Appl Microbiol Biotechnol (2014) 98:9473–9481 DOI 10.1007/s00253-014-6088-6
BIOENERGY AND BIOFUELS
The effect of iron on growth, lipid accumulation, and gene expression profile of the freshwater microalga Chlorella sorokiniana Minxi Wan & Xuejie Jin & Jinlan Xia & Julian N. Rosenberg & Geng Yu & Zhenyuan Nie & George A. Oyler & Michael J. Betenbaugh
Received: 28 April 2014 / Revised: 2 September 2014 / Accepted: 9 September 2014 / Published online: 25 September 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract The effects of iron on the growth, lipid accumulation, and gene expression profiles of the limnetic Chlorella sorokiniana CCTCC M209220 under photoautotrophy were investigated. The addition of iron up to 10−5 mol l-l increased final cell densities by nearly 2-fold at 2.3×107 cells/ml, growth rate by 2-fold, and the length of the exponential phase by 5 days as compared to unsupplemented controls while 10−3 mol l−1 iron was toxic. The lipid content increased from 12 % for unsupplemented cultures to 33 % at 10−4 mol l−1 iron while the highest overall lipid yield reached 179 mg l−1. A ge nefis hing a nd qPCR co mpar ison b etween th e C. sorokiniana at low and high iron levels indicated increases in the expression of several genes, including carbonic anhydrase involved in microalgal cell growth, as well as acc1 and choline transporter related to lipid synthesis. This study provides insights into changes in gene expression and metabolism that accompany iron supplementation to Chlorella as well as potential metabolic engineering targets for improving growth and lipid synthesis in microalgae. M. Wan State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, People’s Republic of China M. Wan : X. Jin : J. Xia (*) : Z. Nie School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, People’s Republic of China e-mail: [email protected] M. Wan : J. N. Rosenberg : G. Yu : M. J. Betenbaugh (*) Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, MD222, Baltimore, MD 21218, USA e-mail: [email protected] G. A. Oyler Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Keywords Chlorella sorokiniana . Fe . Lipid biosynthesis . qPCR . Genefishing
Introduction Iron (Fe) is an essential nutrient for the survival of all organisms. For photosynthetic organisms in particular, iron is a critical cofactor for multiple elements of their electron transport system associated with the chloroplast (Briat et al. 2007). As a cofactor in photosystem I, iron can affect the ability of the photosynthetic apparatus to use light energy, perform interphotosystem electron transport, and attain suitable rates of carbon fixation and growth (Greene et al. 1992; Ivanov et al. 2000; Vassiliev et al. 1995). Iron deficiency can lead to a reduction in photosynthetic activity, iron-containing respiratory complexes, oxygen consumption, and growth rate (Andaluz et al. 2006; Lopez-Millan et al. 2000; Terauchi et al. 2010; Vigani et al. 2009). Although iron is widespread on Earth, it is often poorly available in the environment due to its low solubility in aerobic solutions and some chemical forms. Therefore, iron can be a major limiting factor for the growths of plants, including algae (Paz et al. 2007) and is recognized as a key factor in regulating microalgae biomass in ocean and oligotrophic waters (Behrenfeld et al. 2006). Microalgae are widely considered a potential feedstock for biofuel production, due to their relative rapid growth, high lipid productivity, and lower requirements of fertile land, relative to some other alternative sources (Rosenberg et al. 2008). Enhancing the lipid content may represent a useful approach to increase the yields of lipid precursors of biofuel from microalgae. As a result, the effect of iron on growth and lipid content of microalgae has received increasing attention (Xu et al. 2001).
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To date, the effect of iron on algae has principally focused on Chlamydomonas (Spijkerman 2011; Terauchi et al. 2010), while other microalgae, such as Chlorella, have not been extensively investigated. However, Chlorella strains have significant potential to be an important commercial species for generating biofuel precursors and other biochemical products (Chisti 2007; Illman et al. 2000; Rosenberg et al. 2014; Liang et al. 2009; Miao and Wu 2004; Songa et al. 2008). To reach the commercial potential and viability for biodiesel precursor production, methods for increasing the lipid content in these cells have been considered including reducing the supply of some elements such as nitrogen or phosphorous (Rodolfi et al. 2008; Xu et al. 2001). However, in these cases, the growth rate is greatly inhibited, and the biomass reduced, so that overall lipid productivity is not optimized. Liu et al. (2008) reported that the microalga Chlorella vulgaris C7 supplemented with 10−5 mol l−1 iron exhibited a lipid content up to 56.6 % of the biomass by dry weight, which was 3–7-fold higher than that for other media sources supplemented with lower iron concentrations. However, the effect of iron on the growth and lipid content of other freshwater Chlorella species has not been explored in depth. Furthermore, even though iron can affect metabolic pathways related to growth and lipid content, the impact at the expression level has not been extensively investigated. Thus, identification and evaluation of differentially expressed genes responding to different iron concentrations are an important objective for understanding lipid metabolism. In the present study, the differential expression of genes from the transcriptome of Chlorella sorokiniana cultured under different iron concentrations was sought using genefishing technology, a specific differential display PCR technique. Genefishing represents a rapid screening method for identifying differentially expressed genes among RNA samples along different developmental stages in a eukaryotic system (Liao et al. 2009) as well as in different biological or environmental conditions (Han et al. 2010). With this technology, different gene fragments can be amplified using annealing control primers (ACP). We also used qPCR to estimate the differential expression of genes under various iron concentrations to validate the results from genefishing and to study those genes related to lipid accumulation in more detail.
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height 106 cm) containing 10-l working volume. Mixing was achieved by agitating with filter-sterilized air with 0.03 % CO2 supply at 0.3 % (v/v) per min. Illumination was provided by cool-white fluorescent lamps to give light intensities of 200 μmoles m−2 s−1 with a photoperiod of light/dark 12 h:12 h. Cells were initially cultured in medium without addition of iron until the cell density reached 106 ml−1. C. sorokiniana inocula were then cultured at five levels of iron (FeCl 3 ·6H 2O/EDTA): 10−4 , 10−5 , 10 -6, 10 −7, and 0 mol l−1. The initial pH in each culture was adjusted to 7. FeCl3·6H2O/EDTA was prepared according to the following procedure: 1 g Na2EDTA and 81 mg FeCl3 6H2O were dissolved in 50 ml distilled water and 50 ml HCl (0.1 N), respectively; then, the solutions were mixed to prepare 3 mM FeCl3·6H2O/EDTA. The cell density of each culture was counted by hemocytometer (Neubauer), and specific growth rates were calculated to evaluate the effect of Fe on cell growth. All experiments were performed in triplicate, and thus, error bars shown resulted from replicates of experimental runs. Biomass, lipid, protein, N, and P analysis
Materials and methods
Microalgal cells were harvested by centrifugation (8000×g for 10 min), washed with dH2O and then lyophilized by the freeze dry vacuum (LGJ-25, Xiangyi, China), and measured gravimetrically. Total lipids were extracted from the dried cells by methanol–chloroform extraction (Bligh and Dyer 1959). chloroform–methanol (2:1, v/v) were added to the dried cells, then ultrasonication was used to break cells, and then the addition of methanol and water was added to achieve a final solvent ratio of chloroform/methanol/water of 1:1:0.9. After 3-h standing, chloroform and aqueous methanol layers were separated. The chloroform layer with total lipids was washed with a 5 % NaCl solution and evaporated to dryness. Thereafter, the total lipids were measured gravimetrically (Bligh and Dyer 1959). Lipid content is shown as the percent of lipid weight in the cell dry weight. Protein content was determined by the method of Bradford (1976). The N and P concentrations were determined according to the salicylic acid colorimetry (Hecht and Mohr 1990) and the ammonium molybdate spectrophotometric method (Worsfold et al. 1987), respectively.
Algal cultures and growth analysis
Total RNA extraction and cDNA synthesis
C. sorokiniana CCTCC M209220 (collected by China Center for Type Culture Collection, Wuhan, China) was isolated from the Inner Mongolia Province of China and was cultured in freshwater BG11 medium (Linden et al. 2007) at 25±2 °C in 12-l bubble column photobioreactors (diameter 12.1 cm,
Based on growth curves collected for strain CCTCC M209220 under various iron concentrations, the samples were obtained in stationary phases from C. sorokiniana cultures with 0, 10−5, and 10−4 mol l−1 iron, when these cells were cultured on the 23th day. Total RNA was isolated and purified
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with RNAprep pure Plant Kit (Tiangen, China). Total RNA was quantified at OD260 and OD280 with a NanoDrop® ND1000 spectrophotometer (NanoDrop Technologies, Wilmington, USA), and the integrity of total RNA was assessed by 1.5 % agarose gel electrophoresis and ethidium bromide staining. After confirming the absence of DNA from total RNA by checking amplification of the control gene (18S rRNA) using RNA as PCR templates, total RNA served as the template to synthesize cDNA with RevertAidTM H Minus First Strand cDNA Synthesis Kit and random primers (Fermentas, China).
