Biology subjects

Rey, A.

Publications and source records attributed to Rey, A..

12 recordsLinked to original sources

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↗

bioRxiv2025

Determination of nucleotide-nucleotide and nucleotide-amino acid binding interactions from all-atom potential-of-mean-force calculations

Biomolecular condensates emerge from multivalent interactions between proteins and nucleic acids and are frequently modeled using coarse-grained molecular dynamics simulations. The parametrization of these models critically depends on atomistic data describing the underlying molecular interactions. In this work, we employ all-atom molecular dynamics simulations and potential-of-mean-force (PMF) calculations to investigate the interaction landscape between RNA nucleotides and protein amino acids. We begin by characterizing nucleotide-nucleotide binding modes through canonical base-pairing analysis, observing notable agreement in the predictions from both AMBER03ws and CHARMM36 force fields. Further rationalization of different nucleotide-nucleotide interaction modes involves the calculation of PMFs for ribose-ribose, phosphate-phosphate, and RNA tertiary interactions such as G-quadruplex formation. We also examine the effect of salt concentration on these interactions, finding a reduction on electrostatic self-repulsion for phosphate-phosphate binding upon increasing the ionic strength. Extending our analysis to amino acids, we first benchmark the performance of both AMBER03ws and a99SB-disp force fields for describing pairwise amino acid interactions, and then we evaluate different nucleotide-amino acid binding profiles. Our findings reveal a subset of amino acids--Lys and Arg (positively charged), Asp and Glu (negatively charged), and Gln, Ser, and Asn (polar residues)--that consistently engage with the nitrogenous bases of different nucleotides. Such binding is primarily mediated by hydrogen bonding and, in some cases, cation-{pi} interactions. Furthermore, we identify strong{pi} -{pi} stacking interactions with aromatic residues and phosphate-Arg contacts as key contributors to condensate cohesion in RNA-protein condensates. Our comprehensive analysis provides a detailed library of nucleotide-amino acid interactions, offering quantitative insights to inform coarse-grained model parametrization and deepening our understanding of condensate self-assembly, nucleic acid recognition, and phase-separation regulation at submolecular scale.

biophysics↗

bioRxiv2025

Membrane Proteome of Phytophthora parasitica Zoospores: How Does Sensing Occur?

Phytophthora plant pathogens rely on motile biflagellated zoospores to efficiently locate and colonise host tissues. While rhizospheric signals guiding zoospore movement toward roots are known, the protein composition of membranes mediating these responses remains unclear. Here, we used liquid chromatography with tandem mass spectrometry (LC-MS/MS) and proteomic data mining to analyse membrane fractions from the flagella and cell bodies of Phytophthora parasitica zoospores. Major classes of membrane proteins (receptors, transporters and enzymes) were identified and their subcellular distribution between flagella and cell bodies quantified. Immunolocalization revealed that while most membrane proteins are evenly distributed, a subset localizes to the flagella, suggestive of specialized roles in sensing and movement regulation, particularly for sterol recruitment and ion flux variations. These findings advance our understanding of protein-mediated dispersal and host targeting by zoospores and support the hypothesis that zoospores use polarized signal perception mechanisms for environmental sensing and movement.

microbiology↗

bioRxiv2025

Compositional control of ageing kineticsin TDP-43 condensates

Biomolecular compartments orchestrate the spatiotemporal organisation of cells. The spontaneous assembly of proteins and nucleic acids through liquid-liquid phase separation into biomolecular condensates has been shown to ubiquitously contribute to the functional compartmentalisation of the cytoplasm and nucleoplasm. However, some condensates can undergo an additional phase transition from functional liquid states to pathological solid-like assemblies (i.e., ageing). This liquid-to-solid transition, driven by the accumulation of protein cross-{beta}-sheet structures, represents a hallmark of multiple neurodegenerative disorders. In this study, we employ Molecular Dynamics simulations to explore the role of various biomolecules in regulating the ageing kinetics of condensates scaffolded by TDP-43, a key RNA-binding protein linked to amyotrophic lateral sclerosis and frontotemporal dementia. We find that the recruitment of arginine-rich peptides, such as those produced by the C9orf72 gene, accelerates the nucleation of cross-{beta}-sheet structures. In contrast, the inclusion of poly-Uridine RNA and the HSP70 chaperone significantly slows the emergence of these structures. Remarkably, we observe a correlation between the compactness of the low-complexity domain of TDP-43-- which drives the transition to cross-{beta}-sheet structures--and the condensate ageing kinetics as we vary the composition of the condensates. Moreover, we find that near-interfacial regions of TDP-43 condensates exhibit faster {beta}-sheet transitions than the bulk core of the condensate. Together, our findings underscore the critical role of client biomolecules in modulating protein conformational ensembles and intermolecular interactions, thereby controlling the propensity of condensates to transition into harmful solid-like states.

