- Research article
- Open Access
De novo transcriptomic analysis of Chlorella sorokiniana reveals differential genes expression in photosynthetic carbon fixation and lipid production
© The Author(s). 2016
- Received: 17 April 2015
- Accepted: 14 September 2016
- Published: 26 September 2016
Microalgae, which can absorb carbon dioxide and then transform it into lipid, are promising candidates to produce renewable energy, especially biodiesel. The paucity of genomic information, however, limits the development of genome-based genetic modification to improve lipid production in many microalgae. Here, we describe the de novo sequencing, transcriptome assembly, annotation and differential expression analysis for Chlorella sorokiniana cultivated in different conditions to reveal the change of genes expression associated with lipid accumulation and photosynthetic carbon fixation.
Six cultivation conditions were selected to cultivate C. sorokiniana. Lipid content of C. sorokiniana under nitrogen-limited condition was 2.96 times than that under nitrogen-replete condition. When cultivated in light with nitrogen-limited supply, C. sorokiniana can use carbon dioxide to accumulate lipid. Then, transcriptome of C. sorokiniana was sequenced using Illumina paired-end sequencing technology, and 244,291,069 raw reads with length of 100 bp were produced. After preprocessed, these reads were de novo assembled into 63,811 contigs among which 23,528 contigs were found homologous sequences in public databases through Blastx. Gene expression abundance under six conditions were quantified by calculating FPKM value. Ultimately, we found 385 genes at least 2-fold up-regulated while 71 genes at least 2-fold down-regulated in nitrogen-limited condition. Also, 204 genes were at least 2-fold up-regulated in light while 638 genes at least 2-fold down-regulated. Finally, 16 genes were selected to conduct RT-qPCR and 15 genes showed the similar results as those identified by transcriptomic analysis in term of differential expression.
De novo transcriptomic analyses have generated enormous information over C. sorokiniana, revealing a broad overview of genomic information related to lipid accumulation and photosynthetic carbon fixation. The genes with expression change under different conditions are highly likely the potential targets for genetic modification to improve lipid production and CO2 fixation efficiency in oleaginous microalgae.
- Chlorella sorokiniana
- Lipid accumulation
- Photosynthetic carbon fixation
The demand of energy is increasing as the world population and global economy continue to grow. Microalgae-based biodiesel, which can realize carbon-neutral by photosynthetic carbon fixation via the microalgae’s growth , is a renewable and sustainable energy source. Chlorella, one of eukaryotic, unicellular and photosynthetic microorganism, widely distributes in freshwater environment and is capable of accumulating excess lipid in nitrogen-limited condition. Moreover, Chlorella were used as a model system for investigating photosynthetic carbon fixation [2, 3]. Due to its various and robust metabolic capacities, Chlorella has received increasingly attention as promising microalgae to produce biomass , biodiesel  as well as high additional-value products .
Currently, Chlorella is one of the best microalgae as oil feedstock for the production of biodiesel . Particularly, in nitrogen-limited condition, Chlorella can alter the metabolic pathways to accumulate a high proportion of lipid which can be used for biodiesel production [8–10]. Although the metabolic transition has been identified in the lipid accumulation process , many global changes remain poorly understood, such as genomic information, differential genes expression. As a consequence, the lipid production from naturally occurring Chlorella strains is much lower than the theoretical maximum , making the cost of biodiesel prohibitively high . One primary cause is the limited understanding of the metabolic pathways in microalgae regulating the lipid metabolism in general and lipid biosynthesis and accumulation in particular . Another cause is the lack of genomic information of some oleaginous but non-model microalgae, which largely hampers the investigation of the transcribed genes and genetic modification in these microalgae to accumulate lipid and other valuable products [15–17].
Transcriptome sequencing could be an efficient and relatively economical method to obtain the functional genomic information without the genomic information [17, 18], providing an initial, broad view of lipid accumulation in nitrogen-limited condition  and photosynthetic carbon fixation. A growing number of transcriptomes of oleaginous microalgae were de novo sequenced, assembled and annotated to investigate the regulatory mechanism of lipid accumulation [15–18].
