Memory type 1 T helper (T(H)1) cells are characterized by the stable expression of interferon (IFN)-? as well as by the epigenetic imprinting of the IFNG locus. Among innate cells, NK cells play a crucial role in the defense against cytomegalovirus (CMV) and represent the main source of IFN-?. Recently, it was shown that memory-like features can be observed in NK cell subsets after CMV infection. However, the molecular mechanisms underlying NK cell adaptive properties have not been completely defined. In the present study, we demonstrated that only NKG2Chi NK cells expanded in human CMV (HCMV) seropositive individuals underwent epigenetic remodeling of the IFNG conserved non-coding sequence (CNS) 1, similar to memory CD8(+) T cells or T(H)1 cells. The accessibility of the CNS1 was required to enhance IFN-? transcriptional activity in response to NKG2C and 2B4 engagement, which led to consistent IFN-? production in NKG2C(hi) NK cells. Thus, our data identify epigenetic imprinting of the IFNG locus as selective hallmark and crucial mechanism driving strong and stable IFN-? expression in HCMV-specific NK cell expansions, providing a molecular basis for the regulation of adaptive features in innate cells.
The epidermal growth factor receptor (EGFR) signaling network is activated in most solid tumors, and small-molecule drugs targeting this network are increasingly available. However, often only specific combinations of inhibitors are effective. Therefore, the prediction of potent combinatorial treatments is a major challenge in targeted cancer therapy. In this study, we demonstrate how a model-based evaluation of signaling data can assist in finding the most suitable treatment combination. We generated a perturbation data set by monitoring the response of RAS/PI3K signaling to combined stimulations and inhibitions in a panel of colorectal cancer cell lines, which we analyzed using mathematical models. We detected that a negative feedback involving EGFR mediates strong cross talk from ERK to AKT. Consequently, when inhibiting MAPK, AKT activity is increased in an EGFR-dependent manner. Using the model, we predict that in contrast to single inhibition, combined inactivation of MEK and EGFR could inactivate both endpoints of RAS, ERK and AKT. We further could demonstrate that this combination blocked cell growth in BRAF- as well as KRAS-mutated tumor cells, which we confirmed using a xenograft model.
A single gene cluster of Penicillium chrysogenum contains genes involved in the biosynthesis and secretion of the mycotoxins roquefortine C and meleagrin. Five of these genes have been silenced by RNAi. Pc21g15480 (rds) encodes a nonribosomal cyclodipeptide synthetase for the biosynthesis of both roquefortine C and meleagrin. Pc21g15430 (rpt) encodes a prenyltransferase also required for the biosynthesis of both mycotoxins. Silencing of Pc21g15460 or Pc21g15470 led to a decrease in roquefortine C and meleagrin, whereas silencing of the methyltransferase gene (Pc21g15440; gmt) resulted in accumulation of glandicolin B, indicating that this enzyme catalyzes the conversion of glandicolin B to meleagrin. All these genes are transcriptionally coregulated. Our results prove that roquefortine C and meleagrin derive from a single pathway.
The integrated analysis of omics datasets covering different levels of molecular organization has become a central task of systems biology. We investigated the transcriptional and metabolic response of yeast exposed to increased (37 degrees C) and lowered (10 degrees C) temperatures relative to optimal reference conditions (28 degrees C) in the context of known metabolic pathways. Pairwise metabolite correlation levels were found to carry more pathway-related information and to extend to farther distances within the metabolic pathway network than associated transcript level correlations. Metabolites were detected to correlate stronger to their cognate transcripts (metabolite is reactant of the enzyme encoded by the transcript) than to more remote or randomly chosen transcripts reflecting their close metabolic relationship. We observed a pronounced temporal hierarchy between metabolic and transcriptional molecular responses under heat and cold stress. Changes of metabolites were most significantly correlated to transcripts encoding metabolic enzymes, when metabolites were considered leading in time-lagged correlation analyses. By applying the concept of Granger causality, we detected directed relationships between metabolites and their cognate transcripts. When interpreted as substrate-to-product directions, most of these directed Granger causality pairs agreed with the KEGG-annotated preferred reaction direction. Thus, the introduced Granger causality approach may prove useful for determining the preferred direction of metabolic reactions in cellular systems. The metabolites glutamic acid and serine were identified as central causative metabolites influencing transcript levels at later time points. Selected examples are presented illustrating the intertwined relationships between metabolites and transcripts in the yeast temperature stress adaptation process.
