Biology subjects

Bonnaffoux, A.

Publications and source records attributed to Bonnaffoux, A..

7 recordsLinked to original sources

bioRxiv2026

CRY-NLRP3 complexes define a circadian checkpoint controlling inflammasome activation

Innate immune sensors such as the NLRP3 inflammasome can trigger inflammatory responses within minutes, raising the question of how circadian clocks influence such rapid decisions. Here, we identify a protein-level circadian checkpoint that links core clock components to NLRP3 inflammasome activation. We show that NLRP3 associates with the circadian repressors CRY1 and CRY2, forming oscillatory complexes that restrain inflammasome activation and rapidly dissociate upon stimulation. Pharmacological stabilization of CRY proteins preserves CRY-NLRP3 association and attenuates inflammasome assembly, IL-1{beta} secretion and pyroptotic cell death in primary human macrophages. In synchronized macrophages, both NLRP3 inflammasome activation and its inhibition by the NLRP3 inhibitor MCC950 vary with circadian time. Finally, a subset of NLRP3 variants reported in cohorts of patients with Cryopyrin-Associated Periodic Syndromes (CAPS), a group of hereditary fever syndromes caused by mutations in NLRP3, weaken CRY binding and are associated with altered time-of-day patterns of inflammasome activation and MCC950 responsiveness. Together, these findings define CRY-NLRP3 complexes as a circadian checkpoint that modulates inflammasome activity and drug response, revealing time of day as a critical dimension of NLRP3-driven inflammation.

immunology↗

bioRxiv2025

Inference and validation of a dynamic gene regulatory network influencing cell fate during Duchenne muscular dystrophy initiation

The onset of gene regulatory disturbances leading to developmental disorders remains poorly understood, particularly when symptoms begin after birth. Using induced pluripotent stem cells, single-cell transcriptomics and computational modeling, we determined the gene expression waves guiding early myogenesis and how they are affected by a disease-causing mutation. We uncovered a dynamic gene regulatory network organized in two kernels that function as a toggle-switch controlling stochastic cell fate decisions. In cells derived from patients with Duchenne muscular dystrophy, these waves of regulation are less pronounced and the switch remains in the OFF state with a higher probability, resulting in convergence into a restricted cell fate. Experimental reactivation of the switch through RNA interference led to a rescued transcriptomic state and to a refined network topology. This work uncovers a dynamic regulatory mechanism underlying early cell fate divergence and provides a general framework to infer and validate dynamic gene regulatory networks involved in disease initiation.

bioinformatics↗

bioRxiv2023

VData: Temporally annotated data manipulation and storage

BackgroundRecent advances in both single-cell sequencing technologies and gene expression simulation algorithms have led to the production of increasingly large datasets. Larger datasets (tens or hundreds of Gigabytes) can no longer fit on regular computers RAM and thus pose important challenges for storage and manipulation. Existing solutions offer partial solutions but do not explicitly handle the temporal dimension of simulated data and still require large amounts of RAM to run. ResultsVData is a Python extension to the widely used AnnData format that solves these issues by extending 2D dataframes to 3 dimensions (cells, genes and time). VData is built on top of Ch5mpy, a custom built Python library for easily working with hdf5 files and which allows to reduce the memory footprint to the minimum. ConclusionsVData allows to store and manipulate very large datasets of (empirical or simulated) time-stamped data. Since it follows the original Ann-Data format, it is compatible with the scverse tools and AnnData users will find it easy to use.

bioinformatics↗

bioRxiv2023

TopoDoE: A Design of Experiment strategy for selection and refinement in ensembles of executable Gene Regulatory Networks