In our previous work, we have already identified the metabolic pathways related to lipid accumulation in C. sorokiniana based on two transcriptome datasets . In this present study, we sequenced another four transcriptome datasets and analyzed all six transcriptome datasets together to elucidate differential gene expression involved in the lipid accumulation and photosynthetic carbon fixation. In our experiments, the quantity of lipid accumulated under nitrogen-limited condition can be 2.96 times than that under nitrogen-replete condition, making C. sorokiniana a promising microalgae strain to produce biodiesel. Then all the six transcriptome datasets were de novo assembled, annotated together, and differential genes expression was analyzed as well. Finally, RT-qPCR was conducted for 16 genes involving in the lipid accumulation and photosynthetic carbon fixation. Our results provide an insight into the regulation of lipid metabolism and photosynthetic carbon fixation in C. sorokiniana at the transcriptomic level and may contribute to genetic modification in microalgae to improve lipid productivity and carbon fixation efficiency.
Biomass and lipid content under different cultivation conditions
The general information for each sample
4 % glucose
4 % glucose
4 % glucose
4 % glucose
4 % glucose
4 % (CO2/air, v/v)
100 ~ 120 umol-photon · m−2 · s−1
20.03 ± 1.42
24.83 ± 1.45
20.26 ± 0.15
26.10 ± 0.61
22.91 ± 1.79
2.63 ± 0.09
106.36 ± 5.32
340.42 ± 19.13
17.46 ± 2.33
115.31 ± 7.60
324.723 ± 32.09
72.93 ± 2.31
SRA accession number
When C. sorokiniana was cultivated in light, the fluorescence intensity increased steadily from 12.02 ± 1.85 at third day to 72.93 ± 2.31 at eighth day, increasing by 6.4 times and the OD680 increased continuously from 0.11 ± 0.01 to 2.63 ± 0.14 over the whole cultivation period (Fig. 1c). These indicated that C. sorokiniana could absorb CO2 as carbon source to reproduce and also transform it into lipid accumulated in cells, which provided a promising strategy to alleviate global warming and energy crisis. When cultured heterotrophically in darkness with nitrogen-limited condition, C. sorokiniana accumulated lipid as well and the fluorescence intensity increased by 28.89 times at 84 h (324.72 ± 32.09, Fig. 1d). Compared with photoautotrophy, heterotrophy could make C. sorokiniana yield more biomass and achieve higher lipid productivity (Fig. 1c, d).
Sequencing and de novo assembly
Annotation of contigs
Function classification and Transcription factor analysis
Transcription factor families identified in C. sorokiniana
Transcription factor family
Number of contigs
Number of TF IDa
The transcription factors with at least 2-fold expression change
Transcription factor ID
Transcription factor family
Transcription factor ID
Transcription factor family
up-regulated* in nitrogen-limited condition
up-regulated in light
down-regulated in light
down-regulated# in nitrogen-limited condition
Up to now, it have been proved that Dof-type transcription factor and bHLH family have the function of regulating lipid accumulation in plants [25–27]. In this study, two transcription factors (IGS.gm_27_00071 and IGS.gm_8_00085) in bHLH family were identified and found both up-regulated in nitrogen-limited condition, which further confirm the significance of bHLH family in the in lipid accumulation (Additional file 4). At the same time, others transcription factors assigned to other families were also found with at least 2-fold expression change in respond to nitrogen deprivation (Table 3), and most of these transcription factor families were also reported to be up-regulated in Chlamydomonas reinhardtii cultivated in N-deprived condition especially the MYB-related, SBP and C3H family [28, 29]. Thus, regulating these transcription factors would be a potential approach to increase the lipid accumulation [30, 31]. Moreover, many transcription factors related to photosynthetic carbon fixation were also found to be up-regulated or down-regulated in light (Table 3, Additional file 4). These results would be very useful for the improvement of photosynthetic carbon assimilation in microalgae as few transcription factors involving in photosynthetic carbon assimilation were investigated [32–34].
