Widening the landscape of transcriptional regulation of green algal photoprotection.

Availability of light and CO2, substrates of microalgae photosynthesis, is frequently far from optimal. Microalgae activate photoprotection under strong light, to prevent oxidative damage, and the CO2 Concentrating Mechanism (CCM) under low CO2, to raise intracellular CO2 levels. The two processes are interconnected; yet, the underlying transcriptional regulators remain largely unknown. Employing a large transcriptomic data compendium of Chlamydomonas reinhardtii's responses to different light and carbon supply, we reconstruct a consensus genome-scale gene regulatory network from complementary inference approaches and use it to elucidate transcriptional regulators of photoprotection. We show that the CCM regulator LCR1 also controls photoprotection, and that QER7, a Squamosa Binding Protein, suppresses photoprotection- and CCM-gene expression under the control of the blue light photoreceptor Phototropin. By demonstrating the existence of regulatory hubs that channel light- and CO2-mediated signals into a common response, our study provides an accessible resource to dissect gene expression regulation in this microalga.

Transcriptomic and Metabolomic Analysis of a Pseudomonas-Resistant versus a Susceptible Arabidopsis Accession.

Accessions of one plant species may show significantly different levels of susceptibility to stresses. The Arabidopsis thaliana accessions Col-0 and C24 differ significantly in their resistance to the pathogen Pseudomonas syringae pv. tomato (Pst). To help unravel the underlying mechanisms contributing to this naturally occurring variance in resistance to Pst, we analyzed changes in transcripts and compounds from primary and secondary metabolism of Col-0 and C24 at different time points after infection with Pst. Our results show that the differences in the resistance of Col-0 and C24 mainly involve mechanisms of salicylic-acid-dependent systemic acquired resistance, while responses of jasmonic-acid-dependent mechanisms are shared between the two accessions. In addition, arginine metabolism and differential activity of the biosynthesis pathways of aliphatic glucosinolates and indole glucosinolates may also contribute to the resistance. Thus, this study highlights the difference in the defense response strategies utilized by different genotypes.

Sulfur deficiency-induced genes affect seed protein accumulation and composition under sulfate deprivation.

Sulfur deficiency-induced proteins SDI1 and SDI2 play a fundamental role in sulfur homeostasis under sulfate-deprived conditions (-S) by downregulating glucosinolates. Here, we identified that besides glucosinolate regulation under -S, SDI1 downregulates another sulfur pool, the S-rich 2S seed storage proteins in Arabidopsis (Arabidopsis thaliana) seeds. We identified that MYB28 directly regulates 2S seed storage proteins by binding to the At2S4 promoter. We also showed that SDI1 downregulates 2S seed storage proteins by forming a ternary protein complex with MYB28 and MYC2, another transcription factor involved in the regulation of seed storage proteins. These findings have significant implications for the understanding of plant responses to sulfur deficiency.

Comparative Analysis Based on Transcriptomics and Metabolomics Data Reveal Differences between Emmer and Durum Wheat in Response to Nitrogen Starvation.

Mounting evidence indicates the key role of nitrogen (N) on diverse processes in plant, including development and defense. Using a combined transcriptomics and metabolomics approach, we studied the response of seedlings to N starvation of two different tetraploid wheat genotypes from the two main domesticated subspecies: emmer and durum wheat. We found that durum wheat exhibits broader and stronger response in comparison to emmer as seen from the expression pattern of both genes and metabolites and gene enrichment analysis. They showed major differences in the responses to N starvation for transcription factor families, emmer showed differential reduction in the levels of primary metabolites while durum wheat exhibited increased levels of most of them to N starvation. The correlation-based networks, including the differentially expressed genes and metabolites, revealed tighter regulation of metabolism in durum wheat in comparison to emmer. We also found that glutamate and γ-aminobutyric acid (GABA) had highest values of centrality in the metabolic correlation network, suggesting their critical role in the genotype-specific response to N starvation of emmer and durum wheat, respectively. Moreover, this finding indicates that there might be contrasting strategies associated to GABA and glutamate signaling modulating shoot vs. root growth in the two different wheat subspecies.

Low-temperature tolerance of the Antarctic species Deschampsia antarctica: A complex metabolic response associated with nutrient remobilization.

