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- Department Psychologie (6)
- Institut für Jüdische Studien und Religionswissenschaft (6)
Background: The EXO (EXORDIUM) gene was identified as a potential mediator of brassinosteroid (BR)-promoted growth. It is part of a gene family with eight members in Arabidopsis. EXO gene expression is under control of BR, and EXO overexpression promotes shoot and root growth. In this study, the consequences of loss of EXO function are described. Results: The exo loss of function mutant showed diminished leaf and root growth and reduced biomass production. Light and scanning electron microscopy analyses revealed that impaired leaf growth is due to reduced cell expansion. Epidermis, palisade, and spongy parenchyma cells were smaller in comparison to the wild-type. The exo mutant showed reduced brassinolide-induced cotyledon and hypocotyl growth. In contrast, exo roots were significantly more sensitive to the inhibitory effect of synthetic brassinolide. Apart from reduced growth, exo did not show severe morphological abnormalities. Gene expression analyses of leaf material identified genes that showed robust EXO-dependent expression. Growth-related genes such as WAK1, EXP5, and KCS1, and genes involved in primary and secondary metabolism showed weaker expression in exo than in wild-type plants. However, the vast majority of BR-regulated genes were normally expressed in exo. HA- and GFP-tagged EXO proteins were targeted to the apoplast. Conclusion: The EXO gene is essential for cell expansion in leaves. Gene expression patterns and growth assays suggest that EXO mediates BR-induced leaf growth. However, EXO does not control BR-levels or BR-sensitivity in the shoot. EXO presumably is involved in a signalling process which coordinates BR-responses with environmental or developmental signals. The hypersensitivity of exo roots to BR suggests that EXO plays a diverse role in the control of BR responses in the root.
The temporal dynamics of hydrological model performance gives insights into errors that cannot be obtained from global performance measures assigning a single number to the fit of a simulated time series to an observed reference series. These errors can include errors in data, model parameters, or model structure. Dealing with a set of performance measures evaluated at a high temporal resolution implies analyzing and interpreting a high dimensional data set. This paper presents a method for such a hydrological model performance assessment with a high temporal resolution and illustrates its application for two very different rainfall-runoff modeling case studies. The first is the Wilde Weisseritz case study, a headwater catchment in the eastern Ore Mountains, simulated with the conceptual model WaSiM-ETH. The second is the Malalcahuello case study, a headwater catchment in the Chilean Andes, simulated with the physicsbased model Catflow. The proposed time-resolved performance assessment starts with the computation of a large set of classically used performance measures for a moving window. The key of the developed approach is a data-reduction method based on self-organizing maps (SOMs) and cluster analysis to classify the high-dimensional performance matrix. Synthetic peak errors are used to interpret the resulting error classes. The final outcome of the proposed method is a time series of the occurrence of dominant error types. For the two case studies analyzed here, 6 such error types have been identified. They show clear temporal patterns, which can lead to the identification of model structural errors.
Background: Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites (P-sites) has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites. Results: We characterized the spatial context of phosphorylation sites and assessed its usability for improved phosphorylation site predictions. We identified 750 non-redundant, experimentally verified sites with three-dimensional (3D) structural information available in the protein data bank (PDB) and grouped them according to their respective kinase family. We studied the spatial distribution of amino acids around phosphorserines, phosphothreonines, and phosphotyrosines to extract signature 3D-profiles. Characteristic spatial distributions of amino acid residue types around phosphorylation sites were indeed discernable, especially when kinase-family-specific target sites were analyzed. To test the added value of using spatial information for the computational prediction of phosphorylation sites, Support Vector Machines were applied using both sequence as well as structural information. When compared to sequence-only based prediction methods, a small but consistent performance improvement was obtained when the prediction was informed by 3D-context information. Conclusion: While local one-dimensional amino acid sequence information was observed to harbor most of the discriminatory power, spatial context information was identified as relevant for the recognition of kinases and their cognate target sites and can be used for an improved prediction of phosphorylation sites. A web-based service (Phos3D) implementing the developed structurebased P-site prediction method has been made available at http://phos3d.mpimp-golm.mpg.de.