9475 Table 1 Primers for qPCR detection of expression of genes related to the lipid accumulation in C. sorokiniana CCTCC M209220 Genea
Primer sequence
accD
F: TTTGGTTTGTGCTTCTGGTG R: CACCACCAGTTGTTGGAGAA F: TGACCGTGAAAAAGCATCTG R: CGACATATTCGCCTGATTGA F: ATACCGTGTGGAGGACCTTG R: AGCCAGTTCCAGGTGAAGAA F: CCTGCGGCTTAATTTGACTC R: GCGAACCAACCGTGACTATT F: AGGACATCTACGCGGTGTTC R: GCGGGTCAATCATCTACACA F: CGCGACATCCAGAAGAAGAT R: CTACACTGCTGCTGCTAGGC F: AGCTGCACTTTGAGCAGGAC R: GTTGGATTGGAGGAAGAGCA F: CTTTGCCGTCATCTGCTGTA R: AGCGCAGTGTTGAAAAACCT F: AGCACCCTAGCACTGCTTTC R: GAGAGGGGGATCATATGCAA F: TTTCAGAGCACACCTTACGC R: GATCTGAACGGAGACGCTGT F: GCACCGCTGTCACCTACC R: CGCATGTGTTGATTACACTGTC F: GGGGTCCCAAGAAGAAGAAC R: TAGTGGCGGGTTCAGAACAT F: CACTCGCAATTTGGTGTTTG R: ACAAATGTGACAGGCAGCAG
acc1 rbcL 18S No. 1
Genefishing analysis No. 2
Differentially expressed genes were screened by the ACP-based PCR GenefishingTM DEG kits (Seegene, South Korea). Briefly, total RNA as the template to synthesize cDNA with Omniscript Reverse Transcription Kit (Qiagen, USA) and primer dT-ACP1 included in the genefishing kit. According to the protocol of the kit, genefishing PCRs were performed by using these primer pairs: arbitrary ACP and each of the 20 arbitrary ACPs (from ACP1 to ACP20). Then, the PCR products were analyzed by 2 % agarose gel electrophoresis. The differentially expressed bands were extracted from the agarose gel by using QIAquick Gel Extraction Kit (Qiagen, USA). The obtained DNA fragments were cloned into the TA pDriver cloning vector (Qiagen, USA). After subcloning the vector in Escherichia coli DH5α (Invitrogen USA), the DNA fragments inserted in the vectors were sequenced (Genewiz, USA). Sequence identification was initially estimated by using of the BLASTN and BLASTX facility of the National Center for Biotechnology Information (http://www.ncbi.nlm.nih. gov/BLAST/). These sequences were submitted to Genbank (www.ncbi.nlm.nih.gov), and then, these accession numbers were distributed for Sequence DEG1 (accession JK990955), DEG2 (accession JK990956), DEG3 (accession JK990957), DEG4 (accession JK990958), DEG5 (accession JK990959), DEG6 (accession JK990960), DEG8 (accession JK990961), DEG9 (accession JK990962). Primer design for qPCR According to gene sequences obtained from strain C. sorokiniana in the genefishing analysis and that involved in lipid biosynthesis found in our previous studies (Wan et al. 2011), qPCR primers were designed by using Primer Premier 5.0 and then synthesized by the Invitrogen Company, USA. All primer pairs of selected genes are listed in Table 1. 18S rRNA gene of C. sorokiniana was used as the internal standard. The
No. 3 No. 4 No. 5 No. 6 No. 7 No. 8 No. 9 a
The numbers of genes were from the genefishing result showed in Fig. 3
primers were diluted to obtain 10-mM working stock solutions, and then, the qualities of primers were confirmed by sequencing the relative PCR products. qPCR analysis The qPCR was performed in iCycler iQ Real-time PCR detection system (Bio-Rad Laboratories, USA) using SYBR® Green Real-time PCR Master Mix (Toyobo, Japan) according to the manufacturer’s instructions: 1 cycle of 95 °C for 30 s, and then 40 cycles of 95 °C for 15 s, 55 °C for 15 s, and 72 °C for 30s. In order to normalize the amount of transcripts in each sample, the relative abundance of 18S rRNA was also determined and used as the internal standard. At the completion of each run, melting curves for the amplicons were measured by raising stepwise the temperature 0.5 °C from 55 to 95 °C while monitoring fluorescence. The specificity of the PCR amplification was checked by examining the melting curve for melting temperature Tm, its symmetry, and the lack of nonspecific peaks. The gene expression data was analyzed using 2 −ΔΔCt method (Livak and Schmittgen 2001).
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Results
Table 2 The final cell densities and growth rates under different iron concentrations
Effect of iron on growth and lipid accumulation
Cultures under different iron concentration (mol l−1)
Final cell densities (×107 cells ml−1)
Growth rates (×105 cells ml−1 day−1)
10−4 mol l−1 10−5 mol l−1 10−6 mol l−1 10−7 mol l−1 Control (no iron)
2.1 2.3 1.8 1.7 1.2
7.5 8.3 6.5 5.8 3.9
The growth rates of C. sorokiniana cultured in BG-11 medium supplemented with 10−4, 10−5, 10−6, and 10−7 mol l−1 iron were higher than cultures without iron (Fig. 1). However, C. sorokiniana was unable to grow in higher iron concentrations of 10−3 mol l−1. After 7 days, cultures with 10−5 mol l−1 iron exhibited the highest growth compared to others. After 17 days, the control sample reached the stationary phase while cultures supplemented with iron remained in the exponential growth phase. The iron-supplemented cultures reached the stationary phase by the 23rd day, which was at least 4 days longer than those in the absence of Fe3+. Furthermore, as shown in Table 2, the final cell densities and growth rates increased with increasing iron concentrations up to 10−5 mol l−1. The maximal final cell densities and growth rates were achieved in cultures supplemented with 10−5 mol l−1 iron, reaching levels that were almost double with those of the control cultures, respectively. As iron concentrations exceeded 10−5 mol l−1, both the final cell density and the growth rate declined. The trends in cell densities with increasing iron concentration were consistent with the changes in final biomass concentrations shown in Fig. 2. Effect of iron on lipid accumulation
Cell numbers (106 cells/ml)
Once cultures were approaching the stationary phase (day 23 for cultures with 10−4 and 10−5 mol l−1 iron; day 21 for cultures with 10−6 and 10−7 mol l−1 iron; day 17 for culture without iron), the lipid content (% dry weight in biomass) was determined using the gravimetric method. Cultures with elevated concentrations of iron exhibited an increased lipid content as shown in Fig. 2. The highest lipid content of 33 % (dry 24 22 20 18 16 14 12 10 8 6 4 2 0
10-3 10-4 10-5 10-6 10-7 0
0
2
4
6
8
10 12 14 16 18 20 22 24 26
Time (Day) Fig. 1 Growth curves of C. sorokiniana under supplements of different iron concentrations
weight, w/w) was observed with 10−4 mol l−1 of iron, 2.8-fold that without iron (12 %). However, the overall growth rates were not maximal 10−4 mol l−1 of iron to indicate that maximizing lipid content does not correspond exactly to maximizing growth rate. Considering both growth and lipid content, the highest overall lipid yield was 179 mg l−1 for 10−4 mol l−1 of iron, which was slightly higher than the yields obtained at 10−5 mol l−1. Expression analysis using genefishing To study the effect of iron on gene expression levels, we utilized the “genefishing” technique to compare mRNA expression profiles of cell samples grown with 10−6, 10−5, and 10−4 mol l−1 iron (Fig. 3). Twenty different annealing control primers (ACPs) were first used to amplify cDNA fragments from the gene library followed by separation of the resulting gene fragments according to their molecular weight using agarose gel electrophoresis. The apparent intensity of each band is then related to the expression level of the corresponding gene such that expression levels of the same gene amplified by the same ACP pair can be compared across different samples. More than 50 genes showed clear expression profiles under different iron concentrations. The length of most amplified fragments detected on the gel was between 200 and 400 bp. Most of these bands had the same relative intensity among the three samples, suggesting that the mRNA transcripts were expressed similarly and not significantly affected by changes in iron concentration (10−6, 10−5, or 10−4 mol l−1). However, nine genes with differences in expression levels were identified among the three conditions. The majority of the differentially expressed genes (DEGs) detected with genefishing suggested that they were expressed higher in the higher iron concentrations of 10−5 and/or 10−4 mol l−1 (DEG No. 1, 2, 3, 4, and 9). While DEG numbers 5 and 8 were also candidates, additional confirmation by qPCR analysis was needed as expression levels at lower iron concentrations were not readily evident for these bands. Alternatively, two genes amplified using ACPs 6 and 7 exhibited lower expression in the higher iron concentrations. In order to elucidate the metabolic
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9477 Lipid content
40
Biomass
Lipid yield
300
1.0
250
35
0.8
30
200
-1
Biomass (g l )
Lipid content (%)
-1
Overall lipid yield (mg l )
Fig. 2 Effect of iron on the extractable lipid content, biomass, and overall lipid yield of C. sorokiniana
0.6
25 20
0.4 15 10
0.2
150
100
50
5 0
0
implications of these expression profiles, the DNA fragments of these genes were purified from the agarose gels and sequenced (Table 3). The BLAST results indicated that the genes affected by iron serve as a range of activities, including plasma-membrane choline transport, ribosomal protein production, as well as RNA helicase and oligoribonuclease activity among other biological functions. Differential gene expression of select genes in C. sorokiniana at different iron concentrations Subsequent qPCR analyses were performed to check the reliability of genefishing. The 28S/18S rRNA ratio was
Fig. 3 Differentially expressed genes (DEGs) screening results using different concentrations of iron. ACP1-ACP20, the number of annealing control primer in PCR. Microalgae were cultured with iron (labels 1, 2, and 3 correspond to 10−6, 10−5, and 10−4 mol/l of iron, respectively), M
-7
-6
-5
10 10 10 -1 Iron Concentration (mol l )
10
-4
0.0
0
found to be less than 1.50 based on gel analysis, indicating that RNA was both integral and of high quality. As indicated in Table 4, the results of qPCR quantitatively confirmed the finding indicated by the genefishing analysis. Almost of the identified genes were indeed expressed at higher levels in elevated iron concentrations while DEG No. 6 and 7 were expressed as lower levels as indicated. The three additional genes, rbcL, accD, and acc1, involved in carbon dioxide capture and lipid biosynthesis also exhibited the same trends in that the expression levels were also slightly higher with increasing iron concentrations in C. sorokiniana.
