Biology subjects

Lebre, S.

Publications and source records attributed to Lebre, S..

6 recordsLinked to original sources

bioRxiv2023

Optimizing data integration improves Gene Regulatory Network inference in Arabidopsis thaliana

MotivationsGene Regulatory Networks (GRN) are traditionnally 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. ResultsWe address this issue for two common regression-based GRN inference models, an integrative Random Forest (weigthedRF) and a generalized linear model with stability selection estimated under a weighted LASSO penalty (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 for optimizing data integration strength in a hypothesis-driven, gene-specific manner. The resulting integration scheme reveals a strong diversity of optimal integration intensities between genes. In addition, it provides a good trade-off between prediction error minimization and validation on experimental interactions, while master regulators of nitrate induction can be accurately retrieved. Availability and implementationThe R code and notebooks demonstrating the use of the proposed approaches are available in the repository https://github.com/OceaneCsn/integrative_GRN_N_induction.

systems biology↗

bioRxiv2023

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 the plant mineral content, which might pose a significant threat to food security in the coming decades. To date, very few genes have been identified as having a role in the negative effect of elevated CO2 on plant mineral composition. Yet, several studies have shown a certain degree of diversity in the ionomes response to elevated CO2, associated with genotypic variation. This suggests the existence of genetic factors controlling the effect of CO2 on ionome composition. However, no large-scale studies have been carried out to date to explore the genetic diversity of the ionome responses to elevated CO2. Here, we used six hundred Arabidopsis thaliana accessions, representing geographical distributions ranging from worldwide to regional and local environments, to analyze the natural genetic variation underlying the negative effect of elevated CO2 on the ionome composition in plants. We show that the growth under elevated CO2 leads to a global and important decrease of the ionome content whatever the geographic distribution of the population. We also observed a high range of genetic diversity in the response of the ionome composition to elevated CO2, and we identified sub-populations, showing effects on their ionome ranging from the most pronounced to resilience or even to a benefit in response to elevated CO2. Using genome-wide association mapping on the response of each mineral element to elevated CO2 or on integrative traits, we identified a large set of QTLs and genes associated with the ionome response to elevated CO2. Finally, we demonstrate that the function of one of these genes is associated to the negative effect of elevated CO2 on the plant mineral composition. This resource will contribute to understand the genetic mechanisms underlying the negative effect of elevated CO2 on plant mineral nutrition, and could help towards the development of crops adapted to a high-CO2 world.

plant biology↗

bioRxiv2022

Systematic analysis of the genomic features involved in the binding preferences of transcription factors

Transcription factors (TFs) orchestrate gene expression and are at the core of cell-specific phenotypes and functions. One given TF can therefore have different binding sites depending on cell type and conditions. However, the TF core motif, as represented by Position Weight Matrix for instance, are often, if not invariably, cell agnostic. Likewise, paralogous TFs recognize very similar motifs while binding different genomic regions. We propose a machine learning approach called TFscope aimed at identifying the DNA features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two cell types or treatments or two paralogous TFs. TFscope systematically investigates differences in i) core motif, ii) nucleotide environment around the binding site and iii) presence and location of co-factor motifs. It provides the main DNA features that have been detected, and the contribution of each of these features to explain the binding differences. TFscope has been applied to more than 350 pairs of ChIP-seq. Our experiments showed that the approach is accurate and that the genomic features distinguishing TF binding in two different settings vary according to the TFs considered and/or the conditions. Several samples are presented and discussed to illustrate these findings. For TFs in different cell types or with different treatments, co-factors and nucleotide environment often explain most of the binding-site differences, while for paralogous TFs, subtle differences in the core motif seem to be the main reason for the observed differences in our experiments. The source code (python), data and results of the experiments described in this article are available at https://gite.lirmm.fr/rromero/tfscope.

bioinformatics↗

bioRxiv2020

Identification of long regulatory elements in the genome of Plasmodium falciparum and other eukaryotes

Long regulatory elements (LREs), such as CpG islands, polydA:dT tracts or AU-rich elements, are thought to play key roles in gene regulation but, as opposed to conventional binding sites of transcription factors, few methods have been proposed to formally and automatically characterize them. We present here a computational approach named DExTER dedicated to the identification of LREs and apply it to the analysis of the genomes of different eukaryotes including P. falciparum. Our analyses show that all tested genomes contain several LREs that are somewhat conserved along evolution, and that gene expression can be predicted with surprising accuracy on the basis of these long regions only. Regulation by LREs exhibits very different behaviours depending on species and conditions. On Apicomplexa organisms, the process appears highly dynamic, with different LREs involved at different phases of their life cycle. For multicellular organisms, the same LREs are involved in all tissues, but a dynamic behavior is observed along embryonic development stages. In P. falciparum, whose genome is known to be strongly depleted of transcription factors, LREs appear to be of especially high importance, and our analyses show that they are involved in both transcriptomic and post-transcriptomic regulation mechanisms. Moreover, we demonstrated the biological relevance of one the LREs discovered by DExTER in P. falciparum using an in vivo reporter assay. The source code (python) of DExTER is available at address https://gite.lirmm.fr/menichelli/DExTER.

bioinformatics↗

bioRxiv2017

Modeling transcription factor combinatorics in promoters and enhancers

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 combina-tions among different gene classes, regulatory regions and cell types. We propose a new approach named TFcoop to infer the TF combinations involved in the binding of a tar-get TF in a particular cell type. TFcoop aims to predict the binding sites of the target TF upon 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 combina-tions 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 pre-dicting 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 pro-moters. Second, for a given TF and cell type, we observe that TF combinations are different between promoters and en-hancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Analysis of the TFs cooperating with the dif-ferent 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 dis-tinguish promoters associated with specific biological processes.

bioinformatics↗

bioRxiv2017

Gene nucleotide composition accurately predicts expression and is linked to topological chromatin domains

Gene expression is orchestrated by distinct regulatory regions (e.g. promoters, enhancers, UTRs) to ensure a wide variety of cell types and functions. A challenge is to identify which regulatory regions are active, what are their associated features and how they work together in each cell type. Several approaches have tackled this problem by modeling gene expression based on epigenetic marks (e.g. ChIP-seq, methylation, DNase hypersensitivity), with the ultimate goal of identifying driving genomic regions and mutations that are clinically relevant in particular in precision medicine. However, these models rely on experimental data, which are limited to specific samples (even often to cell lines) and cannot be generated for all regulators and all patients. In addition, we show here that, although these approaches are accurate in predicting gene expression, their biological interpretation can be misleading. Finally these methods are not designed to capture potential regulation instructions present at the sequence level, before the binding of regulators or the opening of the chromatin. We develop here a method for predicting mRNA levels based solely on sequence features collected from distinct regulatory regions, which is as accurate as methods based on experimental data. Our approach confirms the importance of nucleotide composition in predicting gene expression and ranks regulatory regions according to their contribution. It also unveils strong influence of gene body sequence, in particular introns. We further provide evidence that the contribution of nucleotide content can be linked to co-regulations associated with genome 3D architecture and to associations of genes within topologically associated domains.

bioinformatics↗