Integration of transcriptomics and metabolomics data specifies the metabolic response of Chlamydomonas to rapamycin treatment

Differential toxicity of various mineral nanoparticles to Synechocystis sp.: With and without ciprofloxacin

Mineral nanoparticles (M-NPs) are ubiquitous in aquatic environments, but their potential harms to primary producers and impacts on the toxicity of coexisting pollutants are largely unknown. Herein, the toxicity mechanisms of various M-NPs (i.e., SiO2, Fe2O3, Al2O3, and TiO2 NPs) to Synechocystis sp. in absence and presence of ciprofloxacin (CIP) were comprehensively investigated. The heteroaggregation of cells and M-NPs can hinder substrate transfer or light acquisition. The attraction between Synechocystis sp. and M-NPs increased in the order of SiO2 < Fe2O3 < Al2O3 ≈ TiO2 NPs. Therefore, SiO2 and Fe2O3 NPs exerted slight effects on physiology and proteome of Synechocystis sp.. Al2O3 NPs with the rod-like shape caused physical damage to cells. Differently, TiO2 NPs with photocatalytic activities provided photogenerated electrons for Synechocystis sp., promoting photosynthesis and the Calvin cycle for CO2 fixation. SiO2, Fe2O3, and Al2O3 NPs alleviated the toxicity of CIP in an adsorption-depended manner. Conversely, the combination of CIP and TiO2 NPs exerted more pronounced toxic effects compared to their individuals, and CIP disturbed the extracellular electron transfer from TiO2 NPs to cells. The findings highlight the different effects of TiO2 NPs from other M-NPs on cyanobacteria, either alone or in combination with CIP, and improve the understanding of toxic mechanisms of M-NPs.

Model-driven insights into the effects of temperature on metabolism

Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.

Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models

Genome-scale metabolic models (GEMs) are extensively used to simulate cell metabolism and predict cell phenotypes. GEMs can also be tailored to generate context-specific GEMs, using omics data integration approaches. To date, many integration approaches have been developed, however, each with specific pros and cons; and none of these algorithms systematically outperforms the others. The key to successful implementation of such integration algorithms lies in the optimal selection of parameters, and thresholding is a crucial component in this process. To improve the predictive accuracy of context-specific models, we introduce a new integration framework that improves the ranking of related genes and homogenizes the expression values of those gene sets using single-sample Gene Set Enrichment Analysis (ssGSEA). In this study, we coupled ssGSEA with GIMME and validated the advantages of the proposed framework to predict the ethanol formation of yeast grown in the glucose-limited chemostats, and to simulate metabolic behaviors of yeast growth in four different carbon sources. This framework enhances the predictive accuracy of GIMME which we demonstrate for predicting the yeast physiology in nutrient-limited cultures.

From Feasting to Fasting: The Arginine Pathway as a Metabolic Switch in Nitrogen-Deprived Chlamydomonas reinhardtii

The metabolism of the model microalgae Chlamydomonas reinhardtii under nitrogen deprivation is of special interest due to its resulting increment of triacylglycerols (TAGs), that can be applied in biotechnological applications. However, this same condition impairs cell growth, which may limit the microalgae's large applications. Several studies have identified significant physiological and molecular changes that occur during the transition from an abundant to a low or absent nitrogen supply, explaining in detail the differences in the proteome, metabolome and transcriptome of the cells that may be responsible for and responsive to this condition. However, there are still some intriguing questions that reside in the core of the regulation of these cellular responses that make this process even more interesting and complex. In this scenario, we reviewed the main metabolic pathways that are involved in the response, mining and exploring, through a reanalysis of omics data from previously published datasets, the commonalities among the responses and unraveling unexplained or non-explored mechanisms of the possible regulatory aspects of the response. Proteomics, metabolomics and transcriptomics data were reanalysed using a common strategy, and an in silico gene promoter motif analysis was performed. Together, these results identified and suggested a strong association between the metabolism of amino acids, especially arginine, glutamate and ornithine pathways to the production of TAGs, via the de novo synthesis of lipids. Furthermore, our analysis and data mining indicate that signalling cascades orchestrated with the indirect participation of phosphorylation, nitrosylation and peroxidation events may be essential to the process. The amino acid pathways and the amount of arginine and ornithine available in the cells, at least transiently during nitrogen deprivation, may be in the core of the post-transcriptional, metabolic regulation of this complex phenomenon. Their further exploration is important to the discovery of novel advances in the understanding of microalgae lipids' production.

Deciphering the function and evolution of the target of rapamycin signaling pathway in microalgae

Microalgae constitute a highly diverse group of photosynthetic microorganisms that are widely distributed on Earth. The rich diversity of microalgae arose from endosymbiotic events that took place early in the evolution of eukaryotes and gave rise to multiple lineages including green algae, the ancestors of land plants. In addition to their fundamental role as the primary source of marine and freshwater food chains, microalgae are essential producers of oxygen on the planet and a major biotechnological target for sustainable biofuel production and CO2 mitigation. Microalgae integrate light and nutrient signals to regulate cell growth. Recent studies identified the target of rapamycin (TOR) kinase as a central regulator of cell growth and a nutrient sensor in microalgae. TOR promotes protein synthesis and regulates processes that are induced under nutrient stress such as autophagy and the accumulation of triacylglycerol and starch. A detailed analysis of representative genomes from the entire microalgal lineage revealed that the highly conserved central components of the TOR pathway are likely to have been present in the last eukaryotic common ancestor, and the loss of specific TOR signaling elements at an early stage in the evolution of microalgae. Here we examine the evolutionary conservation of TOR signaling components in diverse microalgae and discuss recent progress of this signaling pathway in these organisms.

