Oil accumulation mechanisms of the oleaginous microalga Chlorella protothecoides revealed through its genome, transcriptomes, and proteomes
- Chunfang Gao†1, 3,
- Yun Wang†2,
- Yue Shen†2,
- Dong Yan1,
- Xi He1,
- Junbiao Dai1Email author and
- Qingyu Wu1Email author
© Gao et al.; licensee BioMed Central Ltd. 2014
Received: 29 December 2013
Accepted: 1 July 2014
Published: 10 July 2014
Microalgae-derived biodiesel is a promising substitute for conventional fossil fuels. In particular, the green alga Chlorella protothecoides sp. 0710 is regarded as one of the best candidates for commercial manufacture of microalgae-derived biofuel. This is due not only to its ability to live autotrophically through photosynthesis, but also to its capacity to produce a large amount of biomass and lipid through fermentation of glucose. However, until the present study, neither its genome sequence nor the platform required for molecular manipulations were available.
We generated a draft genome for C. protothecoides, and compared its genome size and gene content with that of Chlorella variabilis NC64A and Coccomyxa subellipsoidea C-169. This comparison revealed that C. protothecoides has a reduced genome size of 22.9 Mbp, about half that of its close relatives. The C. protothecoides genome encodes a smaller number of genes, fewer multi-copy genes, fewer unique genes, and fewer genome rearrangements compared with its close relatives. In addition, three Chlorella-specific hexose-proton symporter (HUP)-like genes were identified that enable the consumption of glucose and, consequently, heterotrophic growth. Furthermore, through comparative transcriptomic and proteomic studies, we generated a global perspective regarding the changes in metabolic pathways under autotrophic and heterotrophic growth conditions. Under heterotrophic conditions, enzymes involved in photosynthesis and CO2 fixation were almost completely degraded, either as mRNAs or as proteins. Meanwhile, the cells were not only capable of quickly assimilating glucose but also showed accelerated glucose catabolism through the upregulation of glycolysis and the tricarboxylic acid (TCA) cycle. Moreover, the rapid synthesis of pyruvate, upregulation of most enzymes involved in fatty acid synthesis, and downregulation of enzymes involved in fatty acid degradation favor the synthesis of fatty acids within the cell.
Despite similarities to other Chlorella, C. protothecoides has a smaller genome than its close relatives. Genes involved in glucose utilization were identified, and these genes explained its ability to grow heterotrophically. Transcriptomic and proteomic results provided insight into its extraordinary ability to accumulate large amounts of lipid. The C. protothecoides draft genome will promote the use of this species as a research model.
KeywordsMicroalgae Chlorella protothecoides Genome sequence Proteomic Oil accumulation Hexose-proton symporter Transcriptome Lipid
World demand for fuel, currently supplied mainly in the form of non-renewable fossil fuel, is continuously rising, leading to an increased interest in alternative and sustainable energy sources. Biodiesel derived from microalgae has opened up a new, promising path to solve the energy crisis . Compared with oil-producing plants, the most attractive advantage of microalgae is their high oil production capacity; the annual oil yield per hectare is 10 times higher than that of oil seed crops . However, to meet industrial scale requirements, the cost of biofuel production must be further reduced . One promising strategy is to apply technologies developed in metabolic engineering and synthetic biology. However, this approach requires an oleaginous algal model with high oil production capacity, known genetic information, and established methods for molecular manipulation.
Chlorella protothecoides sp. 0710 (hereafter referred to as Cp0710) is a unicellular green alga with great potential for biodiesel production. It has been proposed as one of the best candidates for commercial manufacture of microalgae-derived biofuel . One special characteristic that distinguishes Cp0710 from many other algae is its ability to not only live autotrophically through photosynthesis but also to grow heterotrophically by using extracellular organic carbon sources such as glucose [3–5]. Furthermore, when the organism switches from autotrophism to heterotrophism, the chloroplast disappears and is replaced by lipid bodies, leading to high oil accumulation . Based on this finding, a photosynthesis fermentation approach (PFA) was developed to cultivate Cp0710. This approach combines autotrophic and heterotrophic growth, resulting in 69% higher oil yield and 61.5% less CO2 emission compared with typical heterotrophic cultivation . In addition, alternative carbon sources and the combination of different cultivation approaches have been applied to further reduce the cost and to boost biomass/lipid production [8–11]. Despite all of this progress in recent years, both the genome sequence of Cp0710 and the platform for molecular manipulations are still missing.
In the present study, we generated the whole-genome sequence of Cp0710 using multiple strategies, and built a de novo assembly, which demonstrated that this alga has a small genome that encodes the lowest number of genes among all sequenced green microalgae. The Cp0710 genome was annotated and characterized by comparative genomic analysis. In addition, to investigate the differences between autotrophic and heterotrophic cells in metabolic pathways and related regulation patterns, differential expression of mRNA and proteins in the two cell types were studied and a potential regulatory pathway that could result in accumulation of oil in heterotrophically growing Cp0710 was identified. Together, the genome, transcriptome, and proteome analyses presented in this study yield new insights into the molecular basis for the accumulation of oil and provide a rich resource of genetic information that will be useful for the development of Cp0710 as an oleaginous model microalga.
Genome sequencing, assembly, and annotation
The contigs and scaffolds of C. protothecoides sp. 0710 genome
Number (>100 bp)
Number (>2 kb)
To assess genome coverage and completeness of the whole genome, we also estimated the genome size for Cp0710 utilizing 17-base k-mers and their Poisson distribution in the sequencing reads . The estimated Cp0710 genome size was 27.6 Mbp (Additional file 1 Figure S2 and Additional file 1 Table S1). Considering the presence of sequencing errors, the sequencing depth is expected to be underestimated and, consequently, the Cp0710 genome size should be smaller than 27.6 Mbp. Using this estimated genome size, the assembled contigs and scaffolds should at least cover about 80% and 83% of the whole genome, respectively. Further, the completeness of the genome was assessed using CEGMA v2.4 (Core Eukaryotic Genes Mapping Approach) based on mapping of the 248 most highly conserved core eukaryotic genes (CEGs) . The completeness of the assembled genome was 90.73% (Additional file 1 Table S2).
