Biology subjects

Gonze, D.

Publications and source records attributed to Gonze, D..

11 recordsLinked to original sources

bioRxiv2026

How p53 stress memory could redirect JAK/STAT1 antiviral signalling: a model-based prediction.

Viral infection can co-activate interferon (IFN)-JAK/STAT1 signalling and the p53 -Mdm2 stress-response pathway, two modules that jointly shape antiviral defence and cell-fate decisions. Here, we focus on viral infection contexts capable of inducing genotoxic stress associated with DNA double-strand breaks, thereby triggering oscillatory or sustained p53-Mdm2 dynamics. Whether p53 acts merely as a parallel stress pathway, or actively reshapes how an activated JAK/STAT1 response is temporally decoded and functionally routed, remains unclear. We develop a coupled ordinary-differential-equation model linking an IFN-{gamma}-centred JAK/STAT1 core, a p53 -Mdm2 module, downstream antiviral and apoptotic effectors, and a coarse-grained viral-burden layer, with p53 regulation placed downstream of STAT1 activation. We find that p53 does not simply increase nuclear STAT1 availability; it redistributes the response towards DNA-bound STAT1 persistence, transcriptional memory and STAT1-driven feedback, producing a persistence-recovery trade-off in which prior p53 stress prolongs the transcriptionally active STAT1 state but delays re-inducibility after repeated IFN stimulation. When IFN and p53-associated stress are both driven by viral burden, p53 is not a uniform amplifier of host defence: p53 preactivation strengthens the upstream memory layer, but downstream effectors buffer rather than mirror this priming. The model further separates antiviral-state engagement from realised viral control: strong effector activation does not guarantee suppression of poorly sensitive viral classes, whereas sensitive viral classes can be cleared before apoptosis. The origin of the stimulus also matters: exogenous IFN or p53 stimulation allows us to assess the hosts intrinsic response capacity, whereas virus-induced IFN and p53 stress remain coupled to viral persistence. Persistent viral burden thus emerges as the dynamical link between IFN induction, p53 stress-memory, antiviral maintenance, viral control and the choice between JAK/STAT-IRF1-associated, p53-autonomous or dual apoptotic routing.

systems biology↗

bioRxiv2025

STAT1 -- p53 Dynamics Program Cell Fate through p21 and PUMA

Signalling pathways tightly regulate stress-induced cell fate decisions, with p53 stabilization via attenuation of the p53-Mdm2 feedback being central to effective responses. Signal Transducer and Activator of Transcription 1 (STAT1) both represses Mdm2 transcription and co-activates p53 targets (p21 and PUMA), yet these dual roles have not been quantified within a unified framework. We build a mechanistic model that couples a biologically calibrated stress scale (S) and STAT1 activity ({Sigma}) to the canonical p53-Mdm2 core and to downstream p21 and PUMA modules. Key processes are not described by fixed parameters but by explicit functions of S, allowing the model to self-adjust across stress levels without manual re-fitting. This formulation ensures that oscillatory, damped, or plateau responses naturally emerge by varying S alone. STAT1 acts as a dynamical gain on the core (controlling mean level, pulse amplitude, and duration) and as a transcriptional co-activator scaling promoter strengths. Bifurcation analysis reveals a two-dimensional Hopf region in the (S, {Sigma}) plane; increasing {Sigma} shifts this region to higher S and progressively narrows it, ultimately quenching oscillations at large {Sigma} . Simulations of a generic cell with time-varying S and {Sigma}, combined with a two-stage decision rule (early transient detection followed by stationary readout), map p53 dynamics to fate: sustained moderate oscillations align with arrest, damped intermediate responses with senescence, and strongly damped high plateaus with apoptosis. The model reproduces cell fate distributions reported in literature for different cell lines (MCF-7, HCT116, U2OS) without kinetic parameter re-fitting, and highlights cell-type-specific sensitivity to p21 versus PUMA. Our framework identifies STAT1 as a tunable amplifier and oscillation quencher of the stress-responsive p53 network, providing testable dynamics-based predictions for fate control.

