TY - JOUR A1 - Brotman, Yariv A1 - Landau, Udi A1 - Pnini, Smadar A1 - Lisec, Jan A1 - Balazadeh, Salma A1 - Müller-Röber, Bernd A1 - Zilberstein, Aviah A1 - Willmitzer, Lothar A1 - Chet, Ilan A1 - Viterbo, Ada T1 - The LysM Receptor-Like Kinase LysM RLK1 is required to activate defense and abiotic-stress responses induced by overexpression of fungal chitinases in arabidopsis plants JF - Molecular plant N2 - Application of crab shell chitin or pentamer chitin oligosaccharide to Arabidopsis seedlings increased tolerance to salinity in wild-type but not in knockout mutants of the LysM Receptor-Like Kinase1 (CERK1/LysM RLK1) gene, known to play a critical role in signaling defense responses induced by exogenous chitin. Arabidopsis plants overexpressing the endochitinase chit36 and hexoaminidase excy1 genes from the fungus Trichoderma asperelleoides T203 showed increased tolerance to salinity, heavy-metal stresses, and Botrytis cinerea infection. Resistant lines, overexpressing fungal chitinases at different levels, were outcrossed to lysm rlk1 mutants. Independent homozygous hybrids lost resistance to biotic and abiotic stresses, despite enhanced chitinase activity. Expression analysis of 270 stress-related genes, including those induced by reactive oxygen species (ROS) and chitin, revealed constant up-regulation (at least twofold) of 10 genes in the chitinase-overexpressing line and an additional 76 salt-induced genes whose expression was not elevated in the lysm rlk1 knockout mutant or the hybrids harboring the mutation. These findings elucidate that chitin-induced signaling mediated by LysM RLK1 receptor is not limited to biotic stress response but also encompasses abiotic-stress signaling and can be conveyed by ectopic expression of chitinases in plants. KW - abiotic stress KW - chitin-induced signaling KW - chitinases KW - LysM receptor kinase KW - Trichoderma Y1 - 2012 U6 - https://doi.org/10.1093/mp/sss021 SN - 1674-2052 VL - 5 IS - 5 SP - 1113 EP - 1124 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Catchpole, Gareth A1 - Platzer, Alexander A1 - Weikert, Cornelia A1 - Kempkensteffen, Carsten A1 - Johannsen, Manfred A1 - Krause, Hans A1 - Jung, Klaus A1 - Miller, Kurt A1 - Willmitzer, Lothar A1 - Selbig, Joachim A1 - Weikert, Steffen T1 - Metabolic profiling reveals key metabolic features of renal cell carcinoma JF - Journal of cellular and molecular medicine : a journal of translational medicine N2 - Recent evidence suggests that metabolic changes play a pivotal role in the biology of cancer and in particular renal cell carcinoma (RCC). Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were applied to characterize the metabolic signature of RCC and to explore features of metastasized tumours. The findings were validated in a second independent dataset. Vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined the metabolic profile of RCC. alpha-tocopherol, hippuric acid, myoinositol, fructose-1-phosphate and glucose-1-phosphate contributed most to the tumour/normal discrimination and all showed pronounced concentration changes in RCC. The identified metabolic profile was characterized by a low recognition error of only 5% for tumour versus normal samples. Data on metastasized tumours suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle. These results illustrate the potential of mass spectroscopy based metabolomics in conjunction with sophisticated data analysis methods to uncover the metabolic phenotype of cancer. Differentially regulated metabolites, such as vitamin E compounds, hippuric acid and myoinositol, provide leads for the characterization of novel pathways in RCC. KW - kidney cancer KW - metabolism KW - metabolomics KW - metastasis Y1 - 2011 U6 - https://doi.org/10.1111/j.1582-4934.2009.00939.x SN - 1582-1838 VL - 15 IS - 1 SP - 109 EP - 118 PB - Wiley-Blackwell CY - Malden ER - TY - JOUR A1 - de Abreu e Lima, Francisco Anastacio A1 - Li, Kun A1 - Wen, Weiwei A1 - Yan, Jianbing A1 - Nikoloski, Zoran A1 - Willmitzer, Lothar