Genomic features of sequenced chlorophyte green algae
Assembly length (Mb)
GC content (%)
Repeat sequences (%)
Number of gene
Average gene length (bp)
Average number of exons per gene
Average exon length (bp)
Mean length of introns (bp)
Coding sequence ratio (%)
In addition, the complete sequences of the mitochondrial and chloroplast genomes were recovered from sequencing reads after gap-repair, revealing two circular molecules that were 57,274 bp and 84,580 bp in size, respectively. The detailed analysis of these genomes will be reported elsewhere (Yan et al., manuscript in preparation).
C. protothecoides is a member of the class Trebouxiophyceae in the phylum Chlorophyta. Presently, only two strains in the Trebouxiophyceae, C. variabilis NC64A  and C. subellipsoidea C-169 , have been fully sequenced. To gain insight into the novel features of the Cp0710 genome, we compared it with not only these two strains, but also several other sequenced chlorophytes (Table 2), including two in the Chlorophyceae (C. reinhardtii and Volvox carteri) and two in the Mamiellophyceae (Micromonas pusilla CCMP1545 and Ostreococcus tauri).
The size of the genome was regarded as a consequence of evolution , as evolutionarily related species usually have similar genome size. Accordingly, M. pusilla CCMP1545 and O. tauri, two ancient green algae belonging to the Prasinophyceae [17, 18], have the smallest genomes, at 22 Mbp and 13 Mbp, respectively. In fact, of the available algal genome sequences, all members of the Prasinophyceae contain genomes that range in size from 12 to 22 Mbp . On the other hand, among the five sequenced chlorophytes, genome sizes range from 46 Mbp (C. vulgaris NC64A) to as large as 140 Mbp (V. carteri), and the model green alga C. reinhardtii has a 121 Mbp genome. These values are consistent with the hypothesis that differences in genome size correlate with the evolutionary position of related species. Surprisingly, though, the size of the Cp0710 genome is only 22.9 Mbp, making it the smallest genome reported so far in the sequenced chlorophytes, and falling in the range of the prasinophytes. C. variabilis NC64A and C. subellipsoidea C-169, the two closest relatives, have genomes that are double this size, suggesting that a dramatic change in the size of the genome happened after the diversification of these algae.
The unexpectedly small size of the Cp0710 genome led us to reconsider its origin and its evolutionary relationship with other Chlorella species. Therefore, we analyzed its coding proteins by comparing them with the TrEMBL protein database. The total number of best-hit proteins from all organisms was counted and is shown in Figure 1B. The result indicated that the majority of proteins encoded in the Cp0710 genome possess best-hit homologs in C. variabilis NC64A, and these account for 64% of the total proteins. Only 452 (6.4%) and 360 (5.1%) proteins had best-hit homologs in V. carteri and C. reinhardtii, respectively. In addition, although the average G/C content of Cp0710 was slightly lower than that of C. variabilis NC64, both genomes had G/C content greater than 60% (Table 2), consistent with the high G/C feature in the genomes of Chlorella. Furthermore, using 300 single-copy orthologues among the seven green algae, we built a maximum likelihood phylogenetic tree that indicated that C. variabilis NC64A is the closest relative of Cp0710 (Figure 1C). Finally, we compared the gene families among the three closest species (C. protothecoides, C. variabilis NC64A, and C. subellipsoidea C-169). As shown in Figure 1D, of the 5,887 gene families in Cp0710, nearly 50% (2954) are shared with C. variabilis NC64A and C. subellipsoidea C-169. There are 3,465 shared gene families between Cp0710 and C. variabilis NC64A, which account for 58.9% and 47.1% of the total gene families, respectively. Further comparison with the model alga C. reinhardtii identified a large number of unique genes (10,599; ~64%) in this species, suggesting a more distant relationship between C. reinhardtii and the other three green algae (Additional file 1 Figure S4). Together, these results confirmed the evolutionary proximity of Cp0710 and C. variabilis NC64A.
Nitrogen transport and assimilation
In plants, both carbon (C) and nitrogen (N) are crucial for many fundamental cellular activities, and are required not only as nutrients but also as environmental signals to control metabolism and cellular responses . Different organisms have their own preferred N sources, and there are different transporters and assimilation systems involved. For Cp0710, we showed in an earlier study that it prefers ammonium and amino acids as nitrogen sources, and it has lost the ability to grow on nitrate- or urea-containing medium. We also identified and characterized one ammonium transporter gene (CpAMT1) . To further our understanding of the nitrogen preference of Cp0710, a survey of proteins involved in nitrogen transport and assimilation in the Cp0710 genome was performed, leading to the identification of 13 genes (Additional file 1 Table S7). Not surprisingly, both the nitrate and the urea transporter were missing from the Cp0710 genome but were present in other green algae. Meanwhile, three nitrogen-related transporters (the nitrite, ammonium, and amino acid transporters) were identified, consistent with our findings on the choice of nitrogen sources in Cp0710, as described previously .
The ornithine-urea cycle (OUC), a metabolic pathway that is important for detoxification of excessive ammonia in animals , is absent from green plants and algae, though it was recently identified in diatoms . Similarly, the Cp0710 genome does not encode the requisite proteins for a complete OUC; it has lost two of the key enzymes: arginase, which catalyzes the conversion of arginine to ornithine and urea, and urease, which breaks down urea to produce ammonia. However, Cp0710 has all the genes necessary for the aspartate-argininosuccinate shunt, which connects the OUC and tricarboxylic acid (TCA) cycles and is well-established in animal cells . Recently, a similar connection was also found in diatoms, which included not only OUC and TCA cycles, but also the glutamine synthetase/glutamate synthase cycle, although the biological function of this coupling in these photosynthetic microalgae are still not clear . The presence of these genes suggested a conserved pathway might also exist in Cp0710. To further support this hypothesis, we also identified the nitrogen regulatory protein PII and glutamate synthase, two key components in regulating carbon and nitrogen metabolism . Nitrogen regulatory protein PII is a sensor of 2-oxoglutarate and ATP, while glutamate synthase catalyzes glutamate synthesis from 2-oxoglutarate. Both of these link the TCA cycle and nitrogen assimilation through 2-oxoglutarate, and thus control the flow of carbon and nitrogen.