DNA ladder (the length of bands from top to bottom: 2,000, 1,200, 800, 400, 200, and 100 bp). Genes with obvious differences in expression levels among three samples were marked by number 1 to 9 in the figure
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Table 3 The BLAST results of differentially expressed genes (DEGs) from genefishing DEG No.
1 2 3 4 5 6 7 8 9 a
The highest similar sequence in Genbanka
Accession
Description (organism)
Accession
Identity (%)
hypothetical Plasma-membrane choline transporter (Chlorella variabilis) hypothetical 60S ribosomal protein L12 (Chlorella variabilis) NFb hypothetical keratin associated protein 12–4 (Pan troglodytes) hypothetical ATP-dependent RNA helicase (Zea mays) 40S ribosomal protein S14 (Puccinia graminis) NF hypothetical oligoribonuclease (Chlorella variabilis) hypothetical Carbonic anhydrase (Chlorella variabilis)
EFN54827 EFN56333
71 93
NC_006488 NP_001146308 XP_003334111 NF EFN58252 EFN52746
31 30 37 NF 83 83
JK990955 JK990956 JK990957 JK990958 JK990959 JK990960 c
JK990961 JK990962
The webpage of Genbank is www.ncbi.nlm.nih.gov
b
NF no significant similarity found in GenBank
c
The sequence is shorter than 200 bp and cannot be submitted to Genbank. The DNA sequence is shown as follows:
GCTTCACCGCCCCGCCGTCGGCACCGCTGTCACCTACCGCTGCCGGCGCTGAGAGGAGTTAGTGATAGGCCTGCTGAAGGCTGACC TTTGGTTTGTATGCTACTATTGACAGTGTAATCAACACATGCGAAAAAAGAAAAAAAAAAAAAAAAAAAAAAAAA
Discussion Iron is an essential element for the survival of all living organisms, including photosynthetic organisms that have a special requirement for iron as a cofactor in multiple elements of their electron transport system. Due to its low solubility in aerobic solutions (Boyd et al. 2000), the addition of iron may Table 4 Expression difference of genes: accD, acc1, rbcL, and genes No.1 to No.9 from the genefishing, under different iron concentrations Genea Expression Expression levels under different iron concentration changes when iron 10−6 mol l−1 10−5 mol l−1 10−4 mol l−1 increased of iron of iron of iron rbcL accD acc1 No.1 No.2 No.3 No.4 No.5 No.6
UP UP UP UP UP UP UP UP down
(3.4±0.6)×102 (5.8±0.9)×101 0 (6.1±0.4)×10−4 (2.4±0.7)×10−4 (4.8±0.1)×10−4 (1.5±0.2)×10−3 (5.8±0.6)×10−4 (1.2±0.1)×10−3
(3.6±0.6)×102 (6.1±0.8)×101 (1.4±0.2)×10−2 (12±0.1)×10−4 (15±01)×10−4 (4.9±0.2)×10−4 (1.7±0.2)×10−3 (11±0.2)×10−4 (1±0.2)×10−3
(3.8±0.5)×102 (7.3±0.9)×101 (1.7±0.4)×10−2 (13±0.1)×10−4 (15±01)×10−4 (5.6±1)×10−4 (1.7±0.1)×10−3 (13±1)×10−4 (0.9±0.1)×10−3
No.7 No.8 No.9 18S
down UP UP
(1.6±0.2)×10−2 (1.7±0.3)×10−4 (9.7±0.2)×10−4 1
(1.3±0.2)×10−2 (1.8±0.1)×10−4 (15±1)×10−4 1
(0.73±0.06)×10−2 (2.2±0.1)×10−4 (17±2)×10−4 1
The results were normalized against the expression levels of the control gene, 18S rRNA gene, to correct the variation of sample-to-sample a
The numbers of genes were from the genefishing results showed in Fig. 3
improve its bioavailability in order to enhance microalgal growth. Therefore, iron limitations can cause photoautotrophic cells to lose their photosynthetic capacity (Terauchi et al. 2010), which can affect microalgal growth. Indeed, iron is an important cofactor associated with photosystem I for photosynthetic organisms. In our study, C. sorokiniana exhibited even more rapid growth rates under optimal iron concentrations with the highest cell density and fastest growth rate reaching 1.9-fold and 2.1-fold of that without iron, respectively. Furthermore, iron supplementation is shown in the current study to extend the exponential growth period by multiple days. Even without iron supplementation, cell numbers increased for 19 days, probably because existing iron present in the inoculum met the minimum requirements for cell growth, although growth rates and cell densities were significantly lower than those under the optimal iron concentrations. Lipids are usually synthesized and accumulated as carbon and energy stores in oil-rich microalgae under unfavorable or stressful conditions (Merzlyak et al. 2007; Songa et al. 2008; Wan et al. 2011). In the present study, cell densities in cultures with 10 −5 mol l −1 of iron was higher than that with 10−4 mol l−1 of iron, suggesting that 10−5 mol l−1 of iron is more suitable to cell growth than 10−4 mol l−1 of iron. Alternatively, iron at the higher concentration (10−4 mol l−1) may engender a stressful environment for Chlorella cell growth but a beneficial effect on lipid accumulation (higher lipid content 33 %, compared to 25 % in 10−5 mol l−1 of iron). This phenomenon may ultimately lead to the same outcome as reducing the supply of some elements (N and P) which can lower cell growth rate but improve lipid content (Rodolfi et al. 2008; Xu et al. 2001).
