Table 2. As shown in Table 2, the phyco- been considered as factors that may potentially affect the biliprotein amounts for O. agardhii and S. nidulans ranged response function: phycobiliprotein amount (Y1, mg/L). A from 5 to 22 mg/L and from 2 to 11 mg/L, respectively, total of 13 runs were used to optimize the range and levels depending on the physical conditions of experiments. of the chosen variables. Each run had been completed in 10 days. All experiments were performed in triplicates and their average values were reported. Response surface methodology for The mathematical relationship of the response of phycobiliprotein extracted from O. agardhii these variables can be approximated by quadratic (second degree) polynomial equation; A number of process parameters play a critical role on phy- 2 2 Y=β0+β1X1+β2 X2+β12X1X2+β11X1 +β22X2 (1) cobiliprotein production, such as biological, chemical and where Y represents the response variable, β0 is model con- physical factors including the type of organism, inoculum stant, β1 and β2 are linear coefficients, β12 is interaction level, substrate composition, pH, temperature, agitation, effect coefficient and β11 and β22 are quadratic coefficients, light and etc. In this study, agitation rate and light intensity

Table 3: Analysis of variance (ANOVA) of the model for phycobiliprotein amount of O. agardhii.

Source Sum of squares Degree of freedom Mean square F-value p>F

Model 280.64 5 56.13 6.11 0.0172

X1: Agitation 6.72 1 6.72 0.73 0.4210

X2: Light intensity 0.11 1 0.11 0.012 0.9148

X1X2 0.19 1 0.19 0.021 0.8887 2 X1 51.89 1 51.89 5.64 0.0492 2 X2 246.58 1 246.58 26.82 0.0013 Residual 64.35 7 9.19 Lack of fit 42.00 3 14.00 2.51 0.1980 Pure error 22.35 4 5.59 Cor. total 344.99 12 Std. dev. 3.03 R-Squared 0.8135 Mean 13.21 Adj. R-Squared 0.6802 C.V.% 22.95 Pred. R-Squared 0.0330 Press 333.62 Adeq. precision 5.862 İrem Deniz et al.: Phycobiliprotein extraction from cyanobacteria 485

Table 4: Analysis of variance (ANOVA) of the model for phycobiliprotein amount of S. nidulans.

Source Sum of squares Degree of freedom Mean square F-value p>F

Model 43.42 5 8.68 15.47 0.0012

X1: Agitation 25.78 1 25.78 45.64 0.0003

X2: Light intensity 11.64 1 11.64 20.60 0.0027

X1X2 9.000E-004 1 9.000E-004 1.593E-003 0.9693 2 X1 2.30 1 2.30 4.07 0.0833 2 X2 4.43 1 4.43 7.83 0.0266 Residual 3.95 7 0.56 Lack of fit 2.97 3 0.99 4.01 0.1066 Pure error 0.99 4 0.25 Cor. total 47.37 12 Std. dev. 0.75 R-Squared 0.9165 Mean 7.42 Adj. R-Squared 0.8569 C.V.% 10.13 Pred. R-Squared 0.5221 Press 22.64 Adeq. precision 11.756 as physical factors play a dynamic role in the stimulation model. The adjusted determination coefficient represents of phycobiliprotein amounts of cyanobacterial strains. The the high significance of the model [24]. Furthermore, the statistical testing of the model was done by Fisher’s F test adjusted R-squared value increases if unnecessary terms for analysis of variance (ANOVA) as shown in Table 3. The are deleted [26,27]. In this study, the adjusted R-squared quality of fit explained by the model was given by the mul- value was 0.6802, indicating a need for the improvement tiple coefficient of determined R squared (R2) value, a good in the model by the elimination of the non-significant coefficient value accepted for biological sample was R2>0.7 model terms. [21,22]. The regression coefficient, R2 of 0.8135 indicated The F-value of 6.11 and p value was less than 0.05, that the regression model represented 81.35% of the exper- both implying that the quadratic model was significant. imental results, representing a good fit of the response. Moreover, the ‘‘Lack of fit F-value” of 2.51 implied that the By adding factors to the model, the R-squared value ‘‘Lack of fit” was not significant relative to the pure error. (R2) always increases whether the added factor is signif- There was a 19.80% chance for the model that a ‘‘Lack of icant or not [23–25]. Generally, the adjusted R-squared fit F-value” of this large could occur due to noise. A qua- value (Adj. R2) value does not increase as factors are added dratic polynomial equation for physical process condi- to the model. In fact, large differences between R2 and Adj. tions of O. agardhii was made by using coded values, as R2 indicate that non-significant terms are involved in the given in Equation 2:

