For reprint orders, please contact [email protected]

Review

Improving biofuel production in phototrophic microorganisms with systems biology Biofuels (2011) 2(2), 125–144

Robert E Jinkerson1, Venkataramanan Subramanian1,2 & Matthew C Posewitz†1 Biofuels derived from algal energy carriers, including lipids, starch and hydrogen, offer a promising, renewable alternative to fossil fuels. Unfortunately, native algal metabolisms are not optimized for the accumulation of these renewable bioenergy carriers. Systems biology, which includes genomics, transcriptomics, proteomics, metabolomics and lipidomics, can inform and provide key insights to advance algal strain development for biotechnological applications. Recent advances in analytical technologies have enabled these sophisticated, high-throughput, holistic ‘omics’ techniques to generate highly accurate and quantitative datasets that can be leveraged to improve biofuel phenotypes in phototrophic microorganisms. The study of algal genomes and transcriptomes allows for the identification of genes, metabolic pathways and regulatory networks. Investigations of algal proteomes reveal protein levels, locations and post-translational modifications, while study of the metabolome reveals metabolite fluxes and intermediates. All of these systems-biology tools are integral for investigating algal metabolism from the whole-cell perspective. This review focuses on how systems biology has been applied to studying metabolic networks in algae and cyanobacteria, and how these technologies can be used to improve bioenergy-carrier accumulation.

Microalgae and cyanobacteria have high photosyntheticconversion efficiencies, rapid growth rates, diverse metabolic capabilities, accumulate relatively little recalcitrant biomass (e.g., cellulose and lignin) and are able to synthesize a diversity of biological energy carr­iers (e.g., starch, lipids and H2) that are relevant to renewable-bioenergy missions [1] . Algae store energy in two predominate forms, lipids and polyglucans, which can be converted into diesel-fuel surrogates and metabolized into a variety of biofuels (e.g., alcohols, H2 and lipids), respectively. Although intensive efforts are underway worldwide to produce biofuel feedstocks from water-oxidizing, photo­ trophic microorganisms, commercial success in the production of biofuels from these organisms remains an unmet challenge. The production of algal biofuels in an economically and ecologically sustainable manner will require a sophisticated understanding of algal physio­ logy and metabolism, in addition to advances in algal biotechnology. The existing information regarding the

mechanisms by which anabolic metabolisms, respir­atory and fermentative processes, and diel metabolic cycles are integrated and modulate photosynthetic efficiencies is not sufficient to optimize phototroph metabolism for biofuel production. A detailed understanding of primary and secondary metabolisms will inform physiological culturing parameters, as well as potential genetic-engineering strategies, aimed at optimizing the synthesis of targeted bioenergy carriers. Existing know­ledge gaps will need to be addressed in order to develop bio­engineering approaches that allow scientists to tailor metabolic circuits for bioenergy production. Even in model organisms, such as Chlamydomonas reinhardtii, the function of many gene products remain unknown  [2] , hampering current efforts to devise metabolic and regul­atory models that accurately predict metabolic fluxes under photoautotrophic growth conditions. Several species of algae are amenable to genetic modification and many transgenic strategies have been suggested and attempted to improve bioenergy-carrier

Department of Chemistry and Geochemistry, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, USA; Biosciences Center, National Renewable Enery Laboratory, 1617 Cole Blvd, Golden, CO 80401, USA † Author for correspondence: Tel.: +1 303 384 6350; E-mail: [email protected] 1 2

future science group

10.4155/BFS.11.7 © 2011 Future Science Ltd

ISSN 1759-7269

125

Review  Jinkerson, Subramanian & Posewitz

yields [3] . In algae, various physiological stresses stimulate the accum­ ulation of polyglucans and/or lipid bodies; with nutrient (e.g., S, N, P and Fe) deprivation, being the most common mechanism. In addition, sulphur deprivation can induce susPhotosynthetic efficiency: The tained H2 photo­production, which effectiveness of photosynthesis in plants and algae to convert light energy facilitates anaerobiosis and induces into chemical energy. hydrogenase activity. Even though Bioenergy carrier: Compounds it is known that these conditions synthesized by algae to store energy result in the production of bio­ captured during photosynthesis and energy carriers, the exact metabolic include, but are not restricted to, and regulatory mechanisms that polysaccharides, lipids and hydrogen. cause the drastic reorganization of Systems biology: Study of the cellular metabolism are unknown. components of a biological system, their interactions, and how these A n improved biotechno­l og y interacting systems give rise to toolkit is required to fully leveran organism. age the metabolic capabilities of ‘Omics’: Used to describe the photo­trophic microorganisms and techniques that comprise systems systems-biology studies will likely biology (e.g., genomics, proteomics and metabolomics). play a pivotal role in understanding and manipulating algal metabolism Alkanes: Fungible hydrocarbons (general formula for linear alkanes: to optimize the accumulation of CnH2n+2) that can be used directly as desired bioenergy carriers. a fuel. Systems biology, or ‘omics’, tools include genomics, transcript­ omics, proteomics, metabolomics and lipidomics, which attempt to accurately quantify and functionally character­ize genetic elements, mRNA, proteins, metabo­lites and lipids, respectively. An overview of the advantages and disadvantages of these techniques is given in Figure  1. Omics techniques are generally top-down holistic methods that are high throughput and generate extensive datasets [4,5] . Advances in nucleotide-sequencing technology have revolutionized the speed and accuracy with which genome data can be acquired. Several algal genomes have now been Key terms

Cyanobacteria: Previously known as blue-green algae, are water-oxidizing, photosynthetic bacteria that, according to endosymbiotic theory, are the evolutionary precursors of chloroplasts in eukaryotic algae and higher plants.

Genomics

completed and several additional genome-sequencing projects are underway, which have provided critical insights into the metabolic pathways available in algal cells and revealed an extensive set of metabolic capabilities in most organisms. Moreover, these efforts have provided vital tools for gene identification, initial insilico metabolic-pathway maps, the study of gene function and establishment of putative regulatory networks. Specifically, nucleotide-sequencing techno­logies have facilitated the quantification of gene transcripts. Highthroughput analytical techniques, based primarily on advances in MS, have enabled the quantification of proteins and metabolites. Systems-biology techniques have already been applied to many other organisms [6,7] where they have been used extensively to understand human diseases [8] , and to metabolically engineer organisms such as Escherichia coli to a high level [9] . The utilization of systems-biology tools on algal systems has lagged behind these more extensively studied organisms; however, the intense interest in biofuels from phototrophic microorganisms will lead to explosive growth in omicsbased studies in photosynthetic algae and cyanobacteria in the coming years. Recently, the first wave of high-throughput omics studies on algae have begun to appear in the literature and are being used to analyze the dynamics of the transcript­ome, proteome and metabolome under diverse environmental conditions (e.g., variable light and CO2 levels, anoxia and nutrient stress) in several species. The majority of these studies have focused on C. reinhardtii or Phaeodactylum tricornutum, a model green alga and diatom, respectively, for which genome-sequence information is publicly available, and for which relatively advanced genetic techniques are established. To guide hypothesis-driven research for the production of targeted energy-dense metabolites, or to engineer improved biofuels phenotypes, we must develop an understanding of how central metabolism is regulated and how the

Transcriptomics

Proteomics

DNA

mRNA

Advantages

• Identify new genes • Determine gene structure • Find putative genes • Comparative genomics • Evaluate evolutionary relationships

• Determine gene regulation • Find genes important to phenotype • Changes in gene expression

Disadvantages

• No gene function • Expression levels • Regulation

• Protein half-life • Difficult to get complete • Post-translational modification coverage of the proteome • No intracellular protein locations

Metabolomics

Protein • Identify proteins and protein levels • Post-translational modifications • Intracellular location • Protein complexes

Metabolites • Identify key metabolites • Changes in metabolites

• Difficult to assess all metabolites • Time course needed for flux analysis

Figure 1. Overview of system biology, with individual advantages and disadvantages for each technique listed.

126

Biofuels (2011) 2(2)

future science group

Improving biofuel production in phototrophic microorganisms with systems biology  Review

activities of critical enzymes are controlled. It is clear that algae are metabolically versatile, that subcellular compartments (e.g., mitochondria and chloroplast) uniquely contribute to overall cell metabolism and that several genes encoding critical metabolic enzymes are transcriptionally regulated by mechanisms that remain largely undefined. The primary goals of algal omics-based research include: ƒƒ Characterizing the effects of lesions that alter the activities of specific branches of central metabolism, lipid and carbohydrate metabolism, cellular energetics and metabolite fluxes, and photosynthetic activity; ƒƒ Physically locating specific metabolic enzymes (and

metabolomes) to understand the potential trafficking of metabolites between organelles and how that integrates with whole cell metabolism; ƒƒ Investigating transcriptional, translational and post-

translational processes that are used to regulate central metabolism. This article reviews studies focused on phototrophic microorganisms (algae and cyanobacteria) systems biology; how these relate to biofuel production; and how these results and systems-biology tools can be leveraged by scientists and engineers to increase algal biofuel production in native and engineered organisms. Genomics The advent of high-throughput, low-cost nucleotidesequencing technologies has produced a wealth of organismal genetic information, the study of which is termed genomics. Genomics is the generation and anal­­ ysis of nucleotide sequences from genomes and cDNA collections, from which individual genes, repeat elements, gene arrangement and organization, and intergenome comparisons can be elucidated [10] . At its most basic level, the genome of an organism reveals insight into the structure and organization of the DNA, as well as potentially active metabolic circuits based on gene homology. Comparisons between genome sequences (comparative genomics) can identify conserved genes and metabolic pathways between species and can determine phylogenetic relatedness. Functional genomics, the genome-wide study of the function of genes and nongenetic elements within an organism, can determine the precise role of a gene product and its integra­tion in metabolic pathways  [4,11] . The combination of biochemical, physio­logical and genomic data can be used to reveal active metabolic pathways and regulatory mechanisms, providing insights that can be leveraged to understand how individual pathways are integrated and can be potentially manipulated to optimize energy production and utilization [12] .

future science group

Several algal genomes and organellar genomes have been sequenced. In Chlorophyta, whole genomes have been completed for C. reinhardtii [2] , Chlorella variabilis  [13] , Micromonas pusilla [14] , Ostreococcus lucimarinus  [15] and Ostreococcus tauri [16] , while in Bacillariophyta, P. tricornutum [17] and Thalassiosira pseudonana [18] genomes have been completed. In addition, the red alga Cyanidoschyzon merolae [19] and the Cryptophyta Guillardia theta [20] have been fully sequenced. There are also several unfinished algal genome-sequencing projects including those for the following species: Bacillariophyta – Cyclotella meneghiniana, Fragilariopsis cylindrus, Pseudo-nitzschia, Seminavis robusta and Thalassiosira rotula; Chlorophyta – Botryococcus braunii, Chlorella sp., Chlorella vulgaris, Coccomyxa sp., Dunaliella salina, Nephroselmis olivacea, Prototheca wickerhamii, Scenedesmus obliquus, Volvox carteri; and Rhodophyta – Chondrus crispus, Galdieria sulphuraria, Porphyra purpurea and Porphyra yezoensis; Stramenopiles – Aureococcus annophageferrens [21] . To date, more cyanobacterial genomes have been sequenced than eukaryotic genomes, owing to their reduced size and complexity. Some of these include Acaryochloris marina [22] , Anabaena sp. (Nostoc sp.) [23] , Microcystis aeruginosa [24] , Prochlorococcus [25] , Synechococcus sp. [26] , Synechocystis sp. [27] , Thermosynechococcus elongatus [28] and others [21] . Although cyanobacteria are not microalgae, they are water-oxidizing photosynthetic microorganisms that have attracted significant interest in bioenergy-production applications. ƒƒ Comparative genomics

