TY  - JOUR
A1  - Birkhofer, Klaus
A1  - Schöning, Ingo
A1  - Alt, Fabian
A1  - Herold, Nadine
A1  - Klarner, Bernhard
A1  - Maraun, Mark
A1  - Marhan, Sven
A1  - Oelmann, Yvonne
A1  - Wubet, Tesfaye
A1  - Yurkov, Andrey
A1  - Begerow, Dominik
A1  - Berner, Doreen
A1  - Buscot, Francois
A1  - Daniel, Rolf
A1  - Diekötter, Tim
A1  - Ehnes, Roswitha B.
A1  - Erdmann, Georgia
A1  - Fischer, Christiane
A1  - Fösel, Baerbel
A1  - Groh, Janine
A1  - Gutknecht, Jessica
A1  - Kandeler, Ellen
A1  - Lang, Christa
A1  - Lohaus, Gertrud
A1  - Meyer, Annabel
A1  - Nacke, Heiko
A1  - Näther, Astrid
A1  - Overmann, Jörg
A1  - Polle, Andrea
A1  - Pollierer, Melanie M.
A1  - Scheu, Stefan
A1  - Schloter, Michael
A1  - Schulze, Ernst-Detlef
A1  - Schulze, Waltraud X.
A1  - Weinert, Jan
A1  - Weisser, Wolfgang W.
A1  - Wolters, Volkmar
A1  - Schrumpf, Marion
T1  - General relationships between abiotic soil properties and soil biota across spatial scales and different land-use types
JF  - PLoS one
N2  - Very few principles have been unraveled that explain the relationship between soil properties and soil biota across large spatial scales and different land-use types. Here, we seek these general relationships using data from 52 differently managed grassland and forest soils in three study regions spanning a latitudinal gradient in Germany. We hypothesize that, after extraction of variation that is explained by location and land-use type, soil properties still explain significant proportions of variation in the abundance and diversity of soil biota. If the relationships between predictors and soil organisms were analyzed individually for each predictor group, soil properties explained the highest amount of variation in soil biota abundance and diversity, followed by land-use type and sampling location. After extraction of variation that originated from location or land-use, abiotic soil properties explained significant amounts of variation in fungal, meso-and macrofauna, but not in yeast or bacterial biomass or diversity. Nitrate or nitrogen concentration and fungal biomass were positively related, but nitrate concentration was negatively related to the abundances of Collembola and mites and to the myriapod species richness across a range of forest and grassland soils. The species richness of earthworms was positively correlated with clay content of soils independent of sample location and land-use type. Our study indicates that after accounting for heterogeneity resulting from large scale differences among sampling locations and land-use types, soil properties still explain significant proportions of variation in fungal and soil fauna abundance or diversity. However, soil biota was also related to processes that act at larger spatial scales and bacteria or soil yeasts only showed weak relationships to soil properties. We therefore argue that more general relationships between soil properties and soil biota can only be derived from future studies that consider larger spatial scales and different land-use types.
Y1  - 2012
U6  - https://doi.org/10.1371/journal.pone.0043292
SN  - 1932-6203
VL  - 7
IS  - 8
PB  - PLoS
CY  - San Fransisco
ER  - 
TY  - JOUR
A1  - Christian, Jan-Ole
A1  - Braginets, Rostyslav
A1  - Schulze, Waltraud X.
A1  - Walther, Dirk
T1  - Characterization and prediction of protein phosphorylation hotspots in Arabidopsis thaliana
JF  - Frontiers in plant science
N2  - The regulation of protein function by modulating the surface charge status via sequence-locally enriched phosphorylation sites (P-sites) in so called phosphorylation "hotspots" has gained increased attention in recent years. We set out to identify P-hotspots in the model plant Arabidopsis thaliana. We analyzed the spacing of experimentally detected P-sites within peptide-covered regions along Arabidopsis protein sequences as available from the PhosPhAt database. Confirming earlier reports (Schweiger and Lanial, 2010), we found that, indeed, P-sites tend to cluster and that distributions between serine and threonine P-sites to their respected closest next P-site differ significantly from those for tyrosine P-sites. The ability to predict P-hotspots by applying available computational P-site prediction programs that focus on identifying single P-sites was observed to be severely compromised by the inevitable interference of nearby P-sites. We devised a new approach, named HotSPotter, for the prediction of phosphorylation hotspots. HotSPotter is based primarily on local amino acid compositional preferences rather than sequence position-specific motifs and uses support vector machines as the underlying classification engine. HotSPotter correctly identified experimentally determined phosphorylation hotspots in A. thaliana with high accuracy. Applied to the Arabidopsis proteome, HotSPotter-predicted 13,677 candidate P-hotspots in 9,599 proteins corresponding to 7,847 unique genes. Hotspot containing proteins are involved predominantly in signaling processes confirming the surmised modulating role of hotspots in signaling and interaction events. Our study provides new bioinformatics means to identify phosphorylation hotspots and lays the basis for further investigating novel candidate P-hotspots. All phosphorylation hotspot annotations and predictions have been made available as part of the PhosPhAt database at http://phosphat.mpimp-golm.mpg.de.
KW  - protein phosphorylation
KW  - hotspots
KW  - Arabidopsis thaliana
KW  - support vector machines
KW  - regulation
Y1  - 2012
U6  - https://doi.org/10.3389/fpls.2012.00207
SN  - 1664-462X
VL  - 3
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  -