Biology subjects

Farcot, E.

Publications and source records attributed to Farcot, E..

8 recordsLinked to original sources

bioRxiv2026

Mathematical modeling of SUMOylation reveals tissue-specific and dynamic responses to abiotic stress

SUMOylation is an essential and evolutionarily conserved post-translational modification that enables rapid cellular adaptation to environmental stress. Despite its importance, how the SUMOylation machinery controls its dynamics remains poorly understood. Here, we integrated a mathematical model of the plant SUMO cycle with quantitative imaging and biochemical data to decode the regulatory logic of SUMOylation dynamics. The model reveals a fundamental principle: steady-state SUMOylation levels are determined not by the full complement of SUMO-cycle components, but by the abundance of SUMO modifier proteins and E1 enzymes, together with SUMO protease abundance and activity. Strikingly, when we integrated fluorescence measurements from reporters for SUMO-cycle components in Arabidopsis roots after three hours of abiotic stress, the model predicted decreased SUMOylation levels across several tissues, contrary to prevailing evidence. Motivated by this unexpected prediction, we performed time-resolved imaging to better characterize the dynamics of key SUMO-cycle components after salt and osmotic stress. These data revealed highly dynamic component levels within the first three hours of stress that were not captured in the previous measurements. Incorporating this temporal data into the model uncovered a twin-peaked SUMOylation response to salt stress: an early surge underpinned by protease dynamics, followed by a decline and a second peak driven by elevated SUMO modifier and E2 levels. Western blot analysis revealed a corresponding twin-peaked pattern of SUMOylation. Together, our findings demonstrate that the plant SUMO cycle is a highly dynamic system that achieves tissue- and stress-specific control of SUMOylation through the coordinated interplay of multiple SUMO-cycle components.

plant biology↗

bioRxiv2026

A general mathematical framework for modelling subnetworks of the nuclear auxin pathway

Auxins are a family of plant hormones involved in various processes across plant tissues and species. The Nuclear Auxin Pathway (NAP) consists of interacting transcription factors (ARFs) and repressors (Aux/IAAs), which govern an individual cell's response to changes in auxin concentration. These components are present in all land plants, and many species possess multiple copies of each signalling component. We present a general framework for ODE-based models of NAP submodules with the flexibility to model the promotion and repression of target genes by any combination of transcriptional regulators. We analyse published data and show that auxin treatment in Arabidopsis thaliana roots triggers a range of characteristically distinct temporal response profiles-for both target genes and the signalling components themselves. Using our modelling framework, we recapitulate aspects of this behaviour by presenting examples of real and theoretical NAP subnetworks, and by analysing the effect that these network dynamics have on auxin-mediated transcriptional responses. This work demonstrates the utility of our modelling framework as a general-purpose tool for understanding the function of certain protein-protein and protein-DNA interactions through their effects on the NAP. This exploration of the rich dynamics of more complex signalling pathways promises to advance our understanding of the NAP.

plant biology↗

bioRxiv2026

Graph-theoretic comparisons of structural covariance networks: quantifying the false discovery rate

Structural covariance networks (SCNs) represent spatial patterns of covariation in brain morphology, often as a network of connections between nodes representing correlations in grey matter volume or cortical thickness measured by magnetic resonance imaging (MRI). SCNs have been suggested to reveal differences in functional organisation that are reflected in coordinated alterations to brain structure, and often these differences are sought in graph-theoretic measures such as the degree of clustering, segregation into distinct modules, or the characteristic path length between nodes. A common practice is to calculate SCNs for groups of interest, and use permutation testing to determine if they are significantly different for the measure of interest. However, the statistical validity of group comparisons using SCN-derived graph measures remains poorly understood. Here, we systematically evaluate the reliability of SCN estimation and downstream graph-theoretic analyses using structural MRI data from the Human Connectome Project (n = 1,096). We use simulations to show the effects of sample size and atlas dimensionality on SCN reliability. Using bootstrapping to characterise the distribution of SCN graph measures, we establish that small sample sizes systematically bias graph-theoretic measures including clustering, characteristic path length and modularity. Finally, we use simulations based on extrema from the bootstrapping distribution to characterise the statistical power and false discovery rate (FDR) for graph-theoretic between-group comparisons of SCNs, showing that at small sample sizes (n [≤] 30) permutation testing is no better than chance. These findings suggest that many significant SCN group differences, particularly those using small samples and high-dimensional parcellations, may reflect sampling noise rather than true biological differences. We recommend that future SCN studies use larger samples, coarser parcellations, and explicitly evaluate reliability before interpreting group differences.

