Linking proteome responses with physiological and biochemical effects in herbicide-exposed Chlamydomonas reinhardtii

Exposure to a toxicant causes proteome alterations in an organism. In ecotoxicology, analysis of these changes may allow linking them to physiological and biochemical endpoints, providing insights into subcellular exposure effects and responses and, ultimately mechanisms of action. Based on this, useful protein markers of exposure can be identified. We investigated the proteome changes induced by the herbicides paraquat, diuron, and norflurazon in the green alga Chlamydomonas reinhardtii. Shotgun proteome profiling and spectral counting quantification in combination with G-test statistics revealed significant changes in protein abundance. Functional enrichment analysis identified protein groups that responded to the exposures. Significant changes were observed for 149-254 proteins involved in a variety of metabolic pathways. While some proteins and functional protein groups responded to several tested exposure conditions, others were affected only in specific cases. Expected as well as novel candidate markers of herbicide exposure were identified, the latter including the photosystem II subunit PsbR or the VIPP1 protein. We demonstrate that the proteome response to toxicants is generally more sensitive than the physiological and biochemical endpoints, and that it can be linked to effects on these levels. Thus, proteome profiling may serve as a useful tool for ecotoxicological investigations in green algae.

The nuclear proteome of the green alga Chlamydomonas reinhardtii

Nuclear proteins play a central role in regulating gene expression. Their identification is important for understanding how the nuclear repertoire changes over time under different conditions. Nuclear proteins are often underrepresented in proteomic studies due to the frequently low abundance of proteins involved in regulatory processes. So far, only few studies describing the nuclear proteome of plant species have been published. Recently, the genome sequence of the unicellular green alga Chlamydomonas reinhardtii has been obtained and annotated, allowing the development of further detailed studies for this organism. However, a detailed description of its nuclear proteome has not been reported so far. Here, we present an analysis of the nuclear proteome of the sequenced Chlamydomonas strain cc503. Using LC-MS/MS, we identified 672 proteins from nuclei isolates with a maximum 1% peptide spectrum false discovery rate. Besides well-known proteins (e.g. histones), transcription factors and other transcriptional regulators (e.g. tubby and HMG) were identified. The presence of protein motifs in nuclear proteins was investigated by computational tools, and specific over-represented protein motifs were identified. This study provides new insights into the complexity of the nuclear environment and reveals novel putative protein targets for further studies of nuclear mechanisms.

Examination of triacylglycerol biosynthetic pathways via de novo transcriptomic and proteomic analyses in an unsequenced microalga

Biofuels derived from algal lipids represent an opportunity to dramatically impact the global energy demand for transportation fuels. Systems biology analyses of oleaginous algae could greatly accelerate the commercialization of algal-derived biofuels by elucidating the key components involved in lipid productivity and leading to the initiation of hypothesis-driven strain-improvement strategies. However, higher-level systems biology analyses, such as transcriptomics and proteomics, are highly dependent upon available genomic sequence data, and the lack of these data has hindered the pursuit of such analyses for many oleaginous microalgae. In order to examine the triacylglycerol biosynthetic pathway in the unsequenced oleaginous microalga, Chlorella vulgaris, we have established a strategy with which to bypass the necessity for genomic sequence information by using the transcriptome as a guide. Our results indicate an upregulation of both fatty acid and triacylglycerol biosynthetic machinery under oil-accumulating conditions, and demonstrate the utility of a de novo assembled transcriptome as a search model for proteomic analysis of an unsequenced microalga.

Metabolomics of a single vacuole reveals metabolic dynamism in an alga Chara australis

Metabolomics is the most reliable analytical method for understanding metabolic diversity in single organelles derived from single cells. Although metabolites such as phosphate compounds are believed to be localized in different organelles in a highly specific manner, the process of metabolite compartmentalization in the cell is not thoroughly understood. The analysis of metabolites in single organelles has consequently presented a significant challenge. In this study, we used a metabolomic method to elucidate the localization and dynamics of 125 known metabolites isolated from the vacuole and cytoplasm of a single cell of the alga Chara australis. The amount of metabolites in the vacuole and the cytoplasm fluctuated asynchronously under various stress conditions, suggesting that metabolites are spatially regulated within the cell. Metabolite transport across the vacuolar membrane can be directly detected using the microinjection technique, which may reveal a previously unknown function of the vacuole.

