Biology subjects

Chevalier, M.

Publications and source records attributed to Chevalier, M..

10 recordsLinked to original sources

bioRxiv2026

Synapse-based bispecific immune cell engager model predicts invariance in synapse behavior across different effector-to-tumor cell ratios

Immune cell engagers (ICE) such as bispecific antibodies (bsAbs), within an immunological synapse, bind and link CD3 on a T cell to a target antigen (TAA) on a cancer cell, forming a trimer (CD3:bsAb:TAA complex). With sufficient trimer numbers within the synapse, the T cell can become activated and promote cancer cell killing. Elranatamab, a CD3-bispecific antibody for multiple myeloma, has received FDA and EMA filing acceptance (August 2023 and December 2023, respectively) adding to a growing list of bsAbs that are treating patients. In the drug development stages of ICE bsAbs, mechanistic modeling approaches are often used to attain a greater quantitative understanding of the modality, preclinically, and provide human pharmacokinetic and efficacious dose predictions to aide in Phase 1 trial design. To date, the majority of ordinary differential equation (ODE) trimer models treat the tumor compartment as well-mixed and trimer formation is governed by a bulk population reaction not accounting for individual synapses. This lack of discrimination can lead to imprecise analysis when analyzing results across E:T ratios using metrics like trimers per T cell or trimers per target cell. To this end we developed an ODE trimer model based on single-synapse complexes (one target cell/one immune cell) with 2D cross-linking trimer formation. We show computationally that the number of trimers per synapse is invariant to the value of the E:T ratio for a given free bsAb concentration, a property that cannot be captured by non-synapse models. A simple demonstration of this discrepancy using the well-known Betts trimer model is presented. We then apply the Betts trimer model coupled to a tumor growth inhibition (TGI) module to show that our synapse-based trimer model is easy to substitute in to model TGI, including the addition of a trimer-per-synapse activation threshold function for cell killing. Overall, our model attempts to balance mechanistic fidelity while limiting the complexity of the model.

pharmacology and toxicology↗

bioRxiv2026

Integrating coastal microbiome observations for human, oyster and environmental protection

The Reseau dObservatoires de Microbiologie Environnementale integree (ROME) was a pilot study conducted in France from September 2020 to August 2023 aiming to establish a network of eDNA-based observatories across four estuarine ecosystems associated with oyster farming: the Bay of Veys (Normandy), the Bay of Brest (Brittany), Marennes-Oleron (Nouvelle-Aquitaine), and the Thau Lagoon (Occitania). Within a One Health framework, the study assessed the influence of river inputs on estuarine microbiome structuring and the emergence of microbiological hazards affecting human, aquaculture, and ecosystem health. Over 2,000 samples were collected during the study, including biweekly surface water and monthly adult oyster samples. Environmental nucleic acids were analysed using metabarcoding (bacterial and protist communities) and metagenomics (human RNA viruses). The coastal microbiome, including pathogenic and harmful taxa relevant to humans and aquatic invertebrates, was characterized. River influence on microbial community composition was examined through spatial comparisons of stations exposed to varying levels of freshwater runoff, while oysters acted as bio-integrators of local microbial diversity. Results revealed coherent coastal-to-offshore microbiome structuring across all ecosystems, with local variations linked to riverine inputs. eDNA metabarcoding allowed to detect a wide range of prokaryotic and eukaryotic pathogens, as well as harmful algal bloom (HAB) genera, several not captured by conventional monitoring. These findings demonstrate the potential of the ROME eDNA observatory network for high-resolution, integrative surveillance of microbial biodiversity and early detection of biological risks in estuarine environments.

genomics↗

bioRxiv2025

Engineering Infrared Light Detection in Blind Human Retina Using Ultrasensitive Human TRPV1 Channels

Engineering infrared light sensitivity in the blind human retina could restore visual function in patients with regional retinal degeneration. However, current approaches are complex and contain non-human biological components. Using rational protein design we engineered human TRPV1 channels ({Delta}786-840) with temperature sensitivity shifted from 45 to 41{degrees}C that enabled near-infrared light- induced heat activation of mammalian cells at close to physiological temperatures. When expressed in ganglion cells of human retinal explants, {Delta}786-840 TRPV1 generated robust spiking responses to brief near-infrared light-induced temperature transients. Additionally, increasing intensity of radiation evoked graded responses correlating with increasing firing frequencies. Unlike previous approaches that used non-human TRPV1 channels, which risk immune reactions and a multicomponent system that poses barriers to clinical implementation, this single component human-derived approach eliminates immunogenicity concerns, addressing a major challenge to clinical translation, and allow gene delivery using adeno-associated viral vectors.

