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Enhancing biomass production of Dunaliella salina via optimized combinational application of phytohormones
Aquaculture 503 (2019) 146–155
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Enhancing biomass production of Dunaliella salina via optimized combinational application of phytohormones ⁎
Hexin Lva, , Qiao-e Wangb, Shilei Wanga, Bingbing Qia, Jiatong Hea, Shiru Jiaa, a b
T
⁎
Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, China Beijing Key Lab of Plant Resource Research and Development, Beijing Technology and Business University, Beijing 100048, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Dunaliella salina Phytohormone Response surface methodology Biomass production Plackett–Burman design Box-Behnken design
Dunaliella salina is an important source of natural β-carotene. Phytohormones have growth-promoting and other physiological effects on many microalgae. However, there are only few systematic studies on the screening and optimization of key phytohormones for D. salina production. To enhance the biomass production of D. salina, the growth and physiological effects of seven phytohormones, myo-inositol (MI), 6-benzylaminopurine (6-BA), naphthyl acetic acid (NAA), indoleacetic acid (IAA), 2,4-dichlorophenoxyacetic acid (2,4-D), gibberellic acid (GA), and abscisic acid (ABA), on D. salina were investigated. The results showed that the seven selected phytohormones exerted significant effects on growth, photosynthesis, respiration and β-carotene biosynthesis at specific concentrations and time points. Three significant variables affecting biomass production were revealed via a Plackett–Burman design. The statistical model for maximum biomass production was constructed via response surface methodology using a Box-Behnken design. Statistical analysis showed that MI, IAA, and ABA have a stronger promoting effect on D. salina growth than the other four phytohormones, and their optimum concentrations were found to be 552 mg·L−1, 0.14 mg·L−1 and 0.22 mg·L−1, respectively. Validation experiments showed that the biomass increased by 19% using this optimized combination of phytohormones.
1. Introduction The photosynthetic green microalga Dunaliella salina can produce lipids, vitamins, glycerol and large amounts of pigments under abiotic stress conditions, and is currently one of the main commercial sources of natural β-carotene (Hosseini Tafreshi and Shariati, 2009). It has been extensively cultivated in open ponds or closed bioreactors mainly in Australia, Israel, China, and Spain. The phototrophic cultivation of D. salina is generally divided into a biomass production stage and a pigment induction or accumulation stage. The yield of dry biomass of D. salina in the former stage is around one gram per liter. Similar to other microalgae that are cultivated for biodiesel, nutraceuticals and aquatic feeds, the low productivity of biomass of D. salina is the main hurdle for cost reduction in large-scale production processes. Many studies aimed at enhancing biomass productivity have been performed, including the optimizations of inorganic nutrients such as nitrogen (Hosseini Tafreshi and Shariati, 2009), phosphate (Gibor, 1956; Sukenik and Shelef, 1984), sulfate (Hosseini Tafreshi and Shariati, 2009), CO2 (GarcíaGonzález et al., 2003) and bicarbonate (Hejazi et al., 2003), culture
parameters such as temperature (Ben-Amotz, 1995), pH (Ben-Amotz, 1995) and light intensity (Ben-Amotz and Avron, 1989), as well as culture systems such as open ponds (Borowitzka, 1999; Ben-Amotz, 1995; García-González et al., 2003) and closed photobioreactors (Borowitzka, 1999; Borowitzka, 1996). Moreover, an optimized mixotrophic culture medium for improved biomass and β-carotene production was also designed (Morowvat and Ghasemi, 2016). Plant hormones play crucial regulatory roles in multicellular higher plants. Although specific features of hormone metabolism in different groups of algae remain largely unknown (Tarakhovskaya et al., 2007; Kiseleva et al., 2012), many studies have shown that plant hormones have various effects on the metabolism of unicellular algae. For example, ABA was shown to promote the morphogenetic transition of vegetative cells of Haematococcus pluvialis into mature red cyst cells (Kobayashi et al., 1997). ABA promotes the growth and oxidative stress tolerance of Chlamydomonas reinhardtii by enhancing antioxidant enzymes activity (Yoshida et al., 2003). ABA significantly promoted the growth and triacylglycerol accumulation of Chlorella saccharophila (Contreras-Pool et al., 2016). Brassinosteroids stimulate the synthesis of
Abbreviation: MI, myo-inositol; 6-BA, 6-benzylaminopurine; NAA, naphthyl acetic acid; IAA, indoleacetic acid; 2,4-D, 2,4-dichlorophenoxyacetic acid; GA, gibberellic acid; ABA, abscisic acid; PBD, Plackett–Burman design; BBD, Box-Behnken design. ⁎ Corresponding author at: TEDA, 13th street, Tianjin 300457, China. E-mail address: [email protected] (H. Lv). https://doi.org/10.1016/j.aquaculture.2018.12.077 Received 30 April 2018; Received in revised form 25 December 2018; Accepted 27 December 2018 Available online 28 December 2018 0044-8486/ © 2018 Elsevier B.V. All rights reserved.
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2.2. Measurement of photosynthetic and respiratory rates
nucleic acids and proteins in Chlorella vulgaris (Bajguz, 2000). IAA, GA, kinetin, and 1-triacontanol increased the cell concentration and size coupled with changes of morphology and protein contents of the cells of Chlamydomonas reinhardtii (Wonkun et al., 2013). IAA promoted the accumulation of saturated- and monounsaturated fatty acids as well as the biomass productivity, the activity of antioxidant enzymes and expression levels of RuBisCO and ACCase in Chlorella sorokiniana (Babu et al., 2017). NAA treatment had a remarkable promoting effect on cell growth and lipid biosynthesis in Chlorella vulgaris (Liu et al., 2017). In D. salina, MI significantly promoted the growth and showed a concentration-dependent manner, and influenced the composition of fatty acid methyl ester, but did not have a significant effect on total carotenoid content (Cho et al., 2015). Therefore, elucidating the physiological effects of phytohormones on D. salina and optimizing their combinations will be of great significance for understanding the regulatory mechanisms of phytohormones in D. salina and their practical applications in large-scale cultivation. Process optimization plays an important role in industrial production, it has been used in many industrial fields, such as the optimization of nanoparticle composite (Asfaram et al., 2017a; Asfaram et al., 2018a; Asfaram et al., 2018b), the optimization of methylene blue and Cd(II) removal from binary aqueous solution by natural walnut carbon (Mazaheri et al., 2017), the optimization of biosorption of dyes from aqueous solutions (Asfaram et al., 2016; Asfaram et al., 2017b). Under some occasions, small efficiency improvements in the processes of biotechnological production, are critical for successful commercial production, such as the increase of production or reduction of cost. Optimization of the quantities of nutritional components, the physical parameters values, and strain performance is the major method to enhance the microbial metabolites productivity in nowadays microbial fermentation industry. Many statistical methodologies are adopted in process optimization. The Plackett–Burman multifactorial designs could screen the main factors from many process variables. The objective of the Plackett–Burman multifactorial designs is to determine which variables will be retained or removed in the following optimization experiments (Morowvat et al., 2015; Plackett and Burman, 1946). The response surface methodology (RSM) can determine the suitable combination of variables based on statistics equation calculated from given experimental values and thus it is a very efficient and powerful tool to optimize conditions of a multivariable system (Box and Wilson, 1951). In the present study, the effects of a number of phytohormones on the growth and physiological state on D. salina were investigated. These included MI, 6-BA, GA, ABA, and IAA together with its analogs NAA and 2,4-D. Finally, the factors exerted significant effects on cell growth and an optimal combination of phytohormones for the greatest biomass production were obtained by statistical design combining PB with RSM.
