@article{JargoschKroegerGralinskaetal.2016, author = {Jargosch, M. and Kroeger, S. and Gralinska, E. and Klotz, Ulrike and Fang, Z. and Chen, W. and Leser, U. and Selbig, Joachim and Groth, Detlef and Baumgrass, Ria}, title = {Data integration for identification of important transcription factors of STAT6-mediated cell fate decisions}, series = {Genetics and molecular research}, volume = {15}, journal = {Genetics and molecular research}, publisher = {FUNPEC}, address = {Ribeirao Preto}, issn = {1676-5680}, doi = {10.4238/gmr.15028493}, pages = {17}, year = {2016}, abstract = {Data integration has become a useful strategy for uncovering new insights into complex biological networks. We studied whether this approach can help to delineate the signal transducer and activator of transcription 6 (STAT6)-mediated transcriptional network driving T helper (Th) 2 cell fate decisions. To this end, we performed an integrative analysis of publicly available RNA-seq data of Stat6-knockout mouse studies together with STAT6 ChIP-seq data and our own gene expression time series data during Th2 cell differentiation. We focused on transcription factors (TFs), cytokines, and cytokine receptors and delineated 59 positively and 41 negatively STAT6-regulated genes, which were used to construct a transcriptional network around STAT6. The network illustrates that important and well-known TFs for Th2 cell differentiation are positively regulated by STAT6 and act either as activators for Th2 cells (e.g., Gata3, Atf3, Satb1, Nfil3, Maf, and Pparg) or as suppressors for other Th cell subpopulations such as Th1 (e.g., Ar), Th17 (e.g., Etv6), or iTreg (e.g., Stat3 and Hifla) cells. Moreover, our approach reveals 11 TFs (e.g., Atf5, Creb3l2, and Asb2) with unknown functions in Th cell differentiation. This fact together with the observed enrichment of asthma risk genes among those regulated by STAT6 underlines the potential value of the data integration strategy used here. Thus, our results clearly support the opinion that data integration is a useful tool to delineate complex physiological processes.}, language = {en} } @article{EdlichMuthMurayaAltmannetal.2016, author = {Edlich-Muth, Christian and Muraya, Moses M. and Altmann, Thomas and Selbig, Joachim}, title = {Phenomic prediction of maize hybrids}, series = {Biosystems : journal of biological and information processing sciences}, volume = {146}, journal = {Biosystems : journal of biological and information processing sciences}, publisher = {Elsevier}, address = {Oxford}, issn = {0303-2647}, doi = {10.1016/j.biosystems.2016.05.008}, pages = {102 -- 109}, year = {2016}, abstract = {Phenomic experiments are carried out in large-scale plant phenotyping facilities that acquire a large number of pictures of hundreds of plants simultaneously. With the aid of automated image processing, the data are converted into genotype-feature matrices that cover many consecutive days of development. Here, we explore the possibility of predicting the biomass of the fully grown plant from early developmental stage image-derived features. We performed phenomic experiments on 195 inbred and 382 hybrid maizes varieties and followed their progress from 16 days after sowing (DAS) to 48 DAS with 129 image-derived features. By applying sparse regression methods, we show that 73\% of the variance in hybrid fresh weight of fully-grown plants is explained by about 20 features at the three-leaf-stage or earlier. Dry weight prediction explained over 90\% of the variance. When phenomic features of parental inbred lines were used as predictors of hybrid biomass, the proportion of variance explained was 42 and 45\%, for fresh weight and dry weight models consisting of 35 and 36 features, respectively. These models were very robust, showing only a small amount of variation in performance over the time scale of the experiment. We also examined mid-parent heterosis in phenomic features. Feature heterosis displayed a large degree of variance which resulted in prediction performance that was less robust than models of either parental or hybrid predictors. Our results show that phenomic prediction is a viable alternative to genomic and metabolic prediction of hybrid performance. In particular, the utility of early-stage parental lines is very encouraging. (C) 2016 Elsevier Ireland Ltd. All rights reserved.