Sabrina Kleessen

Max Planck Institute of Molecular Plant Physiology, Potsdam, Brandenburg, Germany

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Publications (15)55.4 Total impact

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    ABSTRACT: Metabolomics comprises of the monitoring of small molecules present in a biological system as a function of time and space. Coupled with emerging modeling approaches, it facilitates predictions of reaction sequences. Here, we explore the potential of metabolomic tools for analyzing the complex chemical systems in a model reaction, the hydrothermal reforming (HTR) of glycine. The profiles for more than 20 monitored compounds were used to reconstruct the glycine reaction network. The mechanism of glycine conversion into serine and alanine was validated, where new carbon–carbon (C–C) bonds are formed from the C2-position of glycine. We thus demonstrated that metabolomic methods are useful for the analysis of complex combinatorial problems in chemistry.
    RSC Advances 04/2014; 4(32). · 3.71 Impact Factor
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    ABSTRACT: Growth often involves a trade-off between the performance of contending tasks; metabolic plasticity can play an important role. Here we grow 97 Arabidopsis thaliana accessions in three conditions with a differing supply of carbon and nitrogen and identify a trade-off between two tasks required for rosette growth: increasing the physical size and increasing the protein concentration. We employ the Pareto performance frontier concept to rank accessions based on their multitask performance; only a few accessions achieve a good trade-off under all three growth conditions. We determine metabolic efficiency in each accession and condition by using metabolite levels and activities of enzymes involved in growth and protein synthesis. We demonstrate that accessions with high metabolic efficiency lie closer to the performance frontier and show increased metabolic plasticity. We illustrate how public domain data can be used to search for additional contending tasks, which may underlie the sub-optimality in some accessions.
    Nature Communications 01/2014; 5:3537. · 10.02 Impact Factor
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    Sabrina Kleessen, Sebastian Klie, Zoran Nikoloski
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    ABSTRACT: The accumulation of high-throughput data from different experiments has facilitated the extraction of condition-specific networks over the same set of biological entities. Comparing and contrasting of such multiple biological networks is in the center of differential network biology, aiming at determining general and condition-specific responses captured in the network structure (i.e., included associations between the network components). We provide a novel way for comparison of multiple networks based on determining network clustering (i.e., partition into communities) which is optimal across the set of networks with respect to a given cluster quality measure. To this end, we formulate the optimization-based problem of concurrent conditional clustering of multiple networks, termed COCONETS, based on the modularity. The solution to this problem is a clustering which depends on all considered networks and pinpoints their preserved substructures. We present theoretical results for special classes of networks to demonstrate the implications of conditionality captured by the COCONETS formulation. As the problem can be shown to be intractable, we extend an existing efficient greedy heuristic and applied it to determine concurrent conditional clusters on coexpression networks extracted from publically available time-resolved transcriptomics data of Escherichia coli under five stresses as well as on metabolite correlation networks from metabolomics data set from Arabidopsis thaliana exposed to eight environmental conditions. We demonstrate that the investigation of the differences between the clustering based on all networks with that obtained from a subset of networks can be used to quantify the specificity of biological responses. While a comparison of the Escherichia coli coexpression networks based on seminal properties does not pinpoint biologically relevant differences, the common network substructures extracted by COCONETS are supported by existing experimental evidence. Therefore, the comparison of multiple networks based on concurrent conditional clustering offers a novel venue for detection and investigation of preserved network substructures.
    PLoS ONE 01/2014; 9(8):e103637. · 3.73 Impact Factor
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    ABSTRACT: Seed metabolites are critically important both for plant development and human nutrition; however, the natural variation in their levels remains poorly characterized. Here we profiled 121 metabolites in mature seeds of a wide panel Oryza sativa japonica and indica cultivars, revealing correlations between the metabolic phenotype and geographic origin of the rice seeds. Moreover, japonica and indica subspecies differed significantly not only in the relative abundances of metabolites but also in their corresponding metabolic association networks. These findings provide important insights into metabolic adaptation in rice subgroups, bridging the gap between genome and phenome, and facilitating the identification of genetic control of metabolic properties that can serve as a basis for the future improvement of rice quality via metabolic engineering.