An Oxylab Clark-type electrode (Hansatech, Cambridge, UK) was used to determine the oxygen concentration in culture and the photosynthetic and respiratory rates of D. salina were calculated based on the changes in oxygen concentrations. The detailed procedures were described in our previous report (Lv et al., 2016). Briefly, the cell culture in the reaction chamber was exposed to light and dark for 3 min each. The changes of oxygen concentration in the medium were monitored and used for the calculation the respiratory oxygen consumption and photosynthetic oxygen evolution. For each measurement, at least three experimental rounds were monitored. The photosynthetic and respiratory rates were calculated the equations as below: Y1(pmol·cell−1·h−1)= C2 − C1 , Y2(pmol·cell−1·h−1)= C2 − C3 , where Y1 is ρ×t
ρ×t
the net photosynthetic rate, ρ is cell concentration, t is the reaction time (h), C1 is the initial oxygen concentration, C2 is the oxygen concentration under light for 3 min; where Y2 is the respiration rate, ρ is cell concentration, t is the reaction time (h), C2 is the oxygen concentration under light for 3 min, and C3 is the oxygen concentration under dark for 3 min. The real photosynthetic rate = Y1 + Y2. 2.3. Carotenoid analysis The quantifications of β-carotene were described in our previous study (Lv et al., 2016). Briefly, cultures were centrifuged at 2000g for 2 min to harvest cells. and methanol/methylene chloride (75/25, v/v) were used to extract β-carotene in dark conditions. a Dionex P680 instrument (Sunnyvale California, USA) and BDS HYPERSIL C18 column (250 × 4.6 mm, 5.0 μm, Thermo, USA) were used to HPLC analysis. Acetonitrile/methylene chloride/methanol (85/10/5, v/v/v) was used as the mobile phase and the flow rate was 1 mL min−1. The detection wavelength was 450 nm. The column temperature was 28 °C. The standard of β-carotene was bought from Sigma-Aldrich, USA. The A + 0.396 equation: X = 2.184 ,was used to calculate the β-carotene content, X and A represents the β-carotene content (mg·L−1) and the area count, respectively. 2.4. Experimental optimization and statistical analysis In order to determine the significant variables for biomass production, phytohormones with significant growth promoting or physiological effects were investigated and identified using the Plackett–Burman design of experiment in Minitab 17 (Minitab Inc., Pennsylvania, USA). The seven phytohormones represent 7 parameters. Each parameter was set 2 levels. A total of 12 runs were included for selection. A two-sided confidence interval was adopted. The confidence level for intervals was set as 95. No Box-Cox transformation and stepwise procedure were used. The mean values of the four repeats were set as responses. The Box-Behnken design (BBD) of response surface methodology (RSM) was adopted for the maximization of biomass production by formulating the optimum values of significant parameters (Designer Expert 7.0.0, Stat-Ease, Inc., Minneapolis, MN, USA). Three significant parameters obtained from the Plackett–Burman design experiment were selected. Each parameter was estimated at three levels. One block with five center points was adopted. The design matrix with 17 runs that were used to assess the growth promoting effects of the three significant variables and their corresponding results. The data transformation equation was not used because the ratio of maximum response value to the minimum response value is < 3. The quadratic model was chosen to perform ANOVA analysis based on the significance of terms, the insignificance of lack of fit and whether the model is aliased during fit analysis. At least 4 repeats were performed for each experiment in this study. Data and figures were analyzed and generated using Origin 9.0 (OriginLab, MA, USA). The significance of differences was analyzed by Student's t-test in Excel (Microsoft Corp., USA).
2. Materials and methods 2.1. Culture conditions and determination of cell concentration The D. salina strain was isolated from Tanggu, China and was cultured in a medium previously described in early study (Lv et al., 2016), the culture media were added with corresponding phytohormones, including ABA (Solarbio, China), MI, 6-BA, GA, and IAA together with its analogs NAA and 2,4-D (Sigma-Aldrich, USA). Cells were cultured in 250 mL of medium in a 500 mL Erlenmeyer flasks under continuous 60 μmol photons m−2 s−1 illumination using fluorescent lamp. The culture temperature is 30.0 ± 1.0 °C. The cultures were shaken manually three times a day. Before inoculation, two 16/8-h light/dark cycles were used to synchronize the cell growth phases. 0.25% glutaraldehyde was used to fix cells and then an Olympus CX40 microscope (Olympus Corporation, Tokyo, Japan) and a hemocytometer were used to determine cell concentrations.
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Fig. 1. The optical densities of D. salina cultures in media spanning a gradient of phytohormone concentrations. The X axis indicates the 6 gradient concentrations of the 7 kinds of phytohormones (MI, 6-BA, NAA, IAA, 2,4-D, GA, and ABA) under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C. The Y axis indicates the optical density at 435 nm (OD435) on the 13th and 15th days. Data shown is the mean ± SD, n = 4.
3. Results and discussion
measured and the optimal growth-promoting concentration was determined for each phytohormone (Fig. 2). In agreement with a previous report (Cho et al., 2015), MI at 540 mg/mL significantly promoted the biomass accumulation during the log phase. However, the higher levels of MI repressed the biomass accumulation. Among the auxin family phytohormones, 6-BA and GA, lower levels did not exert any statically significant effects on biomass production but higher levels repressed the biomass production significantly. Lower concentrations of ABA promoted the biomass production during the late log and stationary phase, while higher concentrations repressed the biomass production of D. salina. As shown can be seen in Figs. 1 and 2, for each phytohormone, the concentrations were clearly not consistent between the maximal optical density and maximal biomass production. This result indicated that the phytohormones affected the distribution of cellular components, which in turn influenced the optical absorbance characteristics. Consequently, the optical density was not suitable for monitoring the biomass production of D. salina cells cultured in media with added phytohormones. The results, therefore, indicated that the tested phytohormones also exerted effects on D. salina, similar to the effects of IAA and GA in Chlamydomonas reinhardtii (Wonkun et al., 2013), IAA in Chlorella sorokiniana (Babu et al., 2017), NAA in Chlorella vulgaris (Liu et al., 2017), and MI in D. salina (Cho et al., 2015), as well as ABA in Haematococcus pluvialis (Kobayashi et al., 1997), Chlamydomonas
3.1. Single factor analysis of effects of phytohormones on D. salina growth The production of β-carotene by D. salina could be divided into two steps, the aims of the two steps are the production of cells and β-carotene induction and accumulation, respectively. Therefore, the cell density of the first step is correlated with biomass in the second step. In the present study, the biomass production was expressed as cell density. To enhance the biomass production of the cells of D. salina, the growth effects of seven phytohormones, myo-inositol (MI), 2,4-dichlorophenoxyacetic acid (2,4-D), indoleacetic acid (IAA), naphthyl acetic acid (NAA), 6-benzylaminopurine (6-BA), gibberellic acid (GA) and abscisic acid (ABA), were analyzed by single factor experiments. Firstly, to screen the phytohormones that influence D. salina growth, the optical density (OD) of cultures grown in media with a gradient of hormone concentrations were monitored on the 13th and 15th day, at which the D. salina cells were already in the log to stationary transition phase or stationary phase generally. As shown in Fig. 1, it was confirmed that all seven phytohormones exert effects on cells growth in a dose-dependent manner. Because OD is not only affected by cell density, but also by cell size, light absorption, scattering properties of cells and other factors, therefore, the cell dry weights (CDW) were further 148
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Fig. 2. Cell concentrations of D. salina cultured in media spanning a gradient of phytohormone concentrations. The X axis indicates the 8 sampling time points of the 6 gradient concentrations of the 7 kinds of phytohormones (MI, 6-BA, NAA, IAA, 2,4-D, GA, and ABA) under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C. and the Y axis indicates the cell concentration of each sampling time point. Data shown is the mean ± SD, n = 4.
respiration under higher MI concentrations explained the decline of biomass production under these growth conditions. Although 2,4-D, NAA, and IAA have all been categorized as belonging to the auxin family based on their similar physiological functions in higher plants, they displayed marked differences in their effects on the growth and physiology of D. salina. All three phytohormones displayed complex effects on photosynthesis and respiration at certain concentrations and growth stages (Fig. 3B, C, and D), albeit with different characteristics. As shown in Fig. 3, lower levels of 2,4-D promoted the photosynthesis at the early growth stage. By contrast, both concentrations of NAA showed repressive effects on photosynthesis on the second and sixth day. The higher levels of IAA promoted the photosynthesis on the 2nd and 10th day. As shown in Fig. 4B and C, both concentrations of 2,4-D and IAA promoted the respiration on the 2nd day, but the lower level repressed the respiration on the 6th day and thereafter. It is worth noting that no respiration was detected on the 2nd day, while there was a sharp increase on the 6th day followed by a decrease on the 10th day under lower levels of NAA. The higher level 6-BA, which resulted in the repression of respiration on the 2nd day (Fig. 4E), promoted the photosynthetic activity of D. salina on the 6th day (Fig. 3E). The lower level of GA promoted the photosynthesis but the higher level repressed it
reinhardtii (Yoshida et al., 2003) and Chlorella saccharophila (ContrerasPool et al., 2016). These results suggested that phytohormones may share similar response mechanisms in all chlorophytes. 3.2. Effects of phytohormones on photosynthesis and β-carotene accumulation Photosynthesis and respiration are two key metabolic processes that determine the biomass productivity of photosynthetic organisms. On the other hand, phytohormones may exert minor physiological effects that are not reflected by changes of biomass production in D. salina. Therefore, the respiratory and photosynthetic rates were investigated to estimate the influence of phytohormones on the carbon fixation ability of the strain. Two levels of each phytohormone were selected, which showed a promoting and repressing effect on biomass production, respectively (Fig. 3 and 4). Both levels of MI promoted the photosynthesis at similar levels, especially during the early growth stage, and the promoting effects gradually declined along with time (Fig. 3A). The respiration of D. salina was greatly promoted by the addition MI, and at higher concentrations it was 3.4 times higher than at the optimal concentration for biomass production (Fig. 4A). these sharp increases of 149
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Fig. 3. Photosynthetic rates of D. salina cells cultured in media spanning a gradient of phytohormone concentrations. The X axis indicates the 5 culture time points of the 3 gradient concentrations of the 7 kinds of phytohormones (MI, 6-BA, NAA, IAA, 2,4-D, GA, and ABA) under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C. The Y axis indicates the photosynthetic rate of each time point. Data shown is the mean ± SD, n = 4.
response to stress was remodeled by phytohormones in D. salina. MI showed a stronger effect on growth, photosynthesis, and respiration of D. salina cells than the other six tested phytohormones. The auxin family phytohormones NAA, IAA and, 2,4-D displayed different effects on the D. salina physiology and growth. With the exception of ABA, lower levels of the other six phytohormones all delayed the β-carotene accumulation during the stationary growth stage.
