New Features on the Environmental Regulation of Metabolism Revealed by Modeling the Cellular Proteomic Adaptations Induced by Light, Carbon, and Inorganic Nitrogen in Chlamydomonas reinhardtii (original) (raw)
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BMC Systems Biology, 2014
Background: In photosynthetic organisms, the influence of light, carbon and inorganic nitrogen sources on the cellular bioenergetics has extensively been studied independently, but little information is available on the cumulative effects of these factors. Here, sequential statistical analyses based on design of experiments (DOE) coupled to standard least squares multiple regression have been undertaken to model the dependence of respiratory and photosynthetic responses (assessed by oxymetric and chlorophyll fluorescence measurements) upon the concomitant modulation of light intensity as well as acetate, CO 2 , nitrate and ammonium concentrations in the culture medium of Chlamydomonas reinhardtii. The main goals of these analyses were to explain response variability (i.e. bioenergetic plasticity) and to characterize quantitatively the influence of the major explanatory factor(s). Results: For each response, 2 successive rounds of multiple regression coupled to one-way ANOVA F-tests have been undertaken to select the major explanatory factor(s) (1st-round) and mathematically simulate their influence (2nd-round). These analyses reveal that a maximal number of 3 environmental factors over 5 is sufficient to explain most of the response variability, and interestingly highlight quadratic effects and second-order interactions in some cases. In parallel, the predictive ability of the 2nd-round models has also been investigated by k-fold cross-validation and experimental validation tests on new random combinations of factors. These validation procedures tend to indicate that the 2nd-round models can also be used to predict the responses with an inherent deviation quantified by the analytical error of the models. Conclusions: Altogether, the results of the 2 rounds of modeling provide an overview of the bioenergetic adaptations of C. reinhardtii to changing environmental conditions and point out promising tracks for future in-depth investigations of the molecular mechanisms underlying the present observations.
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
Journal of Visualized Experiments
Metabolic models are reconstructed based on an organism's available genome annotation and provide predictive tools to study metabolic processes at a systemslevel. Genome-scale metabolic models may include gaps as well as reactions that are unverified experimentally. Reconstructed models of newly isolated microalgal species will result in weaknesses due to these gaps, as there is usually sparse biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology is an effective, high-throughput method that functionally determines cellular metabolic activities in response to a wide array of entry metabolites. Combining the high throughput phenotypic assays with metabolic modeling can allow existing metabolic network models to be rapidly reconstructed or optimized by providing biochemical evidence to support and expand genomic evidence. This work will show the use of PM assays for the study of microalgae by using the green microalgal model species Chlamydomonas reinhardtii as an example. Experimental evidence for over 254 reactions obtained by PM was used in this study to expand and refine a genome-scale C. reinhardtii metabolic network model, iRC1080, by approximately 25 percent. The protocol created here can be used as a basis for functionally profiling the metabolism of other microalgae, including known microalgae mutants and new isolates. network models can guide the rational designs for the rapid development of optimization strategies 1 , 2 , 3 , 4. Although approximately 160 microalgal species have been sequenced 5 , there are, to our knowledge, only 44 algal
Metabolic modelling and simulation of the light and dark metabolism of Chlamydomonas reinhardtii
The Plant Journal
A metabolic network model of the green microalga Chlamydomonas reinhardtii was used to characterize photoautotrophic and heterotrophic (i.e. growth on stored compounds) growth under light and dark, respectively. The metabolic network comprised 2514 reactions distributed among nine intracellular compartments and the extracellular space. The metabolic network included all the key biochemical pathways for synthesis and metabolism of starch and triacylglycerols (TAGs). Under light and nitrogen limitation, the model simulated the accumulation of the energy-rich compounds (TAGs and starch) in the cell. In the dark, the model could simulate cell growth and maintenance on stored compounds. The model-predicted consumption rates of storage compounds (starch or TAGs) to enable growth in the dark, were found to be greater than the rates of synthesis under light. This implied utilization of the storage compounds for cell maintenance in the dark. Under constant illumination, the simulations of cell growth and intracellular starch content agreed closely with independent experimental data. In other simulations, compared with the case without photorespiration, light uptake rate increased 1.04-fold when the ratio of the rates of oxygenation and carboxylation (Rubisco) was 0.1. Although extensive experimental work exists on culture and physiology of microalgae, it does not allow quantitative predictions of the influence of dark metabolism on the productivity of metabolites to be made. This limitation is overcome using the present model. A metabolic network model of Chlamydomonas reinhardtii is shown to simulate growth and synthesis of energy-rich compounds (triacylglycerols and starch) under light. The same model also simulates dark growth and maintenance through consumption of the stored energy-rich compounds.
We investigated the systems response of metabolism and growth after an increase in irradiance in the nonsaturating range in the algal model Chlamydomonas reinhardtii. In a three-step process, photosynthesis and the levels of metabolites increased immediately, growth increased after 10 to 15 min, and transcript and protein abundance responded by 40 and 120 to 240 min, respectively. In the first phase, starch and metabolites provided a transient buffer for carbon until growth increased. This uncouples photosynthesis from growth in a fluctuating light environment. In the first and second phases, rising metabolite levels and increased polysome loading drove an increase in fluxes. Most Calvin-Benson cycle (CBC) enzymes were substrate-limited in vivo, and strikingly, many were present at higher concentrations than their substrates, explaining how rising metabolite levels stimulate CBC flux. Rubisco, fructose-1,6-biosphosphatase, and seduheptulose-1,7-bisphosphatase were close to substrate saturation in vivo, and flux was increased by posttranslational activation. In the third phase, changes in abundance of particular proteins, including increases in plastidial ATP synthase and some CBC enzymes, relieved potential bottlenecks and readjusted protein allocation between different processes. Despite reasonable overall agreement between changes in transcript and protein abundance (R 2 = 0.24), many proteins, including those in photosynthesis, changed independently of transcript abundance.
