Biology subjects

Prigent, S.

Publications and source records attributed to Prigent, S..

13 recordsLinked to original sources

bioRxiv2026

Predictive metabolomics to decipher plant eco-evolutive tendencies and physiological traits

Plant ecological and evolutionary strategies are shaped by interactions between phylogenetic history and environmental constraints, resulting in leaf and stomatal traits. However, traditional trait-based and phylogenetic approaches often fail to fully explain biochemical mechanisms underlying ecological strategies, particularly for leaf and stomatal traits. Plant metabolomes integrate genetic, physiological, and environmental information and therefore represent a promising intermediate phenotype for investigating links between biochemical diversity, functional traits, and evolutionary patterns. We analysed metabolomic profiles from 74 plant species with various growth forms and ecological types. Using machine learning approaches, we explored whether metabolic variation could predict plant functional divisions, growth forms and phenological types, but also physiological traits related to drought resistance. Metabolomic data contained structured information associated with variation in plant functional traits, ecological strategies, and phylogenetic relationships. Machine learning models identified with high accuracy distinct metabolic signatures linked to differences among plant functional divisions, growth forms, phenology, and trait values. Our study demonstrates that predictive metabolomics provides a powerful and integrative framework to investigate plant ecological and evolutionary strategies. By linking biochemical diversity with plant phylogeny, and ecophysiological traits across multiple species, this approach offers new opportunities to explore the mechanistic basis of plant evolution.

plant biology↗

bioRxiv2026

Non-fibrillar prion protein oligomers transmit structural information during early assembly

The prion paradigm is founded on the transmission of structural information from aggregated assemblies to soluble protein substrates, a process classically attributed to fibril-end-mediated templating. Whether non-fibrillar assemblies or transient oligomeric states can also participate in folding information transfer remains unclear. Here, using recombinant prion protein (PrP), rational mutagenesis, hetero-oligomerization assays, arrested reaction conditions, and single-particle atomic force microscopy coupled to infrared nano-spectroscopy (AFM-IR), we examine the earliest stages of PrP assembly from the perspective of folding information transmission. We show that polymerization-defective PrP variants can be incorporated into oligomeric assemblies through structural complementation, in which folding information supplied by wild-type PrP restores their assembly competence. Under arrested reaction conditions, transient polymerization-competent conformers further contribute to folding information transfer at subcritical concentrations. Domain-resolved analyses reveal a modular oligomeric architecture in which a {beta}-sheet-rich B domain constitutes the primary scaffold for folding information transfer. Preformed O1 oligomers then act as autonomous conformational templates that promote mutant incorporation and undergo hierarchical condensation through accretion of a structurally distinct E domain. Together, these findings demonstrate that non-fibrillar PrP oligomers and transient assembly intermediates can store and transmit folding information and may function as oligomer-based secondary nucleation platforms, expanding the conceptual framework of prion assembly beyond fibril-end elongation alone.

biochemistry↗

bioRxiv2026

Predictive metabolomics reveals leaf systemic metabolic signatures associated with floral traits in Camellia

The genus Camellia comprises more than 200 evergreen species of major economic and ornamental importance, characterised by high morphological and chemical diversity. While several species have been extensively studied for their bioactive compounds, the metabolic basis of floral trait variation across the genus remains poorly understood. In this study, a predictive metabolomics framework was applied to investigate the relationship between leaf metabolic profiles and floral traits, focusing on flower colour and floral form. Leaves from 315 individual trees, including 15 Camellia species and representing 1,160 samples, were analysed by untargeted metabolomics, generating a large-scale metabolic profiling dataset. A dedicated quality control strategy was implemented to ensure analytical stability across multiple injection series and flowering seasons. Penalised generalised linear models were used to uncover robust metabolic predictors associated with floral traits and to evaluate model performance through internal and external validation. Distinct sets of metabolites were associated with flower colour and floral form, with limited overlap between traits. Predictive performance was consistently higher for colour than for floral form, indicating more structured metabolic signatures for chromatic traits. The selected predictors spanned multiple major chemical classes, supporting a systemic organisation of the metabolome rather than reliance on single biosynthetic pathways. Consistently high predictive accuracies were obtained, reaching approximately 87% for both flower colour and floral form, and remaining clearly above the corresponding no-information rates ({approx} 43%). Together, these results demonstrate that leaf metabolomics can be used to robustly predict floral traits in Camellia and highlight the potential of predictive metabolomics as a tool for early phenotype inference, quality control and selection in long-lived ornamental species.

