Biology subjects

Negrao, S.

Publications and source records attributed to Negrao, S..

9 recordsLinked to original sources

bioRxiv2026

Integrating longitudinal hyperspectral phenotyping with AI and GWAS to dissect barley waterlogging responses

Waterlogging is a major constraint on barley productivity, yet its dynamic, multi-phase nature makes it challenging to dissect using traditional phenotyping approaches. High-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations, but generate complex datasets that demand new analytical frameworks. Here, we imaged 230 barley accessions over 14 days of waterlogging stress and seven days of recovery using visible, chlorophyll fluorescence, and hyperspectral sensors. Explainable AI was applied to classify stress responses into early stress, late stress, and recovery phases, achieving 86% classification accuracy, and to identify the hyperspectral indices most informative for each phase. Water index (WATER1) and structure insensitive pigment index (SIPI) emerged as primary predictors of stress response. Longitudinal genome-wide association studies (GWAS), using a treatment-by-marker interaction model, identified 236 significant loci across 12 linkage disequilibrium blocks, implicating candidate genes involved in oxidative stress regulation, transcriptional control, and auxin transport. MYB transcription factors were consistently identified across all stress phases, underscoring their central role in waterlogging adaptation. To support interpretation of longitudinal GWAS results, we developed 3D-QTLVis, an interactive visualisation tool that extends Manhattan plots across time, enabling clearer identification of dynamic genomic regions underlying stress tolerance.

plant biology↗

bioRxiv2025

Botanic Spectrum Analyser: A Deep Learning GUI for Plant Image Segmentation in Hyperspectral and RGB Phenotyping

Plant phenotyping systematically quantifies plant traits such as growth, morphology, physiology, or yield, assessing genetic and environmental influences on plant performance. The integration of advanced phenotyping technologies, including imaging sensors and data analytics, facilitates the non-destructive and longitudinal acquisition of high-throughput data. Nevertheless, the sheer volume of such phenotyping data introduces significant challenges for researchers, particularly related to data processing. To overcome these challenges, researchers are turning to artificial intelligence (AI), a tool that can autonomously process and learn from large amounts of data. Despite this advantage, accurate image segmentation remains a key hurdle due to the complexity of plant morphology and environmental noise. In this study, we present the Botanical Spectrum Analyser (BSA), a user-friendly graphical user interface (GUI) that integrates a modified U-Net deep neural network for plant image segmentation. Designed for accessibility, BSA enables non-technical users to apply advanced AI segmentation to RGB and hyperspectral (VNIR and SWIR) imagery. We evaluated BSAs performance across three case studies involving wheat, barley, and Arabidopsis, demonstrating its robustness across species and imaging modalities. Our results show that BSA achieves an average accuracy of 99.7%, with F1-scores consistently exceeding 98% and strong Jaccard and recall performance across datasets. For challenging root segmentation tasks, BSA outperformed commercial algorithms, achieving a 76% F1-score compared to 24%, representing a 50% improvement. These results highlight the adaptability of the BSA framework for diverse phenotyping scenarios, bridging the gap between advanced deep learning methods and accessible plant science applications.

plant biology↗

bioRxiv2024

Back to the Future: Environmental genomic selection to take advantage of polygenic local adaptation

The genetic basis of adaptation is a fundamental question in evolutionary genetics. Environmental association analysis (EAA) and various allele frequency comparisons in genomic environmental association (GEA) have become standard approaches for investigating the genetic basis of adaptation to natural environments. While these analyses provide insight into local adaptation, they have not been widely adopted in breeding or conservation programs. This may be attributable to the difficulty in identifying the best individuals for transplantation/relocation in conservation efforts or identification of the best parents in breeding programs. To explore the use of EAA and GEA for future breeding programs, we used a cereal crop - barley (Hordeum vulgare L.) as our case-study species due to its wide adaptability to different environments and agro-ecologies, ranging from marginal and low input fields to high-productive farms. Here, we use publicly available data to conduct environmental genomic selection (EGS) on 753 landrace barley accessions using a mini-core of 31 landrace accessions and a de-novo core of 100 as the training populations. Environmental genomic selection is to environmental association analysis (EAA) what genomic selection is to genome-wide association studies (GWAS). Since local adaptation to the environment is polygenic, a whole-genome approach is likely to be more accurate for selecting for environmental adaptation. Here we show distinct genetic background and population differences and how an integrative approach coupling environmental genomic selection and species distribution modelling can help identify key parents for breeding for adaptation to specific environmental variables and geographies to minimize linkage drag.

plant biology↗

bioRxiv2023

Deciphering Salt Stress Responses in Solanum pimpinellifolium through High-Throughput Phenotyping.