Table 5: Results of obtaining kinetic parameters of different cyanobacterial strains for each experimental run.

O. agardhii S. nidulans

Runs Biomass concentration Specific growth rate Doubling time Biomass concentration Specific growth rate Doubling time (g/L) (µ, day-1) (D.T. day) (g/L) (µ, day-1) (D.T. day)

1 2.07±0.09 0.09 7.45 2.35±0.39 0.10 7.05 2 3.57±0.08 0.11 6.53 3.69±0.11 0.13 5.32 3 2.71±0.08 0.09 7.53 2.00±0.16 0.09 7.95 4 5.38±0.64 0.15 4.49 2.03±0.58 0.09 7.33 5 5.61±0.08 0.16 4.34 1.84±0.42 0.09 7.88 6 3.29±0.20 0.09 7.21 1.54±0.15 0.09 7.70 7 3.11±0.32 0.09 7.81 1.27±0.31 0.10 7.29 8 5.51±0.57 0.16 4.41 2.31±0.29 0.08 8.33 9 4.73±0.37 0.14 4.96 2.81±0.05 0.10 6.72 10 4.67±0.20 0.14 5.01 1.48±0.35 0.08 8.43 11 4.48±0.36 0.13 5.20 3.20±0.28 0.12 5.99 12 4.35±0.23 0.13 5.33 1.37±0.28 0.11 6.57 13 2.46±0.38 0.09 8.08 0.57±0.07 0.07 8.35 486 İrem Deniz et al.: Phycobiliprotein extraction from cyanobacteria

Table 6: Optimum conditions for maximum phycobiliprotein amount of cyanobacterial strains.

Factors-responses Goal Lower limit Upper limit Opt. solution Opt. solution Desirability O. agardhii S. nidulans

Agitation rate, X1, (rpm) Is in range 115 185 155.85 185

Light intensity, X2, Is in range 40 90 64.82 45.88 (μmol photons m-2s-1) PBP (mg/L) Maximize 5.73 21.55 18.63 0.816 O. agardhii PBP (mg/L) Maximize 3.32 10.33 9.95 0.946 S. nidulans

Point prediction

Response Prediction SE Mean 95% CI low 95% CI high SE Pred 95% PI low 95% PI high

PBP (mg/L), O. agardhii 18.63 1.35 15.44 21.82 3.32 10.79 26.48 PBP (mg/L), S. nidulans 9.95 0.50 8.76 11.14 0.90 7.81 12.09