}, language = {en} } @misc{RajasundaramSelbig2016, author = {Rajasundaram, Dhivyaa and Selbig, Joachim}, title = {analysis}, series = {Current opinion in plant biology}, volume = {30}, journal = {Current opinion in plant biology}, publisher = {Elsevier}, address = {London}, issn = {1369-5266}, doi = {10.1016/j.pbi.2015.12.010}, pages = {57 -- 61}, year = {2016}, abstract = {The development of 'omics' technologies has progressed to address complex biological questions that underlie various plant functions thereby producing copious amounts of data. The need to assimilate large amounts of data into biologically meaningful interpretations has necessitated the development of statistical methods to integrate multidimensional information. Throughout this review, we provide examples of recent outcomes of 'omics' data integration together with an overview of available statistical methods and tools.}, language = {en} } @article{ChildsGrimbsSelbig2015, author = {Childs, Dorothee and Grimbs, Sergio and Selbig, Joachim}, title = {Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns}, series = {Bioinformatics}, volume = {31}, journal = {Bioinformatics}, number = {12}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btv243}, pages = {214 -- 220}, year = {2015}, abstract = {Motivation: Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system's Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling. Results: Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase.}, language = {en} } @article{KanzleiterJaehnertSchulzeetal.2015, author = {Kanzleiter, Timo and Jaehnert, Markus and Schulze, Gunnar and Selbig, Joachim and Hallahan, Nicole and Schwenk, Robert Wolfgang and Sch{\"u}rmann, Annette}, title = {Exercise training alters DNA methylation patterns in genes related to muscle growth and differentiation in mice}, series = {American journal of physiology : Endocrinology and metabolism}, volume = {308}, journal = {American journal of physiology : Endocrinology and metabolism}, number = {10}, publisher = {American Chemical Society}, address = {Bethesda}, issn = {0193-1849}, doi = {10.1152/ajpendo.00289.2014}, pages = {E912 -- E920}, year = {2015}, abstract = {The adaptive response of skeletal muscle to exercise training is tightly controlled and therefore requires transcriptional regulation. DNA methylation is an epigenetic mechanism known to modulate gene expression, but its contribution to exercise-induced adaptations in skeletal muscle is not well studied. Here, we describe a genome-wide analysis of DNA methylation in muscle of trained mice (n = 3). Compared with sedentary controls, 2,762 genes exhibited differentially methylated CpGs (P < 0.05, meth diff >5\%, coverage > 10) in their putative promoter regions. Alignment with gene expression data (n = 6) revealed 200 genes with a negative correlation between methylation and expression changes in response to exercise training. The majority of these genes were related to muscle growth and differentiation, and a minor fraction involved in metabolic regulation. Among the candidates were genes that regulate the expression of myogenic regulatory factors (Plexin A2) as well as genes that participate in muscle hypertrophy (Igfbp4) and motor neuron innervation (Dok7). Interestingly, a transcription factor binding site enrichment study discovered significantly enriched occurrence of CpG methylation in the binding sites of the myogenic regulatory factors MyoD and myogenin. These findings suggest that DNA methylation is involved in the regulation of muscle adaptation to regular exercise training.}, language = {en} } @article{BordagKlieJuerchottetal.2015, author = {Bordag, Natalie and Klie, Sebastian and J{\"u}rchott, Kathrin and Vierheller, Janine and Schiewe, Hajo and Albrecht, Valerie and Tonn, J{\"o}rg-Christian and Schwartz, Christoph and Schichor, Christian and Selbig, Joachim}, title = {Glucocorticoid (dexamethasone)-induced metabolome changes in healthy males suggest prediction of response and side effects}, series = {Scientific reports}, volume = {5}, journal = {Scientific reports}, publisher = {Nature Publ. Group}, address = {London}, issn = {2045-2322}, doi = {10.1038/srep15954}, pages = {12}, year = {2015}, abstract = {Glucocorticoids are indispensable anti-inflammatory and decongestant drugs with high prevalence of use at (similar to)0.9\% of the adult population. Better holistic insights into glucocorticoid-induced changes are crucial for effective use as concurrent medication and