@article{TimmerTheissJuerchottetal.2010, author = {Timmer, Marco and Theiss, Hans and J{\"u}rchott, Katrin and Ries, Christian and Paron, Igor and Franz, W. and Selbig, Joachim and Guo, Ketai and Tonn, J{\"o}rg and Schichor, Christian}, title = {Stromal-Derived Factor 1a (Sdf-1a), a Homing Factor for Mesenchymal Progenitor Cells, Is Elevated in Tumor Tissue and Plasma of Glioma Patients}, issn = {1522-8517}, year = {2010}, abstract = {Malignant gliomas are a fatal disease lacking sufficient possibilities for early diagnosis and chemical markers to detect remission or relapse. The recruitment of progenitor cells such as mesenchymal stem cells (MSC) is a main feature of gliomas. Stromal cell-derived factor-1 (SDF-1), a chemokine produced in glioma cell lines, enhances migration in MSC and has been associated with cell survival and apoptosis in gliomas. Therefore, this study was performed to evaluate (i) whether SDF-1 and its receptors are expressed in human malignant gliomas in situ and (ii) if SDF-1 might potentially play a role in recruiting MSCs into human glioma. In glioblastoma tissue, immunohistochemistry revealed that SDF-1 and its receptor CXCR4 are expressed in regions of angiogenesis and necrosis, and qPCR showed that SDF-1 is elevated. Public expression data indicated that CXCR4 was upregulated. The latter data also illustrate that SDF-1 could be up- or downregulated in glioma compared to normal brain in a transcript-specific manner. In plasma, SDF-1 is elevated in glioma patients. The level is reduced by both dexamethasone intake and surgery. Dexamethasone also decreased SDF-1 production in cells in vitro. The undirected migration of human MSC (hMSC) was not enhanced by the addition of SDF-1. However, SDF-1 stimulated directed invasion of hMSC in a dose-dependent manner. Taken together, we show that SDF-1 is a potent chemoattractant of progenitor cells such as hMSCs and that its expression is elevated in glioma tissue, which results in elevated SDF-1 levels in the patient's plasma samples with concomittant decrease after tumor resection. The fact that elevated SDF-1 plasma levels are significantly decreased after tumor surgery could be a first hint that SDF-1 might act as tumor marker for malignant gliomas in order to detect disease progression or remission, respectively.}, language = {en} } @article{SulpicePylIshiharaetal.2009, author = {Sulpice, Ronan and Pyl, Eva-Theresa and Ishihara, Hirofumi and Trenkamp, Sandra and Steinfath, Matthias and Witucka-Wall, Hanna and Gibon, Yves and Usadel, Bj{\"o}rn and Poree, Fabien and Piques, Maria Conceicao and von Korff, Maria and Steinhauser, Marie Caroline and Keurentjes, Joost J. B. and Guenther, Manuela and Hoehne, Melanie and Selbig, Joachim and Fernie, Alisdair R. and Altmann, Thomas and Stitt, Mark}, title = {Starch as a major integrator in the regulation of plant growth}, issn = {0027-8424}, doi = {10.1073/pnas.0903478106}, year = {2009}, abstract = {Rising demand for food and bioenergy makes it imperative to breed for increased crop yield. Vegetative plant growth could be driven by resource acquisition or developmental programs. Metabolite profiling in 94 Arabidopsis accessions revealed that biomass correlates negatively with many metabolites, especially starch. Starch accumulates in the light and is degraded at night to provide a sustained supply of carbon for growth. Multivariate analysis revealed that starch is an integrator of the overall metabolic response. We hypothesized that this reflects variation in a regulatory network that balances growth with the carbon supply. Transcript profiling in 21 accessions revealed coordinated changes of transcripts of more than 70 carbon-regulated genes and identified 2 genes (myo-inositol-1- phosphate synthase, a Kelch-domain protein) whose transcripts correlate with biomass. The impact of allelic variation at these 2 loci was shown by association mapping, identifying them as candidate lead genes with the potential to increase biomass production.}, language = {en} } @article{SulpiceNikoloskiTschoepetal.2013, author = {Sulpice, Ronan and Nikoloski, Zoran and Tschoep, Hendrik and Antonio, Carla and Kleessen, Sabrina and Larhlimi, Abdelhalim and Selbig, Joachim and Ishihara, Hirofumi and Gibon, Yves and Fernie, Alisdair R. and Stitt, Mark}, title = {Impact of the Carbon and Nitrogen Supply on Relationships and Connectivity between Metabolism and Biomass in a Broad Panel of Arabidopsis Accessions(1[W][OA])}, series = {Plant physiology : an international journal devoted to physiology, biochemistry, cellular and molecular biology, biophysics and environmental biology of plants}, volume = {162}, journal = {Plant physiology : an international journal devoted to physiology, biochemistry, cellular and molecular biology, biophysics and environmental biology of plants}, number = {1}, publisher = {American Society of Plant Physiologists}, address = {Rockville}, issn = {0032-0889}, doi = {10.1104/pp.112.210104}, pages = {347 -- 363}, year = {2013}, abstract = {Natural genetic diversity provides a powerful tool to study the complex interrelationship between metabolism and growth. Profiling of metabolic traits combined with network-based and statistical analyses allow the comparison of conditions and identification of sets of traits that predict biomass. However, it often remains unclear why a particular set of metabolites is linked with biomass and to what extent the predictive model is applicable beyond a particular growth condition. A panel