Biology subjects

Torres-Lomas, E.

Publications and source records attributed to Torres-Lomas, E..

5 recordsLinked to original sources

bioRxiv2026

Low-cost rhizotron imaging and zero-shot deep-learning resolve temporal, spatial, and genetic variation in grapevine rootstock root systems

Root system architecture shapes how grapevine rootstocks take up water and nutrients, yet roots remain the least phenotyped grapevine organ because they are hidden and hard to image. We present a low-cost phenotyping pipeline that pairs custom acrylic rhizotrons (about US$30 each) with a consumer flatbed scanner and BiRefNet, a general-purpose deep-learning model used without training on root images, followed by automated mask cleaning, skeleton-based trait extraction, and soil moisture mapping. We tested it on nine commercial rootstocks scanned 16 times over 42 days after transplanting (DAT), with half under a ten-day water deficit. From 1,108 images we extracted 21 whole-root, depth-resolved, and topological traits. Genotypes differed in nearly every trait and in how they changed over time. Heritability of size and branching traits peaked at 0.92-0.93 between 21 and 31 DAT and fell for width, depth, and convex hull once roots reached the rhizotron walls, defining the best measurement window. The image-derived soil moisture map accurately tracked the deficit and its recovery. Deficit plants shifted new root growth to deeper soil without growing less overall, and the substrate dried fastest around older and denser roots. Root brightness decreased with root age and local moisture, and transport segments (axes serving several tips) were brighter than terminal laterals in every genotype. Root system size was associated with stomatal conductance in well-watered plants, and stomatal recovery after re-watering correlated with new root growth. The pipeline turns simple hardware into a quantitative, time-resolved root phenotyping platform suitable for breeding.

plant biology↗

bioRxiv2026

Harnessing Vitis germplasm diversity to dissect and predict adventitious rooting traits in grapevine

Adventitious root formation (ARF) is a critical trait for the cost-effective propagation of grapevines in commercial nurseries. Poor rooting ability can limit the use and adoption of new rootstocks derived from underutilized Vitis species, constraining breeding efforts largely to the traditional trio: Vitis riparia, V. rupestris, and V. berlandieri. Despite its agronomic relevance, the genetic basis of ARF remains poorly characterized across the broader Vitis genus. In this study, we evaluated 308 accessions representing 18 Vitis species over three growing seasons, quantifying rooting performance at two developmental stages, callus-stage and post-transplant, alongside root biomass, cutting weight, and a derived transplant-response index. We observed extensive phenotypic variation both within and across species, and species rankings depended on the trait considered. V. riparia, V. rupestris and V. californica ranked among the top five species for all four rooting traits, whereas V. cinerea and V. candicans ranked among the lowest for root weight and post-transplant rooting. V. arizonica and V. acerifolia rooted well at the callus stage but were intermediate after transplanting, and V. berlandieri was among the weakest at the callus stage yet intermediate for post-transplant rooting. Repeatability was moderate to high for root weight (0.74) and callus-stage rooting (0.66), and lower for post-transplant rooting (0.47), reflecting both genetic control and season-to-season variation. Between-species differences accounted for 68% of the genetic variance in callus-stage rooting but only 10% in cutting weight. Rooting was associated with the climate of each accession's wild site of origin: after removing differences among species, accessions originating from sites with lower dry-season precipitation rooted better and produced more root biomass. Genome-wide association analysis using 3.4 million SNPs identified 54 significant SNPs resolving into 18 independent loci across four traits, with root weight contributing 12 of them. Candidate genes in linkage with these loci include a mitogen-activated protein kinase, a SCARECROW-LIKE GRAS transcription factor, PASTICCINO1, expansin A1, an AP2/ERF-RAV1 transcription factor, a tandem array of caffeoyl-CoA O-methyltransferases, and several sugar, peptide and nitrate transporters, implicating auxin-linked cell proliferation, cell wall and lignin remodeling, and solute transport. Genomic and phenomic prediction models yielded moderate accuracies across traits and seasons; up to r = 0.67 for post-transplant rooting within a season and r = 0.65 for previously unevaluated accessions. Moreover, the integration of spectral and genotypic data further improved predictive performance. Prediction accuracy was essentially flat between 5,000 and 50,000 markers. This study establishes a foundational framework for the genetic improvement of grapevine rootstocks, promoting broader use of resilient, high-performing, and clonally-propagable germplasm in viticulture.

