Nitrogen removal affect lipid production and expression levels of accD gene through cultivation of Chlorella in synthetic wastewater

doi:10.21203/rs.3.rs-5099629/v1

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Nitrogen removal affect lipid production and expression levels of accD gene through cultivation of Chlorella in synthetic wastewater

https://doi.org/10.21203/rs.3.rs-5099629/v1

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Microalgae are considered to be a potential feedstock for biodiesel production. The study resulted in developing an integrated lipid enhancement strategy by culturing C-hlorella vulgaris in wastewater containing nitrogen and phosphorus. An ideal experimental design was carried out based on central composite design (CCD) with response surface methodology (RSM). This RSM was used to calculate the effects of nitrogen and phosphorus concentration, and their interaction with nitrogen removal and accD gene expression corresponding to lipid synthetize. ACCase with four subunits is the key enzyme for fatty acid synthesis which expression of the beta subunit (accD) synthesized in the chloroplast is decisive to the levels of heteromeric ACCase. Under this experimental design there were 13 different runs with various concentrations of nitrogen and phosphorus. Based on the analysis of variance (ANOVA), the nitrogen removal and gene expression model proved to be of very good fit with a very low probability value (< 0.0001). Optimum nitrogen removal (50.5%) and accD gene expression (8.5-fold) occurred at low nitrogen concentration (100 mg/L) and maximum phosphorus concentration (100 mg/L).

Microalgae have recently been used to remove nutrients from wastewater, fix carbon dioxide and produce biofuel. Despite the remarkable advantages of microalgae over other biodiesel feedstocks, commercial algal biofuels production is still not economically viable due to the high cultivation cost for algal biomass generation and challenges in scaling up of harvesting (Amini et al., 2020; Babaei et al., 2016). One possible solution for improving the economics of microalgal cultivation is cultivating in wastewater and altering the cultivation conditions. Also, enhancement of the lipid accumulation in microalgae could improve the economics of the biodiesel production process. A numbers of factors are known to influence the lipid accumulation in microalgae, such as nitrogen starvation or limitation, phosphate limitation, high salinity, metal and CO₂ concentration, light intensity, temperature and petrochemical concentration, etc. (Mirizadeh et al., 2020; Nzayisenga et al., 2020; Zhao et al., 2019; Singh et al., 2015; Madadi et al., 2021). Alteration of nutrient concentrations in the cultivation medium could be the viable approach to lipid enhancement which is easy, scalable and can be applied to various types of microalgal cultivation systems. Wastewater is an inexpensive source of nutrients (nitrogen and phosphorus) for effective growth of microalgae and biofuel production. Microalgae can play a beneficial role in removing nutrients from wastewater and producing biofuel simultaneously (Babaei et al., 2018; Praveen et al., 2016). Previous studies have reported nitrogen (N) and phosphorous (P) limitations are widely applied strategies for lipid enhancement; however, these parameters are associated with compromised biomass production which ultimately affects the overall lipid productivity (Chu et al., 2014).

Different genes are involved in lipid production in microalgae. For instance, Acetyl coenzyme A (Acetyl-CoA) carboxylase (ACCase) show crucial role in fatty acid biosynthesis, which catalyzes acetyl-CoA to convert to malonyl-CoA that is the first major step in fatty acid biosynthesis (Li et al., 2015). AccD is the subunit of ACCase enzyme which is synthesized in the chloroplast. The expression of accD gene in the chloroplast genome is very important to the levels of heteromeric ACCase (Nakkaew et al. 2008). Some studies have conducted to elucidate the effect of nitrogen and phosphorus on the gene expression of microalgae (Guldhe et al., 2019; Singh et al., 2017; Budhauliya and Purkayastha, 2017; Kumar et al., 2017). The nitrogen and phosphorus stress resulted in an increase in the expression of accD gene (Fan et al., 2014; Singh et al., 2015).

