Institute of Oceanology, Chinese Academy of Sciences
Article Information
- LIANG Sijie(梁思杰), GUO Li(郭栗), LIN Genmei(林根妹), ZHANG Zhongyi(张忠义), DING Haiyan(丁海燕), WANG Yamei(王亚梅), YANG Guanpin(杨官品)
- Improvement of Nannochloropsis oceanica growth performance through chemical mutation and characterization of fast growth physiology by transcriptome profiling
- Chinese Journal of Oceanology and Limnology, 35(4): 792-802
- http://dx.doi.org/10.1007/s00343-017-6023-7
Article History
- Received Jan. 27, 2016
- accepted in principle Apr. 25, 2016
- accepted for publication Jun. 13, 2016
2 College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China;
3 Institutes of Evolution and Marine Biodiversity, Ocean University of China, Qingdao 266003, China
The development of environmentally friendly bioenergy is in increasing demand as it has been predicted that fossil fuels will be exhausted in the foreseeable future (Tilman et al., 2009). Microalgae, the primary producers of diverse ecological systems, grow rapidly and have the potential to afford significant industrial biomass and biofuel yields (Wijffels and Barbosa, 2010). Unfortunately, it is far from reality for microalgae to produce biofuel directly (Petkov et al., 2012). One restriction on biofuel biosynthesis is the lack of elite microalgal varieties. Early research has mainly focused on the isolation and tentative culture of biofuel-producing microalgae (Ortiz-Marquez et al., 2013); however, neither domestication nor genetic improvement of these microalgae has been initiated. It can be realistically expected that effective methods for improving terrestrial crops will function well in microalgae (Chepurnov et al., 2011). Actually, various strategies have indeed been applied for microalgal genetic improvement. To date, protoplast fusion (Tjahjono et al., 1994), genetic transformation (Kawata et al., 1991; Xue et al., 2015), chemical mutation (Zhang and Lee, 1997), atmosphere and room temperature plasma (ARTP) mutation (Fang et al., 2013), genetic engineering (Ortiz-Marquez et al., 2013), selective breeding after genome shuffling (Takouridis et al., 2015), and diverse molecular techniques, such as genome editing, knocking-out, and knocking-down (Jinkerson and Jonikas, 2015), have been attempted in microalgae. Among these diverse methods for genetic improvement, both chemical and physical mutations have contributed significantly to crop breeding and performance. Such contributions have also been realized in microalgae, as in N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) mutated Chlorococcum sp. that possess a two-fold higher astaxanthin content (Zhang and Lee, 1997). However, mutation techniques have not advanced genetic improvement of microalgae as a group of organisms, and the new tools are not available for nonmodel and in-culture microalgae. The yield of biomass (or growth speed), accumulation of desirable materials, and resistance to diverse stresses are the most important microalgal traits desirable for large-scale culture. Unfortunately, studies regarding these traits are scarce.
With the popularization of next-generation sequencing technology, transcriptome profiling has evolved into a routine approach to decipher the physiogenetic mechanisms of desirable traits of animals, plants, and microorganisms, including microalgae (Mardis, 2008; He et al., 2012). For example, transcriptome analysis has provided insights into C4-like photosynthesis and the oil body-forming mechanism in Myrmecia incise (Ouyang et al., 2013), the adaptation mechanisms of polar Chlamydomonas sp. (Kim et al., 2013) and N. oceanica (Guo and Yang, 2015), and the oil accumulation mechanisms of Chlorella protothecoides (Gao et al., 2014) and Neochloris oleoabundans (Rismani-Yazdi et al., 2012). However, such approaches have been little applied to unveiling the physiological processes and genetic mechanisms of the growth performance of microalgae.
The microalga Nannochloropsis oceanica possesses the potential for being a biofuel synthesizer (Boussiba et al., 1987; Sukenik et al., 1989). This species is known to be monoploid and asexual (Pan et al., 2011) and is thus suitable for modification of both its nuclear and cytoplasmic genes through mutation. Among currently available mutation methods, chemical mutation is the most convenient. As a preliminary study in this laboratory, N. oceanica was mutated chemically with MNNG. From the resulting mutated population, the fastest and slowest growth mutants (positive and negative growth, PMU and NMU, respectively) were selected, and the two mutants and the wild strain (WT) were subjected to transcriptome profiling with the goal of understanding the physiological mechanisms underlining fast growth behavior.
