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BMC Bioinformatics

Open Access

Exploring the transcriptome of non-model oleaginous microalga Dunaliella tertiolecta through high-throughput sequencing and high performance computing

  • Lina Yao1,
  • Kenneth Wei Min Tan1,
  • Tin Wee Tan2, 3 and
  • Yuan Kun Lee1Email author
BMC BioinformaticsBMC series – open, inclusive and trusted201718:122

https://doi.org/10.1186/s12859-017-1551-x

Received: 30 October 2016

Accepted: 16 February 2017

Published: 22 February 2017

Abstract

Background

RNA-Seq technology has received a lot of attention in recent years for microalgal global transcriptomic profiling. It is widely used in transcriptome-wide analysis of gene expression., particularly for microalgal strains with potential as biofuel sources. However, insufficient genomic or transcriptomic information of non-model microalgae has limited the understanding of their regulatory mechanisms and hampered genetic manipulation to enhance biofuel production. As such, an optimal microalgal transcriptomic database construction is a subject of urgent investigation.

Results

Dunaliella tertiolecta, a non-model oleaginous microalgal species, was sequenced via Illumina MISEQ and HISEQ 4000 in RNA-Seq studies. The high quality high-throughout sequencing data were explored using high performance computing (HPC) in a petascale data center and subjected to de novo assembly and parallelized mpiBLASTX search with multiple species. As a result, a transcriptome database of 17,845 was constructed (~95% completeness). This enlarged database constructed fueled the RNA-Seq data analysis, which was validated by a nitrogen deprivation (ND) study that induces triacylglycerol (TAG) production.

Conclusions

The new paralleled assembly and annotation method under HPC presented here allows the solution of large-scale data processing problems in acceptable computation time. There is significant increase in the number of transcriptomic data achieved and observable heterogeneity in the performance to identify differentially expressed genes in the ND treatment paradigm. The results provide new insights as to how response to ND treatment in microalgae is regulated. ND analyses highlight the advantages of this database generated in this study that could also serve as a useful resource for future gene manipulation and transcriptome-wide analysis. We thus demonstrate the usefulness of exploring the transcriptome as an informative platform for functional studies and genetic manipulations in similar species.

Keywords

Microalgae Dunaliella tertiolecta HPCTranscriptomeRNA-Seq

Background

RNA-Seq is a recently developed approach for transcriptomic profiling, which uses deep-sequencing technologies to elucidate the complexity of eukaryotic transcriptomes [1]. It has been applied in quantifying transcriptional expression in microalgae mutants and microalgae cultured under different culture conditions to elucidate their metabolic regulatory mechanism. Various high-throughput sequencing technologies have been used for RNA-Seq, such as Roche 454 (Life Science), Ion Torrent ((PGM), Thermo Fisher), SOLiD (Applied Biosystems), and MISEQ/HISEQ (Illumina) systems. Following sequencing, the output reads are either aligned to a reference genome or transcriptome, or assembled a transcription map for each gene [1]. RNA-Seq based transcriptome assemblers have been developed in the past few years, which are largely reference-based [24] and highly dependent on a high-quality reference genome. As of now, only few comprehensively annotated model microalgae genomes are available, such as Coccomyxa subellipsoidea C-169 v2 [5], Chlorella variabilis NC64A v1 [6], Chlamydomonas reinhardtii v4 [7], Micromonas pusilla RCC299 v3 [8], Ostreococcus lucimarinus v2 [9], and Volvox carteri [10]. However, a large amount of microalgae with unique traits, which have advantages over model microalgae as feedstock for biofuel production and many other valuable biomolecules are not completely sequenced, thus has limited the potential of genetic engineering and comparative analysis of the transcriptome data [11]. Fortunately, in recent years, workflows for transcriptome analysis on non-model microalgae have been developed [1116], which paves the way for the development of elite algal strains for biofuels production. De novo assembly approach is applied for species with no reliable reference genome. Currently, a number of de novo assemblers are available in the market, such as Velvet [17], ABySS [18], SOAPdenovo [19], Trinity [3, 20], and Bridger [2]. However, some of them rely too much on the genome-assembly methods, or are too memory/time consuming. Among them, Trinity assembler was reported to have the highest number of assembled transcripts matching the non-redundant (Nr) database [2, 15, 21].

With the advances in sequencing technologies that most widely using Illumina MISEQ/HISEQ today, and as sequencing depth becomes higher, the assembly of raw data now requires high capacity processing, which still could not be fulfilled by off-the-shelf PCs [22]. Herein, high performance computing (HPC) in a petascale data center was introduced in our study. The use of HPC has opened up great opportunities for applications in many areas, including next-generation sequencing (NGS) data analysis [23].