Digital models in biotechnology: Towards multi-scale integration and implementation

Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.

Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations

Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data.

Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks.

Availability:Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes.

Genome-Scale Modeling Specifies the Metabolic Capabilities of Rhizophagus irregularis

Rhizophagus irregularis is one of the most extensively studied arbuscular mycorrhizal fungi (AMF) that forms symbioses with and improves the performance of many crops. Lack of transformation protocol for R. irregularis renders it challenging to investigate molecular mechanisms that shape the physiology and interactions of this AMF with plants. Here, we used all published genomics, transcriptomics, and metabolomics resources to gain insights into the metabolic functionalities of R. irregularis by reconstructing its high-quality genome-scale metabolic network that considers enzyme constraints. Extensive validation tests with the enzyme-constrained metabolic model demonstrated that it can be used to (i) accurately predict increased growth of R. irregularis on myristate with minimal medium; (ii) integrate enzyme abundances and carbon source concentrations that yield growth predictions with high and significant Spearman correlation (ρS = 0.74) to measured hyphal dry weight; and (iii) simulate growth rate increases with tighter association of this AMF with the host plant across three fungal structures. Based on the validated model and system-level analyses that integrate data from transcriptomics studies, we predicted that differences in flux distributions between intraradical mycelium and arbuscles are linked to changes in amino acid and cofactor biosynthesis. Therefore, our results demonstrated that the enzyme-constrained metabolic model can be employed to pinpoint mechanisms driving developmental and physiological responses of R. irregularis to different environmental cues. In conclusion, this model can serve as a template for other AMF and paves the way to identify metabolic engineering strategies to modulate fungal metabolic traits that directly affect plant performance.

IMPORTANCE Mounting evidence points to the benefits of the symbiotic interactions between the arbuscular mycorrhiza fungus Rhizophagus irregularis and crops; however, the molecular mechanisms underlying the physiological responses of this fungus to different host plants and environments remain largely unknown. We present a manually curated, enzyme-constrained, genome-scale metabolic model of R. irregularis that can accurately predict experimentally observed phenotypes. We show that this high-quality model provides an entry point into better understanding the metabolic and physiological responses of this fungus to changing environments due to the availability of different nutrients. The model can be used to design metabolic engineering strategies to tailor R. irregularis metabolism toward improving the performance of host plants.

Role of Autophagy in Haematococcus lacustris Cell Growth under Salinity

The microalga Haematococcus lacustris (formerly H. pluvialis) is able to accumulate high amounts of the carotenoid astaxanthin in the course of adaptation to stresses like salinity. Technologies aimed at production of natural astaxanthin for commercial purposes often involve salinity stress; however, after a switch to stressful conditions, H. lacustris experiences massive cell death which negatively influences astaxanthin yield. This study addressed the possibility to improve cell survival in H. lacustris subjected to salinity via manipulation of the levels of autophagy using AZD8055, a known inhibitor of TOR kinase previously shown to accelerate autophagy in several microalgae. Addition of NaCl in concentrations of 0.2% or 0.8% to the growth medium induced formation of autophagosomes in H. lacustris, while simultaneous addition of AZD8055 up to a final concentration of 0.2 µM further stimulated this process. AZD8055 significantly improved the yield of H. lacustris cells after 5 days of exposure to 0.2% NaCl. Strikingly, this occurred by acceleration of cell growth, and not by acceleration of aplanospore formation. The level of astaxanthin synthesis was not affected by AZD8055. However, cytological data suggested a role of autophagosomes, lysosomes and Golgi cisternae in cell remodeling during high salt stress.

A hybrid kinetic and constraint-based model of leaf metabolism allows predictions of metabolic fluxes in different environments

While flux balance analysis (FBA) provides a framework for predicting steady-state leaf metabolic network fluxes, it does not readily capture the response to environmental variables without being coupled to other modelling formulations. To address this, we coupled an FBA model of 903 reactions of soybean (Glycine max) leaf metabolism with e-photosynthesis, a dynamic model that captures the kinetics of 126 reactions of photosynthesis and associated chloroplast carbon metabolism. Successful coupling was achieved in an iterative formulation in which fluxes from e-photosynthesis were used to constrain the FBA model and then, in turn, fluxes computed from the FBA model used to update parameters in e-photosynthesis. This process was repeated until common fluxes in the two models converged. Coupling did not hamper the ability of the kinetic module to accurately predict the carbon assimilation rate, photosystem II electron flux, and starch accumulation of field-grown soybean at two CO2 concentrations. The coupled model also allowed accurate predictions of additional parameters such as nocturnal respiration, as well as analysis of the effect of light intensity and elevated CO2 on leaf metabolism. Predictions included an unexpected decrease in the rate of export of sucrose from the leaf at high light, due to altered starch-sucrose partitioning, and altered daytime flux modes in the tricarboxylic acid cycle at elevated CO2 . Mitochondrial fluxes were notably different between growing and mature leaves, with greater anaplerotic, tricarboxylic acid cycle and mitochondrial ATP synthase fluxes predicted in the former, primarily to provide carbon skeletons and energy for protein synthesis.

