Key Proteomics Tools for Fundamental and Applied Microalgal Research

Microscopic, photosynthetic prokaryotes and eukaryotes, collectively referred to as microalgae, are widely studied to improve our understanding of key metabolic pathways (e.g., photosynthesis) and for the development of biotechnological applications. Omics technologies, which are now common tools in biological research, have been shown to be critical in microalgal research. In the past decade, significant technological advancements have allowed omics technologies to become more affordable and efficient, with huge datasets being generated. In particular, where studies focused on a single or few proteins decades ago, it is now possible to study the whole proteome of a microalgae. The development of mass spectrometry-based methods has provided this leap forward with the high-throughput identification and quantification of proteins. This review specifically provides an overview of the use of proteomics in fundamental (e.g., photosynthesis) and applied (e.g., lipid production for biofuel) microalgal research, and presents future research directions in this field.

Effects of Trophic Acclimation on Growth and Expression Profiles of Genes Encoding Enzymes of Primary Metabolism and Plastid Transporters of Chlamydomonas reinhardtii

In this paper, the effect of prolonged trophic acclimation on the subsequent growth of Chlamydomonas reinhardtii batch cultures was studied. The mixotrophic (light + acetate) acclimation stimulated subsequent growth at both mixotrophy and autotrophy conditions and altered the expression profile of genes encoding enzymes of primary metabolism and plastid transporters. Besides the trophic effect, the influence of Chlamydomonas culture growth stage on gene expression was determined. Under mixotrophic conditions, this effect was most pronounced in the first half of the exponential growth with partial retention of the previous acclimation period traits. The autotrophy acclimation effect was more complex and its significance was enhanced at the end of the growth and in the stationary phase.

Carbon flux through photosynthesis and central carbon metabolism show distinct patterns between algae, C3 and C4 plants

Photosynthesis-related pathways are regarded as a promising avenue for crop improvement. Whilst empirical studies have shown that photosynthetic efficiency is higher in microalgae than in C3 or C4 crops, the underlying reasons remain unclear. Using a tailor-made microfluidics labelling system to supply 13CO2 at steady state, we investigated in vivo labelling kinetics in intermediates of the Calvin Benson cycle and sugar, starch, organic acid and amino acid synthesis pathways, and in protein and lipids, in Chlamydomonas reinhardtii, Chlorella sorokiniana and Chlorella ohadii, which is the fastest growing green alga on record. We estimated flux patterns in these algae and compared them with published and new data from C3 and C4 plants. Our analyses identify distinct flux patterns supporting faster growth in photosynthetic cells, with some of the algae exhibiting faster ribulose 1,5-bisphosphate regeneration and increased fluxes through the lower glycolysis and anaplerotic pathways towards the tricarboxylic acid cycle, amino acid synthesis and lipid synthesis than in higher plants.

PANOMICS meets germplasm

Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.

Overexpression of Sedoheptulose-1,7-Bisphosphatase Enhances Photosynthesis in Chlamydomonas reinhardtii and Has No Effect on the Abundance of Other Calvin-Benson Cycle Enzymes

The productivity of plants and microalgae needs to be increased to feed the growing world population and to promote the development of a low-carbon economy. This goal can be achieved by improving photosynthesis via genetic engineering. In this study, we have employed the Modular Cloning strategy to overexpress the Calvin-Benson cycle (CBC) enzyme sedoheptulose-1,7-bisphosphatase (SBP1) up to threefold in the unicellular green alga Chlamydomonas reinhardtii. The protein derived from the nuclear transgene represented ∼0.3% of total cell protein. Photosynthetic rate and growth were significantly increased in SBP1-overexpressing lines under high-light and elevated CO2 conditions. Absolute quantification of the abundance of all other CBC enzymes by the QconCAT approach revealed no consistent differences between SBP1-overexpressing lines and the recipient strain. This analysis also revealed that the 11 CBC enzymes represent 11.9% of total cell protein in Chlamydomonas. Here, the range of concentrations of CBC enzymes turned out to be much larger than estimated earlier, with a 128-fold difference between the most abundant CBC protein (rbcL) and the least abundant (triose phosphate isomerase). Accordingly, the concentrations of the CBC intermediates are often but not always higher than the binding site concentrations of the enzymes for which they act as substrates. The enzymes with highest substrate to binding site ratios might represent good candidates for overexpression in subsequent engineering steps.

Improved Algal Toxicity Test System for Robust Omics-Driven Mode-of-Action Discovery in Chlamydomonas reinhardtii

Algae are key components of aquatic food chains. Consequently, they are internationally recognised test species for the environmental safety assessment of chemicals. However, existing algal toxicity test guidelines are not yet optimized to discover molecular modes of action, which require highly-replicated and carefully controlled experiments. Here, we set out to develop a robust, miniaturised and scalable Chlamydomonas reinhardtii toxicity testing approach tailored to meet these demands. We primarily investigated the benefits of synchronised cultures for molecular studies, and of exposure designs that restrict chemical volatilisation yet yield sufficient algal biomass for omics analyses. Flow cytometry and direct-infusion mass spectrometry metabolomics revealed significant and time-resolved changes in sample composition of synchronised cultures. Synchronised cultures in sealed glass vials achieved adequate growth rates at previously unachievably-high inoculation cell densities, with minimal pH drift and negligible chemical loss over 24-h exposures. Algal exposures to a volatile test compound (chlorobenzene) yielded relatively high reproducibility of metabolic phenotypes over experimental repeats. This experimental test system extends existing toxicity testing formats to allow highly-replicated, omics-driven, mode-of-action discovery.

Delineating the molecular responses of a halotolerant microalga using integrated omics approach to identify genetic engineering targets for enhanced TAG production

BACKGROUND

Harnessing the halotolerant characteristics of microalgae provides a viable alternative for sustainable biomass and triacylglyceride (TAG) production. Scenedesmus sp. IITRIND2 is a fast growing fresh water microalga that has the capability to thrive in high saline environments. To understand the microalga's adaptability, we studied its physiological and metabolic flexibility by studying differential protein, metabolite and lipid expression profiles using metabolomics, proteomics, real-time polymerase chain reaction, and lipidomics under high salinity conditions.

