Biology subjects

Cassan, O.

Publications and source records attributed to Cassan, O..

4 recordsLinked to original sources

bioRxiv2025

A genome-wide, machine learning-guided exploration of the cis-regulatory code involved in neuronal differentiation

Gene expression is controlled by proximal and distal cis-regulatory elements (CREs), containing DNA motifs bound by various transcription factors (TFs). Other sequence features, such as specific k-mers or low complexity regions, have also been implicated [1-3]. However, in a dynamic biological process such as cell differentiation, we lack an understanding of how the transcriptional activity of CREs progressively change and what sequence features underlie these transitions, which may reflect common and/or coordinated regulatory processes. Here, we use single-cell ATAC-seq and RNA-seq to follow, at a genome scale, CREs along differentiation of induced pluripotent stem cells into cortical neurons and develop a method to automatically identify the diversity of CRE profiles and their underlying sequence features. We propose a machine-learning guided clustering algorithm, STOIC (Statistical learning TO Inform Clustering), that jointly learns an unsupervised clustering of the CREs in the space of the activity profiles and a supervised predictor associated with each cluster in the DNA-sequence space. STOIC is specifically designed to provide readily interpretable results. We show that the method identifies CRE profiles associated with highly predictive sequence features and outperforms methods solely concerned with co-activity clustering on this task. Orthogonal data collected in the same settings link the inferred CRE clusters to specific enhancer or promoter signatures. Furthermore, we show that the DNA features unveiled by STOIC reflect biologically relevant regulators and offer a valuable basis to dissect elements of the cisregulatory grammar. Finally, we demonstrate the general applicability of STOIC by analyzing five bulk CAGE datasets of human cells responding to various treatments.

bioinformatics↗

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↗

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↗