of 97 genetically diverse Arabidopsis (Arabidopsis thaliana) accessions was grown in near-optimal carbon and nitrogen supply, restricted carbon supply, and restricted nitrogen supply and analyzed for biomass and 54 metabolic traits. Correlation-based metabolic networks were generated from the genotype-dependent variation in each condition to reveal sets of metabolites that show coordinated changes across accessions. The networks were largely specific for a single growth condition. Partial least squares regression from metabolic traits allowed prediction of biomass within and, slightly more weakly, across conditions (cross-validated Pearson correlations in the range of 0.27-0.58 and 0.21-0.51 and P values in the range of <0.001-<0.13 and <0.001-<0.023, respectively). Metabolic traits that correlate with growth or have a high weighting in the partial least squares regression were mainly condition specific and often related to the resource that restricts growth under that condition. Linear mixed-model analysis using the combined metabolic traits from all growth conditions as an input indicated that inclusion of random effects for the conditions improves predictions of biomass. Thus, robust prediction of biomass across a range of conditions requires condition-specific measurement of metabolic traits to take account of environment-dependent changes of the underlying networks.}, language = {en} } @article{SteuerHumburgSelbig2006, author = {Steuer, Ralf and Humburg, Peter and Selbig, Joachim}, title = {Validation and functional annotation of expression-based clusters based on gene ontology}, series = {BMC bioinformatics}, volume = {7}, journal = {BMC bioinformatics}, number = {380}, publisher = {BioMed Central}, address = {London}, issn = {1471-2105}, doi = {10.1186/1471-2105-7-380}, pages = {12}, year = {2006}, abstract = {Background: The biological interpretation of large-scale gene expression data is one of the paramount challenges in current bioinformatics. In particular, placing the results in the context of other available functional genomics data, such as existing bio-ontologies, has already provided substantial improvement for detecting and categorizing genes of interest. One common approach is to look for functional annotations that are significantly enriched within a group or cluster of genes, as compared to a reference group. Results: In this work, we suggest the information-theoretic concept of mutual information to investigate the relationship between groups of genes, as given by data-driven clustering, and their respective functional categories. Drawing upon related approaches (Gibbons and Roth, Genome Research 12: 1574-1581, 2002), we seek to quantify to what extent individual attributes are sufficient to characterize a given group or cluster of genes. Conclusion: We show that the mutual information provides a systematic framework to assess the relationship between groups or clusters of genes and their functional annotations in a quantitative way. Within this framework, the mutual information allows us to address and incorporate several important issues, such as the interdependence of functional annotations and combinatorial combinations of attributes. It thus supplements and extends the conventional search for overrepresented attributes within a group or cluster of genes. In particular taking combinations of attributes into account, the mutual information opens the way to uncover specific functional descriptions of a group of genes or clustering result. All datasets and functional annotations used in this study are publicly available. All scripts used in the analysis are provided as additional files.}, language = {en} } @article{SteuerGrossSelbigetal.2006, author = {Steuer, Ralf and Gross, Thilo and Selbig, Joachim and Blasius, Bernd}, title = {Structural kinetic modeling of metabolic networks}, series = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {103}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {32}, publisher = {National Academy of Sciences}, address = {Washington}, issn = {0027-8424}, doi = {10.1073/pnas.0600013103}, pages = {11868 -- 11873}, year = {2006}, abstract = {To develop and investigate detailed mathematical models of metabolic processes is one of the primary challenges in systems biology. However, despite considerable advance in the topological analysis of metabolic networks, kinetic modeling is still often severely hampered by inadequate knowledge of the enzyme-kinetic rate laws and their associated parameter values. Here we propose a method that aims to give a quantitative account of the dynamical capabilities of a metabolic system, without requiring any explicit information about the functional form of the rate equations. Our approach is based on constructing a local linear model at each point in parameter space, such that each element of the model is either directly experimentally accessible or amenable to a straightforward biochemical interpretation. This ensemble of local linear models, encompassing all possible explicit kinetic models, then allows for a statistical exploration of the comprehensive parameter space. The method is exemplified on two paradigmatic metabolic systems: the glycolytic pathway of yeast and a realistic-scale representation of the photosynthetic Calvin cycle.