genetics↗

bioRxiv2026

Disentangling blade and vasculature shape in grapevine leaves

The leaf blade and vasculature develop together within a shared morphological space. Despite shared molecular patterning pathways, it is unknown if developmental and evolutionary variation affect these tissues separately or together in a coordinated way. Grapevine leaves have a morphometric history and abundant data measuring the shape of the blade and vasculature together. Using a combination of topological data analysis and deep learning, we perform reciprocal semantic segmentation of leaf blade and vasculature. Each tissue contains sufficient information to predict the other. We hypothesize that this is due to a one-to-one relationship between blade and vein. Using thin plate splines to swap and warp different combinations of blade and vein shapes, we show that a set of leaves with a many-to-one relationship of blade and vein are distinguishable from true leaves. We also swap blade and vein across the developmental series and between species and show that only reversing the developmental series disrupts the relationship between blade and vasculature. We end by discussing the evolutionary and developmental implications that there is a unique, one-to-one mapping between blade and vein that allows each to be predicted from the other. Author summaryLeaves are made of two closely connected parts: the flat blade that captures light and the network of veins that transports water, nutrients, and developmental signals. Although these tissues grow together and share common molecular patterning pathways, it has remained unclear whether a particular blade shape is uniquely linked to a specific vein pattern. In this study, we use grapevine leaves as a model system and combine mathematical shape analysis with deep learning to examine this relationship. We show that the shape of the blade alone can accurately predict the vein network, and that the vein network can likewise predict the blade. This finding suggests a near one-to-one relationship between these two tissues. To test this idea, we created artificial leaves in which blade and vein shapes were deliberately mismatched. Although these synthetic leaves appeared realistic at a global level, a neural network was able to distinguish them from real leaves based on subtle differences. We further show that this tight coupling is maintained by the developmental sequence of leaf growth rather than by species identity, revealing a conserved constraint linking leaf form and internal structure.

plant biology↗

bioRxiv2025

Leveraging foundation models to dissect the genetic basis of cluster compactness and yield in grapevine

Grape cluster compactness is a key trait that influence fruit quality, yield, and disease susceptibility. Understanding the genetic basis of this trait is essential for optimizing vineyard management and improving grapevine cultivars. In this study, we performed quantitative trait locus (QTL) mapping to identify genomic regions associated with cluster architecture and yield components in a bi-parental population derived from Vitis vinifera cv. Riesling x Cabernet Sauvignon. A total of 138 full-sibling progeny were evaluated over two growing seasons at Oakville, Napa Valley, California. Traditional yield-related traits were measured, including cluster number, total cluster weight, and average cluster weight. Additionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention. Trait correlations revealed that compact clusters tended to have a higher berry count but smaller berry size, highlighting the role of compactness in modulating cluster structure. Heritability estimates varied across traits, with berry dimensions and compactness displaying moderate to high heritability, indicating strong genetic control. Two parental linkage maps were constructed using a pseudo-test cross strategy. QTL mapping identified multiple loci associated with cluster architecture and yield components, with several stable QTLs detected across both years. Notably, a QTL for cluster compactness was found in both seasons on chromosome 1 in Cabernet Sauvignon. Other stable QTLs were associated with berry size (chromosomes 6 and 17) and berry count (chromosome 5 in Cabernet Sauvignon and chromosome 7 in Riesling). Additional QTLs were detected in a single year, reflecting the influence of environmental variation. Our findings provide valuable insights into the application of foundation models requiring no prior training and minimal intervention for high-quality segmentation and enhance our understanding of the genetic architecture of cluster compactness and yield traits. The genomic regions identified in this study offer promising targets for breeding programs aimed at improving grape quality and disease resistance.

genetics↗

bioRxiv2024

A high resolution model of the grapevine leaf morphospace predicts synthetic leaves

O_LIGrapevine leaves are a model morphometric system. Sampling over ten thousand leaves using dozens of landmarks, the genetic, developmental, and environmental basis of leaf shape has been studied and a morphospace for the genus Vitis predicted. Yet, these representations of leaf shape fail to capture the exquisite features of leaves at high resolution. C_LIO_LIWe measure the shapes of 139 grapevine leaves using 1672 pseudo-landmarks derived from 90 homologous landmarks with Procrustean approaches. From hand traces of the vasculature and blade, we have derived a method to automatically detect landmarks and place pseudo-landmarks that results in a high-resolution representation of grapevine leaf shape. Using polynomial models, we create continuous representations of leaf development in 10 Vitis spp. C_LIO_LIWe visualize a high-resolution morphospace in which genetic and developmental sources of leaf shape variance are orthogonal to each other. Using classifiers, V. vinifera, Vitis spp., rootstock and dissected leaf varieties as well as developmental stages are accurately predicted. Theoretical eigenleaf representations sampled from across the morphospace that we call synthetic leaves can be classified using models. C_LIO_LIBy predicting a high-resolution morphospace and delimiting the boundaries of leaf shapes that can plausibly be produced within the genus Vitis, we can sample synthetic leaves with realistic qualities. From an ampelographic perspective, larger numbers of leaves sampled at lower resolution can be projected onto this high-resolution space; or, synthetic leaves can be used to increase the robustness and accuracy of machine learning classifiers. C_LI Societal Impact StatementGrapevine leaves are emblematic of the strong visual associations people make with plants. At a glance, leaf shape is immediately recognizable, and it is because of this reason it is used to distinguish grape varieties. In an era of computationally-enabled, machine learning-derived representations of reality, we can revisit how we view and use the shapes and forms that plants display to understand our relationship with them. Using computational approaches combined with time-honored methods, we can predict theoretical leaves that are possible to understand the genetics, development, and environmental responses of plants in new ways.

plant biology↗