(Fig. 3F). By contrast, both levels of GA promoted the respiration on the 6th day and repress the respiration on the 10th day (Fig. 4F). Both levels of ABA promoted the photosynthesis on the 10th day, leading to an increase by 3.3 times under the lower level of ABA in comparison to that of the blank control (Fig. 3G). Respiration was promoted by the higher level of ABA on the 2nd day but was repressed thereafter. By contrast, respiration was repressed at the lower level of ABA at all the analyzed time points (Fig. 4G). Taken together, all seven phytohormones had effects on both anabolism and catabolism with different effect sizes and times. Moreover, β-carotene in D. salina is a stress indicator which is accumulated under abiotic stresses such as high salt and light, as well as under nutrient deprivation during the late growth stage (Lv et al., 2016; Coesel et al., 2008; Saha et al., 2013). As shown in Fig. 5, none of the seven phytohormones had a significant effect on β-carotene accumulation before the stationary growth stage. Nevertheless, higher levels of MI, 6-BA and ABA promoted the β-carotene accumulations during the stationary stages (Fig. 5A, C, E), whereas lower levels of MI and 6-BA repressed β-carotene accumulation (Fig. 5A, C). By contrast, the lower level of ABA had no significant effect on β-carotene accumulation (Fig. 5E). Auxin family phytohormones showed repressive effects on βcarotene accumulations at both levels (Fig. 5B, D, and F). The repressive effect of GA was similar to that of NAA, but with a smaller size (Fig. 5D, G). The results above indicated that the cellular metabolic
3.3. Identifying significant phytohormones by Plackett–Burman design As indicated by the above single factor experiments, the seven phytohormones exerted different effects of different levels on the growth of D. salina. To further screen the significant variables affecting biomass production of D. salina, the effects of the seven phytohormones on biomass production were analyzed using a Plackett–Burman design (Table 1). This method permits the investigation of a vast number of critical factors through a smaller number of experimental studies compared to central composite designs (Morowvat et al., 2015). The design matrix selected for the screening of significant variables for biomass production and the corresponding responses is shown in Table 2. The model was calculated by factorial regression and the statistically significant variables were screened based on p-values of < 0.05 (Table 3). The model was considered statistically significant based on the p-value of 0.041. The S and R2 values of the model were 150
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Fig. 4. Respiration rates of D. salina cells cultured in media spanning a gradient of phytohormone concentrations. The X axis indicates the 5 culture time points of the 3 gradient concentrations of the 7 kinds of phytohormones (MI, 6-BA, NAA, IAA, 2,4-D, GA, and ABA) under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C. The axis Y indicated the respiration rate of each time point. Data shown is the mean ± SD, n = 4.
absence of a significant lack-of-fit F-value (0.6320) implied that the quadratic model is a good fit. The fitness of the quadratic model was also supported by the lack of fit (0.6321) and p-values (< 0.0001). The final equation was listed below, coded factors and the coefficients of the quadratic model were indicated:
0.1090 and 92.32, respectively, which suggested that the model explains 92.32% of the variation in the data. MI was identified to be the phytohormone with the strongest effect on biomass production (pvalue = 0.02,). Two additional phytohormones, ABA and IAA, had significant p-values of 0.025 and 0.036, respectively. The lower p-values suggested stronger effects on the cell biomass production. Among the three significant factors screened, MI exerted a positive effect on the biomass production, whereas the other two factors, ABA and IAA, exerted negative effects. The other four phytohormones did not exert significant effects, as indicated by their high p-values.
R = 9.24 + 0.20A–0.34C–0.28F–0.075AC–0.0075AF + 0.02CF–0.62A2–0.30C2–0.45F 2 where R is cell concentration, A is MI, C is IAA and F is ABA. As shown in Table 7, the R2 (the coefficient of determination) for the regression equation was 0.9898, indicating that the model could properly explain 98.98% of the value variation in the response presented by Box-Behnken design experiment. The pred R2 (predicted R2) of 0.9364 was in an acceptable agreement with the Adj R2 (adjusted R2) of 0.9766. The R2 can have values in the range from 0 to 1, and the greater the value is, the more reasonable the fit of the model. A signalto-noise ratio, which was reflected by the adeq precision (adequacy of precision), > 4 is necessary for a model. In the present study, the adequacy of precision ratio is 23.34, therefore the model had an adequate signal, suggesting that the model generated by Box-Behnken design can be used to guide the design space. Fig. 6A shows the plot of the predicted biomass production from the model versus the actual experimental values. It was evident that none of the experimental points deviated significantly from the predicted responses. The model thus had a sufficient correlation between the primary variables and the biomass production in the media with the addition of combinations of phytohormones.
3.4. Obtaining the optimal combination of phytohormones for biomass production by Box-Behnken design The three independent phytohormones, MI, ABA, and IAA were selected to implement response surface methodology using a BoxBehnken design. The design matrix and corresponding results of the Box-Behnken design that were used to determine the effects of three variables are shown in Table 4. The fitting results indicated that the quadratic model generated by Box-Behnken design was highly statically significant (p < 0.0001) (Table 5). Importantly, the quadratic model was also not aliased and was without a significant lack of fit or higher adjusted and predicted R2 (Tables 6 and 7). The ANOVA for the response surface quadratic model suggested that the model terms A, C, F, A2, C2 and F2 were all significant (p-value < 0.05). By contrast, there were no significant interactions between the variables, as shown by the high p-values (Table 6). The high model F-value (75.16) and the 151
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Fig. 5. β-carotene contents of D. salina cells cultured in media spanning a gradient of phytohormone concentrations. The X axis indicates the 8 culture time points of the 3 gradient concentrations of the 7 kinds of phytohormones (MI, 6-BA, NAA, IAA, 2,4-D, GA, and ABA) under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C. The Y axis indicated the β-carotene content of each time point. Data shown is the mean ± SD, n = 4. Table 1 Seven experimental variables at two different levels used for biomass production of D. salina via a Plackett-Burman design. Variable (mg·L MI 2,4-D IAA NAA 6-BA ABA GA
Symbol code
−1
) A B C D E F G
Table 2 Twelve runs of a Plackett-Burman design matrix for seven variables with coded values along with the observed and predicted cell concentration at the 19th day of the culture under continuous 60 μmol·m−2·s−1 illumination and 30.0 ± 1.0 °C. A: MI; B: 2,4-D; C: IAA; D: NAA; E: 6-BA; F: ABA; G: GA. Observed data shown is the mean, n = 4.