Background: Mitochondrial genomes are a valuable source of data for analysing phylogenetic relationships. Besides sequence information, mitochondrial gene order may add phylogenetically useful information, too. Sipuncula are unsegmented marine worms, traditionally placed in their own phylum. Recent molecular and morphological findings suggest a close affinity to the segmented Annelida. Results: The first complete mitochondrial genome of a member of Sipuncula, Sipunculus nudus, is presented. All 37 genes characteristic for metazoan mtDNA were detected and are encoded on the same strand. The mitochondrial gene order (protein-coding and ribosomal RNA genes) resembles that of annelids, but shows several derivations so far found only in Sipuncula. Sequence based phylogenetic analysis of mitochondrial protein-coding genes results in significant bootstrap support for Annelida sensu lato, combining Annelida together with Sipuncula, Echiura, Pogonophora and Myzostomida. Conclusion: The mitochondrial sequence data support a close relationship of Annelida and Sipuncula. Also the most parsimonious explanation of changes in gene order favours a derivation from the annelid gene order. These results complement findings from recent phylogenetic analyses of nuclear encoded genes as well as a report of a segmental neural patterning in Sipuncula.
For three small, mountainous catchments in Germany two medium-range forecast systems are compared that predict precipitation for up to 5 days in advance. One system is composed of the global German weather service (DWD) model, GME, which is dynamically downscaled using the COSMO-EU regional model. The other system is an empirical (expanded) downscaling of the ECMWF model IFS. Forecasts are verified against multi-year daily observations, by applying standard skill scores to events of specified intensity. All event classes are skillfully predicted by the empirical system for up to five days lead time. For the available prediction range of one to two days it is superior to the dynamical system.
ChlamyCyc : an integrative systems biology database and web-portal for Chlamydomonas reinhardtii
(2009)
Background: The unicellular green alga Chlamydomonas reinhardtii is an important eukaryotic model organism for the study of photosynthesis and plant growth. In the era of modern highthroughput technologies there is an imperative need to integrate large-scale data sets from highthroughput experimental techniques using computational methods and database resources to provide comprehensive information about the molecular and cellular organization of a single organism. Results: In the framework of the German Systems Biology initiative GoFORSYS, a pathway database and web-portal for Chlamydomonas (ChlamyCyc) was established, which currently features about 250 metabolic pathways with associated genes, enzymes, and compound information. ChlamyCyc was assembled using an integrative approach combining the recently published genome sequence, bioinformatics methods, and experimental data from metabolomics and proteomics experiments. We analyzed and integrated a combination of primary and secondary database resources, such as existing genome annotations from JGI, EST collections, orthology information, and MapMan classification. Conclusion: ChlamyCyc provides a curated and integrated systems biology repository that will enable and assist in systematic studies of fundamental cellular processes in Chlamydomonas. The ChlamyCyc database and web-portal is freely available under http://chlamycyc.mpimp-golm.mpg.de.
Background: The loss of photosynthesis has occurred often in eukaryotic evolution, even more than its acquisition, which occurred at least nine times independently and which generated the evolution of the supergroups Archaeplastida, Rhizaria, Chromalveolata and Excavata. This secondary loss of autotrophic capability is essential to explain the evolution of eukaryotes and the high diversity of protists, which has been severely underestimated until recently. However, the ecological and evolutionary scenarios behind this evolutionary ‘‘step back’’ are still largely unknown. Methodology/Principal Findings: Using a dynamic model of heterotrophic and mixotrophic flagellates and two types of prey, large bacteria and ultramicrobacteria, we examine the influence of DOC concentration, mixotroph’s photosynthetic growth rate, and external limitations of photosynthesis on the coexistence of both types of flagellates. Our key premises are: large bacteria grow faster than small ones at high DOC concentrations, and vice versa; and heterotrophic flagellates are more efficient than the mixotrophs grazing small bacteria (both empirically supported). We show that differential efficiency in bacteria grazing, which strongly depends on cell size, is a key factor to explain the loss of photosynthesis in mixotrophs (which combine photosynthesis and bacterivory) leading to purely heterotrophic lineages. Further, we show in what conditions an heterotroph mutant can coexist, or even out-compete, its mixotrophic ancestor, suggesting that bacterivory and cell size reduction may have been major triggers for the diversification of eukaryotes. Conclusions/Significance: Our results suggest that, provided the mixotroph’s photosynthetic advantage is not too large, the (small) heterotroph will also dominate in nutrient-poor environments and will readily invade a community of mixotrophs and bacteria, due to its higher efficiency exploiting the ultramicrobacteria. As carbon-limited conditions were presumably widespread throughout Earth history, such a scenario may explain the numerous transitions from phototrophy to mixotrophy and further to heterotrophy within virtually all major algal lineages. We challenge prevailing concepts that affiliated the evolution of phagotrophy with eutrophic or strongly light-limited environments only.
