Laboratoire Jean Perrin

A bstract Protein filaments play fundamental functions in the cell, ranging from scaffolding like in the cytoskeleton to sensing and transmitting forces and torques. Here we address the case of the nucleoprotein filaments (NPFs) of homologous recombination (HR) formed by the polymerization of the RecA protein on DNA. In contrast to the cytoskeleton filaments, the HR filaments are not known to exert or sense forces. However the stress in the stretched and unwound DNA bound to those filaments was shown to play a role in promoting DNA strand exchange during the early stage of the HR mechanism. Here we use molecular dynamics simulations to examine whether the strain in the nucleoprotein filament upon strand exchange progression and D-loop formation may influence subsequent steps of the HR process. Our results indicate that the filament mechanical properties are sensitive to the length of DNA incorporated in the D-loop. The response we observe upon increasing the D-loop length is first elastic, up to a threshold that we estimate to be 27 incorporated base pairs, after which the NPF enters a new stage where the protein-DNA connectivities are reorganized. Notably, the DNA displaced strand locally switches from site II to site III, a newly characterized binding site. We discuss the possible consequence of this mechanical response of the NPFs for the course of the HR process.

Understanding how collective neuronal activity in the brain orchestrates behavior is a central question in integrative neuroscience. Addressing this question requires models that can offer a unified interpretation of multimodal data. In this study, we jointly examine video-recordings of zebrafish larvae freely exploring their environment and calcium imaging of the Anterior Rhombencephalic Turning Region (ARTR) circuit, which is known to control swimming orientation, recorded in vivo under tethered conditions. We show that both behavioral and neural data can be accurately modeled using a Hidden Markov Model (HMM) with three hidden states. In the context of behavior, the hidden states correspond to leftward, rightward, and forward swimming. The HMM robustly captures the key statistical features of the swimming motion, including bout-type persistence and its dependence on bath temperature, while also revealing inter-individual phenotypic variability. For neural data, the three states are found to correspond to left- and right-lateral activation of the ARTR circuit, known to govern the selection of left vs. right reorientation, and a balanced state, which likely corresponds to the behavioral forward state. To further unify the two analyses, we exploit the generative nature of the HMM, using neural sequences to generate synthetic swimming trajectories, whose statistical properties are similar to the behavioral data. Overall, this work demonstrates how state-space models can be used to link neuronal and behavioral data, providing insights into the mechanisms of self-generated action.

Mechanical contributions are crucial regulators of diverse biological processes, yet their in vivo measurement remains challenging due to limitations of current techniques that can be destructive or require complex dedicated setups. This study introduces a novel method to synthesize biocompatible, self-functionalizing stress sensors based on inverted emulsions that can be used to probe stresses inside tissues but can also locally perturb the biological environment through specific binder presentation or drug delivery. We engineered an optimal design for these inverted emulsions, focusing on finding the balance between the two contradictory constraints: achieving low surface tension for deformability while maintaining emulsion instability for efficient self-functionalization and drug release. Proof-of-concept experiments in both agarose gels and complex biological systems, including brain organoids and zebrafish embryos, confirm the droplets ability to deform in response to mechanical stress applied within the tissue, to self-functionalize and to release encapsulated molecules locally. These versatile sensors offer a method for noninvasive stress measurements and targeted chemical delivery within living biological tissues, giving the potential to overcome current technical barriers in biophysical studies.Graphical abstractLeft 3 panels: Inverted emulsion droplets self-functionalize over time once injected in a biological medium. Right panel: After self-functionalization, the deformability of the droplets allows for the measurment of local stresses.

We present experiments on chiral active polar particles, realized as vibrated granular rods, revealing the formation of robust ``skipping orbits'' at hard boundaries. These edge states exhibit a net circulation opposite to the particles' intrinsic rotation and lead to a pronounced accumulation at the boundary, stronger than for their achiral counterparts. The directed nature of these orbits provides a simple yet high-fidelity mechanism for chiral sorting -- even for solitary particles, unlike in T Barois et al., Phys. Rev. Lett. 125 , 238003 (2020). We propose a unified theoretical framework for boundary interactions of both chiral and achiral particles. In this model, an effective outward radial force, proportional to motility and chirality, explains the observed boundary-hugging. Our theory predicts, and our experiments confirm, a transition in the pairing of two particles of the same chirality, from apolar spinners to polar circle walkers, with increasing packing fraction of an ambient medium of beads.