The linkage between carbon and nitrogen metabolism might be especially important in Cp0710, since the interchange between autotrophic and heterotrophic growth is achieved through the availability of C and N sources (or C/N ratio). High nitrogen supplementation in the media will block the ability of the cells to switch from autotrophic to heterotrophic growth, as indicated by persistent chlorophyll, regardless of the presence or absence of organic carbon sources. Future study will focus on understanding how the carbon and nitrogen signal is detected and transferred, and whether and/or how this signal will directly contribute to the high oil content in Cp0710.
To utilize glucose from the heterotrophic medium, Cp0710 must be able to actively transport the sugar into cells. This task is normally carried out by H+/hexose co-transporters [26–28]. In Chlorella kessleri, a species which is also capable of using glucose to support cell growth, three H+/hexose co-transporter genes (HUP1, HUP2, and HUP3) have been identified . Therefore, we performed homologous search using the HUPs from C. kessleri as queries, and identified homologs in all seven green algae species (Additional file 1 Table S8). However, the number of homologous proteins among these algae was different. They were most abundant in C. subellipsoidea C-169 (12 proteins), and 9 proteins were identified in Cp0710.
To evaluate the evolutionary relationship of the HUPs, a phylogenetic tree was generated (Additional file 1 Figure S5). The homologs of H+/hexose co-transporters were categorized into three classes, of which two were common in green algae. Interestingly, the third class, which includes the three HUP proteins, was only present in the three Chlorella species and the other related alga C. subellipsoidea C-169. This result illustrated that these HUPs were restricted to Chlorella, suggesting that the inability of other green algae to utilize glucose might be due to the absence of these proteins. In addition, hydrophobicity analysis of the three HUP-like proteins (Cpr004256.1, Cpr001753.1, and Cpr003452.1) indicated that each of them contained 12 transmembrane domains, similar to the prototypical HUP proteins in C. kessleri. Moreover, it has been shown that six conserved amino acid residues are responsible for hexose recognition , and we found that all of them are conserved in the HUP-like proteins in Cp0710 (Additional file 1 Figure S6). Therefore, our data suggested that the three identified HUP-like proteins might be responsible for the ability of Cp0710 to rapidly utilize glucose under heterotrophic conditions. Studies on these glucose transporters are being actively pursued.
Transcriptomes and proteomes under autotrophic and heterotrophic growth conditions
To dissect the potential mechanisms underlying the highly elevated oil accumulation in heterotrophic cells, the transcriptomes and proteomes of cells under the two growth conditions were analyzed. A total of 40.2 M and 37.9 M clean reads were obtained from RNA-seq in autotrophic and heterotrophic samples, with 80.7% and 81.2% mapping ratios to the genome, respectively (Additional file 1 Table S9). Overall, 30.4% of the genes were expressed differently during heterotrophic growth, with 984 over-expressed and 1,136 downregulated (Additional file 2 Table S10). In addition, through one dimension protein separation followed by mass spectrometry analysis, we identified a total of 1,931 proteins (Additional file 1 Figure S7). Among these, 674 proteins had peptide spectrum matching (PSM) ≥ 5, and these were used for the subsequent differential expression analysis. Compared with the autotrophic cells, in heterotrophic cells, 205 proteins were upregulated while 293 proteins were downregulated. In addition, comparison between the proteomic and transcriptomic data indicated that the two datasets largely agreed with each other (Additional file 3 Figure S11), despite the fact that fewer genes were identified in the proteomic study, demonstrating the reliability of both methodologies.
Compared with autotrophic cells, the strongly upregulated genes in heterotrophic cells are involved in glycolysis/gluconeogenesis, the TCA cycle, pyruvate metabolism, oxidative phosphorylation, and fatty acid biosynthesis (Figure 3B, Additional file 1 Figure S9 and Additional file 4 Table S12). This result is not unexpected, since the increase in glycolysis, pyruvate metabolism, and the TCA cycle could result in the rapid accumulation of ATP and other important precursors such as acetyl-CoA, which are critical for fatty acid synthesis in heterotrophic algae. In addition, the pentose phosphate pathway is another significantly upregulated process. This pathway is important for glucose consumption coupled with NADPH generation, and the latter is necessary for fatty acid synthesis. Furthermore, energy metabolism shifts dramatically from photophosphorylation to oxidative phosphorylation under heterotrophic conditions, suggesting a high demand for energy, presumably for rapid cell growth and lipid accumulation. Overall, all of the upregulated pathways can directly or indirectly enhance the accumulation of fatty acids in heterotrophic algae.
Cp0710 is a green alga capable of growing both autotrophically and heterotrophically, and accumulating large amounts of lipid within the cells under heterotrophic growth conditions. This phenotype implies that a specific mechanism, though still elusive, may be encoded in its genome. Here, we generated the first draft of the genome sequence of Cp0710, and also analyzed the transcriptomes and proteomes for cells growing under autotrophic and heterotrophic conditions. One of the biggest surprises resulting from our analyses was that Cp0710 contains a very small nuclear genome, about half the size of the C. variabilis NC64A or C. subellipsoidea C-169 genome. The high degree of similarity of the proteins of Cp0710 and C. variabilis NC64A indicates that the two algae are evolutionarily related, and that Cp0710 is a bona fide species of Chlorella. Meanwhile, several possible reasons for the smaller genome in Cp0710 were considered, including fewer encoded genes, fewer copies of orthologues, and fewer genome duplications and rearrangements (Figure 2 and Table 2).
Another interesting finding after analyzing the Cp0710 genome was the lack of both nitrate and urea transporters, while other nitrogen-related transporters were present, including nitrite, ammonium, and amino acid transporters. This result was not a surprise to us since our previous study showed that this alga could not grow in medium using nitrate or urea as nitrogen sources ; however, no other green alga sequenced so far has lost both of these transporters. Since C. variabilis NC64A and C. subellipsoidea C-169 have both transporters, it is possible that the loss of these transporters from the Cp0710 genome might have happened recently, after the divergence of these species from their last common ancestor. On the other hand, the increased number of amino acid transporters, as revealed by domain analysis using PFAM (Additional file 3 Figure S11), suggests that Cp0710 has adapted to an environment rich in amino acids, and this is consistent with its previously reported N-source preference .