The completion of multiple algal genomes has allowed the comparison of these genomes with different algal species, higher plants and other organisms. Comparative genomics is a useful tool for determining the genetic foundations of phenotypes that are not easily elucidated by the ana­lysis of an individual genome. A comparison of two genomes will reveal conserved genes and pathways that often permit identification of common features among organisms, while unique genes are often responsible for the distinct traits that distinguish two species [29] . This technique can be used to identify genes responsible for unique phenotypes or metabolisms, which are influential for biofuel production. Genomic comparison of the red algae G. sulphuraria and C. merolae has revealed unique carbohydrate metabolisms not common to algae or plants and numerous unique carbohydrate transporters [30] . These findings could be used to increase intercellular carbohydrate levels or be leveraged in secretion pathways. Recently, comparative genomics was used to elucidate the genes involved in the microbial biosynthesis of alkanes [31] . Several species of cyanobacteria are known to produce

www.future-science.com

127

Review  Jinkerson, Subramanian & Posewitz

alkanes; therefore, a comparative biochemical and genomic approach was taken to determine the requisite genes. A total of 11 strains of cyanobacteria were selected and evaluated for alkane production. Reverse genetics: Approach for Ten strains were determined to discovering the effect of a known gene produce alkanes, while one strain, on an organism’s phenotype via targeted gene disruption. Synechococcus sp. PCC7002, did not synthesize these hydrocarbons. The Synechococcus sp. PCC7002 genome was subtracted from the intersection of all ten alkane-producing cyanobacterial genomes, resulting in 17 genes common only to the alkane-producing strains. Of these, ten were of a known function and it was determined that two of these, an acyl-acyl carrier protein reductase and aldehyde decarbonylase, were responsible for alkane synthesis [31] . The discovery of the metabolic pathway for alkane synthesis will allow for the incorporation of this pathway into non-alkane-producing algae and cyano­ bacteria, facilitating the photosynthetic production of alkanes in biofuel processes. The most extensively studied unicellular, eukaryotic alga in the green lineage is C.  reinhardtii. The C. reinhardtii genome has been thoroughly examined since its draft release in 2003 and whole genome publication in 2007 [2] . A set of gene models was created for the full C. reinhardtii genome using ab initio, homologybased gene prediction and expressed sequence tag evidence [2] . These gene models were then compared phylo­ genomically to the gene models for other photosynthetic organisms in the green lineage, such as Ostreococcus and Arabidopsis. Proteins common to these photosynthetic organisms, excluding those found in nonphotosynthetic organisms, were identified and called GreenCut proteins. The GreenCut consists of 349 proteins involved in plastid biogenesis, photosynthetic electron transport, antioxidant generation, carbon fixation and plastidlocalized lipid and starch metabolism [2] . Of these 349 proteins, approximately 100 have unknown functions. One method that has been used to gain insight into these GreenCut proteins with unknown functions has been to examine their cyanobacterial analogs and determine if they are included into operons that may hold a clue to their function. For instance, two such proteins have been found to be present in a putative cyano­bacterial operon associated with isoprenoid bio­s ynthesis  [32] . Since the generation of the initial GreenCut, a number of these unknown proteins have been functionally characterized, some of which have been defined as new proteins involved in the breakdown of chlorophyll [33] and others involved in regulating photosynthetic functions [34,35] . The functional characterization of such proteins involved in photosynthesis has validated the Key terms

Forward genetics: Approach for discovering the genetic basis of a phenotype which employs the creation of random mutations in an organism’s genome.

128

Biofuels (2011) 2(2)

predictive power of using comparative-genomic techniques such as the GreenCut in order to determine sets of proteins that are likely to be associated with specific cellular functions [32] . Comparative-genomic techniques similar to the GreenCut, can also be applied to other sets of genomes that are of interest from a biofuels perspective. This could enable genes assosiated with lipid (e.g., triacyl­ glycerol [TAG]) or hydrogen metabolism to be identified. For example, in order to characterize genes involved in TAG metabolism, the genomes of oleaginous eukaryotic algae in addition to oleaginous fungi and bacteria [36] could be compared to determine a list of common proteins. Proteins found within most cyanobacteria genomes could be excluded, since they cannot synthesize TAGs. The remaining set of proteins, which could be termed the ‘OilyCut’, would probably contain proteins associated with TAG biosynthesis and catabolism. The relatively small number of proteins found in such sets, compared with an organism’s complete set of proteins helps to focus study on proteins that are likely to be important in the desired metabolism. The classical forward genetics requirement of creating genome scale numbers of mutants is not required with this technique. Owing to the small number of identified proteins, reverse-genetic methods can be used to generate a mutant library of these proteins, some of which will probably be aberrant in the metabolism in question. This mutant pool can then be pheno­t ypically evaluated to elucidate gene function and metabolic significance [32] . Comparative genomic data, such as the GreenCut, and predictive gene models, can be utilized to generate algal gene targets for use in reverse-genetic strategies. These strategies can be used to elucidate gene function or to alter genes that are influential for bioenergy-carrier production. RNA interference has been used to alter photosynthetic antenna sizes, which can increase light penetrance in algal cultures [37,38], and to investigate lipid droplets [39] . Genomic information has successfully been utilized in PCR screens of mutant libraries to isolate algal strains with specific mutations relevant for biofuel production. In one study, an insertional mutant library was screened to identify putativesulfate transporters, which play an important role in intercellular sulfur transport and ultimately hydrogen production [40] . Genomics can provide a wealth of information on the presence of putative genes and gene arrangements, but does not provide a complete picture of all cellular processes. The mere presence of genes is not suff­ icient in itself to understand cellular processes, since genes may be differentially expressed, or their protein products may catalyze slightly different chemistries.

future science group

Improving biofuel production in phototrophic microorganisms with systems biology  Review

Genomics focuses on DNA, which is generally considered static information and does not appreciably change in response to short-term environmental changes. In contrast, transcriptomics is used as a tool to interrogate relative biological responses to various environmental stimuli at the transcriptional level. Transcriptomics Transcriptomics is the measurement of global gene expression through a quantitative assessment of mRNA levels. Intracellular mRNA transcript levels generally reflect the genes that are actively being expressed at specified time points under defined environmental conditions. Transcriptomics has three specific aims: ƒƒ Identify all components of a transcriptome, which includes mRNAs, small RNAs and noncoding RNAs; ƒƒ Determine how genes are transcribed, specifically 5´

and 3´ ends, untranslated regions and splicing patterns; ƒƒ To quantify changes in gene transcript levels under

various physiological and environmenta conditions [41] . Four high-throughput methods have emerged for transcriptomic studies: ƒƒ With no a priori gene knowledge, genes differentially expressed under different experimental conditions are identified by differential display, serial ana­lysis of gene expression, or by suppression subtractive hybridization; ƒƒ With a priori gene knowledge, cDNA or oligonucle-

otide microarrays can be used to assess differences in gene expression [42] ; ƒƒ Direct sequencing of expressed sequence tags and

cDNA library clones [43] ; ƒƒ Direct sequencing of RNA or cDNA without any

cloning steps (RNA-Seq) [41] . For more in-depth technical overviews of microarray development see Hegde [44], and for more information on nucleotide-sequencing technologies for transcriptomics see Pariset [45] . Transcriptomics can be a useful tool for understanding and increasing algal biofuel production. Evaluation of transcriptome data can resolve the differential regul­ ation of metabolic pathways under various environmental conditions and stresses. In addition, mutant strains can be used to determine how complex algal metabolic systems compensate for specific genetic lesions. The C.  reinhardtii transcriptome is the most extensively studied algal system, owing to a large research community, the early availability of genomic data, and the completion of several macroarrays and microarrays [42,46–48] . The advent of microarrays for C.  reinhardtii has allowed for the extensive study of its transcriptome

future science group

under a variety of environmental situations and in various mutant backgrounds. The transcriptome of C. reinhardtii has been examined under conditions of high and low light [46] , inorganic carbon availability [48] , oxidative stress [49] , anaerobiosis [50,51] and under sulfur  [42,47] and phosphorus [52] deprivation. In addition to C. reinhardtii, DNA microarrays have been created for Euglena gracilis [53] , the red alga C. merolae [54] and the green alga Haematococcus pluvialis [55] . The transcriptome of C. reinhardtii has been extensively studied under conditions of dark anoxia, which is of particular interest for bioenergy-carrier production. In the dark without oxygen, an extensive set of fermentation pathways are activated, in which a variety of biofuel-relevant products are produced, such as ethanol, formate, acetate, succinate and hydrogen [50,51] . Microarrays and quantitative PCR were used to investigate global changes in gene expression as C. reinhardtii cells acclimate to conditions of anoxia, and initiate fermentative metabolism. During this acclimation to anoxia over 500 transcripts increased significantly, some of which encode proteins responsible for the production of ethanol, organic acids and hydrogen. In addition, transcripts encoding transcription and translation regulators, hybrid cluster proteins, proteases, catalase, prolyl hydroxylases, transhydrogenases and proteins of unknown function were also differentially expressed. In fact, the majority of differentially expressed genes detected by microarray ana­lysis, greater than 70%, encode putative proteins of unknown function [50] . These proteins of unknown function are probably involved in the regulation and metabolic partitioning of fermentative pathways, and are thus excellent biofuel-relevant targets for further study. The transcriptome of several algal mutants have also been studied. Analysis of specific mutant backgrounds can bring insights to how cells can adjust metabolic fluxes when specific pathways are blocked. The transcript­ome of the C. reinhardtii hydEF mutant, defective in an essential radical S-adenosylmethionine protein required for the assembly of an active [Fe–Fe] hydrogenase [56] , has been extensively studied to determine how this mutant metabolically compensates for its inability to produce hydrogen [51] . Although the majority of the transcriptional changes exhibited under anoxia are common to both the parental wild-type strain and the hydEF mutant, there are some marked differences. In the mutant, the pyruvate carboxylase and malic enzyme transcript levels increase significantly, which correlate with the increased production of succinate seen in the mutant [51] . The hydrogenase is thought to function as an electron valve; however, without this outlet, the cell appears to compensate by producing succinate in order to oxidize reducing equivalents and

www.future-science.com

129

Review  Jinkerson, Subramanian & Posewitz

continue glycolysis. The investigation of this and other algal mutants show that algae often have complex and dynamic metabolic networks that can compensate for metabolic perturbations and that single-gene disruptions can significantly alter transcription and metabolite levels. Transcriptomics can thus provide insights into how cells metabolically compensate for these changes. Transcriptomics holds the potential to unlock key findings that can greatly advance the field of biofuels. Study of the transcriptome can identify unknown genes responsible for specific phenotypes, metabolic pathways and biological processes. One gene, or set of genes, that are currently unknown, but are of critical importance to biofuel production are the ‘lipid trigger’ genes. The algal ‘lipid trigger(s)’ is a gene (or set of genes) that are hypothesized to be responsible for the drastic re­organization of carbon-energy stores into neutral lipids under nutrient stress, most notably during nitrogen deprivation. At the conclusion of the Aquatic Species Program – an 18-year program to develop renewable transportation fuels from algae operated at the National Renewable Energy Laboratory – no obvious ‘lipid trigger’ was found based on conventional-genetic and molecular-biology techniques [57] . Transcriptomics could reveal such a ‘lipid trigger’ if it exists. Tracking changes in the transcriptome when algae transition into nutrient-stress conditions may reveal unknown genes or transcription factors that play a role in lipid-body formation and accumulation. Comparison of several transcriptomes, or comparative transcriptomics, of different species of nitrogen-deprived oleaginous algae could elucidate conserved proteins involved in acclimation to nitrogen deprivation, some of which would be associated with TAG biosynthesis. High-throughput nucleotide sequencing has allowed several new methodologies for studying algal transcript­ omes. RNA can be converted into cDNA, with or without amplification, and then directly sequenced, a process termed RNA sequencing or RNA-Seq [41] . Alternatively, RNA can be directly sequenced, without the creation of cDNA [58] . For a comparison of the advantages and disadvantages of RNA-Seq and microarrays see Figure  2 . One major advantage of RNA-Seq over microarray-based technologies is that a priori genomic knowledge is not needed to determine the transcriptome. This technique is ideally suited for transcriptome ana­lysis of nonmodel algae without complete genome sequences. RNA-Seq has low-background signals and can detect a larger dynamic range of expression levels than micro­a rrays [41] . RNA-Seq is already gaining widespread use for Arabidopsis  [59] and yeast [60] and is starting to be used for algae. In a recent study, the transcriptome of a wild-type C.  reinhardtii and a mutant defective in SNRK2.1

130

Biofuels (2011) 2(2)

– a Ser-Thr kinase required for acclimation to sulfur deprivation – were evaluated via standard microarray and RNA-Seq technologies [61] . Quantitative real-time PCR data revealed that the microarray ana­ly­ sis was less sensitive than the RNA-Seq at estimating changes in low abundance transcripts and that RNA-Seq has an overall larger dynamic range. The RNA-Seq assessments have yielded extensive quantitative data sets on the transcriptional responses to sulfur deprivation in the wild-type and mutant strains, a nutrient-stress condition that is important for bioenergy-carrier accum­ ulation  [61] . The transcriptome of C. reinhardtii has also been evaluated using RNA-Seq under conditions of nitrogen limitation [62] , a stress condition that is of particular interest for biofuel production due to the accumulation of starch and TAG. Genes involved in lipid biosynthesis such as pyruvate decarboxylase and diacylglycerol acyltransferase, enzymes in the pentose phosphate cycle, genes involved in nitrogen assimilation, and gametogenesis genes were all upregulated while expression levels of genes encoding lipases, glyoxylate cycle enzymes and gluconeogenesis enzymes all dropped under nitrogen deprivation [62] . These studies have demonstrated several advantages of using RNASeq technology over standard microarrays for algal transcriptional ana­lysis. RNA-Seq has the potential to increase the ease and throughput of transcriptome anal­y sis in many strains of algae of importance for biofuel production, and will probably be a useful tool for future algal research, especially in nonmodel algal systems where no microarrays exist. Transcriptomics can inform researchers of changes in gene regulation and expression, and how algae respond to different environmental stimuli. However, transcript­ omics does not address transcript-translation rates, protein abundance, post-translational modification or intercellular locations of proteins and metabolic pathways. Proteomics and metabolomics can address some of these issues with protein abundance and location, in addition to metabolite concentrations and flux. Proteomics Proteomics is the study of an organism’s accumulated proteins, their intracellular location, abundance and any post-translational modifications. The proteome is dependent on environmental conditions and the physio­logical status of an organism, and can help reveal metabo­lic processes that are active. Preliminary evidence for the existence of a gene can be obtained from genomic and transcriptomic analyses; however, the direct evidence required to verify gene translation can only be obtained by isolating and identifying a protein via its corresponding sequence. Proteomics is used to evaluate whether genes are translated into protein,

future science group

Improving biofuel production in phototrophic microorganisms with systems biology  Review