Genes expression quantification
We also investigated the gene expression profiles for cultivation with 48 h and 84 h, and found 385 genes at least 2-fold up-regulated in nitrogen-limited condition at both cultivation times (Fig. 5a), while 71 genes at least 2-fold down-regulated (Fig. 5b). The expression change of most genes (1429 genes at 48 h, 1179 genes at 84 h, respectively) were less than 2-fold, and 920 genes were found identical at both time (Fig. 5c). Interestingly, we found more genes with at least 2-fold up-regulation and less genes with at least 2-fold down-regulation at 84 h compared with the counterparts at 48 h (Fig. 5a b). The reason for this may be the concentration of nitrogen in the media declined with C. sorokiniana growing, which could induce more genes to increase its transcriptional level as a response to the lower concentration of nitrogen.
Differential gene expression in lipid accumulation related pathways
Log2FCa (48 h)
Log2FC (84 h)
Fatty acid biosynthyesis pathway
malonyl-CoA ACP transacylase
KAS Beta-ketoacyl-ACP synthase
KAS Beta-ketoacyl-ACP synthase
Triacylglycerol biosynthesis pathway
Fatty acid catabolism pathway
Starch biosynthesis and catabolism, and ethanol fermentation pathway
1,4-α-glucan branching enzyme
Pyruvate dehydrogenase complex
EC:188.8.131.52, 184.108.40.206, 220.127.116.11
Differential gene expression in Calvin cycle
ribulose-bisphosphate carboxylase large chain
fructose-bisphosphate aldolase, class I
ribose 5-phosphate isomerase A
Real-time quantitative PCR analysis
Moreover, we found the down-regulation of starch biosynthesis pathway (starch synthase, SS; 1,4-α-glucan branching enzyme, BE) in nitrogen-limited condition, indicating that the starch biosynthesis pathway might be inhibited (Fig. 6a). This result was also reported in N. oleoabundans . 4 genes involving in carbon fixation pathway (ribulose-bisphosphate carboxylase large chain, RBCL; sedoheptulose-bisphosphatase, SEBP; ribulose-phosphate 3-epimerase, RPE; fructose-bisphosphate aldolase, ALDO) were found all up-regulated in light (Fig. 6b).
Among the 16 genes with RT-qPCR analysis, 15 genes showed the similar expression patterns as those identified by the transcriptomic analysis. Only the gene coding starch synthase showed inconsistent result in term of differential expression between the RT-qPCR analysis and transcriptomic analysis at 84 h. Based on the result of RT-qPCR analysis, this gene was found down-regulated in nitrogen-limited condition at 84 h, while the transcriptomic analysis result showed it was up-regulated in the corresponding condition.
This study not only provided transcriptome datasets of C. sorokiniana under six different conditions, but also new biological insights into the expression of genes associated with lipid accumulation and photosynthetic carbon fixation. Based on our study, it is clear that the application of this approach can contribute to the generation of interesting hypotheses for both fundamental and applied research. Moreover, the C. sorokiniana’s transcriptome data could be a contribution for elucidating the physiology and evolution of the chlorophyceans.
Strain and culture conditions for RNA-seq
C. sorokiniana (UTEX 1602) was obtained from the Culture Collection of Alga at University of Texas (UTEX, Austin, TX, USA) and cultivated using modified Kuhl medium (Additional file 7). To induce the differential expression of genes involving in lipid accumulation and photosynthetic carbon fixation, six conditions were selected for transcriptome sequencing, including: 0.2 % nitrate supply with cultivation of 48 h (nitrogen-limited, sample A); 0.2 % nitrate supply with cultivation of 84 h (nitrogen-limited, sample B); 0.8 % nitrate supply with cultivation of 48 h (nitrogen-replete, sample C); 0.8 % nitrate supply with cultivation of 84 h (nitrogen-replete, sample D); 0.2 % nitrate supply with cultivation in darkness of 84 h (heterotrophy with nitrogen-limited, sample E); 0.033 % nitrate supply, white fluorescent light (100 ~ 120 μmol photons · m−2 · s−1) and agitation by air containing 4 % (v/v) CO2 with cultivation of 8 d (photoautotrophy with nitrogen-limited, sample F) (Table 1). The transcriptomes of sample A, C, E and F were sequenced in this study and the datasets of sample B and D were sequenced before (SRX354137 and SRX354141, respectively) , which we analyze together with the purpose of getting the most comprehensive transcript pool using de novo assembly method.