The species Deschampsia antarctica (DA) is one of the only two native vascular species that live in Antarctica. We performed ecophysiological, biochemical, and metabolomic studies to investigate the responses of DA to low temperature. In parallel, we assessed the responses in a non-Antarctic reference species (Triticum aestivum [TA]) from the same family (Poaceae). At low temperature (4°C), both species showed lower photosynthetic rates (reductions were 70% and 80% for DA and TA, respectively) and symptoms of oxidative stress but opposite responses of antioxidant enzymes (peroxidases and catalase). We employed fused least absolute shrinkage and selection operator statistical modelling to associate the species-dependent physiological and antioxidant responses to primary metabolism. Model results for DA indicated associations with osmoprotection, cell wall remodelling, membrane stabilization, and antioxidant secondary metabolism (synthesis of flavonols and phenylpropanoids), coordinated with nutrient mobilization from source to sink tissues (confirmed by elemental analysis), which were not observed in TA. The metabolic behaviour of DA, with significant changes in particular metabolites, was compared with a newly compiled multispecies dataset showing a general accumulation of metabolites in response to low temperatures. Altogether, the responses displayed by DA suggest a compromise between catabolism and maintenance of leaf functionality.

A Biostimulant Obtained from the Seaweed Ascophyllum nodosum Protects Arabidopsis thaliana from Severe Oxidative Stress.

Abiotic stresses cause oxidative damage in plants. Here, we demonstrate that foliar application of an extract from the seaweed Ascophyllum nodosum, SuperFifty (SF), largely prevents paraquat (PQ)-induced oxidative stress in Arabidopsis thaliana. While PQ-stressed plants develop necrotic lesions, plants pre-treated with SF (i.e., primed plants) were unaffected by PQ. Transcriptome analysis revealed induction of reactive oxygen species (ROS) marker genes, genes involved in ROS-induced programmed cell death, and autophagy-related genes after PQ treatment. These changes did not occur in PQ-stressed plants primed with SF. In contrast, upregulation of several carbohydrate metabolism genes, growth, and hormone signaling as well as antioxidant-related genes were specific to SF-primed plants. Metabolomic analyses revealed accumulation of the stress-protective metabolite maltose and the tricarboxylic acid cycle intermediates fumarate and malate in SF-primed plants. Lipidome analysis indicated that those lipids associated with oxidative stress-induced cell death and chloroplast degradation, such as triacylglycerols (TAGs), declined upon SF priming. Our study demonstrated that SF confers tolerance to PQ-induced oxidative stress in A. thaliana, an effect achieved by modulating a range of processes at the transcriptomic, metabolic, and lipid levels.

Cytochrome respiration pathway and sulphur metabolism sustain stress tolerance to low temperature in the Antarctic species Colobanthus quitensis.

Understanding the strategies employed by plant species that live in extreme environments offers the possibility to discover stress tolerance mechanisms. We studied the physiological, antioxidant and metabolic responses to three temperature conditions (4, 15, and 23°C) of Colobanthus quitensis (CQ), one of the only two native vascular species in Antarctica. We also employed Dianthus chinensis (DC), to assess the effects of the treatments in a non-Antarctic species from the same family. Using fused LASSO modelling, we associated physiological and biochemical antioxidant responses with primary metabolism. This approach allowed us to highlight the metabolic pathways driving the response specific to CQ. Low temperature imposed dramatic reductions in photosynthesis (up to 88%) but not in respiration (sustaining rates of 3.0-4.2 µmol CO2  m-2  s-1 ) in CQ, and no change in the physiological stress parameters was found. Its notable antioxidant capacity and mitochondrial cytochrome respiratory activity (20 and two times higher than DC, respectively), which ensure ATP production even at low temperature, was significantly associated with sulphur-containing metabolites and polyamines. Our findings potentially open new biotechnological opportunities regarding the role of antioxidant compounds and respiratory mechanisms associated with sulphur metabolism in stress tolerance strategies to low temperature.

Plasticity in metabolism underpins local responses to nitrogen in Arabidopsis thaliana populations.