Microarrays

RNA-Seq

Advantages

• Genome-wide analysis • Amenable for large sample number • Short turnaround time • Streamlined protocols • Relatively inexpensive

• Sequence data not needed • Allows for transcript discovery and genome annotation in non-model organisms • Ability to detect small RNAs

Disadvantages

• Sequence data needed • Sensitivity limitations due to hybridization • Usually developed for model systems only

• Data analysis tools not as developed • rRNA removal needed from samples • Currently relatively expensive

Figure 2. Advantages and disadvantages of transcriptome-measurement techniques: microarrays and RNA-Seq.

determine protein levels and localization, de­lineate intermolecular protein interactions and to identify protein post-translational modifications. Since the advent of polyacrylamide gel electro­phoresis (PAGE), the field of proteomics has been continuously evolving. The most commonly used proteomic techniques involve the use of sodium dodecyl sulfate (SDS)-PAGE gels to separate proteins based on molecular weight followed by the identification of individual proteins using antibodies. However, this approach is generally used to identify only a small set of proteins from a whole proteome. A higher throughput approach is 2D electrophoresis (2-DE). This involves separating proteins based on their isoelectric point, in the first dimension, followed by its molecular weight, in the second dimension [63] . High-quality protein extracts are subjected to isoelectric focusing on immobilized pH gradient gels, followed by separation based on individual protein molecular weight using SDS-PAGE gels. Proteins can then be visualized by staining using colored or florescent dyes to provide qualitative as well as quantitative information about the proteome. This technique is useful for determining differential protein expression levels across various culture conditions. Depending on the complexity of the protein extract, several hundred proteins can be detected on 2D gels. To determine amino acid sequences, individual protein spots can be cut from these gels and then identified using various MS techniques such as MALDI-TOF and LC–MS/MS. An improvement to this technique is the differential in-gel electrophoresis (DIGE) technique. This involves labeling proteins with fluorescent dyes such as Cy3, Cy5 or Cy2, prior to separation in the first dimension. The advantage of this technique is that two different protein extracts (e.g., from two different growth conditions) could be labeled individually with two different dyes (typically Cy3 or Cy5) and compared on the same gel, thereby halving the number of SDS-PAGE gels needed for any experiment. A third protein extract is prepared

future science group

that contains a mixture of both the protein extracts (half the amount of what was used for labeling with Cy3 or Cy5) and is labeled with the third dye (typically Cy2). This mixture is used as an internal control in all DIGE experiments, where every protein from each sample is represented. This step improves confidence in matching spots from different gels as well as strengthens the statistical significance of matched protein spots. An alternative to 2-DE is to sequentially cut out sections of individual lanes from 1D PAGE gels, digest all proteins with a protease such as trypsin, extract peptides and separate peptide fragments using column chromatography in line with a MS. The principal advantage of this technique is that it is much faster than conventional 2-DE. However, a disadvantage is having a mixture of proteins in every gel slice, thereby adding the requirement of improved MS instrumentation and/or bioinformatics capabilities that are able to identify proteins from complex mixtures. Advances in MS technologies are progressively making protein identification from biological samples easier. Genome-sequence data, in addition to gene annotations, can also assist in protein identification. Identifications of pure/single proteins can be made with simpler technologies such as MALDI-TOF. However, more advanced instruments such as the LC–MS/MS, MALDI-TOF/TOF are needed for identification of proteins from complex mixtures. Shotgun sequencing involves isolating protein extracts from whole cells or cell organelles, fragmenting the proteins and directly identifying individual proteins contained in the mixture using MS. Protein fragments are separated on chromato­ graphy columns that are connected in line with mass spectrometers. Advantages of this technique include identification of thousands of proteins in a single run along with reduced experimental time and complexity. Shotgun sequencing has been used in both qualitative and quantitative experiments. MS has an additional advantage of mapping post-translational modifications

www.future-science.com

131

Review  Jinkerson, Subramanian & Posewitz

Key term

such as glycosylation and phosphorylation. Advances in quantitative shotgun technologies include stable-isotope labeling of amino acids in cell cultures (SILAC), isotope-coded affinity tagging (iCAT), isobaric tags for relative and absolute quantification (iTRAQ), global internal-standard technology (GIST), in-gel stable isotope-labeling (ISIL) and isotope-dilution strategies [64–72] . Similarly to other ‘omic’ techniques, algal proteomics has primarily focused on the model green alga, C. reinhardtii. Many proteomic studies in this organism have aimed to understand the proteins involved in photosynthesis, which is essential for algal biofuel production. Increasing algal photosynthetic efficiencies can improve algal growth and can be leveraged to direct photosynthate into targeted metabolites for biofuel production. Proteomic studies focusing on photosynthesis have emphasized examination of the chloroplast, specifically the thylakoid membranes, where the photosynthetic machinery is located. In an attempt to better understand light harvesting efficiency in C. reinhardtii, Hippler et al. separated and identified 30 light harvesting complex proteins from thylakoid membranes using 2-DE followed by protein identification using nano­ electrospray MS [73] . This study revealed that more hydrophobic membrane proteins such as PsaA polypeptide, which contains 11 transmembrane domains, could be separated using 2-DE. Subsequently, the same group revealed the presence of at least nine Lhca proteins and eight Lhcb proteins in the thylakoid membranes [74] . This approach could be used for the identification and comparison of light-harvesting complex proteins from algal strains with high-photosynthetic efficiencies to determine which proteins are responsible for this pheno­t ype. Once known, these proteins could be expressed into other algal strains in an attempt to increase photo­synthetic efficiencies. In order to understand the translational regulation of these and other chloroplast-associated proteins, a proteomic evaluation of the chloroplast-translational machinery was performed by Yamaguchi et al. [54] . They identified 20 different ribosomal proteins among which, three proteins, S2, S3 and S5, were found to be larger than their orthologs and were predicted to be interacting with S1 and PSRP-7 proteins, which is a unique aspect of this organism and of importance for chloroplastic-protein expression. Heterologous-protein expression in the chloroplast may be an efficient way to directly access metabolites produced from photosynthesis or to produce hydrogen from inducible gene expression systems [75] . The mitochondrion is also an important organelle for biofuel synthesis, particularly for photo­biological hydrogen production. Mitochondrial respiration

Photosynthate: Chemical product of photosynthesis.

132

Biofuels (2011) 2(2)

consumes oxygen produced during the water-splitting activity of photosystem II that would otherwise inhibit hydrogenase activity. Furthermore, acetyl coenzyme A, which is an important precursor in fatty acidbiosynthesis pathways, is also generated in the mitochondria. Using a combination of blue native-PAGE 2-DE, and N-terminal sequencing, the major oxidative phospho­r ylation complexes, namely F1F0-ATP synthase, NADH-ubiquinone oxidoreductase, ubiquinol-cytochrome c reductase and cytochrome c oxidase, were resolved in addition to the identification of other proteins such as the chaperone HSP60, alternate oxidase, aconitase and the ADP/ATP carrier proteins from the C. reinhardtii mitochondrial fractions [76] . Studies that map proteins from the mitochondrion and chloroplast, provide insight into the relatively less studied complex interplay between the two energy-generating and consuming organelles that could be exploited for efficient carbon fixation and channeling of photosynthate into biofuel-precursor production pathways. Efficient light assimilation and biomass-accumulation rates are also important qualities for biofuel-production strains. Cell division and its regulation affect biomass accumulation, so cell cycle manipulation could be used as a method to increase biomass yields. Increasing biomass, but limiting cell division is one approach to increase the accumulation of useful storage products in algae. Centrioles are organelles that act as basal bodies for assembly of cilia and flagella during the interphase of cell division. In order to improve the understanding of the function of algal centrioles, and thus cell division in C. reinhardtii, MS-based protein-identification technology was employed that identified 45 centrioleassociated proteins [77] . Although, this study was not directed towards biofuels research, information generated from this and similar studies can be extrapolated towards biofuel research. Eyespot and flagella are two other organelles that are given extreme importance in C. reinhardtii research. Efficient regulation and understanding of the phototactic signal transmission from the eyespot to the flagella is important to allow for efficient carbon sequestration, which is in turn translated into useful biomass or storage molecule accumulation for biofuel research. The eyespot study revealed 202 proteins that were shown to be involved in structural organization of the eyespot apparatus as well as photo­tactic movement [78] . Furthermore, the authors also showed the role of one of the identified eyespot protein kinases, CK1, in several physiological functions of the cell. Flagella proteomics has primarily concentrated on the identification of phosphorylated proteins in this organelle [79] . Phosphorylation appears to play an important role in molecular signaling in this organelle, which is also highlighted by the fact that several kinases and

future science group

Improving biofuel production in phototrophic microorganisms with systems biology  Review

phosphatases have been found in previous studies of flagella and cilia [80–82] . These reports elucidate the importance of post-translational modifications (e.g., phosphorylation) in this organism. Further evidence for the presence of phosphorylation was recently provided by Wagner et al., where 32 phosphoproteins were identified using a MS approach [83] . Understanding the role of phosphorylation, as well as other post-translational modifications, in algae under biofuel-producing conditions will be critical in understanding the interplay between metabolic-pathway regulation and protein levels. Redox regulation is another mechanism of protein regulation that impacts biofuel-producing organisms. Thioredoxin-mediated thiol-disulfide interchange is an area of study that has recently been gaining attention in algae because it has been observed in land plants [84] . A similar mode of regulation is probably present in algae, which is supported by the numerous thioredoxin targets that have been identified using proteomics techniques in C. reinhardtii [85] . Production of bioenergy carriers and their precursors often affect the redox balance of the cell, so future study on how these changes effect redox regulation and overall biofuel yields needs to be conducted. Other studies on C.  reinhardtii have focused on understanding the physiology of this alga under diff­ erent environmental stress conditions. Generation of algal strains that are capable of thriving under changing environmental (e.g., light and temperature) and/or other stress conditions (e.g., nutrient deprivation, high/low pH, salinity and toxicants) will be an important trait of biofuel-production strains cultured in open environmental conditions  [3] . Forster et al. identified 105 proteins in C. reinhardtii that were diff­erentially regulated in response to high and low light treatments when compared between the wild-type and two very high lightresistant mutant strains [86] . Among the other proteins identified, these mutants specifically were found to have altered their light-harvesting and photosystem II complexes, as shown by altered levels of NAB1 and RB38 proteins, in addition to demonstrating increased levels of chaperone proteins. Another study was based on the response of C. reinhardtii towards the toxic heavy metal, cadmium  [87] . This study revealed an increase in oxidative stress response proteins and decreases in photosynthetic and carbon fixation-pathway proteins, among others, as a response towards cadmium toxicity. This was followed by a study by Naumann et al. to understand the adaptive response of green alga towards iron deficiency [88] . Specifically, they found a decrease in photosystem I polypeptides and an increase in some photosystem II polypeptides in response to iron-stress  [88] . Furthermore, Cid et al. recently showed the response of this alga towards acid stress, which specifically revealed a

future science group

drastic reduction in carbon fixation and photosynthetic proteins [89] . It is evident from these studies that the two photosystems are uniquely altered in response to varying stress conditions, and insights from such studies could provide a basis for the development of genetically altered strains of algae that are resistant to heavy metal toxicity, varying salinity levels, high light intensities and other fluctuating culture conditions. Although there are transcriptomic studies that probe the physiology and regulation of genes during hydrogen production in C. reinhardtii [50,51,90] , studies of gene products and their mode of regulation in this organism are more limited. Several recently published proteomic articles assaying anaerobic metabolism and sulfurdeprived conditions are the only direct studies thus far to investigate hydrogen production by this green algae from a proteome perspective [91,92] . Evidence of metabolic rerouting has been observed under these different hydrogen-producing conditions that gives a better overall view of metabolic changes that occur during hydrogen production in C. reinhardtii. Even the C. reinhardtii proteome has thus far been relatively unexplored in areas of research that will directly improve biofuel production. Future research efforts will probably study the diff­ erent conditions under which renewable biofuels such as hydrogen and biofuel precursors (e.g., fatty acids) are produced by this organism. Such studies would provide a linkage between transcriptomic and metabolomic data, bridging the gap between the genome and the final accumulation of metabolic products. In addition to C. reinhardtii, the proteomes of several other species of algae have been evaluated. The green alga D. salina has been studied for its ability to tolerate high-saline environments. This adaptation has been of interest owing to its potential to reveal the physiological mechanisms necessary to protect against salt stress; a strategy that could be employed in other algal strains, in addition to traditional crops, to improve their survival under highly saline conditions. Blue nativePAGE or 2-DE, in combination with LC–MS/MS, has been commonly used in proteomic studies of D. salina [93,94] . The photosynthetic activity in most plants and cyanobacteria is known to be inhibited by high salt conditions. However, D. salina responds to this stress condition by enhancing CO2 assimilation and by accumulation of the osmolyte, glycerol [93,94] , which is a unique mode of adaptation. Another study has involved shotgun proteomics to study the effect of hyper-saline conditions on flagellar proteins in this organism [95] . Seawater and saline aquifers are likely to be the most available and low-cost water resources for algal culturing. Insights into the haloprotection mechanisms used by D. salina will be of importance for understanding what is required for photo­synthesis