Experimental value Lower
Higher
450 0.25 0.10 0.02 0.45 0.13 0.12
630 0.75 0.30 0.06 0.75 0.40 0.20
Run order
1 2 3 4 5 6 7 8 9 10 11 12
Further, as shown in Fig. 6 (C, D, and E), a 3D response surface was generated to explore the optimal levels of the three phytohormones for maximum D. salina biomass production and to investigate the influence of variation in the factors' levels. Response surface plots show the influence of each phytohormone on D. salina biomass production on the Z-axis when optimizing two phytohormones, while maintaining another phytohormone at central point values. The interaction between the ABA and IAA concentrations on biomass production is shown in Fig. 6C, the 152
Cell concentration (106 cells·ml−1)
Experimental values
A
B
C
D
E
F
G
Observed
Predicted
−1 1 −1 −1 −1 1 −1 1 1 1 −1 1
−1 1 −1 1 −1 1 1 −1 −1 −1 1 1
−1 −1 −1 −1 1 −1 1 1 −1 1 1 1
1 1 −1 −1 1 1 −1 −1 −1 1 1 −1
1 −1 −1 −1 1 1 1 −1 1 −1 −1 1
1 −1 −1 1 −1 −1 −1 −1 1 1 1 1
−1 −1 −1 1 1 1 −1 1 1 −1 1 −1
2.35 2.96 3.05 2.52 2.90 2.92 2.54 2.36 2.79 2.84 2.73 2.90
2.38 2.84 2.97 2.46 2.89 2.92 2.44 2.49 2.82 2.87 2.79 2.97
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Table 3 Estimated effect, regression coefficient and corresponding T- and p-values for cell concentrations in the seven-variable Plackett-Burman design experiment. A: MI; B: 2,4-D; C: IAA; D: NAA; E: 6-BA; F: ABA; G: GA. Term
Effect
Coef
SE Coef
T-Value
p-Value
Constant A B C D E F G
– 0.23 0.03 −0.20 −0.06 −0.17 −0.22 −0.12
2.74 0.12 0.01 −0.10 −0.03 −0.09 −0.11 −0.06
0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
87 3.73 0.47 −3.12 −1.01 −2.77 −3.48 −1.85
0.000a 0.020b 0.665a 0.036c 0.371a 0.050a 0.025c 0.138a
a b c
Table 6 ANOVA analysis of the quadratic model developed for biomass production of D. salina cultured under different combinations of phytohormones.
Non-significant at P < 0.05. Significant positive effect. Significant negative effect.
Table 4 Seventeen runs of a Box-Behnken design matrix using a quadratic model with actual values. The cell concentrations were measured at the 15th day of culturing under continuous 60 μmol·m−2·s−1 illumination and 30.0 ± 1.0 °C. Cell concentration shown is the mean, n = 4. Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
MI (mg·L−1)
540 540 540 600 540 540 540 540 480 540 480 480 600 540 600 480 600
IAA (mg·L−1)
0.10 0.20 0.20 0.30 0.20 0.30 0.30 0.10 0.30 0.20 0.10 0.20 0.10 0.20 0.20 0.20 0.20
ABA mg·L−1)
Cell concentration (105 cells·ml−1)
0.40 0.26 0.26 0.26 0.26 0.13 0.40 0.13 0.26 0.26 0.26 0.40 0.26 0.26 0.13 0.13 0.40
8.46 9.15 9.21 8.07 9.23 8.47 7.90 9.11 7.81 9.40 8.41 7.72 8.97 9.21 8.62 8.21 8.10
Source
Sum of Squares
DF
Mean Square
F-value
p-value
VIF
Model A-MI C-IAA F-ABA AC AF CF A2 C2 F2 Residual Lack of Fit Pure Error Cor Total
5.0713 0.3240 0.9113 0.6216 0.0225 0.0002 0.0016 1.6382 0.3821 0.8669 0.0525 0.0169 0.0356 5.1238
9 1 1 1 1 1 1 1 1 1 7 3 4 16
0.5635 0.3240 0.9112 0.6216 0.0225 0.0002 0.0016 1.6382 0.3821 0.8669 0.0075 0.0056 0.0089
75.16 43.22 121.5 82.92 3.001 0.0300 0.2134 218.5 50.97 115.6
< 0.0001 0.0003 < 0.0001 < 0.0001 0.1268 0.8674 0.6581 < 0.0001 0.0002 < 0.0001
1.00 1.00 1.00 1.00 1.00 1.00 1.01 1.01 1.01
0.6320
0.6321
Table 7 Analysis of variance (ANOVA) of the parameters of the response surface methodology fitted to a second-order polynomial equation. Parameter
Value
Parameter
Value
Std. Dev. Mean C.V. % PRESS
0.087 8.59 1.01 0.33
R-Squared Adj R-Squared Pred R-Squared Adeq Precision
0.9898 0.9766 0.9364 23.34
shown in Table 4, which suggested that the optimal variables generated by the BBD in this study are valid. The 15th-day biomass production under phytohormone free culture conditions was 7.30 × 105 cells·ml−1. The biomass thus increased by 28% in response to the calculated optimal phytohormone addition. Additional validation experiments with a smaller inoculum size were investigated, and the results showed that the cell concentration of D. salina cells cultured in media with the calculated combined phytohormone addition increased by 19% (Fig. 6B). Therefore, the results from validation experiments further confirmed the validity of the model for predicting the maximum biomass production.
interaction between the IAA and MI concentrations in Fig. 6D and the interaction between the ABA and MI concentrations in Fig. 6E. All three interactions showed that the biomass production was influenced by all three parameters, and the response peaks were clearly shown due to the lack of significant interaction effects among any of the three variables. The corresponding biomass production values increased and then decrease with the increase of each variable. The proposed optimal combination for the three variables was calculated based on this secondorder polynomial model, yielding the values of 552 mg·L−1 of MI, 0.14 mg·L−1 of IAA and 0.22 mg·L−1 ABA, and predicted biomass of 9.41 × 105 cells·mL−1. The proposed values of the variables and corresponding biomass production were obviously close to the values of the center point of the BBD design matrix and the experimental values
4. Conclusion In this study, the growth promoting effects of seven phytohormones on D. salina were evaluated, and the results indicated that phytohormones from higher plant also exert effects on the growth of this important industrially used microalga at specific concentrations and growth stages. The physiological effects on photosynthesis, respiration and β-carotene biosynthesis were investigated, revealing that the tested phytohormones exerted different effects with different patterns. MI showed a stronger effect on growth, photosynthesis, and respiration of D. salina cells than the other six tested phytohormones. The auxin family phytohormones NAA, IAA, and 2,4-D displayed different effects on the growth and physiology of D. salina. With the exception of ABA,
Table 5 Sequential model sum of squares of fit analysis of Box-Behnken design. Source
Sum of squares
DF
Mean square
Mean vs Total Linear vs Mean 2FI vs Linear Quadratic vs 2FI Cubic vs Quadratic Residual Total
1255 1.86 0.024 3.19 0.017 0.036 1259
1 3 3 3 3 4 17
1255 0.62 8.108E-003 1.06 5.625E-003 8.900E-003 74.11
Underline indicates p-value < 0.05. 153
F Value
p-value Prob > F
2.46 0.025 141.85 0.63
0.1087 0.9943 < 0.0001 0.6321
Suggested Aliased
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Fig. 6. Three-dimensional response surfaces and validation experiment. The plot of the predicted biomass production versus the actual experimentally measured biomass of 17 runs experiments in the Box-Behnken design. (A); Time courses of biomass production of D. salina under optimized and unoptimized conditions. PC: Cell concentrations of 9 time points of D. salina cultured in media with the addition of the optimized phytohormone combination (551.96 mg·L−1 of MI, 0.14 mg·L−1 of IAA, and 0.22 mg·L−1 ABA) under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C; Control: Cell concentration of D. salina cultured in modified Johnson's media without phytohormones under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C. Data shown is the mean ± SD, n = 4. (B); Three-dimensional response surfaces of the interaction of the three independent significant variables. (C, D and E).
lower levels of the other six phytohormones all delayed the β-carotene accumulation during the stationary growth stage. MI, ABA, and IAA were determined to be the significant variables by a Plackett–Burman design. A statistical model for maximum biomass production was constructed and the optimal concentration combination of the three significant variables was determined to be 552 mg·L−1 of MI, 0.14 mg·L−1 of IAA and 0.22 mg·L−1 ABA, which was verified by a validation experiment. There were no significant interaction effects between any two significant variables. The results of this research provide a new method for enhancing the biomass production of D. salina in industry, as well as insights into the physiological function of phytohormones in D. salina and other microalgae. On the other hand, scale-up experiments in large volume open pond and closed photobioreactors under different cultivation conditions, are needed in the future, because the optimal concentration combination of the phytohormones obtained in this study were performed in lab scale. Moreover, studies coupled with other cultivation factors, such as nutrient salts, temperature, and light, are also needed in the future to further enhance the biomass productivity and economic feasibility.
Technology and Business University (PRRD-2017-YB1) and the National Natural Science Foundation of China (No. 31401029). Declaration of authors contributions Conception and design (SJ), Provision of study materials, drafting of the article and obtaining of funding (HL), Statistical expertise (JH), Collection and assembly of data (BQ), article revision (QW), and analysis and interpretation of the data (SW). Declaration of authors agreement to authorship and submission of the manuscript for peer review All authors approved the authorship and submission of the manuscript for peer review of the article. Conflict of interest statement None conflict of interest declared.