Our study unveiled a fine-tuned pathway for lipid synthesis in Cp0710 under heterotrophic growth conditions. First, the glucose in the medium is transported into the cell via the glucose transporters Cpr004256.1, Cpr001753.1, and Cpr003452.1. Preliminary data also suggested that the three transporters responded differently upon exposure to media containing different amounts of glucose. This was first hinted at by the transcriptomic comparison between autotrophic and heterotrophic cells. One or more of these transporters may enable the alga not only to grow heterotrophically, but also to accumulate large amounts of glucose in the cell to fulfill the energy requirement for lipid synthesis. Then, after glucose enters the cells, it is broken down through glycolysis and the TCA cycle. In heterotrophic cells of Cp0710, nearly all the glycolytic enzymes, including rate-limiting enzymes such as 6-phosphofructokinase (Cpr001888.1) and pyruvate kinase (Cpr002921.1 and Cpr000517.3) were significantly increased (Figure 4A). The TCA cycle was also enhanced under heterotrophic conditions. The increased production of the enzymes in these two pathways ensured a high rate of catabolism of glucose to meet the demand for energy and intermediates for rapid growth and lipid synthesis. As such, high glycolytic ability may be one of the bases for the ability of Cp0710 to accumulate large amounts of lipid. By contrast, a similar function in C. reinhardtii seems much weakened despite the fact that this species can also utilize glucose after a transporter is introduced. However, its growth under heterotrophic conditions is limited and the cells fail to accumulate large amounts of lipids . Secondly, acetyl-coenzyme A carboxylase (ACCase) is regarded as one of the key enzymes in regulating the carbon flux to fatty acid biosynthesis, and over-expression of this enzyme could increase the lipid content . Coincidentally, we found that expression of both the biotin carboxyl carrier subunit (Cpr002575.8) and the biotin carboxylase (Cpr003121.1) subunit of the heteromeric ACCase were elevated in heterotrophic cells (Figure 4A). In addition, all components of the fatty acid synthase subunits, which catalyze the elongation of fatty acids, are also upregulated (Figure 4B). The presence and elevated expression of these enzymes indicated that Cp0710 has adopted a unique pathway to ensure the rapid biosynthesis of fatty acids. Finally, the fatty acyl-CoA generated during de novo synthesis is esterified to make TAG, which will eventually be stored inside the lipid bodies. This requires glycerol-3-phosphate (G3P), a product derived from either glycerol or dihydroxyacetone phosphate (DHAP). In Cp0710, the upregulation of the two isozymes of glycerol-3-phosphate dehydrogenase (Cpr001038.1 and Cpr002587.4), which produce G3P from DHAP, ensures the supply of G3P. To meet the cells’ consequent requirement for DHAP, the alga adopted a mechanism generally used in higher plants: inactivation of triose phosphate isomerase (encoded by Cpr001755.1 and Cpr003712.8 in Cp0710, Figure4B) [35, 36], a key enzyme that catalyzes the isomerization between DHAP and glyceraldehyde 3-phosphate (GAP), directly linking glycolysis and TAG synthesis. A previous study revealed that deficiency of triose phosphate isomerase increased the fatty acid or oil content in root cells of plants . Therefore, we speculate that in Cp0710, a similar mechanism is deployed and the triose phosphate isomerase may be the key enzyme in regulating the carbon flux to fatty acid synthesis.
We report in this paper an annotated draft genome of the oleaginous alga Cp0710, a candidate for use as a model alga for biofuel production. Several unique features of the genome were revealed in this analysis. First, the genome size is greatly reduced and the gene number is relatively small, but most of the genes are homologous to C. variabilis NC64A, implying that Cp0710 is evolutionarily related but probably distant from other Chlorella. Secondly, the undersized genome of Cp0710 may be due to a smaller number of genes encoded in the genome, fewer copies of duplicated and unique genes, and/or a more ancient genome with fewer duplications and rearrangements. Thirdly, three HUP-like genes, which are unique to Chlorella, were identified in the genome, providing a molecular basis for Cp0710 to be able to consume glucose and grow heterotrophically. Finally, Cp0710 is the only sequenced green alga that lacks both the nitrate and urea transporter, suggesting that it may be unique in nitrogen metabolism. In addition, through comparative transcriptomic and proteomic studies on autotrophic and heterotrophic Cp0710, we generated a global view regarding the changes in metabolic pathways under the two growth conditions. We observed that under heterotrophic conditions, enzymes involved in photosynthesis and CO2 fixation are almost completely degraded, either as mRNAs or as proteins. Meanwhile, the cells are capable of not only quickly absorbing glucose but also accelerating glucose catabolism through the upregulation of glycolysis and the TCA cycle, which can presumably provide enough energy for both rapid cell growth and enhanced lipid synthesis. Moreover, the rapid synthesis of pyruvate, the upregulation of most enzymes involved in fatty acid synthesis, and the downregulation of enzymes involved in fatty acid degradation favor the accumulation of fatty acids within the cell. This suggests that Cp0710 encodes a fine-tuned pathway for the accumulation of large amounts of lipid under heterotrophic growth conditions.
Strain and culture conditions
The microalga C. protothecoides strain 0710 was described previously . The medium and culture conditions were the same as described in our previous study . For autotrophic cultivation, the concentration of glycine used was 5 g/L. For heterotrophic cultivation, glucose was added at a concentration of 10 g/L and glycine was reduced to 0.1 g/L.
