Biology subjects

Desroches, M.

Publications and source records attributed to Desroches, M..

12 recordsLinked to original sources

bioRxiv2026

Loss of function of LprG-Rv1410c and MmpL11 homologues in Mycobacterium smegmatis leads to altered glycopeptidolipid profile and decreased cellular c-di-GMP levels during biofilm formation

The cell envelope of many bacteria possesses components that play central roles in bacterial pathogenesis. Unique to mycobacteria is the presence of a lipid-rich cell wall which is critical for the virulence of pathogenic mycobacteria such as Mycobacterium tuberculosis (Mtb). The biosynthesis of mycobacterial cell wall lipids has been well characterized, but the mechanisms of lipid transport still remain largely unknown. MmpL (mycobacterial membrane protein large) proteins have been implicated in the biosynthesis and/or transport of mycobacterial cell wall lipids, but due to their cellular location, it is still unclear how cell wall lipids are transported beyond the inner membrane. Here, we further investigate the role of two conserved lipid transport pathways LprG-Rv1410c and MmpL11 in cell envelope biogenesis during biofilm formation in Mycobacterium smegmatis. We found that deletion of the lprG-rv1410c operon homologues MSMEG_3070-3069 and mmpL11 (MSMEG_0241) simultaneously led to similar biofilm defects as observed in the MSMEG_3070-3069 and mmpL11 mutants. Analysis of pellicle biofilms, total lipid extracts, gene expression and cellular c-di-GMP levels revealed that the shared biofilm defect is directly correlated with significant decreases in cellular c-di-GMP levels, but may also be due to changes in the synthesis and/or localization of glycopeptidolipids (GPLs), and changes in the expression of GPL biosynthesis and transport genes. Our findings therefore suggest that while both LprG-Rv1410c and MmpL11 pathways are involved in modulating cellular c-di-GMP levels during biofilm formation, only LprG-Rv1410c are important for regulating GPL synthesis and/or surface localization, and MmpL11 may play a broad role in fine-tuning GPL levels

microbiology↗

bioRxiv2026

Geometric characterization of the HSV - 1 glycoprotein B - amyloid β interaction in Alzheimer's disease using Forman-Ricci curvature

Alzheimer's disease is characterized by the accumulation and aggregation of amyloid-{beta}(A{beta}), but the molecular mechanisms linking environmental and infectious factors to A$\beta$ conformational changes remain incompletely understood. Herpes simplex virus type 1 (HSV-1) has been proposed as a potential contributor to AD pathology, and interactions between the viral glycoprotein B (gB) and A$\beta$ may influence the conformational behaviour of the peptide. Molecular dynamics (MD) simulations provide atomic-scale information on such interactions, but conventional structural descriptors may not fully capture changes in the organization of residue interaction networks. Here, we introduce a graph-geometric framework based on Forman-Ricci curvature to characterize the evolution of residue interaction networks during MD simulations. Each simulation frame is represented as a residue interaction graph based on C--C contacts, and residue-wise curvature profiles are analysed across time. We apply the framework to A{beta}1-42 in isolation and in complex with HSV-1 gB. Conventional MD analyses indicate stable association of the simulated complex, favourable interaction energetics, and conformational changes in A{beta}, including a transition from -helical structure toward {beta}-turn-rich conformations over the simulated timescale. Forman-Ricci curvature reveals pronounced and spatially localized remodelling of the A{beta} residue interaction network in the complex, with the strongest changes concentrated in the C-terminal region. These regions also exhibit reduced temporal curvature fluctuations and progressively distinct geometric behaviour throughout the simulation. Hierarchical clustering further identifies cooperative groups of residues with coordinated curvature dynamics, including a prominent C-terminal domain. Together, these results demonstrate that Forman-Ricci curvature provides a complementary description of biomolecular dynamics by capturing changes in the geometric organization of residue interaction networks that are not directly represented by conventional structural descriptors. The framework provides a general computational approach for studying network-level structural remodelling in protein molecular dynamics and offers a quantitative perspective on the conformational consequences of HSV-1 gB--A{beta} association.

bioinformatics↗

bioRxiv2026

An astro-neural-field model with application to cortical spreading depolarization