Coral symbionts evolved a functional polycistronic flavodiiron gene

Photosynthesis in cyanobacteria, green algae, and basal land plants is protected against excess reducing pressure on the photosynthetic chain by flavodiiron proteins (FLV) that dissipate photosynthetic electrons by reducing O₂. In these organisms, the genes encoding FLV are always conserved in the form of a pair of two-type isozymes (FLVA and FLVB) that are believed to function in O₂ photo-reduction as a heterodimer. While coral symbionts (dinoflagellates of the family Symbiodiniaceae) are the only algae to harbor FLV in photosynthetic red plastid lineage, only one gene is found in transcriptomes and its role and activity remain unknown. Here, we characterized the FLV genes in Symbiodiniaceae and found that its coding region is composed of tandemly repeated FLV sequences. By measuring the O₂-dependent electron flow and P700 oxidation, we suggest that this atypical FLV is active in vivo. Based on the amino-acid sequence alignment and the phylogenetic analysis, we conclude that in coral symbionts, the gene pair for FLVA and FLVB have been fused to construct one coding region for a hybrid enzyme, which presumably occurred when or after both genes were inherited from basal green algae to the dinoflagellate. Immunodetection suggested the FLV polypeptide to be cleaved by a post-translational mechanism, adding it to the rare cases of polycistronic genes in eukaryotes. Our results demonstrate that FLV are active in coral symbionts with genomic arrangement that is unique to these species. The implication of these unique features on their symbiotic living environment is discussed.

Metabolic systems biology and multi-omics of cyanobacteria: Perspectives and future directions

Cyanobacteria are oxygenic photoautotrophs whose metabolism contains key biochemical pathways to fix atmospheric CO₂ and synthesize various metabolites. The development of bioengineering tools has enabled the manipulation of cyanobacterial chassis to produce various valuable bioproducts photosynthetically. However, effective utilization of cyanobacteria as photosynthetic cell factories needs a detailed understanding of their metabolism and its interaction with other cellular processes. Implementing systems and synthetic biology tools has generated a wealth of information on various metabolic pathways. However, to design effective engineering strategies for further improvement in growth, photosynthetic efficiency, and enhanced production of target biochemicals, in-depth knowledge of their carbon/nitrogen metabolism, pathway fluxe distribution, genetic regulation and integrative analyses are necessary. In this review, we discuss the recent advances in the development of genome-scale metabolic models (GSMMs), omics analyses (metabolomics, transcriptomics, proteomics, fluxomics), and integrative modeling approaches to showcase the current understanding of cyanobacterial metabolism.

Synthetic biology approaches to actinomycete strain improvement

Their biochemical versatility and biotechnological importance make actinomycete bacteria attractive targets for ambitious genetic engineering using the toolkit of synthetic biology. But their complex biology also poses unique challenges. This mini review discusses some of the recent advances in synthetic biology approaches from an actinomycete perspective and presents examples of their application to the rational improvement of industrially relevant strains.

Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis

Thermodynamic metabolic flux analysis (TMFA) can narrow down the space of steady-state flux distributions, but requires knowledge of the standard Gibbs free energy for the modelled reactions. The latter are often not available due to unknown Gibbs free energy change of formation [Formula: see text], of metabolites. To optimize the usage of data on thermodynamics in constraining a model, reaction lumping has been proposed to eliminate metabolites with unknown [Formula: see text]. However, the lumping procedure has not been formalized nor implemented for systematic identification of lumped reactions. Here, we propose, implement, and test a combined procedure for reaction lumping, applicable to genome-scale metabolic models. It is based on identification of groups of metabolites with unknown [Formula: see text] whose elimination can be conducted independently of the others via: (1) group implementation, aiming to eliminate an entire such group, and, if this is infeasible, (2) a sequential implementation to ensure that a maximal number of metabolites with unknown [Formula: see text] are eliminated. Our comparative analysis with genome-scale metabolic models of Escherichia coli, Bacillus subtilis, and Homo sapiens shows that the combined procedure provides an efficient means for systematic identification of lumped reactions. We also demonstrate that TMFA applied to models with reactions lumped according to the proposed procedure lead to more precise predictions in comparison to the original models. The provided implementation thus ensures the reproducibility of the findings and their application with standard TMFA.

Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism

Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }\hbox {C}$$\end{document}∘C chilling conditions. To gain mechanistic insights into the metabolic effects of this ribosome biogenesis defect on metabolism, we developed TC-iReMet2, a constraint-based modelling approach that integrates relative metabolomics and transcriptomics time-course data to predict differential fluxes on a genome-scale level. We employed TC-iReMet2 with metabolomics and transcriptomics data from the Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin–Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.

Clavulanic Acid Production by Streptomyces clavuligerus: Insights from Systems Biology, Strain Engineering, and Downstream Processing

Clavulanic acid (CA) is an irreversible β-lactamase enzyme inhibitor with a weak antibacterial activity produced by Streptomyces clavuligerus (S. clavuligerus). CA is typically co-formulated with broad-spectrum β‑lactam antibiotics such as amoxicillin, conferring them high potential to treat diseases caused by bacteria that possess β‑lactam resistance. The clinical importance of CA and the complexity of the production process motivate improvements from an interdisciplinary standpoint by integrating metabolic engineering strategies and knowledge on metabolic and regulatory events through systems biology and multi-omics approaches. In the large-scale bioprocessing, optimization of culture conditions, bioreactor design, agitation regime, as well as advances in CA separation and purification are required to improve the cost structure associated to CA production. This review presents the recent insights in CA production by S. clavuligerus, emphasizing on systems biology approaches, strain engineering, and downstream processing.

MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics

Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner: the Systems Biology Markup Language (SBML) simulation engine. Three examples are provided to demonstrate how to use MASSpy: (1) a validation of the MASSpy modeling tool through dynamic simulation of detailed mechanisms of enzyme regulation; (2) a feature demonstration using a workflow for generating ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify biological uncertainty, and (3) a case study in which MASSpy is utilized to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenges that arise in dynamic modeling of metabolic networks, both at small and large scales.

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

TOR kinase activity in Chlamydomonas reinhardtii is modulated by cellular metabolic states

Target of rapamycin (TOR) kinase is a sensor and a central integrator of internal and external metabolic cues. However, in algae and in higher plants, the components of TOR kinase signaling are yet to be characterized. Here, we establish an assay system to study TOR kinase activity in Chlamydomonas reinhardtii using the phosphorylation status of its putative downstream target, CrS6K. Using this assay, we probe the modulation of cellular TOR kinase activity under various physiological states such as photoautotrophy, heterotrophy, mixotrophy, and nitrogen (N) starvation. Importantly, we uncover that excess acetate in the medium leads to high cellular reactive oxygen species levels, triggering autophagy and a concomitant drop in TOR kinase activity in a dose-dependent manner, thus leading to a N-starvation-like cellular phenotype, even when nitrogen is present.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

Flux-based hierarchical organization of Escherichia coli's metabolic network

Biological networks across scales exhibit hierarchical organization that may constrain network function. Yet, understanding how these hierarchies arise due to the operational constraint of the networks and whether they impose limits to molecular phenotypes remains elusive. Here we show that metabolic networks include a hierarchy of reactions based on a natural flux ordering that holds for every steady state. We find that the hierarchy of reactions is reflected in experimental measurements of transcript, protein and flux levels of Escherichia coli under various growth conditions as well as in the catalytic rate constants of the corresponding enzymes. Our findings point at resource partitioning and a fine-tuning of enzyme levels in E. coli to respect the constraints imposed by the network structure at steady state. Since reactions in upper layers of the hierarchy impose an upper bound on the flux of the reactions downstream, the hierarchical organization of metabolism due to the flux ordering has direct applications in metabolic engineering.

Systems metabolomics: from metabolomic snapshots to design principles

Metabolomics is a rapidly expanding technology that finds increasing application in a variety of fields, form metabolic disorders to cancer, from nutrition and wellness to design and optimization of cell factories. The integration of metabolic snapshots with metabolic fluxes, physiological readouts, metabolic models, and knowledge-informed Artificial Intelligence tools, is required to obtain a system-level understanding of metabolism. The emerging power of multi-omic approaches and the development of integrated experimental and computational tools, able to dissect metabolic features at cellular and subcellular resolution, provide unprecedented opportunities for understanding design principles of metabolic (dis)regulation and for the development of precision therapies in multifactorial diseases, such as cancer and neurodegenerative diseases.

Can metabolic tightening and expansion of co-expression network play a role in stress response and tolerance?

Plants respond and adapt to changes in their environment by employing a wide variety of genetic, molecular, and biochemical mechanisms. When so doing, they trigger large-scale rearrangements at the metabolic and transcriptional levels. The dynamics and patterns of these rearrangements and how they govern a stress response is not clear. In this opinion, we discuss a plant's response to stress from the perspective of the metabolic gene co-expression network and its rearrangement upon stress. As a case study, we use publicly available expression data of Arabidopsis thaliana plants exposed to heat and drought stress to evaluate and compare the co-expression networks of metabolic genes. The analysis highlights that stress conditions can lead to metabolic tightening and expansion of the co-expression network. We argue that this rearrangement could play a role in a plant's response to stress and thus may be an additional tool to assess and understand stress tolerance/sensitivity. Additional studies are needed to evaluate the metabolic network in response to multiple stresses at various intensities and across different genetic backgrounds (e.g., intra- and inter-species, sensitive and tolerant eco/genotypes).

Microalgal Target of Rapamycin (TOR): A Central Regulatory Hub for Growth, Stress Response and Biomass Production

Target of rapamycin (TOR) is an evolutionarily conserved protein kinase that plays an important role in the regulation of cell growth and the sensing of nutrient and energy status in eukaryotes. In yeasts and mammals, the roles of TOR have been very well described and various functions of TOR signaling in plant lineages have also been revealed over the past 20 years. In the case of microalgae, the functions of TOR have been primarily studied in the model green alga Chlamydomonas reinhardtii and were summarized in an earlier single review article. However, the recent development of tools for the functional analysis of TOR has helped to reveal the involvement of TOR in various functions, including autophagy, transcription, translation, accumulation of energy storage molecules, etc., in microalgae. In the present review, we discuss recent novel findings relating to TOR signaling and its roles in microalgae along with relevant information on land plants and also provide details of topics that must be addressed in future studies to reveal how TOR regulates various physiological functions in microalgae.