RESULTS

On exposure to salinity, the microalga rewired its cellular reserves and ultrastructure, restricted the ions channels, and modulated its surface potential along with secretion of extrapolysaccharide to maintain homeostasis and resolve the cellular damage. The algal-omics studies suggested a well-organized salinity-driven metabolic adjustment by the microalga starting from increasing the negatively charged lipids, up regulation of proline and sugars accumulation, followed by direction of carbon and energy flux towards TAG synthesis. Furthermore, the omics studies indicated both de-novo and lipid cycling pathways at work for increasing the overall TAG accumulation inside the microalgal cells.

CONCLUSION

The salt response observed here is unique and is different from the well-known halotolerant microalga; Dunaliella salina, implying diversity in algal response with species. Based on the integrated algal-omics studies, four potential genetic targets belonging to two different metabolic pathways (salt tolerance and lipid production) were identified, which can be further tested in non-halotolerant algal strains.

Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases

In the past decade, advances in liquid chromatography-mass spectrometry (LC-MS) have revolutionized untargeted metabolomics analyses. By mining metabolomes more deeply, researchers are now primed to uncover key metabolites and their associations with diseases. The employment of untargeted metabolomics has led to new biomarker discoveries and a better mechanistic understanding of diseases with applications in precision medicine. However, many major pertinent challenges remain. First, compound identification has been poor, and left an overwhelming number of unidentified peaks. Second, partial, incomplete metabolomes persist due to factors such as limitations in mass spectrometry data acquisition speeds, wide-range of metabolites concentrations, and cellular/tissue/temporal-specific expression changes that confound our understanding of metabolite perturbations. Third, to contextualize metabolites in pathways and biology is difficult because many metabolites partake in multiple pathways, have yet to be described species specificity, or possess unannotated or more-complex functions that are not easily characterized through metabolomics analyses. From a translational perspective, information related to novel metabolite biomarkers, metabolic pathways, and drug targets might be sparser than they should be. Thankfully, significant progress has been made and novel solutions are emerging, achieved through sustained academic and industrial community efforts in terms of hardware, computational, and experimental approaches. Given the rapidly growing utility of metabolomics, this review will offer new perspectives, increase awareness of the major challenges in LC-MS metabolomics that will significantly benefit the metabolomics community and also the broader the biomedical community metabolomics aspire to serve.

Analysis of bZIP Transcription Factor Family and Their Expressions under Salt Stress in Chlamydomonas reinhardtii

The basic leucine-region zipper (bZIP) transcription factors (TFs) act as crucial regulators in various biological processes and stress responses in plants. Currently, bZIP family members and their functions remain elusive in the green unicellular algae Chlamydomonas reinhardtii, an important model organism for molecular investigation with genetic engineering aimed at increasing lipid yields for better biodiesel production. In this study, a total of 17 C. reinhardtii bZIP (CrebZIP) TFs containing typical bZIP structure were identified by a genome-wide analysis. Analysis of the CrebZIP protein physicochemical properties, phylogenetic tree, conserved domain, and secondary structure were conducted. CrebZIP gene structures and their chromosomal assignment were also analyzed. Physiological and photosynthetic characteristics of C. reinhardtii under salt stress were exhibited as lower cell growth and weaker photosynthesis, but increased lipid accumulation. Meanwhile, the expression profiles of six CrebZIP genes were induced to change significantly during salt stress, indicating that certain CrebZIPs may play important roles in mediating photosynthesis and lipid accumulation of microalgae in response to stresses. The present work provided a valuable foundation for functional dissection of CrebZIPs, benefiting the development of better strategies to engineer the regulatory network in microalgae for enhancing biofuel and biomass production.

Dynamic Modeling of Indole Glucosinolate Hydrolysis and Its Impact on Auxin Signaling

Plants release chemicals to deter attackers. Arabidopsis thaliana relies on multiple defense compounds, including indol-3-ylmethyl glucosinolate (I3G), which upon hydrolysis initiated by myrosinase enzymes releases a multitude of bioactive compounds, among others, indole-3-acetonitrile and indole-3-acetoisothiocyanate. The highly unstable isothiocyanate rapidly reacts with other molecules. One of the products, indole-3-carbinol, was reported to inhibit auxin signaling through binding to the TIR1 auxin receptor. On the contrary, the nitrile product of I3G hydrolysis can be converted by nitrilase enzymes to form the primary auxin molecule, indole-3-acetic acid, which activates TIR1. This suggests that auxin signaling is subject to both antagonistic and protagonistic effects of I3G hydrolysis upon attack. We hypothesize that I3G hydrolysis and auxin signaling form an incoherent feedforward loop and we build a mathematical model to examine the regulatory network dynamics. We use molecular docking to investigate the possible antagonistic properties of different I3G hydrolysis products by competitive binding to the TIR1 receptor. Our simulations reveal an uncoupling of auxin concentration and signaling, and we determine that enzyme activity and antagonist binding affinity are key parameters for this uncoupling. The molecular docking predicts that several I3G hydrolysis products strongly antagonize auxin signaling. By comparing a tissue disrupting attack - e.g., by chewing insects or necrotrophic pathogens that causes rapid release of I3G hydrolysis products - to sustained cell-autonomous I3G hydrolysis, e.g., upon infection by biotrophic pathogens, we find that each scenario gives rise to distinct auxin signaling dynamics. This suggests that plants have different defense versus growth strategies depending on the nature of the attack.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

Absolute Quantification of Major Photosynthetic Protein Complexes in Chlamydomonas reinhardtii Using Quantification Concatamers (QconCATs)

For modeling approaches in systems biology, knowledge of the absolute abundances of cellular proteins is essential. One way to gain this knowledge is the use of quantification concatamers (QconCATs), which are synthetic proteins consisting of proteotypic peptides derived from the target proteins to be quantified. The QconCAT protein is labeled with a heavy isotope upon expression in E. coli and known amounts of the purified protein are spiked into a whole cell protein extract. Upon tryptic digestion, labeled and unlabeled peptides are released from the QconCAT protein and the native proteins, respectively, and both are quantified by LC-MS/MS. The labeled Q-peptides then serve as standards for determining the absolute quantity of the native peptides/proteins. Here, we have applied the QconCAT approach to Chlamydomonas reinhardtii for the absolute quantification of the major proteins and protein complexes driving photosynthetic light reactions in the thylakoid membranes and carbon fixation in the pyrenoid. We found that with 25.2 attomol/cell the Rubisco large subunit makes up 6.6% of all proteins in a Chlamydomonas cell and with this exceeds the amount of the small subunit by a factor of 1.56. EPYC1, which links Rubisco to form the pyrenoid, is eight times less abundant than RBCS, and Rubisco activase is 32-times less abundant than RBCS. With 5.2 attomol/cell, photosystem II is the most abundant complex involved in the photosynthetic light reactions, followed by plastocyanin, photosystem I and the cytochrome b₆/f complex, which range between 2.9 and 3.5 attomol/cell. The least abundant complex is the ATP synthase with 2 attomol/cell. While applying the QconCAT approach, we have been able to identify many potential pitfalls associated with this technique. We analyze and discuss these pitfalls in detail and provide an optimized workflow for future applications of this technique.