systems biology↗

bioRxiv2025

Temperature signals drive grass secondary cell wall thickening

In grasses, stem elongation is driven by intercalary meristems at node-internode junctions, where cells divide, elongate, and in some cell types secondary wall maturation. Cellulose is the predominant polymer in plant cells and the most abundant biopolymer on Earth. It is synthesized at the plasma membrane by multi-protein complexes that include CELLULOSE SYNTHASE A (CESA) proteins. To investigate the spatiotemporal regulation of cellulose deposition during development, we developed a CESA8 luciferase gene expression reporter system in Brachypodium distachyon. High bioluminescence was observed in stem nodes, a specific region of elongating internodes, and the inflorescence, indicating sites of active secondary wall deposition. Within internodes, luminescence followed a distinct pattern, with a "dark zone" directly above the node with minimal signal, followed by a "bright zone" approximately 5 mm above the node where bioluminescence peaked. Histological, biophysical, and transcript analysis confirmed that luminescence intensity correlates with thickened secondary cell walls, increased cellulose crystallinity, and elevated CESA8 transcript levels. Time-lapse imaging revealed that CESA8 expression follows a robust diurnal rhythm governed by thermocycles alone, with peak expression occurring in the early morning. Temperature pulse experiments revealed an immediate but transient response of CESA8 to temperature shifts, which we modeled as an incoherent feed-forward loop. Finally, we found a strong correlation between CESA8 expression and stem elongation, highlighting the role of secondary cell wall thickening in supporting upright growth. These findings provide new insights into the regulation of secondary wall formation and its integration with environmental cues, advancing our understanding of grass stem development. SIGNIFICANCEUnderstanding how grasses build strong stems is essential for improving biomass production and crop resilience. In grasses, stem elongation and secondary cell wall thickening occur in distinct zones, yet the precise timing and regulation of this process remain unclear. To investigate this phenomenon, we developed a real-time imaging system to track the expression of CESA8, a key gene involved in cellulose synthesis. Our findings reveal that secondary wall thickening follows a daily rhythm controlled by temperature rather than light. These insights provide a foundation for optimizing plant architecture in bioenergy crops, improving their efficiency and sustainability.

plant biology↗

bioRxiv2024

Discovery of alternative stable states in a synthetic human gut microbial community

AbstractSeveral human-associated microbial communities exist in multiple configurations and can change their composition in response to perturbations, remaining in an altered state even after the perturbation ends. Multistability has been previously proposed to explain this behavior for gut microbiota in particular, but has not been clearly demonstrated experimentally. Here, we first investigated the life history strategies of three common human gut bacteria to identify mechanisms driving alternative states. We then used this data to build and parameterize a kinetic model, which predicted that alternative states emerge due to phenotype switching between subpopulations of the same species. Perturbation experiments supported these predictions, and confirmed the existence of alternative states. Finally, simulations showed that phenotype switching can also explain alternative states in larger communities. Thus, a transient perturbation combined with metabolic flexibility is sufficient for alternative communities to emerge, implying that they are not necessarily explained by differences between individuals. One-Sentence SummaryWe demonstrate the existence of alternative states in a human gut microbial community and propose phenotype switching as a mechanism explaining their emergence.

microbiology↗

bioRxiv2024

Quantification of beta cell carrying capacity in prediabetes

Prediabetes, a subclinical state of high glucose, carries a risk of transition to diabetes. One cause of prediabetes is insulin resistance, which impairs the ability of insulin to control blood glucose. However, many individuals with high insulin resistance retain normal glucose due to compensation by enhanced insulin secretion by beta cells. Individuals seem to differ in their maximum compensation level, termed beta cell carrying capacity, such that low carrying capacity is associated with a higher risk of prediabetes and diabetes. Carrying capacity has not been quantified using a mathematical model and cannot be estimated directly from measured glucose and insulin levels in patients, unlike insulin resistance and beta cell function which can be estimated using HOMA-IR and HOMA-B formula. Here we present a mathematical model of beta cell compensation and carrying capacity, and develop a new formula called HOMA-C to estimate it from glucose and insulin measurements. HOMA-C estimates the maximal potential beta cell function of an individual, rather than the current beta cell function. We test this approach using longitudinal cohorts of prediabetic people, finding 10-fold variation in carrying capacity. Low carrying capacity is associated with higher risk of transitioning to diabetes. We estimate the timescales of beta cell compensation and insulin resistance using large datasets, showing that, unlike previous mathematical models, the new model can explain the slow rise in glucose over decades. Our mathematical understanding of beta cell carrying capacity may help to assess the risk of prediabetes in each individual.

