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Photorespiration is a central component of photosynthesis; however to better understand its role it should be viewed in the context of an integrated metabolic network rather than a series of individual reactions that operate independently. Isotopically nonstationary C metabolic flux analysis (INST-MFA), which is based on transient labeling studies at metabolic steady state, offers a comprehensive platform to quantify plant central metabolism. In this chapter, we describe the application of INST-MFA to investigate metabolism in leaves. Leaves are an autotrophic tissue, assimilating CO over a diurnal period implying that the metabolic steady state is limited to less than 12 h and thus requiring an INST-MFA approach. This strategy results in a comprehensive unified description of photorespiration, Calvin cycle, sucrose and starch synthesis, tricarboxylic acid (TCA) cycle, and amino acid biosynthetic fluxes. We present protocols of the experimental aspects for labeling studies: transient CO labeling of leaf tissue, sample quenching and extraction, mass spectrometry (MS) analysis of isotopic labeling data, measurement of sucrose and amino acids in vascular exudates, and provide details on the computational flux estimation using INST-MFA.
MOTIVATION - Matrix Assisted Laser Desorption Ionization-Imaging Mass Spectrometry (MALDI-IMS) in 'omics' data acquisition generates detailed information about the spatial distribution of molecules in a given biological sample. Various data processing methods have been developed for exploring the resultant high volume data. However, most of these methods process data in the spectral domain and do not make the most of the important spatial information available through this technology. Therefore, we propose a novel streamlined data analysis pipeline specifically developed for MALDI-IMS data utilizing significant spatial information for identifying hidden significant molecular distribution patterns in these complex datasets.
METHODS - The proposed unsupervised algorithm uses Sliding Window Normalization (SWN) and a new spatial distribution based peak picking method developed based on Gray level Co-Occurrence (GCO) matrices followed by clustering of biomolecules. We also use gist descriptors and an improved version of GCO matrices to extract features from molecular images and minimum medoid distance to automatically estimate the number of possible groups.
RESULTS - We evaluated our algorithm using a new MALDI-IMS metabolomics dataset of a plant (Eucalypt) leaf. The algorithm revealed hidden significant molecular distribution patterns in the dataset, which the current Component Analysis and Segmentation Map based approaches failed to extract. We further demonstrate the performance of our peak picking method over other traditional approaches by using a publicly available MALDI-IMS proteomics dataset of a rat brain. Although SWN did not show any significant improvement as compared with using no normalization, the visual assessment showed an improvement as compared to using the median normalization.
AVAILABILITY AND IMPLEMENTATION - The source code and sample data are freely available at http://exims.sourceforge.net/.
CONTACT - firstname.lastname@example.org or email@example.com
SUPPLEMENTARY INFORMATION - Supplementary data are available at Bioinformatics online.
© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: firstname.lastname@example.org.
Improving plant productivity is an important aim for metabolic engineering. There are few comprehensive methods that quantitatively describe leaf metabolism, although such information would be valuable for increasing photosynthetic capacity, enhancing biomass production, and rerouting carbon flux toward desirable end products. Isotopically nonstationary metabolic flux analysis (INST-MFA) has been previously applied to map carbon fluxes in photoautotrophic bacteria, which involves model-based regression of transient (13)C-labeling patterns of intracellular metabolites. However, experimental and computational difficulties have hindered its application to terrestrial plant systems. We performed in vivo isotopic labeling of Arabidopsis thaliana rosettes with (13)CO2 and estimated fluxes throughout leaf photosynthetic metabolism by INST-MFA. Plants grown at 200 µmol m(-2)s(-1) light were compared with plants acclimated for 9 d at an irradiance of 500 µmol⋅m(-2)⋅s(-1). Approximately 1,400 independent mass isotopomer measurements obtained from analysis of 37 metabolite fragment ions were regressed to estimate 136 total fluxes (54 free fluxes) under each condition. The results provide a comprehensive description of changes in carbon partitioning and overall photosynthetic flux after long-term developmental acclimation of leaves to high light. Despite a doubling in the carboxylation rate, the photorespiratory flux increased from 17 to 28% of net CO2 assimilation with high-light acclimation (Vc/Vo: 3.5:1 vs. 2.3:1, respectively). This study highlights the potential of (13)C INST-MFA to describe emergent flux phenotypes that respond to environmental conditions or plant physiology and cannot be obtained by other complementary approaches.