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While nitrogen and phosphorous limitations can increase microalgal lipid content, this approach often requires more than 20 days of cultivation in order to reach N and/or P starvation. In order to confirm that iron rather than N and P deprivation primarily affected the cellular lipid content in the current investigation, the initial and final concentrations of N and P were measured. Initial concentrations (N 17 mM; P 0.17 mM) decreased slightly to the final levels (N 12.1 mM; P 0.11 mM); however, the concentrations of N and P that lead to nitrogen and phosphorous starvation and increased lipid content are typically lower than 0.5 and 0.02 mM (Rodolfi et al. 2008; Xu et al. 2001). As a result, N and P were likely sufficient throughout the culture at all iron levels. Increasing the lipid content of C. sorokiniana suggests that the flux through certain metabolic pathways related to the lipid accumulation may be modulated by higher iron concentrations for multiple algal species. To study the effect of iron on gene expression levels, the “genefishing” technique was utilized to compare mRNA expression profiles of cell samples grown with 10−6, 10−5, and 10−4 mol l−1 iron. Subsequent qPCR analyses were performed to check the reliability of genefishing and confirm the hypothesis that iron alters the expression levels of these genes. In terms of patterns of upregulation and downregulation of the genes identified by genefishing, two specific genes (DEG No. 1 and 9) exhibited approximately 2-fold increases in expression levels at higher iron concentrations. According to the BLAST results, Gene 1 was found to be closest in sequence identity to the gene for plasma membrane-bound choline transporter-like protein. Choline is predominantly utilized for the synthesis of essential lipid components of the cell membranes, including phosphatidylcholine and sphingomyelin, and for the production of potent lipid mediators, such as lysophosphatidylcholine (Zeisel et al. 1991). Indeed, the function of the plasma membrane choline transporter is to translocate choline for phospholipid synthesis and/ or choline recycling (Michel et al. 2006). In addition, DEG No. 9 exhibits high similarity to the gene encoding carbonic anhydrase (CA) that catalyzes the conversion of carbon dioxide and water to bicarbonate and protons (Badger and Price 1994). As a result, CA can play a role in the acquisition and concentration of CO2 for photosynthesis and is related to atmospheric carbon fixation for the synthesis of more energy-dense molecules, such as sucrose and lipids (Portis and Parry 2007). Its upregulation is consistent with an increase in carbon fixation during increased growth and lipid synthesis at higher iron concentrations (Iida et al. 2009; Spreitzer and Salvucci 2002). The expression levels of five other genes were also upregulated (DEG No. 2–5 and 8). The physiological role of these genes in relation to iron levels, if any, in Chlorella remains to be established. Indeed, these proteins may potentially serve to enhance growth or lipid synthesis associated with increased
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iron concentrations; however, their exact impact will require further investigation. In addition, to the nine genes identified by genefishing, three other important pathway genes related to carbon flux were added to the qPCR analyses to further study the effect of iron on gene expression involved in lipid biosynthesis. These three genes code for ribulose 1,5-bisphosphate carboxylase oxygenase (RuBisCO) large subunit (rbcL gene), heteromeric/ bacterial Acetyl-CoA beta subunit (accD gene), and homomeric/eukaryotic ACCase (acc1 gene), which represent important enzyme components in carbon dioxide capture and lipid accumulation. The rbcL gene encodes the catalytic large subunit of ribulose RuBisCO, which catalyzes the carboxylation of ribulose-1,5-bisphosphate (RuBP) with carbon dioxide during the first reaction of carbon fixation in the Calvin cycle (Iida et al. 2009; Ki and Han 2007). Heteromeric ACCase catalyzes the conversion of acetyl-CoA to malonyl-CoA in the chloroplast in order for metabolites to enter the pathway of lipid biosynthesis and includes the beta subunit (accD) synthesized in the chloroplast. The acc1 gene encodes homomeric ACCase, which provides the material for the synthesis of long-chain fatty acids, flavonoid, and anthocyanins in the cytosol, and can replace heteromeric ACCase in the chloroplast of some plants (Sasaki and Nagano 2004). Given the potential roles of acc1, accD, and rbcL in lipid synthesis and CO2 capture, their elevation in C. sorokiniana at higher iron and lipid levels is reasonable. In particular, the level of acc1, which codes for homomeric Accase, increased from undetectable levels at 10−6 mol l−1 to significant levels at higher iron concentration to suggest a potentially important role for the enzyme in lipid synthesis of iron-supplemented microalgae cultures. The present study represents the first time that the effect of iron on the expression of specific genes has been evaluated in a particular Chlorella species. Interestingly, a similar analysis of a photosynthetic cyanobacterium in iron-deficient versus iron-sufficient media also showed that more genes were upregulated in Fe-sufficient media (Singh et al. 2003). Furthermore, deficiencies in iron can lead to low abundances of proteins in which iron is a cofactor, such as photosystem I (PSI) and photosystem II (PSII) (Singh et al. 2003). Photoautotrophic cells are also known to lose their photosynthetic capacity when iron is restricted (Terauchi et al. 2010), while sufficient iron levels can enhance the photosynthetic capacity and consequent growth rates of photoautotrophic cells as observed here. In addition to growth, the addition of iron also supports increased lipid content as observed here and in the microalga C. vulgaris (Liu et al. 2008). In conclusion, C. sorokiniana had a higher growth rate (8.3×105 cells ml−1 day−1) and lipid content (33 %, dry cell weight) when cultured with iron than without iron supplementation (growth rate 3.9×105 cells ml−1 day−1; lipid content 12 %, dry cell weight). As such, iron may either directly or
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indirectly function in regulating pathways of lipid metabolism and cell growth in the freshwater microalga C. sorokiniana. Genefishing and qPCR analyses further showed that iron has a significant positive effect on the expression levels of specific genes including acc1 related to lipid synthesis as well as CA involved in carbon capture and cell growth, and choline transporter related to lipid membrane assembly. This study has characterized the impact of iron on both growth and lipid synthesis in C. sorokiniana and provides insights into the enzymes and metabolic pathways which may altered in responses to changes in the iron levels present in the environment. Acknowledgments This work was supported by the Chinese Natural Science Foundation For Distinguished Group (No. 50621063), Graduate Innovation Project of Central South University (No. 2010bsxt05), and NSF EFRI Grant (No. 1332344).
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Appl Microbiol Biotechnol (2014) 98:9473–9481 Ivanov AG, Park YI, Miskiewicz E, Raven JA, Huner NPA, Oquist G (2000) Iron stress restricts photosynthetic intersystem electron transport in Synechococcus sp, PCC 7942. Febs Lett 485(2–3):173–177 Ki JS, Han MS (2007) Nuclear rDNA and chloroplast rbcL, rbcS and IGS sequence data, and their implications from the Japanese, Korean, and North American harmful algae, Heterosigma akashiwo (Raphidophyceae). Environ Res 103(3):299–304 Liang Y, Sarkany N, Cui Y (2009) Biomass and lipid productivities of Chlorella vulgaris under autotrophic, heterotrophic and mixotrophic growth conditions. Biotechnol Lett 31(7):1043–1049 Liao F-T, Lee Y-J, Ko J-L, Tsai C-C, Tseng C-J, Sheu G-T (2009) Hepatitis delta virus epigenetically enhances clusterin expression via histone acetylation in human hepatocellular carcinoma cells. J Gen Virol 90(5):1124–1134 Linden SK, Driessen KM, McGuckin MA (2007) Improved in vitro model systems for gastrointestinal infection by choice of cell line, pH, microaerobic conditions, and optimization of culture conditions. Helicobacter 12(4):341–353 Liu ZY, Wang GC, Zhou BC (2008) Effect of iron on growth and lipid accumulation in Chlorella vulgaris. Bioresour Technol 99(11): 4717–4722 Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) Method. Methods (San Diego, Calif) 25(4):402–408 Lopez-Millan AF, Morales F, Andaluz S, Gogorcena Y, Abadia A, De Las Rivas J, Abadia J (2000) Responses of sugar beet roots to iron deficiency. Changes in carbon assimilation and oxygen use. Plant Physiol 124(2):885–897 Merzlyak MN, Chivkunova OB, Gorelova OA, Reshetnikova IV, Solovchenko AE, Khozin-Goldberg I, Cohen Z (2007) Effect of nitrogen starvation on optical properties, pigments, and arachidonic acid content of the unicellular green alga Parietochloris incisa (Trebouxiophyceae, Chlorophyta). J Phycol 43(4):833–843 Miao X, Wu Q (2004) High yield bio-oil production from fast pyrolysis by metabolic controlling of Chlorella protothecoides. J Biotechnol 110(1):85–93 Michel V, Yuan ZF, Ramsubir S, Bakovic M (2006) Choline transport for phospholipid synthesis. Exp Biol Med 231(5):490–504 Paz Y, Shimoni E, Weiss M, Pick U (2007) Effects of iron