Acknowledgments Statement of informed consent, human/animal rights This work is supported by the Open Research Fund Program of Beijing Key Lab of Plant Resource Research and Development, Beijing
No conflicts, informed consent, human or animal rights applicable. 154
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Enhancing biomass production of Dunaliella salina via optimized combinational application of phytohormones ⁎
Hexin Lva, , Qiao-e Wangb, Shilei Wanga, Bingbing Qia, Jiatong Hea, Shiru Jiaa, a b
T
⁎
Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, China Beijing Key Lab of Plant Resource Research and Development, Beijing Technology and Business University, Beijing 100048, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Dunaliella salina Phytohormone Response surface methodology Biomass production Plackett–Burman design Box-Behnken design
Dunaliella salina is an important source of natural β-carotene. Phytohormones have growth-promoting and other physiological effects on many microalgae. However, there are only few systematic studies on the screening and optimization of key phytohormones for D. salina production. To enhance the biomass production of D. salina, the growth and physiological effects of seven phytohormones, myo-inositol (MI), 6-benzylaminopurine (6-BA), naphthyl acetic acid (NAA), indoleacetic acid (IAA), 2,4-dichlorophenoxyacetic acid (2,4-D), gibberellic acid (GA), and abscisic acid (ABA), on D. salina were investigated. The results showed that the seven selected phytohormones exerted significant effects on growth, photosynthesis, respiration and β-carotene biosynthesis at specific concentrations and time points. Three significant variables affecting biomass production were revealed via a Plackett–Burman design. The statistical model for maximum biomass production was constructed via response surface methodology using a Box-Behnken design. Statistical analysis showed that MI, IAA, and ABA have a stronger promoting effect on D. salina growth than the other four phytohormones, and their optimum concentrations were found to be 552 mg·L−1, 0.14 mg·L−1 and 0.22 mg·L−1, respectively. Validation experiments showed that the biomass increased by 19% using this optimized combination of phytohormones.
1. Introduction The photosynthetic green microalga Dunaliella salina can produce lipids, vitamins, glycerol and large amounts of pigments under abiotic stress conditions, and is currently one of the main commercial sources of natural β-carotene (Hosseini Tafreshi and Shariati, 2009). It has been extensively cultivated in open ponds or closed bioreactors mainly in Australia, Israel, China, and Spain. The phototrophic cultivation of D. salina is generally divided into a biomass production stage and a pigment induction or accumulation stage. The yield of dry biomass of D. salina in the former stage is around one gram per liter. Similar to other microalgae that are cultivated for biodiesel, nutraceuticals and aquatic feeds, the low productivity of biomass of D. salina is the main hurdle for cost reduction in large-scale production processes. Many studies aimed at enhancing biomass productivity have been performed, including the optimizations of inorganic nutrients such as nitrogen (Hosseini Tafreshi and Shariati, 2009), phosphate (Gibor, 1956; Sukenik and Shelef, 1984), sulfate (Hosseini Tafreshi and Shariati, 2009), CO2 (GarcíaGonzález et al., 2003) and bicarbonate (Hejazi et al., 2003), culture
parameters such as temperature (Ben-Amotz, 1995), pH (Ben-Amotz, 1995) and light intensity (Ben-Amotz and Avron, 1989), as well as culture systems such as open ponds (Borowitzka, 1999; Ben-Amotz, 1995; García-González et al., 2003) and closed photobioreactors (Borowitzka, 1999; Borowitzka, 1996). Moreover, an optimized mixotrophic culture medium for improved biomass and β-carotene production was also designed (Morowvat and Ghasemi, 2016). Plant hormones play crucial regulatory roles in multicellular higher plants. Although specific features of hormone metabolism in different groups of algae remain largely unknown (Tarakhovskaya et al., 2007; Kiseleva et al., 2012), many studies have shown that plant hormones have various effects on the metabolism of unicellular algae. For example, ABA was shown to promote the morphogenetic transition of vegetative cells of Haematococcus pluvialis into mature red cyst cells (Kobayashi et al., 1997). ABA promotes the growth and oxidative stress tolerance of Chlamydomonas reinhardtii by enhancing antioxidant enzymes activity (Yoshida et al., 2003). ABA significantly promoted the growth and triacylglycerol accumulation of Chlorella saccharophila (Contreras-Pool et al., 2016). Brassinosteroids stimulate the synthesis of
Abbreviation: MI, myo-inositol; 6-BA, 6-benzylaminopurine; NAA, naphthyl acetic acid; IAA, indoleacetic acid; 2,4-D, 2,4-dichlorophenoxyacetic acid; GA, gibberellic acid; ABA, abscisic acid; PBD, Plackett–Burman design; BBD, Box-Behnken design. ⁎ Corresponding author at: TEDA, 13th street, Tianjin 300457, China. E-mail address: [email protected] (H. Lv). https://doi.org/10.1016/j.aquaculture.2018.12.077 Received 30 April 2018; Received in revised form 25 December 2018; Accepted 27 December 2018 Available online 28 December 2018 0044-8486/ © 2018 Elsevier B.V. All rights reserved.
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2.2. Measurement of photosynthetic and respiratory rates
nucleic acids and proteins in Chlorella vulgaris (Bajguz, 2000). IAA, GA, kinetin, and 1-triacontanol increased the cell concentration and size coupled with changes of morphology and protein contents of the cells of Chlamydomonas reinhardtii (Wonkun et al., 2013). IAA promoted the accumulation of saturated- and monounsaturated fatty acids as well as the biomass productivity, the activity of antioxidant enzymes and expression levels of RuBisCO and ACCase in Chlorella sorokiniana (Babu et al., 2017). NAA treatment had a remarkable promoting effect on cell growth and lipid biosynthesis in Chlorella vulgaris (Liu et al., 2017). In D. salina, MI significantly promoted the growth and showed a concentration-dependent manner, and influenced the composition of fatty acid methyl ester, but did not have a significant effect on total carotenoid content (Cho et al., 2015). Therefore, elucidating the physiological effects of phytohormones on D. salina and optimizing their combinations will be of great significance for understanding the regulatory mechanisms of phytohormones in D. salina and their practical applications in large-scale cultivation. Process optimization plays an important role in industrial production, it has been used in many industrial fields, such as the optimization of nanoparticle composite (Asfaram et al., 2017a; Asfaram et al., 2018a; Asfaram et al., 2018b), the optimization of methylene blue and Cd(II) removal from binary aqueous solution by natural walnut carbon (Mazaheri et al., 2017), the optimization of biosorption of dyes from aqueous solutions (Asfaram et al., 2016; Asfaram et al., 2017b). Under some occasions, small efficiency improvements in the processes of biotechnological production, are critical for successful commercial production, such as the increase of production or reduction of cost. Optimization of the quantities of nutritional components, the physical parameters values, and strain performance is the major method to enhance the microbial metabolites productivity in nowadays microbial fermentation industry. Many statistical methodologies are adopted in process optimization. The Plackett–Burman multifactorial designs could screen the main factors from many process variables. The objective of the Plackett–Burman multifactorial designs is to determine which variables will be retained or removed in the following optimization experiments (Morowvat et al., 2015; Plackett and Burman, 1946). The response surface methodology (RSM) can determine the suitable combination of variables based on statistics equation calculated from given experimental values and thus it is a very efficient and powerful tool to optimize conditions of a multivariable system (Box and Wilson, 1951). In the present study, the effects of a number of phytohormones on the growth and physiological state on D. salina were investigated. These included MI, 6-BA, GA, ABA, and IAA together with its analogs NAA and 2,4-D. Finally, the factors exerted significant effects on cell growth and an optimal combination of phytohormones for the greatest biomass production were obtained by statistical design combining PB with RSM.