2 2 Y=18.56+0.92X1-0.12X2+0.22X1X2-2.73X1 -5.95X2 (2) agardhii. Different works have been aimed to the selec- where Y is the predicted response; phycobiliprotein (mg/L), tive extraction and analysis of the phycobiliproteins from and X1 and X2 are the coded values of the test variables; algae, such as Herrero et al. [28], Simó et al. [29] and agitation rate (rpm) and light intensity (μmol photons m-2s- Herrero et al. [30] In general, phycobiliproteins are made 1 ), respectively. The coefficient of X1 was larger than that up of chromophore-bearing polypeptides containing α of X2 regardless of linear or quadratic terms in Equation and β subunits, which have a molecular weight of around 2, demonstrating that the agitation rate was a dominant 20 kDa [5,31]. Oi et al. [32] have proposed to use cyano- factor in obtaining high phycobiliprotein amount for O. bacterial antenna pigments, the biliproteins, for labeling agardhii, followed by the interaction between agitation because of their water solubility; this approach has proven rate and light intensity. useful for external labeling using either natural bichromo- As shown in Figure 1a, the response surface 3D plots phoric systems or non-covalent interactions between two indicates the effect of interaction between agitation rate chromoproteins [33,34]. and light intensity (varying from 100–200 rpm and 40–100 μmol photons m-2s-1, respectively) on the phycobiliprotein amount of O. agardhii. The shape of the response surface Response surface methodology for also shows a moderate interaction between the two factors. phycobiliprotein extracted from S. nidulans A weak effect on the response was observed for the agitation rate of 115 rpm at the maximum and minimum levels One of the most important requirements in obtaining of the light intensity. In this figure, the second order effect phycobiliproteins from cyanobacteria is achieving an of both terms was clearly observed. The phycobiliprotein optimum cultivation process. For the physical cultivation amount increased with decreasing light intensity from 90 process parameters of S. nidulans, analysis of variance to 65 μmol photons m-2s-1 within the studied range of agita- (ANOVA) was used to analyze the responses as defined tion rate. As predicted by the model, the maximum phyco- by the design, as shown in Table 4. Regression analy- biliprotein amount was occurred when the light intensity sis revealed a coefficient of determination (R2) value of was 65 μmol photons m-2s-1 at the agitation rate of 156 rpm. 0.9165, indicating that the sample variation of only 8.35% In addition, higher and lower levels of both agitation rate of the total variation was not explained by the model. The and light intensity did not result in higher phycobilipro- adjusted determination coefficient (Adj. R2=0.8569) was tein amount, as shown by the contour plot (Figure 1b). also high, implying that the model had high significance. However, the trend changes completely when the interme- The model resulted in an F-value of 15.47 with a low diate agitation rate and light intensity were utilized in the p-value (0.0012) for phycobiliprotein amount, implying region in which a large response was predicted. The prom- that the model was adequate for the response variables inent interaction between these variables was evident that were tested. The associated p value was used to judge from the elliptical nature of the contour plot. whether F was large enough to indicate statistical signif- It is important to note that agitation rate plays an icance or not [35]. With very low p-value (0.0001) from important role on phycobiliprotein formation for O. the analysis of variance, the second-order polynomial İrem Deniz et al.: Phycobiliprotein extraction from cyanobacteria 487

(a) (b) 90.00 10.2854 11.9547 13.624 22 X1=A: Agitation X2=B: Light 17.75 77.50 13.5

9.25

PBP ( O sc.) 65.00 5 B: Light 15.2933 185.00 16.9627 167.50 52.50 A: Agitation 150.00 11.9547 13.624 132.50 40.00 10.2854 11.9547 65.00 52.50 40.00 115.00 90.00 77.50 115.00 132.50 150.00 167.50 185.00 B: Light A: Agitation

Figure 1: 3D response surface plot (a) and contour line (b) of central composite design showing the mutual effects of agitation rate (rpm) and light intensity (μmol photons m-2s-1) on phycobiliprotein amount (mg/L) of O. agardhii. model is highly significant and sufficient to represent the variables; agitation rate (rpm) and light intensity (μmol actual relationship between the response function and photons m-2s-1), respectively. This quadratic equation the independent variables [14]. Furthermore, the lack of could be used for predicting response at any combination fit F-value of 0.1066 implied that lack of fit was not signif- of two variables in the experimental range. icant in relation to pure error. A very high degree of pre- With the help of Design Expert 7.0.0, the model graph cision and a good deal of reliability of the experimental of the response for S. nidulans was established in Figure values were indicated by a low value of the coefficient of 2a and convex response surface was found. It could be variation (C.V.=10.13%). seen that both terms followed an almost linear trend. At