Quantitative Phosphoproteomic and System-Level Analysis of TOR Inhibition Unravel Distinct Organellar Acclimation in Chlamydomonas reinhardtii

Rapamycin is an inhibitor of the evolutionary conserved Target of Rapamycin (TOR) kinase which promotes and coordinates translation with cell growth and division. In heterotrophic organisms, TOR regulation is based on intra- and extracellular stimuli such as amino acids level and insulin perception. However, how plant TOR pathways have evolved to integrate plastid endosymbiosis is a remaining question. Despite the close association of the TOR signaling with the coordination between protein turn-over and growth, proteome and phosphoproteome acclimation to a rapamycin treatment have not yet been thoroughly investigated in Chlamydomonas reinhardtii. In this study, we have used in vivo label-free phospho-proteomic analysis to profile both protein and phosphorylation changes at 0, 24, and 48 h in Chlamydomonas cells treated with rapamycin. Using multivariate statistics we highlight the impact of TOR inhibition on both the proteome and the phosphoproteome. Two-way ANOVA distinguished differential levels of proteins and phosphoproteins in response either to culture duration and rapamycin treatment or combined effects. Finally, protein-protein interaction networks and functional enrichment analysis underlined the relation between plastid and mitochondrial metabolism. Prominent changes of proteins involved in sulfur, cysteine, and methionine as well as nucleotide metabolism on the one hand, and changes in the TCA cycle on the other highlight the interplay of chloroplast and mitochondria metabolism. Furthermore, TOR inhibition revealed changes in the endomembrane trafficking system. Phosphoproteomics data, on the other hand, highlighted specific differentially regulated phosphorylation sites for calcium-regulated protein kinases as well as ATG7, S6K, and PP2C. To conclude we provide a first combined Chlamydomonas proteomics and phosphoproteomics dataset in response to TOR inhibition, which will support further investigations.

Metabolite profiling at the cellular and subcellular level reveals metabolites associated with salinity tolerance in sugar beet

Sugar beet is among the most salt-tolerant crops. This study aimed to investigate the metabolic adaptation of sugar beet to salt stress at the cellular and subcellular levels. Seedlings were grown hydroponically and subjected to stepwise increases in salt stress up to 300 mM NaCl. Highly enriched fractions of chloroplasts were obtained by non-aqueous fractionation using organic solvents. Total leaf metabolites and metabolites in chloroplasts were profiled at 3 h and 14 d after reaching the maximum salinity stress of 300 mM NaCl. Metabolite profiling by gas chromatography-mass spectrometry (GC-MS) resulted in the identification of a total of 83 metabolites in leaves and chloroplasts under control and stress conditions. There was a lower abundance of Calvin cycle metabolites under salinity whereas there was a higher abundance of oxidative pentose phosphate cycle metabolites such as 6-phosphogluconate. Accumulation of ribose-5-phosphate and ribulose-5-phosphate coincided with limitation of carbon fixation by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco). Increases in glycolate and serine levels indicated that photorespiratory metabolism was stimulated in salt-stressed sugar beet. Compatible solutes such as proline, mannitol, and putrescine accumulated mostly outside the chloroplasts. Within the chloroplast, putrescine had the highest relative level and probably assisted in the acclimation of sugar beet to high salinity stress. The results provide new information on the contribution of chloroplasts and the extra-chloroplast space to salinity tolerance via metabolic adjustment in sugar beet.

Metabolic responses to ethanol and butanol in Chlamydomonas reinhardtii

BACKGROUND

Microalgae have been demonstrated to be among the most promising phototrophic species for producing renewable biofuels and chemicals. Ethanol and butanol are clean energy sources with good chemical and physical properties as alternatives to gasoline. However, biosynthesis of these two biofuels has not been achieved due to low tolerance of algal cells to ethanol or butanol.

RESULTS

With an eye to circumventing these problems in the future and engineering the robust alcohol-producing microalgal hosts, we investigated the metabolic responses of the model green alga Chlamydomonas reinhardtii to ethanol and butanol. Using a quantitative proteomics approach with iTRAQ-LC-MS/MS technologies, we detected the levels of 3077 proteins; 827 and 730 of which were differentially regulated by ethanol and butanol, respectively, at three time points. In particular, 41 and 59 proteins were consistently regulated during at least two sampling times. Multiple metabolic processes were affected by ethanol or butanol, and various stress-related proteins, transporters, cytoskeletal proteins, and regulators were induced as the major protection mechanisms against toxicity of the organic solvents. The most highly upregulated butanol response protein was Cre.770 peroxidase.

CONCLUSIONS

The study is the first comprehensive view of the metabolic mechanisms employed by C. reinhardtii to defend against ethanol or butanol toxicity. Moreover, the proteomic analysis provides a resource for investigating potential gene targets for engineering microalgae to achieve efficient biofuel production.

The Eukaryotic CO2-Concentrating Organelle Is Liquid-like and Exhibits Dynamic Reorganization

Approximately 30%-40% of global CO2 fixation occurs inside a non-membrane-bound organelle called the pyrenoid, which is found within the chloroplasts of most eukaryotic algae. The pyrenoid matrix is densely packed with the CO2-fixing enzyme Rubisco and is thought to be a crystalline or amorphous solid. Here, we show that the pyrenoid matrix of the unicellular alga Chlamydomonas reinhardtii is not crystalline but behaves as a liquid that dissolves and condenses during cell division. Furthermore, we show that new pyrenoids are formed both by fission and de novo assembly. Our modeling predicts the existence of a "magic number" effect associated with special, highly stable heterocomplexes that influences phase separation in liquid-like organelles. This view of the pyrenoid matrix as a phase-separated compartment provides a paradigm for understanding its structure, biogenesis, and regulation. More broadly, our findings expand our understanding of the principles that govern the architecture and inheritance of liquid-like organelles.

