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Based on large-scale data for the yeast Saccharomyces cerevisiae (protein and mRNA abundance, translational status, transcript length), we investigate the relation of transcription, translation, and protein turnover on a genome-wide scale. We elucidate variations between different spatial cell compartments and functional modules by comparing protein-to-mRNA ratios, translational activity, and a novel descriptor for protein-specific degradation (protein half-life descriptor). This analysis helps to understand the cell’s strategy to use transcriptional and post-transcriptional regulation mechanisms for managing protein levels. For instance, it is possible to identify modules that are subject to suppressed translation under normal conditions (“translation on demand”). In order to reduce inconsistencies between the datasets, we compiled a new reference mRNA abundance dataset and we present a novel approach to correct large microarray signals for a saturation bias. Accounting for ribosome density based on transcript length rather than ORF length improves the correlation of observed protein levels to translational activity. We discuss potential causes for the deviations of these correlations. Finally, we introduce a quantitative descriptor for protein degradation (protein half-life descriptor) and compare it to measured half-lives. The study demonstrates significant post-transcriptional control of protein levels for a number of different compartments and functional modules, which is missed when exclusively focusing on transcript levels.

Plants are able to acclimate to new growth conditions on a relatively short time-scale. Recently, we showed that the progeny of plants exposed to various abiotic stresses exhibited changes in genome stability, methylation patterns and stress tolerance. Here, we performed a more detailed analysis of methylation patterns in the progeny of Arabidopsis thaliana (Arabidopsis) plants exposed to 25 and 75 mM sodium chloride. We found that the majority of gene promoters exhibiting changes in methylation were hypermethylated, and this group was overrepresented by regulators of the chromatin structure. The analysis of DNA methylation at gene bodies showed that hypermethylation in the progeny of stressed plants was primarily due to changes in the 5′ and 3′ ends as well as in exons rather than introns. All but one hypermethylated gene tested had lower gene expression. The analysis of histone modifications in the promoters and coding sequences showed that hypermethylation and lower gene expression correlated with the enrichment of H3K9me2 and depletion of H3K9ac histones. Thus, our work demonstrated a high degree of correlation between changes in DNA methylation, histone modifications and gene expression in the progeny of salt-stressed plants.

As in other eukaryotes, cell division in plants is highly conserved and regulated by cyclin-dependent kinases (CDKs) that are themselves predominantly regulated at the posttranscriptional level by their association with proteins such as cyclins. Although over the last years the knowledge of the plant cell cycle has considerably increased, little is known on the assembly and regulation of the different CDK complexes. To map protein–protein interactions between core cell cycle proteins of Arabidopsis thaliana, a binary protein–protein interactome network was generated using two complementary high-throughput interaction assays, yeast two-hybrid and bimolecular fluorescence complementation. Pairwise interactions among 58 core cell cycle proteins were tested, resulting in 357 interactions, of which 293 have not been reported before. Integration of the binary interaction results with cell cycle phase-dependent expression information and localization data allowed the construction of a dynamic interaction network. The obtained interaction map constitutes a framework for further in-depth analysis of the cell cycle machinery.

• To enable easy access and interpretation of heterogenous and scattered data, we have developed a user-friendly tool for data mining and integration in Arabidopsis, named CORNET. This tool allows the browsing of microarray data, the construction of coexpression and protein–protein interaction (PPI) networks and the exploration of diverse functional annotations. Here, we present the new functionalities of CORNET 2.0 for data integration in plants. • First of all, CORNET allows the integration of regulatory interaction datasets accessible through the new transcription factor (TF) tool that can be used in combination with the coexpression tool or the PPI tool. In addition, we have extended the PPI tool to enable the analysis of gene–gene associations from AraNet as well as newly identified PPIs. Different search options are implemented to enable the construction of networks centered around multiple input genes or proteins. New functional annotation resources are included to retrieve relevant literature, phenotypes, plant ontology and biological pathways. • We have also extended CORNET to attain the construction of coexpression and PPI networks in the crop species maize. • Networks and associated evidence of the majority of currently available data types are visualized in Cytoscape. CORNET is available at https://bioinformatics.psb.ugent.be/cornet.