According to the model, agitation rate (X1) and light the lowest light intensity, an increase in the agitation rate intensity (X2) were significant model terms. The linear term enhanced the phycobiliprotein amount. The observed phe- of X1 was highly significant (p<0.01) and X2 was significant nomenon occurred as increasing the agitation rate tended (p<0.05). On the other hand, the interaction coefficient to induce phycobiliprotein formation and the increase of term of X1X2 was insignificant (p>0.05), indicating the less light intensity resulted in a negative impact on phycobilip- effect of the interaction between the two factors on phyco- rotein formation. Two-dimensional contour plot for the biliprotein amount. A second-order polynomial equation effect of the interaction variables on phycobiliprotein was was obtained by applying multiple regression analysis in generated in present study to determine the interaction terms of coded factors for S. nidulans, as follows: among these two factors and their optimum concentration 2 2 Y=8.26+1.80X1-1.21X2-0.015X1X2-0.58X1 –0.80X2 (3) values. From Figure 2b, it could be found that the elliptical where Y is the predicted response; phycobiliprotein shape was not obvious as Figure 1b, which indicated that

(mg/L), and X1 and X2 are the coded values of the test the interaction of agitation rate and light intensity was not

(a) (b) 90.00 10.4 4.91257 X1=A: Agitation X2=B: Light 5.92033 8.625 77.50 6.92808 7.93584 6.85 65.00 5.075 8.9436 B: Light PBP (Syn.) 3.3 52.50 185.00 167.50 40.00 150.00 52.50 40.00 A: Agitation 65.00 115.00 132.50 150.00 167.50 185.00 132.50 77.50 115.00 90.00 B: Light A: Agitation

Figure 2: 3D response surface plot (a) and contour line (b) of central composite design showing the mutual effects of agitation rate (rpm) and light intensity (μmol photons m-2s-1) on phycobiliprotein amount (mg/L) of S. nidulans. 488 İrem Deniz et al.: Phycobiliprotein extraction from cyanobacteria

(a) 7 phycocyanin B. The high yield of energy transfer from O. agardhii the phycobilisomes to the small amount of chlorophyll 6 S. nidulans in photosystem II requires that allophycocyanin B be pre- 5 cisely located on the photosynthetic lamallae with respect 4 to this chlorophyll [37].

3 2 Evaluation of the cultivation conditions of

Biomass concentration (g/L) concentration Biomass 1 cyanobacterial strains

0 1 2 3 4 5 6 7 8 9 10 11 12 13 The effects of different physical conditions on the growths Run of O. agardhii and S. nidulans were simultaneously inves- (b) 6 tigated for 10 days of each experimental run. As shown O. agardhii S. nidulans in Figure 3a, the maximum biomass concentration of 5 5.61±0.08 g/L, which corresponded to the specific growth −1 4 rate of 0.16 day , was obtained in EM medium under the light intensity of 65 µmol photons m-2s-1 at the agitation rate 3 of 150 rpm on the 10th day of the fifth run of O. agardhii cultivation, whereas the biomass concentration of S. nidulans 2 was 3.05 times lower compared to the biomass concentra- Chlorophyll-a (mg/L) Chlorophyll-a 1 tion of O. agardhii under the same physical conditions. On the other hand, biomass concentration reached the 0 1 2 3 4 5 6 7 8 9 10 11 12 13 maximum level of 3.69±0.11 g/L, which corresponded to Run the specific growth rate of 0.13 day−1 at 185 rpm under the (c) 3 light intensity of 40 µmol photons m-2s-1 for the second run O. agardhii S. nidulans of S. nidulans cultivation (Table 5). It should be noted here that the high agitation rate stimulated faster growth than 2 increased light intensity for the cultivation of S. nidulans. As reported by Van Liere and Mur [38], the maximum specific growth rate of 0.003 h-1 (0.072 day–1) was obtained C-PC (%) C-PC under the temperature of 20°C with the pH value of 8.0 at 1 an irradiance of 0.5 Wm–2 for O. agardhii. At low irradiance, cyanobacteria had a higher growth rate, as compared with green algae. The biomass productivity of 124.0±3.2 mgL− 0 1 −1 −1 −1 1 2 3 4 5 6 7 8 9 10 11 12 13 day and lipid productivity of 35.9±0.5 mgL day for Syn- Run echococcus sp. were obtained under the light intensity of -2 -1 150 µmol m s with Na2CO3 concentration of 1.5 g/L [39]. Figure 3: Effect of each experimental run on the growths and metabo- As shown in Figure 3b, the amount of chlorophyll-a lite productions of cyanobacterial strains: (a) Biomass concentration (between the values of 2 and 3.5 mg/L) of S. nidulans was (g/L), (b) Chlorophyll-a amount (mg/L), (c) C-Pycocyanin amount (%). more than of O. agardhii, in general. The maximum chlorophyll-a amount of 3.17±0.02 mg/L was found in EM medium significant when the phycobiliprotein amounts were con- at the agitation rate of 100 rpm under the light intensity of sidered. 65 µmol photons m-2s-1 for S. nidulans, which indicated that It is also important to underline that light has pro- cells could adjust well to the growth conditions. The lowest found quantitative and qualitative effects on phycobilip- chlorophyll-a amount (1.61±0.03 mg/L) was obtained at the rotein formation. When the cellular phycobiliprotein level agitation rate of 150 rpm under 30 µmol photons m-2s-1 for becomes too low to maintain photosynthetic activity, the O. agardhii. This was due to the fact that at low light inten- light-harvesting role of these pigments is assumed by sities, less photosynthesis could occur. chlorophyll-a [36]. The pathway of energy transfer from The growth of Oscillatoria quadripunctulata was the extralamellar phycobilisomes to the intralamellar studied by inoculating 1 g of wet cell mass to the freshly chlorophyll-a of photosystem II (PSII) continues via allo- prepared ASN III medium and incubating in 12-h light/12-h İrem Deniz et al.: Phycobiliprotein extraction from cyanobacteria 489