Dunaliella tertiolecta, a non-model unicellular halophilic green alga that has fast growth rate and high accumulation of lipids, was used as the experimental organism in this study [15]. Recently, the whole genome sequence of the D. salina v1.0 was released (genome. jgi-psf.org), which aids in our comparison study as the most related species. Equipped with more input sequencing data (from Illumina HISEQ 4000), advanced de novo assembler, a wider reference species annotation database (all plants and bacterial proteins), and HPC in high performance data center, a much more complete D. tertiolecta transcriptome database (~95% of the total gene numbers) was constructed herein and applied in a case study of RNA-Seq data analysis from nitrogen-deprived cells. From the nitrogen deprivation (ND) study, potential regulatory mechanisms of cell growth and triacylglycerol (TAG) accumulation were proposed. Further, alternative-splicing variants in D. tertiolecta was predicted and compared with related species for the first time. This approach could be applied to other non-model microalgae for further applications.

Methods

Cultivation of microalgal samples

The algal culture D. tertiolecta strain UTEX LB-999 was obtained from the UTEX Culture Collection of Algae (University of Texas at Austin, TX) and cultured in 250 mL flasks containing ATCC-1174 DA liquid medium (American Type Culture Collection at Manassas, Virginia) with 0.5 M NaCl, 5 mM KNO3 under 30 μE m-2 s-1 as low light condition (with 400 μE m-2 s-1 for one set of high light culture. The rest cultures intended for transcriptomic data assembly were cultured under low light condition with the other culture conditions unchanged, as detailed in Table 1). For the ND study, D. tertiolecta were cultured in reduced nitrogen (0.5 mM KNO3), with the other culture conditions unchanged. Biological duplicates of ND and its WT were cultured for subsequent sequencing.
Table 1

Input raw data and post-analyzed data from MISEQ and HISEQ

Data name

Data source

Number of protein-coding contigs

Dt_G (HISEQ 4000)

Yao et al. 2016 [63]

27,797

Dt_Shin (MISEQ)

Shin et al. 2015 [15]

13,861

Dt_KR (MISEQ)

Tan et al. 2016 [58]

25,475

Dt_v10 (MISEQ)

Yao et al. 2015 [16]

20,229

Merged contigs

-

87,197

Non-redundant contigs

-

17,845

Measurement of dry cell weight, TAG and fatty acid content

Dry cell weight (DCW) measurement was performed by harvesting 10 mL of cells and collected by filtration on pre-weighed Advantec GB-140 filter paper (0.4 μm pore size; diameter 47 mm). The filter paper was then washed with isotonic 0.5 M ammonium formate (40 mL) to remove salts without causing the cells to burst. Cells captured on filter paper discs were dried in oven at 95 °C, and measured for DCW.

A modified Nile red staining method [16] was used to quantify intracellular TAGs. Briefly, cells were harvested by centrifugation (3000 g for 10 min at 4 °C), supernatant was removed and the pellet resuspended in fresh 0.5 M ATCC-1174 DA media to an OD680 of 0.3. Two hundred microliters of triolein standards (40, 20, 10, 5, 2.5, 0 μg/mL) and cell suspensions were loaded as technical triplicates onto a 96-well black, clear bottom plate (CLS3603; Sigma-Aldrich). Prior to staining, Nile red stock is diluted in acetone to obtain a working solution (25 μg/mL), and 2 μL of the Nile red working solution is added to each well of sample and standard, followed by a 5 min incubation in the dark. Fluorescence of each sample was detected using a microplate reader (Infinite M200 PRO, Tecan) at excitation and emission wavelengths of 524 nm and 586 nm. Fluorescence imaging of Nile Red-stained cells was performed with an automated fluorescence microscope (Olympus BX63). Acquisition and processing of data was done using the cellSens software.