bioengineering↗

bioRxiv2023

SIN3 acts in distinct complexes to regulate the germline transcriptional program in C. elegans

The SIN3 transcriptional coregulator influences gene expression through multiple interactions that include histone deacetylases (HDACs). Haploinsufficiency and mutations in SIN3 are the underlying cause of Witteveen-Kolk syndrome and related intellectual disability (ID)/autism syndromes, emphasizing its key role in development. However, little is known about the diversity of its interactions and functions in developmental processes. Here we show that loss of SIN-3, the single SIN3 homologue in Caenorhabditis elegans, results in maternal effect sterility associated with deregulation of the germline transcriptome, including desilencing of X-linked genes. We identify at least two distinct SIN3 complexes containing specific HDACs, and show that they differentially contribute to fertility. Single cell smFISH reveals that in sin-3 mutants, the X chromosome becomes re-expressed prematurely and in a stochastic manner in individual germ cells. Furthermore, we identify histone residues whose acetylation increases in the absence of SIN3. Together, this work provides a powerful framework for the in vivo study of SIN3 and associated proteins.

genetics↗

bioRxiv2023

High-throughput quantitative screening of glucose-stimulated insulin secretion and insulin content using automated MAL-DI-TOF mass spectrometry

Type 2 diabetes (T2D) is a metabolic disorder characterized by loss of pancreatic {beta}- cell function, decreased insulin secretion and increased insulin resistance, that affects more than 400 million people worldwide. Although several treatments are proposed to patients suffering from T2D, long-term control of glycemia remains a challenge. Therefore, identifying new potential drugs and targets that positively affect {beta}-cell function and insulin secretion remains crucial. Here, we developed an automated approach to allow the identification of new compounds or genes potentially involved in {beta}-cell function in a 384-well plate format, using the murine {beta}-cell model Min6. Using MALDI-TOF mass spectrometry, we have implemented a high-throughput screening (HTS) strategy based on the automation of a cellular assay allowing to detect insulin secretion in response to glucose, quantitative detection of insulin, in a miniaturized system. As a proof of concept, we screened siRNA targeting well-know {beta}-cell genes and 1600 chemical compounds and identified several molecules as potential regulators of insulin secretion and/or synthesis, demonstrating that our approach allows HTS of insulin secretion in vitro.

physiology↗

bioRxiv2022

Essential ingredients in Joint Species Distribution Models: influence on interpretability, explanatory and predictive power

Joint Species Distribution Models (jSDM) are increasingly used to explain and predict biodiversity patterns. By accounting for species co-occurrence patterns and potentially including species-specific information, jSDMs capture the processes that shape ecological communities. Yet, factors like missing covariates or omitting ecologically-important species may alter the interpretability and effectiveness of jSDMs. Additionally, while the specific formulation of a jSDM directly affects its performances, the effects of choices related to model structure, such as inclusion, or not of phylogeny or trait information, are not well-explored. Here, we developed a multifaceted framework to comprehensively assess performances of alternative jSDM formulations at both species and community levels. We applied this framework to four alternative models fitted on presence/absence and abundance data of a polychaete assemblage sampled in two coastal habitats over 500 km and 8 years. Relative to a benchmark jSDM only capturing the effects of abiotic predictors and residual co-occurrence patterns, we explored the performance of alternative formulations that also included species phylogeny, traits, or some additional 179 non-target species, which were sampled alongside the species of interest. For both presence/absence and abundance data, explanatory power was good for all models but their interpretability and predictive power varied. Relative to the benchmark model, predictive errors on species abundances decreased by 95% or 53%, when including non-target species, or phylogeny, respectively. These differences across models relate to changes in both species-environment relationships and residual co-occurrence patterns. While considering trait data did not improve explanatory or predictive power, it facilitated interpretation of trait-mediated species response to environmental gradients. This study demonstrates trade-offs in jSDM formulation for explaining or predicting species data, highlighting the importance of using a comprehensive framework to compare models. Furthermore, our study provides some guidance for model selection tailored to specific objectives and available data.

ecology↗

bioRxiv2021

Determinants of haemosporidian single and co-infections risks in western paleartic birds