25 Process optimization for cyanobacterial strains 20 In the optimization stage, the physical process variables 15 (agitation rate and light intensity) were set within the range between low (-1) and high (+1) and the response was

PBP (mg/L) 10 set to the maximum value. The constraints used for each variable of the optimization study are shown in Table 6 5 coupled with the point predictions for cyanobacterial strains. The optimization solution of O. agardhii (approx- 0 imately at the agitation rate of 156 rpm under the light 0 2 4 6 8 10 intensity of 65 μmol photons m-2s-1) was selected because it Time (day) resulted in the highest predicted response with the highest desirability. Furthermore, the optimal phycobiliprotein Figure 4: Phycobiliprotein (PBP; mg/L) profile under optimum condi- amount of S. nidulans was in agreement with the predicted tions. (□) O. agardhii, (Δ) S. nidulans. value, with a relative desirability of 0.95, meaning that the model showed high desirability. dark cycles under 36 W white fluorescent lamp illumina- To verify the predicted results, validation experiment tion (130 µmol photons m-2s-1) at 27±2°C and the maximum was performed in triplicate tests. Validations under the chlorophyll-a amount of 200 µg/mL was obtained on optimized conditions were performed in a 250-mL Erlen- the 26th day of cultivation period [3]. As reported by meyer flask containing 100 mL EM medium (Figure 4). Macias-Sanchez et al. [4], the most appropriate operat- Under optimum conditions, the predicted phycobilip- ing conditions to obtain the best yield in the extraction of rotein amount of O. agardhii was 18.63 mg/L, while the chlorophyll-a (1 µg Chl-a/mg cell) were found under the experimental result was found as 19.95±0.57 mg/L, indi- pressure of 500 bar at 60°C using the process of supercriti- cating that the optimized results were reliable. For the cal carbon dioxide extraction for Synechococcus sp. phycobiliprotein amount of S. nidulans, the results exhibit 1.87% phycocyanin per dry matter was reached at 150 that 9.23±0.71 mg/L phycobiliprotein amount was close to rpm under 65 µmol photons m-2s-1 for O. agardhii, whereas the predicted value (9.95 mg/L) at the agitation rate of 185 1.54% phycocyanin per dry matter was obtained at 100 rpm under the light intensity of 46 μmol photons m-2s-1. As rpm under 65 µmol photons m-2s-1 for S. nidulans (Figure a result, the data could be accurately fitted the model and 3c). In general for this study, the phycocyanin content the model could be used for the prediction of the variables of O. agardhii was richer than that of S. nidulans. It was within the given ranges. also recorded that no significant increase in phycocyanin content was observed at each experimental run. It is also worth noting that the light intensity is the main factor affecting the pigment concentration in cyanobacteria Conclusion cultivations. In another study, 20 mg pure phycocyanin was The RSM was applied to assess the effect of agitation rate obtained from 10 g of Oscillatoria quadripunctulata cells and light intensity on the phycobiliprotein amounts for using the extraction of repeated freeze–thaw cycles. Oscil- cyanobacterial strains. The values of two variables for O. latoria sp. culture has the ability to produce high amount agardhii, agitation rate (156 rpm), and light intensity (65 of C-PC when grown under normal white light, suggesting µmol photons m-2s-1), were found to be optimum for the it to be a good candidate for the production and purifica- phycobiliprotein amount with a satisfied desirability. The tion of phycocyanin [3]. As reported by Gupta and Sainis optimal physical process conditions were found at 185 [40], the yield of phycocyanin was 80–100 mg g−1 dry rpm under the light intensity of 46 μmol photons m-2s-1 weight of cells extracted from the cyanobacterium Syn- with the phycobiliprotein amount of 9.95 mg/L for S. nid- echococcus sp. (Anacystis nidulans BD1). Despite its low ulans. The developed mathematical models fitted reason- content in lipophilic pigments, Spirulina is very rich in ably well with the experimental results for all the response blue phycobiliprotein aqueous pigments, with 7.0% c-phy- variables and were accurate to predict the phycobilipro- cocyanin and 4.4% allophycocyanin per dry matter, corre- tein amount. Furthermore, the physical process parame- sponding to 15.6% of Spirulina’s crude protein (44%) [41]. ters were also evaluated for the growth of O. agardhii and 490 İrem Deniz et al.: Phycobiliprotein extraction from cyanobacteria