To analyze the accumulation of total lipids, cells were harvested, snap-frozen in liquid nitrogen and stored at -80 °C until analysis. Frozen culture samples were lyophilized by freeze-drying and lipids were extracted by hexane using direct transesterification [24] as it was reported to be a convenient and accurate method for analyzing total fatty acids [25]. Biomass quantities of between 5 and 10 mg of biomass were weighed into glass 55-mL PYREX culture tubes with polytetrafluoroethylene (PTFE)-lined phenolic caps (25 mm diameter × 150 mm height, PYREX #9826-25, Corning). To each sample, 0.2 mL of chloroform-methanol (2:1, v/v) was added and mixed by vortexing, followed by simultaneous transesterification of lipids with 0.3 mL of 1.25 M methanolic HCl and vortexed to mix. An internal standard (100 μg Methyl tridecanoate, C13-Fatty Acid Methyl Ester, C13-FAME; Cat. no. 91558, Sigma-Aldrich) was included to correct for the loss of FAME during the reaction, and to correct for subsequent incomplete extraction of hexane [26]. The culture tube was then incubated in a 50 °C waterbath overnight. After 24 hours, 1 mL of hexane was added and mixed by vortex, and incubated at room temperature for 1 h. The upper organic phase containing FAMEs was removed using a glass pipette, filtered through a 0.22-μm PTFE syringe filter (Agilent Technologies), and collected in a 250-μL glass vial insert (Part no. 5181-1270, Agilent Technologies). FAME extracts were injected into a GC system (Model 7890B, Agilent Technologies) equipped with an Agilent Agilent HP-5 ms Ultra Inert column (30 m x 250 μm x 0.25 μm) (Cat. no. 19091S-433UI, Agilent Technologies) interfaced with a mass spectrometric detector (Model 5977A, Agilent Technologies). Injection volume was set at 1 μL with a 5:1 split ratio at a GC inlet temperature of 250 °C. Helium was used as the carrier gas in a fixed flow of 1 mL/min throughout. Temperature program is as follows: initial oven temperature of 70 °C held for 3 mins, ramp to 130 °C at 20 °C/min, 178 °C at 4 °C/min, 190 °C at 1 °C/min, and 290 °C at 10 °C/min. The total run time was 40 min. Shifting of retention times (RTs) were eliminated by comparing the RTs of each FA compound to the C13-FAME internal standard. Analysis was performed using the MassHunter WorkStation Qualitative Analysis B.07.00 software (Agilent Technologies) and compounds were identified with the NIST mass spectral library (National Institute of Standards and Technology, Data Version: NIST 14).

Preparation for sequencing and de novo transcriptome assembly

Cells were harvested at different culture stages by Allegra® X-30 centrifuge (Beckman Coulter) at 4000 × g for 10 min at 4 °C. The cell pellets were immediately frozen in liquid nitrogen and total RNA was extracted using RNeasy plant RNA Mini kit (Qiagen). After cDNA libraries were constructed [16], validation and quality assessment of each library was performed from gel electrophoresis and bioanalyzer (Agilent Technologies; Santa Clara, CA, USA). The concentration of each library was quantified via KAPA Library Quantification Kit (Illumina® platforms). The resulting libraries were sequenced by Illumina MISEQ sequencer (KR represents data from D. tertiolecta ND and highlight cultures) and Illumina HISEQ sequencer (G represents data from D. tertiolecta mutant cultures).

After sequencing reads were trimmed by QA/QC, Trinity assembler v2.2.0 was used to obtain strand-specific paired-end short reads data, with the default setting. To have a more complete transcriptome database, draft datasets constructed by Shin et al. [15] (http://cholab.or.kr/data/) and Yao et al. [16] from Illumina MISEQ sequencer were also adopted and compared in the following pipeline.

Annotation of the transcriptome

To have the functional annotation of the assembled transcripts, Basic Local Alignment Search Tool (BLAST suite), was used to compare against the ‘best’ proteins in the comprehensively annotated plant and bacterial Nr database from NCBI website (http://www.ncbi.nlm.nih.gov/refseq/). Protein IDs and their hypothetical function names were obtained for corresponding transcripts. To avoid multiple counting of contigs, only the best alignment (‘top hit’) from BLASTX was kept.

Generally, we filtered our BLASTX results using a three-step criterion: 1) The best alignment was chosen with E-value < = 1E-10; 2) Length percentage of the query sequence > = 80% of the subject protein sequence alignment; 3) Redundant contigs with the same ncbi_proteinID were deleted. The resulting transcripts were served as the protein coding sequences (Dtertiolecta_v11.transcript_primaryTranscriptOnly.fa, short as ‘Dt_v11’ below). Transcripts associated with a Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway or a Gene Ontology (GO) biological process were predicted to represent a certain expression pattern [12]. To identify it, we applied the online KEGG database for conversion of BLASTX results (ncbi_proteinID) into KEGG gene, KEGG Orthology (KO), GO and GO definition through KEGG (http://www.kegg.jp) and GenomeNet (http://www.genome.jp) websites for pathway mapping and GO analysis based on the KEGG/GO enrichment scores.