plant biology↗

bioRxiv2026

MetaNetMap: automatic mapping of metabolomic data onto metabolic networks

MotivationMetabolic networks represent genome-derived information about the biochemical reactions that cells are capable of performing. Mapping omic data onto these networks is important to refine model simulations. However, metabolomic data mapping remains very challenging due to difficulties in identifier reconciliation between annotation profiles and metabolic networks. ResultsMetaNetMap is a Python package designed to automatise the process of mapping metabolomic data onto metabolic networks. It includes several layers of identifier matching, the use of customisable databases, and molecular ontology integration to suggest the most matches between experimentally-identified metabolites and molecules defined in the network. We demonstrate its usability and the quality of automated mapping using two datasets. Availability and ImplementationMetaNetMap is an open source python package and publicly available under the GPLv3 licence. Source code is freely available on GitHub: https://github.com/coraliemuller/metanetmap. Data and code used in the application cases of this paper can be found at: https://doi.org/10.57745/ESFLR8.

systems biology↗

bioRxiv2025

Molecular characterization of flower bud dormancy under constrained temperatures unveils a shallow dormancy stage induced by cold deprivation

Mild winters are becoming increasingly common in temperate regions due to climate change, which may have important impacts on ecosystems and agriculture. In particular, rising temperatures affect the progression of winter dormancy--a crucial developmental stage in perennial plants--making tree development and reproduction particularly vulnerable to climate change. A better understanding of how future temperature conditions will disrupt dormancy in cultivated fruit trees is crucial for anticipating the impeding consequences and identifying potential adaptation strategies. We investigated the effect of very constrained temperature conditions, i.e. several levels of cold deprivation and early cold exposure, on sweet cherry flower buds during dormancy onset and maintenance, using phenological observations and transcriptomic analyses. We show that temperature is a major driver of dormancy progression as cold deprivation and early cold exposure strongly modify the timing of phenological phases as well as gene expression patterns. We identified genes and signaling pathways specifically activated and/or repressed by cold temperatures, and therefore potentially involved in the optimal progression of dormancy. Finally, thanks to an integrative analysis of molecular data obtained under natural and prolonged warm conditions, we characterize a distinct shallow dormancy phase induced by cold deprivation, with a unique molecular signature. HighlightsOur phenological and molecular analysis of sweet cherry dormancy under constrained conditions reveals a gene expression timeline in response to temperature and uncovers a shallow dormancy stage induced by cold deprivation.

plant biology↗

bioRxiv2023

Convergent and divergent responses of the rhizosphere chemistry and bacterial communities to a stress gradient in the Atacama Desert.

Plants can modulate their rhizosphere chemistry, thereby influencing microbe communities. Although our understanding of rhizosphere chemistry is growing, knowledge of its responses to abiotic constraints is limited, especially in realistic ecological contexts. Here, we combined predictive metabolomics with bacterial sequencing data to investigate whether rhizosphere chemistry responded to environmental constraints and shaped bacterial communities across an elevation gradient in the Atacama Desert. We found that metabolic adjustments of rhizosphere chemistry predicted the environment of four plant species independently of year, identifying important rhizosphere metabolic biomarkers. Inter-species predictions unveiled significant biochemical convergences. Subsequently, we linked metabolic predictors to variation in the abundance of operational taxonomic units (OTUs). Chemical response influenced distinct and common bacterial families between species and vegetation belts. The annotation of chemical markers and correlated bacterial families highlighted critical biological processes such as nitrogen starvation, metal pollution and plant development and defence. Overall, this study demonstrates a unique metabolic set likely involved in improving plant resilience to harsh edaphic conditions. Besides, the results emphasise the need to integrate ecology with plant metabolome and microbiome approaches to explore plant-soil interactions and better predict their responses to climate change and consequences for ecosystem dynamics.