2 MATERIAL AND METHOD 2.1 N. oceanica and its cultureN. oceanica was obtained from Key Laboratory of Mariculture of Chinese Ministry of Education, Ocean University of China. The alga was cultured in f/2 medium (pH 7.8, salinity 30; Guillard and Ryther, 1962; Guillard, 1975). The f/2 medium was prepared with filtrated seawater, which was autoclaved at 121℃ for 30 min. The alga was cultured at a constant temperature of 26℃ and under 70 μmol photons/ (m2∙s) following a 12 h/12 h light/dark regime.
2.2 MNNG mutation and mutant selectionAlgal cells in the exponential growth phase were collected and washed twice with PBS (pH 7.2–7.4), resuspended and adjusted to 107 cells/mLin PBS, and subjected to 3.5 mg/mL MNNG mutation at room temperature (~25℃) for 1 h. The cells were then washed with PBS, resuspended in f/2 medium, and incubated in darkness overnight before being spread on f/2 solid medium (Nečas, 1975; Lee and Jones, 1976). One month later, clear and well-separated colonies were selected and cultured to the stationary growth phase on 24-well plates, one colony in each well.
WT cultures at the stationary phase were diluted to various degrees while monitoring the OD680 and the dilutions' cell density measured to build an OD680/cell density curve. At different initial OD680 values (0.001 4, 0.007 0, and 0.035 0), WT suspensions were batch cultured in multiple flasks for each dilution in f/2 medium (20 mL inoculum in a 100-mL conicalfl ask), with the OD680 measured every other day, one flask each time. Individual colonies were inoculated onto 24-well plates and cultured in f/2 medium to stationary phase, at which point each was inoculated into new flasks (initial OD680 0.001 4–0.035 0) and batch cultured as with the WT cultures.
2.3 RNA extraction and transcriptome sequencingCells were harvested at the exponential growth phase, which was on day 12 for WT and days 10 and 14 for positive and negative cells, respectively, by centrifugation at 5 000×g for 7 min and immediately frozen in liquid nitrogen for RNA extraction. Total RNA of each mutant and WT was extracted using TRIZOL reagent (Invitrogen Corp., Carlsbad, CA, USA), following the manufacturer's instructions. RNA degradation and genomic DNA contamination were evaluated through 1% agarose gel electrophoresis. RNA purity was evaluated using a NanoPhotometer® (Implen GmbH, Munchen, Germany). RNA concentration was measured using a Qubit® RNA Assay Kit in Qubit® 2.0 Fluorometer (Life Technologies, Inc., Carlsbad, CA, USA). RNA integrity was assessed using an RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 System (Agilent Technologies, Inc., CA, USA). The 3′ poly (A)-tailed mRNA was isolated from the total RNA using oligo (dT)-linked magnetic beads (Oligotex mRNA Kits, Qiagen GmbH, Hilden, Germany), following manufacturer's instructions, then fragmented, and used as template for first-strand cDNA synthesis with reverse transcriptase and random hexamers. The second-strand cDNA was synthesized using RNase H and DNA polymerase I. After polishing the cDNA ends using T4 DNA polymerase and Klenow DNA polymerase at 20℃ for 30 min, a single adenine base was added to the cDNA 3′-ends. Illumina mRNA-Seq Kit specific adaptors (Illumina Inc., San Diego, CA, USA) were then ligated to the resulting 3′-ends. The modified cDNA was gel purified and PCR amplified, yielding a cDNA library the size and concentration of which was determined on an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc.). The cDNA library was sequenced commercially with an Illumina HiSeq 2000 Sequencer (Beijing Novogene Bioinformatics Technology Co., Ltd. Beijing, China).
2.4 Data processingAfter removing low quality sequencing reads, clean reads were assembled using Trinity (Grabherr et al., 2011) by setting the minimum_kmer_coverage at 2 and other parameters at default. Gene functions were annotated by referring to NCBI non-redundant protein sequences (Nr), NCBI non-redundant nucleotide sequences (Nt), protein family (Pfam), clusters of orthologous groups of proteins (KOG/ COG), a manually annotated and reviewed protein sequence database (Swiss-Prot), KEGG ortholog (KO), and gene ontology (GO).
The abundance of gene transcripts was determined using RSEM (Li and Dewey, 2011). Clean reads were stacked onto the assembled transcriptome and each counted for its gene number. The reads were normalized using the expected number of fragments per kilobase of transcript sequence per millions base pairs sequenced (FPKM), which avoided the effects of sequencing depth and transcript length (Trapnell et al., 2010). The read counts were adjusted using the edgeR software package with one scaling normalizing factor. Differential expression of any two samples was analyzed using the DEGseq (2010) R package. The P-value was adjusted using q-value software (Storey and Tibshirani, 2003). The differential expression was considered significant if q-value was < 0.005 and log2|foldchange|>1.