The resulted transcriptome annotation information was compared to available transcriptome information from C. reinhardtii v5.5 (https://phytozome.jgi.doe.gov/pz/portal.html#!info?alias=Org_Creinhardtii), V. carteri v2.1 (https://phytozome.jgi.doe.gov/pz/portal.html#!info?alias=Org_Vcarteri), D. salina v1.0 (https://phytozome.jgi.doe.gov/pz/portal.html#!info?alias=Org_Dsalina_er).

Differential gene expression analysis

We used Nr transcript dataset constructed as the reference for mapping the sequencing reads using RSEM version 1.2.29 with default settings [27], and subsequently imported and normalized in EBSeq for gene differential expression analysis [28]. Deferentially transcribed contigs upon ND is obtained using a cutoff of fold change (post) > = 2 or < = -2, and PPEE (FDR) value < = 0.05.

Retrieval of redundant contigs

Normally, Nr transcripts after BLASTX best-hit search were selected for transcriptome database construction. There are four basic types of non-top-hit events: 1) false de novo assembly; 2) isoforms with alternative transcription starts or ends; 3) breakage of an integrated gene; 4) alternative splicing (exon skipping, intron retention, alternative 5′ or 3′ splices site, mutually exclusive exons) [2931]. Python scripts using ClustalW algorithm [32] were proposed to check the similarities of all the redundant contigs, which hit the same ncbi_proteinID. Alternative-splicing variants from A. thaliana, C. reinhardtii, V. carteri, D. salina were extracted and their homogeneities were compared.

Results and discussion

System environment for experimental software

In general, the complete workflow follows Fig. 1. Experiments using HPC were completed on the petascale National Supercomputing Centre (NSCC), which comprises of 1288 nodes (dual socket, 12 cores/CPU E5-2690v3), 128 GB DDR4/ node. Additionally, 9 nodes are equipped with more than 1 TB memory RAM for enabling large memory applications. All the software settings used for construction and analyses of the transcriptome in this study are described in Additional file 1.
Fig. 1

Pipeline of RNA-Seq data analysis workflow from short sequence raw data. The general pipeline includes workflow for transcriptome database construction, annotation, and differential gene expression analysis. Workflow in data center mainly consists of de novo assembly, mpiBLASTX. Dt_G and Dt_KR are in-house constructed samples. Dt_Shin and Dt_Yao_v10 are two published datasets. 10% of each datasets are randomly picked for mpiBLASTX test to get the best parameters for mpiBLASTX with NCBI database for the merged D. tertiolecta datasets. The mpiBLASTX output was further extracted and filtered for annotation using python scripts

It is reported that the conventional BLASTX is computationally intensive and embarrassingly parallel [33]. As the input high-throughput data size continuously increases, time cost becomes the major issue. An open-source parallelization of BLASTX (mpiBLASTX version 1.6.0), that segments and distributes a BLASTX database among cluster nodes such that each node searches a unique portion of the database was a great advantage for speedup than the conventional single-core BLASTX. Thus, instead of using normal single-core BLASTX, we used mpiBLASTX. In mpiBLASTX, database needs to be segmented into 24 fragments prior to do BLAST by using mpiformatdb [33]. The database segmentation can save time from producing heavy intercommunication between nodes to realize the elimination of high overhead of disk I/O [33]. Figure 2 shows the scalability test based on the subsampling from the four sources of datasets, where we increased the number of cores in the system for mpiBLASTX application from 24 (1 node) to 1680 (70 nodes) cores and measured the speedup achieved. It was concluded that using 960 (40 nodes) cores was optimal regarding the time cost in this study. The performance flourishes when increasing core count from 24 to 960, as a result of the abundant parallelism. For configurations with more than 960 cores, however, the performance begins to diminish because the communication cost becomes the predominant factor, rendering the computing cores underutilized. Therefore, prompted by the subsampling results, the optimal configuration was used in our real study.
Fig. 2

Speedup achieved by mpiBLASTX calculated over run of 24 cores. It shows the scalability test based on the subsampling from the four sources of datasets, where we increased the number of cores in the system for mpiBLASTX application from 24 (1 node) to 1680 (70 nodes) cores and measured the speedup achieved. It was concluded that using 960 (40 nodes) cores was optimal regarding the time cost in this study

De novo assembly of D. tertiolecta transcriptome

Strand-specific RNA sequencing data from each condition were pooled together for de novo transcriptome assembly of D. tertiolecta transcriptome database and subsequent gene expression analysis. Due to the unavailability of complete genome or transcriptome information of D. tertiolecta LB 999, the de-novo assembled RNA-seq data can be used to identify genes. In particular, bigger input pooled data could enlarge the output transcriptome. To evaluate the transcripts created from different input datasets, assembly statistics between different raw data were compared and pooled together (Table 1). As a result, 87,197 contigs were merged for annotation.