Most of the studies are either focused on nitrogen or phosphorus stress or single abiotic stress. The effect of nitrogen and phosphorus concentrations individually and in combination on nutrient removal and expression levels of accd gene is a scarcely exploited area. The response surface methodology (RSM) can be used to design statistical experiments, reduce number of experimental runs, and develop a mathematical model to predict out-put response. This statistical tool provides the synergistic effect of selected variables towards the response (Alipourzadeh et al., 2016).

In this study Chlorella vulgaris was cultivated in wastewater to investigate the parameters influencing nutrient removal and lipid production. Nitrogen and phosphorus concentration were investigated to achieve maximum nutrient removal and expression levels of accd gene. RSM was used across 3 levels to develop a model and to optimize the effective parameters on nutrient removal and lipid productivity simultaneously and observe the interaction between operational factors. In addition, expression level of key gene responsible for lipid biosynthesis in C. vulgaris was compared in the presence of different N and P concentrations using real-time PCR.

2.1. Microalgae strain

A pure strain of Chlorella vulgaris was prepared by Research Institute of Applied Science, Tehran, Iran. Microalgae were precultivated in sterile N-8 medium for 10 days at ambient temperature under fluorescent light illumination (Amini et al., 2020).

2.2. Experimental Procedure

The experiments were conducted in a batch mode using 1000 mL Erlenmeyer flasks. In each experiment, microalgae medium was transferred from elementary cultivated sources into the flasks contained autoclaved synthetic wastewater and 10-day-old microalgal culture. The flasks were then agitated at 140 rpm in a shaker. In all experiments, initial biomass concentration was 0.3 g/L. The flasks were illuminated by fluorescent with light intensity of 2000 lux and under a photoperiod of 12:12-h light/dark. Synthetic wastewater contained inorganic carbon and different concentration of total nitrogen (100, 550, 1000 mg/l) and phosphorous (10, 55, 100 mg/l). In all experiments, pH was maintained constant at 7 by adding base and acid.

2.3. Experimental design

In this study, RSM was used as a useful mathematical-statistical method to design experiments and assess the relationship between response and variables, as well as, to optimize variable conditions to predict the best value of response. Therefore, central composite design (CCD), which is ideal for sequential experimentation, was carried out. CCD confirms plenty amount of information to check the lack of fit when sufficient number of experimental value exists (Alipourzadeh et al., 2016). RSM and CCD were generated with the help of Design Expert software (version 0.0.7). Nitrogen and phosphorus concentration were the significant independent variables in this study which were varied over tree levels between − 1 to 1 at the determined ranges. Also, response variables were nitrogen removal and gene expression level. Table 1 provides the independent variable and their variation levels in actual and coded values. The total numbers of experiments for the two factors with two replications to enhance error were obtained as 10. According to the CCD, a full quadratic approximation, which can be used for developing a second-order response surface models, is written in general forms as follows:

Y = β₀ + β₁X₁ + β₂X₂ + β₁₁X₁² + β₂₂X₂² + β₁₂X₁X₂ + e_i (1)

Where y is the response; X₁ and X₂ are the variable; \(\:{\beta\:}_{0}\) is constant coefficient; β₁ and β₂ are linear coefficients; β₁₁ and β₂₂ are the squared term coefficients; β₁₂ is the interaction coefficients; and \(\:{e}_{i}\) is the error. The modality of the fit of the polynomial model was represented by the coefficient of determination (R²) and the adjusted R² (\(\:{\:R}_{adj}^{2}\)) (Bashir et al., 2010).

Table 1

Factors, level in coded and actual values for experimental design
Factor	Actual factor name	units	Level in coded and actual values
Factor	Actual factor name	units	Low actual	High actual	Low coded	High coded
A	Nitrogen concentration	mg/L	100	1000	-1	1
B	Phosphorus concentration	mg/L	10	100	-1	1

2.4. Analytical method

Biomass concentration was determined via measuring optical density of microalgae suspension by a spectrometer (Model 2100, UNICO, US) at a wavelength of 580 nm using dry weight method (Babaei et al., 2016). Liquid samples were withdrawn to measure COD, Nitrogen and Phosphate every 24 hr based on standard methods (APHA 2005).