    Scientific reports. 01/2014; 4:5067.
  • Sabrina Kleessen, Sebastian Klie, Zoran Nikoloski
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    ABSTRACT: Plant behaviors across levels of cellular organization, from biochemical components to tissues and organs, relate and reflect growth habitats. Quantification of the relationship between behaviors captured in various phenotypic characteristics and growth habitats can help reveal molecular mechanisms of plant adaptation. The aim of this article is to introduce the power of using statistics originally developed in the field of geographic variability analysis together with prominent network models in elucidating principles of biological organization. We provide a critical systematic review of the existing statistical and network-based approaches that can be employed to determine patterns of covariation from both uni- and multivariate phenotypic characteristics in plants. We demonstrate that parameter-independent network-based approaches result in robust insights about phenotypic covariation. These insights can be quantified and tested by applying well-established statistics combining the network structure with the phenotypic characteristics. We show that the reviewed network-based approaches are applicable from the level of genes to the study of individuals in a population of Arabidopsis thaliana. Finally, we demonstrate that the patterns of covariation can be generalized to quantifiable biological principles of organization. Therefore, these network-based approaches facilitate not only interpretation of large-scale data sets, but also prediction of biochemical and biological behaviors based on measurable characteristics.
    The Plant Cell 06/2013; · 9.25 Impact Factor
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    ABSTRACT: Understanding molecular factors determining local adaptation is a key challenge, particularly relevant for plants, which are sessile organisms coping with a continuously fluctuating environment. Here we introduce a rigorous network-based approach for investigating the relation between geographic location of accessions and heterogeneous molecular phenotypes. We demonstrate for Arabidopsis accessions that not only genotypic variability but also flowering and metabolic phenotypes show a robust pattern of isolation-by-distance. Our approach opens new avenues to investigate relations between geographic origin and heterogeneous molecular phenotypes, like metabolite profiles, which can easily be obtained in species where genome data is not yet available.
    Nature Communications 12/2012; 3:1319. · 10.02 Impact Factor
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    Sabrina Kleessen, Zoran Nikoloski
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    ABSTRACT: Flux balance analysis (FBA) together with its extension, dynamic FBA, have proven instrumental for analyzing the robustness and dynamics of metabolic networks by employing only the stoichiometry of the included reactions coupled with adequately chosen objective function. In addition, under the assumption of minimization of metabolic adjustment, dynamic FBA has recently been employed to analyze the transition between metabolic states. Here, we propose a suite of novel methods for analyzing the dynamics of (internally perturbed) metabolic networks and for quantifying their robustness with limited knowledge of kinetic parameters. Following the biochemically meaningful premise that metabolite concentrations exhibit smooth temporal changes, the proposed methods rely on minimizing the significant fluctuations of metabolic profiles to predict the time-resolved metabolic state, characterized by both fluxes and concentrations. By conducting a comparative analysis with a kinetic model of the Calvin-Benson cycle and a model of plant carbohydrate metabolism, we demonstrate that the principle of regulatory on/off minimization coupled with dynamic FBA can accurately predict the changes in metabolic states. Our methods outperform the existing dynamic FBA-based modeling alternatives, and could help in revealing the mechanisms for maintaining robustness of dynamic processes in metabolic networks over time.
    BMC Systems Biology 03/2012; 6:16. · 2.98 Impact Factor
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    ABSTRACT: Discrimination of metabolic models based on high throughput metabolomics data, reflecting various internal and external perturbations, is essential for identifying the components that contribute to the emerging behavior of metabolic processes. Here, we investigate 12 different models of the mitochondrial electron transport chain (ETC) in Arabidopsis thaliana during dark-induced senescence in order to elucidate the alternative substrates to this metabolic pathway. Our findings demonstrate that the coupling of the proposed computational approach, based on dynamic flux balance analysis, with time-resolved metabolomics data results in model-based confirmations of the hypotheses that, during dark-induced senescence in Arabidopsis, (i) under conditions where the main substrate for the ETC are not fully available, isovaleryl-CoA dehydrogenase and 2-hydroxyglutarate dehydrogenase are able to donate electrons to the ETC, (ii) phytanoyl-CoA does not act even as an indirect substrate of the electron transfer flavoprotein/electron-transfer flavoprotein:ubiquinone oxidoreductase complex, and (iii) the mitochondrial γ-aminobutyric acid transporter has functional significance in maintaining mitochondrial metabolism. Our study provides a basic framework for future in silico studies of alternative pathways in mitochondrial metabolism under extended darkness whereby the role of its components can be computationally discriminated based on available molecular profile data.