In this study, we selected six experimental conditions to compare the expression level of genes and each experimental condition have one biological replicate (n = 1). To keep the concordance of cultivation, the culture method of Sample A, C and E was the same as that of sample B and D , using 250 mL Erlenmeyer flask with 100 mL medium shaking at 220 rpm at 37 °C. Sample F was cultivated using Φ1x50 mm Cylindrical glass tube with 300 mL medium agitating with air at room temperature (25 ± 2 °C). After cultivation, cells were harvested by centrifugation (Eppendorf, Germany) at 4000 rpm, for 5 min, at 4 °C. The cell pellets were immediately frozen in liquid nitrogen and stored at −80 °C until further analysis.
RNA extraction, library construction and sequencing
Total RNA of four samples (sample A, C, E and F) were extracted separately using General Total RNA Extraction Kit (QIAGEN, Germany) according to the manufacturer’s instructions. After the elimination of the contaminant DNA, oligo (dT) beads were used to isolate mRNA from total RNA, followed by mRNA was cut randomly into short fragments. These fragmented RNA was reverse-transcribed to the first-strand cDNA with reverse transcriptase (Invitrogen, USA) that was then used as template to synthesis the second-strand of cDNA with DNA polymeraseIand RNase H (Invitrogen). The resulting short cDNA fragments were purified using QiaQuik PCR Extraction Kit (QIAGEN) and resolved in an elution buffer for end reparation and addition of a single adenine base to 3’ends. Then the cDNA fragments were linked with sequencing adapters and separated in gels by electrophoresis. The fragments with a desirable size were cut from gels and eluted for PCR amplification. After qualified with Agilent 2100, each cDNA library was sequenced with Illumina Hiseq2000 platform (Illumina, USA). These RNA extraction and library construction processes were the same as those used for sample B and D.
Analysis of biomass and lipid content
The biomass of C. sorokiniana was determined by measuring the OD680 using the microplate reader (Molecular Devices, USA). Lipid content was determined using the modified nile red staining method . The culture was diluted with corresponding medium until the OD680 was between 0.1 and 0.3. Then 1 mL of this algal suspension was stained with 3.33 μL nile red solution (7.8 × 10−4 mol · L−1 dissolved in acetone) and then excited at 486 nm before measuring the emission at 570 nm using the microplate reader. Glucose concentration was measured using HPLC method (Agilent Technologies, USA).
Preprocessing, de novo assembly and function annotation
The 100 bp paired-end raw reads generated from Illumina Hiseq2000 were analyzed by FastQC tool (v0.10.1)  for quality assessment and preprocessed using Python scripts (Additional file 8), including: (a) remove low quality bases with Phred score < 20, (b) remove ambiguous base ‘N’, (c) discard short reads with length < 25 bp. Followed by the high quality reads were de novo assembled using Trinity (v2.0.6)  with default parameters to construct contigs. Final clustering of contigs were conducted using the Cluster Database at High Identity with Tolerance (CD-HIT) EST suits  with minimum similarity cut-off of 90 % to generate the non-redundant contigs used for the following analysis.
For the functional annotation, the non-redundant contigs were searched against with the NCBI's non-redundant (Nr) database and Clusters of Orthologous Groups (COG) database [40, 41] using Blastx algorithm  with E-value ≤ 10−5 and 10−10, respectively, and other default parameters. Putative gene function, coding sequence and predicted proteins of corresponding contigs could be obtained by parsing the features of the best hit from each Blastx result. For the contigs that had no hit in any databases, the Transdecoder was used to predict potential coding sequences with default parameters. The Blastx results from COG database were used to identify the cluster of orthologous groups. To identify BRITE functional hierarchies , the non-redundant contigs were also submitted to the KEGG Automatic Annotation Server (KAAS)  with bi-directional best hit assignment method. KAAS could annotate each submitted sequence with KEGG orthology (KO), corresponding enzyme commission number (EC number) with the threshold of Blast bit scores > 60. Putative transcription factors were also identified by searching Plant Transcription Factor Database (PlnTFDB)  using Blastx algorithm  with E-value Blas−10. Chlorella sp. NC64A  was selected as the candidate to search against in order to predict the transcription factors in C. sorokiniana.