ecology↗

bioRxiv2023

Phylogenetically diverse wild plant species use common biochemical strategies to thrive in the Atacama Desert

The best ideotypes are under mounting pressure due to increased aridity in many parts of the world. Understanding the conserved molecular mechanisms that evolve in wild plant species adapted to harsh environments is crucial in developing new strategies for sustainable agriculture. Yet our knowledge of such mechanisms in wild species is scant, particularly in extreme environments. We performed metabolic pathway reconstruction using transcriptome information from 32 Atacama plant species and phylogenetically related plant species that do not live in Atacama (Sister species). We analyzed pathway and reaction enrichment to understand the biochemical commonalities and differences of wild Atacama plant species. To gain insights into the mechanisms that ensure plant survival, we compared expressed gene isoform numbers and gene expression patterns between the annotated biochemical reactions from 32 Atacama and Sister species. We found significant biochemical convergences in primary and secondary metabolism characterized by reactions enriched in at least 50% of the Atacama species across major plant phylogenetic lineages. Analysis of the annotation indicated potential advantages against drought, salinity, high solar irradiance, and nitrogen starvation. These findings suggest that the adaptation in the Atacama Desert may result in part from shared genetic legacies governing the expression of key metabolic pathways to face harsh environmental conditions. Enriched reactions corresponded to ubiquitous compounds common to extreme and agronomic species and were congruent with our previous metabolomic analyses in these Atacama species. Hence, genes underlying these adaptive traits offer promising candidates for improving abiotic stress resilience in crop species.

plant biology↗

bioRxiv2022

STracking: a free and open-source python library for particle tracking and analysis

SummaryAnalysis of intra and extra cellular dynamic like vesicles transport involves particle tracking algorithms. Designing a particle tracking pipeline is a routine but tedious task. Therefore, particle dynamics analysis is often performed by combining several pieces of software (filtering, detection, tracking...) requiring a lot of manual operations, and therefore leading to poorly reproducible results. A good synergy between particle detector with a tracker is of paramount importance. In addition, a user-friendly interface to control the quality of estimated trajectories is necessary. To address these issues, we developed STracking a python library that allows to combine algorithms into standardized particle tracking pipelines. Availability and ImplementationSTracking is available as a python library using "pip install" and the source code is publicly available on GitHub (https://github.com/sylvainprigent/stracking). A graphical interface is available using two napari plugins: napari-stracking and napari-tracks-reader. These napari plugins can be installed via the napari plugins menu or using "pip install". The napari plugin source codes are available on GitHub (https://github.com/sylvainprigent/napari-tracks-reader, https://github.com/sylvainprigent/napari-stracking). Contactsylvain.prigent@inria.fr, cesar-augusto.valades-cruz@curie.fr

bioinformatics↗

bioRxiv2022

SPITFIR(e): A supermaneuverable algorithm for restoring 2D-3D fluorescence images and videos,and background subtraction