systems biology↗

bioRxiv2023

Starvation response strategies of human gut bacteria Bacteroides thetaiotaomicron and Roseburia intestinalis and their impact on co-culture dynamics

Bacterial growth often alters the environment, which in turn can impact interspecies interactions among bacteria. Here, we used an in vitro batch system containing mucin beads to emulate the dynamic host environment and to study its impact on the interactions between two abundant and prevalent human gut bacteria, the primary fermenter Bacteroides thetaiotaomicron and the butyrate producer Roseburia intestinalis. By combining machine learning and flow cytometry, we found that the number of viable B. thetaiotaomicron cells decreases with glucose consumption due to acid production, while R. intestinalis survives post-glucose depletion by entering a slow growth mode. Both species attach to mucin beads, but only viable cell counts of B. thetaiotaomicron increase significantly. The number of viable co-culture cells varies significantly over time compared to those of monocultures. A combination of targeted metabolomics and RNA-seq showed that the slow growth mode of R. intestinalis represents a diauxic shift towards acetate and lactate consumption, whereas B. thetaiotaomicron survives glucose depletion and low pH by foraging on mucin sugars. In addition, most of the mucin monosaccharides we tested inhibited the growth of R. intestinalis but not B. thetaiotaomicron. We encoded these causal relationships in a kinetic model, which reproduced the observed dynamics. In summary, we explored how R. intestinalis and B. thetaiotaomicron respond to nutrient scarcity and how this affects their dynamics. We highlight the importance of understanding bacterial metabolic strategies to effectively modulate microbial dynamics in changing conditions.

microbiology↗

bioRxiv2021

Quantifying the impact of ecological memory on the dynamics of interacting communities

Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications. Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the outcomes of a well-characterized interaction model are affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity. Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory on ecological communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions. Author summaryAn ecosystem is said to exhibit ecological memory when its future states do not only depend on its current state but also on its initial state and trajectory. Memory may arise through various mechanisms as organisms adapt to their environment, modify it, and accumulate biotic and abiotic material. It may also emerge from phenotypic heterogeneity at the population level. Despite its commonness in nature, ecological memory and its potential influence on ecosystem dynamics have been so far overlooked in many applied contexts. Here, we use modeling to investigate how memory can influence the dynamics, composition, and stability landscape of ecological communities. We incorporate long-term memory effects into a multi-species model recently introduced to investigate alternative stable states in microbial communities. We assess the impact of memory on key aspects of model behavior and validate our findings using a model parameterized by empirical data from the human gut microbiota. Our approach for modeling memory and studying its implications has the potential to improve our understanding of microbial community dynamics and ultimately our ability to predict, manipulate, and experimentally design microbial ecosystems. It could also be applied more broadly in the study of systems composed of interacting components.

systems biology↗

bioRxiv2020

Magnesium maintains length of circadian period in Arabidopsis thaliana

The circadian clock coordinates the physiological response of a biological system to day and night rhythms through complex loops of transcriptional/ translational regulation. It can respond to external stimuli and adjust generated circadian oscillations accordingly to keep an endogenous period close to 24 h. To date, the interaction between nutritional status and circadian rhythms in plants is poorly understood. Magnesium (Mg) is essential for numerous biological processes in plants and its homeostasis is crucial to maintain optimal development and growth. Magnesium deficiency in young Arabidopsis thaliana seedlings increased the circadian period of pCCA1:LUC oscillations and dampened its amplitude in constant light in a dose-dependent manner. Although circadian period increase by Mg deficiency was light dependent, it did not depend on active photosynthesis. Mathematical modelling of the Mg input to the circadian clock reproduced the experimental increase of the circadian period and suggested that Mg is likely to affect global transcription/translation levels rather than a single component of the circadian oscillator. The model prediction was supported by a synergistic interaction between Mg deficiency and cyclohexamide, an inhibitor of translation. These findings suggest that proper Mg supply is required to support proper timekeeping in plants. One sentence summaryMagnesium maintains the circadian period in Arabidopsis seedlings and interferes with the circadian oscillator most likely through translational mechanisms.