2.5. Lipid Analysis

Lipids were extracted from the biomass obtained for each experiment solvent (chloroform and methanol) extraction and gravimetrically (Bligh and Dyer, 1959). The percentage lipid content was determined based on lipid recovered from known weight of dry biomass. Lipid productivity was calculated by the following expression (Eq. 2) (Singh et al., 2016).

Lipid Productivity (mg/l.d) = Biomass productivity (mg/l.d) ˟ percentage lipid content (2)

2.6. Gene expression analysis

2.6.1. Total RNA isolation and complementary DNA (cDNA) synthesis

For extracting the total RNA, 50 mg of the wet biomass was disrupted in liquid nitrogen before adding 1 ml TRIzol reagent (Khamoushi et al., 2020). After the extraction, the genomic DNA was removed with DNaseI treatment. Finally, Concentrations and integrity of RNA were confirmed spectrophotometrically. Extracted RNA was electrophoresed on 1% agarose gel at 120V. RNA extracts were used to synthesize cDNA by using the TAKARA cDNA synthesis kit. The synthesized cDNA was visualized on an agarose gel (2%) and utilized as the template for the qPCR reaction and the gene expression.

2.6.2. Realtime PCR analysis

Primers for accd and reference genes were designed using PRIMER3 (Table 2) and then synthesized by the Sinaclon Company, Iran. In this study, 18S rRNA was used as reference gene for normalization the gene expression data. The qPCR assay was performed

using the BIORAD iCycler iQ Real-Time PCR Detection System and the SYBR Green Master Mix. The real-time PCR cycle parameters were 15 s at 95°C for denaturing, 25 s at 55°C for annealing and 20 s at 72°C for extension. Melting curves were routinely checked for lack of non-specific peaks and abnormalities. The data of the gene expression was analyzed using 2^−ΔΔCT method and represented the fold change of the target gene in the treated sample relative to the control sample (BG11) (Schmittgen and Livak 2008).

Table 2

Primers for real-time PCR detection of expression of *accD* genes in *Chlorella vulgaris*
Gene	Primer sequence	Length (bp) of production
accD-F	5' CTCGTACTACTCGTGTGCTCT 3'	108
accD-R	5' CATCATCAAAGGGACGCCAC 3'	108
18S rRNA-F	5' CCTGCGGCTTAATTTGACTCA 3'	103
18S rRNA-R	5' TGCACCACCACCCATAGAAT 3'	103

2.6.3. Statistical analysis

The comparison between the treatments was conducted in Sigma Plot 11.0. Statistical analyses were performed for the biomass and lipid productivity, nutrient removal and gene expression level using one-way analysis of variance (ANOVA) (P < 0.05). In order to determine which system had a significant difference with others, Holm-Sidak test was conducted for every binary combination of the systems. Analysis of variance (ANOVA) with the 95% confidence level was used to evaluate the interaction between the effective variable and the response. Error bars in all figures represent the three replicates.

3.1. Biomass and lipid productivity

Biomass and lipid productivity was calculated at the late log phase for all the runs based on their growth curve analysis. As shown in Fig. 1, the biomass productivity was affected by different nitrogen and phosphorus concentrations. Based on the results, the highest biomass productivity (166.3 ± 5.3 mg/l.d) was obtained at the concentration of 550 and 55 mg/l nitrogen and phosphorus, respectively, which was not significantly different from the biomass productivity at the concentration of 1000 mg/l nitrogen and 100 mg/l phosphorus. Results demonstrated that the maximum biomass productivity achieved at optimum ratio of N/P (around 10) while the highest content of lipid was observed at limited concentration of nitrogen or phosphorus (N/P > 10 or N/P < 3.7) because under this condition, cellular metabolism (Biomass production) moves to fatty acid biosynthesis (Miller et al., 2010).

