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A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses.

ABSTRACT: Microalgae have reemerged as organisms of prime biotechnological interest due to their ability to synthesize a suite of valuable chemicals. To harness the capabilities of these organisms, we need a comprehensive systems-level understanding of their metabolism, which can be fundamentally achieved through large-scale mechanistic models of metabolism. In this study, we present a revised and significantly improved genome-scale metabolic model for the widely-studied microalga, Chlamydomonas reinhardtii. The model, iCre1355, represents a major advance over previous models, both in content and predictive power. iCre1355 encompasses a broad range of metabolic functions encoded across the nuclear, chloroplast and mitochondrial genomes accounting for 1355 genes (1460 transcripts), 2394 and 1133 metabolites. We found improved performance over the previous metabolic model based on comparisons of predictive accuracy across 306 phenotypes (from 81 mutants), lipid yield analysis and growth rates derived from chemostat-grown cells (under three conditions). Measurement of macronutrient uptake revealed carbon and phosphate to be good predictors of growth rate, while nitrogen consumption appeared to be in excess. We analyzed high-resolution time series transcriptomics data using iCre1355 to uncover dynamic pathway-level changes that occur in response to nitrogen starvation and changes in light intensity. This approach enabled accurate prediction of growth rates, the cessation of growth and accumulation of triacylglycerols during nitrogen starvation, and the temporal response of different growth-associated pathways to increased light intensity. Thus, iCre1355 represents an experimentally validated genome-scale reconstruction of C. reinhardtii metabolism that should serve as a useful resource for studying the metabolic processes of this and related microalgae.

SUBMITTER: Imam S

PROVIDER: S-EPMC4715634 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses.

Imam Saheed S Schäuble Sascha S Valenzuela Jacob J López García de Lomana Adrián A Carter Warren W Price Nathan D ND Baliga Nitin S NS

The Plant journal : for cell and molecular biology 20151130 6

Microalgae have reemerged as organisms of prime biotechnological interest due to their ability to synthesize a suite of valuable chemicals. To harness the capabilities of these organisms, we need a comprehensive systems-level understanding of their metabolism, which can be fundamentally achieved through large-scale mechanistic models of metabolism. In this study, we present a revised and significantly improved genome-scale metabolic model for the widely-studied microalga, Chlamydomonas reinhardt ...[more]

PMID: 26485611

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Project description:To understand the molecular basis underlying increased triacylglycerol accumulation in starchless Chlamydomonas mutants, we undertook comparative time course transcriptomics of sta6 (CC-4348), a cw strain purported to represent the parental STA6 strain (CC-4349), and 3 independent STA6 strains (STA6-C2, STA6-C4 and STA6-C6, generated by complementation of sta6, CC-4565, M-bM-^@M-^S4566 and M-bM-^@M-^S4567) in the context of nitrogen deprivation. Despite the N starvation induced dramatic remodeling of the transcriptome, there are relatively few differences (5 x 102) between sta6 and STA6, the most dramatic of which are in the abundance of transcripts encoding key regulated or rate-limiting steps in central carbon metabolism, specifically ICL1, MAS1, TAL1, FBP1 and PCK1, suggestive of increased carbon movement towards hexosephosphate in sta6 by up-regulation of the glyoxylate pathway and gluconeogenesis. Enzyme assays validated the increase in isocitrate lyase and malate synthase activities. Targeted metabolite analysis indicates increased succinate and malate, increased Glc-6-P and decreased fructose-1,6-bisphosphate, which documents the impact of these changes. Comparisons of the independent datasets in multiple strains allowed the delineation of a sequence of events in a global N-starvation response in Chlamydomonas, starting within minutes with up-regulation of alternative N assimilation routes and carbohydrate synthesis and subsequently a more gradual up-regulation of genes encoding enzymes of TAG synthesis. Finally, genome re-sequencing analysis indicated that i) the deletion in sta6 extends into the neighboring gene, RBO1, and ii) a commonly-used STA6 strain (CC-4349) as well as the sequenced reference (CC-503) are not congenic with respect to sta6 (CC-4348), underscoring the importance of using complemented strains for more rigorous assignment of phenotype to genotype. The transcriptomes of five different Chlamydomonas strains (wild type CC-4349, sta6 mutant (CC-4348),and three indepdent STA6 strains (STA6-C2, STA6-C4 and STA6-C6) are profiled by RNA-Seq in timecourse experiments after the transition to minus nitrogen