Annotation of de novo-assembled transcriptome

Using the de novo-assembled contigs, annotation based on BLASTX with plant and bacterial Nr protein database was performed, to obtain protein identification from the taxonomy of plant and bacteria. De novo assembly methods are known to produce false positive contigs proportional to the sequencing depth [34]. Among the total 87,197 contigs obtained from the pooled library, only 17,845 transcripts were matched to proteins falling into our criteria to remove any false positives. The 17,845 annotated D. tertiolecta transcripts were subjected to functional analysis. Transcript length was ranged from 114 bps to 16,518 bps.

Of the 17,845 transcripts, 2525 are associated with at least one GO function, and 10,790 were found to have KEGG gene name and 5227 associated KO through python based scripts from online KEGG database. However, some transcripts with the different KEGG gene name would end into the same KO. For the unique pathway analysis, we further filtered the transcripts that have the same KO by the following criteria: 1) filter out the same KO as a category; 2) accelerating E-value; 2) decelerating sequence length. As a result, 15,336 transcripts (regarded as Dt_v11.2) were generated. Subsequently, 2718 out of the 15,336 transcripts have unique KO and associated with at least one metabolic pathway.

Analysis of the D. tertiolecta transcriptome information

GC content is an indicator for many features of an organism, and it is correlated with various genomic features, including repeat element distribution, methylation pattern, and gene density [3538]. The transcriptome of D. tertiolecta (54%) and D. salina (55%) showed higher GC content than higher plants studied here, but lowest GC content among the microalgal species and even lower than moss species (Fig. 3a). This phenomenon may reveal new insights into the gene regulatory mechanisms required for evolution among Viridiplantae, or green plants [39] according to the ancestor nodes from Phytozome database (https://phytozome.jgi.doe.gov/pz/portal.html#!search).
Fig. 3

D. tertiolecta transcriptome information. a GC content in D. tertiolecta transcriptome compared with other species; b Identification and verification of the protein-coding transcripts in D. tertiolecta. This pie chart is the result from BLASTX output. The sum of top hit transcripts from each individual species. The right side species names: descending numbers of hits

We also looked into the best-hit annotation models in the BLASTX search. Majority (73.7%) of the best-hit annotations in D. tertiolecta were found to derive from green algal species (Fig. 3b). D. tertiolecta transcriptome appears to mostly resemble that of V. carteri (26.4%), followed by C. reinhardtii (23.1%), Monoraphidium neglectum (11.9%), C. subellipsoidea C-169 (6.6%), and C. variabilis (5.7%) (Fig. 3b).

Furthermore, approximately 97.2% of the core eukaryotic genes (CEGs) were found in the 17,845 transcripts, suggesting a rather high coverage of transcripts that has been obtained to represent the D. tertiolecta LB 999 transcriptome [40]. A detailed comparison of transcriptome information of D. tertiolecta with other species is presented in Table 2. This has shown that the newly constructed database (Dt_v11.2) has a relatively good coverage (~95% that of D. salina) of transcripts and high assembly and annotation quality. N50, maximum contig length, total size of contigs, number of protein-coding transcripts, and average contig length all increased based on the current available D. tertiolecta databases. To this end, this enhanced transcriptome database comprising core genes in D. tertiolecta was used as a reference for following studies.
Table 2

Transcriptome assembly and annotation descriptions of different species

 

C. reinhardtii v5.5

V. carteri v2.1

D. salina v1.0

D. tertiolecta Shin et al.

Dt_v11/Dt_v11.2

Genome description

111.1 Mb arranged on 17 chromosomes and 37 minor scaffolds

131.2 Mb arranged in 434 scaffolds

343.7 Mb arranged in 5512 scaffolds

-

-

N50 (bp)

3938

4188

2291

1540

1797

Maximum contig length (bp)

72,700

24,197

17,353

15,234

16,518

Total size of contigs (bp)

63,797,006

51,775,597

33,246,103

16,600,538

24,538,468

Protein-coding transcripts

19,526

16,075

18,801

13,861

-

transcript_primaryTranscriptOnly

17,741

14,247

16,697

9839

17,845 / 15,336

Average length (bp)

3267

3220

1768

1197

1375

Alternatively spliced transcripts

1785

1828

2104

-

-

The entry in italic represents data from Dt_v11.2

Microalgae are a highly diverse group with largely unexplored genetic information, and there was the enormous amount (67.7% distinct) of diversity among microalgae at genetic level, which indicated that the functional genetic information is very diverse and case-dependent in microalgae, though they could be morphologically similar [41]. Therefore, the enrichment of D. tertiolecta transcriptome database is a necessity for accurate genetic engineering and RNA-Seq analysis, using larger input data, and multiple annotation species.