Protein phosphorylation is an important post-translational modification influencing many aspects of dynamic cellular behavior. Site-specific phosphorylation of amino acid residues serine, threonine, and tyrosine can have profound effects on protein structure, activity, stability, and interaction with other biomolecules. Phosphorylation sites can be affected in diverse ways in members of any species, one such way is through single nucleotide polymorphisms (SNPs). The availability of large numbers of experimentally identified phosphorylation sites, and of natural variation datasets in Arabidopsis thaliana prompted us to analyze the effect of non-synonymous SNPs (nsSNPs) onto phosphorylation sites.
The PhosPhAt database of Arabidopsis phosphorylation sites was initially launched in August 2007. Since then, along with 10-fold increase in database entries, functionality of PhosPhAt (phosphat.mpimp-golm.mpg.de) has been considerably upgraded and re-designed. PhosPhAt is now more of a web application with the inclusion of advanced search functions allowing combinatorial searches by Boolean terms. The results output now includes interactive visualization of annotated fragmentation spectra and the ability to export spectra and peptide sequences as text files for use in other applications. We have also implemented dynamic links to other web resources thus augmenting PhosPhAt-specific information with external protein-related data. For experimental phosphorylation sites with information about dynamic behavior in response to external stimuli, we display simple time-resolved diagrams. We have included predictions for pT and pY sites and updated pS predictions. Access to prediction algorithm now allows on-the-fly prediction of phosphorylation of any user-uploaded protein sequence. Protein Pfam domain structures are now mapped onto the protein sequence display next to experimental and predicted phosphorylation sites. Finally, we have implemented functional annotation of proteins using MAPMAN ontology. These new developments make the PhosPhAt resource a useful and powerful tool for the scientific community as a whole beyond the plant sciences.
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.
Phosphoproteomics involves identification of phosphoproteins, precise mapping, and quantification of phosphorylation sites, and eventually, revealing their biological function. In plants, several systematic phosphoproteomic analyses have recently been performed to optimize in vitro and in vivo technologies to reveal components of the phosphoproteome. The discovery of novel substrates for specific protein kinases is also an important issue. Development of a new tool has enabled rapid identification of potential kinase substrates such as kinase assays using plant protein microarrays. Progress has also been made in quantitative and dynamic analysis of mapped phosphorylation sites. Increased quantity of experimentally verified phosphorylation sites in plants has prompted the creation of dedicated web-resources for plant-specific phosphoproteomics data. This resulted in development of computational prediction methods yielding significantly improved sensitivity and specificity for the detection of phosphorylation sites in plants when compared to methods trained on less plant-specific data. In this review, we present an update on phosphoproteomic studies in plants and summarize the recent progress in the computational prediction of plant phosphorylation sites. The application of the experimental and computed results in understanding the phosphoproteomic networks of cellular and metabolic processes in plants is discussed. This is a continuation of our comprehensive review series on plant phosphoproteomics.
The identification and annotation of protein-coding genes is one of the primary goals of whole-genome sequencing projects, and the accuracy of predicting the primary protein products of gene expression is vital to the interpretation of the available data and the design of downstream functional applications. Nevertheless, the comprehensive annotation of eukaryotic genomes remains a considerable challenge. Many genomes submitted to public databases, including those of major model organisms, contain significant numbers of wrong and incomplete gene predictions. We present a community-based reannotation of the Aspergillus nidulans genome with the primary goal of increasing the number and quality of protein functional assignments through the careful review of experts in the field of fungal biology.
Biochemical, molecular and genetic studies emphasize the role of the potato vacuolar invertase Pain-1 in the accumulation of reducing sugars in potato tubers upon cold storage, and thereby its influence on the quality of potato chips and French fries. Previous studies showed that natural Pain-1 cDNA alleles were associated with better chip quality and higher tuber starch content. In this study, we focused on the functional characterization of these alleles. A genotype-dependent transient increase of total Pain-1 transcript levels in cold-stored tubers of six different genotypes as well as allele-specific expression patterns were detected. 3D modelling revealed putative structural differences between allelic Pain-1 proteins at the molecules surface and at the substrate binding site. Furthermore, the yeast SUC2 mutant was complemented with Pain-1 cDNA alleles and enzymatic parameters of the heterologous expressed proteins were measured at 30 and 4?°C. Significant differences between the alleles were detected. The observed functional differences between Pain-1 alleles did not permit final conclusions on the mechanism of their association with tuber quality traits. Our results show that natural allelic variation at the functional level is present in potato, and that the heterozygous genetic background influences the manifestation of this variation.
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