Differentially expressed genes (DEGs) were enriched in Gene Ontology (GO) terms with the GOseq R software package, based on Wallenius' noncentral hypergeometric distribution (Young et al., 2010), which can avoid length bias in DEGs. KEGG (Kanehisa et al., 2008; http://www.genome.jp/kegg/), a database that aids in understanding the high-level functions and utility of biological systems, and KOBAS software (Mao et al., 2005) were used for enriching DEGs into KEGG pathways.
3 RESULT 3.1 Selection of fast and slow growth mutantsThe WT cell density was found to be linearly proportional to its OD680 (R2=0.999); such linearity also existed among the mutated strains (Fig. 1a). The OD680 of these suspensions might have been associated with a range of cell characteristics, such as cell size and cell growth phase. However, the cell number of a cell line should be the principle factor associated with OD680. WT and the mutant strains were cultured to stationary phase, diluted to different cell densities (OD680 of 0.001 4, 0.007 0, and 0.035 0), and cultured to obtain growth curves with different initial concentrations. The slope of these observed growth curves in exponential growth phase were found to be slightly different among the mutants but, fortunately, their final OD680 values were almost identical (Fig. 1b). These findings allowed the identification of fast and slow growing strains according to their OD680 at stationary phase. Comparison of the final OD680 values of WT and 3 faster and 3 slower growing mutants obtained in triplicate, parallel mutation trials (Fig. 1c) showed that they were OD680 values were proportional to their cell density. The algal colonies were inoculated into multiple well plates, cultured to stationary phase, and transferred into f/2 medium in batch culture flasks. The initial OD680 might vary between cultures, but identical final OD680 values should be reached by stationary phase. According to this understanding, 3 faster (1-39, 2-10, and 3-7) and 3 slower (1-44, 2-23, and 3-36) growing mutants were selected from 153 mutants (clear and well-separated from other colonies) obtained from 3 mutation trials. After comparison, 2-10 and 2-23 were selected as the fastest and slowest growing mutants (PMU and NMU), respectively (Fig. 1c).
3.2 RNA sequencing of WT and mutantsRNA sequencing was carried out for WT, PMU, and NMU. The frequencies of fragments normalized to FPKM distributed normally and were similar among WT, PMU, and NMU. In total, 147 241 944 clean reads were obtained from the three libraries. These reads were pooled for assembling transcripts, with a total of 33 307 unigenes (FPKM>0.3, mainly 15–60 bp) assembled and ranging from 16 425– 201 bp in length with an average of 743 bp; the N50 length was 1 230 bp. Of 33 307 unigenes, 19 051 were annotated against at least one available database (Table 1) and assigned into function classifications of GO, KOG, and KEGG (Fig. 2). In total, 19 611 unigenes were shared by WT, PMU, and NMU, with 1 121, 1 347, and 3 839 unigenes specific for WT, PMU, and NMU, respectively.
3.3 Difference among librariesBetween PMU and WT, 62 DEGs were detected (Fig. 3) and, of these, 13 and 49 were upregulated and downregulated, respectively. Between PMU and WT, no DEG was significantly categorized into GO terms, and only the peroxisome pathway was enriched (Table 2, Fig. 4a). Focusing on the direction of regulatory changes, it was found that the KEGG pathways of fatty acid metabolism and peroxisome were downregulated (Table 3). Among proteins encoded by these DEGs, ELOVL6 limits C12–C16 fatty acid elongation and ACSL degrades C12–C22 fatty acids and synthesizes acyl-CoA. Downregulation of their encoding genes indicated that fatty acid synthesis was decreased in PMU. The observed DEGs also included genes encoding MCM2, a member of the MCM family, which functions as a six-polymer complex in DNA prereplication, allowing cells to enter S phase, and PSMD1, a part of the proteasome, which participates in ubiquitin-mediated proteolysis, ensuring normal running of the cell cycle. Upregulation of genes encoding MCM2 and PSMD1 observed here might have indicated acceleration of cell division and enhancement of cell cycling by PMU.
