Biology subjects

Tognon, M.

Publications and source records attributed to Tognon, M..

6 recordsLinked to original sources

bioRxiv2026

Dexamethasone-induced PPARG expression in osteogenic differentiation in vitro: impact on SOX9 and RUNX2 levels.

BackgroundThe effects of dexamethasone during in vitro human osteogenesis present a complex picture. On one side, dexamethasone promotes the osteogenic differentiation of human bone marrow mesenchymal stromal cells (BMSCs) by downregulating SOX9. On the other side, it simultaneously promotes adipogenesis through the upregulation of PPARG. The regulation of SOX9 and PPARG levels appears to be mediated by the transactivation function of the glucocorticoid receptor (GR), suggesting an indirect effect of dexamethasone on SOX9 downregulation. This study aims to determine whether PPAR-{gamma} affects the expression levels of SOX9, as suggested by several studies. MethodsHuman BMSCs were isolated from bone marrow and cultured in different osteogenic induction media containing 10 or 100 nM dexamethasone. Undifferentiated cells were used as control. Cells were treated either with a pharmacological PPAR-{gamma} inhibitor (T0070907) or with a PPARG-targeting siRNA. Differentiation markers or PPAR-{gamma} target genes were analysed by RT-qPCR. Mineral deposition was assessed by Alizarin Red staining. Two-way ANOVA followed by a Sidak multiple comparison test was used to compare the effects of treatments. ResultsPharmacological inhibition of PPAR-{gamma} had a mild effect on the expression of PPAR-{gamma} target genes but hindered adipocyte formation. Neither RUNX2 nor SOX9 expression were affected by T0070907. siRNA treatment successfully downregulated PPARG expression, as well as that of PPAR-{gamma} target genes LPL, LPAR1, and ADIPOQ. Contrary to expectations, RUNX2 was significantly downregulated by the PPARG-siRNA treatment during osteogenic differentiation both in the absence and presence of dexamethasone, while SOX9 levels were downregulated in undifferentiated cells. Overall, Alizarin Red staining analysis showed no change in mineralization levels when PPARG expression or activity was inhibited. ConclusionsUnderstanding how dexamethasone regulates human BMSC differentiation is crucial to refine current in vitro models. These results suggest that PPAR-{gamma} is not involved in SOX9 or RUNX2 repression during in vitro osteogenic differentiation of human cells.

cell biology↗

bioRxiv2025

CRISPR-HAWK: Haplotype- and Variant-aware guide design toolkit for CRISPR-Cas

Current CRISPR guide RNA design tools rely on reference genomes, overlooking how genetic variation impacts editing outcomes. As genome editing advances toward clinical applications, incorporating population diversity becomes essential for ensuring therapeutic efficacy across diverse populations. We present CRISPR-HAWK, a framework integrating individual- and population-scale variants and haplotypes into gRNA design. Analyzing therapeutic targets across 79,648 genomes reveals that genetic variants substantially alter guide performance. For the clinically approved sickle cell disease therapeutic guide targeting BCL11A, we identify haplotypes that completely abolish predicted cutting activity. Across seven therapeutic loci, 82.5% of guides contain variants modifying on-target activity. Variants also create novel protospacer adjacent motif sites generating individual-specific guides invisible to reference-based design. These findings demonstrate that variant-aware selection is critical for equitable genome editing. CRISPR-HAWK is available at https://github.com/pinellolab/CRISPR-HAWK and https://github.com/InfOmics/CRISPR-HAWK

bioinformatics↗

bioRxiv2025

Improving DNA Modeling with WaveDNA: Enhancing Speed, Generalizability, and Interpretability through Wavelet Transformation

Transcription factors (TFs) regulate gene expression by binding to short, specific DNA sequences, known as transcription factor binding sites (TFBSs). Accurate identification of TFBSs is fundamental for understanding transcriptional regulation. By leveraging their ability to capture complex non-linear patterns and hierarchical dependencies underlying TF-DNA binding deep learning (DL) has emerged as the state-of-the-art approach for modeling and identifying TFBSs. However, current models often require extensive pretraining, involve large parameter sets, and offer limited interpretability. To address these limitations, we introduce WaveDNA, a lightweight and interpretable DL framework that encode DNA sequences into two-dimensional representations using wavelet transforms. This approach enables the use of convolutional neural networks pretrained on images, facilitating efficient transfer learning without requiring large-scale genomic data pretraining. Across diverse ENCODE ChIP-seq datasets spanning different TFs, WaveDNA achieves predictive accuracy comparable to state-of-the-art DL models while using approximately fivefold fewer parameters and substantially less computational resources. Moreover, representing DNA sequences as images allows the direct application of established computer vision interpretability techniques to visualize the learned binding patterns. Together, these results demonstrate that WaveDNA offers a scalable, computationally efficient, and interpretable alternative for modeling TF-DNA interactions.