Genome sequencing and assembly
Harvested cells were ground in liquid nitrogen and the total DNA was isolated using the EZgene TM CP Plant Miniprep Kit (GD2621) following the manufacturer’s protocol. A 300-bp (short-insert) and 2-kb (mate-pair) DNA library were prepared according to standard Illumina DNA preparation protocols and sequenced on an Illumina HiSeq 2000 platform. A 400–800-bp DNA library was prepared according to the standard Roche 454 DNA preparation protocol and sequenced on the Roche 454 GS FLX platform. Sequencing data from HiSeq 2000 and Roche 454 GS FLX platforms were assembled into contigs using SOAP de novo 2.01  and Newbler V2.6, respectively , and further combined with Amos 3.1.0 . Paired-end information from all reads was used for scaffold building from contigs. Local assembly was applied to fill intra-scaffold gaps with reads from a read pair, in which one read aligned within a contig uniquely and the other located within the gap. Assembly and annotation data have been uploaded with the version number APJO01000000 on DDBJ/EMBL/GenBank (accession APJO00000000). Sequence reads, including both genome and transcriptomes, have been deposited into the NCBI sequence read archive (accessions SRA115225).
Gene annotation of the Cp0710 genome was accomplished through both a de novo-based approach (Augustus v2.5.5 , SNAP , and Glimmer HMM v3.02 ) and a homolog-based approach. The generated gene sets were further combined into a single consensus gene set by applying GLEAN v1.1 .
Transcriptome data was also used during the gene annotation of the Cp0710 genome. RNA-seq reads were aligned and assembled into transcripts by Tophat v2.0.4  and Cufflinks v2.0.2 , respectively.
Gene sets from genome sequencing data and transcriptome sequencing data were further compared to generate one complete set of genes. For those transcriptome-derived genes with complete ORF structure, if a transcriptome-derived gene covered several genome-derived genes, then these genome-derived genes were replaced by the transcriptome-derived gene. Those transcriptome-derived genes without complete ORF structure were further used to help identify the start and stop site of incomplete genes annotated through the homology-based approach.
Applying InterProScan v4.7 , motifs and domains were annotated by searching against publicly available databases, including ProDom, PRINTS, Pfam, SMART, and PROSITE . GO (Gene Ontology) descriptions for the individual genes were also obtained from the corresponding information from InterPro.
The de novo repetitive sequences, including simple repeats, satellites, LINEs, SINEs, and LTR retrotransposons, were identified using LTR_FINDER v1.05 , PILER v1.0 , RepeatModeler v1.0.5 (http://www.repeatmasker.org/RepeatModeler.html), and RepeatScout v1.0.5 [51, 52]. Known repeats were identified with RepeatMasker v3.2.9 (http://repeatmasker.org) against the Repbase v17.06 database . Additionally, repeat-related proteins were identified using RepeatProteinMask against the protein database in Repbase. The results of the two approaches were combined.
Gene family and phylogenetic tree reconstruction
Gene family identification was performed using TreeFam . BLASTP was used to align all protein sequences against the protein dataset of all species with cut-off E-values of 1e-7. For each gene pair with more than 0.33 of the region aligned to both genes, conjoined fragmental alignments were performed using Solar (Sorting Out Local Alignment Results) (a program in TreeFam). The hierarchical clustering algorithm was applied for gene clustering, with thresholds set to a minimum edge weight of ≥10 and a minimum edge density of ≥0.34.
The protein sequences from 300 single-copy gene families with edge weight >30 and edge density equal to 1, which were conserved among Cp0710, C. variabilis NC64A, C. subellipsoidea C-169, C. reinhardtii, V. carteri, M. pusilla CCMP1545, and O. tauri, were extracted and aligned using MUSCLE v3.8.31 . The multiple alignments were then concatenated into one super sequence for each species. The maximum likelihood tree was constructed with protein sequences using PHYML v3.0  under the WAG with gamma model. Bootstrap values were taken to assess branch reliability.
Gene comparison and synteny analysis
The orthologues of Cp0710, C. variabilis NC64A, and C. subellipsoidea C-169 were identified using the gene family identification method described above. The orthologues were classified as core and pairwise orthologues. In addition, the core orthologues was divided into 1:1:1 orthologues and n:n:n orthologues. The 1:1:1 orthologues were defined as single-copy genes presenting in all three species and the n:n:n orthologues represented putative multiple gene duplications in at least one of the species. On the other hand, pairwise orthologues were assigned when orthology was not detectable in the third species. Some genes lacking clear orthology, but with some similarity in the other species were designated as “homologous” (E-value cut-off of 10-5 using BlastP).
The synteny blocks between Cp0710 and C. variabilis NC64A were identified according to the method described previously . The whole-genome alignments were obtained using the program lastz (Local Alignment Search Tool, blastZ-like, version 1.02.00) in a HOXD55 substitution penalty matrix, and then the aligned gapless segments with the same order and orientation in both species were ligated into “chains” by AXTCHAIN. Subsequently, only a single best alignment for every region of the C. variabilis NC64A genome was selected by CHAINNET.
Transcriptome sequencing and analysis
Total RNAs were isolated from either autotrophic or heterotrophic cells using Trizol Reagent (Invitrogen Inc.), followed by poly (A) mRNA purification with Dynabeads Oligo (dT) (Invitrogen Inc.). The 200-bp cDNA libraries were prepared according to the manufacturer’s instructions (Illumina Inc.). The cDNA libraries derived from enriched mRNA were paired-end sequenced using the Illumina HiSeq™ 2000.
The unigene expression level was determined using RPKM , and statistical differences in expression of unigenes was determined using SAGE . To rigorously identify differentially expressed genes with multiple pairwise comparisons, false discovery rate (FDR) correction was applied to constrain errors . Finally, unigenes with an FDR < = 0.001 and an RPKM change of > =2-fold were marked as significantly differentially expressed genes (DEGs), between the two samples.
KEGG  pathway enrichment and GO  functional enrichment were then carried out for all DEGs. All upregulated or downregulated genes were initially mapped to KEGG or GO terms in the database (KEGG, release 58; GO, release 2012-07-01) using Blastp, then proteins with E-value ≤ 10-5 and coverage ≥ 50% were identified. The gene numbers for each term were calculated. Those significantly enriched in GO and KEGG terms were identified by comparison to the genome background using an ultra geometric test with Bonferroni correction, with statistical significance set at P = 0.05. For GO enrichment, to remove redundancy, if the GOs enrich at different levels with parent–child relationship and have the same gene list, the lowest level is chosen.
