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. This lack of complete and independent evaluation calls for new criteria to robustly estimate the optimal intensity of prior data integration in the inference process. RESULTS: We address this issue for two regression-based GRN inference models, a weighted random forest (weigthedRF) and a generalized linear model estimated under a weighted LASSO penalty with stability selection (weightedLASSO). These approaches are applied to data from the root response to nitrate induction in Arabidopsis thaliana. For each gene, we measure how the integration of transcription factor binding motifs influences model prediction. We propose a new approach, DIOgene, that uses model prediction error and a simulated null hypothesis in order to optimize data integration strength in a hypothesis-driven, gene-specific manner. This integration scheme reveals a strong diversity of optimal integration intensities between genes, and offers good performance in minimizing prediction error as well as retrieving experimental interactions. Experimental results show that DIOgene compares favorably against state-of-the-art approaches and allows to recover master regulators of nitrate induction. AVAILABILITY AND IMPLEMENTATION: The R code and notebooks demonstrating the use of the proposed approaches are available in the repository https://github.com/OceaneCsn/integrative_GRN_N_induction.

Natural genetic variation underlying the negative effect of elevated CO2 on ionome composition in Arabidopsis thaliana.

The elevation of atmospheric CO2 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 CO2 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 CO2, using six hundred Arabidopsis thaliana accessions, representing geographical distributions ranging from worldwide to regional and local environments. We show that growth under elevated CO2 leads to a global decrease of ionome content, whatever the geographic distribution of the population. We observed a high range of genetic diversity, ranging from the most negative effect to resilience or even to a benefit in response to elevated CO2. Using genome-wide association mapping, we identified a large set of genes associated with this response, and we demonstrated that the function of one of these genes is involved in the negative effect of elevated CO2 on plant mineral composition. This resource will contribute to understand the mechanisms underlying the effect of elevated CO2 on plant mineral nutrition, and could help towards the development of crops adapted to a high-CO2 world.

A gene regulatory network in Arabidopsis roots reveals features and regulators of the plant response to elevated CO2.

The elevation of CO2 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 CO2 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 CO2 , nitrate, and iron. We demonstrated that elevated CO2 has a modest effect on root genome expression under nutrient sufficiency, but by contrast leads to massive expression changes under nitrate or iron deficiencies. We demonstrated that elevated CO2 negatively targets nitrate and iron starvation modules at the transcriptional level, associated with a reduction in high-affinity nitrate uptake. Finally, we inferred a gene regulatory network governing the root response to elevated CO2 . This network allowed us to identify candidate transcription factors including MYB15, WOX11, and EDF3 which we experimentally validated for their role in the stimulation of growth by elevated CO2 . Our approach identified key features and regulators of the plant response to elevated CO2 , with the objective of developing crops resilient to climate change.

The decline of plant mineral nutrition under rising CO2: physiological and molecular aspects of a bad deal.

The elevation of atmospheric CO2 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 CO2 levels on plant mineral status are not understood. We provide an update on the main hypotheses and review the increasing evidence that, for nitrogen, this detrimental effect is associated with direct inhibition of key mechanisms of nitrogen uptake and assimilation. We also mention promising strategies for identifying genotypes that will maintain robust nutrient status in a future high-CO2 world.

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. Here, we report that combining mutations for CURLY LEAF (CLF) and LIKE HETEROCHROMATIN PROTEIN1 (LHP1), two Arabidopsis PcG proteins, results in deregulation of expression of active genes that are targeted by PcG proteins or enriched in associated chromatin marks. We show that this deregulation is associated with accumulation of small RNAs corresponding to massive degradation of active gene transcripts. We demonstrate that transcriptionally active genes and especially those targeted by PcG proteins are prone to RNA degradation, even though deregulation of RNA degradation following the loss of function of PcG proteins is not likely to be mediated by a PcG protein-mediated chromatin environment. Therefore, we conclude that PcG protein function is essential to maintain an accurate level of RNA degradation to ensure accurate gene expression.

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. RESULTS: We developed here a user interface called DIANE (Dashboard for the Inference and Analysis of Networks from Expression data) designed to harness the potential of multi-factorial expression datasets from any organisms through a precise set of methods. DIANE interactive workflow provides normalization, dimensionality reduction, differential expression and ontology enrichment. Gene clustering can be performed and explored via configurable Mixture Models, and Random Forests are used to infer gene regulatory networks. DIANE also includes a novel procedure to assess the statistical significance of regulator-target influence measures based on permutations for Random Forest importance metrics. All along the pipeline, session reports and results can be downloaded to ensure clear and reproducible analyses. CONCLUSIONS: We demonstrate the value and the benefits of DIANE using a recently published data set describing the transcriptional response of Arabidopsis thaliana under the combination of temperature, drought and salinity perturbations. We show that DIANE can intuitively carry out informative exploration and statistical procedures with RNA-Seq data, perform model based gene expression profiles clustering and go further into gene network reconstruction, providing relevant candidate genes or signalling pathways to explore. DIANE is available as a web service ( https://diane.bpmp.inrae.fr ), or can be installed and locally launched as a complete R package.

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. TFcoop aims to predict the binding sites of the target TF upon the nucleotide content of the sequences and of the binding affinity of all identified cooperating TFs. The set of cooperating TFs and model parameters are learned from ChIP-seq data of the target TF. We used TFcoop to investigate the TF combinations involved in the binding of 106 TFs on 41 cell types and in four regulatory regions: promoters of mRNAs, lncRNAs and pri-miRNAs, and enhancers. We first assess that TFcoop is accurate and outperforms simple PWM methods for predicting TF binding sites. Next, analysis of the learned models sheds light on important properties of TF combinations in different promoter classes and in enhancers. First, we show that combinations governing TF binding on enhancers are more cell-type specific than that governing binding in promoters. Second, for a given TF and cell type, we observe that TF combinations are different between promoters and enhancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Analysis of the TFs cooperating with the different targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target. Lastly, our models accurately distinguish promoters associated with specific biological processes. CONCLUSIONS: TFcoop appears as an accurate approach for studying TF combinations. Its use on ENCODE and FANTOM data allowed us to discover important properties of human TF combinations in different promoter classes and in enhancers. The R code for learning a TFcoop model and for reproducing the main experiments described in the paper is available in an R Markdown file at address https://gite.lirmm.fr/brehelin/TFcoop .