A1 - Brotman, Yariv T1 - Unraveling lipid metabolism in maize with time-resolved multi-omics data JF - The plant journal N2 - Maize is the cereal crop with the highest production worldwide, and its oil is a key energy resource. Improving the quantity and quality of maize oil requires a better understanding of lipid metabolism. To predict the function of maize genes involved in lipid biosynthesis, we assembled transcriptomic and lipidomic data sets from leaves of B73 and the high-oil line By804 in two distinct time-series experiments. The integrative analysis based on high-dimensional regularized regression yielded lipid-transcript associations indirectly validated by Gene Ontology and promoter motif enrichment analyses. The co-localization of lipid-transcript associations using the genetic mapping of lipid traits in leaves and seedlings of a B73 x By804 recombinant inbred line population uncovered 323 genes involved in the metabolism of phospholipids, galactolipids, sulfolipids and glycerolipids. The resulting association network further supported the involvement of 50 gene candidates in modulating levels of representatives from multiple acyl-lipid classes. Therefore, the proposed approach provides high-confidence candidates for experimental testing in maize and model plant species. KW - Zea mays KW - lipid metabolism KW - omics KW - GFLASSO KW - QTL Y1 - 2018 U6 - https://doi.org/10.1111/tpj.13833 SN - 0960-7412 SN - 1365-313X VL - 93 IS - 6 SP - 1102 EP - 1115 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - de Abreu e Lima, Francisco Anastacio A1 - Willmitzer, Lothar A1 - Nikoloski, Zoran T1 - Classification-driven framework to predict maize hybrid field performance from metabolic profiles of young parental roots JF - PLoS one N2 - Maize (Zea mays L.) is a staple food whose production relies on seed stocks that largely comprise hybrid varieties. Therefore, knowledge about the molecular determinants of hybrid performance (HP) in the field can be used to devise better performing hybrids to address the demands for sustainable increase in yield. Here, we propose and test a classification-driven framework that uses metabolic profiles from in vitro grown young roots of parental lines from the Dent x Flint maize heterotic pattern to predict field HP. We identify parental analytes that best predict the metabolic inheritance patterns in 328 hybrids. We then demonstrate that these analytes are also predictive of field HP (0.64 >= r >= 0.79) and discriminate hybrids of good performance (accuracy of 87.50%). Therefore, our approach provides a cost-effective solution for hybrid selection programs. Y1 - 2018 U6 - https://doi.org/10.1371/journal.pone.0196038 SN - 1932-6203 VL - 13 IS - 4 PB - PLoS CY - San Fransisco ER - TY - JOUR A1 - Duwenig, Elke A1 - Steup, Martin A1 - Willmitzer, Lothar A1 - Kossmann, Jens T1 - Antisense inhibition of cytosolic phosphorylase in potato plants (Solanum tuberosum L.) affects tuber sprouting and flower formation with only little impact on carbohydrate metabolism Y1 - 1997 ER - TY - JOUR A1 - Feher, Kristen A1 - Lisec, Jan A1 - Roemisch-Margl, Lilla A1 - Selbig, Joachim A1 - Gierl, Alfons A1 - Piepho, Hans-Peter A1 - Nikoloski, Zoran A1 - Willmitzer, Lothar T1 - Deducing hybrid performance from parental metabolic profiles of young primary roots of maize by using a multivariate diallel approach JF - PLoS one Y1 - 2014 U6 - https://doi.org/10.1371/journal.pone.0085435 SN - 1932-6203 VL - 9 IS - 1 PB - PLoS CY - San Fransisco ER - TY - GEN A1 - Gärtner, Tanja A1 - Steinfath, Matthias A1 - Andorf, Sandra A1 - Lisec, Jan A1 - Meyer, Rhonda C. A1 - Altmann, Thomas A1 - Willmitzer, Lothar A1 - Selbig, Joachim T1 - Improved heterosis prediction by combining information on DNA- and metabolic markers N2 - Background: Hybrids represent a cornerstone in the success story of breeding programs. The fundamental principle underlying this success is the phenomenon of hybrid vigour, or heterosis. It describes an advantage of the