biophysics↗

bioRxiv2024

Capturing single-molecule properties does not ensure accurate prediction of biomolecular phase diagrams

Intracellular liquid-liquid phase separation of proteins and nucleic acids represents a fundamental mechanism by which cells organise their components into biomolecular condensates that perform multiple biological tasks. Computer simulations provide powerful tools to investigate biomolecular phase separation, offering microscopic insights into the physicochemical principles that regulate these systems. In this study, we investigate the phase behaviour of the low-complexity domain (LCD) of hnRNPA1 and several mutants via Molecular Dynamics simulations. We systematically compare the performance of five state-of-the-art residue-resolution coarse-grained protein models: HPS, HPS-cation-{pi}, CALVADOS2, Mpipi, and Mpipi-Recharged. Our evaluation focuses on how well these models reproduce experimental coexistence densities and single-protein radii of gyration for the LCD-hnRNPA1 set of mutants. While most models yield similar intramolecular contact maps and reasonable estimates of the single-protein radius of gyration compared to in vitro measurements, only Mpipi-Recharged, Mpipi, and CALVADOS2 accurately predict phase diagrams that align with experimental data. This suggests that force field parameterizations optimized solely to reproduce single-protein properties may not always capture the phase behaviour of protein solutions. Additionally, our findings reveal that some residue-resolution coarse-grained models can lead to significant discrepancies in predicting the roles of individual intermolecular interactions, even for relatively simple intrinsically disordered proteins like the low-complexity domain of hnRNPA1. Our work highlights the importance of balancing both single-molecule and collective properties of proteins to accurately predict condensate formation and material properties.

biophysics↗

bioRxiv2024

Phase behaviour of hnRNPA1 low-complexity domain mutants described by different sequence-dependent models

Intracellular liquid-liquid phase separation (LLPS) of proteins and nucleic acids is a fundamental mechanism by which cells compartmentalize their components and perform essential biological functions. Molecular simulations play a crucial role in providing microscopic insights into the physicochemical processes driving this phenomenon. In this study, we systematically compare six state-of-the-art sequence-dependent, residue-resolution models to evaluate their performance in reproducing the phase behaviour and material properties of condensates formed by seven variants of the low-complexity domain (LCD) of the hnRNPA1 protein (A1-LCD)--a protein implicated in the pathological liquid-to-solid transition of stress granules. Specifically, we assess the HPS, HPS-cation-{pi}, HPS-Urry, CALVADOS2, Mpipi, and Mpipi-Recharged models in their predictions of the condensate saturation concentration, critical solution temperature, and condensate viscosity for the A1-LCD variants. Our analyses demonstrate that, among the tested models, Mpipi, Mpipi-Recharged, and CALVADOS2 provide accurate descriptions of the critical solution temperatures and saturation concentrations for the various A1-LCD variants tested. Regarding the prediction of material properties for condensates of A1-LCD and its variants, Mpipi-Recharged stands out as the most reliable model. Overall, this study benchmarks a range of residue-resolution coarse-grained models for the study of the thermodynamic stability and material properties of condensates and establishes a direct link between their performance and the ranking of intermolecular interactions these models consider.

biophysics↗

bioRxiv2024

An optimized method to visualize lipid droplets in brain tissue demonstrates their substantial accumulation in aged brains

