Publications by authors named "Jan Huege"

Perennial ryegrass () is integral to temperate pastoral agriculture, which contributes most of the milk and meat production worldwide. Chemical profiles and diversity of ryegrass offer several opportunities to harness specific traits and elucidate underlying biological mechanisms for forage improvement. We conducted a large-scale metabolomics study of perennial ryegrass comprising 715 genotypes, representing 118 populations from 21 countries.

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Background: Pyrrolizidine alkaloids (PAs) are a class of secondary metabolites that function as feeding deterrents in a range of different plant species. In perennial ryegrass (Lolium perenne L.) the only PAs that have been identified are the thesinine-rhamnoside group, which displays significant genetic variation.

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The efficiency of inorganic nitrogen (N) assimilation is a critical component of fertilizer use by plants and of forage production in Lolium perenne, an important pasture species worldwide. We present a spatiotemporal description of nitrate use efficiency in terms of metabolic responses and carbohydrate remobilization, together with components of cytokinin signal transduction following nitrate addition to N-impoverished plants. Perennial ryegrass (L.

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Liquid chromatography coupled to mass spectrometry (LCMS) is widely used in metabolomics due to its sensitivity, reproducibility, speed and versatility. Metabolites are detected as peaks which are characterised by mass-over-charge ratio () and retention time (rt), and one of the most critical but also the most challenging tasks in metabolomics is to annotate the large number of peaks detected in biological samples. Accurate measurements enable the prediction of molecular formulae which provide clues to the chemical identity of peaks, but often a number of metabolites have identical molecular formulae.

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Isotope labelling experiments with stable or radioactive isotopes have long been an integral part of biological and medical research. Labelling experiments led to the discovery of new metabolic pathways and made it possible to calculate the fluxes responsible for a metabolic phenotype, i.e.

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In this chapter we illustrate the methodology for high-throughput metabolic flux analysis. Central to this is developing an end to end data pipeline, crucial for integrating the wet lab experiments and analytics, combining hardware and software automation, and standardizing data representation providing importers and exporters to support third party tools. The use of existing software at the start, data extraction from the chromatogram, and the end, MFA analysis, allows for the most flexibility in this workflow.

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Photosynthesis is the basis for life, and its optimization is a key biotechnological aim given the problems of population explosion and environmental deterioration. We describe a method to resolve intracellular fluxes in intact Arabidopsis thaliana rosettes based on time-dependent labeling patterns in the metabolome. Plants photosynthesizing under limiting irradiance and ambient CO2 in a custom-built chamber were transferred into a (13)CO2-enriched environment.

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Background: Metabolic flux analysis has become an established method in systems biology and functional genomics. The most common approach for determining intracellular metabolic fluxes is to utilize mass spectrometry in combination with stable isotope labeling experiments. However, before the mass spectrometric data can be used it has to be corrected for biases caused by naturally occurring stable isotopes, by the analytical technique(s) employed, or by the biological sample itself.

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Research on plant metabolism is currently experiencing the common use of various omics methods creating valuable information on the concentrations of the cell's constituents. However, little is known about in vivo reaction rates, which can be determined by Metabolic Flux Analysis (MFA), a combination of isotope labeling experiments and computer modeling of the metabolic network. Large-scale applications of this method so far have been hampered by tedious procedures of tissue culture, analytics, modeling and simulation.

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Using metabolic and transcriptomic phenotyping, we studied acclimation of cyanobacteria to low inorganic carbon (LC) conditions and the requirements for coordinated alteration of metabolism and gene expression. To analyse possible metabolic signals for LC sensing and compensating reactions, the carboxysome-less mutant ΔccmM and the photorespiratory mutant ΔglcD1/D2 were compared with wild-type (WT) Synechocystis. Metabolic phenotyping revealed accumulation of 2-phosphoglycolate (2PG) in ΔccmM and of glycolate in ΔglcD1/D2 in LC- but also in high inorganic carbon (HC)-grown mutant cells.

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Here we describe an integrative protocol for metabolite extraction and the measurement of three cellular constituents, chlorophyll a, total protein, and glycogen from the same small volume of cyanobacterial cultures that can be used as alternative sample amount parameters for data adjustment in comparative metabolome studies. We conducted recovery experiments to assess the robustness and reproducibility of the measurements obtained for the cellular constituents. Also, we have chosen three profile-intrinsic parameters derived from gas chromatography-mass spectrometry (GC/MS) data in order to test their utility for spectral data adjustment.

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Background: Recent studies using transcript and metabolite profiles of wild-type and gene deletion mutants revealed that photorespiratory pathways are essential for the growth of Synechocystis sp. PCC 6803 under atmospheric conditions. Pool size changes of primary metabolites, such as glycine and glycolate, indicated a link to photorespiration.

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Metabolomics is the comprehensive analysis of the small molecules that compose an organism's metabolism. The main limiting step in microbial metabolomics is the requirement for fast and efficient separation of microbes from the culture medium under conditions in which metabolism is rapidly halted. In this article we compare three different sampling strategies, quenching, filtering, and centrifugation, for arresting the metabolic activities of two morphologically diverse cyanobacteria, the unicellular Synechocystis sp.

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The amount of inorganic carbon represents one of the main environmental factors determining productivity of photoautotrophic organisms. Using the model cyanobacterium Synechocystis sp. PCC 6803, we performed a first metabolome study with cyanobacterial cells shifted from high CO(2) (5% in air) into conditions of low CO(2) (LC; ambient air with 0.

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The established GC-EI-TOF-MS method for the profiling of soluble polar metabolites from plant tissue was employed for the kinetic metabolic phenotyping of higher plants. Approximately 100 typical GC-EI-MS mass fragments of trimethylsilylated and methoxyaminated metabolite derivatives were structurally interpreted for mass isotopomer analysis, thus enabling the kinetic study of identified metabolites as well as the so-called functional group monitoring of yet non-identified metabolites. The monitoring of isotope dilution after (13)CO(2) labelling was optimized using Arabidopsis thaliana Col-0 or Oryza sativa IR57111 plants, which were maximally labelled with (13)C.

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