An Oxylab Clark-type electrode (Hansatech, Cambridge, UK) was used to determine the oxygen concentration in culture and the photosynthetic and respiratory rates of D. salina were calculated based on the changes in oxygen concentrations. The detailed procedures were described in our previous report (Lv et al., 2016). Briefly, the cell culture in the reaction chamber was exposed to light and dark for 3 min each. The changes of oxygen concentration in the medium were monitored and used for the calculation the respiratory oxygen consumption and photosynthetic oxygen evolution. For each measurement, at least three experimental rounds were monitored. The photosynthetic and respiratory rates were calculated the equations as below: Y1(pmol·cell−1·h−1)= C2 − C1 , Y2(pmol·cell−1·h−1)= C2 − C3 , where Y1 is ρ×t
ρ×t
the net photosynthetic rate, ρ is cell concentration, t is the reaction time (h), C1 is the initial oxygen concentration, C2 is the oxygen concentration under light for 3 min; where Y2 is the respiration rate, ρ is cell concentration, t is the reaction time (h), C2 is the oxygen concentration under light for 3 min, and C3 is the oxygen concentration under dark for 3 min. The real photosynthetic rate = Y1 + Y2. 2.3. Carotenoid analysis The quantifications of β-carotene were described in our previous study (Lv et al., 2016). Briefly, cultures were centrifuged at 2000g for 2 min to harvest cells. and methanol/methylene chloride (75/25, v/v) were used to extract β-carotene in dark conditions. a Dionex P680 instrument (Sunnyvale California, USA) and BDS HYPERSIL C18 column (250 × 4.6 mm, 5.0 μm, Thermo, USA) were used to HPLC analysis. Acetonitrile/methylene chloride/methanol (85/10/5, v/v/v) was used as the mobile phase and the flow rate was 1 mL min−1. The detection wavelength was 450 nm. The column temperature was 28 °C. The standard of β-carotene was bought from Sigma-Aldrich, USA. The A + 0.396 equation: X = 2.184 ,was used to calculate the β-carotene content, X and A represents the β-carotene content (mg·L−1) and the area count, respectively. 2.4. Experimental optimization and statistical analysis In order to determine the significant variables for biomass production, phytohormones with significant growth promoting or physiological effects were investigated and identified using the Plackett–Burman design of experiment in Minitab 17 (Minitab Inc., Pennsylvania, USA). The seven phytohormones represent 7 parameters. Each parameter was set 2 levels. A total of 12 runs were included for selection. A two-sided confidence interval was adopted. The confidence level for intervals was set as 95. No Box-Cox transformation and stepwise procedure were used. The mean values of the four repeats were set as responses. The Box-Behnken design (BBD) of response surface methodology (RSM) was adopted for the maximization of biomass production by formulating the optimum values of significant parameters (Designer Expert 7.0.0, Stat-Ease, Inc., Minneapolis, MN, USA). Three significant parameters obtained from the Plackett–Burman design experiment were selected. Each parameter was estimated at three levels. One block with five center points was adopted. The design matrix with 17 runs that were used to assess the growth promoting effects of the three significant variables and their corresponding results. The data transformation equation was not used because the ratio of maximum response value to the minimum response value is < 3. The quadratic model was chosen to perform ANOVA analysis based on the significance of terms, the insignificance of lack of fit and whether the model is aliased during fit analysis. At least 4 repeats were performed for each experiment in this study. Data and figures were analyzed and generated using Origin 9.0 (OriginLab, MA, USA). The significance of differences was analyzed by Student's t-test in Excel (Microsoft Corp., USA).
2. Materials and methods 2.1. Culture conditions and determination of cell concentration The D. salina strain was isolated from Tanggu, China and was cultured in a medium previously described in early study (Lv et al., 2016), the culture media were added with corresponding phytohormones, including ABA (Solarbio, China), MI, 6-BA, GA, and IAA together with its analogs NAA and 2,4-D (Sigma-Aldrich, USA). Cells were cultured in 250 mL of medium in a 500 mL Erlenmeyer flasks under continuous 60 μmol photons m−2 s−1 illumination using fluorescent lamp. The culture temperature is 30.0 ± 1.0 °C. The cultures were shaken manually three times a day. Before inoculation, two 16/8-h light/dark cycles were used to synchronize the cell growth phases. 0.25% glutaraldehyde was used to fix cells and then an Olympus CX40 microscope (Olympus Corporation, Tokyo, Japan) and a hemocytometer were used to determine cell concentrations.
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Fig. 1. The optical densities of D. salina cultures in media spanning a gradient of phytohormone concentrations. The X axis indicates the 6 gradient concentrations of the 7 kinds of phytohormones (MI, 6-BA, NAA, IAA, 2,4-D, GA, and ABA) under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C. The Y axis indicates the optical density at 435 nm (OD435) on the 13th and 15th days. Data shown is the mean ± SD, n = 4.
3. Results and discussion
measured and the optimal growth-promoting concentration was determined for each phytohormone (Fig. 2). In agreement with a previous report (Cho et al., 2015), MI at 540 mg/mL significantly promoted the biomass accumulation during the log phase. However, the higher levels of MI repressed the biomass accumulation. Among the auxin family phytohormones, 6-BA and GA, lower levels did not exert any statically significant effects on biomass production but higher levels repressed the biomass production significantly. Lower concentrations of ABA promoted the biomass production during the late log and stationary phase, while higher concentrations repressed the biomass production of D. salina. As shown can be seen in Figs. 1 and 2, for each phytohormone, the concentrations were clearly not consistent between the maximal optical density and maximal biomass production. This result indicated that the phytohormones affected the distribution of cellular components, which in turn influenced the optical absorbance characteristics. Consequently, the optical density was not suitable for monitoring the biomass production of D. salina cells cultured in media with added phytohormones. The results, therefore, indicated that the tested phytohormones also exerted effects on D. salina, similar to the effects of IAA and GA in Chlamydomonas reinhardtii (Wonkun et al., 2013), IAA in Chlorella sorokiniana (Babu et al., 2017), NAA in Chlorella vulgaris (Liu et al., 2017), and MI in D. salina (Cho et al., 2015), as well as ABA in Haematococcus pluvialis (Kobayashi et al., 1997), Chlamydomonas
3.1. Single factor analysis of effects of phytohormones on D. salina growth The production of β-carotene by D. salina could be divided into two steps, the aims of the two steps are the production of cells and β-carotene induction and accumulation, respectively. Therefore, the cell density of the first step is correlated with biomass in the second step. In the present study, the biomass production was expressed as cell density. To enhance the biomass production of the cells of D. salina, the growth effects of seven phytohormones, myo-inositol (MI), 2,4-dichlorophenoxyacetic acid (2,4-D), indoleacetic acid (IAA), naphthyl acetic acid (NAA), 6-benzylaminopurine (6-BA), gibberellic acid (GA) and abscisic acid (ABA), were analyzed by single factor experiments. Firstly, to screen the phytohormones that influence D. salina growth, the optical density (OD) of cultures grown in media with a gradient of hormone concentrations were monitored on the 13th and 15th day, at which the D. salina cells were already in the log to stationary transition phase or stationary phase generally. As shown in Fig. 1, it was confirmed that all seven phytohormones exert effects on cells growth in a dose-dependent manner. Because OD is not only affected by cell density, but also by cell size, light absorption, scattering properties of cells and other factors, therefore, the cell dry weights (CDW) were further 148
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Fig. 2. Cell concentrations of D. salina cultured in media spanning a gradient of phytohormone concentrations. The X axis indicates the 8 sampling time points of the 6 gradient concentrations of the 7 kinds of phytohormones (MI, 6-BA, NAA, IAA, 2,4-D, GA, and ABA) under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C. and the Y axis indicates the cell concentration of each sampling time point. Data shown is the mean ± SD, n = 4.
respiration under higher MI concentrations explained the decline of biomass production under these growth conditions. Although 2,4-D, NAA, and IAA have all been categorized as belonging to the auxin family based on their similar physiological functions in higher plants, they displayed marked differences in their effects on the growth and physiology of D. salina. All three phytohormones displayed complex effects on photosynthesis and respiration at certain concentrations and growth stages (Fig. 3B, C, and D), albeit with different characteristics. As shown in Fig. 3, lower levels of 2,4-D promoted the photosynthesis at the early growth stage. By contrast, both concentrations of NAA showed repressive effects on photosynthesis on the second and sixth day. The higher levels of IAA promoted the photosynthesis on the 2nd and 10th day. As shown in Fig. 4B and C, both concentrations of 2,4-D and IAA promoted the respiration on the 2nd day, but the lower level repressed the respiration on the 6th day and thereafter. It is worth noting that no respiration was detected on the 2nd day, while there was a sharp increase on the 6th day followed by a decrease on the 10th day under lower levels of NAA. The higher level 6-BA, which resulted in the repression of respiration on the 2nd day (Fig. 4E), promoted the photosynthetic activity of D. salina on the 6th day (Fig. 3E). The lower level of GA promoted the photosynthesis but the higher level repressed it
reinhardtii (Yoshida et al., 2003) and Chlorella saccharophila (ContrerasPool et al., 2016). These results suggested that phytohormones may share similar response mechanisms in all chlorophytes. 3.2. Effects of phytohormones on photosynthesis and β-carotene accumulation Photosynthesis and respiration are two key metabolic processes that determine the biomass productivity of photosynthetic organisms. On the other hand, phytohormones may exert minor physiological effects that are not reflected by changes of biomass production in D. salina. Therefore, the respiratory and photosynthetic rates were investigated to estimate the influence of phytohormones on the carbon fixation ability of the strain. Two levels of each phytohormone were selected, which showed a promoting and repressing effect on biomass production, respectively (Fig. 3 and 4). Both levels of MI promoted the photosynthesis at similar levels, especially during the early growth stage, and the promoting effects gradually declined along with time (Fig. 3A). The respiration of D. salina was greatly promoted by the addition MI, and at higher concentrations it was 3.4 times higher than at the optimal concentration for biomass production (Fig. 4A). these sharp increases of 149
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Fig. 3. Photosynthetic rates of D. salina cells cultured in media spanning a gradient of phytohormone concentrations. The X axis indicates the 5 culture time points of the 3 gradient concentrations of the 7 kinds of phytohormones (MI, 6-BA, NAA, IAA, 2,4-D, GA, and ABA) under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C. The Y axis indicates the photosynthetic rate of each time point. Data shown is the mean ± SD, n = 4.
response to stress was remodeled by phytohormones in D. salina. MI showed a stronger effect on growth, photosynthesis, and respiration of D. salina cells than the other six tested phytohormones. The auxin family phytohormones NAA, IAA and, 2,4-D displayed different effects on the D. salina physiology and growth. With the exception of ABA, lower levels of the other six phytohormones all delayed the β-carotene accumulation during the stationary growth stage.