management of adverse effects. The profiles of 214 metabolites from plasma of 20 male healthy volunteers were recorded prior to and after ingestion of a single dose of 4 mg dexamethasone (+20 mg pantoprazole). Samples were drawn at three predefined time points per day: seven untreated (day 1 midday - day 3 midday) and four treated (day 3 evening - day 4 evening) per volunteer. Statistical analysis revealed tremendous impact of dexamethasone on the metabolome with 150 of 214 metabolites being significantly deregulated on at least one time point after treatment (ANOVA, Benjamini-Hochberg corrected, q < 0.05). Inter-person variability was high and remained uninfluenced by treatment. The clearly visible circadian rhythm prior to treatment was almost completely suppressed and deregulated by dexamethasone. The results draw a holistic picture of the severe metabolic deregulation induced by single-dose, short-term glucocorticoid application. The observed metabolic changes suggest a potential for early detection of severe side effects, raising hope for personalized early countermeasures increasing quality of life and reducing health care costs.}, language = {en} } @article{RajasundaramRunavotGuoetal.2014, author = {Rajasundaram, Dhivyaa and Runavot, Jean-Luc and Guo, Xiaoyuan and Willats, William G. T. and Meulewaeter, Frank and Selbig, Joachim}, title = {Understanding the relationship between cotton fiber properties and non-cellulosic cell wall polysaccharides}, series = {PLoS one}, volume = {9}, journal = {PLoS one}, number = {11}, publisher = {PLoS}, address = {San Fransisco}, issn = {1932-6203}, doi = {10.1371/journal.pone.0112168}, pages = {11}, year = {2014}, abstract = {A detailed knowledge of cell wall heterogeneity and complexity is crucial for understanding plant growth and development. One key challenge is to establish links between polysaccharide-rich cell walls and their phenotypic characteristics. It is of particular interest for some plant material, like cotton fibers, which are of both biological and industrial importance. To this end, we attempted to study cotton fiber characteristics together with glycan arrays using regression based approaches. Taking advantage of the comprehensive microarray polymer profiling technique (CoMPP), 32 cotton lines from different cotton species were studied. The glycan array was generated by sequential extraction of cell wall polysaccharides from mature cotton fibers and screening samples against eleven extensively characterized cell wall probes. Also, phenotypic characteristics of cotton fibers such as length, strength, elongation and micronaire were measured. The relationship between the two datasets was established in an integrative manner using linear regression methods. In the conducted analysis, we demonstrated the usefulness of regression based approaches in establishing a relationship between glycan measurements and phenotypic traits. In addition, the analysis also identified specific polysaccharides which may play a major role during fiber development for the final fiber characteristics. Three different regression methods identified a negative correlation between micronaire and the xyloglucan and homogalacturonan probes. Moreover, homogalacturonan and callose were shown to be significant predictors for fiber length. The role of these polysaccharides was already pointed out in previous cell wall elongation studies. Additional relationships were predicted for fiber strength and elongation which will need further experimental validation.}, language = {en} } @article{KlieNikoloskiSelbig2014, author = {Klie, Sebastian and Nikoloski, Zoran and Selbig, Joachim}, title = {Biological cluster evaluation for gene function prediction}, series = {Journal of computational biology}, volume = {21}, journal = {Journal of computational biology}, number = {6}, publisher = {Liebert}, address = {New Rochelle}, issn = {1066-5277}, doi = {10.1089/cmb.2009.0129}, pages = {428 -- 445}, year = {2014}, abstract = {Recent advances in high-throughput omics techniques render it possible to decode the function of genes by using the "guilt-by-association" principle on biologically meaningful clusters of gene expression data. However, the existing frameworks for biological evaluation of gene clusters are hindered by two bottleneck issues: (1) the choice for the number of clusters, and (2) the external measures which do not take in consideration