Comparative metabolic profiling of the lipid-producing green microalga Chlorella reveals that nitrogen and carbon metabolic pathways contribute to lipid metabolism

Microalgae are a promising feedstock for biofuel production. Microalgal metabolic pathways are heavily influenced by environmental factors. For instance, lipid metabolism can be induced by nitrogen-limiting conditions. However, the underlying mechanisms of lipid biosynthesis are unclear. In this study, we analyzed the global metabolic profiles of three genetically closely related Chlorella strains (C1, C2, and C3) with significant differences in lipid productivity to identify the contributions of key metabolic pathways to lipid metabolism. We found that nitrogen obtained from amino acid catabolism was assimilated via the glutamate-glutamine pathway and then stored as amino acids and intermediate molecules (particularly proline, alanine, arginine, succinate, and gamma-aminobutyrate) via the corresponding metabolic pathways, which led to carbon-nitrogen disequilibrium. Excess carbon obtained from photosynthesis or glycolysis was re-distributed into carbon-containing compounds, such as glucose-6-phosphate, fructose-6-phosphate, phosphoenolpyruvate, lactate, citrate, 3-hydroxybutyrate, and leucine, and then diverted into lipid metabolism for the production of storage lipids via the gamma-aminobutyrate pathway, glycolysis, and the tricarboxylic acid cycle. These results were substantiated in the model green alga Chlamydomonas reinhardtii by analyzing various mutants deficient in glutamate synthase/NADH-dependent, glutamate synthase/Fd-dependent, glutamine synthetase, aspartate aminotransferase, alanine aminotransferase, pyruvate kinase, and citrate synthase. Our study suggests that not only carbon but also nitrogen assimilation and distribution pathways contribute to lipid biosynthesis. Furthermore, these findings may facilitate genetic engineering efforts to enhance microalgal biofuel production.

Quantitative in vivo phosphoproteomics reveals reversible signaling processes during nitrogen starvation and recovery in the biofuel model organism Chlamydomonas reinhardtii

BACKGROUND

Nitrogen deprivation and replenishment induces massive changes at the physiological and molecular level in the green alga Chlamydomonas reinhardtii, including reversible starch and lipid accumulation. Stress signal perception and acclimation involves transient protein phosphorylation. This study aims to provide the first experimental phosphoprotein dataset for the adaptation of C. reinhardtii during nitrogen depletion and recovery growth phases and its impact on lipid accumulation.

RESULTS

To decipher the signaling pathways involved in this dynamic process, we applied a label-free in vivo shotgun phosphoproteomics analysis on nitrogen-depleted and recovered samples. 1227 phosphopeptides belonging to 732 phosphoproteins were identified and quantified. 470 phosphopeptides showed a significant change across the experimental set-up. Multivariate statistics revealed the reversible phosphorylation process and the time/condition-dependent dynamic rearrangement of the phosphoproteome. Protein-protein interaction analysis of differentially regulated phosphoproteins identified protein kinases and phosphatases, such as DYRKP and an AtGRIK1 orthologue, called CDPKK2, as central players in the coordination of translational, photosynthetic, proteomic and metabolomic activity. Phosphorylation of RPS6, ATG13, and NNK1 proteins points toward a specific regulation of the TOR pathway under nitrogen deprivation. Differential phosphorylation pattern of several eukaryotic initiation factor proteins (EIF) suggests a major control on protein translation and turnover.

CONCLUSION

This work provides the first phosphoproteomics dataset obtained for Chlamydomonas responses to nitrogen availability, revealing multifactorial signaling pathways and their regulatory function for biofuel production. The reproducibility of the experimental set-up allows direct comparison with proteomics and metabolomics datasets and refines therefore the current model of Chlamydomonas acclimation to various nitrogen levels. Integration of physiological, proteomics, metabolomics, and phosphoproteomics data reveals three phases of acclimation to N availability: (i) a rapid response triggering starch accumulation as well as energy metabolism while chloroplast structure is conserved followed by (ii) chloroplast degradation combined with cell autophagy and lipid accumulation and finally (iii) chloroplast regeneration and cell growth activation after nitrogen replenishment. Plastid development seems to be further interconnected with primary metabolism and energy stress signaling in order to coordinate cellular mechanism to nitrogen availability stress.

Global Metabolic Regulation of the Snow Alga Chlamydomonas nivalis in Response to Nitrate or Phosphate Deprivation by a Metabolome Profile Analysis

In the present work, Chlamydomonas nivalis, a model species of snow algae, was used to illustrate the metabolic regulation mechanism of microalgae under nutrient deprivation stress. The seed culture was inoculated into the medium without nitrate or phosphate to reveal the cell responses by a metabolome profile analysis using gas chromatography time-of-flight mass spectrometry (GC/TOF-MS). One hundred and seventy-one of the identified metabolites clustered into five groups by the orthogonal partial least squares discriminant analysis (OPLS-DA) model. Among them, thirty of the metabolites in the nitrate-deprived group and thirty-nine of the metabolites in the phosphate-deprived group were selected and identified as “responding biomarkers” by this metabolomic approach. A significant change in the abundance of biomarkers indicated that the enhanced biosynthesis of carbohydrates and fatty acids coupled with the decreased biosynthesis of amino acids, N-compounds and organic acids in all the stress groups. The up- or down-regulation of these biomarkers in the metabolic network provides new insights into the global metabolic regulation and internal relationships within amino acid and fatty acid synthesis, glycolysis, the tricarboxylic acid cycle (TCA) and the Calvin cycle in the snow alga under nitrate or phosphate deprivation stress.