Metabolomics to Exploit the Primed Immune System of Tomato Fruit

Tomato is a major crop suffering substantial yield losses from diseases, as fruit decay at a postharvest level can claim up to 50% of the total production worldwide. Due to the environmental risks of fungicides, there is an increasing interest in exploiting plant immunity through priming, which is an adaptive strategy that improves plant defensive capacity by stimulating induced mechanisms. Broad-spectrum defence priming can be triggered by the compound ß-aminobutyric acid (BABA). In tomato plants, BABA induces resistance against various fungal and bacterial pathogens and different methods of application result in durable protection. Here, we demonstrate that the treatment of tomato plants with BABA resulted in a durable induced resistance in tomato fruit against Botrytis cinerea, Phytophthora infestans and Pseudomonas syringae. Targeted and untargeted metabolomics were used to investigate the metabolic regulations that underpin the priming of tomato fruit against pathogenic microbes that present different infection strategies. Metabolomic analyses revealed major changes after BABA treatment and after inoculation. Remarkably, primed responses seemed specific to the type of infection, rather than showing a common fingerprint of BABA-induced priming. Furthermore, top-down modelling from the detected metabolic markers allowed for the accurate prediction of the measured resistance to fruit pathogens and demonstrated that soluble sugars are essential to predict resistance to fruit pathogens. Altogether, our results demonstrate that metabolomics is particularly insightful for a better understanding of defence priming in fruit. Further experiments are underway in order to identify key metabolites that mediate broad-spectrum BABA-induced priming in tomato fruit.

Combined Analysis of the Metabolome and Transcriptome Identified Candidate Genes Involved in Phenolic Acid Biosynthesis in the Leaves of Cyclocarya paliurus

To assess changes of metabolite content and regulation mechanism of the phenolic acid biosynthesis pathway at different developmental stages of leaves, this study performed a combined metabolome and transcriptome analysis of Cyclocarya paliurus leaves at different developmental stages. Metabolite and transcript profiling were conducted by ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometer and high-throughput RNA sequencing, respectively. Transcriptome identification showed that 58 genes were involved in the biosynthesis of phenolic acid. Among them, 10 differentially expressed genes were detected between every two developmental stages. Identification and quantification of metabolites indicated that 14 metabolites were located in the phenolic acid biosynthetic pathway. Among them, eight differentially accumulated metabolites were detected between every two developmental stages. Association analysis between metabolome and transcriptome showed that six differentially expressed structural genes were significantly positively correlated with metabolite accumulation and showed similar expression trends. A total of 128 transcription factors were identified that may be involved in the regulation of phenolic acid biosynthesis; these include 12 MYBs and 10 basic helix–loop–helix (bHLH) transcription factors. A regulatory network of the phenolic acid biosynthesis was established to visualize differentially expressed candidate genes that are involved in the accumulation of metabolites with significant differences. The results of this study contribute to the further understanding of phenolic acid biosynthesis during the development of leaves of C. paliurus.

Reciprocal regulation of photosynthesis and mitochondrial respiration by TOR kinase in Chlamydomonas reinhardtii

While the role of TOR kinase in the chloroplast biogenesis and transcriptional regulation of photosynthesis is well documented in Arabidopsis, the functional relevance of this metabolic sensor kinase in chloroplast-mitochondria cross talk is unknown. Using Chlamydomonas reinhardtii as the model system, we demonstrate the role of TOR kinase in the regulation of chloroplast and mitochondrial functions: We show that TOR kinase inhibition impairs the maintenance of high ETR associated with PSII and low NPQ and inhibits efficient state transitions between PSII and PSI. While compromised photosynthetic functions are observed in TOR kinase inhibited cells, same conditions lead to augmentation in mitochondrial basal respiration rate by twofold and concomitantly a rise in ATP production. Interestingly, such upregulated mitochondrial functions in TOR-inhibited cells are mediated by fragmented mitochondria via upregulating COXIIb and downregulating Hxk1 and AOX1 protein levels. We propose that TOR kinase may act as a sensor that counter-regulates chloroplast versus mitochondrial functions in a normal C. reinhardtii cell.

Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom

Diatoms outcompete other phytoplankton for nitrate, yet little is known about the mechanisms underpinning this ability. Genomes and genome-enabled studies have shown that diatoms possess unique features of nitrogen metabolism however, the implications for nutrient utilization and growth are poorly understood. Using a combination of transcriptomics, proteomics, metabolomics, fluxomics, and flux balance analysis to examine short-term shifts in nitrogen utilization in the model pennate diatom in Phaeodactylum tricornutum, we obtained a systems-level understanding of assimilation and intracellular distribution of nitrogen. Chloroplasts and mitochondria are energetically integrated at the critical intersection of carbon and nitrogen metabolism in diatoms. Pathways involved in this integration are organelle-localized GS-GOGAT cycles, aspartate and alanine systems for amino moiety exchange, and a split-organelle arginine biosynthesis pathway that clarifies the role of the diatom urea cycle. This unique configuration allows diatoms to efficiently adjust to changing nitrogen status, conferring an ecological advantage over other phytoplankton taxa.