the structure of the analyzed data and the ontology of the existing biological knowledge. Here, we address the identified bottlenecks by developing a novel framework that allows not only for biological evaluation of gene expression clusters based on existing structured knowledge, but also for prediction of putative gene functions. The proposed framework facilitates propagation of statistical significance at each of the following steps: (1) estimating the number of clusters, (2) evaluating the clusters in terms of novel external structural measures, (3) selecting an optimal clustering algorithm, and (4) predicting gene functions. The framework also includes a method for evaluation of gene clusters based on the structure of the employed ontology. Moreover, our method for obtaining a probabilistic range for the number of clusters is demonstrated valid on synthetic data and available gene expression profiles from Saccharomyces cerevisiae. Finally, we propose a network-based approach for gene function prediction which relies on the clustering of optimal score and the employed ontology. Our approach effectively predicts gene function on the Saccharomyces cerevisiae data set and is also employed to obtain putative gene functions for an Arabidopsis thaliana data set.}, language = {en} } @article{GuoFuJuerchottetal.2014, author = {Guo, Ke-Tai and Fu, Peng and Juerchott, Kathrin and Motaln, Helena and Selbig, Joachim and Lah, Tamara T. and Tonn, J{\"o}rg-Christian and Schichor, Christian}, title = {The expression of Wnt-inhibitor DKK1 (Dickkopf 1) is determined by intercellular crosstalk and hypoxia in human malignant gliomas}, series = {Journal of cancer research and clinical oncology : official organ of the Deutsche Krebsgesellschaft}, volume = {140}, journal = {Journal of cancer research and clinical oncology : official organ of the Deutsche Krebsgesellschaft}, number = {8}, publisher = {Springer}, address = {New York}, issn = {0171-5216}, doi = {10.1007/s00432-014-1642-2}, pages = {1261 -- 1270}, year = {2014}, abstract = {Objective Wnt signalling pathways regulate proliferation, motility and survival in a variety of human cell types. Dickkopf 1 (DKK1) gene codes for a secreted Wnt inhibitory factor. It functions as tumour suppressor gene in breast cancer and as a pro-apoptotic factor in glioma cells. In this study, we aimed to demonstrate whether the different expression of DKK1 in human glioma-derived cells is dependent on microenvironmental factors like hypoxia and regulated by the intercellular crosstalk with bone-marrow-derived mesenchymal stem cells (bmMSCs). Methods Glioma cell line U87-MG, three cell lines from human glioblastoma grade IV (glioma-derived mesenchymal stem cells) and three bmMSCs were selected for the experiment. The expression of DKK1 in cell lines under normoxic/hypoxic environment or co-culture condition was measured using real-time PCR and enzyme-linked immunoadsorbent assay. The effect of DKK1 on cell migration and proliferation was evaluated by in vitro wound healing assays and sulphorhodamine assays, respectively. Results Glioma-derived cells U87-MG displayed lower DKK1 expression compared with bmMSCs. Hypoxia led to an overexpression of DKK1 in bmMSCs and U87-MG when compared to normoxic environment, whereas co-culture of U87-MG with bmMSCs induced the expression of DKK1 in both cell lines. Exogenous recombinant DKK1 inhibited cell migration on all cell lines, but did not have a significant effect on cell proliferation of bmMSCs and glioma cell lines. Conclusion In this study, we showed for the first time that the expression of DKK1 was hypoxia dependent in human malignant glioma cell lines. The induction of DKK1 by intracellular crosstalk or hypoxia stimuli sheds light on the intense adaption of glial tumour cells to environmental alterations.}, language = {en} } @article{FeherLisecRoemischMargletal.2014, author = {Feher, Kristen and Lisec, Jan and Roemisch-Margl, Lilla and Selbig, Joachim and Gierl, Alfons and Piepho, Hans-Peter and Nikoloski, Zoran and Willmitzer, Lothar}, title = {Deducing hybrid performance from parental metabolic profiles of young primary roots of maize by using a multivariate diallel approach}, series = {PLoS one}, volume = {9}, journal = {PLoS one}, number = {1}, publisher = {PLoS}, address = {San Fransisco}, issn = {1932-6203}, doi = {10.1371/journal.pone.0085435}, pages = {9}, year = {2014}, language = {en} }