The lipid content of Chlorella in BG-11 medium was about 14%, which has increased in all concentrations of nitrogen and phosphorus (Fig. 2a). The maximum lipid content (40.2 ± 1.2%) was obtained in nitrogen and phosphorus concentrations of 100 and 100 mg/l, respectively. According to the Fig. 2a, in low concentration of nitrogen (100 mg/l) the lipid content raised by increasing phosphorus concentration up to 100 mg/l due to accumulate consumed phosphorus as polyphosphate in microalgae. Polyphosphate plays an important role in the metabolism of protein, RNA and ATP, these products are a source of energy for lipid accumulation in microalgae under limited nitrogen conditions (Chu et al., 2014).

The results also show that in conditions where there is a limit concentration of phosphorus (10 mg/l), the lipid content increased by increasing the concentration of nitrogen to 1000 mg/l. It has been reported that under phosphorus limitation, lipid accumulation in chlorella is higher than carbohydrate accumulation (Liang et al., 2013). Phosphate limitation in medium could lead to the breakdown of phospholipids from cell wall into neutral lipids in order to obtain required phosphate for other metabolic processes. Therefore, the lipid content increases under this condition.

According to low biomass productivity under nitrogen or phosphorus deficient, parameter of lipid productivity should be analyzed to optimize lipid production. The highest lipid productivity (41.8 ± 1 mg/l.d) was obtained at nitrogen and phosphorus concentrations of 100 and 55 mg/l, respectively. As shown in Fig. 2b, in nitrogen and phosphorus concentrations of 1000 and 10 mg/l and 100 and 100 mg / l, respectively, despite the high lipid content, the lipid production efficiency is low because in these conditions (limitation of nitrogen and phosphorus concentration), the levels of ribose 1 and 5-carboxylic bisphosphate enzymes (involved in carbon dioxide consumption) and tryptophan synthesis are reduced. These enzymes affect photosynthetic function and reduce the growth of microalgae (Msanne et al. 2012).

3.2. Nitrogen removal

As shown in Fig. 3, the highest percentage of nitrogen removal is obtained at low concentrations of nitrogen (100 mg/l). The maximum nitrogen removal efficiency was obtained at concentration of 100 and 55 mg/L nitrogen and phosphorus, respectively, due to limitation value of nitrogen in the culture medium. It has been reported when the ratio of nitrogen to phosphorus is less than 3.7, there is a limit to the concentration of nitrogen, the microalgae consume more nitrogen and this consumption is used to keep the cell alive (Alketife et al., 2017). Because in this condition, the cell produces lipids instead of producing biomass to keep itself alive that is consistent with the results of biomass production in Fig. 1. It can be concluded that biomass yield is directly proportional to N concentration in culture media while lipid accumulation of microalgal cells is inversely proportional to N concentration. In general, microalgae need both of nitrogen and carbon source to grow and do photosynthesis. In this study, the only source of carbon was carbon dioxide in aeration and organic carbon (COD), which was not enough for the consumption of microalgae.

3.3 Effect of combined nitrogen and phosphorus concentration on the expression of accD gene of C. Vulgaris

ACCase catalyzes the first rate limiting step in the fatty-acid biosynthetic pathway through the formation of malonyl-CoA from acetyl-CoA. In this study, the expression of heteromeric unit of ACCase (accD gene) has been evaluated as a function of combined nitrogen and phosphorus concentration changes.

To confirm gene expression in the samples, the PCR product was applied to the electrophoresis gel, which is shown in Fig. 4. As shown in the gel electrophoresis, the specific bands for accD gene expression were not seen in negative control and NRT control. The NRT control sample was placed for all samples and in Fig. 4–10, only the NRT gel image of the control is shown. Also, due to the high color of some bands, the results show that the rate of gene expression in samples 2, 3, 4, 5 and 7 is higher than other samples.