Senthilkumar N, et al. C-Phycocyanin from Oscillatoria tenuis S. nidulans. In this paper, the noteworthy observation was exhibited an antioxidant and in vitro antiproliferative activity that the high agitation rate stimulated the faster growth through induction of apoptosis and G0/G1 cell cycle arrest. than increased the light intensity for the growths of cya- Food Chem 2013; 140(1-2):262–72. nobacterial strains. Furthermore, the light had profound [11] Cisneros M, Rito-Palomares M. A simplified strategy for the quantitative and qualitative effects on phycobiliprotein release and primary recovery of c-phycocyanin produced by formation. Our study provides experimental approach for Spirulina maxima. Chem Biochem Eng Q 2004; 18(4):385–90. [12] Chaiklahan R, Chirasuwan N, Loha V, Tia S, Bunnag B. further investigations about the optimization of physical Separation and purification of phycocyanin from Spirulina process parameters of cyanobacterial strains which are sp. using a membrane process. Bioresour Technol 2011; able to produce the phycobiliprotein. 102(14):7159–64. [13] Lee H, Song M, Hwang S. Optimizing bioconversion of deproteinated cheese whey to mycelia of Ganoderma lucidum. Acknowledgements: This study is a part of MAREX Process Biochem 2003; 38:1685–93. (Novel marine bioactive compounds for European indus- [14] Guo J, Zhuang Y, Chen L, Liu J, Li D, Ye N. Process optimization tries) project and the authors would like to thank the for microwave-assisted direct liquefaction of Sargassum European Community’s Seventh Framework Programme polycystum C.Agardh using response surface methodology. Bioresour Technol 2012; 120:19–25. (FP7/2010–2014) under grant agreement number 245137 [15] Janssen M, Tramper J, Mur LR, Wijffels RH. Enclosed outdoor (FP7-KBBE-2009-3) for the financial support. photobioreactors: light regime, photosynthetic efficiency, Conflict of Interest: The authors have no conflict of interest. scale-up, and future prospects. Biotechnol Bioeng 2003; 81(2):193–210. [16] Lee YK. Microalgal mass culture systems and methods: Their limitation and potential. J Appl Phycol 2001; 13:307–15. References [17] Castellanos CS. Batch and continuous studies of Chlorella vulgaris in photobioreactors, M.E.Sc. Thesis. University of [1] Cohen Z. Chemicals from microalgae. Taylor & Francis, London Western Ontario, Ontario 2013. 1999. [18] Schreiber E. Die Reinkultur von marinen phytoplankton und [2] Halim R, Danquah MK. Bioprocess Development for Chlorophyll deren bedeutung für die erforschung der produktionsfähigkeit Extraction from Microalgae. In (Ed. Lee JW) Advanced Biofuels des meerwassers. Wiss Meeresuntersuch NF 1927; 10:1–34. and Bioproducts Springer, New York 2014. p. 807–32. [19] Talling JF, Driver D. Some problems in the estimation of chloro- [3] Soni B, Kalavadia B, Trivedi U, Madamwar D. Extraction, phyll-a in phytoplankton. In (Ed. Doty MS) Proceedings of the purification and characterization of phycocyanin from Conference on Primary Productivity Measurement, Marine and Oscillatoria quadripunctulata-Isolated from the rocky shores Freshwater, U.S. Atomic Energy Comm., New York 1963. p. of Bet-Dwarka, Gujarat, India. Process Biochem 2006; 142–4. 