Metabolic flux analysis (MFA) is a powerful approach for quantifying plant central carbon metabolism based upon a combination of extracellular flux measurements and intracellular isotope labeling measurements. In this chapter, we present the method of isotopically nonstationary (13)C MFA (INST-MFA), which is applicable to autotrophic systems that are at metabolic steady state but are sampled during the transient period prior to achieving isotopic steady state following the introduction of (13)CO2. We describe protocols for performing the necessary isotope labeling experiments, sample collection and quenching, nonaqueous fractionation and extraction of intracellular metabolites, and mass spectrometry (MS) analysis of metabolite labeling. We also outline the steps required to perform computational flux estimation using INST-MFA. By combining several recently developed experimental and computational techniques, INST-MFA provides an important new platform for mapping carbon fluxes that is especially applicable to autotrophic organisms, which are not amenable to steady-state (13)C MFA experiments.
Natural variation in the regulation of the accumulation of mineral nutrients and trace elements in plant tissues is crucial to plant metabolism, development, and survival across different habitats. Studies of the genetic basis of natural variation in nutrient metabolism have been facilitated by the development of ionomics. Ionomics is a functional genomic approach for the identification of the genes and gene networks that regulate the elemental composition, or ionome, of an organism. In this study, we evaluated the genetic basis of divergence in elemental composition between an inland annual and a coastal perennial accession of Mimulus guttatus using a recombinant inbred line (RIL) mapping population. Out of 20 elements evaluated, Mo and Cd were the most divergent in accumulation between the two accessions and were highly genetically correlated in the RILs across two replicated experiments. We discovered two major quantitative trait loci (QTL) for Mo accumulation, the largest of which consistently colocalized with a QTL for Cd accumulation. Interestingly, both Mo QTLs also colocalized with the two M. guttatus homologues of MOT1, the only known plant transporter to be involved in natural variation in molybdate uptake.
PREMISE OF STUDY - Botanists have long been interested in the reasons for genetic variation among individuals, populations, and species of plants. The anthocyanin pathway is ideal for studying the evolution of such phenotypic variation.
METHODS - We used a combination of quantitative trait loci mapping and association studies to understand the genetic basis of variation in five anthocyanin phenotypes including calyx, corolla, and leaf coloration patterns that vary within and among populations of Mimulus guttatus. We then examined what genes might be responsible for this phenotypic variation and whether one of the traits, calyx spotting, is randomly distributed across the geographic range of the species.
KEY RESULTS - All five phenotypes in M. guttatus were primarily controlled by the same major locus (PLA1), which contains a tandem array of three R2R3-MYB genes known to be involved in the evolution of flower color in a related species of Mimulus. Calyx spotting was nonrandomly distributed across the range of M. guttatus and correlated with multiple climate variables.
CONCLUSIONS - The results of this study suggest that variation in R2R3-MYB genes is the primary cause of potentially important anthocyanin phenotypic variation within and among populations of M. guttatus, a finding consistent with recent theoretical and empirical research on flower color evolution.
In addition to the known metabolites cytochalasin H (1), cytochalasin J (2), and epoxycytochalasin H (3), two new 10-phenyl-(11)-cytochalasans, named cytochalasin Z10 and Z11 (4 and 5, resp.) were isolated from the solid substrate culture of Endothia gyrosa IFB-E023, an endophytic fungus residing inside the healthy leaf of Vatica mangachapo (Dipterocarpaceae). The structure determination of 4 and 5 was accomplished through correlative analyses of their spectral data (UV, ESI-MS, IR, (1)H- and (13)C-NMR, COSY, NOESY, HMQC, and HMBC). Metabolites 1-5 were demonstrated to be substantially cytotoxic to the human leukaemia K562 cell line with the IC(50) values of 10.1, 1.5, 24.5, 28.3, and 24.4 microM, respectively, which are comparable to that of 5-fluorouracil (33.0 microM), co-assayed as the positive reference.
Chaetominine (1), an alkaloidal metabolite with a new framework, was characterized from the solid-substrate culture of Chaetomium sp. IFB-E015, an endophytic fungus on the apparently healthy Adenophora axilliflora leaves. Its structure was determined by a combination of its spectral data and single-crystal X-ray diffraction analysis, with its absolute configuration elucidated by Marfey's method. Chaetominine was more cytotoxic than 5-fluorouracil against the human leukemia K562 and colon cancer SW1116 cell lines. [structure: see text]