As shown in Fig. 5, an increase in expression level of accD gene was observed in all experimental condition as compared to control (BG11). The highest levels of gene expression were obtained at nitrogen and phosphorus concentrations of 100 and 100 mg/l, respectively, which was approximately 8.5-fold increase in expression of accD gene in the control sample (culture in BG-11 medium). This value of gene expression is completely consistent with the optimal concentration of nitrogen and phosphorus that obtained in the previous section (percentage content and efficiency of lipid production). The results show that when there is a limit to the concentration of nitrogen or phosphorus in wastewater, the expression of the accD gene is increased and the microalgae tend to produce more lipids. Nitrogen deprivation conditions could lead to reduced cell division. Reduced cell division shifts the lipid biosynthetic pathways to synthesize more neutral lipids than synthesizing membrane lipids required for the cell wall formation (Singh et al., 2016). Subsequent accumulation of NADH due to the lower photosynthetic rate inhibits enzyme citrate synthase and prevents acetyl-CoA from entering into the TCA cycle. Elevated concentrations of acetyl-CoA activate acetyl-CoA carboxylase, this leads to enhanced lipid accumulation in microalgal cells (Li et al., 2015) Similarly, Fan et al., (2015) reported that both nitrogen and phosphorus limitation resulted in increasing the expression of accD gene in C. pyrenoidosa.

3.4. Predicted model using RSM

Gene expression and nitrogen removal were taken as the response to analyze the combined effect of nitrogen and phosphorus concentration on cultivation of C.vulgaris. With the results obtained from the CCD experimental design and data analysis, reduced quadratic polynomial equations were derived. Insignificant coefficients were omitted to achieve a more suitable regression model using back-ward elimination procedure. Eqs. (3) and (4) indicates the final reduced regression model in terms of coded factors for the nitrogen removal and gene expression, respectively:

Nitrogen removal = 32.26–6.54 * A + 2.87 * B + 14.89 * A²- 5.56 * B² (3)

Gene expression = 4.54–1.07 * A- 0.45 * B – 2.4 * AB + 0.9740 * A² (4)

Based on previous studies, R² should be a minimum of 0.8 to fit a proper model (Joglekar et al., 1987). In both of these equations, R² were approximately 0.96; meaning that the models were fit. In addition, the value of the adjusted determination coefficient for both of the responses (adjusted R² = 0.94) suggested that about 6% of the total variation could not be explained by these models.

From the Table 3 it can be concluded that both factors were significant parameters on response nitrogen removal variation, while the nutrient concentration with most notable impact on nitrogen removal was nitrogen concentration (p < 0.0001). F-Value of 52.52 and p-value less than 0.0001 stated that the reduced quadratic model was valid. In addition, there was only a 0.01% chance that a model F-value could have occurred due to the

noise. Moreover, ANOVA analysis showed that the lack of fit of dates was not significant, revealing that the indicated model terms were significant for the considered response (Muhammad et al. 2013).

Table 3

**ANOVA analysis for quadratic model of nitrogen removal response**
Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	911.71	4	227.93	52.52	< 0.0001	significant
A-A	249.62	1	249.62	57.52	< 0.0001
B-B	49.31	1	49.31	11.36	0.0098
A²	612.74	1	612.74	141.20	< 0.0001
B²	85.23	1	85.23	19.64	0.0022
Residual	34.72	8	4.34
Lack of Fit	28.80	4	7.20	4.87	0.0771	not significant
Pure Error	5.91	4	1.48
Cor Total	946.43	12

The ANOVA regression for gene expression response is shown in Table 4. Based on data, the parameter influence on gene expression response could be arranged as AB < A < A² < B. Hence, nitrogen concentration had the most considerable effect on the gene expression.

