Biology subjects

De Diego, N.

Publications and source records attributed to De Diego, N..

5 recordsLinked to original sources

bioRxiv2026

A hyperbolic topological atlas reveals polyamine steering of a shared developmental manifold in Arabidopsis

High-throughput plant phenotyping captures development at scale, yet image-rich screens are still often reduced to static trait summaries. We tested whether nutrient availability, polyamine priming, concentration, and their transport reshape Arabidopsis rosette development by generating distinct morphologies or by changing residence along a common trajectory. We analyzed 138,223 time-resolved rosette images from Col-0 and five mutants involved in polyamine transport (put1-5) primed to putrescine, spermidine, spermine, dose, and nutrient regimes using a self-supervised vision backbone, Poincare embedding, hyperbolic Mapper, and manifold straightening. The data form a single connected developmental manifold with 410 nodes and 746 edges, organized from an early, low-nutrient-biased hub through high-betweenness transition corridors to two late, nutrient-enriched terminal regions. Polyamine identity stratifies this manifold by developmental phase: putrescine enriches early states, spermidine occupies transition corridors, and spermine marks late compact rosettes. Nutrient richness and dose change distal occupancy, whereas put genotypes alter dwell time within shared regions rather than producing separate topologies. Manifold straightening resolves these effects into a short early lateral deflection followed by convergence, yielding two scalar readouts, early transverse offset and distal occupancy, that summarize treatment action on a common morphodynamic scale. The framework converts large image screens into interpretable developmental geometry for image-based phenomics.

bioinformatics↗

bioRxiv2026

LOCOPOTS: a low-cost high-throughput screening platform for in vitro potato phenotyping under abiotic stress

Potato crop is highly vulnerable to abiotic stresses like salinity and low nutrient availability. Rapid identification of stress-resilient genotypes is therefore essential for breeding, yet conventional phenotyping is often slow, space-demanding and expensive. We present LOCOPOTS -- a LOw-COst high-throughput screening platform for in vitro POTatoes under abiotic Stress -- which combines individual in vitro plant culture, low-cost RGB imaging and machine-learning-based automatic segmentation using a trained model of a convolutional neural network, based on U-Net architecture. LOCOPOTS enabled the automated extraction of growth, colour, and vegetation-index traits and demonstrated robust performance across independent phenotyping rounds. We screened 30 potato varieties under control, low-nutrient and saltinity conditions, identifying contrasting growth and physiological responses. Integrated traits such as final area and height, Area_AUC and height_AUC, together with GLI, Chol, cive and chlorophyll fluorescence parameters, discriminated genotype performance under stress. Metabolic profiling further revealed genotype-specific reprogramming in carbon and nitrogen metabolism under low nutrition and salt stress, including changes in fructose, myo-inositol, {beta}-aminobutyric acid, {gamma}-aminobutyric acid, proline, and certain polyamines, identifying them as specific chemical biomarkers of plant stress responses. LOCOPOTS provides a scalable, affordable and space-efficient platform for early screening of potato genetic diversity and identification of candidate traits associated with stress resilience.

plant biology↗

bioRxiv2026

Rewiring vascular patterning through translational control in Arabidopsis

Plant vascular systems exhibit a wide developmental spectrum, from rigid woody tissues to soft, fleshy storage tissues. We show that increasing polyamine thermospermine transport into wild-type rootstocks, together with cytokinin, reprograms xylem identity from woody to fleshy in Arabidopsis. Our findings establish thermospermine as a mobile developmental signal and suggest a strategy for engineering plant vascular architecture.

plant biology↗

bioRxiv2025

CLPC2 plays specific roles in CLP complex-mediated regulation of growth, photosynthesis, embryogenesis and response to growth-promoting microbial compounds

In Arabidopsis, exposure to growth-promoting microbial volatile compounds (VCs) enhances CLPC2 levels. This chaperone forms part of the CLP protease complex, which ensures the correct functioning of essential processes in plastids. Previous studies indicated considerable functional redundancy of CLPC2 with its dominant paralogue CLPC1. However, the function and action mechanism of CLPC2 still remain unknown. Here we found that CLPC2-lacking clpc2-2 mutants were unresponsive to microbial VCs, whereas clpc1-1 knockout mutants exhibited a WT-like response to VCs when grown on sucrose-containing medium. Unlike clpc1-1, clpc2-2 plants presented a fully functional photosystem II and lower than WT stomatal conductance. Furthermore, clpc2-2 plants, but not clpc1-1 plants, produced wrinkled seeds with delayed embryonic development and reduced postgerminative establishment rates that resembled those of mutants lacking P and R components of the CLP proteolytic core. Proteomic analyses revealed that knocking out of CLPC2 enhanced the levels of chloroplastic proteins that are essential for growth, embryo development and seedling establishment. These changes differed from those promoted by the lack of CLPC1, but partially resembled those promoted by CLPPR core inactivation. Nearly 40% of the proteins differentially accumulated by the lack of CLPC2 were VC-responsive. Notably, 35S promoter-driven CLPC2 expression promoted changes in the proteome similar to those promoted by the lack of CLPC1. Collectively, our findings highlighted contrasting functional and molecular specificities for CLPC1 and CLPC2, and provided strong evidence that CLPC2 plays specific roles in CLP complex-mediated regulation of plant growth, photosynthesis, embryogenesis, postgerminative seedling establishment and microbial VC responsiveness.

plant biology↗

bioRxiv2024

Decrypting the complex phenotyping traits of plants by machine learning

Phenotypes, defining an organisms behaviour and physical attributes, arise from the complex, dynamic interplay of genetics, development, and environment, whose interactions make it enormously challenging to forecast future phenotypic traits of a plant at a given moment. This work reports AMULET, a modular approach that uses imaging-based high-throughput phenotyping and machine learning to predict morphological and physiological plant traits hours to days before they are visible. AMULET streamlines the phenotyping process by integrating plant detection, prediction, segmentation, and data analysis, enhancing workflow efficiency and reducing time. The machine learning models used data from over 30,000 plants, using the Arabidopsis thaliana-Pseudomonas syringae pathosystem. AMULET also demonstrated its adaptability by accurately detecting and predicting phenotypes of in vitro potato plants after minimal fine-tuning with a small dataset. The general approach implemented through AMULET streamlines phenotyping and will improve breeding programs and agricultural management by enabling pre-emptive interventions optimising plant health and productivity.

plant biology↗