BackgroundInference of Gene Regulatory Networks (GRNs) is a difficult and long-standing question in Systems Biology. Numerous approaches have been proposed with the latest methods exploring the richness of single-cell data. One of the current difficulties lies in the fact that many methods of GRN inference do not result in one proposed GRN but in a collection of plausible networks that need to be further refined. In this work, we present a Design of Experiment strategy to use as a second stage after the inference process. It is specifically fitted for identifying the next most informative experiment to perform for deciding between multiple network topologies, in the case where proposed GRNs are executable models. This strategy first performs a topological analysis to reduce the number of perturbations that need to be tested, then predicts the outcome of the retained perturbations by simulation of the GRNs and finally compares predictions with novel experimental data. ResultsWe apply this method to the results of our divide-and-conquer algorithm called WASABI, adapt its gene expression model to produce perturbations and compare our predictions with experimental results. We show that our networks were able to produce in silico predictions on the outcome of a gene knock-out, which were qualitatively validated for 48 out of 49 genes. Finally, we eliminate as many as two thirds of the candidate networks for which we could identify an incorrect topology, thus greatly improving the accuracy of our predictions. ConclusionThese results both confirm the inference accuracy of WASABI and show how executable gene expression models can be leveraged to further refine the topology of inferred GRNs. We hope this strategy will help systems biologists further explore their data and encourage the development of more executable GRN models.

bioinformatics↗

bioRxiv2022

CD8 memory precursor cells generation is a continuous process

In this work, we studied the generation of memory precursor cells following an acute infection by analyzing single-cell RNA-seq data that contained CD8 T cells collected during the postinfection expansion phase. We used different tools to reconstruct the developmental trajectory that CD8 T cells followed after activation. Cells that exhibited a memory precursor signature were identified and positioned on this trajectory. We found that memory precursors are generated continuously with increasing numbers being generated over time. Similarly, expression of genes associated with effector functions was also found to be raised in memory precursors at later time points. The ability of cells to enter quiescence to differentiate into memory cells was confirmed by BrdU pulse-chase experiment in vivo. Analysis of cell counts indicates that the vast majority of memory cells are generated at later time points from cells that have extensively divided.

immunology↗

bioRxiv2022

Evidence for close molecular proximity between reverting and undifferentiated cells

According to Waddingtons epigenetic landscape concept, the differentiation process can be illustrated by a cell akin to a ball rolling down from the top of a hill (proliferation state) and crossing furrows before stopping in basins or "attractor states" to reach its stable differentiated state. However, it is now clear that some committed cells can retain a certain degree of plasticity and reacquire phenotypical characteristics of a more pluripotent cell state. In line with this dynamic model, we have previously shown that differentiating cells (chicken erythrocytic progenitors (T2EC)) retain for 24 hours the ability to self-renew when transferred back in self-renewal conditions. Despite those intriguing and promising results, the underlying molecular state of those "reverting" cells remains unexplored. The aim of the present study was therefore to molecularly characterize the T2EC reversion process by combining advanced statistical tools to make the most of single cell transcriptomic data. For this purpose, T2EC, initially maintained in a self-renewal medium (0H), were induced to differentiate for 24h (24H differentiating cells); then a part of these cells was transferred back to the self-renewal medium (48H reverting cells) and the other part was maintained in the differentiation medium for another 24h (48H differentiating cells). For each time point, cell transcriptomes were generated using scRT-qPCR and scRNAseq. Our results showed a strong overlap between 0H and 48H reverting cells when applying dimensional reduction. Moreover, the statistical comparison of cell distributions and differential expression analysis indicated no significant differences between these two cell groups. Interestingly, gene pattern distributions highlighted that, while 48H reverting cells have gene expression pattern more similar to 0H cells, they retained traces of their engagement in the differentiation process. Finally, Sparse PLS analysis showed that only the expression of 3 genes discriminates 48H reverting and 0H cells. Altogether, we show that reverting cells return to an earlier molecular state almost identical to undifferentiated cells and demonstrate a previously undocumented physiological and molecular plasticity during the differentiation process, which most likely results from the dynamic behavior of the underlying molecular network.

cell biology↗

bioRxiv2018

WASABI: a dynamic iterative framework for gene regulatory network inference

Inference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations. In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-by-one through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a fascinating new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus. In conclusion, WASABI is a versatile algorithm which should help biologists to fully exploit the power of time-stamped single-cell data.

systems biology↗