Quantitation of Vacuolar Sugar Transporter Abundance Changes Using QconCAT Synthtetic Peptides

Measurements of protein abundance changes are important for biological conclusions on protein-related processes such as activity or complex formation. Proteomic analyses in general are almost routine tasks in many laboratories, but a precise and quantitative description of (absolute) protein abundance changes require careful experimental design and precise data quality. Today, a vast choice of metabolic labeling and label-free quantitation protocols are available, but the trade-off between quantitative precision and proteome coverage of quantified proteins including missing value problems remain. Here, we provide an example of a targeted proteomic approach using artificial standard proteins consisting of concatenated peptides of interest (QconCAT) to specifically quantify abiotic stress-induced abundance changes in low abundant vacuolar transporters. An advantage of this approach is the reliable quantitation of alimited set of low-abundant target proteins throughout different conditions. We show that vacuolar ATPase AVP1 and sugar transporters of the ERDL (early responsive to dehydration-like) family and TMT2 (tonoplast monosaccharide transporter 2) showed increased abundance upon salt stress.

Proteomics and Metabolomics: Two Emerging Areas for Legume Improvement

The crop legumes such as chickpea, common bean, cowpea, peanut, pigeonpea, soybean, etc. are important sources of nutrition and contribute to a significant amount of biological nitrogen fixation (>20 million tons of fixed nitrogen) in agriculture. However, the production of legumes is constrained due to abiotic and biotic stresses. It is therefore imperative to understand the molecular mechanisms of plant response to different stresses and identify key candidate genes regulating tolerance which can be deployed in breeding programs. The information obtained from transcriptomics has facilitated the identification of candidate genes for the given trait of interest and utilizing them in crop breeding programs to improve stress tolerance. However, the mechanisms of stress tolerance are complex due to the influence of multi-genes and post-transcriptional regulations. Furthermore, stress conditions greatly affect gene expression which in turn causes modifications in the composition of plant proteomes and metabolomes. Therefore, functional genomics involving various proteomics and metabolomics approaches have been obligatory for understanding plant stress tolerance. These approaches have also been found useful to unravel different pathways related to plant and seed development as well as symbiosis. Proteome and metabolome profiling using high-throughput based systems have been extensively applied in the model legume species, Medicago truncatula and Lotus japonicus, as well as in the model crop legume, soybean, to examine stress signaling pathways, cellular and developmental processes and nodule symbiosis. Moreover, the availability of protein reference maps as well as proteomics and metabolomics databases greatly support research and understanding of various biological processes in legumes. Protein-protein interaction techniques, particularly the yeast two-hybrid system have been advantageous for studying symbiosis and stress signaling in legumes. In this review, several studies on proteomics and metabolomics in model and crop legumes have been discussed. Additionally, applications of advanced proteomics and metabolomics approaches have also been included in this review for future applications in legume research. The integration of these "omics" approaches will greatly support the identification of accurate biomarkers in legume smart breeding programs.

The quest for tolerant varieties: the importance of integrating "omics" techniques to phenotyping

The primary objective of crop breeding is to improve yield and/or harvest quality while minimizing inputs. Global climate change and the increase in world population are significant challenges for agriculture and call for further improvements to crops and the development of new tools for research. Significant progress has been made in the molecular and genetic analysis of model plants. However, is science generating false expectations? Are 'omic techniques generating valuable information that can be translated into the field? The exploration of crop biodiversity and the correlation of cellular responses to stress tolerance at the plant level is currently a challenge. This viewpoint reviews concisely the problems one encounters when working on a crop and provides an outline of possible workflows when initiating cellular phenotyping via "-omic" techniques (transcriptomics, proteomics, metabolomics).

The response of Chlamydomonas reinhardtii to nitrogen deprivation: a systems biology analysis

Drastic alteration in macronutrients causes large changes in gene expression in the photosynthetic unicellular alga Chlamydomonas reinhardtii. Preliminary data suggested that cells follow a biphasic response to this change hinging on the initiation of lipid accumulation, and we hypothesized that drastic repatterning of metabolism also followed this biphasic modality. To test this hypothesis, transcriptomic, proteomic, and metabolite changes that occur under nitrogen (N) deprivation were analyzed. Eight sampling times were selected covering the progressive slowing of growth and induction of oil synthesis between 4 and 6 h after N deprivation. Results of the combined, systems-level investigation indicated that C. reinhardtii cells sense and respond on a large scale within 30 min to a switch to N-deprived conditions turning on a largely gluconeogenic metabolic state, which then transitions to a glycolytic stage between 4 and 6 h after N depletion. This nitrogen-sensing system is transduced to carbon- and nitrogen-responsive pathways, leading to down-regulation of carbon assimilation and chlorophyll biosynthesis, and an increase in nitrogen metabolism and lipid biosynthesis. For example, the expression of nearly all the enzymes for assimilating nitrogen from ammonium, nitrate, nitrite, urea, formamide/acetamide, purines, pyrimidines, polyamines, amino acids and proteins increased significantly. Although arginine biosynthesis enzymes were also rapidly up-regulated, arginine pool size changes and isotopic labeling results indicated no increased flux through this pathway.

Targeted quantitative analysis of a diurnal RuBisCO subunit expression and translation profile in Chlamydomonas reinhardtii introducing a novel Mass Western approach

RuBisCO catalyzes the rate-limiting step of CO2 fixation in photosynthesis. Hypothetical mechanisms for the regulation of rbcL and rbcS gene expression assume that both large (LSU) and small (SSU) RuBisCO subunit proteins (RSUs) are present in equimolar amounts to fit the 1:1 subunit stoichiometry of the holoenzyme. However, the actual quantities of the RSUs have never been determined in any photosynthetic organism. In this study the absolute amount of rbc transcripts and RSUs was quantified in Chlamydomonas reinhardtii grown during a diurnal light/dark cycle. A novel approach utilizing more reliable protein stoichiometry quantification is introduced. The rbcL:rbcS transcript and protein ratios were both 5:1 on average during the diurnal time course, indicating that SSU is the limiting factor for the assembly of the holoenzyme. The oscillation of the RSUs was 9h out of phase relative to the transcripts. The amount of rbc transcripts was at its maximum in the dark while that of RSUs was at its maximum in the light phase suggesting that translation of the rbc transcripts is activated by light as previously hypothesized. A possible post-translational regulation that might be involved in the accumulation of a 37-kDa N-terminal LSU fragment during the light phase is discussed.

BIOLOGICAL SIGNIFICANCE

A novel MS based approach enabling the exact stoichiometric analysis and absolute quantification of protein complexes is presented in this article. The application of this method revealed new insights in RuBisCO subunit dynamics.