We present a novel astro-neural-field population model with application to migraine-related cortical spreading depolarization. The model is composed of four spatio-temporal state variables: excitatory and inhibitory membrane potentials, astrocytic potassium uptake recruitment, and extracellular potassium concentration. Extending a previous neural field model, we incorporate activity-dependent astrocytic potassium clearance via a nonlinear term coupled to astrocyte dynamics. The astrocyte transfer function, like its neural counterpart, exhibits three regimes governed by extracellular potassium, capturing its effect on clearance. This yields a more comprehensive framework, better fits experimental data, and provides new insights into the mechanisms of cortical spreading depolarization.

neuroscience↗

bioRxiv2025

A phenomenological interpretation of multiple bursting patterns in Lateral Habenula neurons

The Lateral Habenula (LHb) is a small brain structure specialized in encoding aversive signals. Bursting activity in the LHb has been consistently linked to mood regulation, with increased bursting activity proposed to promote depressive behaviors. Bursting is a complex dynamic process that has been extensively studied and modeled in other neuronal contexts. However, at the LHb this type of activity has typically been described only as transient periods of high frequency firing. Here, to provide a deeper understanding of LHb bursting, we analyzed this activity from the perspective of dynamical systems. Ex vivo, LHb neurons display a variety of bursting patterns, characterized at one extreme by a dominating square-wave type and in other by parabolic type, plus transitional forms referred to as triangular bursting. Notably, these bursting patterns, which reflect different LHb output modes, can occur within the same neuron, suggesting that they may correspond to distinct dynamic states of the same LHb neuron. To capture these complex behaviors, we propose an idealized multiple-timescale dynamical model. This model successfully reproduces the three main bursting patterns observed in experimental data. Furthermore, we identify a special point in the parameter space, termed the saddle-node homoclinic bifurcation, which acts as an organizing center demarcating the boundary between the two primary bursting patterns and around which the third pattern appear. Our model suggests that LHb bursting activity is structured around distinct dynamic states with potentially diverse and unexplored impacts on mood regulation. By providing new insights into the dynamic principles underlying LHb bursting, this framework may advance our understanding of its biological significance.

neuroscience↗

bioRxiv2024

GABAergic neurons can facilitate the propagation of cortical spreading depolarization: experiments in mouse neocortical slices and a novel neural field computational model

Cortical spreading depolarization (CSD) is a wave of depolarization with local onset and extended propagation implicated in several pathological conditions. Its mechanisms have been extensively investigated, including our recent studies showing with experimental and computational approaches that the hyperactivity of GABAergic neurons can initiate migraine-related CSD because of spiking-generated extracellular potassium (K+) build-up. However, less is known about the role played by GABAergic neurons in CSD propagation. Here we studied mechanisms of CSD propagation, focusing on the role of GABAergic neurons, with experiments performed in mouse brain slices and with a new spatially extended neural field computational model. Experimentally, we induced CSD by applying brief puffs of potassium chloride (KCl) in somatosensory cortex slices from wild type and VGAT-ChR2-tdtomato mice, which specifically express the excitatory opsin channelrhodopsin (ChR2) in GABAergic neurons. We evaluated the role of GABAergic neurons in CSD propagation by modulating their activity with optogenetic illumination and their synaptic connections with pharmacological tools. We have developed the computational model to obtain realistic simulations of both initiation and propagation of CSD. It includes large populations of interconnected excitatory and inhibitory neurons, as well as the effect of extracellular ion concentrations on their features. We found that the decrease of the synaptic activity of GABAergic neurons can enhance CSD propagation, because of the reduction of the inhibitory synaptic weight, whereas their spiking activity can enhance CSD propagation because of extracellular K+ upload. However, differently than for CSD initiation, the latter effect is normally hidden by the action of GABAergic synaptic transmission. A reduction of GABAergic synaptic transmission, which can be observed in pathological states, can reveal the potentiating effect of the K+ upload induced by GABAergic activation. The neural field model that we implemented can generate accurate simulations of CSD, providing testable hypotheses on mechanisms, and can also be used for modeling other (patho)-physiological activities of neuronal networks.