www.future-science.com

133

Review  Jinkerson, Subramanian & Posewitz

to occur in high-saline conditions. Reports on whole cell and subproteome analyses on other marine and halotolerant alga are also available, although the primary interest has been to understand the physiology of these algae under environmental stress conditions or their ability to produce industrial important metabolites (e.g., pigments). Published reports include those from Haematococcus sps., Nannochloropsis sp., Scytosiphon sp. and Pseudokirchneriella sp. [96–100] . In comparison to eukaryotic algae, cyanobacteria are more extensively studied with respect to proteomic analyses; with Synechocystis sp. PCC 6803 being the most intensively studied cyanobacteria. Proteomics in cyanobacteria can be classified into membrane and soluble fraction proteomics. Since the late 1990s, there has been extensive research carried out to identify the regulation of proteins under different environmental, nutrient, and stress conditions in this organism. Since it is estimated that nearly a third of all the proteins in Synechocystis are membrane-bound, special emphasis is given to membrane-based proteomics. These include studies involving all membranes as a whole [101,102] or involving membrane fractions from thylakoids  [103,104] and plasma membranes [105–108] . Most of these reports have used 2-DE in combination with MALDITOF MS. In particular, the presence of major protein complexes such as those belonging to the photosynthetic electron flow, ATP synthesis, NADPH-dehydrogenase and so forth, along with the identification of proteins involved in redox signaling, cell motility and ion transport among others, have been reported in the above studies. Information about its cell structure, physiology, photosynthetic apparatus and nutrient uptake generated from these membrane-bound proteomic studies could provide a mechanism by which metabolic flux could be redirected towards biofuel-precursor production. In terms of soluble protein proteomics, studies have primarily focused on understanding the mechanism by which this organism adapts to changing environmental influences. Specifically, studies involving salt stress, acid stress, heat shock and ultraviolet-light exposure have been performed [109–114] . With its potential to be used in the synthesis of important biofuels and/or their precursors, studies dealing with adaptation to stress conditions are valuable towards developing more environmentally tolerant strains. There are some reports on proteomic studies on the cyanobacterium Synechococcus sp. with respect to proteins involved in cell division and protein interactions that form the structural aspect of the ribulose-1,5-bisphosphate carboxylase/oxygenase- (rubisco) containing organelle, carboxysome [115,116] . The cyanobacterium genus Nostoc has been studied with respect to understanding nitrogen fixation, their life cycle, as well as their potential use in hydrogen

134

Biofuels (2011) 2(2)

production. Under nitrogen limitation, cyanobacteria can fix atmospheric nitrogen into ammonia using the enzyme nitrogenase, which is also capable of producing hydrogen. This enzyme is located in differentiated cells called heterocysts. Proteomics in this organism have used more global approaches when compared with studies of Synechocystis. Specifically, iTRAQ-based ana­lysis has been used to investigate proteomic changes during nitrogen fixation in this organism [117,118] . Collectively, an increase in the levels of proteins involved in energy and carbon metabolism, nitrogen assimilation and photosystem I components has been reported under nitrogen-fixing conditions. Another study involving a combination of gel filtration and 2D-LC–MS/MS technique has been used to analyze the soluble proteome of a different species of this filamentous nitrogen-fixing cyanobacterium in the presence of ammonia as a nitrogen source [119] . A comparison of proteomic profiles between photoautotrophic and diazotrophically grown cells has been reported using 2-DE and MALDI-TOF techniques that revealed the identity of proteins related to stress, motility, secretion, post-translational modifications and those containing thioredoxin targets that were not reported earlier [120] . A detailed ana­lysis of the heterocysts has also been carried out using an iTRAQ approach where protein extracts were labeled with eight different iTRAQ tags followed by LC separation on strong cation exchange columns and identification of proteins using LC–MS/MS [118] . With respect to Anabaena sp., which is another nitrogen-fixing cyano­ bacterial genus, proteomic studies have focused on soluble cell proteomics, in addition to copper homeostasis and the effect of pre-exposure to heat on UV-B toxicity [121–123] . Understanding the cross-talk between the vegetative cells and the nitrogen-fixing heterocysts is a key factor towards developing this organism for hydrogen production. Regulation of photosystems with simultaneous funneling of the reducing equivalents from vegetative cells to the heterocysts is crucial for maintaining an oxygen-free atmosphere and functional nitrogenase enzymes in heterocysts. Such proteomic studies could therefore provide valuable information on the levels of expression of proteins in heterocysts and their regulation with respect to the overall cellular metabolism (including photosynthesis) in this oxygenic photosynthetic organism. The cyanobacterium, Spirulina sp. is primarily known for its use as a food supplement owing to its high protein and fatty acid content. This organism has been investigated extensively by Hongsthong’s group at the level of proteomics using 2-DE and MALDI-TOF MS for understanding their global protein regulation in response to temperature, light, and their two morphological forms [124–126] . Information regarding

future science group

Improving biofuel production in phototrophic microorganisms with systems biology  Review

RNA stability of fatty acid genes, chlorophyll bio­ synthesis, and changes in cell shape in response to altering environmental conditions were obtained in these studies. In addition, there are proteomic studies on other cyanobacteria including Prochlorococcus sp. and Gloeothece sp. in response to light and quorum sensing, respectively [127,128] . Diatoms, which can accumulate large quantities of triacylglycerides, are of significant interest becuase of their lipid productivities [129,130] . There is only one proteomic study to date in the diatom Thalassiosira pseudonana, where a lyse-N-Go shotgun approach was used to investigate biochemical pathways in this organism [131] . The field of renewable energy is expected to benefit immensely from the advent of new-labeling technologies, which provide more quantitative information on protein accumulation. Although, there is a dearth of research aimed directly at biofuel production in microalgae and cyanobacteria, it is only a matter of time before such studies are conducted. Future studies will specifically address the effects of environmental conditions, nutrient deprivation, light levels, temperature fluctuations, CO2 levels and the myriad of other factors that directly influence the production of important biofuels or their precursors in photosynthetic organisms. Quantitative proteomics can be performed between two test conditions using SILAC or iCAT labeling techniques. In addition, comparisons between more than two different growth conditions have become much easier with the availability of iTRAQ-labeling reagents. It should not be ignored that regulation of metabolic pathways can occur by other modes where signal-transduction pathways are affected by post-translational modifications including phosphorylation, glycosylation and ubiquitination. Although a direct approach for detection and quantification of such modifications is still not available, a combination of protein-enrichment techniques foll­owed by MS has been used to map specific post-translational modifications in different systems. Detailed overviews of phosphoproteomics, as well as other posttranslational modifications that could be identified by MS, are available [132–136] . These studies provide a more complete picture of the cellular pathways functioning in their respective organisms, along with an improved understanding of the mechanisms by which individual pathways responsible for bioenergy-carrier production are regulated. This information can be exploited by informing rational genetic-engineering strategies. Metabolomics Metabolomics is the study of small molecules or metabolites, which includes their identification and quantification, at a global level in a biological system. The molecules studied include organic acids, fatty acids, amino

future science group

acids, lipids, carbohydrates and other compounds that have molecular weights of less than approximately 1000  Da. Determining the level of individual compounds and their intermediates, or the metabolome, can reveal metabolic fluxes at any given time point, which, in turn, helps in elucidating the metabolic pathways functioning in an organism. Evaluation of the metabolome can define a particular growth phenotype and the biological state of a cell. Metabolomics has been of interest in the field of medicine and drug discovery owing to its ability to identify and differentiate biochemical pheno­t ypes based on identification of certain key metabolites or biomarkers in a tissue, organ or biofluid [137–140], and is currently of great interest to biofuel researchers. Identification of metabolites is most commonly carried out using two techniques: NMR and/or MS. Differences between the two techniques lie in their sensitivity and the nature of the compounds identified. NMR measures the amount and frequency of energy absorbed by NMR-active nuclei under a magnetic field. 1 H is the most commonly used nuclei with each nuclei contributing to the NMR spectrum. Other commonly used nuclei include 13C, 31P and 15N. NMR is a highly reproducible and quantitative technique, although the level of sensitivity is low (10 µmol to a few nmol) in comparison with MS techniques. The advantage of using NMR is its nondestructive nature [141] . Furthermore, this technique provides detailed structural information on the metabolites identified. An important character­ istic of this technique is that it is not affected by the chemical nature of the metabolites such as the acid diss­ociation constant (pKa) or hydrophobicity, making sample preparation a relatively easy task. MS is a tool that is characterized by its high sensitivity (picomolar range) and specificity. Although it can be used to identify pure compounds with a direct injection, it is combined in most cases with other separation techniques, such as LC, GC or CE. Whereas LC is based on differential partitioning of metabolites between a mobile phase (solvent) and a capillary stationary phase (usually silica-based), GC is based on differential partitioning of metabolites between the mobile phase (carrier gas) and the capillary matrix (stationary phase). CE depends on the separation of metabolites based on its charge-to-mass ratio and is particularly suitable for the detection of polar compounds. GC is traditionally combined with electronimpact ionization MS, where the GC-separated individual compounds are fragmented to provide fingerprint spectra. Compounds are then identified by comparison with available spectral libraries. The disadvantage of this technique is that the compounds of interest need to be volatile or made volatile by derivatization (e.g., silylation, acetylation and methy­lation) using chemical agents,

www.future-science.com

135

Review  Jinkerson, Subramanian & Posewitz

thus adding additional sample preparation steps. LC is a more straightforward technique that does not require sample derivatization. It is generally combined with ESI MS. The resolving power of this technique is lower and depends on the type of column used for separation. CE is traditionally combined with ESI, although it can also be combined with MALDI techniques [142,143] . Metabolomics studies in algae have been conducted primarily on C.  reinhardtii, particularly by Fiehn’s group, who optimized the necessary extraction conditions, as well as identified and quantified metabolites from C. reinhardtii under nutrient-deprived (N, P, S and Fe) conditions [144,145] . They identified over 100 metabolites out of the 800 that were detected from a single sample using GC–TOF–MS. In addition to demonstrating 4-hydroxyproline accumulation exclusively under sulfur-starvation conditions, the study by Fiehn et  al. also revealed that phosphorus depletion induced a deficiency response quite different from that of nitrogen, sulfur or iron [145] . It was found that a wide range of changes in the primary metabolic modules occur in response to nutrient stresses in this organism. Determination of such big alterations suggests that drastic metabolic reorganization occurs in the cell wall during starvation conditions. Subsequently, May et al. used a combination of metabolomics, proteomics and computational-modeling techniques to improve the existing C. reinhardtii genome annotation. Using GC/ GC-TOF–MS and LC–MS/MS for their metabo­lomic and proteomic analyses, they were able to detect 159 metabolites and 1,069 proteins using these two techniques [146] . Wienkoop et al. recently published a more targeted approach to understanding the metabolic changes occurring in autotrophic versus mixotrophic C. reinhardtii cultures [147] . They used a combination of basic (Western blot analyses) and advanced-level proteomic techniques (MS techniques) along with metabolomics using unlabeled samples (GC–TOF-MS analyses) and isotope-labeled metabolic flux analyses (using GC–TOF-MS). A comprehensive view of the dynamic cross-talk occurring between mitochondrial and chloroplast metabolism has been revealed. Such studies using system-wide approaches (i.e., proteomic and metabolic) in combination with the existing genomic databases provide a complete map of the functional metabolic pathways in this organism and their regulation under different growth conditions that could eventually be implemented into better carbon fixing strains with the ability to divert metabolic flux towards biofuels and/ or their precursors. Another green alga that has been studied at the level of metabolomics is Scenedesmus vacuolatus, where the biochemical changes during exposure to the phytotoxic, N-phenyl-2-naphthylamine was evaluated using a GC–MS approach [148] .