Nitrogen (N) is central for plant growth, and metabolic plasticity can provide a strategy to respond to changing N availability. We showed that two local A. thaliana populations exhibited differential plasticity in the compounds of photorespiratory and starch degradation pathways in response to three N conditions. Association of metabolite levels with growth-related and fitness traits indicated that controlled plasticity in these pathways could contribute to local adaptation and play a role in plant evolution.

Identification and characterization of metabolite quantitative trait loci in tomato leaves and comparison with those reported for fruits and seeds.

INTRODUCTION: To date, most studies of natural variation and metabolite quantitative trait loci (mQTL) in tomato have focused on fruit metabolism, leaving aside the identification of genomic regions involved in the regulation of leaf metabolism. OBJECTIVE: This study was conducted to identify leaf mQTL in tomato and to assess the association of leaf metabolites and physiological traits with the metabolite levels from other tissues. METHODS: The analysis of components of leaf metabolism was performed by phenotypying 76 tomato ILs with chromosome segments of the wild species Solanum pennellii in the genetic background of a cultivated tomato (S. lycopersicum) variety M82. The plants were cultivated in two different environments in independent years and samples were harvested from mature leaves of non-flowering plants at the middle of the light period. The non-targeted metabolite profiling was obtained by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). With the data set obtained in this study and already published metabolomics data from seed and fruit, we performed QTL mapping, heritability and correlation analyses. RESULTS: Changes in metabolite contents were evident in the ILs that are potentially important with respect to stress responses and plant physiology. By analyzing the obtained data, we identified 42 positive and 76 negative mQTL involved in carbon and nitrogen metabolism. CONCLUSIONS: Overall, these findings allowed the identification of S. lycopersicum genome regions involved in the regulation of leaf primary carbon and nitrogen metabolism, as well as the association of leaf metabolites with metabolites from seeds and fruits.

AtRsgA from Arabidopsis thaliana is important for maturation of the small subunit of the chloroplast ribosome.

Plastid ribosomes are very similar in structure and function to the ribosomes of their bacterial ancestors. Since ribosome biogenesis is not thermodynamically favorable under biological conditions it requires the activity of many assembly factors. Here we have characterized a homolog of bacterial RsgA in Arabidopsis thaliana and show that it can complement the bacterial homolog. Functional characterization of a strong mutant in Arabidopsis revealed that the protein is essential for plant viability, while a weak mutant produced dwarf, chlorotic plants that incorporated immature pre-16S ribosomal RNA into translating ribosomes. Physiological analysis of the mutant plants revealed smaller, but more numerous, chloroplasts in the mesophyll cells, reduction of chlorophyll a and b, depletion of proplastids from the rib meristem and decreased photosynthetic electron transport rate and efficiency. Comparative RNA sequencing and proteomic analysis of the weak mutant and wild-type plants revealed that various biotic stress-related, transcriptional regulation and post-transcriptional modification pathways were repressed in the mutant. Intriguingly, while nuclear- and chloroplast-encoded photosynthesis-related proteins were less abundant in the mutant, the corresponding transcripts were increased, suggesting an elaborate compensatory mechanism, potentially via differentially active retrograde signaling pathways. To conclude, this study reveals a chloroplast ribosome assembly factor and outlines the transcriptomic and proteomic responses of the compensatory mechanism activated during decreased chloroplast function.

Computational Approaches to Study Gene Regulatory Networks.

The goal of the gene regulatory network (GRN) inference is to determine the interactions between genes given heterogeneous data capturing spatiotemporal gene expression. Since transcription underlines all cellular processes, the inference of GRN is the first step in deciphering the determinants of the dynamics of biological systems. Here, we first describe the generic steps of the inference approaches that rely on similarity measures and group the similarity measures based on the computational methodology used. For each group of similarity measures, we not only review the existing approaches but also describe specifically the detailed steps of the existing state-of-the-art algorithms.

Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data.

