Optimizing data integration improves gene regulatory network inference in Arabidopsis thaliana.

Motivations: Gene regulatory networks (GRNs) are traditionally inferred from gene expression profiles monitoring a specific condition or treatment. In the last decade, integrative strategies have successfully emerged to guide GRN inference from gene expression with complementary prior data. However, datasets used as prior information and validation gold standards are often related and limited to a subset of genes.

Natural genetic variation underlying the negative effect of elevated CO on ionome composition in .

The elevation of atmospheric CO leads to a decline in plant mineral content, which might pose a significant threat to food security in coming decades. Although few genes have been identified for the negative effect of elevated CO on plant mineral composition, several studies suggest the existence of genetic factors. Here, we performed a large-scale study to explore genetic diversity of plant ionome responses to elevated CO, using six hundred accessions, representing geographical distributions ranging from worldwide to regional and local environments.

A gene regulatory network in Arabidopsis roots reveals features and regulators of the plant response to elevated CO.

The elevation of CO in the atmosphere increases plant biomass but decreases their mineral content. The genetic and molecular bases of these effects remain mostly unknown, in particular in the root system, which is responsible for plant nutrient uptake. To gain knowledge about the effect of elevated CO on plant growth and physiology, and to identify its regulatory in the roots, we analyzed genome expression in Arabidopsis roots through a combinatorial design with contrasted levels of CO , nitrate, and iron.

The decline of plant mineral nutrition under rising CO: physiological and molecular aspects of a bad deal.

The elevation of atmospheric CO concentration has a strong impact on the physiology of C3 plants, far beyond photosynthesis and C metabolism. In particular, it reduces the concentrations of most mineral nutrients in plant tissues, posing major threats on crop quality, nutrient cycles, and carbon sinks in terrestrial agro-ecosystems. The causes of the detrimental effect of high CO levels on plant mineral status are not understood.

Loss of Polycomb proteins CLF and LHP1 leads to excessive RNA degradation in Arabidopsis.

Polycomb-group (PcG) proteins are major chromatin complexes that regulate gene expression, mainly described as repressors keeping genes in a transcriptionally silent state during development. Recent studies have nonetheless suggested that PcG proteins might have additional functions, including targeting active genes or acting independently of gene expression regulation. However, the reasons for the implication of PcG proteins and their associated chromatin marks on active genes are still largely unknown.

Inferring and analyzing gene regulatory networks from multi-factorial expression data: a complete and interactive suite.

Background: High-throughput transcriptomic datasets are often examined to discover new actors and regulators of a biological response. To this end, graphical interfaces have been developed and allow a broad range of users to conduct standard analyses from RNA-seq data, even with little programming experience. Although existing solutions usually provide adequate procedures for normalization, exploration or differential expression, more advanced features, such as gene clustering or regulatory network inference, often miss or do not reflect current state of the art methodologies.

Probing transcription factor combinatorics in different promoter classes and in enhancers.

Background: In eukaryotic cells, transcription factors (TFs) are thought to act in a combinatorial way, by competing and collaborating to regulate common target genes. However, several questions remain regarding the conservation of these combinations among different gene classes, regulatory regions and cell types.

Results: We propose a new approach named TFcoop to infer the TF combinations involved in the binding of a target TF in a particular cell type.