136

Biofuels (2011) 2(2)

Several studies have focused on cyanobacterial metabolomics. Lin et al. developed a combined highthroughput MS protocol where LC–MS was followed by an offline microdroplet NMR approach to identify unknown metabolites from the cyanobacterial strain Fischerella ambigua [149] . At each stage of separation, samples were recovered and used for subsequent ana­lysis (e.g., MS and NMR) [149] . Another study optimized metabolomic investigations of two different cyanobacteria, Synechocystis sp. PCC6803 and Nostoc sp. PCC7120, where individual steps for sample harvesting, quenching, extraction and derivatization, as well as sample ana­lysis using GC–MS, were optimized [150] . The diatom P. tricornutum has been studied with an emphasis towards understanding its biochemical adaptation to iron starvation. Both transcriptomic (microarray and RT-PCR) as well as metabolomic (GC–MS) approaches were used in this study [151] . Photosynthetic, mitochondrial electron transport and nitrate assimilatory processes were downregulated in these cultures. In addition, this study also revealed the upregulation of an iron-responsive gene cluster; one function being iron uptake in response to iron starvation. Striking similarities, especially in terms of the accumulation of tricarboxylic acid cycle intermediates, have been observed with C. reinhardtii under iron-starvation conditions [144] . Information on iron regulation is crucial to the field of bioenergy, especially biohydrogen, which involves a complex set of Fe-S containing proteins along with their interplay with the photosynthetic machinery. Another diatom, T. rotula, has been recently studied using a novel band-selective optimized flip-angle shorttransient heteronuclear multiple quantum correlation technique that allowed acquisition of small metabolite NMR spectra in 10–15 s. This technique has provided a means to understand the subtle changes occurring in living cells (in vivo) as opposed to time-consuming conventional MS techniques [152] . Metabolome evaluation of green algae and other prospective biofuel producing organisms, has been relatively limited. Like proteomics, metabolomic studies have concentrated on understanding the global changes occurring under different environmental stress conditions. Little work has been done with an emphasis on biofuel research in these organisms. With the availability of both proteomic and metabolomic data, along with the availability of transcriptomic data, a complete functioning network of metabolic pathways could be generated. This effort would lead to a thorough understanding of these microorganisms at a molecular level that could be exploited in a more focused manner towards biofuel development in the future. Understanding the three major functional ‘omes’ of these organisms will be a key factor in the develop­ment of efficient bioenergy producers in the future.

future science group

Improving biofuel production in phototrophic microorganisms with systems biology  Review

Lipidomics Lipidomics is a subset of metabolomics that specifically evaluates cellular lipids, their associated metabolic pathways and their interactions with proteins, metabolites and other lipids. The complete lipid profile or ‘lipidome’, can be evaluated using various analytical chemistry techniques including GC–MS, GC-flame ionization detector (FID), MALDI-TOF MS, NMR, near infrared and fourier transform infrared spectroscopy [153–156] . Various algal lipidomes have been evaluated  [157] . Using genomic information, lipid-biosynthetic pathways can be reconstructed in silico [158] . In C. reinhardtii, it was found that nearly 260 proteins are associated with TAG-lipid droplets. When one of the major proteins found within lipid droplets was knocked down with RNA interference, TAG-droplet size increased but no overall change in TAG metabolism or content was observed [39] . Determining what proteins are important in TAG biosynthesis and assembly is of great importance to algal biofuel production. Recently, efforts have focused on altering the lipidome of algae to produce lipids that are more amenable for biofuel production. One effort has focused on metabolically engineering fatty acid chain lengths in P. tricornutum [159] . Diesel fuel surrogates produced from shorter chainlength fatty acids (i.e., 14 carbons and less) have better fuel characteristics and cold-flow properties; however, the majority of fatty acids in P. tricornutum have chain lengths of 16, 18 and 20 carbons. To produce shorter fatty acids, Radakovits et al. heterologously expressed acyl-acyl carrier protein thioesterases that were known to produce 12 (lauric acid) and 14 (myristic acid) carbon fatty acids in plants. These thioesterases increased myristic acid production and induced production of lauric acid, which is not natively produced by P. tricornutum, successfully altering the lipidome [159] . Alterations of the lipidome from engineering fatty acid chain lengths, increasing metabolic flux to lipid anabolism and identifying proteins involved in fatty acid synthesis and TAG-lipid droplet formation will directly impact biofuel yields in algae and is an important area of future research. Integration of systems-biology disciplines Single omics approach may not be sufficient to characterize complex biological systems; however, together, ‘omic’ technologies have the capability to generate valuable insights into specific biological processes [160] . Putative genes found in a genome may be nonfunctional, or expressed at low levels negating their metabolic effect. Transcriptomic data on gene expression levels do not provide insight into protein abundance, activity, location or post-translational modifications [4] . Glycolysis has been shown to be controlled at the

future science group

metabolic, proteomic, post-translational and genomic levels [161] and it is likely that most metabolic pathways have multiple levels of regulation. Integrating multiple ‘omic’ studies will be necessary to unravel the complex systems that regulate biological systems on a molecular level. This type of integrated evaluation will require new approaches to analyze and compare large data sets, but should provide insights into important cellular processes [4] . How systems-biology tools can be used to develop biofuel-production strains Alterations of native metabolic pathways and introduction of synthetic metabolic pathways into a micro­ organism can be used to produce desired chemical products, but these alterations can also produce deleterious effects on the host cell’s native metabolism and pathway intermediates. Systems biology can provide insights into the cellular effects of metabolically engineered pathways and, although this has not been demonstrated yet in algae, it has been applied to biofuel production in E. coli. In one study, transcriptomics and metabolomics were used to determine the mechanism of toxicity in an engineered strain of E. coli that produces large quantities of isoprenoids [162] . A synthetic mevalonate pathway intermediate, 3-hydroxy-3methylglutaryl-coenzyme A (HMG-CoA), was found to accumulate intracellularly causing inhibited cell growth and cytotoxicity  [163] . Evaluation of the transcriptome revealed upregulation of fatty acid synthesis genes, while metabolome data showed an accumulation of malonyl-coenzyme A, an intermediate in fatty acid biosynthesis, indicating an inhibition of fatty acid anabolism. Systems-biology tools determined the source of growth inhibition, which in turn informed a strategy of palmitic and oleic acid supplementation that counteracted the cytotoxic effects of HMG-CoA [162] . Assessing whether an engineered metabolic pathway is malfunctioning is often easily determined by ana­lysis of the desired product. However, determining what native pathways are perturbed that result in cell toxicity or slow growth phenotypes, is often more difficult. A systematic evaluation of the perturbed organism with systems-biology tools can reveal insight into metabolic intermediates that may be limited or have toxic effects on normal cellular processes, which can be addressed by further metabolic-engineering strategies. The majority of algal systems-biology studies have been conducted on model strains, such as C.  reinhardtii and P. tricornutum. Model algal systems are often selected for their fast growth rates, hetero­trophic growth ability to assess photosynthesis mutants, ease of genetic manipulation or to investigate specific cell­ ular functions such as phototaxis or sexual cycles  [10] .

www.future-science.com

137

Review  Jinkerson, Subramanian & Posewitz

However, these traits are not necessarily the most desirable attributes for biofuel production. Algal strains that produce large quantities of lipids or starch, have fast growth rates outdoors and are easily harvested and processed, are the most desirable for biofuel production and often considered ‘industrially relevant’ strains [1] . This creates a disparity between the collective basic knowledge of algal systems, which is generally focused on model organisms; whereas knowledge of algal systems needs to be applied more frequently on industrially relevant strains. Systems-biology tools can help bridge this gap by facilitating the transfer of general algal knowledge to industrially relevant strains and by allowing researchers to understand these industrially relevant organisms at a systems level of complexity, giving them insight into algal metabolism and regulatory processes. Figure 3 is a flow diagram overview of one scenario where systems-biology tools can be implemented to transform an industrially relevant algal strain into a strain that can be used as a biofuel-production crop. The first step in this process is nucleotide sequencing. Sequencing of a genome will reveal genome size and complexity, help to identify genes by homology, and determine how closely it is related to available model Model organism

organisms. Transcriptome sequencing of an alga subjected to conditions that are likely to be found in mass cultivation, or under nutrient stress regimes that promote bioenergy-carrier accumulation, is the next likely step. The transcriptome data will inform researchers on which genes are differentially regulated under the assayed conditions, potentially providing insight to what genes are responsible for the desired bioenergy phenotype. Gene models can be verified and detailed knowledge on intron/exon boundaries can be determined, in addition to discovery of endogenous promoters that can be used for foreign gene expression. At this stage, the differences and similarities between the industrially relevant strain and established model organisms’ physiology, genome and transcriptome should be known. Model organisms with high similarity can be used as a primary knowledge base for making rational decisions about metabolic-engineering strategies to improve biofuel yields. With genome data, reverse genetic screens can be employed to find knockouts or knockdowns. For example, it is known that in C. reinhardtii impairing starch synthesis leads to an increase in lipid accumulation [155] , so in industrially relevant strains that produce starch, these pathways could be targeted for knockout.

Genomics Transcriptomics Metabolomics Proteomics

Gene function Metabolic pathways Regulatory networks

Transfer of knowledge Genome sequencing

Transcriptomics Genome

Gene models Gene expression

Genetically organism

Industrially relevant microalgae No

Mass cultivation

Yes

Biofuel production potential?

Proteomics Metabolomics

Altered phenotype

Biofuels © Future Science Group (2011)

Figure 3. How systems-biology tools can be used to develop algal strains for biofuel production. An industrially relevant alga is chosen and its genome sequenced. After transcript profiling, gene models and gene expression profiles can be created. This information, along with a priori knowledge from model algal systems can then be used to make rational genetic modifications targeted at increasing biofuel production. Once genetically modified, these organisms can be subjected to iterative metabolome and proteome profiling to examine biofuel yields in detail. If yields are suboptimal, ‘omics’ techniques can again be leveraged to determine the consequences of genetic modification to reveal existing metabolic bottlenecks that need to be mitigated and the entire process repeated until the desired phenotype is attained, at which point, scaled-up production can be evaluated.

138

Biofuels (2011) 2(2)

future science group

Improving biofuel production in phototrophic microorganisms with systems biology  Review

PCR reverse genetic screens of mutant libraries could be used to identify these or other desired mutants. In addition, any knowledge about deregulating bioenergycarrier production or any known homologous or heterologous genes that increase bioenergy yields in model systems can be implemented in the industrially relevant organism. The genetically modified strains can be re­evaluated to determine the effect of the modification on the bioenergy phenotype. At this point, proteomic and metabolic studies are appropriate. Proteomics could be used to determine transgene-expression levels and localization, while metabolomics gives insight into the levels of bioenergy carriers and their precursors. After this thorough assessment, it can be decided whether the desired bioenergy phenotype has been achieved. If successful, the strain can be tested in scaled-up cultivation. However, if bioenergy yields are insufficient for a commercially relevant process, another round of transcriptomics, followed by genetic modification and phenotype evaluation could be iterated until the desired bioenergy phenotype is achieved. Conclusion & future perspective The cultivation of microalgae for the production of fuels represents a paradigm shift in the way we view agriculture and fuel production. Growing aquatic microorganisms at a scale large enough to provide meaningful quantities of fuel has never before been achieved. This effort will require massive investments in infrastructure for cultivating, harvesting and processing algal biomass. Increasing algal bioenergy-carrier production per photosynthetic footprint is essential for minimizing the area needed for cultivation, capital costs and, ultimately, fuel prices. Terrestrial plants have been selected and bred for thousands of

years to produce the high yielding, economically viable and nutritious crops that we enjoy today. A similar approach to improving bioenergy yields in algae would be laborious and time intensive. Determining optimal algal-culture conditions, in addition to genetically modifying algal strains, will decrease the time needed to transform native-algal species into bio­energy crops. Systems-biology tools will play a major role in this transformation. These tools will facilitate the continued evaluation of photosynthetic microorganisms on a molecular level, which will provide researchers with insights into the mechanisms that are important for biofuel production. Systems biology can reveal how metabolic pathways are differentially regulated under conditions found in mass cultivation and also identify gene targets and pathways for overexpression or knockout that will direct photosynthate into the desired bioenergy carrier. High-throughput systems-biology techniques that specifically evaluate biological molecules at various cell­ ular levels are, and will become, more common place in the laboratory. As these technologies mature, their cost will probably decrease, making their adoption and use more widespread. Emerging tools that take advantage of improvements in nucleotide-sequencing technologies, such as RNA-Seq, will be used in the place of more established technologies such as microarrays. As these tools proliferate, the amount of data generated will be extensive. Data processing, ana­lysis and dissemination will need to keep pace with the expansion of these tools in order to keep their output timely and relevant. The extraction of useful know­ledge from the data generated by systems-biology tools regarding how biological systems are interacting will be crucial to understanding algal biology and, ultimately increasing biofuel yields.