Project description:BackgroundMicroalgae have the potential to deliver biofuels without the associated competition for land resources. In order to realise the rates and titres necessary for commercial production, however, system-level metabolic engineering will be required. Genome scale metabolic reconstructions have revolutionized microbial metabolic engineering and are used routinely for in silico analysis and design. While genome scale metabolic reconstructions have been developed for many prokaryotes and model eukaryotes, the application to less well characterized eukaryotes such as algae is challenging not at least due to a lack of compartmentalization data.ResultsWe have developed a genome-scale metabolic network model (named AlgaGEM) covering the metabolism for a compartmentalized algae cell based on the Chlamydomonas reinhardtii genome. AlgaGEM is a comprehensive literature-based genome scale metabolic reconstruction that accounts for the functions of 866 unique ORFs, 1862 metabolites, 2249 gene-enzyme-reaction-association entries, and 1725 unique reactions. The reconstruction was compartmentalized into the cytoplasm, mitochondrion, plastid and microbody using available data for algae complemented with compartmentalisation data for Arabidopsis thaliana. AlgaGEM describes a functional primary metabolism of Chlamydomonas and significantly predicts distinct algal behaviours such as the catabolism or secretion rather than recycling of phosphoglycolate in photorespiration. AlgaGEM was validated through the simulation of growth and algae metabolic functions inferred from literature. Using efficient resource utilisation as the optimality criterion, AlgaGEM predicted observed metabolic effects under autotrophic, heterotrophic and mixotrophic conditions. AlgaGEM predicts increased hydrogen production when cyclic electron flow is disrupted as seen in a high producing mutant derived from mutational studies. The model also predicted the physiological pathway for H2 production and identified new targets to further improve H2 yield.ConclusionsAlgaGEM is a viable and comprehensive framework for in silico functional analysis and can be used to derive new, non-trivial hypotheses for exploring this metabolically versatile organism. Flux balance analysis can be used to identify bottlenecks and new targets to metabolically engineer microalgae for production of biofuels.

Project description:Systems characterization of immune landscapes in health, disease and clinical intervention cases is a priority in modern medicine. High-throughput transcriptomes accumulated from gene-knockout (KO) experiments are crucial for deciphering target KO signaling pathways that are impaired by KO genes at the systems-level. There is a demand for integrative platforms. This article describes the PathwayKO platform, which has integrated state-of-the-art methods of pathway enrichment analysis, statistics analysis, and visualizing analysis to conduct cutting-edge integrative pathway analysis in a pipeline fashion and decipher target KO signaling pathways at the systems-level. We focus on describing the methodology, principles and application features of PathwayKO. First, we demonstrate that the PathwayKO platform can be utilized to comprehensively analyze real-world mouse KO transcriptomes (GSE22873 and GSE24327), which reveal systemic mechanisms underlying the innate immune responses triggered by non-infectious extensive hepatectomy (2 hours after 85% liver resection surgery) and infectious CASP-model sepsis (12 hours after CASP-model surgery). Strikingly, our results indicate that both cases hit the same core set of 21 KO MyD88-associated signaling pathways, including the Toll-like receptor signaling pathway, the NFκB signaling pathway, the MAPK signaling pathway, and the PD-L1 expression and PD-1 checkpoint pathway in cancer, alongside the pathways of bacterial, viral and parasitic infections. These findings suggest common fundamental mechanisms between these immune responses and offer informative cues that warrant future experimental validation. Such mechanisms in mice may serve as models for humans and ultimately guide formulating the research paradigms and composite strategies to reduce the high mortality rates of patients in intensive care units who have undergone successful traumatic surgical treatments. Second, we demonstrate that the PathwayKO platform model-based assessments can effectively evaluate the performance difference of pathway analysis methods when benchmarked with a collection of proper transcriptomes. Together, such advances in methods for deciphering biological insights at the systems-level may benefit the fields of bioinformatics, systems immunology and beyond.

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A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses.

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A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses.

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