41:2017–23. [20] Boussiba S, Richmond AE. Isolation and characterization of [4] Macias-Sanchez MD, Mantell C, Rodriguez M, Martinez de phycocyanins from the blue-green alga Spirulina platensis. la Ossa E, Lubian LM, et al. Supercritical fluid extraction Arch Microbiol 1979; 120:155,9. of carotenoids and chlorophyll a from Synechococcus sp. J [21] Lundstedt T, Seifert E, Abramo L, Thelin B, Nyström A, et al. Supercrit Fluids 2007; 39:323–9. Experimental design and optimization. Chemom. Intell Lab [5] Abalde J, Betancourt L, Torres E, Cid A, Barwell C. Purification Syst 1998; 42:3–40. and characterization of phycocyanin from marine [22] Hanrahan G, Garza C, Garcia E, Miller K. Experimental Design cyanobacterium Synechococcus sp. 109201. Plant Sci 1998; and Response Surface Modeling: A Method Development 136:109–20. Application for the Determination of Reduced Inorganic Species [6] Golbeck JH, Bryant DA. Photosytem I. In (Ed. Lee CP) in Environmental Samples. J Environ Inform 2007; 9(2):71–9. Current Topics in Bioenergetics, Light Driven Reactions in [23] Montgomery DC. Design and Analysis of Experiments. John Bioenergetics, Academic Press, New York 1991. p. 83–177. Wiley & Sons, New York 1996. [7] Mishra S, Mishra DR, Schluchter WM. A Novel Algorithm for [24] Myers RH, Montgomery D, Anderson-Cook CM. Response Predicting Phycocyanin Concentrations in Cyanobacteria: A surface methodology: process and product optimization using Proximal Hyperspectral Remote Sensing Approach. Remote designed experiments. Wiley, New York 2009. Sens 2009; 1:758–75. [25] Jaliliannosrati H, Amin NA, Talebian-Kiakalaieh A, Noshadi [8] Bermejo R, Acién FG, Ibáñez MJ, Fernández JM, Molina E, I. Microwave assisted biodiesel production from Jatropha Alvarez-Pez JM. Preparative purification of B-phycoerythrin curcas L. seed by two-step in situ process: optimization using from the microalga Porphyridium cruentum by expanded-bed response surface methodology. Bioresour Technol 2013; adsorption chromatography. J Chromatogr B Analyt Technol 136:565–73. Biomed Life Sci 2003; 790(1-2):317–25. [26] Fermoso J, Gil MV, Arias B, Plaza MG, Pevida C, et al. [9] Becker W. Microalgae in Human and Animal Nutrition. In (Ed. Application of response surface methodology to assess the Richmond A) Handbook of Microalgal Culture: Biotechnology and combined effect of operating variables on high-pressure coal Applied Phycology, Blackwell Science, Oxford 2004. p. 312–52. gasification for H2-rich gas production. Int. J. of Hydrogen [10] Thangam R, Suresh V, Asenath Princy W, Rajkumar M, Energ 2009; 35:1191–204 İrem Deniz et al.: Phycobiliprotein extraction from cyanobacteria 491