Gene expression quantification
To determine the gene expression abundance, high quality reads from each condition were mapped to the non-redundant contigs to calculate the FPKM value  using the RSEM (v1.2.7) . Due to the lack of biological replicates, we selected genes whose FPKM value was greater than 0 in all six conditions to study the differential expression and genes with the change of FPKM value greater than 2-fold in comparison of two different conditions were identified as differential expression.
Real-time quantitative PCR
In order to avoid the bias caused by the absence of biological replicates, we selected 16 genes, involving in lipid accumulation and carbon fixation, to perform the RT-qPCR. The same conditions were used to cultivate C. sorokiniana for the RT-qPCR analysis. M-MLV reverse transcription kit (Promega, USA) was used to synthesis the cDNA according to the manufacturer’s instruction. Gene specific primers (Additional file 9) for RT-qPCR were designed using Vecter NTI software. A 10 μL reaction system was performed on the Eco real-time PCR system (Illumina, USA) with the absolute SYBR Green qPCR Kit Master Mix (Toyobo, Japan) according to the manufacturer’s instruction. The cycle threshold value (CT) was determined and differential expression was calculated using the 2-△△CT method  with 18S gene of C. sorokiniana as the endogenous reference. Each sample was run in triplicate to confirm the reproducibility of the results.
This work was supported by the National Basic Research Program of China (973 Program, 2011CB200900).
LL did the experiments, analyzed the transcriptome data and prepared the manuscript; GZ did the experiments; QW designed the experiments, discussed the results and revised the manuscript for publication. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Schenk P, Thomas-Hall S, Stephens E, Marx U, Mussgnug J, Posten C, Kruse O, Hankamer B. Second Generation Biofuels: High-Efficiency Microalgae for Biodiesel Production. Bioenerg Res. 2008;1(1):20–43.View ArticleGoogle Scholar
- Bassham JA, Benson AA, Calvin M. The path of carbon in photosynthesis. J Biol Chem. 1950;185:781–7.PubMedGoogle Scholar
- Barker SA, Bassham JA, Calvin M, Quarck UC. Intermediates in the photosynthetic cycle. Biochim Biophys Acta. 1956;21(2):376–7.Google Scholar
- Lee Y-K. Microalgal mass culture systems and methods: Their limitation and potential. J Appl Phycol. 2001;13(4):307–15.View ArticleGoogle Scholar
- Xu H, Miao X, Wu Q. High quality biodiesel production from a microalga Chlorella protothecoides by heterotrophic growth in fermenters. J Biotechnol. 2006;126(4):499–507.View ArticlePubMedGoogle Scholar
- Pulz O, Gross W. Valuable products from biotechnology of microalgae. Appl Microbiol Biotechnol. 2004;65(6):635–48.View ArticlePubMedGoogle Scholar
- Gouveia L, Oliveira A. Microalgae as a raw material for biofuels production. J Ind Microbiol Biotechnol. 2009;36(2):269–74.View ArticlePubMedGoogle Scholar
- Miao X, Wu Q. Biodiesel production from heterotrophic microalgal oil. Bioresour Technol. 2006;97(6):841–6.View ArticlePubMedGoogle Scholar
- Griffiths M, Harrison SL. Lipid productivity as a key characteristic for choosing algal species for biodiesel production. J Appl Phycol. 2009;21(5):493–507.View ArticleGoogle Scholar
- Chisti Y. Biodiesel from microalgae. Biotechnol Adv. 2007;25(3):294–306.View ArticlePubMedGoogle Scholar
- Xiong W, Liu L, Wu C, Yang C, Wu Q. 13C-tracer and gas chromatography–mass spectrometry analyses reveal metabolic flux distribution in the oleaginous microalga Chlorella protothecoides. Plant Physiol. 2010;154(2):1001–11.View ArticlePubMedPubMed CentralGoogle Scholar