deficiency on iron binding and internalization into acidic vacuoles in Dunaliella salina. Plant Physiol 144(3):1407–1415 Portis AR Jr, Parry MA (2007) Discoveries in Rubisco (Ribulose 1,5bisphosphate carboxylase/oxygenase): a historical perspective. Photosynth Res 94(1):121–143 Rodolfi L, Chini Zittelli G, Bassi N, Padovani G, Biondi N, Bonini G, Tredici MR (2008) Microalgae for oil: strain selection, induction of lipid synthesis and outdoor mass cultivation in a low–cost photobioreactor. Biotechnol Bioeng 102(1):100–112 Rosenberg JN, Oyler GA, Wilkinson L, Betenbaugh MJ (2008) A green light for engineered algae: redirecting metabolism to fuel a biotechnology revolution. Curr Opin Biotechnol 19:430–436 Rosenberg JN, Kobayashi N, Barnes A, Noel EA, Betenbaugh MJ, Oyler GA (2014) Comparative analyses of three Chlorella species in response to light and sugar reveal distinctive lipid accumulation patterns in the microalga C. sorokiniana. PLoS ONE:Under Revision Sasaki Y, Nagano Y (2004) Plant acetyl-CoA carboxylase: structure, biosynthesis, regulation, and gene manipulation for plant breeding. Biosci Biotechnol Biochem 68(6):1175–1184 Singh AK, McIntyre LM, Sherman LA (2003) Microarray analysis of the genome-wide response to iron deficiency and iron reconstitution in the cyanobacterium Synechocystis sp. PCC 6803. Plant Physiol 132(4):1825–1839 Songa D, Fub J, Shib D (2008) Exploitation of oil-bearing microalgae for biodiesel. Chin J Biotechnol 24(3):341–348
Appl Microbiol Biotechnol (2014) 98:9473–9481 Spijkerman E (2011) The expression of a carbon concentrating mechanism in Chlamydomonas acidophila under variable phosphorus, iron, and CO2 concentrations. Photosynth Res 109(1):179–189 Spreitzer RJ, Salvucci ME (2002) Rubisco: structure, regulatory interactions, and possibilities for a better enzyme. Annu Rev Plant Biol 53: 449–475 Terauchi AM, Peers G, Kobayashi MC, Niyogi KK, Merchant SS (2010) Trophic status of Chlamydomonas reinhardtii influences the impact of iron deficiency on photosynthesis. Photosynth Res 105(1):39–49 Vassiliev IR, Kolber Z, Wyman KD, Mauzerall D, Shukla VK, Falkowski PG (1995) Effects of iron limitation on photosystem-II composition and light utilization in Dunaliella-Tertiolecta. Plant Physiol 109(3): 963–972 Vigani G, Maffi D, Zocchi G (2009) Iron availability affects the function of mitochondria in cucumber roots. New Phytol 182(1):127–136
9481 Wan M, Liu P, Xia J, Rosenberg JN, Oyler GA, Betenbaugh MJ, Nie Z, Qiu G (2011) The effect of mixotrophy on microalgal growth, lipid content, and expression levels of three pathway genes in Chlorella sorokiniana. Appl Microbiol Biotechnol 91(3):835–844 Worsfold PJ, Richard Clinch J, Casey H (1987) Spectrophotometric field monitor for water quality parameters: the determination of phosphate. Anal Chim Acta 197:43–50 Xu N, Zhang X, Fan X, Han L, Zeng C (2001) Effects of nitrogen source and concentration on growth rate and fatty acid composition of Ellipsoidion sp. (Eustigmatophyta). J Appl Phycol 13(6):463–469 Zeisel SH, Dacosta KA, Franklin PD, Alexander EA, Lamont JT, Sheard NF, Beiser A (1991) Choline, an essential nutrient for humans. Faseb J 5(7):2093–2098
BIOENERGY AND BIOFUELS
The effect of iron on growth, lipid accumulation, and gene expression profile of the freshwater microalga Chlorella sorokiniana Minxi Wan & Xuejie Jin & Jinlan Xia & Julian N. Rosenberg & Geng Yu & Zhenyuan Nie & George A. Oyler & Michael J. Betenbaugh
Received: 28 April 2014 / Revised: 2 September 2014 / Accepted: 9 September 2014 / Published online: 25 September 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract The effects of iron on the growth, lipid accumulation, and gene expression profiles of the limnetic Chlorella sorokiniana CCTCC M209220 under photoautotrophy were investigated. The addition of iron up to 10−5 mol l-l increased final cell densities by nearly 2-fold at 2.3×107 cells/ml, growth rate by 2-fold, and the length of the exponential phase by 5 days as compared to unsupplemented controls while 10−3 mol l−1 iron was toxic. The lipid content increased from 12 % for unsupplemented cultures to 33 % at 10−4 mol l−1 iron while the highest overall lipid yield reached 179 mg l−1. A ge nefis hing a nd qPCR co mpar ison b etween th e C. sorokiniana at low and high iron levels indicated increases in the expression of several genes, including carbonic anhydrase involved in microalgal cell growth, as well as acc1 and choline transporter related to lipid synthesis. This study provides insights into changes in gene expression and metabolism that accompany iron supplementation to Chlorella as well as potential metabolic engineering targets for improving growth and lipid synthesis in microalgae. M. Wan State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, People’s Republic of China M. Wan : X. Jin : J. Xia (*) : Z. Nie School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, People’s Republic of China e-mail: [email protected] M. Wan : J. N. Rosenberg : G. Yu : M. J. Betenbaugh (*) Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, MD222, Baltimore, MD 21218, USA e-mail: [email protected] G. A. Oyler Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Keywords Chlorella sorokiniana . Fe . Lipid biosynthesis . qPCR . Genefishing
Introduction Iron (Fe) is an essential nutrient for the survival of all organisms. For photosynthetic organisms in particular, iron is a critical cofactor for multiple elements of their electron transport system associated with the chloroplast (Briat et al. 2007). As a cofactor in photosystem I, iron can affect the ability of the photosynthetic apparatus to use light energy, perform interphotosystem electron transport, and attain suitable rates of carbon fixation and growth (Greene et al. 1992; Ivanov et al. 2000; Vassiliev et al. 1995). Iron deficiency can lead to a reduction in photosynthetic activity, iron-containing respiratory complexes, oxygen consumption, and growth rate (Andaluz et al. 2006; Lopez-Millan et al. 2000; Terauchi et al. 2010; Vigani et al. 2009). Although iron is widespread on Earth, it is often poorly available in the environment due to its low solubility in aerobic solutions and some chemical forms. Therefore, iron can be a major limiting factor for the growths of plants, including algae (Paz et al. 2007) and is recognized as a key factor in regulating microalgae biomass in ocean and oligotrophic waters (Behrenfeld et al. 2006). Microalgae are widely considered a potential feedstock for biofuel production, due to their relative rapid growth, high lipid productivity, and lower requirements of fertile land, relative to some other alternative sources (Rosenberg et al. 2008). Enhancing the lipid content may represent a useful approach to increase the yields of lipid precursors of biofuel from microalgae. As a result, the effect of iron on growth and lipid content of microalgae has received increasing attention (Xu et al. 2001).
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To date, the effect of iron on algae has principally focused on Chlamydomonas (Spijkerman 2011; Terauchi et al. 2010), while other microalgae, such as Chlorella, have not been extensively investigated. However, Chlorella strains have significant potential to be an important commercial species for generating biofuel precursors and other biochemical products (Chisti 2007; Illman et al. 2000; Rosenberg et al. 2014; Liang et al. 2009; Miao and Wu 2004; Songa et al. 2008). To reach the commercial potential and viability for biodiesel precursor production, methods for increasing the lipid content in these cells have been considered including reducing the supply of some elements such as nitrogen or phosphorous (Rodolfi et al. 2008; Xu et al. 2001). However, in these cases, the growth rate is greatly inhibited, and the biomass reduced, so that overall lipid productivity is not optimized. Liu et al. (2008) reported that the microalga Chlorella vulgaris C7 supplemented with 10−5 mol l−1 iron exhibited a lipid content up to 56.6 % of the biomass by dry weight, which was 3–7-fold higher than that for other media sources supplemented with lower iron concentrations. However, the effect of iron on the growth and lipid content of other freshwater Chlorella species has not been explored in depth. Furthermore, even though iron can affect metabolic pathways related to growth and lipid content, the impact at the expression level has not been extensively investigated. Thus, identification and evaluation of differentially expressed genes responding to different iron concentrations are an important objective for understanding lipid metabolism. In the present study, the differential expression of genes from the transcriptome of Chlorella sorokiniana cultured under different iron concentrations was sought using genefishing technology, a specific differential display PCR technique. Genefishing represents a rapid screening method for identifying differentially expressed genes among RNA samples along different developmental stages in a eukaryotic system (Liao et al. 2009) as well as in different biological or environmental conditions (Han et al. 2010). With this technology, different gene fragments can be amplified using annealing control primers (ACP). We also used qPCR to estimate the differential expression of genes under various iron concentrations to validate the results from genefishing and to study those genes related to lipid accumulation in more detail.
