Widening the landscape of transcriptional regulation of green algal photoprotection.

Availability of light and CO, substrates of microalgae photosynthesis, is frequently far from optimal. Microalgae activate photoprotection under strong light, to prevent oxidative damage, and the CO Concentrating Mechanism (CCM) under low CO, to raise intracellular CO levels. The two processes are interconnected; yet, the underlying transcriptional regulators remain largely unknown.

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 accessions Col-0 and C24 differ significantly in their resistance to the pathogen pv. (Pst).

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.

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.

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).

A Biostimulant Obtained from the Seaweed Protects from Severe Oxidative Stress.

Abiotic stresses cause oxidative damage in plants. Here, we demonstrate that foliar application of an extract from the seaweed , SuperFifty (SF), largely prevents paraquat (PQ)-induced oxidative stress in . While PQ-stressed plants develop necrotic lesions, plants pre-treated with SF (i.

Plasticity in metabolism underpins local responses to nitrogen in 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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Effect of salt stress on genes encoding translation-associated proteins in Arabidopsis thaliana.

Salinity negatively affects plant growth and disturbs chloroplast integrity. Here, we aimed at identifying salt-responsive translation-related genes in Arabidopsis thaliana with an emphasis on those encoding plastid-located proteins. We used quantitative real-time PCR to test the expression of 170 genes after short-term salt stress (up to 24 h) and identified several genes affected by the stress including: PRPL11, encoding plastid ribosomal protein L11, ATAB2, encoding a chloroplast-located RNA-binding protein presumably functioning as an activator of translation, and PDF1B, encoding a peptide deformylase involved in N-formyl group removal from nascent proteins synthesized in chloroplasts.

PageRank-based identification of signaling crosstalk from transcriptomics data: the case of Arabidopsis thaliana.

The levels of cellular organization, from gene transcription to translation to protein-protein interaction and metabolism, operate via tightly regulated mutual interactions, facilitating organismal adaptability and various stress responses. Characterizing the mutual interactions between genes, transcription factors, and proteins involved in signaling, termed crosstalk, is therefore crucial for understanding and controlling cells' functionality. We aim at using high-throughput transcriptomics data to discover previously unknown links between signaling networks.