Biology subjects

Vandel, J.

Publications and source records attributed to Vandel, J..

4 recordsLinked to original sources

bioRxiv2026

Nasal environment potentiates the pathogenicity of Bordetella pertussis

Pertussis remains a major health concern, as acellular vaccines fail to prevent nasal infection and transmission. The nasal cavity is the primary niche for Bordetella pertussis colonization, but how this particular environment shapes bacterial pathogenicity remains poorly understood. Here we developed bacterial culture conditions that replicate the nasal environment, combining nasal-fluid-like transition metals concentrations with nasal cavity temperature. Using complementary transcriptomic and proteomic approaches, Dual RNA-sequencing, and in vitro and in vivo infection models, we assessed their impact on host-pathogen interactions. Nasal-like conditions reprogram B. pertussis into a state of increased virulence, conferring functional advantages for efficient bacterium-cell interactions and inducing physiological remodeling of human nasal epithelial cells. Finally, we showed that nasal preconditioning promotes efficient early B. pertussis colonization in mice. Altogether, these findings identify the nasal cavity as an instructive niche priming B. pertussis for efficient upper respiratory tract colonization following airborne transmission.

microbiology↗

bioRxiv2025

The Circadian Clock Controls Hepatic Stellate Cell Activation in Liver Fibrosis via a BMAL1/CK1ϵ/REV-ERBα/Transgelin Signaling Pathway.

Liver fibrosis is a progressive and life-threatening condition with no effective targeted treatments. Growing evidence indicates a two-way relationship between circadian rhythm and fibrogenesis, although the specific molecular signaling pathways involved are still not well understood. The molecular clock, which governs circadian rhythms, regulates metabolic and cellular functions, and its pharmacological manipulation has shown potential as a therapy for organ fibrosis. Although the livers molecular clock appeared resilient to the progression of chronic liver disease in humans from steatosis to fibrosis, detectable changes in the daily amplitude of clock genes were observed in a cohort of people living with obesity. We discovered a clock-controlled signaling pathway that drives hepatic stellate cell (HSC) activation, a key event in fibrosis progression. Interfering with this pathway, either by disrupting the core regulator CLOCK:BMAL1 or activating the nuclear receptors REV-ERBs, significantly reduced HSC activation. We also identified transgelin as the downstream effector of clock-regulated HSC contractility, a characteristic of HSC activation. Transgelin is regulated indirectly by a BMAL1-CK1{varepsilon} signaling pathway and directly by REV-ERB. Our findings identify a previously unknown circadian-controlled mechanism that links the molecular clock to HSC activation and cell contractile function, which is relevant to human diseases. This pathway provides several entry points for drugs to target and disrupt fibrogenic signaling. By connecting clock biology to the cellular processes that cause fibrosis, our work also offers a mechanistic basis for chronotherapeutic strategies against chronic liver disease.

molecular 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↗

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↗