While fluorescent microscopy imaging has become the spearhead of modern biology as it is able to generate long-term videos depicting 4D nanoscale cell behaviors, it is still limited by the optical aberrations and the photon budget available in the specimen and to some extend to photo-toxicity. A direct consequence is the necessity to develop flexible and "off-road" algorithms in order to recover structural details and improve spatial resolution, which is critical when pushing the illumination to the low levels in order to limit photo-damages. Moreover, as the processing of very large temporal series of images considerably slows down the analysis, special attention must be paid to the feasibility and scalability of the developed restoration algorithms. To address these specifications, we present a very flexible method designed to restore 2D-3D+Time fluorescent images and subtract undesirable out-of-focus background. We assume that the images are sparse and piece-wise smooth, and are corrupted by mixed Poisson-Gaussian noise. To recover the unknown image, we consider a novel convex and non-quadratic regularizer Sparse Hessian Variation) defined as the mixed norms which gathers image intensity and spatial second-order derivatives. This resulting restoration algorithm named SPITFIR(e) (SParse fIT for Fluorescence Image Restoration) utilizes the primal-dual optimization principle for energy minimization and can be used to process large images acquired with varied fluorescence microscopy modalities. It is nearly parameter-free as the practitioner needs only to specify the amount of desired sparsity (weak, moderate, high). Experimental results in lattice light sheet, stimulated emission depletion, multifocus microscopy, spinning disk confocal, and wide-field microscopy demonstrate the generic ability of the SPITFIR(e) algorithm to efficiently reduce noise and blur, and to subtract undesirable fluorescent background, while avoiding the emergence of deconvolution artifacts.

bioinformatics↗

bioRxiv2021

BioImageIT: Open-source framework for integration of image data-management with analysis

Open science and FAIR principles have become major topics in the field of bioimaging. This is due to both new data acquisition technologies that generate large datasets, and new analysis approaches that automate data mining with high accuracy. Nevertheless, data are rarely shared and rigorously annotated because it requires a lot of manual and tedious management tasks and software packaging. We present BioImageIT, an open-source framework for integrating data management according to FAIR principles with data processing.

bioinformatics↗

bioRxiv2021

High-resolution reconstruction and deconvolution of array detector images

Array detector allows a resolution gain for confocal microscopy by combining images sensed by a set of photomultipliers tubes (or sub-detectors). Several methods have been proposed to reconstruct a high resolution image by linearly combining sub-detector images. To overcome the limitations of these techniques, we propose a new reconstruction method that takes the full stack of spatially reassigned detector signals as input. We show on both calibration slides and real data that our deconvolution method allows to achieve a better reconstruction performance in terms of resolution, image contrast, and spatial intensity homogeneity. The tested algorithms are available in an open source software.

bioinformatics↗

bioRxiv2019

Genome-scale model of Rhodotorula toruloides metabolism

The basidiomycete red yeast Rhodotorula toruloides is a promising platform organism for production of biooils. We present rhto-GEM, the first genome-scale model of R. toruloides metabolism, that was largely reconstructed using RAVEN toolbox. The model includes 4869 genes, 897 reactions, and 3334 metabolites. Lipid metabolism was described using the SLIMEr formalism, which allows direct integration of lipid class and acyl chain experimental distribution data. The simulation results confirmed that the R. toruloides model provides valid growth predictions on glucose, xylose and glycerol, while prediction of genetic engineering targets to increase production of linolenic acid and triacylglycerols highlighted genes that have previously been successfully used to increase lipid production. This renders rtho-GEM useful for future studies to improve the production of other industrially important oleochemicals including both value-added fatty acids and carotenoids, while it will also be valuable tool for system-wide omics-data analysis in R. toruloides. Expanding the portfolio of GEMs for lipid accumulating fungi contributes to both understanding of metabolic mechanisms of the oleaginous phenotype but also uncover particularities of lipid production machinery in R. toruloides.

systems biology↗

bioRxiv2017

Coordinated morphogenesis through tension-induced planar polarity

Tissues from different developmental origins must interact to achieve coordinated morphogenesis at the level of a whole organism. C. elegans embryonic elongation is controlled by actomyosin dynamics which trigger cell shape changes in the epidermis and by muscle contractions, but how the two processes are coordinated is not known. We found that a tissue-wide tension generated by muscle contractions and relayed by tendon-like hemidesmosomes in the dorso-ventral epidermis is required to establish a planar polarity of the apical PAR module in the lateral epidermis. This planar polarized PAR module then controls actin planar organization, thus determining the orientation of cell shape changes and the elongation axis of the whole embryo. This trans-tissular mechanotransduction pathway thus contributes to coordinate the morphogenesis of three embryonic tissues.

developmental biology↗