Table 4

ANOVA analysis for quadratic model of gene expression response
Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	34.13	4	8.53	50.11	< 0.0001	significant
A-A	6.81	1	6.81	39.97	0.0002
B-B	1.22	1	1.22	7.14	0.0283
AB	23.04	1	23.04	135.32	< 0.0001
A²	3.07	1	3.07	18.00	0.0028
Residual	1.36	8	0.1703
Lack of Fit	1.12	4	0.2810	4.72	0.0810	not significant
Pure Error	0.2381	4	0.0595
Cor Total	35.49	12

The adequacy of these models was evaluated using diagnostic plot such as a plot of predicted versus actual values. According to Fig. 6a and b, the predicted values obtained from the presented models match well with the real values. Figure 6c and d demonstrates the three dimensional surface response as a function of the nitrogen and phosphorus concentration. It also indicates the major effects and the interaction between the investigated parameters.

According to the Fig. 6c, in all nitrogen concentrations, the nitrogen removal efficiency rose with increasing the concentration of phosphorus to 55 mg/l. It can be seen that the effect of this increase in low concentrations of nitrogen is greater than high concentrations of nitrogen.

As shown in Fig. 6d, at high concentrations of nitrogen (1000 mg/L), the expression of gene decreased by increasing the concentration of phosphorus, which indicates that the limitation of phosphorus concentration in the cultivation lead the microalgae to increase the expression of accD. Also, the results show that at high concentrations of phosphorus (100 mg/l), the relative expression of the gene dropped by rising the concentration of nitrogen.

3.5. Optimization model for simultaneously nitrogen removal and accD gene expression

Numerical optimization technique was applied to investigate the optimal conditions of the process. The goal was to maximize nitrogen removal and gene expression simultaneously. Predictably, maximum value of N removal and relative accD gene expression would be reached in low nitrogen concentration at 100 mg/L and close to the maximum of phosphorus concentration at 95.7 mg/L. In this optimum values, the maximum predicted N removal and level of gene expression was 51.64% and 8.34, respectively, and the desirability was 0.909.

The main focus of this study was to develop an easily applicable and scalable lipid enhancement strategy based on nutrient for chlorella vulgaris by investigating growth, lipid accumulation, nitrogen removal and expression of accD gene (lipid biosynthesis). In this study, RSM was used for modeling and optimization of the nitrogen removal and accD gene expression simultaneously by changing nitrogen and phosphorus concentration. Maximum nitrogen removal and gene expression was achieved in minimum concentration of nitrogen (100 mg/L) and close to maximum concentration of phosphorus (100 mg/L). The results showed that the high expression of the gene, followed by high lipid production, was achieved by limiting the concentration of nitrogen and phosphorus. Based on the results of the present study, utilizing wastewater containing nitrogen and phosphorus was recommended for the culturing of C. vulgaris and producing biodiesel.

Ethics approval:

Ethical approval was not required for this work.

Competing interests:

The authors declare that they have no competing interests relevant to this manuscript.

Funding:

This research was not funded.

Acknowledgments

The authors express their gratitude for the valuable guidance provided by Islamic Azad University, Tehran, Iran.

Data availability:

The data will be available upon request.

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Nitrogen removal affect lipid production and expression levels of accD gene through cultivation of Chlorella in synthetic wastewater

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Abstract

Figures

1. Introduction

2. Material and Methods

2.1. Microalgae strain

2.2. Experimental Procedure

2.3. Experimental design

2.4. Analytical method

2.5. Lipid Analysis

2.6. Gene expression analysis

2.6.1. Total RNA isolation and complementary DNA (cDNA) synthesis

2.6.2. Realtime PCR analysis

2.6.3. Statistical analysis

3. Result and discussion

3.1. Biomass and lipid productivity

3.2. Nitrogen removal

3.3 Effect of combined nitrogen and phosphorus concentration on the expression of accD gene of C. Vulgaris

3.4. Predicted model using RSM

3.5. Optimization model for simultaneously nitrogen removal and accD gene expression

Conclusion

Declarations

Ethics approval:

Competing interests:

Funding:

Acknowledgments

Data availability:

References

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