Executive summary ƒƒ Water-oxidizing phototrophic microorganisms, specifically algae and cyanobacteria, are well suited for the production of biofuels because they produce a variety of bioenergy carriers amenable for the production of diesel fuel surrogates, alcohols and hydrogen, while lacking large amounts of recalcitrant biomass. ƒƒ Before algae can be used in economically viable biofuel-production processes, bioenergy-carrier yields will need to be increased through metabolic engineering and altered growth regimes. ƒƒ Systems biology, or ‘omics’ (i.e., genomics, transcriptomics, proteomics, metabolomics and lipidomics) can reveal insights regarding an organism’s metabolic underpinnings by accurately quantifying and functionally characterizing genetic elements, mRNA, proteins, metabolites and lipids. ƒƒ Several algal and many cyanobacterial genomes have been completely sequenced, with more currently underway. ƒƒ Comparative genomics can help establish evolutionary relationships among algal strains and also reveal gene targets for metabolisms of interest by employing combinatorial techniques. ƒƒ Transcriptomics can elucidate differential gene regulation and expression under conditions of growth, stress and genomic mutations. ƒƒ Proteomic evaluation can determine protein abundance, intercellular locations and post-translational modifications. ƒƒ Metabolomics can reveal flux of native metabolites, in addition to providing information on how synthetic metabolic pathways reroute metabolites. ƒƒ Evaluation and alteration of the lipidome can directly influence biofuel production in algae. ƒƒ A systems-biology evaluation of an industrially relevant algal strain in concert with a priori knowledge from model algal systems can be used to inform genetic-engineering strategies to produce strains amenable for biofuel production.

future science group

www.future-science.com

139

Review  Jinkerson, Subramanian & Posewitz

Financial & competing interests disclosure The authors of this work were supported by the United States Air Force Office of Scientific Research under grant FA9550–05–1–0365 and the United States Department of Energy Basic Energy Sciences and Biological and Environmental Sciences grants. Robert E Jinkerson was supported by a Graduate Research Fellowship f rom the National Science Foundation. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

8

9

Hood L, Heath JR, Phelps ME, Lin B. Systems biology and new technologies enable predictive and preventative medicine. Science 306(5696), 640–643 (2004). Xu P, Koffas MA. Metabolic engineering of Escherichia coli for biofuel production. Biofuels 1(3), 493–504 (2010).

10

Grossman AR. Paths toward algal genomics. Plant Physiol. 137(2), 410–427 (2005).

11

Meuser JE, Ananyev G, Wittig LE et al. Phenotypic diversity of hydrogen production in chlorophycean algae reflects distinct anaerobic metabolisms. J. Biotechnol. 142(1), 21–30 (2009).

12 Grossman AR, Croft M, Gladyshev VN et al.

Novel metabolism in Chlamydomonas through the lens of genomics. Curr. Opin. Plant Biol. 10(2), 190–198 (2007). The Chlorella variabilis NC64A genome reveals adaptation to photosymbiosis, coevolution with viruses, and cryptic sex. Plant Cell 22(9), 2943–2955 (2010).

Papers of special note have been highlighted as: of interest of considerable interest n

n n

Dismukes GC, Carrieri D, Bennette N, Ananyev GM, Posewitz MC. Aquatic phototrophs: efficient alternatives to land-based crops for biofuels. Curr. Opin. Biotechnol. 19(3), 235–240 (2008).

2

Merchant SS, Prochnik SE, Vallon O et al. The Chlamydomonas genome reveals the evolution of key animal and plant functions. Science 318(5848), 245–250 (2007).

3

n

Radakovits R, Jinkerson RE, Darzins A, Posewitz MC. Genetic engineering of algae for enhanced biofuel production. Eukaryot. Cell 9(4), 486–501 (2010). Presents a variety of genetic-engineering strategies to increase bioenergy yeilds in algae.

4

Zhang W, Li F, Nie L. Integrating multiple ‘omics’ ana­lysis for microbial biology: application and methodologies. Microbiology 156(2), 287–301 (2010).

5

Jamers A, Blust R, De Coen W. Omics in algae: paving the way for a systems biological understanding of algal stress phenomena? Aquat. Toxicol. 92(3), 114–121 (2009).

n n

Review of ‘omics’ in algae applied to environmental issues. Includes a specific case study of subjecting an alga to heavy-metal stress.

6

Ideker T, Galitski T, Hood L. A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2(1), 343–372 (2001).

7

Ideker T, Thorsson V, Ranish JA et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292(5518), 929–934 (2001).

140

Biofuels (2011) 2(2)

Niche adaptation and genome expansion in the chlorophyll d-producing cyanobacterium Acaryochloris marina. Proc. Natl Acad. Sci. USA 105(6), 2005–2010 (2008). 23 Kaneko T, Nakamura Y, Wolk CP et al.

Complete genomic sequence of the filamentous nitrogen-fixing cyanobacterium Anabaena sp. strain PCC 7120. DNA Res. 8(5), 205–213 (2001). 24 Kaneko T, Nakajima N, Okamoto S et al.

Complete genomic structure of the bloom-forming toxic cyanobacterium Microcystis aeruginosa NIES-843. DNA Res. 14(6), 247–256 (2007). 25 Rocap G, Larimer FW, Lamerdin J et al.

Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature 424(6952), 1042– 1047 (2003).

13 Blanc G, Duncan G, Agarkova I et al.

Bibliography

1

22 Swingley WD, Chen M, Cheung PC et al.

26 Palenik B, Brahamsha B, Larimer FW et al.

The genome of a motile marine Synechococcus. Nature 424(6952), 1037–1042 (2003). 27 Kaneko T, Sato S, Kotani H et al. Sequence

14

Worden AZ, Lee JH, Mock T et al. Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes Micromonas. Science 324(5924), 268–272 (2009).

15

Palenik B, Grimwood J, Aerts A et al. The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. Proc. Natl Acad. Sci. USA 104(18), 7705–7710 (2007).

28 Nakamura Y, Kaneko T, Sato S et al.

Derelle E, Ferraz C, Rombauts S et al. Genome  na­lysis of the smallest free-living eukaryote Ostreococcus tauri unveils many unique features. Proc. Natl Acad. Sci. USA 103(31), 11647–11652 (2006).

29 Hardison RC. Comparative genomics.

16

17

Bowler C, Allen AE, Badger JH et al. The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature 456(7219), 239–244 (2008).

18

Armbrust EV, Berges JA, Bowler C et al. The genome of the diatom Thalassiosira pseudonana: ecology, evolution, and metabolism. Science 306(5693), 79–86 (2004).

19

Matsuzaki M, Misumi O, Shin-i T et al. Genome sequence of the ultrasmall unicellular red alga Cyanidioschyzon merolae 10D. Nature 428(6983), 653–657 (2004).

ana­lysis of the genome of the unicellular cyanobacterium Synechocystis sp. strain PCC6803. II. Sequence determination of the entire genome and assignment of potential protein-coding regions. DNA Res. 3(3), 109–136 (1996). Complete genome structure of the thermophilic cyanobacterium Thermosynechococcus elongatus BP-1. DNA Res. 9(4), 123–130 (2002). PLoS Biol. 1(2), 156–160 (2003). 30 Barbier G, Oesterhelt C, Larson MD et al.

Comparative genomics of two closely related unicellular thermo-acidophilic red algae, Galdieria sulphuraria and Cyanidioschyzon merolae, reveals the molecular basis of the metabolic flexibility of Galdieria sulphuraria and significant differences in carbohydrate metabolism of both algae. Plant Physiol. 137(2), 460–474 (2005). 31

32 Grossman A, Karpowicz S, Heinnickel M

et al. Phylogenomic ana­lysis of the Chlamydomonas genome unmasks proteins potentially involved in photosynthetic function and regulation. Photosynth. Res. 160(1–2), 3–17 (2010).

20 Douglas S, Zauner S, Fraunholz M et al. The

highly reduced genome of an enslaved algal nucleus. Nature 410(6832), 1091–1096 (2001). 21

Liolios K, Chen I-MA, Mavromatis K et al. The genomes on line database (GOLD) in 2009: status of genomic and metagenomic projects and their associated metadata. Nucleic Acids Res. 38, D346–D354 (2010).

Schirmer A, Rude MA, Li X, Popova E, del Cardayre SB. Microbial biosynthesis of alkanes. Science 329(5991), 559–562 (2010).

n n

Comparative genomic ana­lysis of Chlamydomonas reinhardtii to establish and evaluate the ‘GreenCut’ proteins.

future science group

Improving biofuel production in phototrophic microorganisms with systems biology  Review 33 Sato Y, Morita R, Katsuma S, Nishimura M,

Tanaka A, Kusaba M. Two short-chain dehydrogenase/reductases, NON-YELLOW COLORING 1 and NYC1-LIKE, are required for chlorophyll b and light-harvesting complex II degradation during senescence in rice. Plant J. 57(1), 120–131 (2009).

44 Hegde P, Qi R, Abernathy K et al. A concise

45

34 DalCorso G, Pesaresi P, Masiero S et al.

A complex containing PGRL1 and PGR5 is involved in the switch between linear and cyclic electron flow in Arabidopsis. Cell 132(2), 273–285 (2008). 35

Duan K, Yi K, Dang L, Huang H, Wu W, Wu P. Characterization of a sub-family of Arabidopsis genes with the SPX domain reveals their diverse functions in plant tolerance to phosphorus starvation. Plant J. 54(6), 965–975 (2008).

37 Mussgnug JH, Thomas-Hall S, Rupprecht J

et al. Engineering photosynthetic light capture: impacts on improved solar energy to biomass conversion. Plant Biotechnol. J. 5(6), 802–814 (2007).

regulation of high light-induced genes in Chlamydomonas reinhardtii. Plant J. 30(3), 301–313 (2002). Vallon O, Grossman AR. Insights into the survival of Chlamydomonas reinhardtii during sulfur starvation based on microarray ana­lysis of gene expression. Eukaryot. Cell 3(5), 1331–1348 (2004).

Acclimation to singlet oxygen stress in Chlamydomonas reinhardtii. Eukaryot. Cell 6(6), 919–930 (2007).

41 Wang Z, Gerstein M, Snyder M. RNA-Seq:

a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10(1), 57–63 (2009). n n

Overview of RNA-Seq. Includes the advantages, limitations and biases of the method.

42 Eberhard S, Jain M, Im C et al. Generation of

an oligonucleotide array for ana­lysis of gene expression in Chlamydomonas reinhardtii. Curr. Genet. 49(2), 106–124 (2006). 43 Frias-Lopez J, Shi Y, Tyson GW et al.

Microbial community gene expression in ocean surface waters. Proc. Natl Acad. Sci. USA 105(10), 3805–3810 (2008).

future science group

Roessler P. A look back at the US Department of Energy’s Aquatic Species Program – biodiesel from algae. Nat. Renew. Energy Lab. Golden, Colorado, Report No. NREL/TP-580–24190 (1998). 58 Ozsolak F, Platt AR, Jones DR et al. Direct

RNA sequencing. Nature 461(7265), 814–818 (2009). 59 Lister R, O’Malley RC, Tonti-Filippini J

et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133(3), 523–536 (2008). 60 Nagalakshmi U, Wang Z, Waern K et al.

The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320(5881), 1344–1349 (2008). 61

50 Mus F, Dubini A, Seibert M, Posewitz MC,

Grossman AR. Anaerobic acclimation in Chlamydomonas reinhardtii: anoxic gene expression, hydrogenase induction, and metabolic pathways. J. Biol. Chem. 282(35), 25475–25486 (2007). 51

Dubini A, Mus F, Seibert M, Grossman AR, Posewitz MC. Flexibility in anaerobic metabolism as revealed in a mutant of Chlamydomonas reinhardtii lacking hydrogenase activity. J. Biol. Chem. 284(11), 7201–7213 (2009).

52

Moseley JL, Chang C-W, Grossman AR. Genome-based approaches to understanding phosphorus deprivation responses and PSR1 control in Chlamydomonas reinhardtii. Eukaryot. Cell 5(1), 26–44 (2006).

40 Pootakham W, Gonzalez-Ballester D,

Grossman AR. Identification and regulation of plasma membrane sulfate transporters in Chlamydomonas reinhardtii. Plant Physiol. 153(4), 1653–1668 (2010).

57 Sheehan J, Dunahay T, Benemann J,

49 Ledford HK, Chin BL, Niyogi KK.

An ancient light-harvesting protein is critical for the regulation of algal photosynthesis. Nature 462(7272), 518–521 (2009). silencing of a major lipid droplet protein affects lipid droplet size in Chlamydomonas reinhardtii. Eukaryot. Cell 9(1), 97–106 (2010).

Zhang L, Seibert M, Ghirardi ML. Discovery of two novel radical S-adenosylmethionine proteins required for the assembly of an active [Fe] hydrogenase. J. Biol. Chem.279(24), 25711–25720 (2004).

48 Miura K, Yamano T, Yoshioka S et al.

Expression profiling-based identification of CO2-responsive genes regulated by CCM1 controlling a carbon-concentrating mechanism in Chlamydomonas reinhardtii. Plant Physiol. 135(3), 1595–1607 (2004).

54 Minoda A, Nagasawa K, Hanaoka M,

Horiuchi M, Takahashi H, Tanaka K. Microarray profiling of plastid gene expression in a unicellular red alga, Cyanidioschyzon merolae. Plant Mol. Biol. 59(3), 375–385 (2005).