- Hu Q, Sommerfeld M, Jarvis E, Ghirardi M, Posewitz M, Seibert M, Darzins A. Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances. Plant J. 2008;54(4):621–39.View ArticlePubMedGoogle Scholar
- Acién FG, Fernández JM, Magán JJ, Molina E. Production cost of a real microalgae production plant and strategies to reduce it. Biotechnol Adv. 2012;30(6):1344–53.View ArticlePubMedGoogle Scholar
- Li Y, Han D, Sommerfeld M, Hu Q. Photosynthetic carbon partitioning and lipid production in the oleaginous microalga Pseudochlorococcum sp. (Chlorophyceae) under nitrogen-limited conditions. Bioresour Technol. 2011;102(1):123–9.View ArticlePubMedGoogle Scholar
- Rismani-Yazdi H, Haznedaroglu BZ, Hsin C, Peccia J: Transcriptomic analysis of the oleaginous microalga Neochloris oleoabundans reveals metabolic insights into triacylglyceride accumulation. Biotechnol Biofuels 2013Google Scholar
- Rismani-Yazdi H, Haznedaroglu BZ, Bibby K, Peccia J. Transcriptome sequencing and annotation of the microalgae Dunaliella tertiolecta: pathway description and gene discovery for production of next-generation biofuels. BMC Genomics. 2011;12:148.View ArticlePubMedPubMed CentralGoogle Scholar
- Zheng M, Tian J, Yang G, Zheng L, Chen G, Chen J, Wang B. Transcriptome sequencing, annotation and expression analysis of Nannochloropsis sp. at different growth phases. Gene. 2013;523(2):117–21.View ArticlePubMedGoogle Scholar
- Guarnieri MT, Nag A, Smolinski SL, Darzins A, Seibert M, Pienkos PT. Examination of triacylglycerol biosynthetic pathways via de novo transcriptomic and proteomic analyses in an unsequenced microalga. PLoS One. 2011;6(10):e25851.View ArticlePubMedPubMed CentralGoogle Scholar
- Li L, Wang Q, Yang H, Wang W: De novo transcriptomic analysis of Chlorella sorokiniana: Pathway description and gene discovery for lipid production. Acta Microbiologica Sinica 2014, 54(9):1010–1021Google Scholar
- Yang S, Guarnieri MT, Smolinski S, Ghirardi M, Pienkos PT: De novo transcriptomic analysis of hydrogen production in the green alga Chlamydomonas moewusii through RNA-Seq. Biotechnol biofuels 2013, 6:118–118Google Scholar
- Wu CH, Tsai MH, Ho CC, Chen CY, Lee HS. De novo transcriptome sequencing of axolotl blastema for identification of differentially expressed genes during limb regeneration. BMC Genomics. 2013;14:434–46.View ArticlePubMedPubMed CentralGoogle Scholar
- Blanc G, Duncan G, Agarkova I, Borodovsky M, Gurnon J, Kuo A, Lindquist E, Lucas S, Pangilinan J, Polle J, et al. The Chlorella variabilis NC64A genome reveals adaptation to photosymbiosis, coevolution with viruses, and cryptic sex. Plant Cell. 2010;22(9):2943–55.View ArticlePubMedPubMed CentralGoogle Scholar
- Yamasaki K, Kigawa T, Inoue M, Tateno M, Yamasaki T, Yabuki T, Aoki M, Seki E, Matsuda T, Nunokawa E, et al. A novel zinc-binding motif revealed by solution structures of DNA-binding domains of Arabidopsis SBP-family transcription factors. J Mol Biol. 2004;337(1):49–63.View ArticlePubMedGoogle Scholar
- Li Z, Thomas TL. PEI1, an Embryo-Specific Zinc Finger Protein Gene Required for Heart-Stage Embryo Formation in Arabidopsis. Plant Cell Online. 1998;10(3):383–98.Google Scholar
- Wang H-W, Zhang B, Hao Y-J, Huang J, Tian A-G, Liao Y, Zhang J-S, Chen S-Y. The soybean Dof-type transcription factor genes, GmDof4 and GmDof11, enhance lipid content in the seeds of transgenic Arabidopsis plants. Plant J. 2007;52(4):716–29.View ArticlePubMedGoogle Scholar
