most, if not all of them, were chosen to be sequenced for reasons other than biofuel production. ‘‘Higher level -omic’’ analyses – transcriptomics, proteomics, and metabolomics – of microalgae have largely focused upon those species with sequenced genomes (for an extensive review, please refer to Jamers, 2009). For example, the transcriptome of the model green alga, Chlamydomonas reinhardtii, was recently characterized under nutrient-replete, anaerobic H2-producing, and sulfur-deplet- ed growth conditions [7,8,9]. Benning and coworkers have also extensively examined the nitrogen-deprivation stress response and its effects upon lipid accumulation in C. reinhardtii, through comparative transcriptomics and lipid droplet proteomics [10,11]. However, the best cellular lipid accumulation noted for this organism is only ,20% in wild type cultivars, though ,40% total lipids have been reported for starchless mutants [12,13]. Many unsequenced wild-type strains have been reported to make more than 50% total lipids under these same growth conditions [1]. It is likely that the dearth of microalgal genomics data has dissuaded biologists from pursuing higher-level systems biology analyses of unsequenced yet potential commercially relevant oleaginous microalgae. Transcriptomic analyses focused upon elucidating specific metabolic pathways have, up until recently, employed microarrays generated from cDNA libraries, or sandwich hybridization assays, as opposed to utilization of the high-throughput, massively parallel, cDNA-sequencing techniques currently available for global transcriptome analysis [14,15,16]. On the other hand, Rismani-Yazdi et al. [17] just reported 454 pyrosequencing to carry out the first de novo transcriptomic sequencing and annotation of the unsequenced microalgae Dunaliella tertiolecta. This work marks a meaningful advance in the pursuit of higher-level systems biology approaches with unsequenced, oleaginous microalgae, and demon- strates the capability of using transcriptomic data to identify pathways and targets of interest for metabolic engineering and functional genomic analyses in non-model microalgae. Despite its power, transcriptomic analysis does not adequately define the control points for metabolic regulation. This is especially true in algae where post-transcriptional regulation is not well understood. For example, translational regulation of chloroplast gene expression occurs in a number of microalgae and higher plants [18,19,20,21,22,23]. Thus, a more complete systems biology analysis is required to provide useful hypotheses for strain improvement strategies. Proteomics can bring us closer to this goal, but the availability of proteome data from unsequenced microalgae is also sparse. Such proteomic analyses have typically targeted specific cellular components (sub-proteomes such as chloroplasts) and yielded relatively low orthologous identification rates [16,24,25]. For example, Wang et al. [25] employed a cross-species protein identification strategy in order to examine the proteome of the Haematococcus pluvialis cell wall. For the identification of proteins with low sequence identity, a conserved motif and domain strategy was also implemented. They observed that approximately one half of the proteins examined failed to be recognized in protein databases. This was attributed to amino acid substitutions and/or post-translational modifications, both of which dramatically reduce the probability of cross-species proteomic identifications. Such analyses underscore the need for increased sequence information on diverse microalgae. As we present in this communication, utilization of a de novo sequenced transcriptome can provide the necessary sequence information to pursue such proteomic analyses. After considering the fact that of the over 40,000 species of microalgae identified to date, fewer than a dozen microalgal genome sequences are available, it is not surprising that elucidation of the key pathways and networks regulating lipid accumulation remains limited [1]. An integrated systems biology examination of Systems Biology Analysis of Chlorella vulgaris these organisms will be critical in order to understand the unique, strain-specific mechanisms of lipid accumulation, and to develop strategies required to engineer improved strains with enhanced lipid production. Furthermore, genetic engineering of unsequenced strains will require identification of unique promoter and untrans- lated region (UTR) sequences for targeted overexpression or silencing of target genes [22]. Transcriptomics and proteomics cannot provide complete sequence data for promoters and UTRs, but can identify genes with desirable expression patterns, thereby directing strain-engineering strategies to a small region rather than the entire genome. And so, with the full potential of transcriptomics and proteomics largely dependent upon genome sequence avail- ability, many promising algal strains have been left unexplored. The oleaginous green alga, Chlorella vulgaris, has been extensively studied due to its relatively fast growth rate and its value as both a food supplement and potential biofuel feedstock. Additionally, C. vulgaris has also recently been examined in light of the genus’ biomedical relevance, demonstrating anti-oxidant and anti- tumorigenic properties, as well as having value in increasing vascular and immune function [26,27,28,29]. C. vulgaris also accumulates .50% lipid under nutrient-deplete conditions, with a favorable fatty acid profile for biodiesel production. Finally, many reports have been published describing successful genetic trans- formation of Chlorella cultivars [30,31]. Taken together, we have concluded that this is an ideal platform to explore algal lipid metabolism and the biosynthetic pathways involved in fatty acid and TAG biosynthesis in oleaginous algae. To date, however, there is no genome sequence available for C. vulgaris (although a genome sequence has been published for the presumably related strain, C. variablis NC64A [32]), hindering the further development of this organism as a food, fuel and biomedical resource. We have taken a different approach and set out to demonstrate the utility of bypassing the genome sequencing step by taking advantage of current high-throughput technologies in order to pursue direct, higher-level systems biology analyses. Herein, we have built upon the de novo transcriptome sequencing approach, and set out to conduct a comparative global transcriptomic and proteomic study of the microalga, C. vulgaris UTEX 395, chosen after screening all ten C. vulgaris cultivars in the UTEX Algae Culture Collection for growth rate and lipid accumulation capability, under conditions that induce high oil production. These conditions have been optimized to yield greater than 60% fatty acid accumulation based on dry cell weight when the algae were grown under nutrient-deplete conditions. cDNA from C. vulgaris was sequenced using Illumina technology and de novo transcriptome assembly was performed using a combination of readily available software and newly generated bioinformatic tools. The proteomic analysis was subsequently undertaken utilizing the assembled C. vulgaris transcriptome as a search model. This work marks the first comprehensive proteomic investigation of lipid accumulation in an unsequenced microalga, as well as the first utilization of a de novo assembled transcriptome as a search model for proteomic analysis in an unsequenced microalga. Our results indicate that this approach can provide a powerful and effective search model for proteomic analysis. Our efforts demonstrate the feasibility of bypassing the bottleneck of genomic sequencing, opening the door for a comprehensive systems biology examina- tion of other unsequenced oleaginous microalgae. Materials and Methods Algal strain and culture conditions Chlorella vulgaris strain UTEX 395 was grown in 1L Roux bottles using modified Bold’s Basal Media (mBBM) containing: 2.94 mM PLoS ONE | www.plosone.org 2 October 2011 | Volume 6 | Issue 10 | e25851