Algal and aquatic plant carbon concentrating mechanisms in relation to environmental change

Carbon dioxide concentrating mechanisms (also known as inorganic carbon concentrating mechanisms; both abbreviated as CCMs) presumably evolved under conditions of low CO(2) availability. However, the timing of their origin is unclear since there are no sound estimates from molecular clocks, and even if there were, there are no proxies for the functioning of CCMs. Accordingly, we cannot use previous episodes of high CO(2) (e.g. the Palaeocene-Eocene Thermal Maximum) to indicate how organisms with CCMs responded. Present and predicted environmental change in terms of increased CO(2) and temperature are leading to increased CO(2) and HCO(3)(-) and decreased CO(3)(2-) and pH in surface seawater, as well as decreasing the depth of the upper mixed layer and increasing the degree of isolation of this layer with respect to nutrient flux from deeper waters. The outcome of these forcing factors is to increase the availability of inorganic carbon, photosynthetic active radiation (PAR) and ultraviolet B radiation (UVB) to aquatic photolithotrophs and to decrease the supply of the nutrients (combined) nitrogen and phosphorus and of any non-aeolian iron. The influence of these variations on CCM expression has been examined to varying degrees as acclimation by extant organisms. Increased PAR increases CCM expression in terms of CO(2) affinity, whilst increased UVB has a range of effects in the organisms examined; little relevant information is available on increased temperature. Decreased combined nitrogen supply generally increases CO(2) affinity, decreased iron availability increases CO(2) affinity, and decreased phosphorus supply has varying effects on the organisms examined. There are few data sets showing interactions amongst the observed changes, and even less information on genetic (adaptation) changes in response to the forcing factors. In freshwaters, changes in phytoplankton species composition may alter with environmental change with consequences for frequency of species with or without CCMs. The information available permits less predictive power as to the effect of the forcing factors on CCM expression than for their overall effects on growth. CCMs are currently not part of models as to how global environmental change has altered, and is likely to further alter, algal and aquatic plant primary productivity.

Green systems biology - From single genomes, proteomes and metabolomes to ecosystems research and biotechnology

Plants have shaped our human life form from the outset. With the emerging recognition of world population feeding, global climate change and limited energy resources with fossil fuels, the relevance of plant biology and biotechnology is becoming dramatically important. One key issue is to improve plant productivity and abiotic/biotic stress resistance in agriculture due to restricted land area and increasing environmental pressures. Another aspect is the development of CO(2)-neutral plant resources for fiber/biomass and biofuels: a transition from first generation plants like sugar cane, maize and other important nutritional crops to second and third generation energy crops such as Miscanthus and trees for lignocellulose and algae for biomass and feed, hydrogen and lipid production. At the same time we have to conserve and protect natural diversity and species richness as a foundation of our life on earth. Here, biodiversity banks are discussed as a foundation of current and future plant breeding research. Consequently, it can be anticipated that plant biology and ecology will have more indispensable future roles in all socio-economic aspects of our life than ever before. We therefore need an in-depth understanding of the physiology of single plant species for practical applications as well as the translation of this knowledge into complex natural as well as anthropogenic ecosystems. Latest developments in biological and bioanalytical research will lead into a paradigm shift towards trying to understand organisms at a systems level and in their ecosystemic context: (i) shotgun and next-generation genome sequencing, gene reconstruction and annotation, (ii) genome-scale molecular analysis using OMICS technologies and (iii) computer-assisted analysis, modeling and interpretation of biological data. Systems biology combines these molecular data, genetic evolution, environmental cues and species interaction with the understanding, modeling and prediction of active biochemical networks up to whole species populations. This process relies on the development of new technologies for the analysis of molecular data, especially genomics, metabolomics and proteomics data. The ambitious aim of these non-targeted 'omic' technologies is to extend our understanding beyond the analysis of separated parts of the system, in contrast to traditional reductionistic hypothesis-driven approaches. The consequent integration of genotyping, pheno/morphotyping and the analysis of the molecular phenotype using metabolomics, proteomics and transcriptomics will reveal a novel understanding of plant metabolism and its interaction with the environment. The analysis of single model systems - plants, fungi, animals and bacteria - will finally emerge in the analysis of populations of plants and other organisms and their adaptation to the ecological niche. In parallel, this novel understanding of ecophysiology will translate into knowledge-based approaches in crop plant biotechnology and marker- or genome-assisted breeding approaches. In this review the foundations of green systems biology are described and applications in ecosystems research are presented. Knowledge exchange of ecosystems research and green biotechnology merging into green systems biology is anticipated based on the principles of natural variation, biodiversity and the genotype-phenotype environment relationship as the fundamental drivers of ecology and evolution.