Construction of transcriptome coverage can vary due to expression differences and input data depth [42]. Theoretically, when addition input reads does not provide new output information, a sequencing saturation depth was hit. Several studies suggested that saturation depths at 95% gene coverage [4346]. However, in this study, the use of the increasing number of high-throughput sequencing data enlarged the de novo transcriptome assembly to ~95% of Dunaliella genes. The enhancement and exploration of the database gave us essential and additional information for comparative analysis of the transcriptome data.

Alternative splicing, an essential mechanism for increasing transcriptome and proteome diversity in eukaryotes, are quite common [39]. It is however less clear, and has few reports in microalgae. Venn diagram in Additional file 2 shows that the alternative-splicing among different species has little homogeneity. The retrieved D. tertiolecta potential splicing variants appear to be diverse and do not resemble much of those in the close related species. Further, different GC content (Fig. 3a) might also cause differences between the species in alternative splicing as reported elsewhere [47, 48]. To further verify the predicted alternative-splicing variants, genome sequencing or third generation sequencing (single-molecule long-read sequencing) is necessary.

Case study of RNA-Seq data from nitrogen-deprived cells

D. tertiolecta ND cells were chosen as a case study for comparing results using RSEM-EbSeq pipeline and Partek software, as the transcriptomic and physiological responses are well documented in microalgae to promote TAG accumulation [49]. We found that nitrogen-deprived D. tertiolecta cells on culture day 5 had comparable DCW but remarkably increase in TAG. It was reported carbohydrate accumulation during the early stages of ND conditions existed, which could account for its little increase in DCW ([15, 50]).

Through Illumina MISEQ sequencing, over 27 million qualified raw reads with 150 bps in length were used. Besides contributing to our large database construction, these data were analyzed for differential gene expression. Raw data were deposited in SRA database (SRR4011625, SRR4011626, SRR4011627, SRR4011628). Using Partek pipelines based on C. reinhardtii annotation [16], differential gene expression, significant GO output were presented in Additional file 3a and Fig. 4, respectively. From the perspective of significant differential expressed genes, the number increased from 482 to 582 after updated to Dt_v11 analysis (Additional file 3a-b). The detailed KEGG and GO lists are presented in Additional file 4a and Additional file 5, respectively. Most GO families showed repressed under ND conditions.
Fig. 4

GO functional enrichment of up-regulated (blue) and down-regulated (red) genes under ND conditions. Categories were filtered by Fisher’s exact test with an FDR-corrected p-value ≤ 0

Equipped with more genes annotated from different plant and bacterial species of the newly constructed database, we reported results of differential gene expression with top fold changes from RSEM and Partek pipelines (Additional file 3b). Based on the Dt_v11 GO annotation, a.gmt file requested by biological analysis with unique GO reflecting to different transcripts.

Regarding KO, given one culture condition a, and one KEGG term KEGGb, the KEGG enrichment score in the network x can be computed by the below equation (Eq.1) [5154]:
$$ \begin{array}{c}\hfill {S}_{G O/ KEGG}\left( a, KEG{G}_b\right)=- lo{g}_{10}\left( p- value\right)\hfill \\ {}\hfill =- lo{g}_{10}\left({\displaystyle \sum_{k= m}^n}\frac{\left(\begin{array}{c}\hfill M\hfill \\ {}\hfill m\hfill \end{array}\right)\left(\begin{array}{c}\hfill N- M\hfill \\ {}\hfill n- m\hfill \end{array}\right)}{\left(\begin{array}{c}\hfill N\hfill \\ {}\hfill n\hfill \end{array}\right)}\right)\hfill \end{array} $$

where N, is the total number of genes with KEGG in a certain number of species (x), M, is the number of proteins that are annotated to the KEGG term KEGGb, n, is the number of proteins in K(a), and m is the number of proteins both in K(a) and annotated to the KEGG term KEGGb, respectively. Larger the enrichment score of one KEGG term is, more overrepresented such a term is under the culture condition a [55, 56].