Metabolite profiling and integrative modeling reveal metabolic constraints for carbon partitioning under nitrogen starvation in the green algae Haematococcus pluvialis

The green alga Hematococcus pluvialis accumulates large amounts of the antioxidant astaxanthin under inductive stress conditions, such as nitrogen starvation. The response to nitrogen starvation and high light leads to the accumulation of carbohydrates and fatty acids as well as increased activity of the tricarboxylic acid cycle. Although the behavior of individual pathways has been well investigated, little is known about the systemic effects of the stress response mechanism. Here we present time-resolved metabolite, enzyme activity, and physiological data that capture the metabolic response of H. pluvialis under nitrogen starvation and high light. The data were integrated into a putative genome-scale model of the green alga to in silico test hypotheses of underlying carbon partitioning. The model-based hypothesis testing reinforces the involvement of starch degradation to support fatty acid synthesis in the later stages of the stress response. In addition, our findings support a possible mechanism for the involvement of the increased activity of the tricarboxylic acid cycle in carbon repartitioning. Finally, the in vitro experiments and the in silico modeling presented here emphasize the predictive power of large scale integrative approaches to pinpoint metabolic adjustment to changing environments.

Carbon partitioning in green algae (chlorophyta) and the enolase enzyme

The exact mechanisms underlying the distribution of fixed carbon within photoautotrophic cells, also referred to as carbon partitioning, and the subcellular localization of many enzymes involved in carbon metabolism are still unknown. In contrast to the majority of investigated green algae, higher plants have multiple isoforms of the glycolytic enolase enzyme, which are differentially regulated in higher plants. Here we report on the number of gene copies coding for the enolase in several genomes of species spanning the major classes of green algae. Our genomic analysis of several green algae revealed the presence of only one gene coding for a glycolytic enolase [EC 4.2.1.11]. Our predicted cytosolic localization would require export of organic carbon from the plastid to provide substrate for the enolase and subsequent re-import of organic carbon back into the plastids. Further, our comparative sequence study of the enolase and its 3D-structure prediction may suggest that the N-terminal extension found in green algal enolases could be involved in regulation of the enolase activity. In summary, we propose that the enolase represents one of the crucial regulatory bottlenecks in carbon partitioning in green algae.

A universal protocol for the combined isolation of metabolites, DNA, long RNAs, small RNAs, and proteins from plants and microorganisms

Here, we describe a method for the combined metabolomic, proteomic, transcriptomic and genomic analysis from one single sample as a major step for multilevel data integration strategies in systems biology. While extracting proteins and DNA, this protocol also allows the separation of metabolites into polar and lipid fractions, as well as RNA fractionation into long and small RNAs, thus allowing a broad range of transcriptional studies. The isolated biomolecules are suitable for analysis with different methods that range from electrophoresis and blotting to state-of-the-art procedures based on mass spectrometry (accurate metabolite profiling, shot-gun proteomics) or massive sequencing technologies (transcript analysis). The low amount of starting tissue, its cost-efficiency compared with the utilization of commercial kits, and its performance over a wide range of plant, microbial, and algal species such as Chlamydomonas, Arabidopsis, Populus, or Pinus, makes this method a universal alternative for multiple molecular isolation from plant tissues.

Rationales and approaches for studying metabolism in eukaryotic microalgae

The generation of efficient production strains is essential for the use of eukaryotic microalgae for biofuel production. Systems biology approaches including metabolite profiling on promising microalgal strains, will provide a better understanding of their metabolic networks, which is crucial for metabolic engineering efforts. Chlamydomonas reinhardtii represents a suited model system for this purpose. We give an overview to genetically amenable microalgal strains with the potential for biofuel production and provide a critical review of currently used protocols for metabolite profiling on Chlamydomonas. We provide our own experimental data to underpin the validity of the conclusions drawn.

Linking toxicity and adaptive responses across the transcriptome, proteome, and phenotype of Chlamydomonas reinhardtii exposed to silver

Understanding mechanistic and cellular events underlying a toxicological outcome allows the prediction of impact of environmental stressors to organisms living in different habitats. A systems-based approach aids in characterizing molecular events, and thereby the cellular pathways that have been perturbed. However, mapping only adverse outcomes of a toxicant falls short of describing the stress or adaptive response that is mounted to maintain homeostasis on perturbations and may confer resistance to the toxic insult. Silver is a potential threat to aquatic organisms because of the increasing use of silver-based nanomaterials, which release free silver ions. The effects of silver were investigated at the transcriptome, proteome, and cellular levels of Chlamydomonas reinhardtii. The cells instigate a fast transcriptome and proteome response, including perturbations in copper transport system and detoxification mechanisms. Silver causes an initial toxic insult, which leads to a plummeting of ATP and photosynthesis and damage because of oxidative stress. In response, the cells mount a defense response to combat oxidative stress and to eliminate silver via efflux transporters. From the analysis of the perturbations of the cell's functions, we derived a detailed mechanistic understanding of temporal dynamics of toxicity and adaptive response pathways for C. reinhardtii exposed to silver.

System-level network analysis of nitrogen starvation and recovery in Chlamydomonas reinhardtii reveals potential new targets for increased lipid accumulation

BACKGROUND

Nitrogen starvation is known to cause drastic alterations in physiology and metabolism leading to the accumulation of lipid bodies in many microalgae, and it thus presents an important alternative for biofuel production. However, despite the importance of this process, the molecular mechanisms that mediate the metabolic remodeling induced by N starvation and especially by stress recovery are still poorly understood, and new candidates for bioengineering are needed to make this process useful for biofuel production.

RESULTS

We have studied the molecular changes involved in the adaptive mechanisms to N starvation and full recovery of the vegetative cells in the microalga Chlamydomonas reinhardtii during a four-day time course. High throughput mass spectrometry was employed to integrate the proteome and the metabolome with physiological changes. N starvation led to an accumulation of oil bodies and reduced Fv/Fm.. Distinct enzymes potentially participating in the carbon-concentrating mechanism (CAH7, CAH8, PEPC1) are strongly accumulated. The membrane composition is changed, as indicated by quantitative lipid profiles. A reprogramming of protein biosynthesis was observed by increased levels of cytosolic ribosomes, while chloroplastidic were dramatically reduced. Readdition of N led to, the identification of early responsive proteins mediating stress recovery, indicating their key role in regaining and sustaining normal vegetative growth. Analysis of the data with multivariate correlation analysis, Granger causality, and sparse partial least square (sPLS) provided a functional network perspective of the molecular processes. Cell growth and N metabolism were clearly linked by the branched chain amino acids, suggesting an important role in this stress. Lipid accumulation was also tightly correlated to the COP II protein, involved in vesicle and lysosome coating, and a major lipid droplet protein. This protein, together with other key proteins mediating signal transduction and adaption (BRI1, snRKs), constitute a series of new metabolic and regulatory targets.