    Journal of Biological Chemistry 02/2012; 287(14):11122-31. · 4.65 Impact Factor
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    Sabrina Kleessen, Alisdair R Fernie, Zoran Nikoloski
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    ABSTRACT: High-throughput phenotyping technologies in combination with genetic variability for the plant model species Arabidopsis thaliana (Arabidopsis) offer an excellent experimental platform to reveal the effects of different gene combinations on phenotypes. These developments have been coupled with computational approaches to extract information not only from the multidimensional data, capturing various levels of biochemical organization, but also from various morphological and growth-related traits. Nevertheless, the existing methods usually focus on data aggregation which may neglect accession-specific effects. Here we argue that revealing the molecular mechanisms governing a desired set of output traits can be performed by ranking of accessions based on their efficiencies relative to all other analyzed accessions. To this end, we propose a framework for evaluating accessions via their relative efficiencies which establish a relationship between multidimensional system's inputs and outputs from different environmental conditions. The framework combines data envelopment analysis (DEA) with a novel valency index characterizing the difference in congruence between the efficiency rankings of accessions under various conditions. We illustrate the advantages of the proposed approach for analyzing genetic variability on a publicly available data set comprising quantitative data on metabolic and morphological traits for 23 Arabidopsis accessions under three conditions of nitrogen availability. In addition, we extend the proposed framework to identify the set of traits displaying the highest influence on ranking based on the relative efficiencies of the considered accessions. As an outlook, we discuss how the proposed framework can be combined with well-established statistical techniques to further dissect the relationship between natural variability and metabolism.
    Frontiers in Plant Science 01/2012; 3:217. · 3.60 Impact Factor
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    ABSTRACT: Every multicellular organism consists of numerous organs, tissues and specific cell types. To gain detailed knowledge about the morphogenesis of these complex structures, it is inevitable to advance biochemical analyses to ultimate spatial and temporal resolution since individual cell types contribute differently to the overall performance of living objects. Single cell sampling combined with systems biological approaches was recently applied to investigations of Arabidopsis thaliana trichomes (leaf hairs). These are single celled structures that provide ideal model systems to address various aspects of plant cell development and differentiation at the level of individual cells. A previously suggested function of trichomes in plant stress responses could thus be confirmed. Furthermore, trichome-specific "omics" data collected in several laboratories are mutually conclusive which demonstrates the applicability of systems biological approaches at the single cell level.
    Phytochemistry 10/2010; 72(10):1061-70. · 3.05 Impact Factor
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    ABSTRACT: Protein phosphorylation is an important post-translational modification influencing many aspects of dynamic cellular behavior. Site-specific phosphorylation of amino acid residues serine, threonine, and tyrosine can have profound effects on protein structure, activity, stability, and interaction with other biomolecules. Phosphorylation sites can be affected in diverse ways in members of any species, one such way is through single nucleotide polymorphisms (SNPs). The availability of large numbers of experimentally identified phosphorylation sites, and of natural variation datasets in Arabidopsis thaliana prompted us to analyze the effect of non-synonymous SNPs (nsSNPs) onto phosphorylation sites. From the analyses of 7,178 experimentally identified phosphorylation sites we found that: (i) Proteins with multiple phosphorylation sites occur more often than expected by chance. (ii) Phosphorylation hotspots show a preference to be located outside conserved domains. (iii) nsSNPs affected experimental phosphorylation sites as much as the corresponding non-phosphorylated amino acid residues. (iv) Losses of experimental phosphorylation sites by nsSNPs were identified in 86 A. thaliana proteins, among them receptor proteins were overrepresented.These results were confirmed by similar analyses of predicted phosphorylation sites in A. thaliana. In addition, predicted threonine phosphorylation sites showed a significant enrichment of nsSNPs towards asparagines and a significant depletion of the synonymous substitution. Proteins in which predicted phosphorylation sites were affected by nsSNPs (loss and gain), were determined to be mainly receptor proteins, stress response proteins and proteins involved in nucleotide and protein binding. Proteins involved in metabolism, catalytic activity and biosynthesis were less affected. We analyzed more than 7,100 experimentally identified phosphorylation sites in almost 4,300 protein-coding loci in silico, thus constituting the largest phosphoproteomics dataset for A. thaliana available to date. Our findings suggest a relatively high variability in the presence or absence of phosphorylation sites between different natural accessions in receptor and other proteins involved in signal transduction. Elucidating the effect of phosphorylation sites affected by nsSNPs on adaptive responses represents an exciting research goal for the future.