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height 106 cm) containing 10-l working volume. Mixing was achieved by agitating with filter-sterilized air with 0.03 % CO2 supply at 0.3 % (v/v) per min. Illumination was provided by cool-white fluorescent lamps to give light intensities of 200 μmoles m−2 s−1 with a photoperiod of light/dark 12 h:12 h. Cells were initially cultured in medium without addition of iron until the cell density reached 106 ml−1. C. sorokiniana inocula were then cultured at five levels of iron (FeCl 3 ·6H 2O/EDTA): 10−4 , 10−5 , 10 -6, 10 −7, and 0 mol l−1. The initial pH in each culture was adjusted to 7. FeCl3·6H2O/EDTA was prepared according to the following procedure: 1 g Na2EDTA and 81 mg FeCl3 6H2O were dissolved in 50 ml distilled water and 50 ml HCl (0.1 N), respectively; then, the solutions were mixed to prepare 3 mM FeCl3·6H2O/EDTA. The cell density of each culture was counted by hemocytometer (Neubauer), and specific growth rates were calculated to evaluate the effect of Fe on cell growth. All experiments were performed in triplicate, and thus, error bars shown resulted from replicates of experimental runs. Biomass, lipid, protein, N, and P analysis
Materials and methods
Microalgal cells were harvested by centrifugation (8000×g for 10 min), washed with dH2O and then lyophilized by the freeze dry vacuum (LGJ-25, Xiangyi, China), and measured gravimetrically. Total lipids were extracted from the dried cells by methanol–chloroform extraction (Bligh and Dyer 1959). chloroform–methanol (2:1, v/v) were added to the dried cells, then ultrasonication was used to break cells, and then the addition of methanol and water was added to achieve a final solvent ratio of chloroform/methanol/water of 1:1:0.9. After 3-h standing, chloroform and aqueous methanol layers were separated. The chloroform layer with total lipids was washed with a 5 % NaCl solution and evaporated to dryness. Thereafter, the total lipids were measured gravimetrically (Bligh and Dyer 1959). Lipid content is shown as the percent of lipid weight in the cell dry weight. Protein content was determined by the method of Bradford (1976). The N and P concentrations were determined according to the salicylic acid colorimetry (Hecht and Mohr 1990) and the ammonium molybdate spectrophotometric method (Worsfold et al. 1987), respectively.
Algal cultures and growth analysis
Total RNA extraction and cDNA synthesis
C. sorokiniana CCTCC M209220 (collected by China Center for Type Culture Collection, Wuhan, China) was isolated from the Inner Mongolia Province of China and was cultured in freshwater BG11 medium (Linden et al. 2007) at 25±2 °C in 12-l bubble column photobioreactors (diameter 12.1 cm,
Based on growth curves collected for strain CCTCC M209220 under various iron concentrations, the samples were obtained in stationary phases from C. sorokiniana cultures with 0, 10−5, and 10−4 mol l−1 iron, when these cells were cultured on the 23th day. Total RNA was isolated and purified
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with RNAprep pure Plant Kit (Tiangen, China). Total RNA was quantified at OD260 and OD280 with a NanoDrop® ND1000 spectrophotometer (NanoDrop Technologies, Wilmington, USA), and the integrity of total RNA was assessed by 1.5 % agarose gel electrophoresis and ethidium bromide staining. After confirming the absence of DNA from total RNA by checking amplification of the control gene (18S rRNA) using RNA as PCR templates, total RNA served as the template to synthesize cDNA with RevertAidTM H Minus First Strand cDNA Synthesis Kit and random primers (Fermentas, China).
9475 Table 1 Primers for qPCR detection of expression of genes related to the lipid accumulation in C. sorokiniana CCTCC M209220 Genea
Primer sequence
accD
F: TTTGGTTTGTGCTTCTGGTG R: CACCACCAGTTGTTGGAGAA F: TGACCGTGAAAAAGCATCTG R: CGACATATTCGCCTGATTGA F: ATACCGTGTGGAGGACCTTG R: AGCCAGTTCCAGGTGAAGAA F: CCTGCGGCTTAATTTGACTC R: GCGAACCAACCGTGACTATT F: AGGACATCTACGCGGTGTTC R: GCGGGTCAATCATCTACACA F: CGCGACATCCAGAAGAAGAT R: CTACACTGCTGCTGCTAGGC F: AGCTGCACTTTGAGCAGGAC R: GTTGGATTGGAGGAAGAGCA F: CTTTGCCGTCATCTGCTGTA R: AGCGCAGTGTTGAAAAACCT F: AGCACCCTAGCACTGCTTTC R: GAGAGGGGGATCATATGCAA F: TTTCAGAGCACACCTTACGC R: GATCTGAACGGAGACGCTGT F: GCACCGCTGTCACCTACC R: CGCATGTGTTGATTACACTGTC F: GGGGTCCCAAGAAGAAGAAC R: TAGTGGCGGGTTCAGAACAT F: CACTCGCAATTTGGTGTTTG R: ACAAATGTGACAGGCAGCAG
acc1 rbcL 18S No. 1
Genefishing analysis No. 2
Differentially expressed genes were screened by the ACP-based PCR GenefishingTM DEG kits (Seegene, South Korea). Briefly, total RNA as the template to synthesize cDNA with Omniscript Reverse Transcription Kit (Qiagen, USA) and primer dT-ACP1 included in the genefishing kit. According to the protocol of the kit, genefishing PCRs were performed by using these primer pairs: arbitrary ACP and each of the 20 arbitrary ACPs (from ACP1 to ACP20). Then, the PCR products were analyzed by 2 % agarose gel electrophoresis. The differentially expressed bands were extracted from the agarose gel by using QIAquick Gel Extraction Kit (Qiagen, USA). The obtained DNA fragments were cloned into the TA pDriver cloning vector (Qiagen, USA). After subcloning the vector in Escherichia coli DH5α (Invitrogen USA), the DNA fragments inserted in the vectors were sequenced (Genewiz, USA). Sequence identification was initially estimated by using of the BLASTN and BLASTX facility of the National Center for Biotechnology Information (http://www.ncbi.nlm.nih. gov/BLAST/). These sequences were submitted to Genbank (www.ncbi.nlm.nih.gov), and then, these accession numbers were distributed for Sequence DEG1 (accession JK990955), DEG2 (accession JK990956), DEG3 (accession JK990957), DEG4 (accession JK990958), DEG5 (accession JK990959), DEG6 (accession JK990960), DEG8 (accession JK990961), DEG9 (accession JK990962). Primer design for qPCR According to gene sequences obtained from strain C. sorokiniana in the genefishing analysis and that involved in lipid biosynthesis found in our previous studies (Wan et al. 2011), qPCR primers were designed by using Primer Premier 5.0 and then synthesized by the Invitrogen Company, USA. All primer pairs of selected genes are listed in Table 1. 18S rRNA gene of C. sorokiniana was used as the internal standard. The
No. 3 No. 4 No. 5 No. 6 No. 7 No. 8 No. 9 a
The numbers of genes were from the genefishing result showed in Fig. 3
primers were diluted to obtain 10-mM working stock solutions, and then, the qualities of primers were confirmed by sequencing the relative PCR products. qPCR analysis The qPCR was performed in iCycler iQ Real-time PCR detection system (Bio-Rad Laboratories, USA) using SYBR® Green Real-time PCR Master Mix (Toyobo, Japan) according to the manufacturer’s instructions: 1 cycle of 95 °C for 30 s, and then 40 cycles of 95 °C for 15 s, 55 °C for 15 s, and 72 °C for 30s. In order to normalize the amount of transcripts in each sample, the relative abundance of 18S rRNA was also determined and used as the internal standard. At the completion of each run, melting curves for the amplicons were measured by raising stepwise the temperature 0.5 °C from 55 to 95 °C while monitoring fluorescence. The specificity of the PCR amplification was checked by examining the melting curve for melting temperature Tm, its symmetry, and the lack of nonspecific peaks. The gene expression data was analyzed using 2 −ΔΔCt method (Livak and Schmittgen 2001).