[27] Mazaheri H, Lee KT, Bhatia S, Mohamed AR. Subcritical [34] Tang K, Zeng XL, Yang Y, Wang ZB, Wu XJ, et al. A minimal water liquefaction of oil palm fruit press fiber in the phycobilisome: fusion and chromophorylation of the truncated presence of sodium hydroxide: an optimisation study using core-membrane linker and phycocyanin. Biochim Biophys Acta response surface methodology. Bioresour Technol 2010; 2012; 1817(7):1030–6. 101(23):9335–41. [35] Zhou X, Xin ZJ, Lu XH, Yang XP, Zhao MR, et al. High efficiency [28] Herrero M, Simó C, Ibáñez E, Cifuentes A. Capillary electro- degradation crude oil by a novel mutant irradiated from phoresis-mass spectrometry of Spirulina platensis proteins Dietzia strain by 12C6+ heavy ion using response surface obtained by pressurized liquid extraction. Electrophoresis methodology. Bioresour Technol 2013; 137:386–93. 2005; 26(21):4215–24. [36] Lemasson C, Marsac NT, Cohen-Bazire G. Role of allophy- [29] Simó C, Herrero M, Neusüss C, Pelzing M, Kenndler E, et al. cocyanin as light-harvesting pigment in cyanobacteria. Proc Characterization of proteins from Spirulina platensis microalga Natl Acad Sci U S A 1973; 70(11):3130–3. using capillary electrophoresis-ion trap-mass spectrometry [37] Ley AC, Butler WL. Isolation and Function of Allophycocyanin B and capillary electrophoresis-time of flight-mass spectrometry. of Porphyridium cruentum. Plant Physiol 1977; 59(5):974–80. Electrophoresis 2005; 26(13):2674–83. [38] Van Liere L, Mur LR. Growth Kinetics of Oscillatoria agardhii [30] Herrero M, Mendiola JA, Plaza M, Ibañez E. Screening for Gomont in Continuous Culture, Limited in its Growth by the Bioactive Compounds from Algae. In (Ed. Lee JW) Advanced Light Energy Supply. J Gen Microbiol 1979; 115:153–60. Biofuels and Bioproducts, Springer, New York 2014. p. 833–73. [39] Silva CSP, Silva-Stenico ME, Fiore MF, Castro HF, Da Rós PCM. [31] Chaiklahan R, Chirasuwan N, Bunnag B. Stability of Optimization of the cultivation conditions for Synechococcus phycocyanin extracted from Spirulina sp.: Influence of sp. PCC7942 (cyanobacterium) to be used as feedstock for temperature, pH and preservatives. Process Biochem 2012; biodiesel production. Algal Res 2014; 3:1–7. 47:659–64 [40] Gupta A, Sainis JK. Isolation of C-phycocyanin from [32] oi VT, Glazer AN, Stryer L. Fluorescent phycobiliprotein Synechococcus sp. (Anacystis nidulans BD1). J Appl Phycol conjugates for analyses of cells and molecules. J Cell Biol 1982; 2010; 22:231–3. 93(3):981–6. [41] Campanella L, Crescentini G, Avino P. Chemical composition [33] Glazer AN. Phycobiliproteins — a family of valuable, widely and nutritional evaluation of some natural and commercial food used fluorophores. J Appl Phycol 1995; 6:105–12. products based on Spirulina. Analusus 1999; 27:533–40.