    BMC Genomics 01/2010; 11:411. · 4.40 Impact Factor
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    60. Tagung 2009 der Vereinigung der Pflanzenzüchter und Saatgutkaufleute Österreichs; 01/2010
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    ABSTRACT: GabiPD ("http://www.gabipd.org/":http://www.gabipd.org/) at the Max Planck Institute of Molecular Plant Physiology constitutes a repository and analysis platform of genomic, transcriptomic, proteomic, and metabolomic plant data provided by the GABI community (Riaño-Pachón et al., 2009). Beside the data, current versions of useful tools are being made accessible via GabiPD, such as MapMan (Usadel et al., 2005) and SATlotyper (Neigenfind et al., 2008). Access to GabiPD is provided via either the web interface or WebServices. In GabiPD, twenty different plant species are currently represented with the model plant A. thaliana on top followed by the crop plants S. tuberosum and H. vulgare. Innovative user interfaces allow interactive access to the different data types, e.g. (i) GreenCards to all text-based information, like sequences and comparative SNP information (e.g., Pajerowska-Mukhtar et al., 2009) (ii) YAMB to comparative genetic maps, (iii) MapManWeb to gene expression or metabolite profiling data mapped onto pathways, or (iv) 2DGelViewer to annotated 2DE gel images. All data types (e.g., clones, protein spots, gene expression data) in GabiPD are pointing to the central Gene GreenCard, where gene information is retrieved from genome annotation projects or UniGene sets (provided by NCBI). With the Gene GreenCards, we provide MapMan and Gene Ontology annotations and ortholog information (currently only between _A. thaliana_ and _O. sativa ssp. japonica_). Recently, a new graphical representation of transcripts was integrated displaying gene structure (untranslated regions, start and stop codons and exon-exon junctions). Protein domains as well as ESTs, are mapped on the transcript displays. In order to ease the transfer of knowledge from model to crop plants, we have performed similarity-based mappings between closely related species, i.e., _O. sativa ssp. japonica_ and _H. vulgare_. By the integration of complex data in a framework of existing knowledge, GabiPD provides new insights and allows for new interpretations of plant data. This work is/was supported by the BMBF (GABI grants 0312272, 0313112, 0315046). Neigenfind J et al. (2008) Haplotype inference from unphased SNP data in heterozygous polyploids based on SAT. _BMC Genomics_ 9:356. Pajerowska-Mukhtar K et al. (2009) Single nucleotide polymorphisms in the allene oxide synthase 2 gene are associated with field resistance to late blight in populations of tetraploid potato cultivars. _Genetics_, doi:"10.1534/genetics.108.094268":http://dx.doi.org/10.1534/genetics.108.094268 Riano-Pachon DM et al. (2009) GabiPD: The GABI Primary Database - a plant integrative ‘omics’ database. _Nucleic Acids Research_ 37(Database issue):D954-9, DOI "10.1093/nar/gkn611":http://dx.doi.org/10.1093/nar/gkn611 Usadel B et al (2006) PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments. _BMC Bioinformatics_ 7:535.
    Nature Precedings 01/2009;