bioinformatics↗

bioRxiv2025

Benchmarking PWM and SVM-based Models for Transcription Factor Binding Site Prediction: A Comparative Analysis on Synthetic and Biological Data

Transcription Factors (TFs) are essential regulatory proteins that control the cellular transcriptional states by binding to specific DNA sequences known as Transcription Factor Binding Sites (TFBSs) or motifs. Accurate TFBS identification is crucial for unraveling regulatory mechanisms driving cellular dynamics. Over the years, various computational approaches have been developed to model TFBSs, with Position Weight Matrices (PWMs) being one of the most widely adopted methods. PWMs provide a probabilistic framework by representing nucleotide frequencies at every position within the binding site. While effective and interpretable, PWMs face significant limitations, such as their inability to capture positional dependencies or model complex interactions. To address these, advanced methods, such as Support Vector Machine (SVM)-based models, have been introduced. Leveraging human ChIP-seq data from ENCODE, this study systematically benchmarks the predictive performance of PWM and SVM-based models across different scenarios. We evaluate the impact of key factors such as training dataset size, sequence length, and kernel functions (for SVMs) on models performance. Additionally, we explore the impact of synthetic versus real biological background data during model training. Our analysis highlights strengths and limitations of both PWM and SVM-based approaches under different conditions, providing practical guidance for selecting and tailoring models to specific biological datasets. To complement our analysis, we present a comprehensive database of pretrained SVM models for TFBS detection, trained on human ChIP-seq data from diverse cell lines and tissues. This resource aims to facilitate broader adoption of SVM-based methods in TFBS prediction and enhance their practical utility in regulatory genomics research.

bioinformatics↗

bioRxiv2021

Multi view based imaging genetics analysis on Parkinson disease

Longitudinal studies integrating imaging and genetic data have recently become widespread among bioinformatics researchers. Combining such heterogeneous data allows a better understanding of complex diseases origins and causes. Through a multi-view based workflow proposal, we show the common steps and tools used in imaging genetics analysis, interpolating genotyping, neuroimaging and transcriptomic data. We describe the advantages of existing methods to analyze heterogeneous datasets, using Parkinsons Disease (PD) as a case study. Parkinsons disease is associated with both genetic and neuroimaging factors, however such imaging genetics associations are at an early investigation stage. Therefore it is desirable to have a free and open source workflow that integrates different analysis flows in order to recover potential genetic biomarkers in PD, as in other complex diseases.

bioinformatics↗

bioRxiv2021

GRAFIMO: variant and haplotype aware motif scanning on pangenome graphs

Transcription factors (TFs) are proteins that promote or reduce the expression of genes by binding short genomic DNA sequences known as transcription factor binding sites (TFBS). While several tools have been developed to scan for potential occurrences of TFBS in linear DNA sequences or reference genomes, no tool exists to find them in pangenome variation graphs (VGs). VGs are sequence-labelled graphs that can efficiently encode collections of genomes and their variants in a single, compact data structure. Because VGs can losslessly compress large pangenomes, TFBS scanning in VGs can efficiently capture how genomic variation affects the potential binding landscape of TFs in a population of individuals. Here we present GRAFIMO (GRAph-based Finding of Individual Motif Occurrences), a command-line tool for the scanning of known TF DNA motifs represented as Position Weight Matrices (PWMs) in VGs. GRAFIMO extends the standard PWM scanning procedure by considering variations and alternative haplotypes encoded in a VG. Using GRAFIMO on a VG based on individuals from the 1000 Genomes project we recover several potential binding sites that are enhanced, weakened or missed when scanning only the reference genome, and which could constitute individual-specific binding events. GRAFIMO is available as an open-source tool, under the MIT license, at https://github.com/pinellolab/GRAFIMO and https://github.com/InfOmics/GRAFIMO. Author summaryTranscription factors (TFs) are key regulatory proteins and mutations occurring in their binding sites can alter the normal transcriptional landscape of a cell and lead to disease states. Pangenome variation graphs (VGs) efficiently encode genomes from a population of individuals and their genetic variations. GRAFIMO is an open-source tool that extends the traditional PWM scanning procedure to VGs. By scanning for potential TBFS in VGs, GRAFIMO can simultaneously search thousands of genomes while accounting for SNPs, indels, and structural variants. GRAFIMO reports motif occurrences, their statistical significance, frequency, and location within the reference or alternative haplotypes in a given VG. GRAFIMO makes it possible to study how genetic variation affects the binding landscape of known TFs within a population of individuals.

bioinformatics↗