We propose two strategies to characterize organisms with respect to their metabolic capabilities. The first, investigative, strategy describes metabolic networks in terms of their capability to utilize different carbon sources, resulting in the concept of carbon utilization spectra. In the second, predictive, approach minimal nutrient combinations are predicted from the structure of the metabolic networks, resulting in a characteristic nutrient profile. Both strategies allow for a quantification of functional properties of metabolic networks, allowing to identify groups of organisms with similar functions. We investigate whether the functional description reflects the typical environments of the corresponding organisms by dividing all species into disjoint groups based on whether they are aerotolerant and/or photosynthetic. Despite differences in the underlying concepts, both measures display some common features. Closely related organisms often display a similar functional behavior and in both cases the functional measures appear to correlate with the considered classes of environments. Carbon utilization spectra and nutrient profiles are complementary approaches toward a functional classification of organism-wide metabolic networks. Both approaches contain different information and thus yield different clusterings, which are both different from the classical taxonomy of organisms. Our results indicate that a sophisticated combination of our approaches will allow for a quantitative description reflecting the lifestyles of organisms.
The ability of an organism to survive depends on its capability to adapt to external conditions. In addition to metabolic versatility and efficient replication, reliable signal transduction is essential. As signaling systems are under permanent evolutionary pressure one may assume that their structure reflects certain functional properties. However, despite promising theoretical studies in recent years, the selective forces which shape signaling network topologies in general remain unclear. Here, we propose prevention of autoactivation as one possible evolutionary design principle. A generic framework for continuous kinetic models is used to derive topological implications of demanding a dynamically stable ground state in signaling systems. To this end graph theoretical methods are applied. The index of the underlying digraph is shown to be a key topological property which determines the so-called kinetic ground state (or off-state) robustness. The kinetic robustness depends solely on the composition of the subdigraph with the strongly connected components, which comprise all positive feedbacks in the network. The component with the highest index in the feedback family is shown to dominate the kinetic robustness of the whole network, whereas relative size and girth of these motifs are emphasized as important determinants of the component index. Moreover, depending on topological features, the maintenance of robustness differs when networks are faced with structural perturbations. This structural off-state robustness, defined as the average kinetic robustness of a network’s neighborhood, turns out to be useful since some structural features are neutral towards kinetic robustness, but show up to be supporting against structural perturbations. Among these are a low connectivity, a high divergence and a low path sum. All results are tested against real signaling networks obtained from databases. The analysis suggests that ground state robustness may serve as a rationale for some structural peculiarities found in intracellular signaling networks.
With the increasing availability of observational data from different sources at a global level, joint analysis of these data is becoming especially attractive. For such an analysis – oftentimes with little prior knowledge about local and global interactions between the different observational variables at hand – an exploratory, data-driven analysis of the data may be of particular relevance. In the present work we used generalized additive models (GAM) in an exemplary study of spatio-temporal patterns in the tropospheric NO2-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We focused on identifying correlations between NO2 and local wind fields, a quantity which is of particular interest in the analysis of spatio-temporal interactions. Formulating general functional, parametric relationships between the observed NO2 distribution and local wind fields, however, is difficult – if not impossible. So, rather than following a modelbased analysis testing the data for predefined hypotheses (assuming, for example, sinusoidal seasonal trends), we used a GAM with non-parametric model terms to learn this functional relationship between NO2 and wind directly from the data. The NO2 observations showed to be affected by winddominated processes over large areas. We estimated the extent of areas affected by specific NO2 emission sources, and were able to highlight likely atmospheric transport “pathways”. General temporal trends which were also part of our model – weekly, seasonal and linear changes – showed to be in good agreement with previous studies and alternative ways of analysing the time series. Overall, using a non-parametric model provided favorable means for a rapid inspection of this large spatio-temporal NO2 data set, with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO2 distribution at a global scale.