Gonzalez-Ballester D, Casero D, Cokus S, Pellegrini M, Merchant SS, Grossman AR. RNA-Seq ana­lysis of sulfur-deprived Chlamydomonas cells reveals aspects of acclimation critical for cell survival. Plant Cell 22(6), 2058–2084 (2010).

62 Miller R, Wu G, Deshpande RR et al.

Changes in transcript abundance in Chlamydomonas reinhardtii following nitrogen-deprivation predict diversion of metabolism. Plant Physiol. 154(4), 1737– 1752 (2010). 63 O’Farrell PH. High resolution two-

dimensional electrophoresis of proteins. J. Biol. Chem. 250(10), 4007–4021 (1975). 64 Ong S-E, Foster LJ, Mann M. Mass

spectrometric-based approaches in quantitative proteomics. Methods 29(2), 124–130 (2003).

53 dos Santos Ferreira V, Rocchetta I,

Conforti V, Bench S, Feldman R, Levin MJ. Gene expression patterns in Euglena gracilis: insights into the cellular response to environmental stress. Gene 389(2), 136–145 (2007).

Eom H, Lee C-G, Jin E. Gene expression profile ana­lysis in astaxanthin-induced Haematococcus pluvialis using a cDNA microarray. Planta 223(6), 1231–1242 (2006).

56 Posewitz MC, King PW, Smolinski SL,

47 Zhang Z, Shrager J, Jain M, Chang C-W,

38 Peers G, Truong TB, Ostendorf E et al.

39 Moellering ER, Benning C. RNA interference

Pariset L, Chillemi G, Bongiorni S, Spica VR, Valentini A. Microarrays and highthroughput transcriptomic ana­lysis in species with incomplete availability of genomic sequences. New Biotechnol. 25(5), 272–279 (2009).

46 Im CS, Grossman AR. Identification and

36 Alvarez AF, Alvarez HM, Kalscheuer R,

Waltermann M, Steinbuchel A. Cloning and characterization of a gene involved in triacylglycerol biosynthesis and identification of additional homologous genes in the oleaginous bacterium Rhodococcus opacus PD630. Microbiology 154(8), 2327–2335 (2008).

55

guide to cDNA microarray ana­lysis. BioTechniques 29, 548–562 (2000).

65

Tannu NS, Hemby SE. Methods for proteomics in neuroscience. In: Progress in Brain Research: Functional Genomics and Proteomics in the Clinical Neurosciences. Hemby SE, Bahn S (Eds). Elsevier, Amsterdam, The Netherlands, 41–82 (2006).

66 Ciordia S, Ríos V, Albar J-P. Contributions

of advanced proteomics technologies to cancer diagnosis. Clin. Transl. Oncol. 8(8), 566–580 (2006).

www.future-science.com

141

Review  Jinkerson, Subramanian & Posewitz 67 Chen X, Sun L, Yu Y, Xue Y, Yang P. Amino

Proteomic ana­lysis of the eyespot of Chlamydomonas reinhardtii provides novel insights into its components and tactic movements. Plant Cell 18(8), 1908–1930 (2006).

68 Maurya P, Meleady P, Dowling P, Clynes M.

Proteomic approaches for serum biomarker discovery in cancer. Anticancer Res. 27(3A), 1247–1255 (2007).

Analysis of flagellar phosphoproteins from Chlamydomonas reinhardtii. Eukaryot. Cell 8(7), 922–932 (2009). 80 Liu Q, Tan G, Levenkova N et al. The

proteome of the mouse photoreceptor sensory cilium complex. Mol. Cell. Proteomics 6(8), 1299–1317 (2007).

70 Pan S, Aebersold R. Quantitative proteomics

by stable isotope labeling and mass spectrometry. In: Mass Spectrometry Data Analysis in Proteomics. Matthiesen R (Ed.). Humana Press, NJ, USA 209–218 (2007). 71 Asara JM, Zhang X, Zheng B, Christofk HH,

Wu N, Cantley LC. In-gel stable-isotope labeling (ISIL): a strategy for mass spectrometry-based relative quantification. J. Proteome Res. 5(1), 155–163 (2005).

74

Rochaix J-D. Potential for hydrogen production with inducible chloroplast gene expression in Chlamydomonas. Proc. Natl Acad. Sci. USA 104(44), 17548–17553 (2007). 76 van Lis R, Atteia A, Mendoza-Hernandez G,

Gonzalez-Halphen D. Identification of novel mitochondrial protein components of Chlamydomonas reinhardtii. A proteomic approach. Plant Physiol. 132(1), 318–330 (2003). n n

Comprehensive ana­lysis of the mitochondrial oxidative phosphorylation complexes using a combination of blue native-polyacrylamide gel electrophoresis and MS.

77 Keller LC, Romijn EP, Zamora I,

Yates JR 3rd, Marshall WF. Proteomic ana­lysis of isolated Chlamydomonas centrioles reveals orthologs of ciliary-disease genes. Curr. Biol. 15(12), 1090–1098 (2005).

142

Biofuels (2011) 2(2)

Terashima M, Specht M, Naumann B, Hippler M. Characterizing the anaerobic response of Chlamydomonas reinhardtii by quantitative proteomics. Mol. Cell. Proteomics 9(7), 1514–1532 (2010).

92 Chen M, Zhao L, Sun Y-L et al. Proteomic

82 Pazour GJ, Agrin N, Leszyk J, Witman GB.

93 Liska AJ, Shevchenko A, Pick U, Katz A.

Proteomic ana­lysis of a eukaryotic cilium. J. Cell Biol. 170(1), 103–113 (2005). 83 Wagner V, Kreimer G, Mittag M. The power

of functional proteomics: components of the green algal eyespot and its light signaling pathway(s). Plant Signal. Behav. 3(7), 433–435 (2008). 84 Buchanan B, Schürmann P, Wolosiuk R,

Jacquot J-P. The ferredoxin/thioredoxin system: from discovery to molecular structures and beyond. Photosynth. Res. 73(1), 215–222 (2002). 85 Lemaire SD, Guillon B, Le Maréchal P,

Stauber EJ, Fink A, Markert C, Kruse O, Johanningmeier U, Hippler M. Proteomics of Chlamydomonas reinhardtii light-harvesting proteins. Eukaryot. Cell 2(5), 978–994 (2003).

75 Surzycki R, Cournac L, Peltier G,

91

ana­lysis of hydrogen photoproduction in sulfur-deprived Chlamydomonas cells. J. Proteome Res. 9(8), 3854–3866 (2010).

73 Hippler M, Klein J, Fink A, Allinger T,

Hoerth P. Towards functional proteomics of membrane protein complexes: ana­lysis of thylakoid membranes from Chlamydomonas reinhardtii. Plant J. 28(5), 595–606 (2001).

Transcriptome for photobiological hydrogen production induced by sulfur deprivation in the green alga Chlamydomonas reinhardtii. Eukaryot. Cell 7(11), 1965–1979 (2008).

Ostrowski LE, Blackburn K, Radde KM et al. A proteomic ana­lysis of human cilia. Mol. Cell. Proteomics 1(6), 451–465 (2002).

81

72 Brun V, Masselon C, Garin J, Dupuis A.

Isotope dilution strategies for absolute quantitative proteomics. J. Proteomics 72(5), 740–749 (2009).

90 Nguyen AV, Thomas-Hall SR, Malnoe A et al.

79 Boesger J, Wagner V, Weisheit W, Mittag M.

69 Xun Z, Kaufman TC, Clemmer DE. Stable

isotope labeling and label-free proteomics of Drosophila parkin null mutants. J. Proteome Res. 8(10), 4500–4510 (2009).

acidophilic strain of Chlamydomonas sp. (Chlorophyta) to natural metal-rich water. Proteomics 10(10), 2026–2036 (2010).

78 Schmidt M, Gessner G, Luff M et al.

acid-coded tagging approaches in quantitative proteomics. Expert Rev. Proteomics 4(1), 25–37 (2007).

n

Enhanced photosynthesis and redox energy production contribute to salinity tolerance in Dunaliella as revealed by homology-based proteomics. Plant Physiol. 136(1), 2806–2817 (2004). 94 Katz A, Waridel P, Shevchenko A, Pick U.

Salt-induced changes in the plasma membrane proteome of the halotolerant alga Dunaliella salina as revealed by blue native gel electrophoresis and nano-LC–MS/MS ana­lysis. Mol. Cell. Proteomics 6(9), 1459–1472 (2007). 95 Jia Y, Xue L, Li J, Liu H. Isolation and

proteomic ana­lysis of the halotolerant alga Dunaliella salina flagella using shotgun strategy. Mol. Biol. Reports 37(2), 711–716 (2010).

Keryer E, Miginiac-Maslow M, Decottignies P. New thioredoxin targets in the unicellular photosynthetic eukaryote Chlamydomonas reinhardtii. Proc. Natl Acad. Sci. USA 101(19), 7475–7480 (2004).

96 Wang SB, Hu Q, Sommerfeld M, Chen F. Cell

Article discusses an interesting affinitybased proteomic approach to isolate and identify thioredoxin-binding proteins.

97 Tran NP, Park JK, Hong SJ, Lee CG.

86 Forster B, Mathesius U, Pogson BJ.

Comparative proteomics of high light stress in the model alga Chlamydomonas reinhardtii. Proteomics 6(15), 4309–4320 (2006). 87 Gillet S, Decottignies P, Chardonnet S,

Le Marechal P. Cadmium response and redoxin targets in Chlamydomonas reinhardtii: a proteomic approach. Photosynth. Res. 89(2–3), 201–211 (2006). 88 Naumann B, Busch A, Allmer J et al.

Comparative quantitative proteomics to investigate the remodeling of bioenergetic pathways under iron deficiency in Chlamydomonas reinhardtii. Proteomics 7(21), 3964–3979 (2007). 89 Cid C, Garcia-Descalzo L,

Casado-Lafuente V, Amils R, Aguilera A. Proteomic ana­lysis of the response of an

wall proteomics of the green alga Haematococcus pluvialis (Chlorophyceae). Proteomics 4(3), 692–708 (2004). Proteomics of proteins associated with astaxanthin accumulation in the green algae Haematococcus lacustris under the influence of sodium orthovanadate. Biotechnol. Lett. 31(12), 1917–1922 (2009). 98 Kim YK, Yoo WI, Lee SH, Lee MY. Proteomic

ana­lysis of cadmium-induced protein profile alterations from marine alga Nannochloropsis oculata. Ecotoxicology 14(6), 589–596 (2005). 99 Contreras L, Moenne A, Gaillard F, Potin P,

Correa JA. Proteomic ana­lysis and identification of copper stress-regulated proteins in the marine alga Scytosiphon gracilis (Phaeophyceae). Aquat. Toxicol. 96(2), 85–89 (2010). 100 Vannini C, Marsoni M, Domingo G,

Antognoni F, Biondi S, Bracale M. Proteomic ana­lysis of chromate-induced modifications in Pseudokirchneriella subcapitata. Chemosphere 76(10), 1372–1379 (2009).

future science group

Improving biofuel production in phototrophic microorganisms with systems biology  Review 101 Herranen M, Battchikova N, Zhang P et al.

113 Gao Y, Xiong W, Li XB et al. Identification

Towards functional proteomics of membrane protein complexes in Synechocystis sp. PCC 6803. Plant Physiol. 134(1), 470–481 (2004). 102 Mata-Cabana A, Florencio FJ, Lindahl M.

of the proteomic changes in Synechocystis sp. PCC 6803 following prolonged UV-B irradiation. J. Exp. Bot. 60(4), 1141–1154 (2009). 114 Pandhal J, Noirel J, Wright PC, Biggs CA.

Membrane proteins from the cyanobacterium Synechocystis sp. PCC 6803 interacting with thioredoxin. Proteomics 7(21), 3953–3963 (2007). 103 Norling B, Zak E, Andersson B, Pakrasi H.

A systems biology approach to investigate the response of Synechocystis sp. PCC6803 to a high salt environment. Saline Systems 5, 8 (2009). 115 Koksharova OA, Klint J, Rasmussen U.

2D-isolation of pure plasma and thylakoid membranes from the cyanobacterium Synechocystis sp. PCC 6803. FEBS Lett. 436(2), 189–192 (1998). 104 Srivastava R, Pisareva T, Norling B.

Comparative proteomics of cell division mutants and wild-type of Synechococcus sp. strain PCC 7942. Microbiology 153(Pt 8), 2505–2517 (2007). 116 Long BM, Badger MR, Whitney SM,

Proteomic studies of the thylakoid membrane of Synechocystis sp. PCC 6803. Proteomics 5(18), 4905–4916 (2005).

Price GD. Analysis of carboxysomes from Synechococcus PCC7942 reveals multiple Rubisco complexes with carboxysomal proteins CcmM and CcaA. J. Biol. Chem. 282(40), 29323–29335 (2007).