Soil salinity is a major environmental stressor affecting agricultural productivity worldwide. Understanding plant responses to salt stress is crucial for developing resilient crop varieties. Wild relatives of cultivated crops, such as wild tomato, Solanum pimpinellifolium, can serve as a useful resource to further expand the resilience potential of the cultivated germplasm, S. lycopersicum. In this study, we employed high-throughput phenotyping in the greenhouse and field conditions to explore salt stress responses of a S. pimpinellifolium diversity panel. Our study revealed extensive phenotypic variations in response to salt stress, with traits such as transpiration rate, shoot mass, and ion accumulation showing significant correlations with plant performance. We found that while transpiration was a key determinant of plant performance in the greenhouse, shoot mass strongly correlated with yield under field conditions. Conversely, ion accumulation was the least influential factor under greenhouse conditions. Through a Genome Wide Association Study, we identified candidate genes not previously associated with salt stress, highlighting the power of high-throughput phenotyping in uncovering novel aspects of plant stress responses. This study contributes to our understanding of salt stress tolerance in S. pimpinellifolium and lays the groundwork for further investigations into the genetic basis of these traits, ultimately informing breeding efforts for salinity tolerance in tomato and other crops.

plant biology↗

bioRxiv2020

Genetic mapping of the early responses to salt stress in Arabidopsis thaliana

Salt stress decreases plant growth prior to significant ion accumulation in the shoot. However, the processes underlying this rapid reduction in growth are still unknown. To understand the changes in salt stress responses through time and at multiple physiological levels, examining different plant processes within a single setup is required. Recent advances in phenotyping has allowed the image-based estimation of plant growth, morphology, colour and photosynthetic activity. In this study, we examined the salt stress-induced responses of 191 Arabidopsis accessions from one hour to seven days after treatment using high-throughput phenotyping. Multivariate analyses and machine learning algorithms identified that quantum yield measured in the light-adapted state (Fv'/Fm') greatly affected growth maintenance in the early phase of salt stress, while maximum quantum yield (QY max) was crucial at a later stage. In addition, our genome-wide association study (GWAS) identified 770 loci that were specific to salt stress, in which two loci associated with QY max and Fv'/Fm' were selected for validation using T-DNA insertion lines. We characterised an unknown protein kinase found in the QY max locus, which reduced photosynthetic efficiency and growth maintenance under salt stress. Understanding the molecular context of the identified candidate genes will provide valuable insights into the early plant responses to salt stress. Furthermore, our work incorporates high-throughput phenotyping, multivariate analyses and GWAS, uncovering details of temporal stress responses, while identifying associations across different traits and time points, which likely constitute the genetic components of salinity tolerance.

plant biology↗

bioRxiv2019

Machine Learning Reveals Spatiotemporal Genome Evolution in Asian Rice Domestication

Domestication is anthropogenic evolution that fulfills mankinds critical food demand. As such, elucidating the molecular mechanisms behind this process promotes the development of future new food resources including crops. With the aim of understanding the long-term domestication process of Asian rice and by employing the Oryza sativa subspecies (indica and japonica) as an Asian rice domestication model, we scrutinized past genomic introgressions between them as traces of domestication. Here we show the genome-wide introgressive region (IR) map of Asian rice, by utilizing 4,587 accession genotypes with a stable outgroup species, particularly at the finest resolution through a machine learning-aided method. The IR map revealed that 14.2% of the rice genome consists of IRs, including both wide IRs (recent) and narrow IRs (ancient). This introgressive landscape with their time calibration indicates that introgression events happened in multiple genomic regions over multiple periods. From the correspondence between our wide IRs and the so-called selective sweep regions, we provide a definitive answer to a long-standing controversy over the evolutionary origin of Asian rice domestication, single or multiple origins: It heavily depends upon which regions you pay attention to, implying that wider genomic regions represent immediate short history of Asian rice domestication as a likely support to the single origin, while its ancient history is interspersed in narrower traces throughout the genome as a possible support to the multiple origin.