}, language = {en} } @article{SteinfathStrehmelPetersetal.2010, author = {Steinfath, Matthias and Strehmel, Nadine and Peters, Rolf and Schauer, Nicolas and Groth, Detlef and Hummel, Jan and Steup, Martin and Selbig, Joachim and Kopka, Joachim and Geigenberger, Peter and Dongen, Joost T. van}, title = {Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach}, issn = {1467-7644}, doi = {10.1111/j.1467-7652.2010.00516.x}, year = {2010}, abstract = {Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments. Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods. By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits. The predictive metabolites can be selected and used subsequently to develop fast, targeted and low-cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker-assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information.}, language = {en} } @article{SteinfathGaertnerLisecetal.2009, author = {Steinfath, Matthias and G{\"a}rtner, Tanja and Lisec, Jan and Meyer, Rhonda Christiane and Altmann, Thomas and Willmitzer, Lothar and Selbig, Joachim}, title = {Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers}, series = {Theoretical and applied genetics : TAG ; international journal of plant breeding research}, volume = {120}, journal = {Theoretical and applied genetics : TAG ; international journal of plant breeding research}, publisher = {Springer}, address = {Berlin}, issn = {0040-5752}, doi = {10.1007/s00122-009-1191-2}, pages = {239 -- 247}, year = {2009}, abstract = {A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80\% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected.}, language = {en} } @article{ScholzKaplanGuyetal.2005, author = {Scholz, Matthias and Kaplan, F. and Guy, C. L. and Kopka, Joachim and Selbig, Joachim}, title = {Non-linear PCA : a missing data approach}, issn = {1367-4803}, year = {2005}, abstract = {Motivation: Visualizing and analysing the potential non-linear structure of a dataset is becoming an important task in molecular biology. This is even more challenging when the data have missing values. Results: Here, we propose an inverse model that performs non-linear principal component analysis (NLPCA) from incomplete datasets. Missing values are ignored while optimizing the model, but can be estimated afterwards. Results are shown for both artificial and experimental datasets. In contrast to linear methods, non-linear methods were able to give better missing value estimations for non-linear structured data. Application: We applied this technique to a time course of metabolite data from a cold stress experiment on the model plant Arabidopsis thaliana, and could approximate the mapping function from any time point to the metabolite responses. Thus, the inverse NLPCA provides greatly improved information for better understanding the complex response to cold stress}, language = {en} } @article{SchichorAlbrechtKorteetal.2012, author = {Schichor, Christian and Albrecht, Valerie and Korte, Benjamin and Buchner, Alexander and Riesenberg, Rainer and Mysliwietz, Josef and Paron, Igor and Motaln, Helena and Turnsek, Tamara Lah and Juerchott, Kathrin and Selbig, Joachim and Tonn, J{\"o}rg-Christian}, title = {Mesenchymal stem cells and glioma cells form a structural as well as a functional syncytium in vitro}, series = {Experimental neurology}, volume = {234}, journal = {Experimental neurology}, number = {1}, publisher = {Elsevier}, address = {San Diego}, issn = {0014-4886}, doi = {10.1016/j.expneurol.2011.12.033}, pages = {208 -- 219}, year = {2012}, abstract = {The interaction of human mesenchymal stem cells (hMSCs) and tumor cells has been investigated in various contexts. HMSCs are considered as cellular treatment vectors based on their capacity to migrate towards a malignant lesion. However, concerns about unpredictable behavior of transplanted hMSCs are accumulating. In malignant gliomas, the recruitment mechanism is driven by glioma-secreted factors which lead to accumulation of both, tissue specific stem cells as well as bone marrow derived hMSCs within the tumor. The aim of the present work was to study specific cellular interactions between hMSCs and glioma cells in vitro. We show, that glioma cells as well as hMSCs differentially express connexins. and that they interact via gap-junctional coupling. Besides this so-called functional syncytium formation, we also provide evidence of cell fusion events (structural syncytium). These complex cellular interactions led to an enhanced migration and altered proliferation of both, tumor and mesenchymal stem cell types in vitro. The presented work shows that glioma cells display signs of functional as well as structural syncytium formation with hMSCs in vitro. The described cellular phenomena provide new insight into the complexity of interaction patterns between tumor cells and host cells. Based on these findings, further studies are warranted to define the impact of a functional or structural syncytium formation on malignant tumors and cell based therapies in vivo.}, language = {en} } @article{RyngajlloChildsLohseetal.2011, author = {Ryngajllo, Malgorzata and Childs, Liam H. and Lohse, Marc and Giorgi, Federico M. and Lude, Anja and Selbig, Joachim and Usadel, Bj{\"o}rn}, title = {SLocX predicting subcellular localization of Arabidopsis proteins leveraging gene expression data}, series = {Frontiers in plant science}, volume = {2}, journal = {Frontiers in plant science}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-462X}, doi = {10.3389/fpls.2011.00043}, pages = {19}, year = {2011}, abstract = {Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mito-chondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.}, language = {en} }