CONCLUSIONS

This work not only provides new insights and corrects previous models by analyzing a complex dataset, but also increases our biochemical understanding of the adaptive mechanisms to N starvation in Chlamydomonas, pointing to new bioengineering targets for increased lipid accumulation, a key step for a sustainable and profitable microalgae-based biofuel production.

From proteomics to systems biology: MAPA, MASS WESTERN, PROMEX, and COVAIN as a user-oriented platform

Genome sequencing and systems biology are revolutionizing life sciences. Proteomics emerged as a fundamental technique of this novel research area as it is the basis for gene function analysis and modeling of dynamic protein networks. Here a complete proteomics platform suited for functional genomics and systems biology is presented. The strategy includes MAPA (mass accuracy precursor alignment; http://www.univie.ac.at/mosys/software.html ) as a rapid exploratory analysis step; MASS WESTERN for targeted proteomics; COVAIN ( http://www.univie.ac.at/mosys/software.html ) for multivariate statistical analysis, data integration, and data mining; and PROMEX ( http://www.univie.ac.at/mosys/databases.html ) as a database module for proteogenomics and proteotypic peptides for targeted analysis. Moreover, the presented platform can also be utilized to integrate metabolomics and transcriptomics data for the analysis of metabolite-protein-transcript correlations and time course analysis using COVAIN. Examples for the integration of MAPA and MASS WESTERN data, proteogenomic and metabolic modeling approaches for functional genomics, phosphoproteomics by integration of MOAC (metal-oxide affinity chromatography) with MAPA, and the integration of metabolomics, transcriptomics, proteomics, and physiological data using this platform are presented. All software and step-by-step tutorials for data processing and data mining can be downloaded from http://www.univie.ac.at/mosys/software.html.

A workflow for mathematical modeling of subcellular metabolic pathways in leaf metabolism of Arabidopsis thaliana

During the last decade genome sequencing has experienced a rapid technological development resulting in numerous sequencing projects and applications in life science. In plant molecular biology, the availability of sequence data on whole genomes has enabled the reconstruction of metabolic networks. Enzymatic reactions are predicted by the sequence information. Pathways arise due to the participation of chemical compounds as substrates and products in these reactions. Although several of these comprehensive networks have been reconstructed for the genetic model plant Arabidopsis thaliana, the integration of experimental data is still challenging. Particularly the analysis of subcellular organization of plant cells limits the understanding of regulatory instances in these metabolic networks in vivo. In this study, we develop an approach for the functional integration of experimental high-throughput data into such large-scale networks. We present a subcellular metabolic network model comprising 524 metabolic intermediates and 548 metabolic interactions derived from a total of 2769 reactions. We demonstrate how to link the metabolite covariance matrix of different Arabidopsis thaliana accessions with the subcellular metabolic network model for the inverse calculation of the biochemical Jacobian, finally resulting in the calculation of a matrix which satisfies a Lyaponov equation. In this way, different strategies of metabolite compartmentation and involved reactions were identified in the accessions when exposed to low temperature.

Carbon acquisition and accumulation in microalgae Chlamydomonas: Insights from "omics" approaches

Understanding the processes and mechanisms of carbon acquisition and accumulation in microalgae is fundamental to enhance the cellular capabilities aimed to environmental and industrial applications. The "omics" approaches have greatly contributed to expanding the knowledge on these carbon-related cellular responses, reporting large data sets on microalgae transcriptome, proteome and metabolome. This review emphasizes the advances made on Chlamydomonas exploration; however, some knowledge acquired from studying this model organism, may be extrapolated to close algae species. The large data sets available for this organism revealed the identity of a vast range of genes and proteins which are integrating carbon-related mechanisms. Nevertheless, these data sets have also highlighted the need for integrative analysis in order to fully explore the information enclosed. Here, some of the main results from "omics" approaches which may contribute to the understanding of carbon acquisition and accumulation in Chlamydomonas were reviewed and possible applications were discussed.

BIOLOGICAL SIGNIFICANCE

A number of important publications in the field of "omics" technologies have been published reporting studies of the model green microalga Chlamydomonas reinhardtii and related to microalgal biomass production. However, there are only few attempts to integrate these data. Publications showing the results from "omics" approaches, such as transcriptome, metabolome and proteome, focused in the study of mechanisms of carbon acquisition and accumulation in microalgae were reviewed. This review contributes to highlight the knowledge recently generated on such "omics" studies and it discusses how these results may be important for the advance of applied sciences, such as microalgae biotechnology.

Systemic cold stress adaptation of Chlamydomonas reinhardtii

Chlamydomonas reinhardtii is one of the most important model organisms nowadays phylogenetically situated between higher plants and animals (Merchant et al. 2007). Stress adaptation of this unicellular model algae is in the focus because of its relevance to biomass and biofuel production. Here, we have studied cold stress adaptation of C. reinhardtii hitherto not described for this algae whereas intensively studied in higher plants. Toward this goal, high throughput mass spectrometry was employed to integrate proteome, metabolome, physiological and cell-morphological changes during a time-course from 0 to 120 h. These data were complemented with RT-qPCR for target genes involved in central metabolism, signaling, and lipid biosynthesis. Using this approach dynamics in central metabolism were linked to cold-stress dependent sugar and autophagy pathways as well as novel genes in C. reinhardtii such as CKIN1, CKIN2 and a hitherto functionally not annotated protein named CKIN3. Cold stress affected extensively the physiology and the organization of the cell. Gluconeogenesis and starch biosynthesis pathways are activated leading to a pronounced starch and sugar accumulation. Quantitative lipid profiles indicate a sharp decrease in the lipophilic fraction and an increase in polyunsaturated fatty acids suggesting this as a mechanism of maintaining membrane fluidity. The proteome is completely remodeled during cold stress: specific candidates of the ribosome and the spliceosome indicate altered biosynthesis and degradation of proteins important for adaptation to low temperatures. Specific proteasome degradation may be mediated by the observed cold-specific changes in the ubiquitinylation system. Sparse partial least squares regression analysis was applied for protein correlation network analysis using proteins as predictors and Fv/Fm, FW, total lipids, and starch as responses. We applied also Granger causality analysis and revealed correlations between proteins and metabolites otherwise not detectable. Twenty percent of the proteins responsive to cold are uncharacterized proteins. This presents a considerable resource for new discoveries in cold stress biology in alga and plants.