Protein preparation and identification
Cells were collected by centrifugation and resuspended in protein extraction buffer (HEPES-40mM, KCl-10mM, MgCl2-10mM, EGTA-5mM, CaCl2-10mM, PVPP- 0.1%, PMSF-1mM, and DTT-1mM). Then, the cells were disrupted with a mini-BeadBeater, and the mixture was centrifuged at 15,000 × g for 10 min, and the supernatant was collected. One volume of SDS/sucrose buffer (sucrose 60%, SDS 4%, and 0.1M Tris–HCl, pH 6.8) was added to the supernatant and blended followed by the addition of one volume of Tris-saturated phenol and vortexed for 10 minutes. After centrifugation at 20,000 × g for 10 minutes, the protein-containing layer was collected, and five volumes of a 0.1 M ice cold ammonium acetate–methanol mix was added. The mixture was then incubated at -20°C overnight for protein precipitation. After centrifugation (20,000 × g for 10 min), the pellet was washed three times with 0.1 M ammonium acetate–methanol and twice with acetone. Finally, the protein was lyophilized and dissolved in sample buffer for SDS-PAGE.
Protein identification was performed as in a previous study . In brief, 100 μg of protein was separated by one dimensional SDS-PAGE. Each lane was cut into 40 slices, and gel digestion was performed for each slice. The digested samples (20 μl) were analyzed by LC-MS/MS with an LTQ Orbitrap Velosmass spectrometer (Thermo Scientific™). Then, 4 μl of each sample was loaded and gradient eluted with a flow rate of 120 nL/min. For the first 2 minutes, the acetonitrile (ACN) content in the wash buffer was 8%, and in the subsequent 43 minutes the ACN content increased to 55%, in the last 15 minutes the ACN content was 95%. The MS analysis was performed in the Orbitrap at 60,000 full width at half-maximum (FWHM) resolutions, and the 20 most abundant ions were selected for the MS/MS analysis performed in the LTQ.
To identify the proteins, the data from mass spectra were searched against the protein database of Cp0710 using SEQUEST of Thermo Proteome Discoverer 220.127.116.11. The tolerance for peptide identification was 20 ppm in MS and 0.8 Da in MS/MS, and the considered modifications included carbamidomethylation and oxidation. Two missed cleavages were permitted. For quantification, only proteins with PSM ≥ 5 were included in the calculation. The PSM and the total peak area of all peptides for a particular protein were divided by the sum of all PSMs or peak areas of that spectrum, respectively, i.e. normalization to total detected peptides within each sample (central tendency normalization ) (Sheet Pro with PSM > =5, Column J to Q in Additional file 2 Table S10). Then, the protein data were compared between the two samples (Sheet Pro with PSM > =5, Colum R and S in Additional file 2 Table S10) to determine the relative change in expression level. This experiment was done twice and only proteins with PSM and area changes ≥ 1.5-fold in both experiments were regarded as significant hits. The raw data from the protein identification is presented in Sheet protein identification in Additional file 2 Table S10. All proteomic data have been submitted to the peptide atlas database (Identifier: PASS00452, http://www.peptideatlas.org/PASS/PASS00452).
We are grateful to Vanita Uppada for providing a critical reading of this paper. This work was supported by NSFC project 41030210, 31370282, MOST project 2011BAD14B05, and 2011CB808804 to QW and by Tsinghua University Initiative Scientific Research Program 2011Z02296 and 2012Z08128 to JD and by Shenzhen New Energy Industry Development Special Foundation Basic Research Key Projects JC201005250091A.
- Halim R, Danquah MK, Webley PA: Extraction of oil from microalgae for biodiesel production: a review. Biotechnol Adv. 2012, 30: 709-732.PubMedView ArticleGoogle Scholar
- Scott SA, Davey MP, Dennis JS, Horst I, Howe CJ, Lea-Smith DJ, Smith AG: Biodiesel from algae: challenges and prospects. Curr Opin Biotechnol. 2010, 21: 277-286.PubMedView ArticleGoogle Scholar
- Miao X, Wu Q: Biodiesel production from heterotrophic microalgal oil. Bioresour Technol. 2006, 97: 841-846.PubMedView ArticleGoogle Scholar
- Perez-Garcia O, Escalante FM, de-Bashan LE, Bashan Y: Heterotrophic cultures of microalgae: metabolism and potential products. Water Res. 2011, 45: 11-36.PubMedView 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: 499-507.PubMedView ArticleGoogle Scholar
- Xiong W, Li X, Xiang J, Wu Q: High-density fermentation of microalga Chlorella protothecoides in bioreactor for microbio-diesel production. Appl Microbiol Biotechnol. 2008, 78: 29-36.PubMedView ArticleGoogle Scholar
- Xiong W, Gao C, Yan D, Wu C, Wu Q: Double CO (2) fixation in photosynthesis-fermentation model enhances algal lipid synthesis for biodiesel production. Bioresour Technol. 2010, 101: 2287-2293.PubMedView ArticleGoogle Scholar
- Lu Y, Ding Y, Wu Q: Simultaneous saccharification of cassava starch and fermentation of algae for biodiesel production. J Appl Phycol. 2011, 23: 115-121.View ArticleGoogle Scholar
- Gao C, Zhai Y, Ding Y, Wu Q: Application of sweet sorghum for biodiesel production by heterotrophic microalga Chlorella protothecoides. Appl Energy. 2010, 87: 756-761.View ArticleGoogle Scholar
- Yan D, Lu Y, Chen Y-F, Wu Q: Waste molasses alone displaces glucose-based medium for microalgal fermentation towards cost-saving biodiesel production. Bioresour Technol. 2011, 102: 6487-6493.PubMedView ArticleGoogle Scholar