Recent advances in metabolomics technologies have resulted in high-quality (time-resolved) metabolic profiles with an increasing coverage of metabolic pathways. These data profiles represent read-outs from often non-linear dynamics of metabolic networks. Yet, metabolic profiles have largely been explored with regression-based approaches that only capture linear relationships, rendering it difficult to determine the extent to which the data reflect the underlying reaction rates and their couplings. Here we propose an approach termed Stoichiometric Correlation Analysis (SCA) based on correlation between positive linear combinations of log-transformed metabolic profiles. The log-transformation is due to the evidence that metabolic networks can be modeled by mass action law and kinetics derived from it. Unlike the existing approaches which establish a relation between pairs of metabolites, SCA facilitates the discovery of higher-order dependence between more than two metabolites. By using a paradigmatic model of the tricarboxylic acid cycle we show that the higher-order dependence reflects the coupling of concentration of reactant complexes, capturing the subtle difference between the employed enzyme kinetics. Using time-resolved metabolic profiles from Arabidopsis thaliana and Escherichia coli, we show that SCA can be used to quantify the difference in coupling of reactant complexes, and hence, reaction rates, underlying the stringent response in these model organisms. By using SCA with data from natural variation of wild and domesticated wheat and tomato accession, we demonstrate that the domestication is accompanied by loss of such couplings, in these species. Therefore, application of SCA to metabolomics data from natural variation in wild and domesticated populations provides a mechanistic way to understanding domestication and its relation to metabolic networks.

A network property necessary for concentration robustness.

Maintenance of functionality of complex cellular networks and entire organisms exposed to environmental perturbations often depends on concentration robustness of the underlying components. Yet, the reasons and consequences of concentration robustness in large-scale cellular networks remain largely unknown. Here, we derive a necessary condition for concentration robustness based only on the structure of networks endowed with mass action kinetics. The structural condition can be used to design targeted experiments to study concentration robustness. We show that metabolites satisfying the necessary condition are present in metabolic networks from diverse species, suggesting prevalence of this property across kingdoms of life. We also demonstrate that our predictions about concentration robustness of energy-related metabolites are in line with experimental evidence from Escherichia coli. The necessary condition is applicable to mass action biological systems of arbitrary size, and will enable understanding the implications of concentration robustness in genetic engineering strategies and medical applications.

A Transcriptional and Metabolic Framework for Secondary Wall Formation in Arabidopsis.

Plant cell walls are essential for plant growth and development. The cell walls are traditionally divided into primary walls, which surround growing cells, and secondary walls, which provide structural support to certain cell types and promote their functions. While much information is available about the enzymes and components that contribute to the production of these two types of walls, much less is known about the transition from primary to secondary wall synthesis. To address this question, we made use of a transcription factor system in Arabidopsis (Arabidopsis thaliana) in which an overexpressed master secondary wall-inducing transcription factor, VASCULAR-RELATED NAC DOMAIN PROTEIN7, can be redirected into the nucleus by the addition of dexamethasone. We established the time frame during which primary wall synthesis changed into secondary wall production in dexamethasone-treated seedlings and measured transcript and metabolite abundance at eight time points after induction. Using cluster- and network-based analyses, we integrated the data sets to explore coordination between transcripts, metabolites, and the combination of the two across the time points. We provide the raw data as well as a range of network-based analyses. These data reveal links between hormone signaling and metabolic processes during the formation of secondary walls and provide a framework toward a deeper understanding of how primary walls transition into secondary walls.

Gene regulatory network inference using fused LASSO on multiple data sets.

Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions.

Genetic Determinants of the Network of Primary Metabolism and Their Relationships to Plant Performance in a Maize Recombinant Inbred Line Population.

Deciphering the influence of genetics on primary metabolism in plants will provide insights useful for genetic improvement and enhance our fundamental understanding of plant growth and development. Although maize (Zea mays) is a major crop for food and feed worldwide, the genetic architecture of its primary metabolism is largely unknown. Here, we use high-density linkage mapping to dissect large-scale metabolic traits measured in three different tissues (leaf at seedling stage, leaf at reproductive stage, and kernel at 15 d after pollination [DAP]) of a maize recombinant inbred line population. We identify 297 quantitative trait loci (QTLs) with moderate (86.2% of the mapped QTL, R(2) = 2.4 to 15%) to major effects (13.8% of the mapped QTL, R(2) >15%) for 79 primary metabolites across three tissues. Pairwise epistatic interactions between these identified loci are detected for more than 25.9% metabolites explaining 6.6% of the phenotypic variance on average (ranging between 1.7 and 16.6%), which implies that epistasis may play an important role for some metabolites. Key candidate genes are highlighted and mapped to carbohydrate metabolism, the tricarboxylic acid cycle, and several important amino acid biosynthetic and catabolic pathways, with two of them being further validated using candidate gene association and expression profiling analysis. Our results reveal a metabolite-metabolite-agronomic trait network that, together with the genetic determinants of maize primary metabolism identified herein, promotes efficient utilization of metabolites in maize improvement.