Here, using the diatom Phaeodactylum tricornutum as a model organism, the authors combine functional genomics, phylogenetics, and metabolic modeling to describe how diatoms might have functionally integrated nitrogen metabolism during evolution and how metabolic flux is regulated across cellular compartments

TOR inhibition interrupts the metabolic homeostasis by shifting the carbon-nitrogen balance in Chlamydomonas reinhardtii

The allocation of nutrient resources to growth and metabolism is an essential function for controlling biomass accumulation in photoautotrophic organisms. One essential protein complex involved in this process is the target of rapamycin (TOR) kinase. It has been shown that the inhibition of TOR leads to a considerable upsurge in the amino acid levels. This molecular phenotype relies mainly on the availability of light, carbon (C) and nitrogen (N). To validate the time-resolved response of C and N metabolites, we used a targeted gas chromatography mass spectrometery (GC-MS)-based metabolomic approach, where we examined the response of Chlamydomonas reinhardtii upon TOR inhibition under C-limited condition, namely extended darkness. Contrary to C-supplemented conditions, the rapid increase in the amino acid levels is suppressed almost completely 4 h after TOR inhibition, confirming that C supply is essential to raise the amino acid levels mediated by their de novo synthesis. An exception to this observation was the levels of aspartate, which is presumably synthesized via the anaplerotic pathway. In agreement with previous reports, TOR repression, under these C-limited conditions, leads to a significant reduction in the C/N ratio, corroborating with the crucial role of the pathway in maintaining the metabolic balance of the cells and consequently propelling growth.

Inhibition of TOR in Chlamydomonas reinhardtii Leads to Rapid Cysteine Oxidation Reflecting Sustained Physiological Changes

The target of rapamycin (TOR) kinase is a master metabolic regulator with roles in nutritional sensing, protein translation, and autophagy. In Chlamydomonas reinhardtii, a unicellular green alga, TOR has been linked to the regulation of increased triacylglycerol (TAG) accumulation, suggesting that TOR or a downstream target(s) is responsible for the elusive “lipid switch” in control of increasing TAG accumulation under nutrient limitation. However, while TOR has been well characterized in mammalian systems, it is still poorly understood in photosynthetic systems, and little work has been done to show the role of oxidative signaling in TOR regulation. In this study, the TOR inhibitor AZD8055 was used to relate reversible thiol oxidation to the physiological changes seen under TOR inhibition, including increased TAG content. Using oxidized cysteine resin-assisted capture enrichment coupled with label-free quantitative proteomics, 401 proteins were determined to have significant changes in oxidation following TOR inhibition. These oxidative changes mirrored characterized physiological modifications, supporting the role of reversible thiol oxidation in TOR regulation of TAG production, protein translation, carbohydrate catabolism, and photosynthesis through the use of reversible thiol oxidation. The delineation of redox-controlled proteins under TOR inhibition provides a framework for further characterization of the TOR pathway in photosynthetic eukaryotes.

Omics Application of Bio-Hydrogen Production Through Green Alga Chlamydomonas reinhardtii

This article summarizes the current knowledge regarding omics approaches, which include genomics, transcriptomics, proteomics and metabolomics, in the context of bio-hydrogen production in Chlamydomonas reinhardtii. In this paper, critical genes (HydA1, Hyd A2, Sulp, Tla1, Sta7, PFL1) involved in H₂ metabolism were identified and analyzed for their function in H₂ accumulation. Furthermore, the advantages of gene microarrays and RNA-seq were compared, as well as their applications in transcriptomic analysis of H₂ production. Moreover, as a useful tool, proteomic analysis could identify different proteins that participate in H₂ metabolism. This review provides fundamental theory and an experimental basis for H₂ production, and further research effort is needed in this field.

Metabolic Model of the Phytophthora infestans-Tomato Interaction Reveals Metabolic Switches during Host Colonization

The oomycete pathogen Phytophthora infestans causes potato and tomato late blight, a disease that is a serious threat to agriculture. P. infestans is a hemibiotrophic pathogen, and during infection, it scavenges nutrients from living host cells for its own proliferation. To date, the nutrient flux from host to pathogen during infection has hardly been studied, and the interlinked metabolisms of the pathogen and host remain poorly understood. Here, we reconstructed an integrated metabolic model of P. infestans and tomato (Solanum lycopersicum) by integrating two previously published models for both species. We used this integrated model to simulate metabolic fluxes from host to pathogen and explored the topology of the model to study the dependencies of the metabolism of P. infestans on that of tomato. This showed, for example, that P. infestans, a thiamine auxotroph, depends on certain metabolic reactions of the tomato thiamine biosynthesis. We also exploited dual-transcriptome data of a time course of a full late blight infection cycle on tomato leaves and integrated the expression of metabolic enzymes in the model. This revealed profound changes in pathogen-host metabolism during infection. As infection progresses, P. infestans performs less de novo synthesis of metabolites and scavenges more metabolites from tomato. This integrated metabolic model for the P. infestans-tomato interaction provides a framework to integrate data and generate hypotheses about in planta nutrition of P. infestans throughout its infection cycle.IMPORTANCE Late blight disease caused by the oomycete pathogen Phytophthora infestans leads to extensive yield losses in tomato and potato cultivation worldwide. To effectively control this pathogen, a thorough understanding of the mechanisms shaping the interaction with its hosts is paramount. While considerable work has focused on exploring host defense mechanisms and identifying P. infestans proteins contributing to virulence and pathogenicity, the nutritional strategies of the pathogen are mostly unresolved. Genome-scale metabolic models (GEMs) can be used to simulate metabolic fluxes and help in unravelling the complex nature of metabolism. We integrated a GEM of tomato with a GEM of P. infestans to simulate the metabolic fluxes that occur during infection. This yields insights into the nutrients that P. infestans obtains during different phases of the infection cycle and helps in generating hypotheses about nutrition in planta.

