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

Nunes Nesi, Adriano. Universidade Federal de Viçosa. Departamento de Biologia Vegetal. Viçosa, Minas Gerais, Brazil.Alseekh, Saleh. Max - Planck- Institute of Molecular Plant Physiology. Potsdam, Germany.Oliveira Silva, Franklin Magnum de. Universidade Federal de Viçosa. Departamento de Biologia Vegetal. Viçosa, Minas Gerais, Brazil.Omranian, Nooshin. Max - Planck- Institute of Molecular Plant Physiology. Potsdam, Germany.Lichtenstein, Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología. Castelar, Buenos Aires, Argentina.Mirnezhad, Mohammad. Leiden University. Plant Ecology, Institute of Biology. The Netherlands.Romero González, Roman R. Leiden University. Plant Ecology. Institute of Biology. The Netherlands.Carrari, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología. Castelar, Buenos Aires, Argentina.1-13Introduction 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

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

Widening the landscape of transcriptional regulation of algal photoprotection

Abstract Availability of light and CO 2 , substrates of microalgae photosynthesis, is frequently far from optimal. Microalgae activate photoprotection under strong light, to prevent oxidative damage, and the CO 2 Concentrating Mechanism (CCM) under low CO 2 , to raise intracellular CO 2 levels. The two processes are interconnected; yet, the underlying transcriptional regulators remain largely unknown. By employing a large transcriptomics data compendium of Chlamydomonas reinhardtii’s responses to different light and carbon supply we reconstructed a consensus genome-scale gene regulatory network from complementary inference approaches and used it to elucidate the transcriptional regulation of photoprotection. We showed 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. Along with demonstrating the existence of regulatory hubs that channel light- and CO 2 -mediated signals into a common response, our study provides a unique resource to dissect gene expression regulation in this microalga

Network-Based Segmentation of Biological Multivariate Time Series

<div><p>Molecular phenotyping technologies (<i>e.g.</i>, 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 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 <i>Saccharomyces cerevisiae</i> 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.</p></div

Optimal segmentation for synthetic data.

<p>The upper part of the table shows the result of the optimal segmentation for synthetic data based on dynamic programming, while the lower part contains the result based on the method of Ramakrishnan <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062974#pone.0062974-Ramakrishnan1" target="_blank">[15]</a>. In the upper table, the first and second columns show the name and the type of network properties used to determine the distances: G stands for global, L for local, and LG for local-global. The third column includes the number of segments that maximize the objective with the dynamic programming approach. The resulting segments are given in the forth column, while the fifth and sixth columns contain the corresponding values of lower () and upper () bound of the tuning parameter . The lower part also includes minimum and maximum length of the segments, i.e., and , as parameters of the contending method.</p

Segmentation for yeast’s metabolic cycle.

<p>The partitions found by applying our method are highlighted in light grey. The phases of the yeast metabolic cycle are indicated with colored rectangles above each panel following Tu <i>et al. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062974#pone.0062974-Tu1" target="_blank">[36]</a>. R/C stands for reductive charging, OX oxidative metabolism, and R/B, reductive metabolism. (a) shows the segmentations caught by relative density as global property; (b) illustrates the segmentations based on degree; (c) and (d) demonstrate segmentations with local-global properties, betweenness and closeness, respectively. The segmentations in panel (a) performs particularly well due to the global changes in the form of global cycles in the data set from yeast.</p

Illustration of the segmentation for synthetic data with relative density as network property.

<p>The resulting partitions are highlighted in light grey and the simulated segmentation points are marked with red bars.</p

Directed acyclic graph (DAG) used as input in Algorithm 1 (Fig. 3).

<p>The DAG for time points is depicted. It contains nodes, including the special nodes and . The label of each node corresponds to the time points , , and .</p