Lipid droplets (LDs) are cellular stores for lipids. These organelles have recently gained interest in neuroscience because they accumulate in various cell types in neurodegenerative diseases. However, their role under physiological conditions is still not fully understood. Classical LD staining methods, which use lipophilic dyes like BODIPY 493/503 (BD493) or antibodies against LD coat proteins, show very few LDs in healthy brain tissue. Our recently developed novel endogenous LD reporter mouse challenges this view. We have been able to detect numerous LDs in healthy brain tissue from both adult and developing mice without staining. To understand why classical staining and endogenous labeling yield different results, we thoroughly investigated the effects of tissue preparation and detergent used in LD detection. We found that BD493 works poorly in brain tissue, while other lipophilic dyes visualize many LDs. We also found that antibody-based LD detection depends on tissue pretreatment and detergent concentration but can reveal a similar number of LDs as observed with the endogenous LD reporter mouse. Taken together, we here present an optimized procedure for LD detection in brain tissue using commercially available dyes and antibodies. Using these methods, we demonstrate that LDs are numerous in healthy brain tissue and substantially accumulate in aged brains in various cell types, including neurons.

neuroscience↗

bioRxiv2023

Functional diversity of NLRP3 gain-of-function mutants associated with CAPS autoinflammation.

NLRP3-associated autoinflammatory disease (NLRP3-AID or CAPS) is an heterogenous group of monogenic autoinflammations associated with NLRP3 gain-of-function mutations. The poor functional characterization of most NLRP3 variants is a barrier to diagnosis although patients can be efficiently treated with anti-IL-1 approaches. In addition, while NLRP3 inflammasome is controlled by coordinated priming and activation signals, gain-of-functions of NLRP3 variants have been only investigated in response to priming. Here, we functionally characterize 34 NLRP3 variants in vitro by determining their activity in response to induction, priming and/or activation signals, and their sensitivity to inhibitors. We highlight the functional diversity of the gain-of-function mutants and describe four groups based on their profile of signals required for their activation, that correlate partly with the symptoms severities. We identify a new group of NLRP3 mutants responding to the activation signal without priming, with patients often misdiagnosed. Our results identify key NLRP3 residues controlling the inflammasome activity and sensitivity to inhibitors. The comparison of four inhibitors on the 34 variants identifies inhibitory mechanisms with broader efficiency for future drug design. Altogether, our results provide new insights on NLRP3 activation and an explanatory mechanism for NLRP3-AID heterogeneity, and original tools for NLRP3-AID diagnosis and anti-inflammatory disease drug development. eTOC SummaryFunctional characterization of 34 CAPS-associated NLRP3 variants identifies polymorphisms versus gain-of-function pathogenic mutants, and highlights diversities in the signals controlling their activation and in their sensitivity to inhibitors. This study provides tools for CAPS diagnosis and anti-inflammation drug development and insights on NLRP3 control mechanisms.

immunology↗

bioRxiv2021

The Evolution of Chunks in Sequence Learning

Chunking mechanisms are central to several cognitive processes and notably to the acquisition of visuo-motor sequences. Individuals segment sequences into chunks of items to perform visuo-motor tasks more fluidly, rapidly, and accurately. However, the exact dynamics of chunking processes in the case of extended practice remain unclear. Using an operant conditioning device, eighteen Guinea baboons (Papio papio) produced a fixed sequence of nine movements during 1,000 trials by pointing to a moving target on a touch screen. Response times analyses revealed a specific chunking pattern of the sequence for each baboon. More importantly, we found that these patterns evolved during the course of the experiment, with chunks becoming progressively fewer and longer. We identified two chunk reorganization mechanisms: the recombination of preexisting chunks and the concatenation of two distinct chunks into a single one. These results provide new evidence on chunking mechanisms in sequence learning and challenge current models of associative and statistical learning.

animal behavior and cognition↗

bioRxiv2020

Litter decomposition rates of biocrust-forming lichens are similar to that of vascular plants and are affected by warming in a semiarid grassland