(Fig. 3F). By contrast, both levels of GA promoted the respiration on the 6th day and repress the respiration on the 10th day (Fig. 4F). Both levels of ABA promoted the photosynthesis on the 10th day, leading to an increase by 3.3 times under the lower level of ABA in comparison to that of the blank control (Fig. 3G). Respiration was promoted by the higher level of ABA on the 2nd day but was repressed thereafter. By contrast, respiration was repressed at the lower level of ABA at all the analyzed time points (Fig. 4G). Taken together, all seven phytohormones had effects on both anabolism and catabolism with different effect sizes and times. Moreover, β-carotene in D. salina is a stress indicator which is accumulated under abiotic stresses such as high salt and light, as well as under nutrient deprivation during the late growth stage (Lv et al., 2016; Coesel et al., 2008; Saha et al., 2013). As shown in Fig. 5, none of the seven phytohormones had a significant effect on β-carotene accumulation before the stationary growth stage. Nevertheless, higher levels of MI, 6-BA and ABA promoted the β-carotene accumulations during the stationary stages (Fig. 5A, C, E), whereas lower levels of MI and 6-BA repressed β-carotene accumulation (Fig. 5A, C). By contrast, the lower level of ABA had no significant effect on β-carotene accumulation (Fig. 5E). Auxin family phytohormones showed repressive effects on βcarotene accumulations at both levels (Fig. 5B, D, and F). The repressive effect of GA was similar to that of NAA, but with a smaller size (Fig. 5D, G). The results above indicated that the cellular metabolic
3.3. Identifying significant phytohormones by Plackett–Burman design As indicated by the above single factor experiments, the seven phytohormones exerted different effects of different levels on the growth of D. salina. To further screen the significant variables affecting biomass production of D. salina, the effects of the seven phytohormones on biomass production were analyzed using a Plackett–Burman design (Table 1). This method permits the investigation of a vast number of critical factors through a smaller number of experimental studies compared to central composite designs (Morowvat et al., 2015). The design matrix selected for the screening of significant variables for biomass production and the corresponding responses is shown in Table 2. The model was calculated by factorial regression and the statistically significant variables were screened based on p-values of < 0.05 (Table 3). The model was considered statistically significant based on the p-value of 0.041. The S and R2 values of the model were 150
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Fig. 4. Respiration rates of D. salina cells cultured in media spanning a gradient of phytohormone concentrations. The X axis indicates the 5 culture time points of the 3 gradient concentrations of the 7 kinds of phytohormones (MI, 6-BA, NAA, IAA, 2,4-D, GA, and ABA) under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C. The axis Y indicated the respiration rate of each time point. Data shown is the mean ± SD, n = 4.
absence of a significant lack-of-fit F-value (0.6320) implied that the quadratic model is a good fit. The fitness of the quadratic model was also supported by the lack of fit (0.6321) and p-values (< 0.0001). The final equation was listed below, coded factors and the coefficients of the quadratic model were indicated:
0.1090 and 92.32, respectively, which suggested that the model explains 92.32% of the variation in the data. MI was identified to be the phytohormone with the strongest effect on biomass production (pvalue = 0.02,). Two additional phytohormones, ABA and IAA, had significant p-values of 0.025 and 0.036, respectively. The lower p-values suggested stronger effects on the cell biomass production. Among the three significant factors screened, MI exerted a positive effect on the biomass production, whereas the other two factors, ABA and IAA, exerted negative effects. The other four phytohormones did not exert significant effects, as indicated by their high p-values.
R = 9.24 + 0.20A–0.34C–0.28F–0.075AC–0.0075AF + 0.02CF–0.62A2–0.30C2–0.45F 2 where R is cell concentration, A is MI, C is IAA and F is ABA. As shown in Table 7, the R2 (the coefficient of determination) for the regression equation was 0.9898, indicating that the model could properly explain 98.98% of the value variation in the response presented by Box-Behnken design experiment. The pred R2 (predicted R2) of 0.9364 was in an acceptable agreement with the Adj R2 (adjusted R2) of 0.9766. The R2 can have values in the range from 0 to 1, and the greater the value is, the more reasonable the fit of the model. A signalto-noise ratio, which was reflected by the adeq precision (adequacy of precision), > 4 is necessary for a model. In the present study, the adequacy of precision ratio is 23.34, therefore the model had an adequate signal, suggesting that the model generated by Box-Behnken design can be used to guide the design space. Fig. 6A shows the plot of the predicted biomass production from the model versus the actual experimental values. It was evident that none of the experimental points deviated significantly from the predicted responses. The model thus had a sufficient correlation between the primary variables and the biomass production in the media with the addition of combinations of phytohormones.
3.4. Obtaining the optimal combination of phytohormones for biomass production by Box-Behnken design The three independent phytohormones, MI, ABA, and IAA were selected to implement response surface methodology using a BoxBehnken design. The design matrix and corresponding results of the Box-Behnken design that were used to determine the effects of three variables are shown in Table 4. The fitting results indicated that the quadratic model generated by Box-Behnken design was highly statically significant (p < 0.0001) (Table 5). Importantly, the quadratic model was also not aliased and was without a significant lack of fit or higher adjusted and predicted R2 (Tables 6 and 7). The ANOVA for the response surface quadratic model suggested that the model terms A, C, F, A2, C2 and F2 were all significant (p-value < 0.05). By contrast, there were no significant interactions between the variables, as shown by the high p-values (Table 6). The high model F-value (75.16) and the 151
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Fig. 5. β-carotene contents of D. salina cells cultured in media spanning a gradient of phytohormone concentrations. The X axis indicates the 8 culture time points of the 3 gradient concentrations of the 7 kinds of phytohormones (MI, 6-BA, NAA, IAA, 2,4-D, GA, and ABA) under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C. The Y axis indicated the β-carotene content of each time point. Data shown is the mean ± SD, n = 4. Table 1 Seven experimental variables at two different levels used for biomass production of D. salina via a Plackett-Burman design. Variable (mg·L MI 2,4-D IAA NAA 6-BA ABA GA
Symbol code
−1
) A B C D E F G
Table 2 Twelve runs of a Plackett-Burman design matrix for seven variables with coded values along with the observed and predicted cell concentration at the 19th day of the culture under continuous 60 μmol·m−2·s−1 illumination and 30.0 ± 1.0 °C. A: MI; B: 2,4-D; C: IAA; D: NAA; E: 6-BA; F: ABA; G: GA. Observed data shown is the mean, n = 4.