Differential metabolic and coexpression networks of plant metabolism.

Recent analyses have demonstrated that plant metabolic networks do not differ in their structural properties and that genes involved in basic metabolic processes show smaller coexpression than genes involved in specialized metabolism. By contrast, our analysis reveals differences in the structure of plant metabolic networks and patterns of coexpression for genes in (non)specialized metabolism. Here we caution that conclusions concerning the organization of plant metabolism based on network-driven analyses strongly depend on the computational approaches used.

Identification and mode of inheritance of quantitative trait loci for secondary metabolite abundance in tomato.

A large-scale metabolic quantitative trait loci (mQTL) analysis was performed on the well-characterized Solanum pennellii introgression lines to investigate the genomic regions associated with secondary metabolism in tomato fruit pericarp. In total, 679 mQTLs were detected across the 76 introgression lines. Heritability analyses revealed that mQTLs of secondary metabolism were less affected by environment than mQTLs of primary metabolism. Network analysis allowed us to assess the interconnectivity of primary and secondary metabolism as well as to compare and contrast their respective associations with morphological traits. Additionally, we applied a recently established real-time quantitative PCR platform to gain insight into transcriptional control mechanisms of a subset of the mQTLs, including those for hydroxycinnamates, acyl-sugar, naringenin chalcone, and a range of glycoalkaloids. Intriguingly, many of these compounds displayed a dominant-negative mode of inheritance, which is contrary to the conventional wisdom that secondary metabolite contents decreased on domestication. We additionally performed an exemplary evaluation of two candidate genes for glycolalkaloid mQTLs via the use of virus-induced gene silencing. The combined data of this study were compared with previous results on primary metabolism obtained from the same material and to other studies of natural variance of secondary metabolism.

Segmentation of biological multivariate time-series data.

Time-series data from multicomponent systems capture the dynamics of the ongoing processes and reflect the interactions between the components. The progression of processes in such systems usually involves check-points and events at which the relationships between the components are altered in response to stimuli. Detecting these events together with the implicated components can help understand the temporal aspects of complex biological systems. Here we propose a regularized regression-based approach for identifying breakpoints and corresponding segments from multivariate time-series data. In combination with techniques from clustering, the approach also allows estimating the significance of the determined breakpoints as well as the key components implicated in the emergence of the breakpoints. Comparative analysis with the existing alternatives demonstrates the power of the approach to identify biologically meaningful breakpoints in diverse time-resolved transcriptomics data sets from the yeast Saccharomyces cerevisiae and the diatom Thalassiosira pseudonana.

Network-based segmentation of biological multivariate time series.

Molecular phenotyping technologies (e.g., transcriptomics, proteomics, and metabolomics) offer the possibility to simultaneously obtain multivariate time series (MTS) data from different levels of information processing and metabolic conversions in biological systems. As a result, MTS data capture the dynamics of biochemical processes and components whose couplings may involve different scales and exhibit temporal changes. Therefore, it is important to develop methods for determining the time segments in MTS data, which may correspond to critical biochemical events reflected in the coupling of the system's components. Here we provide a novel network-based formalization of the MTS segmentation problem based on temporal dependencies and the covariance structure of the data. We demonstrate that the problem of partitioning MTS data into [Formula: see text] segments to maximize a distance function, operating on polynomially computable network properties, often used in analysis of biological network, can be efficiently solved. To enable biological interpretation, we also propose a breakpoint-penalty (BP-penalty) formulation for determining MTS segmentation which combines a distance function with the number/length of segments. Our empirical analyses of synthetic benchmark data as well as time-resolved transcriptomics data from the metabolic and cell cycles of Saccharomyces cerevisiae demonstrate that the proposed method accurately infers the phases in the temporal compartmentalization of biological processes. In addition, through comparison on the same data sets, we show that the results from the proposed formalization of the MTS segmentation problem match biological knowledge and provide more rigorous statistical support in comparison to the contending state-of-the-art methods.