Genome-wide functional annotation and structural verification of metabolic ORFeome of Chlamydomonas reinhardtii

Background

Recent advances in the field of metabolic engineering have been expedited by the availability of genome sequences and metabolic modelling approaches. The complete sequencing of the C. reinhardtii genome has made this unicellular alga a good candidate for metabolic engineering studies; however, the annotation of the relevant genes has not been validated and the much-needed metabolic ORFeome is currently unavailable. We describe our efforts on the functional annotation of the ORF models released by the Joint Genome Institute (JGI), prediction of their subcellular localizations, and experimental verification of their structural annotation at the genome scale.

Results

We assigned enzymatic functions to the translated JGI ORF models of C. reinhardtii by reciprocal BLAST searches of the putative proteome against the UniProt and AraCyc enzyme databases. The best match for each translated ORF was identified and the EC numbers were transferred onto the ORF models. Enzymatic functional assignment was extended to the paralogs of the ORFs by clustering ORFs using BLASTCLUST.

In total, we assigned 911 enzymatic functions, including 886 EC numbers, to 1,427 transcripts. We further annotated the enzymatic ORFs by prediction of their subcellular localization. The majority of the ORFs are predicted to be compartmentalized in the cytosol and chloroplast. We verified the structure of the metabolism-related ORF models by reverse transcription-PCR of the functionally annotated ORFs. Following amplification and cloning, we carried out 454FLX and Sanger sequencing of the ORFs. Based on alignment of the 454FLX reads to the ORF predicted sequences, we obtained more than 90% coverage for more than 80% of the ORFs. In total, 1,087 ORF models were verified by 454 and Sanger sequencing methods. We obtained expression evidence for 98% of the metabolic ORFs in the algal cells grown under constant light in the presence of acetate.

Conclusions

We functionally annotated approximately 1,400 JGI predicted metabolic ORFs that can facilitate the reconstruction and refinement of a genome-scale metabolic network. The unveiling of the metabolic potential of this organism, along with structural verification of the relevant ORFs, facilitates the selection of metabolic engineering targets with applications in bioenergy and biopharmaceuticals. The ORF clones are a resource for downstream studies.

Unpredictability of metabolism--the key role of metabolomics science in combination with next-generation genome sequencing

Next-generation sequencing provides technologies which sequence whole prokaryotic and eukaryotic genomes in days, perform genome-wide association studies, chromatin immunoprecipitation followed by sequencing and RNA sequencing for transcriptome studies. An exponentially growing volume of sequence data can be anticipated, yet functional interpretation does not keep pace with the amount of data produced. In principle, these data contain all the secrets of living systems, the genotype-phenotype relationship. Firstly, it is possible to derive the structure and connectivity of the metabolic network from the genotype of an organism in the form of the stoichiometric matrix N. This is, however, static information. Strategies for genome-scale measurement, modelling and predicting of dynamic metabolic networks need to be applied. Consequently, metabolomics science--the quantitative measurement of metabolism in conjunction with metabolic modelling--is a key discipline for the functional interpretation of whole genomes and especially for testing the numerical predictions of metabolism based on genome-scale metabolic network models. In this context, a systematic equation is derived based on metabolomics covariance data and the genome-scale stoichiometric matrix which describes the genotype-phenotype relationship.