Between WT and NMU, 1428 DEGs were identified (Fig. 3) and, of them, 633 and 795 were upregulated and downregulated, respectively. Between WT and NMU, KEGG pathways of ribosome and fatty acid metabolism were significantly enriched (Table 4, Fig. 4b). Focusing on the direction of regulation, upregulated KEGG pathways included biosynthesis of amino acids, fatty acids, pigments, ribosome, and photosynthetic elements (Table 5). NMU cells divided more slowly than those of WT and showed four DEGs (encoding MCM2, SCF, SMC3, and ATM/ATR) involved in the cell cycle that were downregulated. These downregulated genes were inferred to suggest a closing of the avenue for transitioning from the G1 phase to S phase but, instead, promoted the synthesis of amino acids, fatty acids, and pigments as well as construction of ribosomes and photosynthetic machinery.
Comparison of PMU with NMU demonstrated the differential expression of 1 901 unigenes (Fig. 3) and, of them, 1 014 and 887 were upregulated and downregulated, respectively. The only KEGG pathway enriched by these DEGs was fatty acid metabolism (Table 4, Fig. 4c), in which 11 and 15 genes were upregulated and downregulated, respectively. DEGs for fatty acid biosynthesis showed downregulation while those for fatty acid degradation showed upregulation. Fatty acid degradation yields acetyl-CoA and small intermediate molecules that might enter the tricarboxylic acid cycle and oxidative phosphorylation, generating ATP. In total, 12 DEGs involved in the cell cycle were upregulated, the relationships of which are shown in Figure 5. Downregulated DEGs also included genes responsible for the degradation of amino acids, fatty acids, pigments, and sugar, and construction of ribosome and photosynthetic machinery (Table 3). These findings suggested the negation of the conjectures that (1) fast growth was controlled by genes functioning in cell cycle and (2) cells grew faster by reducing synthesis of amino acids, fatty acids, and sugar, as well as construction of ribosome and photosynthesis machinery.
4 DISCUSSIONBy profiling and comparing the transcriptome of the WT and two mutant strains, it was found that Nannochloropsis cells divided and thus grew more rapidly by downregulating the pathways of peroxisome and fatty acid metabolism, and upregulating gene expression involved in cell division and cycling. These conclusions were supported by the NMU, which showed upregulated biosynthesis of amino acids, fatty acids, pigments, photosynthetic elements, and ribosome, while downregulating genes were involved in cell cycling. Direct comparison between the PMU and NMU indicated that, in PMU, fatty acid biosynthesis was downregulated while fatty acid degradation was upregulated. In PMU, downregulated genes also included genes responsible for metabolism of amino acids, fatty acids, pigments, and sugar as well as photosynthesis and ribosome assembly. These observations also indicated that 12 DEGs functioning in the cell cycle were upregulated. Thus, it was concluded here that Nannochloropsis cells grew faster by upregulating expression of genes functioning in the cell cycle and downregulating genes involved in synthesis of amino acids, fatty acids, and sugar as well as construction of ribosome and photosynthetic machinery (Fig. 6). However, the relationship among genes responsible for cell cycle, metabolism of fatty acids, amino acids among others, and construction of ribosome and photosynthetic machinery remain unclear. Further studies, such as genetic investigations, are required for clarifying the genetic (and thus metabolic) networks underlining the growth performance of Nannochloropsis.
Except for those mutations generated occasionally during DNA replication, DNA can be mutated through diverse natural and artificial methods that include traditional chemical and physical methods as well as some newly developed and widely practiced methods, for example, atmospheric and room temperature plasma (ARTP; Zhang et al., 2014) and low-energy heavy ion beam implantation (Tang and Yu, 2007; Zhang and Yu, 2009). The effects of these methods on DNA differ. In this study, the effectiveness of MNNG mutation was illustrated, but this success did not indicate that MNNG mutation was the most appropriate or applicable method. This study explored a means of creating and breeding microalgal strains, particularly species possessing a monoploid genome and asexual reproduction, such as does Nannochloropsis. In future studies, Nannochloropsis will be mutated by other methods, with the goal of breeding elite strains for maximal production and to develop various interesting strains for genetic studies.
For microalgae, various strategies have been applied for their genetic improvement. For less studied microalgal species, such as Nannochloropsis, new research tools should be developed for their improvement, as genetic transformation of Nannochloropsis has not met much success in most laboratories. Therefore, the identification or development of highly effective transformation tools for this species will promote its improvement and deciphering of its gene functions.
5 CONCLUSIONBased on MNNG mutation and transcriptome profiling, this method of mutation was shown to be effective for improving Nannochloropsis growth and that Nannochloropsis cells divided faster by downregulating pathways for peroxisome and fatty acid metabolism, while upregulating expression of genes involved in the cell cycle. Further research is required to determine the relationship among these observed changes.
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