transcript isoforms corresponding to the same locus or to different loci can have the same top blast hit, multiple transcript isoforms can result in redundant headers, causing errors with the Mascot program. To bypass this problem, multiple occurrences of a given fasta header in the annotated transcriptome file were appended with ascending numbers using a second Python script. Product ion data was searched against forward and reverse concatenated Chlorophyta and six-frame translated de novo assembled C. vulgaris transcriptome databases using the Mascot search program, using identical search parameters. Searching against Chlorophyta, the proteomic analysis identified an average of 1,401 proteins under nitrogen-replete conditions, and 1,347 proteins under nitrogen-deplete conditions, corresponding to 2,061 unique protein identifications between the two conditions (Figure 3b). Searching against the de novo assembled C. vulgaris transcriptome yielded significantly higher positive identifications. Under nitro- gen-replete conditions, an average of 2,312 proteins were identified, and an average of 2,209 were identified under nitrogen-deplete conditions, corresponding to 2,949 unique protein identifications between the two conditions (Figure 3b). Thus, of the 7,067 transcripts identified by blastn search against all Chlorophyta, ,42% were identified in our proteomics analysis. The numbers of matching spectra, unique peptides, mean and median spectra/protein (the average and statistically distributed mid-value of spectral counts, respectively, identifying a given protein), and mean and median unique peptides/protein (the average and statistically distributed mid-value, respectively, of observed pep- tides uniquely matching a given protein) all increased approxi- mately 2-fold using the de novo assembled C. vulgaris transcriptome, clearly indicative of a superior search database (Figure 3b). This identification rate marks the largest number of positive identifi- cations for a microalgal proteomic analysis to date, and represents an order of magnitude increase compared to previously identified microalgal sub-proteomic analyses of unsequenced microalgae. Annotation of protein identifications was completed by matching to transcriptomic blastx results. Of 2,949 positive identifications, 2,660 proteins (90.2%) returned a statistically significant blast hit. We employed molecular function Gene Ontology (GO) enrichment analysis to assess the functional distribution of transcripts in the entire annotated C. vulgaris transcriptome, as well as the 2,949 transcripts corresponding to positive MS/MS identifications in the soluble sub-proteome. The results of GO enrichment are represented as the percent of total transcripts in respective fractions in Figure 4. The GO enrichment represented several categories of molecular function, with transcripts coding for nucleotide and nucleic acid binding proteins comprising the largest percentage (,25%) of all transcripts in both the whole annotated transcriptome and the corresponding soluble sub- proteome fraction (Figure 4). Transcripts coding for proteins with transferase, hydrolase, and lyase activity were also highly enriched in both the whole transcriptome and soluble sub-proteome fraction. The gene distribution amongst the whole transcriptome and the fraction corresponding to the soluble sub-proteome fraction shows relatively equivalent percent distribution amongst the functional groupings. This result suggests a large fraction of proteins that might be expected to reside in the insoluble proteome fraction (e.g., transporters and membrane bound enzymes) were isolated by our lysis method and identified in our proteomic analysis. Indeed, this occurrence is demonstrated in the positive identification of all enzymes along the TAG biosynthetic pathway, comprised of a number of membrane-associated proteins in the endoplasmic reticulum (discussed below). The initial goal of this study was to examine the soluble proteome fraction without specific intent to examine the TAG biosynthetic components, Systems Biology Analysis of Chlorella vulgaris though the identification of these components was a welcome result. The components of the FA biosynthetic pathway are expected to be largely associated with the soluble proteome fraction (not membrane-bound), and as such, the observed identification of these components was expected. Approximately 42% of all annotated transcripts were identified in our proteomic analysis. Given the equivalent distribution across the various GO categories between the proteome and transcrip- tome (Figure 4), this value implies that less than half of the annotated transcribed genes from each category were identified in the proteome. However, the uniform nature of these absences suggests this is a limitation of MS/MS identification capabilities, as opposed to the systematic absence of a given class of proteins. In cases where a genome is available as a search model, identification rates for large-scale proteomic analyses are typically 35–60% [44,45,46], indicating the transcriptome offers a strong search database in cases where a genome is unavailable. The utilization of the de novo assembled C. vulgaris transcriptome led to identification of a number of proteins along the major metabolic and biosynthetic pathways that were initially absent from the data obtained using other Chlorophyta sequence databases. Figure 5 provides more detail for the multiple sequence alignment of peptide fragments of acetyl-CoA acyltransferase (ACAT) identified in MS/MS analysis of C. vulgaris against the top seven Chlorophyta homologs. Despite significantly high sequence similarity (E-values,6e-124) for all homologs, Mascot searching against all Chlorophyta databases failed to identify ACAT. Only when utilizing the de novo assembled C. vulgaris transcriptome was ACAT identified. The utilization of our C. vulgaris transcriptome as a proteomic search model was also successful in identifying otherwise unidentified proteins that play critical roles in fatty acid and triacylglycerol biosynthesis (Figure 6). A significant portion of the FA pathway, including malonyl-CoA:ACP transacylase (MAT), 3- ketoacyl-ACP synthase (KAS), 3-ketoacyl-ACP reductase (KAR), and 3-hydroxyacyl-ACP dehydratase (HD) was absent from our orthologous database analysis results. The components of the TAG biosynthetic pathway, including glycerol-3-phosphate acyl- transferase (GPAT), lyso-phosphatidic acid acyltransferase (LPAAT), phosphatidic acid phosphatase (PAP), lyso-phosphati- dylcholine acyltransferase (LPAT), and diacylglycerol acyltransfer- ase (DGAT) – the last of which is required for commitment into TAG biosynthesis – were also absent from the TAG biosynthetic pathway, when using orthologous search databases (Figure 6). However, these proteins were all identified in significant abundance (.10 spectral counts) using the C. vulgaris UTEX 395 de novo assembled transcriptome, indicating that they went unidentified due to lack of sequence similarity, as opposed to abundance below the limits of detection (1 spectral count). Overall, the number of statistically significant protein identifications increased nearly 2-fold when using the de novo assembled transcriptome as a sequence database (Figure 3b). Differential Protein Abundance in Fatty Acid and Triacylglycerol Biosynthetic Pathways Chloroplastic microalgal fatty acid synthesis is proposed to occur primarily through conversion of acetyl-CoA to malonyl- CoA precursors, followed by four successive condensation reactions, ultimately resulting in the production of an acyl-ACP (Figure 6). Acetyl-CoA carboxylase (ACCase) catalyzes the first committed step of fatty acid synthesis in a two-step reaction that results in the conversion of acetyl-CoA to malonyl-CoA. ACCase inhibition via phosphorylation can be catalyzed by AMP-activated kinase (AMPK). In the next step of fatty acid synthesis, the malonyl PLoS ONE | www.plosone.org 7 October 2011 | Volume 6 | Issue 10 | e25851