neuroscience↗

bioRxiv2024

Modeling impairment of ionic regulation with extended Adaptive Exponential integrate-and-fire models

To model the dynamics of neuron membrane excitability many models can be considered, from the most biophysically detailed to the highest level of phenomenological description. Recent works at the single neuron level have shown the importance of taking into account the evolution of slow variables such as ionic concentration. A reduction of such a model to models of the integrate-and-fire family is interesting to then go to large network models. In this paper, we introduce a way to consider the impairment of ionic regulation by adding a third, slow, variable to the adaptive Exponential integrate-and-fire model (AdEx). We then implement and simulate a network including this model. We find that this network was able to generate normal and epileptic discharges. This model should be useful for the design of network simulations of normal and pathological states.

neuroscience↗

bioRxiv2023

Discovering Secondary Protein Structures via Local Euler Curvature

Protein structure analysis and classification, which is fundamental for predicting protein function, still poses formidable challenges in the fields of molecular biology, mathematics, physics and computer science. In the present work we exploit recent advances in computational topology to define a new intrinsic unsupervised topological fingerprint for proteins. These fingerprints, computed via Local Euler Curvature (LECs), identify secondary protein structures, such as Helices and Sheets, by capturing their distinctive topological signatures. Using an extensive protein residue database, the proposed computational framework not only distinguishes between structural classes via unsupervised clustering but also achieves remarkable accuracy in classifying proteins structures through supervised machine learning classifier. We also show that the internal structure of LEC space embeds the information about the secondary structure of proteins. Beyond its immediate implications for the advancement of critical application areas such as drug design and biotechnology, our approach opens a fascinating avenue towards characterizing the multiscale structures of diverse biopolymers based solely on their geometric and topological attributes.

biochemistry↗

bioRxiv2023

Emergence of High-Order Functional Hubs in the Human Brain

Network theory is often based on pairwise relationships between nodes, which is not necessarily realistic for modeling complex systems. Importantly, it does not accurately capture non-pairwise interactions in the human brain, often considered one of the most complex systems. In this work, we develop a multivariate signal processing pipeline to build high-order networks from time series and apply it to resting-state functional magnetic resonance imaging (fMRI) signals to characterize high-order communication between brain regions. We also propose connectivity and signal processing rules for building uniform hypergraphs and argue that each multivariate interdependence metric could define weights in a hypergraph. As a proof of concept, we investigate the most relevant three-point interactions in the human brain by searching for high-order "hubs" in a cohort of 100 individuals from the Human Connectome Project. We find that, for each choice of multivariate interdependence, the high-order hubs are compatible with distinct systems in the brain. Additionally, the high-order functional brain networks exhibit simultaneous integration and segregation patterns qualitatively observable from their high-order hubs. Our work hereby introduces a promising heuristic route for hypergraph representation of brain activity and opens up exciting avenues for further research in high-order network neuroscience and complex systems.

neuroscience↗

bioRxiv2022

Multiple-timescale dynamics, mixed mode oscillations and mixed affective states in a model of Bipolar Disorder

Mixed affective states in bipolar disorder (BD) is a common psychiatric condition that occurs when symptoms of the two opposite poles coexist during an episode of mania or depression. A four-dimensional model by A. Goldbeter [27, 28] rests upon the notion that manic and depressive symptoms are produced by two competing and auto-inhibited neural networks. Some of the rich dynamics that this model can produce, include complex rhythms formed by both small-amplitude (subthreshold) and large-amplitude (suprathreshold) oscillations and could correspond to mixed bipolar states. These rhythms are commonly referred to as mixed mode oscillations (MMOs) and they have already been studied in many different contexts [7, 50]. In order to accurately explain these dynamics one has to apply a mathematical apparatus that makes full use of the timescale separation between variables. Here we apply the framework of multiple-timescale dynamics to the model of BD in order to understand the mathematical mechanisms underpinning the observed dynamics of changing mood. We show that the observed complex oscillations can be understood as MMOs due to a so-called folded-node singularity. Moreover, we explore the bifurcation structure of the system and we provide possible biological interpretations of our findings. Finally, we show the robustness of the MMOs regime to stochastic noise and we propose a minimal three-dimensional model which, with the addition of noise, exhibits similar yet purely noise-driven dynamics. The broader significance of this work is to introduce mathematical tools that could be used to analyse and potentially control future, more biologically grounded models of BD.