Bacterial populations often have complex spatial structures, which can impact their evolution. Here, we study how spatial structure affects the evolution of antibiotic resistance in a bacterial population. We consider a minimal model of spatially structured populations where all demes (i.e., subpopulations) are identical and connected to each other by identical migration rates. We show that spatial structure can facilitate the survival of a bacterial population to antibiotic treatment, starting from a sensitive inoculum. Specifically, the bacterial population can be rescued if antibiotic resistant mutants appear and are present when drug is added, and spatial structure can impact the fate of these mutants and the probability that they are present. Indeed, the probability of fixation of neutral or deleterious mutations providing drug resistance is increased in smaller populations. This promotes local fixation of resistant mutants in the structured population, which facilitates evolutionary rescue by drug resistance in the rare mutation regime. Once the population is rescued by resistance, migrations allow resistant mutants to spread in all demes. Our main result that spatial structure facilitates evolutionary rescue by antibiotic resistance extends to more complex spatial structures, and to the case where there are resistant mutants in the inoculum.

Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Experimental methods for generating specific binders rely on in vitro selection, which is limited in terms of library size and control over specificity profiles. Additional control was recently demonstrated through high-throughput sequencing and downstream computational analysis. Here we follow such an approach to demonstrate the design of specific antibodies beyond those probed experimentally. We do so in a context where very similar epitopes need to be discriminated, and where these epitopes cannot be experimentally dissociated from other epitopes present in the selection. Our approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies.

Self-interacting random walks (SIRWs) show long-range memory effects that result from the interaction of the random walker at time t with the territory already visited at earlier times t^{'}<t. This class of non-Markovian random walks has applications in contexts as diverse as foraging theory, the behavior of living cells, and even machine learning. Despite this importance and numerous theoretical efforts, the propagator, which is the distribution of the walker's position and arguably the most fundamental quantity to characterize the process, has so far remained out of reach for all but a single class of SIRW. Here we fill this gap and provide an exact and explicit expression for the propagator of two important universality classes of SIRWs, namely, the once-reinforced random walk and the polynomially self-repelling walk. These results give access to key observables, such as the diffusion coefficient, which so far had not been determined. We also uncover an inherently non-Markovian mechanism that tends to drive the walker away from its starting point.

Exposure to pesticide residues during the first 1000 days of life can disrupt body homeostasis and contribute to chronic metabolic diseases. Perinatal chlorpyrifos (CPF) exposure alters gut microbiota (GM) balance, potentially affecting offspring’s health. Given the GM influence on brain function, the primary aim is to determine if pesticide-induced dysbiosis (microbial imbalance) affects indirectly other organs, such as the blood–brain barrier (BBB). The secondary objective is to evaluate the prebiotics protective effects, particularly inulin in promoting microbial balance (symbiosis), in both mothers and offspring. A total of 15 or more female rats were divided in 4 groups: control, oral CPF-exposed (1 mg/kg/day), exposed to inulin (10 g/L), and co-exposed to CPF and inulin from pre-gestation until weaning of pups. Samples from intestines, spleen, liver, and brain microvessels underwent microbiological and biomolecular analyses. Bacterial culture assessed GM composition of living bacteria and their translocation to non-intestinal organs. RT qPCR and Western blotting detected gene expression and protein levels of tight junction markers in brain microvessels. CPF exposure caused gut dysbiosis in offspring, with decreased Lactobacillus and Bifidobacterium and increased Escherichia coli (p < 0.01) leading to bacterial translocation to the spleen and liver. CPF also decreased tight junction’s gene expression levels (50 to 60% decrease of CLDN3, p < 0.05). In contrast, inulin partially mitigated these adverse effects and restored gene expression to control levels. Our findings demonstrate a causal link between GM alterations and BBB integrity disruptions. The protective effects of inulin suggest potential therapeutic strategies to counteract pesticide-induced dysbiosis.

In the field of 3-D resolved computational modelling of Lithium-ion battery electrodes, the arrangement and properties of the Carbon-Binder-Domain (CBD) play a critical role in the ion and electron transport properties through their impact on the electrode tortuosity factor. However, until now, the CBD porosity value -its main descriptor in terms of transport properties and occupied volume- has been determined through educated guesses due to the lack of an experimental approach. Here, we report a novel methodology is reported for the determination of the CBD internal porosity through the combination of computational modelling and experimental electrochemical impedance spectroscopy (EIS). The methodology is based on the creation of a calibration curve that relates tortuosity factor with CBD porosity through digital stochastic generation of electrode microstructures and diffusivity characterization. The curve is then compared to the EIS experimental results and analyzed through a transmission line model, yielding a good estimation of the parameters. In this work, the usefulness and the identified areas of limitation of this approach are demonstrated with three different formulations of LiNi0.3Mn0.3Co0.3O2 (NMC 111) cathodes. To the best of the authors’ knowledge, this is the first reported method for estimating CBD porosity.