Despite the high relevance of communities dominated by lichens, mosses and cyanobacteria living on the soil surface (biocrusts) for ecosystem functioning in drylands worldwide, no study to date has investigated the decomposition of biocrust-forming lichen litter in situ. Thus, we do not know whether the drivers of its decomposition are similar to those for plant litter (e.g., importance of abiotic degradation through UV radiation), the magnitude of lichen decomposition rates and whether they will be affected by climate change. Here we report results from a litter decomposition experiment carried out with two biocrust-forming lichens (Diploschistes diacapsis and Cladonia convoluta) in central Spain. We evaluated how lichen decomposition was affected by warming, rainfall exclusion and the combination of both. We also manipulated the incidence of UV radiation using mesh material that blocked 10% or 90% of incoming UV radiation. Our results indicate that lichens decompose as fast as some plants typical of the region (k~0.3) and that the chemical composition of their thallus drives litter decomposition rates. Warming increased decomposition rates of both lichen species, and mediated the effects of photodegradation. While UV exposure accelerated the decomposition of D. diacapsis, it slowed down that of C. convoluta. Our results indicate that biocrust-forming lichens can decompose in the field at a rate similar to that of vascular plants, and that this process will be affected by warming. Our findings further highlight the need of incorporating biocrusts into carbon cycling models to better understand and forecast climate change impacts on terrestrial biogeochemistry.

ecology↗

bioRxiv2019

Marine water environmental DNA metabarcoding provides a comprehensive fish diversity assessment and reveals spatial patterns in a large oceanic area

Current methods for monitoring marine fish diversity mostly rely on trawling surveys, which are invasive, costly and time-consuming. Moreover, these methods are selective, targeting a subset of species at the time, and can be inaccessible to certain areas. Here, we explore the potential of environmental DNA (eDNA), the DNA present in the water column as part of shed cells, tissues or mucus, to provide comprehensive information about fish diversity in a large marine area. Further, eDNA results were compared to the fish diversity obtained in pelagic trawls. A total of 44 5L-water samples were collected onboard a wide-scale oceanographic survey covering about 120,000 square kilometres in Northeast Atlantic Ocean. A short region of the 12S rRNA gene was amplified and sequenced through metabarcoding generating almost 3.5 million quality-filtered reads. Trawl and eDNA samples resulted in the same most abundant species (European anchovy, European pilchard, Atlantic mackerel and blue whiting), but eDNA metabarcoding resulted in more detected fish and elasmobranch species (116) than trawling (16). Although an overall correlation between fish biomass and number of reads was observed, some species deviated from the common trend, which could be explained by inherent biases of each of the methods. Species distribution patterns inferred from eDNA metabarcoding data coincided with current ecological knowledge of the species, suggesting that eDNA has the potential to draw sound ecological conclusions that can contribute to fish surveillance programs. Our results support eDNA metabarcoding for broad scale marine fish diversity monitoring in the context of Directives such as the Common Fisheries Policy or the Marine Strategy Framework Directive.

ecology↗

bioRxiv2019

Guidelines and considerations for metabarcoding-based port baseline biodiversity surveys: towards improved marine non-indigenous species monitoring

O_LIMonitoring introduction and spread of non-indigenous species via maritime transport and performing risk assessments require port biological baseline surveys. Yet, the comprehensiveness of these surveys is often compromised by the large number of habitats present in a port, the seasonal variability and the time-consuming morphological approach used for taxonomic identification. Metabarcoding represents a promising alternative for rapid comprehensive port biological baseline surveys, but its application in this context requires further assessments. C_LIO_LIWe applied metabarcoding (based on barcodes of the Cytochrome c oxidase subunit I and of the 18S ribosomal RNA gene) to 192 port samples collected i) from diverse habitats (water column - including environmental DNA and zooplankton, sediment and fouling structures), ii) at different sites (from inner to outer estuary), and iii) during the four seasons of the year. C_LIO_LIBy comparing the biodiversity metrics derived from each sample group, we show that each sampling method resulted in a distinct community profile and that environmental DNA alone cannot substitute for organismal sampling, and that, although sampling at different seasons and locations resulted in higher observed biodiversity, operational results can be obtained by sampling selected locations and seasons. C_LIO_LIBy assessing the taxonomic composition of the samples, we show that metabarcoding data allowed the detection of previously recorded non-indigenous species as well as to reveal presence of new ones, even if in low abundance. C_LIO_LISynthesis and application. Our comprehensive assessment of metabarcoding for port biological baseline surveys sets the basics for cost-effective, standardized and comprehensive monitoring of non-indigenous species and for performing risk assessments in ports. This development will contribute to the implementation of the recently entered into force International Convention for the Control and Management of Ships Ballast Water and Sediments. C_LI

ecology↗