neuroscience↗

bioRxiv2022

Geometry of spiking activity in early visual cortex: a Topological Data Analytic approach

In the brain, spiking patterns live in a high-dimensional space of neurons and time. Thus, determining the intrinsic structure of this space presents a theoretical and experimental challenge. To address this challenge, we introduce a new framework for applying topological data analysis (TDA) to spike train data and use it to determine the geometry of spiking patterns in the visual cortex. Key to our approach is a parameterized family of distances based on the timing of spikes that quantifies the dissimilarity between neuronal responses. We applied TDA to visually driven single-unit and multiple single-unit spiking activity in macaque V1 and V2. TDA across timescales reveals a common geometry for spiking patterns in V1 and V2 which, among simple models, is most similar to that of a low-dimensional space endowed with Euclidean or hyperbolic geometry with modest curvature. Remarkably, the inferred geometry depends on timescale, and is clearest for the timescales that are important for encoding contrast, orientation, and spatial correlations.

neuroscience↗

bioRxiv2022

Interaction mechanism between the HSV-1 glycoprotein B and the antimicrobial peptide Amyloid-β

Unravelling the mystery of Alzheimers Disease (AD) requires urgent resolution given the worldwide increase of the aging population. There is a growing concern that the current leading AD hypothesis, the amyloid cascade hypothesis, does not stand up to validation with respect to emerging new data. Indeed, several paradoxes are being discussed in the literature, for instance, both the deposition of the Amyloid-Beta peptide (A{beta}) and the intracellular neurofibrillary tangles (NFTs) could occur within the brain without any cognitive pathology. Thus, these paradoxes suggest that something more fundamental is at play in the onset of the disease and other key and related pathomechanisms have to be investigated. The present study follows our previous investigations on the infectious hypothesis, which posits that some pathogens are linked to late onset AD. Our studies also build upon the shattering finding that A{beta} is a powerful antimicrobial agent capable of inhibiting pathogens as observed in in vitro experiments. Herein, we ask what are the molecular mechanisms in play when A{beta} neutralizes infectious pathogens? To answer this question, we probed at nanoscale lengths with FRET (Forster Resonance Energy Transfer), the interaction between A{beta} peptides and glycoprotein B (responsible of virus-cell binding) within the HSV-1 virion. We concluded that there is indeed a close interaction, likely nonspecific or semi-specific, between the two types of molecules, which participate in virus neutralization.

neuroscience↗

bioRxiv2020

GABAergic neurons and Nav1.1 channel hyperactivity: a novel neocortex-specific mechanism of cortical spreading depression.

Cortical spreading depression (CSD) is a pathologic mechanism of migraine. We have identified a novel neocortex-specific mechanism of CSD initiation and a novel pathological role of GABAergic neurons. Mutations of the NaV1.1 sodium channel (the SCN1A gene), which is particularly important for GABAergic neurons excitability, cause Familial Hemiplegic Migraine type-3 (FHM3), a subtype of migraine with aura. They induce gain-of-function of NaV1.1 and hyperexcitability of GABAergic interneurons in culture. However, the mechanism linking these dysfunctions to CSD and FHM3 has not been elucidated. Here, we show that NaV1.1 gain-of-function, induced by the specific activator Hm1a, or mimicked by optogenetic-induced hyperactivity of cortical GABAergic neurons, is sufficient to ignite CSD by spiking-generated extracellular K+ build-up. This mechanism is neocortex specific because, with these approaches, CSD was not generated in other brain areas. GABAergic and glutamatergic synaptic transmission is not required for optogenetic CSD initiation, but glutamatergic transmission is implicated in CSD propagation. Thus, our results reveal the key role of hyper-activation of Nav1.1 and GABAergic neurons in a novel mechanism of CSD initiation, which is relevant for FHM3 and possibly also for other types of migraine.

neuroscience↗