- Cheng Y, Zhou W, Gao C, Lan K, Gao Y, Wu Q: Biodiesel production from Jerusalem artichoke (Helianthus Tuberosus L.) tuber by heterotrophic microalgae Chlorella protothecoides. J Chem Technol Biotechnol. 2009, 84: 777-781.View ArticleGoogle Scholar
- Li R, Fan W, Tian G, Zhu H, He L, Cai J, Huang Q, Cai Q, Li B, Bai Y, Zhang Z, Zhang Y, Wang W, Li J, Wei F, Li H, Jian M, Li J, Zhang Z, Nielsen R, Li D, Gu W, Yang Z, Xuan Z, Ryder OA, Leung FC, Zhou Y, Cao J, Sun X, Fu Y, et al: The sequence and de novo assembly of the giant panda genome. Nature. 2010, 463: 311-317.PubMed CentralPubMedView ArticleGoogle Scholar
- Parra G, Bradnam K, Korf I: CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics. 2007, 23: 1061-1067.PubMedView ArticleGoogle Scholar
- Blanc G, Duncan G, Agarkova I, Borodovsky M, Gurnon J, Kuo A, Lindquist E, Lucas S, Pangilinan J, Polle J, Salamov A, Terry A, Yamada T, Dunigan DD, Grigoriev IV, Claverie JM, Van Etten JL: The Chlorella variabilis NC64A genome reveals adaptation to photosymbiosis, coevolution with viruses, and cryptic sex. Plant Cell. 2010, 22: 2943-2955.PubMed CentralPubMedView ArticleGoogle Scholar
- Blanc G, Agarkova I, Grimwood J, Kuo A, Brueggeman A, Dunigan DD, Gurnon J, Ladunga I, Lindquist E, Lucas S, Pangilinan J, Pröschold T, Salamov A, Schmutz J, Weeks D, Yamada T, Lomsadze A, Borodovsky M, Claverie JM, Grigoriev IV, Van Etten JL, et al: The genome of the polar eukaryotic microalga Coccomyxa subellipsoidea reveals traits of cold adaptation. Genome Biol. 2012, 13: R39-PubMed CentralPubMedView ArticleGoogle Scholar
- Oliver MJ, Petrov D, Ackerly D, Falkowski P, Schofield OM: The mode and tempo of genome size evolution in eukaryotes. Genome Res. 2007, 17: 594-601.PubMed CentralPubMedView ArticleGoogle Scholar
- Le Bihan T, Martin SF, Chirnside ES, van Ooijen G, Barrios-Llerena ME, O’Neill JS, Shliaha PV, Kerr LE, Millar AJ: Shotgun proteomic analysis of the unicellular alga Ostreococcus tauri. J Proteomics. 2011, 74: 2060-2070.PubMedView ArticleGoogle Scholar
- Palenik B, Grimwood J, Aerts A, Rouze P, Salamov A, Putnam N, Dupont C, Jorgensen R, Derelle E, Rombauts S, et al: The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. Proc Natl Acad Sci USA. 2007, 104: 7705-7710.PubMed CentralPubMedView ArticleGoogle Scholar
- Tirichine L, Bowler C: Decoding algal genomes: tracing back the history of photosynthetic life on Earth. Plant J. 2011, 66: 45-57.PubMedView ArticleGoogle Scholar
- Kumar A, Bennetzen JL: Plant retrotransposons. Annu Rev Genet. 1999, 33: 479-532.PubMedView ArticleGoogle Scholar
- Zheng ZL: Carbon and nitrogen nutrient balance signaling in plants. Plant Signal Behav. 2009, 4: 584-591.PubMed CentralPubMedView ArticleGoogle Scholar
- Yan D, Dai J, Wu Q: Characterization of an ammonium transporter in the oleaginous alga Chlorella protothecoides. Appl Microbiol Biotechnol. 2013, 97: 919-928.PubMedView ArticleGoogle Scholar
- Morris SM: Regulation of enzymes of the urea cycle and arginine metabolism. Annu Rev Nutr. 2002, 22: 87-105.PubMedView ArticleGoogle Scholar
- Allen AE, Dupont CL, Obornik M, Horak A, Nunes-Nesi A, McCrow JP, Zheng H, Johnson DA, Hu H, Fernie AR, Bowler C: Evolution and metabolic significance of the urea cycle in photosynthetic diatoms. Nature. 2011, 473: 203-207.PubMedView ArticleGoogle Scholar
- Commichau FM, Forchhammer K, Stulke J: Regulatory links between carbon and nitrogen metabolism. Curr Opin Microbiol. 2006, 9: 167-172.PubMedView ArticleGoogle Scholar
- Williams LE, Lemoine R, Sauer N: Sugar transporters in higher plants–a diversity of roles and complex regulation. Trends Plant Sci. 2000, 5: 283-290.PubMedView ArticleGoogle Scholar
- McCurdy DW, Dibley S, Cahyanegara R, Martin A, Patrick JW: Functional characterization and RNAi-mediated suppression reveals roles for hexose transporters in sugar accumulation by tomato fruit. Mol Plant. 2010, 3: 1049-1063.PubMedView ArticleGoogle Scholar
- Ozcan S, Johnston M: Function and regulation of yeast hexose transporters. Microbiol Mol Biol Rev. 1999, 63: 554-569.PubMed CentralPubMedGoogle Scholar
- Sauer N, Tanner W: The hexose carrier from Chlorella. cDNA cloning of a eucaryotic H + -cotransporter. FEBS Lett. 1989, 259: 43-46.PubMedView ArticleGoogle Scholar
- Stadler R, Wolf K, Hilgarth C, Tanner W, Sauer N: Subcellular localization of the inducible Chlorella HUP1 monosaccharide-H + symporter and cloning of a Co-induced galactose-H + symporter. Plant Physiol. 1995, 107: 33-41.PubMed CentralPubMedView ArticleGoogle Scholar
- Will A, Caspari T, Tanner W: Km mutants of the Chlorella monosaccharide/H + cotransporter randomly generated by PCR. Proc Natl Acad Sci USA. 1994, 91: 10163-10167.PubMed CentralPubMedView ArticleGoogle Scholar
- Kato Y, Ueno S, Imamura N: Studies on the nitrogen utilization of endosymbiotic algae isolated from Japanese Paramecium bursaria. Plant Sci. 2006, 170: 481-486.View ArticleGoogle Scholar
- Doebbe A, Rupprecht J, Beckmann J, Mussgnug JH, Hallmann A, Hankamer B, Kruse O: Functional integration of the HUP1 hexose symporter gene into the genome of C. reinhardtii: Impacts on biological H (2) production. J Biotechnol. 2007, 131: 27-33.PubMedView ArticleGoogle Scholar
- Roesler K, Shintani D, Savage L, Boddupalli S, Ohlrogge J: Targeting of the Arabidopsis homomeric acetyl-coenzyme A carboxylase to plastids of rapeseeds. Plant Physiol. 1997, 113: 75-81.PubMed CentralPubMedView ArticleGoogle Scholar