In this study, the annotation was performed based on all plants and bacterial protein database; this x is not easily adopted, therefore we could still compare the enrichment score according to the relative values. Through in-house constructed scripts to calculate KEGG enrichment score, the output of significant KEGG pathways was presented in Additional file 4b. Combined with Dt_v10 (MISEQ) [16] as the baseline, we found additional KEGG pathways that were significantly representative in the ND treatment with change of gene expression levels on the basis of a larger annotation. These included nitrogen metabolism, fatty acid biosynthesis, fatty acid metabolism, biotin metabolism, terpenoid backbone biosynthesis, propanoate metabolism, pentose phosphate pathway, oxidative phosphorylation, 2-oxocarboxylic acid metabolism, DNA replication, insulin resistance, starch and sucrose metabolism. Genes participating in fatty acid biosynthesis/metabolism and nitrogen metabolism pathway, which were detected in Dt_v11 analysis but not in Dt_v10 analysis, were summarized in Table 3. Integration of significant genes hit in KEGG biological pathway analysis from Dt_v10 and Dt_v11, KEGG maps were constructed as shown in Additional file 6a-e. Using the ND study, pathway maps showing the well annotated and enriched transcriptome database help shed light on metabolic pathways and regulation of D. tertiolecta and guide rational synthetic biology approaches.
Table 3

Comparison of dry cell weight and TAG content in D. tertiolecta ND culture

 

Dry cell weight (g/L)

TAG content (pg/cell)

Fatty acid content (% DCW)

N-replete

0.31 ± 0.08

0.15 ± 0.02

6.2 ± 0.27

N-deplete

0.34 ± 0.04

1.29 ± 0.12

5.14 ± 0.45

The values are presented as the mean ± the standard deviation

Global gene expression level change under ND conditions

In this ND study, we observed all the photosynthetic genes were repressed (Additional file 6a-b). Consistent with this, Yang et al. observed the similar trend in Phaeodactylum tricurnutum, and extensive degradation of chloroplast membranes under an electron microscope, with photosynthetic efficiency declined [57]. It suggested that nitrogen is an essential factor for photosynthetic activity. As nitrogen is an important component for the synthesis of chlorophyll and photosystem proteins (e.g. light-harvesting complex II, LHCII apoprotein), the reduction in nitrogen availability could hinder the expression of genes related to photosynthesis. Indeed, chlorophyll content and PSII quantum yield were decreased in D. tertiolecta upon ND conditions [58] which corresponds with the decreased gene expression. Multiple studies of nutrient deprivation in microalgae suggest that degradation of thylakoid membranes is responsible for the downregulation of most LHC genes [57, 5961], indicating that ND triggered a cellular response for reorganization of photosynthetic apparatus.

Genes in fatty acid biosynthesis and metabolism were also found to be repressed, while TAG levels significantly increased, indicating that intracellular membrane remodeling might have substantially contributed to the neutral lipid accumulation, instead of de novo lipid synthesis. It is consistent with Table 4, where FA percentage of the nitrogen-deprived cells was comparable and even a little decrease than that of the control. This is similar to observations by other studies which saw declines in total FA content under ND conditions [15]. The parallel increase in TAG coupled with a decline in total FA could be explained by the degradation of thylakoid membranes and rechanneling of carbon towards storage compounds such as TAGs. A recent report showed that when D. tertiolecta is cultured under ND condition, it had significant decreases in the lipid classes diacylglyceryltrimethylhomoserine (DGTS) and digalactosyldiacylglycerol (DGDG), a main component of chloroplast membranes [62], suggesting that a major remodeling of lipid membranes has occurred after ND.
Table 4

Genes participating in important pathways that are exclusively found in Dt_v11

KO

Name

Definition

Fold Change

(N-deplete/ N-replete)

ko00061 Fatty acid biosynthesis & ko01212 Fatty acid metabolism

ko:K00059

fabG

3-oxoacyl-[acyl-carrier protein] reductase [EC:1.1.1.100]

-3.100679049

ko:K00208

fabI

enoyl-[acyl-carrier protein] reductase I [EC:1.3.1.9 1.3.1.10]

-3.38232061

ko:K00645

fabD

[acyl-carrier-protein] S-malonyltransferase [EC:2.3.1.39]

-3.52911698

ko:K01962

accA

acetyl-CoA carboxylase carboxyl transferase subunit alpha [EC:6.4.1.2]

-2.126940035

ko:K01963

accD

acetyl-CoA carboxylase carboxyl transferase subunit beta [EC:6.4.1.2]

-3.075257707

ko:K02160

accB

acetyl-CoA carboxylase biotin carboxyl carrier protein

-7.80359902

ko:K02372

fabZ

3-hydroxyacyl-[acyl-carrier-protein] dehydratase [EC:4.2.1.59]