Advanced Topics in Science and Technology in China, 2013
In this study, we report initial biophysical and biochemical characterization of a spontaneous 'mutant' (referred to as 'IM') of the green alga Chlamydomonas reinhardtii with several unique attributes that has potential for improving photosynthetic productivity, light utilization efficiency, and, perhaps, even protection against environmental stress. Growth rate experiments showed that under low light intensity (10 mol photons m-2 s-1), IM showed 36% higher cell density and 25% higher dry cell weight than the wild type cells (WT), while at higher light intensity (640 mol photons m-2 s-1), the IM did not show any advantage. Chlorophyll a fluorescence transient measurements and subsequent analysis indicated that IM cells grown at a light intensity of 20 mol photons m-2 s-1 had a higher light utilization efficiency in comparison to the WT cells. Interestingly, metabolite profiling analysis showed that during the exponential growth phase with both low and high light intensities (10 and 640 mol photons m-2 s-1), the IM cells had higher concentrations than WT cells for several important metabolites that have been previously shown to help protect against environmental stress.
Journal of Biotechnology, 2015
Please cite this article in press as: Willamme, R., et al., Metabolomic analysis of the green microalga Chlamydomonas reinhardtii cultivated under day/night conditions. J. Biotechnol. (2015), http://dx.a b s t r a c t Biomass composition of Chlamydomonas reinhardtii was studied during two consecutive cycles of 12 h light/12 h dark. As in our experimental conditions the two synchronized divisions were separated by 20 h, it was possible to show that accumulation of dry weight, proteins, chlorophyll and fatty acids mainly depends on cell division, whereas starch accumulation depends on a circadian rhythm as reported previously. Our metabolomics analyses also revealed that accumulation of five (Ser, Val, Leu, Ile and Thr) of the nine free amino acids detected displayed rhythmicity, depending on cell division while Glu was 20-50 times more abundant than the other ones probably because this free amino acid serves not only for protein synthesis but also for biosynthesis of nitrogen compounds. In addition, we performed a thermodynamicmotivated theoretical approach known as 'surprisal analysis'. The results from this analysis showed that cells were close to a steady state all along the 48 h of the experiment. In addition, calculation of free energy of cellular metabolites showed that the transition point, i.e. the state which immediately precedes cell division, corresponds to the most unstable stage of the cell cycle and that division is identified as the greatest drop in the free energy of metabolites.
Plant Physiology, 2010
Like many microalgae, Chlamydomonas reinhardtii forms lipid droplets rich in triacylglycerols when nutrient deprived. To begin studying the mechanisms underlying this process, nitrogen (N) deprivation was used to induce triacylglycerol accumulation and changes in developmental programs such as gametogenesis. Comparative global analysis of transcripts under induced and noninduced conditions was applied as a first approach to studying molecular changes that promote or accompany triacylglycerol accumulation in cells encountering a new nutrient environment. Towards this goal, high-throughput sequencing technology was employed to generate large numbers of expressed sequence tags of eight biologically independent libraries, four for each condition, N replete and N deprived, allowing a statistically sound comparison of expression levels under the two tested conditions. As expected, N deprivation activated a subset of control genes involved in gametogenesis while down-regulating protein biosynthesis. Genes for components of photosynthesis were also down-regulated, with the exception of the PSBS gene. N deprivation led to a marked redirection of metabolism: the primary carbon source, acetate, was no longer converted to cell building blocks by the glyoxylate cycle and gluconeogenesis but funneled directly into fatty acid biosynthesis. Additional fatty acids may be produced by membrane remodeling, a process that is suggested by the changes observed in transcript abundance of putative lipase genes. Inferences on metabolism based on transcriptional analysis are indirect, but biochemical experiments supported some of these deductions. The data provided here represent a rich source for the exploration of the mechanism of oil accumulation in microalgae.
Journal of Proteome Research, 2014
Nitrogen starvation induces a global stress response in microalgae that results in the accumulation of lipids as a potential source of biofuel. Using GC-MS-based metabolite and iTRAQ-labeled protein profiling, we examined and correlated the metabolic and proteomic response of Chlamydomonas reinhardtii under nitrogen stress. Key amino acids and metabolites involved in nitrogen sparing pathways, methyl group transfer reactions, and energy production were decreased in abundance, whereas certain fatty acids, citric acid, methionine, citramalic acid, triethanolamine, nicotianamine, trehalose, and sorbitol were increased in abundance. Proteins involved in nitrogen assimilation, amino acid metabolism, oxidative phosphorylation, glycolysis, TCA cycle, starch, and lipid metabolism were elevated compared with nonstressed cultures. In contrast, the enzymes of the glyoxylate cycle, one carbon metabolism, pentose phosphate pathway, the Calvin cycle, photosynthetic and light harvesting complex, and ribosomes were reduced. A noteworthy observation was that citrate accumulated during nitrogen stress coordinate with alterations in the enzymes that produce or utilize this metabolite, demonstrating the value of comparing protein and metabolite profiles to understand complex patterns of metabolic flow. Thus, the current study provides unique insight into the global metabolic adjustments leading to lipid storage during N starvation for application toward advanced biofuel production technologies.