neuroscience↗

bioRxiv2025

Model of integrin binding as a function of light-induced variations in forces exerted on cells via RGD-terminated, photoswitchable azobenzene surfaces

Photo-responsive biomaterials are attractive because of the ability to non-invasively alter and control the material properties, thus allowing control over the cell response at the interface with the bioamaterial surface. While in silico mathematical models have been implemented for simulating single-cell force spectroscopy (SCFS) experiments on static and some dynamic biomaterials, these models have yet to be extended to light-responsive biointerfaces. So here, we develop a mathematical model that describes and predicts the strength of integrin-mediated cell adhesions to a photoswitchable biomaterial surface. The fluctuating biomaterial comprises photoswitchable azobenzene attached to a glass surface and terminated with peptide c(RGDfK). Upon irradiation with light at 530 nm, the azobenzenes rapidly fluctuate between an extended and contracted conformation, leading to a change in the length of the azobenzene that stimulates integrins bound to c(RGDfK). The mathematical model mimics the nascent adhesion and spatial fluctuations in the extra-cellular matrix (ECM) and is calibrated using single-cell force microscopy retraction curves. It relies on spring-based mechanics to describe the stretch and deformation of a cell and ensembles of integrins when applied to a fluctuating biomaterial. We use the model to simulate retraction curves and the proportion of bound integrins on the surface as the material fluctuates. Additionally, we use the model to predict SCFS retraction curves for varying experimental conditions. This includes the length of time the cell (attached to the tip of the atomic force microscopy cantilever) is kept in contact with the biomaterial before retraction, and for varying frequency of light-induced movement of the azobenzene conformations. These model outcomes provide an attractive route to integrate and rigorously control certain experimental variables, thus accelerating experimental design of the study of cell adhesion to light-responsive biomaterials. Furthermore the model complements experiments by providing estimates of variables that are experimentally inaccessible. Thus, the outcomes of the model provide a valuable resource to aid in the interpretation and design of light-responsive biointerface functionalities.

biophysics↗

bioRxiv2023

Fluctuations in auxin levels depend upon synchronicity of cell divisions in a one-dimensional model of auxin transport

Auxin is a well-studied plant hormone, the spatial distribution of which remains incompletely understood. Here, we investigate the effects of cell growth and divisions on the dynamics of auxin patterning, using a combination of mathematical modelling and experimental observations. In contrast to most prior work, models are not designed or tuned with the aim to produce a specific auxin pattern. Instead, we use well-established techniques from dynamical systems theory to uncover and classify ranges of auxin patterns as exhaustively as possible, as parameters are varied. Previous work using these techniques has shown how a multitude of stable auxin patterns may coexist, each attainable from a specific ensemble of initial conditions. When a key parameter spans a range of values, these steady patterns form a geometric curve with successive folds, often nicknamed a snaking diagram. As we introduce growth and cell divisions into a one-dimensional model of auxin distribution, we observe new behaviour which can be conveniently explained in terms of this diagram. Cell growth changes the shape of the snaking diagram, corresponding to deformations of auxin patterns. As divisions occur this can lead to abrupt creation or annihilation of auxin peaks. We term this phenomenon snake-jumping. Under rhythmic cell divisions, we show how this can lead to stable oscillations of auxin. However, we also show that this requires a high level of synchronisation between cell divisions. Using 18 hour time-lapse imaging of the auxin reporter DII:Venus in roots of Arabidopsis thaliana, we show auxin fluctuates greatly, both in terms of amplitude and periodicity, consistent with the snake-jumping events observed with non-synchronised cell divisions. Periodic signals downstream the auxin signalling pathway have previously been recorded in plant roots. The present work shows that auxin alone is unlikely to play the role of a pacemaker in this context. Author summaryAuxin is a crucial plant hormone, the function of which underpins almost every known plant development process. The complexity of its transport and signalling mechanisms, alongside the inability to image directly, make mathematical modelling an integral part of research on auxin. One particularly intriguing phenomenon is the experimental observation of oscillations downstream of auxin pathway, which serve as initiator for lateral organ formation. Existing literature, with the aid of modelling, has presented both auxin transport and signalling as potential drivers for these oscillations. In this study, we demonstrate how growth and cell divisions may trigger fluctuations of auxin with significant amplitude, which may lead to regular oscillations in situations where cell divisions are highly synchronised. More physiological conditions including variations in the timing of cell divisions lead to much less temporal regularity in auxin variations. Time-lapse microscope images confirm this lack of regularity of auxin fluctuations in the root apical meristem. Together our findings indicate that auxin changes are unlikely to be strictly periodic in tissues that do not undergo synchronous cell divisions and that other factors may have a robust ability to convert irregular auxin inputs into the periodic outputs underpinning root development.