-2.198560822

ko:K09458

fabF

3-oxoacyl-[acyl-carrier-protein] synthase II [EC:2.3.1.179]

-3.332311424

ko00910 Nitrogen metabolism

ko:K00264

GLT1

glutamate synthase (NADPH/NADH) [EC:1.4.1.13 1.4.1.14]

23.8846

ko:K00366

nirA

ferredoxin-nitrite reductase [EC:1.7.7.1]

-13.89942929

ko:K01915

glnA

glutamine synthetase [EC:6.3.1.2]

2.84411

ko:K02575

NRT

MFS transporter, NNP family, nitrate/nitrite transporter

-4.415225463

ko:K10534

NR

nitrate reductase (NAD(P)H) [EC:1.7.1.1 1.7.1.2 1.7.1.3]

-9.464319515

Interestingly, from the nitrogen metabolism, we found the glutamate synthase (NADPH/NADH) was greatly activated under ND conditions. Similarly, Shin et al. also observed similar results, proving that intracellular glutamate levels were the main factor for the regulation of cell growth and carbon accumulation [15]. Further studies on metabolite profiling of glutamate and related amino acid levels could be potential targets to uncover the regulatory mechanism more specifically. As a signal of the growth delimitation under ND, genetic engineering to activate or block certain enzyme coding genes would promote the biofuel-relevant productions in microalgae.

Conclusions

In this study, fueled by a high performance data center (NSCC), high quality high-throughput RNA-Seq data were de novo assembled and annotated, which resulted in 17,845 protein-coding transcripts in D. tertiolecta. Integration of paralleled assembly and annotation method under HPC presented here enables large-scale data processing in a reasonable computation time. Ultimately, a significant increase of transcriptomic data (~95% of the total genes in Dunaliella) was achieved. The enhanced transcriptomic database subjected to the analysis of RNA-Seq data from ND study gave us a new insight of regulation mechanisms of cell growth and lipid biosynthesis, suggesting that the increase of TAGs were mainly derived from internal bioconversion to improve lipid production concurrently with exponential cell growth. Overall, these results laid the foundation for demonstrating integration of supercomputing with large input datasets to improve microalgal transcriptomic database and elucidate the regulatory response of ND conditions for promoting biofuel production. Further, this pipeline written and packaged by python scripts facilitates its use by non-experts. We believe that the uncovered transcriptomic database can play a key role in the development of this microalga for biofuel use and some related models with high-throughput raw data.

Abbreviations

BLAST: 

Basic Local Alignment Search Tool

DCW: 

Dry cell weight

GO: 

Gene Ontology

HPC: 

High performance computing

KEGG: 

Kyoto Encyclopedia of Genes and Genomes

KO: 

KEGG Orthology

ND: 

Nitrogen deprivation

NGS: 

Next-generation sequencing

Nr: 

Non-redundant

NSCC: 

National Supercomputing Centre

TAG: 

Triacylglycerol

Declarations

Acknowledgments

We would like to extend our sincere gratitude and appreciation to Singapore National Supercomputing Centre (NSCC) for kindly providing us with HPC in this study. We wish to thank the input of RNA-Seq raw data of high light cultures from Siti Radiah Binte Safie. We thank Dr Hui Shen and Dr Yi-Kai Ng for their helpful guidance and expertise, and Mr Chin-Seng Low for the general lab supply and technical support.

Funding

This work was funded by the National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) Programme (Grant No. R-182-000-205-592). The motivation for this work was generated from this grant. L.Y. was supported by the NUS Research Scholarship.

Availability of data and materials

The Illumia HISEQ sequencing data were deposited into GEO with accession number of GSE82121). Transcript sequence file Dt_v11.2.fa (Supplementary dataset 1), Dt_v11.2 annotation file (Supplementary dataset 2), Dt_v11.2 transcriptome associated GO annotation file in.gmt format (Supplementary dataset 3), redundant contigs under the same protein identification (Supplementary dataset 4), and the comparison result of D. tertiolecta potential alternative-splicing variants with other species (Supplementary dataset 5) were hosted at the author’s GitHub page (https://github.com/SPURc-Lab/).

Authors’ contributions

LY and YKL conceived the study; KWT cultivated nitrogen deprivation algal samples and performed sequencing; LY designed codes for transcriptomic data assembly, and annotation; TWT supervised the code; LY, KWT, and YKL wrote the paper; All authors contributed to the data interpretation, read, revised and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

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.

Authors’ Affiliations

(1)
Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore
(2)
Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore
(3)
National Supercomputing Centre (NSCC)

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Copyright

© The Author(s). 2017

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