105 Huang F, Parmryd I, Nilsson F et al.

Proteomics of Synechocystis sp. strain PCC 6803: identification of plasma membrane proteins. Mol. Cell. Proteomics 1(12), 956–966 (2002).

117 Stensjo K, Ow SY, Barrios-Llerena ME,

Lindblad P, Wright PC. An iTR AQ-based quantitative ana­lysis to elaborate the proteomic response of Nostoc sp. PCC 7120 under N2 fixing conditions. J. Proteome Res. 6(2), 621–635 (2007).

106 Huang F, Fulda S, Hagemann M, Norling B.

Proteomic screening of salt-stress-induced changes in plasma membranes of Synechocystis sp. strain PCC 6803. Proteomics 6(3), 910–920 (2006).

118 Ow SY, Cardona T, Taton A et al.

Quantitative shotgun proteomics of enriched heterocysts from Nostoc sp. PCC 7120 using 8-plex isobaric peptide tags. J. Proteome Res. 7(4), 1615–1628 (2008).

107 Pisareva T, Shumskaya M, Maddalo G,

Ilag L, Norling B. Proteomics of Synechocystis sp. PCC 6803. Identification of novel integral plasma membrane proteins. FEBS J. 274(3), 791–804 (2007). 108 Zhang LF, Yang HM, Cui SX et al.

Proteomic ana­lysis of plasma membranes of cyanobacterium Synechocystis sp. Strain PCC 6803 in response to high pH stress. J. Proteome Res. 8(6), 2892–2902 (2009). 109 Fulda S, Huang F, Nilsson F, Hagemann M,

Norling B. Proteomics of Synechocystis sp. strain PCC 6803. Identification of periplasmic proteins in cells grown at low and high salt concentrations. Eur. J. Biochem. 267(19), 5900–5907 (2000). 110 Fulda S, Mikkat S, Huang F et al. Proteome

ana­lysis of salt stress response in the cyanobacterium Synechocystis sp. strain PCC 6803. Proteomics 6(9), 2733–2745 (2006). 111 Suzuki I, Simon WJ, Slabas AR. The heat

shock response of Synechocystis sp. PCC 6803 analysed by transcriptomics and proteomics. J. Exp. Bot. 57(7), 1573–1578 (2006). 112 Kurian D, Phadwal K, Maenpaa P.

Proteomic characterization of acid stress response in Synechocystis sp. PCC 6803. Proteomics 6(12), 3614–3624 (2006).

future science group

n n

Novel approach using isobaric tags for relative- and absolute-quantification shotgun proteomics has been used to investigate changes in protein expression in vegetative cells and heterocysts.

119 Anderson DC, Campbell EL, Meeks JC.

A soluble 3D LC–MS/MS proteome of the filamentous cyanobacterium Nostoc punctiforme. J. Proteome Res. 5(11), 3096–3104 (2006). 120 Ran L, Huang F, Ekman M, Klint J,

Bergman B. Proteomic analyses of the photoauto- and diazotrophically grown cyanobacterium Nostoc sp. PCC 73102. Microbiology 153(Pt 2), 608–618 (2007). 121 Barrios-Llerena ME, Reardon KF,

Wright PC. 2-DE proteomic ana­lysis of the model cyanobacterium Anabaena variabilis. Electrophoresis 28(10), 1624–1632 (2007). 122 Bhargava P, Mishra Y, Srivastava AK,

Narayan OP, Rai LC. Excess copper induces anoxygenic photosynthesis in Anabaena doliolum: a homology based proteomic assessment of its survival strategy. Photosynth. Res. 96(1), 61–74 (2008).

123 Mishra Y, Chaurasia N, Rai LC. Heat

pretreatment alleviates UV-B toxicity in the cyanobacterium Anabaena doliolum: a proteomic ana­lysis of cross tolerance. Photochem. Photobiol. 85(3), 824–833 (2009). 124 Hongsthong A, Sirijuntarut M,

Prommeenate P et al. Revealing differentially expressed proteins in two morphological forms of Spirulina platensis by proteomic ana­lysis. Mol. Biotechnol. 36(2), 123–130 (2007). 125 Hongsthong A, Sirijuntarut M,

Prommeenate P et al. Proteome ana­lysis at the subcellular level of the cyanobacterium Spirulina platensis in response to lowtemperature stress conditions. FEMS Microbiol. Lett. 288(1), 92–101 (2008). 126 Jeamton W, Mungpakdee S, Sirijuntarut M

et al. A combined stress response ana­lysis of Spirulina platensis in terms of global differentially expressed proteins, and mRNA levels and stability of fatty acid biosynthesis genes. FEMS Microbiol. Lett. 281(2), 121–131 (2008). 127 Pandhal J, Wright PC, Biggs CA.

A quantitative proteomic ana­lysis of light adaptation in a globally significant marine cyanobacterium Prochlorococcus marinus MED4. J. Proteome Res. 6(3), 996–1005 (2007). 128 Sharif DI, Gallon J, Smith CJ, Dudley E.

Quorum sensing in cyanobacteria: N-octanoyl-homoserine lactone release and response, by the epilithic colonial cyanobacterium Gloeothece PCC6909. ISME J. 2(12), 1171–1182 (2008). 129 Matsumoto M, Sugiyama H, Maeda Y,

Sato R, Tanaka T, Matsunaga T. Marine diatom, Navicula sp. strain JPCC DA0580 and marine green alga, Chlorella sp. strain NKG400014 as potential sources for biodiesel production. Appl. Biochem. Biotechnol. 161(1–8), 483–490 (2010). 130 Huesemann MH, Hausmann TS, Bartha R,

Aksoy M, Weissman JC, Benemann JR. Biomass productivities in wild type and pigment mutant of Cyclotella sp. (Diatom). Appl. Biochem. Biotechnol. 157(3), 507–526 (2009). 131 Nunn BL, Aker JR, Shaffer SA et al.

Deciphering diatom biochemical pathways via whole-cell proteomics. Aquat. Microb. Ecol. 55(3), 241–253 (2009). 132 Grimsrud PA, Swaney DL, Wenger CD,

Beauchene NA, Coon JJ. Phosphoproteomics for the masses. ACS Chem. Biol. 5(1), 105–119 (2010). 133 Collins MO, Yu L, Choudhary JS. Analysis of

protein phosphorylation on a proteome-scale. Proteomics 7(16), 2751–2768 (2007).

www.future-science.com

143

Review  Jinkerson, Subramanian & Posewitz 134 Young NL, Plazas-Mayorca MD, Garcia BA.

146 May P, Wienkoop S, Kempa S et al.

Systems-wide proteomic characterization of combinatorial post-translational modification patterns. Expert Rev. Proteomics 7(1), 79–92 (2010). 135 Amoresano A, Carpentieri A, Giangrande C

Metabolomics- and proteomics-assisted genome annotation and ana­lysis of the draft metabolic network of Chlamydomonas reinhardtii. Genetics 179(1), 157–166 (2008). 147 Wienkoop S, Weiss J, May P et al. Targeted

et al. Technical advances in proteomics mass spectrometry: identification of posttranslational modifications. Clin. Chem. Lab. Med. 47(6), 647–665 (2009).

proteomics for Chlamydomonas reinhardtii combined with rapid subcellular protein fractionation, metabolomics and metabolic flux analyses. Mol. BioSyst. 6(6), 1018–1031 (2010).

136 Farley AR, Link AJ. Identification and

quantification of protein posttranslational modifications. Methods Enzymol. 463, 725–763 (2009). 137 Goonewardena SN, Prevette LE, Desai AA.

Metabolomics and atherosclerosis. Curr. Atheroscler. Rep. 12(4), 267–272 (2010). 138 Oakman C, Tenori L, Biganzoli L et al.

Uncovering the metabolomic fingerprint of breast cancer. Int. J. Biochem.Cell Biol. DOI: 10.1016/j.biocel.2010.05.001 (2010). (Epub ahead of print). 139 Aravindaram K, Yang NS. Anti-inflammatory

plant natural products for cancer therapy. Planta Med. 76(11), 1103–1117 (2010). 140 Young SP, Wallace GR. Metabolomic ana­lysis

of human disease and its application to the eye. J. Ocul. Biol. Dis. Infor. 2(4), 235–242 (2009). 141 Pan Z, Raftery D. Comparing and combining

NMR spectroscopy and mass spectrometry in metabolomics. Anal. Bioanal. Chem. 387(2), 525–527 (2007). 142 Oh E, Hasan MN, Jamshed M et al. Growing

trend of CE at the omics level: the frontier of systems biology. Electrophoresis 31(1), 74–92 (2010). 143 Fonslow BR, Yates JR, 3rd. Capillary

electrophoresis applied to proteomic ana­lysis. J. Sep. Sci. 32(8), 1175–1188 (2009). 144 Bolling C, Fiehn O. Metabolite profiling of

Chlamydomonas reinhardtii under nutrient deprivation. Plant Physiol. 139(4), 1995–2005 (2005). n

Study of the metabolome of Chlamydomonas reinhardtii using a rapid sampling and extraction protocol under nutrient-starvation conditions has been presented using GC– TOF-MS.

n n

Study presents a combined application of the three ‘omics’ approaches, namely, genomics, proteomics and metabolomics, to answer a biological question.

148 Sans-Piche F, Kluender C, Altenburger R,

Schmitt-Jansen M. Anchoring metabolic changes to phenotypic effects in the chlorophyte Scenedesmus vacuolatus under chemical exposure. Mar. Envir. Res. 69, S28–S30 (2010). 149 Lin Y, Schiavo S, Orjala J, Vouros P, Kautz R.

Microscale LC–MS–NMR platform applied to the identification of active cyanobacterial metabolites. Anal. Chem. 80(21), 8045–8054 (2008). 150 Krall L, Huege J, Catchpole G,

Steinhauser D, Willmitzer L. Assessment of sampling strategies for gas chromatographymass spectrometry (GC–MS) based metabolomics of cyanobacteria. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 877(27), 2952–2960 (2009). 151 Allen AE, Laroche J, Maheswari U et al.

Whole-cell response of the pennate diatom Phaeodactylum tricornutum to iron starvation. Proc. Natl Acad. Sci. USA 105(30), 10438–10443 (2008). 152 Motta A, Paris D, Melck D. Monitoring

real-time metabolism of living cells by fast two-dimensional NMR spectroscopy. Anal. Chem. 82(6), 2405–2411 (2010). 153 Vieler A, Wilhelm C, Goss R, Süß R,

Schiller J. The lipid composition of the unicellular green alga Chlamydomonas reinhardtii and the diatom Cyclotella meneghiniana investigated by MALDI-TOF MS and TLC. Chem. Phys. Lipids 150(2), 143–155 (2007).

154 Beal CM, Webber ME, Ruoff RS,

Hebner RE. Lipid ana­lysis of Neochloris oleoabundans by liquid state NMR. Biotechnol. Bioeng. 106(4), 573–583 (2010). 155 Work VH, Radakovits R, Jinkerson RE et al.

Increased lipid accumulation in the Chlamydomonas reinhardtii sta7–10 starchless isoamylase mutant and increased carbohydrate synthesis in complemented strains. Eukaryot. Cell 9(8), 1251–1261 (2010). 156 Laurens L, Wolfrum E. Feasibility of

spectroscopic characterization of algal lipids: chemometric correlation of NIR and FTIR spectra with exogenous lipids in algal biomass. BioEnergy Res. 1–14 (2010). 157 Hu Q, Sommerfeld M, Jarvis E et al.

Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances. Plant J. 54(4), 621–639 (2008). 158 Riekhof WR, Sears BB, Benning C.

Annotation of genes involved in glycerolipid biosynthesis in Chlamydomonas reinhardtii: discovery of the betaine lipid synthase BTA1Cr. Eukaryot. Cell 4(2), 242–252 (2005). 159 Radakovits R, Eduafo PM, Posewitz MC.

Genetic engineering of fatty acid chain length in Phaeodactylum tricornutum. Metab. Eng. 13(1), 89–95 (2010). 160 Gygi SP, Rochon Y, Franza BR, Aebersold R.

Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19(3), 1720–1730 (1999). 161 ter Kuile BH, Westerhoff HV. Transcriptome

meets metabolome: hierarchical and metabolic regulation of the glycolytic pathway. FEBS Lett. 500(3), 169–171 (2001). 162 Kizer L, Pitera DJ, Pfleger BF, Keasling JD.

Application of functional genomics to pathway optimization for increased isoprenoid production. Appl. Envir. Microbiol. 74(10), 3229–3241 (2008). 163 Pitera DJ, Paddon CJ, Newman JD,

Keasling JD. Balancing a heterologous mevalonate pathway for improved isoprenoid production in Escherichia coli. Metabol. Eng. 9(2), 193–207 (2007).

145 Lee do Y, Fiehn O. High quality metabolomic

data for Chlamydomonas reinhardtii. Plant Methods 4, 7 (2008).

144

Biofuels (2011) 2(2)

future science group