Biofuels as a sustainable energy source: an update of the applications of proteomics in bioenergy crops and algae

Sustainable energy is the need of the 21st century, not because of the numerous environmental and political reasons but because it is necessary to human civilization's energy future. Sustainable energy is loosely grouped into renewable energy, energy conservation, and sustainable transport disciplines. In this review, we deal with the renewable energy aspect focusing on the biomass from bioenergy crops to microalgae to produce biofuels to the utilization of high-throughput omics technologies, in particular proteomics in advancing our understanding and increasing biofuel production. We look at biofuel production by plant- and algal-based sources, and the role proteomics has played therein. This article is part of a Special Issue entitled: Translational Plant Proteomics.

Metabolic and morphological changes of an oil accumulating trebouxiophycean alga in nitrogen-deficient conditions

Oil-rich algae have promising potential for a next-generation biofuel feedstock. Pseudochoricystis ellipsoidea MBIC 11204, a novel unicellular green algal strain, accumulates a large amount of oil (lipids) in nitrogen-deficient (-N) conditions. Although the oil bodies are easily visualized by lipophilic staining in the cells, little is known about how oil bodies are metabolically synthesized. Clarifying the metabolic profiles in -N conditions is important to understand the physiological mechanisms of lipid accumulations and will be useful to optimize culture conditions efficiently produce industrial oil. Metabolome and lipidome profiles were obtained, respectively, using capillary electrophoresis- and liquid chromatography-mass spectrometry from P. ellipsoidea in both nitrogen-rich (+N; rapid growth) and -N conditions. Relative quantities of more than 300 metabolites were systematically compared between these two conditions. Amino acids in nitrogen assimilation and N-transporting metabolisms were decreased to 1/20 the amount, or less, in -N conditions. In lipid metabolism, the quantities of neutral lipids increased greatly in -N conditions; however, quantities of nearly all the other lipids either decreased or only changed slightly. The morphological changes in +N and -N conditions were also provided by microscopy, and we discuss their relationship to the metabolic changes. This is the first approach to understand the novel algal strain's metabolism using a combination of wide-scale metabolome analysis and morphological analysis.

The proteomics of heavy metal hyperaccumulation by plants

Hyperaccumulators are distinguished from non-hyperaccumulators on the basis of their capacity to extract heavy metal ions from the soil, their more efficient root-to-shoot translocation of these ions and their greater ability to detoxify and sequester heavy metals in the shoot. The understanding of the mechanisms underlying metal ion accumulation has progressed beyond the relevant biochemistry and physiology to encompass the genetic and molecular regulatory systems which differentiate hyperaccumulators from non-hyperaccumulators. This paper reviews the literature surrounding the application of proteomics technology to plant metal hyperaccumulation, in particular involving the elements As, Cd, Cu, Ni, Pb and Zn. The hyperaccumulation process across a number of unrelated plant species appears to be associated with proteins involved in energy metabolism, the oxidative stress response and abiotic and biotic stress. The relevance of transducers of the metal stress response to the phenomenon of hyperaccumulation is summarized. Proteomic data complement the more voluminous genomic and transcriptomic data sets in providing a more nuanced picture of the process, and should therefore help in the identification of the major genetic determinants of the hyperaccumulation phenomenon.

Mathematical modeling of plant metabolism-from reconstruction to prediction

Due to their sessile lifestyle, plants are exposed to a large set of environmental cues. In order to cope with changes in environmental conditions a multitude of complex strategies to regulate metabolism has evolved. The complexity is mainly attributed to interlaced regulatory circuits between genes, proteins and metabolites and a high degree of cellular compartmentalization. The genetic model plant Arabidopsis thaliana was intensely studied to characterize adaptive traits to a changing environment. The availability of genetically distinct natural populations has made it an attractive system to study plant-environment interactions. The impact on metabolism caused by changing environmental conditions can be estimated by mathematical approaches and deepens the understanding of complex biological systems. In combination with experimental high-throughput technologies this provides a promising platform to develop in silico models which are not only able to reproduce but also to predict metabolic phenotypes and to allow for the interpretation of plant physiological mechanisms leading to successful adaptation to a changing environment. Here, we provide an overview of mathematical approaches to analyze plant metabolism, with experimental procedures being used to validate their output, and we discuss them in the context of establishing a comprehensive understanding of plant-environment interactions.

System response of metabolic networks in Chlamydomonas reinhardtii to total available ammonium

Drastic alterations in macronutrients are known to cause large changes in biochemistry and gene expression in the photosynthetic alga Chlamydomonas reinhardtii. However, metabolomic and proteomic responses to subtle reductions in macronutrients have not yet been studied. When ammonium levels were reduced by 25-100% compared with control cultures, ammonium uptake and growth rates were not affected at 25% or 50% nitrogen-reduction for 28 h. However, primary metabolism and enzyme expression showed remarkable changes at acute conditions (4 h and 10 h after ammonium reduction) compared with chronic conditions (18 h and 28 h time points). Responses of 145 identified metabolites were quantified using gas chromatography-time of flight mass spectrometry; 495 proteins (including 187 enzymes) were monitored using liquid chromatography-ion trap mass spectrometry with label-free spectral counting. Stress response and carbon assimilation processes (Calvin cycle, acetate uptake and chlorophyll biosynthesis) were altered first, in addition to increase in enzyme contents for lipid biosynthesis and accumulation of short chain free fatty acids. Nitrogen/carbon balance metabolism was found changed only under chronic conditions, for example in the citric acid cycle and amino acid metabolism. Metabolism in Chlamydomonas readily responds to total available media nitrogen with temporal increases in short-chain free fatty acids and turnover of internal proteins, long before nitrogen resources are depleted.