Experimental value Lower
Higher
450 0.25 0.10 0.02 0.45 0.13 0.12
630 0.75 0.30 0.06 0.75 0.40 0.20
Run order
1 2 3 4 5 6 7 8 9 10 11 12
Further, as shown in Fig. 6 (C, D, and E), a 3D response surface was generated to explore the optimal levels of the three phytohormones for maximum D. salina biomass production and to investigate the influence of variation in the factors' levels. Response surface plots show the influence of each phytohormone on D. salina biomass production on the Z-axis when optimizing two phytohormones, while maintaining another phytohormone at central point values. The interaction between the ABA and IAA concentrations on biomass production is shown in Fig. 6C, the 152
Cell concentration (106 cells·ml−1)
Experimental values
A
B
C
D
E
F
G
Observed
Predicted
−1 1 −1 −1 −1 1 −1 1 1 1 −1 1
−1 1 −1 1 −1 1 1 −1 −1 −1 1 1
−1 −1 −1 −1 1 −1 1 1 −1 1 1 1
1 1 −1 −1 1 1 −1 −1 −1 1 1 −1
1 −1 −1 −1 1 1 1 −1 1 −1 −1 1
1 −1 −1 1 −1 −1 −1 −1 1 1 1 1
−1 −1 −1 1 1 1 −1 1 1 −1 1 −1
2.35 2.96 3.05 2.52 2.90 2.92 2.54 2.36 2.79 2.84 2.73 2.90
2.38 2.84 2.97 2.46 2.89 2.92 2.44 2.49 2.82 2.87 2.79 2.97
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Table 3 Estimated effect, regression coefficient and corresponding T- and p-values for cell concentrations in the seven-variable Plackett-Burman design experiment. A: MI; B: 2,4-D; C: IAA; D: NAA; E: 6-BA; F: ABA; G: GA. Term
Effect
Coef
SE Coef
T-Value
p-Value
Constant A B C D E F G
– 0.23 0.03 −0.20 −0.06 −0.17 −0.22 −0.12
2.74 0.12 0.01 −0.10 −0.03 −0.09 −0.11 −0.06
0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
87 3.73 0.47 −3.12 −1.01 −2.77 −3.48 −1.85
0.000a 0.020b 0.665a 0.036c 0.371a 0.050a 0.025c 0.138a
a b c
Table 6 ANOVA analysis of the quadratic model developed for biomass production of D. salina cultured under different combinations of phytohormones.
Non-significant at P < 0.05. Significant positive effect. Significant negative effect.
Table 4 Seventeen runs of a Box-Behnken design matrix using a quadratic model with actual values. The cell concentrations were measured at the 15th day of culturing under continuous 60 μmol·m−2·s−1 illumination and 30.0 ± 1.0 °C. Cell concentration shown is the mean, n = 4. Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
MI (mg·L−1)
540 540 540 600 540 540 540 540 480 540 480 480 600 540 600 480 600
IAA (mg·L−1)
0.10 0.20 0.20 0.30 0.20 0.30 0.30 0.10 0.30 0.20 0.10 0.20 0.10 0.20 0.20 0.20 0.20
ABA mg·L−1)
Cell concentration (105 cells·ml−1)
0.40 0.26 0.26 0.26 0.26 0.13 0.40 0.13 0.26 0.26 0.26 0.40 0.26 0.26 0.13 0.13 0.40
8.46 9.15 9.21 8.07 9.23 8.47 7.90 9.11 7.81 9.40 8.41 7.72 8.97 9.21 8.62 8.21 8.10
Source
Sum of Squares
DF
Mean Square
F-value
p-value
VIF
Model A-MI C-IAA F-ABA AC AF CF A2 C2 F2 Residual Lack of Fit Pure Error Cor Total
5.0713 0.3240 0.9113 0.6216 0.0225 0.0002 0.0016 1.6382 0.3821 0.8669 0.0525 0.0169 0.0356 5.1238
9 1 1 1 1 1 1 1 1 1 7 3 4 16
0.5635 0.3240 0.9112 0.6216 0.0225 0.0002 0.0016 1.6382 0.3821 0.8669 0.0075 0.0056 0.0089
75.16 43.22 121.5 82.92 3.001 0.0300 0.2134 218.5 50.97 115.6
< 0.0001 0.0003 < 0.0001 < 0.0001 0.1268 0.8674 0.6581 < 0.0001 0.0002 < 0.0001
1.00 1.00 1.00 1.00 1.00 1.00 1.01 1.01 1.01
0.6320
0.6321
Table 7 Analysis of variance (ANOVA) of the parameters of the response surface methodology fitted to a second-order polynomial equation. Parameter
Value
Parameter
Value
Std. Dev. Mean C.V. % PRESS
0.087 8.59 1.01 0.33
R-Squared Adj R-Squared Pred R-Squared Adeq Precision
0.9898 0.9766 0.9364 23.34
shown in Table 4, which suggested that the optimal variables generated by the BBD in this study are valid. The 15th-day biomass production under phytohormone free culture conditions was 7.30 × 105 cells·ml−1. The biomass thus increased by 28% in response to the calculated optimal phytohormone addition. Additional validation experiments with a smaller inoculum size were investigated, and the results showed that the cell concentration of D. salina cells cultured in media with the calculated combined phytohormone addition increased by 19% (Fig. 6B). Therefore, the results from validation experiments further confirmed the validity of the model for predicting the maximum biomass production.
interaction between the IAA and MI concentrations in Fig. 6D and the interaction between the ABA and MI concentrations in Fig. 6E. All three interactions showed that the biomass production was influenced by all three parameters, and the response peaks were clearly shown due to the lack of significant interaction effects among any of the three variables. The corresponding biomass production values increased and then decrease with the increase of each variable. The proposed optimal combination for the three variables was calculated based on this secondorder polynomial model, yielding the values of 552 mg·L−1 of MI, 0.14 mg·L−1 of IAA and 0.22 mg·L−1 ABA, and predicted biomass of 9.41 × 105 cells·mL−1. The proposed values of the variables and corresponding biomass production were obviously close to the values of the center point of the BBD design matrix and the experimental values
4. Conclusion In this study, the growth promoting effects of seven phytohormones on D. salina were evaluated, and the results indicated that phytohormones from higher plant also exert effects on the growth of this important industrially used microalga at specific concentrations and growth stages. The physiological effects on photosynthesis, respiration and β-carotene biosynthesis were investigated, revealing that the tested phytohormones exerted different effects with different patterns. MI showed a stronger effect on growth, photosynthesis, and respiration of D. salina cells than the other six tested phytohormones. The auxin family phytohormones NAA, IAA, and 2,4-D displayed different effects on the growth and physiology of D. salina. With the exception of ABA,
Table 5 Sequential model sum of squares of fit analysis of Box-Behnken design. Source
Sum of squares
DF
Mean square
Mean vs Total Linear vs Mean 2FI vs Linear Quadratic vs 2FI Cubic vs Quadratic Residual Total
1255 1.86 0.024 3.19 0.017 0.036 1259
1 3 3 3 3 4 17
1255 0.62 8.108E-003 1.06 5.625E-003 8.900E-003 74.11
Underline indicates p-value < 0.05. 153
F Value
p-value Prob > F
2.46 0.025 141.85 0.63
0.1087 0.9943 < 0.0001 0.6321
Suggested Aliased
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Fig. 6. Three-dimensional response surfaces and validation experiment. The plot of the predicted biomass production versus the actual experimentally measured biomass of 17 runs experiments in the Box-Behnken design. (A); Time courses of biomass production of D. salina under optimized and unoptimized conditions. PC: Cell concentrations of 9 time points of D. salina cultured in media with the addition of the optimized phytohormone combination (551.96 mg·L−1 of MI, 0.14 mg·L−1 of IAA, and 0.22 mg·L−1 ABA) under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C; Control: Cell concentration of D. salina cultured in modified Johnson's media without phytohormones under continuous 60 μmol photons m−2 s−1 illumination and 30.0 ± 1.0 °C. Data shown is the mean ± SD, n = 4. (B); Three-dimensional response surfaces of the interaction of the three independent significant variables. (C, D and E).
lower levels of the other six phytohormones all delayed the β-carotene accumulation during the stationary growth stage. MI, ABA, and IAA were determined to be the significant variables by a Plackett–Burman design. A statistical model for maximum biomass production was constructed and the optimal concentration combination of the three significant variables was determined to be 552 mg·L−1 of MI, 0.14 mg·L−1 of IAA and 0.22 mg·L−1 ABA, which was verified by a validation experiment. There were no significant interaction effects between any two significant variables. The results of this research provide a new method for enhancing the biomass production of D. salina in industry, as well as insights into the physiological function of phytohormones in D. salina and other microalgae. On the other hand, scale-up experiments in large volume open pond and closed photobioreactors under different cultivation conditions, are needed in the future, because the optimal concentration combination of the phytohormones obtained in this study were performed in lab scale. Moreover, studies coupled with other cultivation factors, such as nutrient salts, temperature, and light, are also needed in the future to further enhance the biomass productivity and economic feasibility.
Technology and Business University (PRRD-2017-YB1) and the National Natural Science Foundation of China (No. 31401029). Declaration of authors contributions Conception and design (SJ), Provision of study materials, drafting of the article and obtaining of funding (HL), Statistical expertise (JH), Collection and assembly of data (BQ), article revision (QW), and analysis and interpretation of the data (SW). Declaration of authors agreement to authorship and submission of the manuscript for peer review All authors approved the authorship and submission of the manuscript for peer review of the article. Conflict of interest statement None conflict of interest declared.
Acknowledgments Statement of informed consent, human/animal rights This work is supported by the Open Research Fund Program of Beijing Key Lab of Plant Resource Research and Development, Beijing
No conflicts, informed consent, human or animal rights applicable. 154
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