molecular biology↗

bioRxiv2019

Mutualistic cross-feeding in microbial systems generates bistability via an Allee effect

Mutualistic interactions are characterized by the positive influence that two species exert on each other. Such mutualism can lead to bistability. Depending on the initial population size species will either survive or go extinct. Various phenomenological models have been suggested to describe bistability in mutualistic systems. However, these models do not account for interaction mediators such as nutrients. In contrast, nutrient-explicit models do not provide an intuitive understanding of what causes bistability. Here, we reduce a theoretical nutrient-explicit model of two mutualistic cross-feeders in a chemostat, uncovering an explicit relation to a growth model with an Allee effect. We show that the dilution rate in the chemostat leads to bistability by turning a weak Allee effect into a strong Allee effect. This happens as long as there is more production than consumption of cross-fed nutrients. Thanks to the explicit relationship of the reduced model with the underlying experimental parameters, these results allow to predict the biological conditions that sustain or prevent the survival of mutualistic species.

ecology↗

bioRxiv2019

Modeling the dynamics of Let-7-coupled gene regulatory networks linking cell proliferation to malignant transformation

The microRNA Let-7 controls the expression of proteins that belong to two distinct gene regulatory networks, namely a cyclin-dependent kinases (Cdks) network driving the cell cycle and a cell transformation network which can undergo an epigenetic switch between a non-transformed and a malignant transformed cell state.\n\nUsing mathematical modeling and transcriptomic data analysis, we here investigate how Let-7 controls the cdk-dependent cell cycle network, and how it couples the latter with the transformation network. We also determine whether the two networks can be combined into a larger entity that impacts on cancer progression.\n\nOur analysis shows that the switch from a quiescent to a cycling state depends on the relative levels of Let-7 and several cell cycle activators. Numerical simulations further indicate that the Let-7-coupled cell cycle and transformation networks control each other, and our model identifies key players for this mutual control. Transcriptomic data analysis from the The Cancer Genome Atlas (TCGA) suggest that the two networks are activated in cancer, in particular in gastrointestinal cancers, and that the activation levels vary significantly among patients affected with a same cancer type. Our mathematical model, when applied to a heterogeneous cell population, suggests that heterogeneity among tumors results from stochastic switches between a non-transformed cell state with low proliferative capability and a transformed cell state with high proliferative property. The model further predicts that Let-7 may reduce tumor heterogeneity by decreasing the occurrence of stochastic switches towards a transformed, proliferative cell state.\n\nIn conclusion, we identified the key components responsible for the qualitative dynamics of two GRNs interconnected by Let-7. The two GRNs are heterogeneously involved in several cancers, thereby stressing the need to consider patients specific GRN characteristics to optimize therapeutic strategies.

systems biology↗

bioRxiv2018

Integrated culturing, modeling and transcriptomics uncovers complex interactions and emergent behavior in a synthetic gut community

Whereas the composition of the human gut microbiome is relatively well resolved, predictive understanding of its response to perturbations such as diet shifts is still lacking. Here, we followed a bottom-up strategy to explore human gut community dynamics. We established a synthetic community composed of three representative human gut isolates in well-controlled conditions in vitro. We then explored species interactions by performing all mono- and pair-wise fermentation experiments and quantified with a mechanistic community model how well tri-culture dynamics was predicted from mono-culture data. With the model as a reference, we demonstrated that species grown in co-culture behaved differently than in mono-culture and confirmed their altered behavior at the transcriptional level. In addition, we showed with replicate tri-cultures and in simulations that dominance in tri-culture sensitively depends on initial conditions. Our work has important implications for gut microbial community modeling as well as ecological interaction detection from batch cultures.

bioinformatics↗