Co-infections with multiple pathogens are common in the wild and may act as a strong selective pressure on both host and parasite evolution. Yet, contrary to single infection, the factors that shape co-infection risk are largely under-investigated. Here, we explored the extent to which bird ecology and phylogeny impact single and co-infection probabilities by haemosporidian parasites using large datasets from museum collections and a Bayesian phylogenetic modelling framework. While both phylogeny and species attributes (e.g. size of the geographic range, life-history strategy, migration) were relevant predictors of co-infection risk, these factors were less pertinent in predicting the probability of being single infected. Our study suggests that co-infection risk is under a stronger deterministic control than single-infection risk. These results underscore the combined influence of host evolutionary history and species attributes in determining single and co-infection pattern providing new avenues regarding our ability to predict infection risk in the wild.

ecology↗

bioRxiv2019

Loss of genetic diversity in wild fish populations reduces biomass stability over time.

Genetic diversity sustains species adaptation. However, it may also support key ecosystems functions and services that can be altered by the worldwide loss of genetic diversity. Here, using long-term wild fish data, we demonstrate that populations with high genetic diversity do not reach higher biomasses than populations with low genetic diversity. Nonetheless, populations with high genetic diversity have much more stable biomasses over recent decades than populations having suffered from genetic erosion, which has implications for the provision of ecosystem services and the risk of population extinction. Our results strengthen the importance of adopting prominent environmental policies to conserve this important biodiversity facet.

ecology↗

bioRxiv2018

Design and analysis of a Proportional-Integral-Derivative controller with biological molecules

The ability of cells to regulate their function through feedback control is a fundamental underpinning of life. The capability to engineer de novo feedback control with biological molecules is ushering in an era of robust functionality for many applications in biotechnology and medicine. To fulfill their potential, feedback control strategies implemented with biological molecules need to be generalizable, modular and operationally predictable. Proportional-Integral-Derivative (PID) control fulfills this role for technological systems and is a commonly used strategy in engineering. Integral feedback control allows a system to return to an invariant steady-state value after step disturbances, hence enabling its robust operation. Proportional and derivative feedback control used with integral control help sculpt the dynamics of the return to steady-state following perturbation. Recently, a biomolecular implementation of integral control was proposed based on an antithetic motif in which two molecules interact stoichiometrically to annihilate each others function. In this work, we report how proportional and derivative implementations can be layered on top of this integral architecture to achieve a biochemical PID control design. We illustrate through computational and analytical treatments that the addition of proportional and derivative control improves performance, and discuss practical biomolecular implementations of these control strategies.

synthetic biology↗

bioRxiv2017

Goodness of fit checks for binomial N-mixture models

O_LIBinomial N-mixture models are commonly applied to analyze population survey data. By estimating detection probabilities, N-mixture models aim at extracting information about abundances in terms of actual and not just relative numbers. This separation of detection probability and abundance relies on parametric assumptions about the distribution of individuals among sites and of detections of individuals among repeat visits to sites. Current methods for checking assumptions are limited, and their computational complexity have hindered evaluations of their performances.\nC_LIO_LIWe develop computationally efficient graphical goodness of fit checks and measures of overdispersion for binomial N-mixture models. These checks are illustrated in a case study, and evaluated in simulations under two scenarios. The two scenarios assume overdispersion in the abundance distribution via a negative binomial distribution or in the detection probability via a beta-binomial distribution. We evaluate the ability of the checks to detect lack of fit, and how lack of fit affects estimates of abundances.\nC_LIO_LIThe simulations show that if the parametric assumptions are incorrect there can be severe biases in estimated abundances: negatively if there is overdispersion in abundance relative to the fitted model and positively if there is overdispersion in detection. Our goodness of fit checks performed well in detecting lack of fit when the abundance distribution is overdispersed, but struggled to detect lack of fit when detections were overdispersed. We show that the inability to detect lack of fit due to overdispersed detection is caused by a fundamental similarity between N-mixture models with beta-binomial detections and N-mixture models with negative binomial abundances.\nC_LIO_LIThe strong biases in estimated abundances that can occur in the binomial N-mixture model when the distribution of individuals among sites, or the detection model, is mis-specified implies that checking goodness of fit is essential for sound inference in ecological studies that use these methods. To check the assumptions we provide computationally efficient goodness of fit checks that are available in an R-package nmixgof. However, even when a binomial N-mixture model appears to fit the data well, estimates are not robust in the presence of overdispersion unless additional information about detection is collected.\nC_LI

ecology↗