Plant Metabolic Networks
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Registration Fees Additional Fee Info. Plant Molecular Biology June 14 - 19, Contact Us Call Us: You have no notifications. Claudia Schmidt-Dannert Univ. Mary Schuler Univ. Kazuki Saito Chiba University, Japan "Integration of transcriptomics and metabolomics for finding regulatory networks in plant metabolism". Bert van der Zaal Leiden University, The Netherlands "The use of artificial transcription factor libraries for mutant discovery".
RESULTS AND DISCUSSION
The working hypothesis is that a network of metabolic associations represents a snapshot response of the underlying biochemical network at a given biological situation, which then can be used to observe changes between different genotypes 8 , 9. Specifically, the effects on metabolite pool concentrations may be higher than the alteration in enzymatic flux control or enzyme activities.
Recently, silent yeast phenotypes were discriminated from WT strains by using multivariate statistics applied to NMR 12 or MS 13 based metabolic fingerprints, with the objective to cluster genotypes together that were defective in genes with similar functions.
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However, resolution of the NMR and MS data prohibited the actual determination of individual metabolites to underpin the biochemical basis of the clustering results. Here, we present an approach to the study of silent plant phenotypes. We propose to use unbiased metabolite detection concomitant with differential topology analysis of metabolic correlation networks as complementary tools to classical univariate and multivariate statistical tests.
As a test case for a silent plant phenotype, an antisense potato plant line was used that was constitutively reduced in gene expression encoding for the sucrose synthase isoform II SS2 under the control of the 35S promoter. The primary biochemical action of sucrose synthase is sucrose cleavage to UDP-glucose and fructose, which may also work bidirectionally in vivo in tubers In general, several isoforms of sucrose synthase are known to play pivotal roles in plant development, carbon partitioning, phloem unloading, and sink strength 15 — However, it is difficult to allocate specific functions of particular isoforms.
When two genes encoding sucrose synthase are expressed in the same cell, the proteins form homo- or heterotetramers 20 — 22 , suggesting that the isozymes are interchangeable in at least some cellular roles.
Fluxes through plant metabolic networks: measurements, predictions, insights and challenges.
This gene redundancy might lead to silent phenotypes if specific isoforms have decreased activities using the antisense approach. Specifically, no change in total enzyme activity can be anticipated. The objective of this study was now to investigate with metabolomic and statistical tools whether i the silent SS2 antisense genotype can be discriminated from the parental line and whether ii biochemical effects on primary metabolism can be found as an important classifier in discrimination.
Plants were grown in controlled greenhouse conditions in a randomized plot. One-hundred-milligram FW tuber slices 5 mm i. For leaves, mg FW disks were sampled. Samples were extracted and fractionated into a polar and a lipophilic fraction as given in ref. Joseph, MI and chromatof software Fig. For identification and alignment, peaks were matched against a customized reference spectrum database, based on retention indices and mass spectral similarities.
Relative quantification was performed on ion traces chosen by optimal selectivity from coeluting compounds. Artifact peaks such as column bleeding, phtalates, and polysiloxanes were removed.
Topological assessment of metabolic networks reveals evolutionary information | Scientific Reports
All data were normalized to plant mg FW and to internal references ribitol and nonadecanoic acid methyl ester. For all statistical tests, data were log-transformed, resulting in more Gaussian-type distributions Fig. For multivariate statistics, empty cells were replaced by genotype means.
Principle component analysis was performed by using pirouette 2. By Northern blot analysis using specific cDNAs for the sucrose synthase isoforms SS1, SS2, and SS3, it was shown that all sucrose synthase genes were expressed in all potato tissues, but that expression levels varied Fig.
The expression of SS2 was most specific for leaf veins, implying a role in carbon partitioning for long-distance transport. For this reason, we expected metabolic effects in mature leaves as carbon source and tubers carbon sinks. Expression of the SS2 antisense construct was tissue specific. In leaf tissue, the sense SS2 band was completely suppressed concomitant with a strong antisense signal whereas in tuber the sense band was still expressed beside a strong antisense band Fig. However, the total sucrose synthase activity was not found to be significantly decreased due to the coexpression of the other two enzyme isoforms.
This phenomenon is often observed in plant molecular biology. In some cases, the total activity may even be found to have increased after suppression or knockout of a specific gene In maize mutants, a loss of the sucrose synthase isoform sus1 was reported to have no phenotypic effect but was associated with ectopic expression of the other gene isoform sh1 gene complementing sus1 RNA expression analysis of sucrose synthase isoforms in potato plants.