- Kamisaka Y, Tomita N, Kimura K, Kainou K, Uemura H. DGA1 (diacylglycerol acyltransferase gene) overexpression and leucine biosynthesis significantly increase lipid accumulation in the Deltasnf2 disruptant of Saccharomyces cerevisiae. Biochem J. 2007;408(1):61–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Riano-Pachon DM, Correa LG, Trejos-Espinosa R, Mueller-Roeber B. Green transcription factors: a chlamydomonas overview. Genetics. 2008;179(1):31–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Rachel M, Guangxi W, Rahul RD, Astrid V, Katrin G, Xiaobo L, Eric RM, Simone A, Adam JC, Bensheng L, et al. Changes in Transcript Abundance in Chlamydomonas reinhardtii following Nitrogen Deprivation Predict Diversion of Metabolism. Plant Physiol. 2010;154(4):1737–52.View ArticleGoogle Scholar
- Hexin L, Ge Q, Xizhen Q, Lina L, Chaoguang T, Yanhe M. Transcriptome analysis of Chlamydomonas reinhardtii during the processof lipid accumulation. Genomics. 2013;101(4):229–37.View ArticleGoogle Scholar
- Noemie MDC, Albert P, Bei W, Christopher QL. Enhancement of lipid production using biochemical, genetic and transcription factor engineering approaches. J Biotechnol. 2009;141(1–2):31–41.Google Scholar
- Alejandro IS, Sergio RM, Alejandro RU, Jocelin IRA, Ignacio LH, Araceli HT. Over-expression of Dof-type transcription factor increases lipidproduction in Chlamydomonas reinhardtii. J Biotechnol. 2014;183:27–38.Google Scholar
- Silvia P, Enrique F, Antonia H. The LysR-type transcription factor PacR is a global regulator of photosynthetic carbon assimilation in Anabaena. Environ Microbiol. 2015;17(9):3341–51.View ArticleGoogle Scholar
- Satoshi Y, Fumiya T, Kenji M, Takeshi I, Takashi Y, Hideya F. The Novel Myb Transcription Factor LCR1 Regulates the CO2-Responsive Gene Cah1, Encoding a Periplasmic Carbonic Anhydrase in Chlamydomonas reinhardtii. Plane Cell. 2004;16:1466–77.View ArticleGoogle Scholar
- Shawn D, Anthony DK, Marla JC, Robert LB. Regulation of the Cyanobacterial CO2-Concentrating Mechanism Involves Internal Sensing of NADP+ and a- Ketogutarate Levels by Transcription Factor CcmR. PLoS ONE. 2012;7(7):e41286.View ArticleGoogle Scholar
- Chen H, Lin L, Xie M, Zhang G, Su W. De novo Sequencing, Assembly and Characterization of Antennal Transcriptome of Anomala corpulenta Motschulsky (Coleoptera: Rutelidae). PLoS ONE. 2014;10(4):e0127303.View ArticleGoogle Scholar
- Elsey D, Jameson D, Raleigh B, Cooney MJ. Fluorescent measurement of microalgal neutral lipids. J Microbiol Methods. 2007;68(3):639–42.View ArticlePubMedGoogle Scholar
- Anders S: FastQC. Babraham Bioinformatics 2015. http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc. Accessed 2 Jan 2015.
- Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29(7):644–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics. 2010;26(5):680–2.View ArticlePubMedPubMed CentralGoogle Scholar
- Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28(1):33–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Tatusov RL, Koonin EV, Lipman DJ. A Genomic Perspective on Protein Families. Science. 1997;278(5338):631–7.View ArticlePubMedGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–402.View ArticlePubMedPubMed CentralGoogle Scholar
- Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40(Database issue):D109–114.View ArticlePubMedGoogle Scholar
- Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007;35 suppl 2:W182–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Riano-Pachon DM, Ruzicic S, Dreyer I, Mueller-Roeber B. PlnTFDB: an integrative plant transcription factor database. BMC Bioinformatics. 2007;8:42.View ArticlePubMedPubMed CentralGoogle Scholar
- Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.View ArticlePubMedPubMed CentralGoogle Scholar
- Livak KJ, Schmittgen TD. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2 − ΔΔCT Method. Methods. 2001;25(4):402–8.View ArticlePubMedGoogle Scholar