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.

The magic 'hammer' of TOR: the multiple faces of a single pathway in the metabolic regulation of plant growth and development

The target of rapamycin (TOR) pathway has emerged as a central hub synchronizing plant growth according to the nutrient/energy status and environmental inputs. Molecular mechanisms through which TOR promotes plant growth involve the positive regulation of transcription of cell proliferation-associated genes, mRNA translation initiation and ribosome biogenesis, to cite a few examples. Phytohormones, light, sugars, and sulfur have been found to broadly regulate TOR activity. TOR operates as a metabolic homeostat to fine-tune anabolic processes and efficiently enable plant growth under different circumstances. However, little is known about the multiple effectors that act up- and downstream of TOR. Here, we mainly discuss recent findings related to the TOR pathway in the context of plant metabolism and highlight areas of interest that need to be addressed to keep unravelling the intricate networks governing the regulation of TOR and its function in controlling biosynthetic growth.

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

Integration of Plant Metabolomics Data with Metabolic Networks: Progresses and Challenges

In the last decade, plant genome-scale modeling has developed rapidly and modeling efforts have advanced from representing metabolic behavior of plant heterotrophic cell suspensions to studying the complex interplay of cell types, tissues, and organs. A crucial driving force for such developments is the availability and integration of "omics" data (e.g., transcriptomics, proteomics, and metabolomics) which enable the reconstruction, extraction, and application of context-specific metabolic networks. In this chapter, we demonstrate a workflow to integrate gas chromatography coupled to mass spectrometry (GC-MS)-based metabolomics data of tomato fruit pericarp (flesh) tissue, at five developmental stages, with a genome-scale reconstruction of tomato metabolism. This method allows for the extraction of context-specific networks reflecting changing activities of metabolic pathways throughout fruit development and maturation.

Multi-Omics and Genome-Scale Modeling Reveal a Metabolic Shift During C. elegans Aging

In this contribution, we describe a multi-omics systems biology study of the metabolic changes that occur during aging in Caenorhabditis elegans. Sampling several time points from young adulthood until early old age, our study covers the full duration of aging and include transcriptomics, and targeted MS-based metabolomics. In order to focus on the metabolic changes due to age we used two strains that are metabolically close to wild-type, yet are conditionally non-reproductive. Using these data in combination with a whole-genome model of the metabolism of C. elegans and mathematical modeling, we predicted metabolic fluxes during early aging. We find that standard Flux Balance Analysis does not accurately predict in vivo measured fluxes nor age-related changes associated with the Citric Acid cycle. We present a novel Flux Balance Analysis method where we combined biomass production and targeted metabolomics information to generate an objective function that is more suitable for aging studies. We validated this approach with a detailed case study of the age-associated changes in the Citric Acid cycle. Our approach provides a comprehensive time-resolved multi-omics and modeling resource for studying the metabolic changes during normal aging in C. elegans.

Advances in metabolic flux analysis toward genome-scale profiling of higher organisms

Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.

Proteome evolution under non-substitutable resource limitation

Resource limitation is a major driver of the ecological and evolutionary dynamics of organisms. Short-term responses to resource limitation include plastic changes in molecular phenotypes including protein expression. Yet little is known about the evolution of the molecular phenotype under longer-term resource limitation. Here, we combine experimental evolution of the green alga Chlamydomonas reinhardtii under multiple different non-substitutable resource limitation regimes with proteomic measurements to investigate evolutionary adaptation of the molecular phenotype. We demonstrate convergent proteomic evolution of core metabolic functions, including the Calvin-Benson cycle and gluconeogenesis, across different resource limitation environments. We do not observe proteomic changes consistent with optimized uptake of particular limiting resources. Instead, we report that adaptation proceeds in similar directions under different types of non-substitutable resource limitation. This largely convergent evolution of the expression of core metabolic proteins is associated with an improvement in the resource assimilation efficiency of nitrogen and phosphorus into biomass.

Organisms could respond to essential resource limitation by increasing metabolic efficiency or resource acquisition ability. Here, the authors experimentally evolve green algae under different resource limitations and show convergent evolution of core metabolism rather than resource specialization.

The potential effect of low cell osmolarity on cell function through decreased concentration of enzyme substrates

Some freshwater algae have lower (<130 osmol m-3) intracellular osmolarities than most others (>180 osmol m-3). Low osmolarities are related to the presence of flagella and the low energy cost of active water efflux following downhill water influx unconstrained by cell walls covering the plasmalemma, and the low resource cost of cell wall synthesis with the same mechanical degree of safety. One consequence of low intracellular osmolarity is limitation on the concentration of metabolites, that is, substrates and products of enzyme activity. Models of the flux through metabolic pathways, and hence the specific growth rate, using steady-state concentrations of enzymes and metabolites have involved organisms with intracellular metabolite osmolarities >280 osmol m-3, where the metabolite concentrations are much greater than the total osmolarity of some freshwater algae. Since the protein concentration (mol m-3) in the cells and the specific growth rates of freshwater cells with low and with higher intracellular osmolarity are highly similar, the models of trade-offs between enzyme and metabolite concentrations for cells with high intracellular osmolarity need modification for cells with low intracellular osmolarity. The soluble free-radical scavenger ascorbate can constitute as little as 0.2% of the low intracellular metabolite concentration (mol m-3) of low-intracellular-osmolarity cells.

Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling

Metabolism is an emerging stem cell hallmark tied to cell fate, pluripotency, and self-renewal, yet systems-level understanding of stem cell metabolism has been limited by the lack of genome-scale network models. Here, we develop a systems approach to integrate time-course metabolomics data with a computational model of metabolism to analyze the metabolic state of naive and primed murine pluripotent stem cells. Using this approach, we find that one-carbon metabolism involving phosphoglycerate dehydrogenase, folate synthesis, and nucleotide synthesis is a key pathway that differs between the two states, resulting in differential sensitivity to anti-folates. The model also predicts that the pluripotency factor Lin28 regulates this one-carbon metabolic pathway, which we validate using metabolomics data from Lin28-deficient cells. Moreover, we identify and validate metabolic reactions related to S-adenosyl-methionine production that can differentially impact histone methylation in naive and primed cells. Our network-based approach provides a framework for characterizing metabolic changes influencing pluripotency and cell fate.

The target of rapamycin kinase affects biomass accumulation and cell cycle progression by altering carbon/nitrogen balance in synchronized Chlamydomonas reinhardtii cells

Several metabolic processes tightly regulate growth and biomass accumulation. A highly conserved protein complex containing the target of rapamycin (TOR) kinase is known to integrate intra- and extracellular stimuli controlling nutrient allocation and hence cellular growth. Although several functions of TOR have been described in various heterotrophic eukaryotes, our understanding lags far behind in photosynthetic organisms. In the present investigation, we used the model alga Chlamydomonas reinhardtii to conduct a time-resolved analysis of molecular and physiological features throughout the diurnal cycle after TOR inhibition. Detailed examination of the cell cycle phases revealed that growth is not only repressed by 50%, but also that significant, non-linear delays in the progression can be observed. By using metabolomics analysis, we elucidated that the growth repression was mainly driven by differential carbon partitioning between anabolic and catabolic processes. Accordingly, the time-resolved analysis illustrated that metabolic processes including amino acid-, starch- and triacylglycerol synthesis, as well RNA degradation, were redirected within minutes of TOR inhibition. Here especially the high accumulation of nitrogen-containing compounds indicated that an active TOR kinase controls the carbon to nitrogen balance of the cell, which is responsible for biomass accumulation, growth and cell cycle progression.

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.

iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models

MOTIVATION

Understanding the rerouting of metabolic reaction fluxes upon perturbations has the potential to link changes in molecular state of a cellular system to alteration of growth. Yet, differential flux profiling on a genome-scale level remains one of the biggest challenges in systems biology. This is particularly relevant in plants, for which fluxes in autotrophic growth necessitate time-consuming instationary labeling experiments and costly computations, feasible for small-scale networks.

RESULTS

Here we present a computationally and experimentally facile approach, termed iReMet-Flux, which integrates relative metabolomics data in a metabolic model to predict differential fluxes at a genome-scale level. Our approach and its variants complement the flux estimation methods based on radioactive tracer labeling. We employ iReMet-Flux with publically available metabolic profiles to predict reactions and pathways with altered fluxes in photo-autotrophically grown Arabidopsis and four photorespiratory mutants undergoing high-to-low CO2 acclimation. We also provide predictions about reactions and pathways which are most strongly regulated in the investigated experiments. The robustness and variability analyses, tailored to the formulation of iReMet-Flux, demonstrate that the findings provide biologically relevant information that is validated with external measurements of net CO2 exchange and biomass production. Therefore, iReMet-Flux paves the wave for mechanistic dissection of the interplay between pathways of primary and secondary metabolisms at a genome-scale.

AVAILABILITY AND IMPLEMENTATION

The source code is available from the authors upon request.

CONTACT

nikoloski@mpimp-golm.mpg.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The TOR Signaling Network in the Model Unicellular Green Alga Chlamydomonas reinhardtii

Cell growth is tightly coupled to nutrient availability. The target of rapamycin (TOR) kinase transmits nutritional and environmental cues to the cellular growth machinery. TOR functions in two distinct multiprotein complexes, termed TOR complex 1 (TORC1) and TOR complex 2 (TORC2). While the structure and functions of TORC1 are highly conserved in all eukaryotes, including algae and plants, TORC2 core proteins seem to be missing in photosynthetic organisms. TORC1 controls cell growth by promoting anabolic processes, including protein synthesis and ribosome biogenesis, and inhibiting catabolic processes such as autophagy. Recent studies identified rapamycin-sensitive TORC1 signaling regulating cell growth, autophagy, lipid metabolism, and central metabolic pathways in the model unicellular green alga Chlamydomonas reinhardtii. The central role that microalgae play in global biomass production, together with the high biotechnological potential of these organisms in biofuel production, has drawn attention to the study of proteins that regulate cell growth such as the TOR kinase. In this review we discuss the recent progress on TOR signaling in algae.

Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed "unsteady-state flux balance analysis" (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through ¹³C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.