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Results
Table 2 The final cell densities and growth rates under different iron concentrations
Effect of iron on growth and lipid accumulation
Cultures under different iron concentration (mol l−1)
Final cell densities (×107 cells ml−1)
Growth rates (×105 cells ml−1 day−1)
10−4 mol l−1 10−5 mol l−1 10−6 mol l−1 10−7 mol l−1 Control (no iron)
2.1 2.3 1.8 1.7 1.2
7.5 8.3 6.5 5.8 3.9
The growth rates of C. sorokiniana cultured in BG-11 medium supplemented with 10−4, 10−5, 10−6, and 10−7 mol l−1 iron were higher than cultures without iron (Fig. 1). However, C. sorokiniana was unable to grow in higher iron concentrations of 10−3 mol l−1. After 7 days, cultures with 10−5 mol l−1 iron exhibited the highest growth compared to others. After 17 days, the control sample reached the stationary phase while cultures supplemented with iron remained in the exponential growth phase. The iron-supplemented cultures reached the stationary phase by the 23rd day, which was at least 4 days longer than those in the absence of Fe3+. Furthermore, as shown in Table 2, the final cell densities and growth rates increased with increasing iron concentrations up to 10−5 mol l−1. The maximal final cell densities and growth rates were achieved in cultures supplemented with 10−5 mol l−1 iron, reaching levels that were almost double with those of the control cultures, respectively. As iron concentrations exceeded 10−5 mol l−1, both the final cell density and the growth rate declined. The trends in cell densities with increasing iron concentration were consistent with the changes in final biomass concentrations shown in Fig. 2. Effect of iron on lipid accumulation
Cell numbers (106 cells/ml)
Once cultures were approaching the stationary phase (day 23 for cultures with 10−4 and 10−5 mol l−1 iron; day 21 for cultures with 10−6 and 10−7 mol l−1 iron; day 17 for culture without iron), the lipid content (% dry weight in biomass) was determined using the gravimetric method. Cultures with elevated concentrations of iron exhibited an increased lipid content as shown in Fig. 2. The highest lipid content of 33 % (dry 24 22 20 18 16 14 12 10 8 6 4 2 0
10-3 10-4 10-5 10-6 10-7 0
0
2
4
6
8
10 12 14 16 18 20 22 24 26
Time (Day) Fig. 1 Growth curves of C. sorokiniana under supplements of different iron concentrations
weight, w/w) was observed with 10−4 mol l−1 of iron, 2.8-fold that without iron (12 %). However, the overall growth rates were not maximal 10−4 mol l−1 of iron to indicate that maximizing lipid content does not correspond exactly to maximizing growth rate. Considering both growth and lipid content, the highest overall lipid yield was 179 mg l−1 for 10−4 mol l−1 of iron, which was slightly higher than the yields obtained at 10−5 mol l−1. Expression analysis using genefishing To study the effect of iron on gene expression levels, we utilized the “genefishing” technique to compare mRNA expression profiles of cell samples grown with 10−6, 10−5, and 10−4 mol l−1 iron (Fig. 3). Twenty different annealing control primers (ACPs) were first used to amplify cDNA fragments from the gene library followed by separation of the resulting gene fragments according to their molecular weight using agarose gel electrophoresis. The apparent intensity of each band is then related to the expression level of the corresponding gene such that expression levels of the same gene amplified by the same ACP pair can be compared across different samples. More than 50 genes showed clear expression profiles under different iron concentrations. The length of most amplified fragments detected on the gel was between 200 and 400 bp. Most of these bands had the same relative intensity among the three samples, suggesting that the mRNA transcripts were expressed similarly and not significantly affected by changes in iron concentration (10−6, 10−5, or 10−4 mol l−1). However, nine genes with differences in expression levels were identified among the three conditions. The majority of the differentially expressed genes (DEGs) detected with genefishing suggested that they were expressed higher in the higher iron concentrations of 10−5 and/or 10−4 mol l−1 (DEG No. 1, 2, 3, 4, and 9). While DEG numbers 5 and 8 were also candidates, additional confirmation by qPCR analysis was needed as expression levels at lower iron concentrations were not readily evident for these bands. Alternatively, two genes amplified using ACPs 6 and 7 exhibited lower expression in the higher iron concentrations. In order to elucidate the metabolic
Appl Microbiol Biotechnol (2014) 98:9473–9481
9477 Lipid content
40
Biomass
Lipid yield
300
1.0
250
35
0.8
30
200
-1
Biomass (g l )
Lipid content (%)
-1
Overall lipid yield (mg l )
Fig. 2 Effect of iron on the extractable lipid content, biomass, and overall lipid yield of C. sorokiniana
0.6
25 20
0.4 15 10
0.2
150
100
50
5 0
0
implications of these expression profiles, the DNA fragments of these genes were purified from the agarose gels and sequenced (Table 3). The BLAST results indicated that the genes affected by iron serve as a range of activities, including plasma-membrane choline transport, ribosomal protein production, as well as RNA helicase and oligoribonuclease activity among other biological functions. Differential gene expression of select genes in C. sorokiniana at different iron concentrations Subsequent qPCR analyses were performed to check the reliability of genefishing. The 28S/18S rRNA ratio was
Fig. 3 Differentially expressed genes (DEGs) screening results using different concentrations of iron. ACP1-ACP20, the number of annealing control primer in PCR. Microalgae were cultured with iron (labels 1, 2, and 3 correspond to 10−6, 10−5, and 10−4 mol/l of iron, respectively), M
-7
-6
-5
10 10 10 -1 Iron Concentration (mol l )
10
-4
0.0
0
found to be less than 1.50 based on gel analysis, indicating that RNA was both integral and of high quality. As indicated in Table 4, the results of qPCR quantitatively confirmed the finding indicated by the genefishing analysis. Almost of the identified genes were indeed expressed at higher levels in elevated iron concentrations while DEG No. 6 and 7 were expressed as lower levels as indicated. The three additional genes, rbcL, accD, and acc1, involved in carbon dioxide capture and lipid biosynthesis also exhibited the same trends in that the expression levels were also slightly higher with increasing iron concentrations in C. sorokiniana.
DNA ladder (the length of bands from top to bottom: 2,000, 1,200, 800, 400, 200, and 100 bp). Genes with obvious differences in expression levels among three samples were marked by number 1 to 9 in the figure
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Table 3 The BLAST results of differentially expressed genes (DEGs) from genefishing DEG No.
1 2 3 4 5 6 7 8 9 a
The highest similar sequence in Genbanka
Accession
Description (organism)
Accession
Identity (%)
hypothetical Plasma-membrane choline transporter (Chlorella variabilis) hypothetical 60S ribosomal protein L12 (Chlorella variabilis) NFb hypothetical keratin associated protein 12–4 (Pan troglodytes) hypothetical ATP-dependent RNA helicase (Zea mays) 40S ribosomal protein S14 (Puccinia graminis) NF hypothetical oligoribonuclease (Chlorella variabilis) hypothetical Carbonic anhydrase (Chlorella variabilis)
EFN54827 EFN56333
71 93
NC_006488 NP_001146308 XP_003334111 NF EFN58252 EFN52746
31 30 37 NF 83 83
JK990955 JK990956 JK990957 JK990958 JK990959 JK990960 c
JK990961 JK990962
The webpage of Genbank is www.ncbi.nlm.nih.gov
b
NF no significant similarity found in GenBank
c
The sequence is shorter than 200 bp and cannot be submitted to Genbank. The DNA sequence is shown as follows:
GCTTCACCGCCCCGCCGTCGGCACCGCTGTCACCTACCGCTGCCGGCGCTGAGAGGAGTTAGTGATAGGCCTGCTGAAGGCTGACC TTTGGTTTGTATGCTACTATTGACAGTGTAATCAACACATGCGAAAAAAGAAAAAAAAAAAAAAAAAAAAAAAAA
Discussion Iron is an essential element for the survival of all living organisms, including photosynthetic organisms that have a special requirement for iron as a cofactor in multiple elements of their electron transport system. Due to its low solubility in aerobic solutions (Boyd et al. 2000), the addition of iron may Table 4 Expression difference of genes: accD, acc1, rbcL, and genes No.1 to No.9 from the genefishing, under different iron concentrations Genea Expression Expression levels under different iron concentration changes when iron 10−6 mol l−1 10−5 mol l−1 10−4 mol l−1 increased of iron of iron of iron rbcL accD acc1 No.1 No.2 No.3 No.4 No.5 No.6
UP UP UP UP UP UP UP UP down
(3.4±0.6)×102 (5.8±0.9)×101 0 (6.1±0.4)×10−4 (2.4±0.7)×10−4 (4.8±0.1)×10−4 (1.5±0.2)×10−3 (5.8±0.6)×10−4 (1.2±0.1)×10−3
(3.6±0.6)×102 (6.1±0.8)×101 (1.4±0.2)×10−2 (12±0.1)×10−4 (15±01)×10−4 (4.9±0.2)×10−4 (1.7±0.2)×10−3 (11±0.2)×10−4 (1±0.2)×10−3
(3.8±0.5)×102 (7.3±0.9)×101 (1.7±0.4)×10−2 (13±0.1)×10−4 (15±01)×10−4 (5.6±1)×10−4 (1.7±0.1)×10−3 (13±1)×10−4 (0.9±0.1)×10−3
No.7 No.8 No.9 18S
down UP UP
(1.6±0.2)×10−2 (1.7±0.3)×10−4 (9.7±0.2)×10−4 1
(1.3±0.2)×10−2 (1.8±0.1)×10−4 (15±1)×10−4 1
(0.73±0.06)×10−2 (2.2±0.1)×10−4 (17±2)×10−4 1
The results were normalized against the expression levels of the control gene, 18S rRNA gene, to correct the variation of sample-to-sample a
The numbers of genes were from the genefishing results showed in Fig. 3
improve its bioavailability in order to enhance microalgal growth. Therefore, iron limitations can cause photoautotrophic cells to lose their photosynthetic capacity (Terauchi et al. 2010), which can affect microalgal growth. Indeed, iron is an important cofactor associated with photosystem I for photosynthetic organisms. In our study, C. sorokiniana exhibited even more rapid growth rates under optimal iron concentrations with the highest cell density and fastest growth rate reaching 1.9-fold and 2.1-fold of that without iron, respectively. Furthermore, iron supplementation is shown in the current study to extend the exponential growth period by multiple days. Even without iron supplementation, cell numbers increased for 19 days, probably because existing iron present in the inoculum met the minimum requirements for cell growth, although growth rates and cell densities were significantly lower than those under the optimal iron concentrations. Lipids are usually synthesized and accumulated as carbon and energy stores in oil-rich microalgae under unfavorable or stressful conditions (Merzlyak et al. 2007; Songa et al. 2008; Wan et al. 2011). In the present study, cell densities in cultures with 10 −5 mol l −1 of iron was higher than that with 10−4 mol l−1 of iron, suggesting that 10−5 mol l−1 of iron is more suitable to cell growth than 10−4 mol l−1 of iron. Alternatively, iron at the higher concentration (10−4 mol l−1) may engender a stressful environment for Chlorella cell growth but a beneficial effect on lipid accumulation (higher lipid content 33 %, compared to 25 % in 10−5 mol l−1 of iron). This phenomenon may ultimately lead to the same outcome as reducing the supply of some elements (N and P) which can lower cell growth rate but improve lipid content (Rodolfi et al. 2008; Xu et al. 2001).