offspring as compared to the two parental lines with respect to parameters such as growth and resistance against abiotic or biotic stress. Dominance, overdominance or epistasis based models are commonly used explanations. Conclusion/Significance: The heterosis level is clearly a function of the combination of the parents used for offspring production. This results in a major challenge for plant breeders, as usually several thousand combinations of parents have to be tested for identifying the best combinations. Thus, any approach to reliably predict heterosis levels based on properties of the parental lines would be highly beneficial for plant breeding. Methodology/Principal Findings: Recently, genetic data have been used to predict heterosis. Here we show that a combination of parental genetic and metabolic markers, identified via feature selection and minimum-description-length based regression methods, significantly improves the prediction of biomass heterosis in resulting offspring. These findings will help furthering our understanding of the molecular basis of heterosis, revealing, for instance, the presence of nonlinear genotype-phenotype relationships. In addition, we describe a possible approach for accelerated selection in plant breeding. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - paper 142 Y1 - 2009 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-45132 ER - TY - GEN A1 - Hische, Manuela A1 - Larhlimi, Abdelhalim A1 - Schwarz, Franziska A1 - Fischer-Rosinský, Antje A1 - Bobbert, Thomas A1 - Assmann, Anke A1 - Catchpole, Gareth S. A1 - Pfeiffer, Andreas F. H. A1 - Willmitzer, Lothar A1 - Selbig, Joachim A1 - Spranger, Joachim T1 - A distinct metabolic signature predictsdevelopment of fasting plasma glucose T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - Background High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods. Methods We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort. Results We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis. Conclusions We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 850 KW - prediction KW - fasting glucose KW - type 2 diabetes KW - metabolomics KW - plasma KW - random forest KW - metabolite KW - regression KW - biomarker Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-427400 SN - 1866-8372 IS - 850 ER - TY - JOUR A1 - Juerchott, Kathrin A1 - Guo, Ke-Tai A1 - Catchpole, Gareth A1 - Feher, Kristen A1 - Willmitzer, Lothar A1 - Schichor, Christian A1 - Selbig, Joachim T1 - Comparison of metabolite profiles in U87 glioma cells and mesenchymal stem cells JF - Biosystems : journal of biological and information processing sciences N2 - Gas chromatography-mass spectrometry (GC-MS) profiles were generated from U87 glioma cells and human mesenchymal stem cells (hMSC). 37 metabolites representing glycolysis intermediates, TCA cycle metabolites, amino acids and lipids were selected for a detailed analysis. The concentrations of these. metabolites were compared and Pearson correlation coefficients were used to calculate the relationship between pairs of metabolites. Metabolite profiles and correlation patterns differ significantly between the two cell lines. These profiles can be considered as a signature of the underlying biochemical system and provide snap-shots of the metabolism in mesenchymal stem cells and tumor cells. KW - Metabolite profiles KW - Correlation networks KW - U87 glioma cells KW - Human mesenchymal stem cells Y1 - 2011 U6 - https://doi.org/10.1016/j.biosystems.2011.05.005 SN - 0303-2647 VL - 105 IS - 2 SP - 130 EP - 139 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Kehr, Julia A1 - Haebel, Sophie A1 - Blechschmidt-Schneider, Sabine A1 - Willmitzer, Lothar A1 - Steup, Martin A1 - Fisahn, Joachim T1 - Analysis of phloem protein patterns from different organs of Cucurbita maxima Duch. by matrix-assisted laser desorption/ionization time of flight mass spectroscopy combined with sodium dodecyl sufate-polyacryilamide gel electrophoresis Y1 - 1999 ER -