- Chen M, Thelen JJ: The plastid isoform of triose phosphate isomerase is required for the postgerminative transition from heterotrophic to autotrophic growth in Arabidopsis. Plant Cell. 2010, 22: 77-90.PubMed CentralPubMedView ArticleGoogle Scholar
- Olah J, Orosz F, Keseru GM, Kovari Z, Kovacs J, Hollan S, Ovadi J: Triosephosphate isomerase deficiency: a neurodegenerative misfolding disease. Biochem Soc Trans. 2002, 30: 30-38.PubMedView ArticleGoogle Scholar
- Li R, Zhu H, Ruan J, Qian W, Fang X, Shi Z, Li Y, Li S, Shan G, Kristiansen K, Li S, Yang H, Wang J, Wang J: De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 2010, 20: 265-272.PubMed CentralPubMedView ArticleGoogle Scholar
- Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen Z, Dewell SB, Du L, Fierro JM, Gomes XV, Godwin BC, He W, Helgesen S, Ho CH, Irzyk GP, Jando SC, Alenquer ML, Jarvie TP, Jirage KB, Kim JB, Knight JR, Lanza JR, Leamon JH, Lefkowitz SM, Lei M, Li J, et al: Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005, 437: 376-380.PubMed CentralPubMedGoogle Scholar
- Phillippy AM, Schatz MC, Pop M: Genome assembly forensics: finding the elusive mis-assembly. Genome Biol. 2008, 9: R55-PubMed CentralPubMedView ArticleGoogle Scholar
- Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern B: AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 2006, 34: W435-W439.PubMed CentralPubMedView ArticleGoogle Scholar
- Korf I: Gene finding in novel genomes. BMC Bioinformatics. 2004, 5: 59-PubMed CentralPubMedView ArticleGoogle Scholar
- Majoros WH, Pertea M, Salzberg SL: TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics. 2004, 20: 2878-2879.PubMedView ArticleGoogle Scholar
- Elsik CG, Mackey AJ, Reese JT, Milshina NV, Roos DS, Weinstock GM: Creating a honey bee consensus gene set. Genome Biol. 2007, 8: R13-PubMed CentralPubMedView ArticleGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009, 25: 1105-1111.PubMed CentralPubMedView ArticleGoogle Scholar
- Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L: Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010, 28: 511-515.PubMed CentralPubMedView ArticleGoogle Scholar
- Bairoch A, Boeckmann B, Ferro S, Gasteiger E: Swiss-Prot: juggling between evolution and stability. Brief Bioinform. 2004, 5: 39-55.PubMedView ArticleGoogle Scholar
- Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28: 27-30.PubMed CentralPubMedView ArticleGoogle Scholar
- Hunter S, Jones P, Mitchell A, Apweiler R, Attwood TK, Bateman A, Bernard T, Binns D, Bork P, Burge S, de Castro E, Coggill P, Corbett M, Das U, Daugherty L, Duquenne L, Finn RD, Fraser M, Gough J, Haft D, Hulo N, Kahn D, Kelly E, Letunic I, Lonsdale D, Lopez R, Madera M, Maslen J, McAnulla C, McDowall J, et al: InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 2012, 40: D306-D312.PubMed CentralPubMedView ArticleGoogle Scholar
- Xu Z, Wang H: LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 2007, 35: W265-W268.PubMed CentralPubMedView ArticleGoogle Scholar
- Edgar RC, Myers EW: PILER: identification and classification of genomic repeats. Bioinformatics. 2005, 21 (Suppl 1): i152-i158.PubMedView ArticleGoogle Scholar
- Price AL, Jones NC, Pevzner PA: De novo identification of repeat families in large genomes. Bioinformatics. 2005, 21 (Suppl 1): i351-i358.PubMedView ArticleGoogle Scholar
- Benson G: Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999, 27: 573-580.PubMed CentralPubMedView ArticleGoogle Scholar
- Jurka J, Kapitonov VV, Pavlicek A, Klonowski P, Kohany O, Walichiewicz J: Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 2005, 110: 462-467.PubMedView ArticleGoogle Scholar
- Li H, Coghlan A, Ruan J, Coin LJ, Hériché JK, Osmotherly L, Li R, Liu T, Zhang Z, Bolund L, Wong GK, Zheng W, Dehal P, Wang J, Durbin R: TreeFam: a curated database of phylogenetic trees of animal gene families. Nucleic Acids Res. 2006, 34: D572-D580.PubMed CentralPubMedView ArticleGoogle Scholar
- Edgar RC: MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004, 32: 1792-1797.PubMed CentralPubMedView ArticleGoogle Scholar
- Guindon S, Gascuel O: A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol. 2003, 52: 696-704.PubMedView ArticleGoogle Scholar
- Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D: Evolution’s cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003, 100: 11484-11489.PubMed CentralPubMedView ArticleGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008, 5: 621-628.PubMedView ArticleGoogle Scholar
- Audic S, Claverie JM: The significance of digital gene expression profiles. Genome Res. 1997, 7: 986-995.PubMedGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: a Prctical and Powerful Approach to Multiple Testing. J R Stat Soc. 1995, 57: 12-Google Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25: 25-29.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhang N, Zhang Z, Feng S, Wang Q, Malamud D, Deng H: Quantitative analysis of differentially expressed saliva proteins in human immunodeficiency virus type 1 (HIV-1) infected individuals. Anal Chim Acta. 2013, 774: 61-66.PubMed CentralPubMedView ArticleGoogle Scholar
- Callister SJ, Barry RC, Adkins JN, Johnson ET, Qian WJ, Webb-Robertson BJ, Smith RD, Lipton MS: Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J Proteome Res. 2006, 5: 277-286.PubMed CentralPubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.