plant biology↗

bioRxiv2022

Selective degradation of ARF monomers controls auxin response in Marchantia

The plant signaling molecule auxin controls a variety of growth and developmental processes in land plants. Auxin regulates gene expression through a nuclear auxin signaling pathway (NAP) consisting of a ubiquitin ligase auxin receptor TIR1/AFB, its Aux/IAA degradation substrate, and the DNA-binding ARF transcription factors. While extensive qualitative understanding of the pathway and its interactions has been obtained by studying the flowering plant Arabidopsis thaliana, it is so far unknown how these translate to quantitative system behaviour in vivo, a problem that is confounded by large NAP gene families in this species. Here we used the minimal NAP of the liverwort Marchantia polymorpha to quantitatively map NAP protein accumulation and dynamics in vivo through the use of knock-in fluorescent fusion proteins. Beyond revealing the native accumulation profile of the entire NAP protein network, we discovered that the two central ARFs MpARF1 and MpARF2 are proteasomally degraded. This degradation serves two functions: it tunes the stoichiometry of auxin-responsive, positively acting MpARF1 and auxin-independent, negatively acting MpARF2, thereby permitting auxin response. Secondly, through mapping a minimal degradation motif, we found that degradation is likely selective for MpARF2 monomers and favours accumulation of dimers. Interfering with MpARF1:MpARF2 stoichiometry or preventing degradation of MpARF2 monomers caused strong growth defects associated with auxin response defects. Thus, quantitative analysis of the entire Marchantia NAP, allowed to identify a novel regulatory mechanism in auxin response, built on regulated ARF degradation.

plant biology↗

bioRxiv2022

In silico evidence for the utility of parsimonious root phenotypes for improved vegetative growth and carbon sequestration under drought

Drought is a primary constraint to crop yields and climate change is expected to increase the frequency and severity of drought stress in the future. It has been hypothesized that crops can be made more resistant to drought and better able to sequester atmospheric carbon in the soil by selecting appropriate root phenotypes. We introduce OpenSimRoot_v2, an upgraded version of the functional-structural plant/soil model OpenSimRoot, and use it to test the utility of a maize root phenotype with fewer and steeper axial roots, reduced lateral root branching density, and more aerenchyma formation (i.e. the Steep, Cheap, and Deep (SCD) ideotype) and different combinations of underlying SCD root phene states under rainfed and drought conditions in three distinct maize growing pedoclimatic environments in the USA, Nigeria, and Mexico. In all environments where plants are subjected to drought stress the SCD ideotype as well as several intermediate phenotypes lead to greater shoot biomass after 42 days. As an additional advantage, the amount of carbon deposited below 50 cm in the soil is twice as great for the SCD phenotype as for the reference phenotype in 5 out of 6 simulated environments. We conclude that crop growth and deep soil carbon deposition can be improved by breeding maize plants with fewer axial roots, reduced lateral root branching density, and more aerenchyma formation.

plant biology↗

bioRxiv2021

The making of cauliflowers: the story of unsuccessful flowers

The arrangement of plant organs, called phyllotaxis, produce remarkable spiral or whorled patterns. Cauliflowers present a unique phyllotaxis with a multitude of spirals over a wide range of scales. How such a self-similar fractal organization emerges from developmental mechanisms has remained elusive. Combining experimental assays with modeling, we found that cauliflowers arise due to the hysteresis of the bistable floral network that generates inflorescences imprinted by a transient floral state. We further show how additional mutations affecting meristem growth dynamics can induce the production of conical phyllotactic structures reminiscent of the conspicuous fractal Romanesco shape. This study reveals how the spectacular morphological modification of the inflorescences in cauliflower and Romanesco shape arises from the hysteresis of the genetic programs controlling inflorescence development. One Sentence SummaryThe molecular making of cauliflowers

plant biology↗