Consistent with this background knowledge, it was not expected to find a strong phenotype for the antisense expression of the SS2 isoform in potato. No significant change in plant morphology or development, or target metabolites such as starch, sucrose, glucose, or fructose contents was observed in comparison with the parental line Table 1 ; see also Fig.
However, we assumed that the antisense expression of sucrose synthase should still have measurable effects on related key enzymes in carbohydrate metabolism that counteracted the primary effect of the altered biochemical network in a pleiotropic way. Based on the strength of the SS2 suppression in leaves, enzyme activity measurements focused on this organ.
No significant difference was found for the acid and alkaline invertases, fructobisphosphatase, fructokinase, and the maximum catalytic activity V max of sucrosephosphate synthase SPS. This finding means that the SPS enzyme was less sensitive to inhibition by inorganic phosphate the SPS allosteric inhibitor , which suggests a higher sucrose mobilization in SS2 plants.
Intriguingly, the maximal catalytic activity of sucrose synthase was increased in the antisense tubers Table 1 , pointing to an increased sucrose influx. In the next sections, we describe how metabolic networks respond to differences in the SPS activation state. Although these data sets were sufficient to distinguish metabolic states of clearly different plant genotypes, they did not extract the full information contained in these chromatograms.
Topology of plant metabolic networks
Compared with quadrupole MS, GC-TOF data acquisition has the great advantage that far more spectra can be acquired across a chromatographic peak. Additionally, relative ion intensities in mass spectra remain constant over the chromatographic elution profile. Both properties largely enhance the suitability of i finding peaks in a completely unbiased manner, even for low abundant trace compounds, and ii deconvoluting coeluting mass spectra in complex chromatograms, in which very often more than two side peaks overlap with any specific metabolite.
This MS deconvolution facilitates correct peak annotation by spectra purification.
The deconvolution power is demonstrated by detection of low abundant hexose isomers like psicose, tagatose, and allose Fig. Limits of mass spectral deconvolution were found for trace compounds that coeluted with 1,fold more concentrated peaks as given for the example of allose and fructose Fig. GC-TOF analysis deconvoluting uncommon monosaccharides such as tagatose, psicose, and allose in polar extracts of potato leaves.
Mass spectra for coeluting peaks a and b allose and fructose are given in Fig. Among these compounds, the only metabolite that could be identified was galactonic acid. The other 14 were therefore primary candidates for de novo structural elucidation; 7 of these were classified as sugarrelated compounds by their corresponding mass spectra. Only one lipophilic compound was found among these hits, implying that leaf metabolism was more significantly affected for polar phase metabolites than for lipophilics.
For tubers, far fewer significant changes were found. Mean values of metabolite levels directly involved in the enzymatic reaction of sucrose synthase, i. Next, genotype discrimination was tested by clustering tools to find further evidence for biochemical alterations in the silent SS2 phenotype.
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Apart from the fairly small differences in mean values between the two genotypes, this result is probably caused by the high amount of non-genotype related variance caused by all other metabolites. By using lower-order vectors, metabolic phenotypes could partly or fully be discriminated between SS2 and WT plants Fig. On leaf metabolic phenotypes, glucose, fructose, and sucrose had almost no impact on this discrimination, but rather those compounds that were already observed to have significantly different mean values.
For tubers, glucose and fructose were found among the most important metabolites for classification based on loading scores for vector 4 4. In accordance with the alteration in sucrose synthase and specific SPS activities, this finding indicated a subtle alteration in glucose and fructose metabolism between SS2 and WT tubers. Alternatively, supervised learning methods such as DFA can be used to classify metabolite data sets By using a nonparametric normal kernel DFA, genotypes could be discriminated by using internal cross-validation.
Investigation of the discriminant functions, however, pointed to the same metabolites that had already been demonstrated to be significantly different in mean levels, e. In this respect, DFA was not helpful for gaining further insights into metabolic alterations in the SS2 plants or pointing to the primary cause of the genetic defect. Analyzing a large number of snapshots of the same genotype permitted the search for metabolic correlations Fig.
In a recent study, we demonstrated that metabolic fluctuations may cause linear associations between metabolite levels as a consequence of the underlying reaction pathway structure 8 , 9. However, experimentally observed correlations between variables are not straightforwardly interpretable in a complex system 9 , 30 , In our study, several thousand metabolite pairs were found meeting such low thresholds. Metabolite—metabolite correlations given in scatter plots. Central to RA2 is an integrated analysis of the plant microbiota metabolism, taking advantage of reductionist approaches.
Toggle navigation. Alga Zuccaro azuccaro[at]uni-koeln. Paul Schulze-Lefert schlef[at]mpipz. Petra Bauer. Michael Bonkowski. Marcel Bucher.