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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.
The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coil, Saccharomycies cerevisiae, and Arabidopsis thaliana.
Plasticity of human growth
(2020)
Background:
This systematic review aimed at collecting, analyzing and summarizing scientific studies focusing on psychosocial factors that influence linear growth among humans.
Methods:
The online database "PubMed" was used in order to acquire suitable scientific studies. These studies were evaluated based on clearly defined criteria that determine whether a study was to be excluded or included in the literature review. In the end, a total sum of 36 studies remained, which were carefully analyzed and used to generate an overview of the association between psychosocial factors and linear growth.
Results:
In the 36 reviewed studies, different social and psychological factors, such as socioeconomic status, parental education or emotional deprivation were set in relation to physical growth among humans. The studies were listed and summarized, depending on the investigated psychosocial factor. A clear association between psychosocial factors and growth could be observed in most of the reviewed studies. Discussion: Based on the results of the reviewed studies it could be concluded that the regulation of linear growth is also subject to different psychosocial factors. The way in which the developing human and the specific social environment interact seemed to have a major impact on linear growth. Statusspecific stress was discussed as one possible explanation for the regulating mechanism of human linear growth.