Appl Microbiol Biotechnol (2014) 98:9473–9481
While nitrogen and phosphorous limitations can increase microalgal lipid content, this approach often requires more than 20 days of cultivation in order to reach N and/or P starvation. In order to confirm that iron rather than N and P deprivation primarily affected the cellular lipid content in the current investigation, the initial and final concentrations of N and P were measured. Initial concentrations (N 17 mM; P 0.17 mM) decreased slightly to the final levels (N 12.1 mM; P 0.11 mM); however, the concentrations of N and P that lead to nitrogen and phosphorous starvation and increased lipid content are typically lower than 0.5 and 0.02 mM (Rodolfi et al. 2008; Xu et al. 2001). As a result, N and P were likely sufficient throughout the culture at all iron levels. Increasing the lipid content of C. sorokiniana suggests that the flux through certain metabolic pathways related to the lipid accumulation may be modulated by higher iron concentrations for multiple algal species. To study the effect of iron on gene expression levels, the “genefishing” technique was utilized to compare mRNA expression profiles of cell samples grown with 10−6, 10−5, and 10−4 mol l−1 iron. Subsequent qPCR analyses were performed to check the reliability of genefishing and confirm the hypothesis that iron alters the expression levels of these genes. In terms of patterns of upregulation and downregulation of the genes identified by genefishing, two specific genes (DEG No. 1 and 9) exhibited approximately 2-fold increases in expression levels at higher iron concentrations. According to the BLAST results, Gene 1 was found to be closest in sequence identity to the gene for plasma membrane-bound choline transporter-like protein. Choline is predominantly utilized for the synthesis of essential lipid components of the cell membranes, including phosphatidylcholine and sphingomyelin, and for the production of potent lipid mediators, such as lysophosphatidylcholine (Zeisel et al. 1991). Indeed, the function of the plasma membrane choline transporter is to translocate choline for phospholipid synthesis and/ or choline recycling (Michel et al. 2006). In addition, DEG No. 9 exhibits high similarity to the gene encoding carbonic anhydrase (CA) that catalyzes the conversion of carbon dioxide and water to bicarbonate and protons (Badger and Price 1994). As a result, CA can play a role in the acquisition and concentration of CO2 for photosynthesis and is related to atmospheric carbon fixation for the synthesis of more energy-dense molecules, such as sucrose and lipids (Portis and Parry 2007). Its upregulation is consistent with an increase in carbon fixation during increased growth and lipid synthesis at higher iron concentrations (Iida et al. 2009; Spreitzer and Salvucci 2002). The expression levels of five other genes were also upregulated (DEG No. 2–5 and 8). The physiological role of these genes in relation to iron levels, if any, in Chlorella remains to be established. Indeed, these proteins may potentially serve to enhance growth or lipid synthesis associated with increased
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iron concentrations; however, their exact impact will require further investigation. In addition, to the nine genes identified by genefishing, three other important pathway genes related to carbon flux were added to the qPCR analyses to further study the effect of iron on gene expression involved in lipid biosynthesis. These three genes code for ribulose 1,5-bisphosphate carboxylase oxygenase (RuBisCO) large subunit (rbcL gene), heteromeric/ bacterial Acetyl-CoA beta subunit (accD gene), and homomeric/eukaryotic ACCase (acc1 gene), which represent important enzyme components in carbon dioxide capture and lipid accumulation. The rbcL gene encodes the catalytic large subunit of ribulose RuBisCO, which catalyzes the carboxylation of ribulose-1,5-bisphosphate (RuBP) with carbon dioxide during the first reaction of carbon fixation in the Calvin cycle (Iida et al. 2009; Ki and Han 2007). Heteromeric ACCase catalyzes the conversion of acetyl-CoA to malonyl-CoA in the chloroplast in order for metabolites to enter the pathway of lipid biosynthesis and includes the beta subunit (accD) synthesized in the chloroplast. The acc1 gene encodes homomeric ACCase, which provides the material for the synthesis of long-chain fatty acids, flavonoid, and anthocyanins in the cytosol, and can replace heteromeric ACCase in the chloroplast of some plants (Sasaki and Nagano 2004). Given the potential roles of acc1, accD, and rbcL in lipid synthesis and CO2 capture, their elevation in C. sorokiniana at higher iron and lipid levels is reasonable. In particular, the level of acc1, which codes for homomeric Accase, increased from undetectable levels at 10−6 mol l−1 to significant levels at higher iron concentration to suggest a potentially important role for the enzyme in lipid synthesis of iron-supplemented microalgae cultures. The present study represents the first time that the effect of iron on the expression of specific genes has been evaluated in a particular Chlorella species. Interestingly, a similar analysis of a photosynthetic cyanobacterium in iron-deficient versus iron-sufficient media also showed that more genes were upregulated in Fe-sufficient media (Singh et al. 2003). Furthermore, deficiencies in iron can lead to low abundances of proteins in which iron is a cofactor, such as photosystem I (PSI) and photosystem II (PSII) (Singh et al. 2003). Photoautotrophic cells are also known to lose their photosynthetic capacity when iron is restricted (Terauchi et al. 2010), while sufficient iron levels can enhance the photosynthetic capacity and consequent growth rates of photoautotrophic cells as observed here. In addition to growth, the addition of iron also supports increased lipid content as observed here and in the microalga C. vulgaris (Liu et al. 2008). In conclusion, C. sorokiniana had a higher growth rate (8.3×105 cells ml−1 day−1) and lipid content (33 %, dry cell weight) when cultured with iron than without iron supplementation (growth rate 3.9×105 cells ml−1 day−1; lipid content 12 %, dry cell weight). As such, iron may either directly or
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indirectly function in regulating pathways of lipid metabolism and cell growth in the freshwater microalga C. sorokiniana. Genefishing and qPCR analyses further showed that iron has a significant positive effect on the expression levels of specific genes including acc1 related to lipid synthesis as well as CA involved in carbon capture and cell growth, and choline transporter related to lipid membrane assembly. This study has characterized the impact of iron on both growth and lipid synthesis in C. sorokiniana and provides insights into the enzymes and metabolic pathways which may altered in responses to changes in the iron levels present in the environment. Acknowledgments This work was supported by the Chinese Natural Science Foundation For Distinguished Group (No. 50621063), Graduate Innovation Project of Central South University (No. 2010bsxt05), and NSF EFRI Grant (No. 1332344).
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