University of Potsdam

About: University of Potsdam is a education organization based out in Potsdam, Germany. It is known for research contribution in the topics: Population & Computer science. The organization has 9629 authors who have published 26740 publications receiving 759745 citations. The organization is also known as: Universität Potsdam.

Quantitative phosphoproteomics reveals the role of the AMPK plant ortholog SnRK1 as a metabolic master regulator under energy deprivation

Abstract: Since years, research on SnRK1, the major cellular energy sensor in plants, has tried to define its role in energy signalling. However, these attempts were notoriously hampered by the lethality of a complete knockout of SnRK1. Therefore, we generated an inducible amiRNA::SnRK1α2 in a snrk1α1 knock out background (snrk1α1/α2) to abolish SnRK1 activity to understand major systemic functions of SnRK1 signalling under energy deprivation triggered by extended night treatment. We analysed the in vivo phosphoproteome, proteome and metabolome and found that activation of SnRK1 is essential for repression of high energy demanding cell processes such as protein synthesis. The most abundant effect was the constitutively high phosphorylation of ribosomal protein S6 (RPS6) in the snrk1α1/α2 mutant. RPS6 is a major target of TOR signalling and its phosphorylation correlates with translation. Further evidence for an antagonistic SnRK1 and TOR crosstalk comparable to the animal system was demonstrated by the in vivo interaction of SnRK1α1 and RAPTOR1B in the cytosol and by phosphorylation of RAPTOR1B by SnRK1α1 in kinase assays. Moreover, changed levels of phosphorylation states of several chloroplastic proteins in the snrk1α1/α2 mutant indicated an unexpected link to regulation of photosynthesis, the main energy source in plants.

Interleukin-22 protects intestinal stem cells against genotoxic stress

Abstract: Environmental genotoxic factors pose a challenge to the genomic integrity of epithelial cells at barrier surfaces that separate host organisms from the environment. They can induce mutations that, if they occur in epithelial stem cells, contribute to malignant transformation and cancer development1-3. Genome integrity in epithelial stem cells is maintained by an evolutionarily conserved cellular response pathway, the DNA damage response (DDR). The DDR culminates in either transient cell-cycle arrest and DNA repair or elimination of damaged cells by apoptosis4,5. Here we show that the cytokine interleukin-22 (IL-22), produced by group 3 innate lymphoid cells (ILC3) and γδ T cells, is an important regulator of the DDR machinery in intestinal epithelial stem cells. Using a new mouse model that enables sporadic inactivation of the IL-22 receptor in colon epithelial stem cells, we demonstrate that IL-22 is required for effective initiation of the DDR following DNA damage. Stem cells deprived of IL-22 signals and exposed to carcinogens escaped DDR-controlled apoptosis, contained more mutations and were more likely to give rise to colon cancer. We identified metabolites of glucosinolates, a group of phytochemicals contained in cruciferous vegetables, to be a widespread source of genotoxic stress in intestinal epithelial cells. These metabolites are ligands of the aryl hydrocarbon receptor (AhR)6, and AhR-mediated signalling in ILC3 and γδ T cells controlled their production of IL-22. Mice fed with diets depleted of glucosinolates produced only very low levels of IL-22 and, consequently, the DDR in epithelial cells of mice on a glucosinolate-free diet was impaired. This work identifies a homeostatic network protecting stem cells against challenge to their genome integrity by AhR-mediated 'sensing' of genotoxic compounds from the diet. AhR signalling, in turn, ensures on-demand production of IL-22 by innate lymphocytes directly regulating components of the DDR in epithelial stem cells.

Noise-induced phase synchronization and synchronization transitions in chaotic oscillators.

Abstract: Whether common noise can induce complete synchronization in chaotic systems has been a topic of great relevance and long-standing controversy. We first clarify the mechanism of this phenomenon and show that the existence of a significant contraction region, where nearby trajectories converge, plays a decisive role. Second, we demonstrate that, more generally, common noise can induce phase synchronization in nonidentical chaotic systems. Such a noise-induced synchronization and synchronization transitions are of special significance for understanding neuron encoding in neurobiology.

Components of uncertainty in species distribution analysis: a case study of the Great Grey Shrike.

Abstract: Sophisticated statistical analyses are common in ecological research, particularly in species distribution modeling. The effects of sometimes arbitrary decisions during the modeling procedure on the final outcome are difficult to assess, and to date are largely unexplored. We conducted an analysis quantifying the contribution of uncertainty in each step during the model-building sequence to variation in model validity and climate change projection uncertainty. Our study system was the distribution of the Great Grey Shrike in the German federal state of Saxony. For each of four steps (data quality, collinearity method, model type, and variable selection), we ran three different options in a factorial experiment, leading to 81 different model approaches. Each was subjected to a fivefold cross-validation, measuring area under curve (AUC) to assess model quality. Next, we used three climate change scenarios times three precipitation realizations to project future distributions from each model, yielding 729 projections. Again, we analyzed which step introduced most variability (the four model-building steps plus the two scenario steps) into predicted species prevalences by the year 2050. Predicted prevalences ranged from a factor of 0.2 to a factor of 10 of present prevalence, with the majority of predictions between 1.1 and 4.2 (inter-quartile range). We found that model type and data quality dominated this analysis. In particular, artificial neural networks yielded low cross-validation robustness and gave very conservative climate change predictions. Generalized linear and additive models were very similar in quality and predictions, and superior to neural networks. Variations in scenarios and realizations had very little effect, due to the small spatial extent of the study region and its relatively small range of climatic conditions. We conclude that, for climate projections, model type and data quality were the most influential factors. Since comparison of model types has received good coverage in the ecological literature, effects of data quality should now come under more scrutiny.