plant biology↗

bioRxiv2019

Genomic history and ecology of the geographic spread of rice

Rice (Oryza sativa) is one of the worlds most important food crops. We reconstruct the history of rice dispersal in Asia using whole-genome sequences of >1,400 landraces, coupled with geographic, environmental, archaeobotanical and paleoclimate data. We also identify extrinsic factors that impact genome diversity, with temperature a leading abiotic factor. Originating [~]9,000 years ago in the Yangtze Valley, rice diversified into temperate and tropical japonica during a global cooling event [~]4,200 years ago. Soon after, tropical rice reached Southeast Asia, where it rapidly diversified starting [~]2,500 yBP. The history of indica rice dispersal appears more complicated, moving into China [~]2,000 yBP. Reconstructing the dispersal history of rice and its climatic correlates may help identify genetic adaptation associated with the spread of a key domesticated species.\n\nOne sentence summaryWe reconstructed the ancient dispersal of rice in Asia and identified extrinsic factors that impact its genomic diversity.

evolutionary biology↗

bioRxiv2019

A diversity of traits contributes to salinity tolerance of wild Galapagos tomatoes seedlings

Traits of modern crops have been heavily selected in agriculture, causing the commercial lines to be more susceptible to harsh conditions, which their wild relatives are naturally better able to withstand. Understanding the developed mechanisms of tolerance present in wild relatives can enhance crop performance under stress. In this study, salinity tolerance traits of two species of wild tomato endemic to the Galapagos Islands, Solanum cheesmaniae and Solanum galapagense, were investigated. Since these tomatoes grow well despite being constantly splashed with seawater, they could be a valuable genetic resource for improving salinity tolerance in commercial tomatoes. To explore their potential, over 20 traits reflecting plant growth, physiology and ion content were recorded in 67 accessions of S. cheesmaniae and S. galapagense and two commercial tomato lines of Solanum lycopersicum. Salt treatments of 200 mM NaCl were applied for ten days, using supported hydroponics. Great natural variation was evident in the responses of the Galapagos tomatoes to salt stress and they also displayed greater tolerance to salt stress than the commercial lines tested, based on multivariate trait analyses. Although Galapagos tomatoes in general exhibited better tolerance to salt stress than the commercial lines tested, the accessions LA0317, LA1449 and LA1403 showed particularly high salinity tolerance based on growth maintenance under stress. Thus, Galapagos tomatoes should be further explored using forward genetic studies to identify and investigate the genes underlying their high tolerance and be used as a resource for increasing salinity tolerance of commercial tomatoes. The generated data, along with useful analysis tools, have been packaged and made publicly available via an interactive online application (https://github.com/mmjulkowska/La_isla_de_tomato) to facilitate trait selection and the use of Galapagos tomatoes for the development of salt tolerant commercial tomatoes.

plant biology↗

bioRxiv2017

The genome sequence of the wild tomato Solanum pimpinellifolium provides insights into salinity tolerance

Solanum pimpinellifolium, a wild relative of cultivated tomato, offers a wealth of breeding potential for several desirable traits such as tolerance to abiotic and biotic stresses. Here, we report the genome and annotation of S. pimpinellifolium LA0480. The LA0480 genome size (811 Mb) and the number of annotated genes (25,970) are within the range observed for other sequenced tomato species. We developed and utilized the Dragon Eukaryotic Analyses Platform (DEAP) to functionally annotate the LA0480 protein-coding genes. Additionally, we used DEAP to compare protein function between S. pimpinellifolium and cultivated tomato. Our data suggest enrichment in genes involved in biotic and abiotic stress responses. Moreover, we present phenotypic data from one field experiment that demonstrate a greater salinity tolerance for fruit-and yield-related traits in S. pimpinellifolium compared with cultivated tomato. To understand the genomic basis for these differences in S. pimpinellifolium and S. lycopersicum, we analyzed 15 genes that have previously been shown to mediate salinity tolerance in plants. We show that S. pimpinellifolium has a higher copy number of the inositol-3-phosphate synthase and phosphatase genes, which are both key enzymes in the production of inositol and its derivatives. Moreover, our analysis indicates that changes occurring in the inositol phosphate pathway may contribute to the observed higher salinity tolerance in LA0480. Altogether, our work provides essential resources to understand and unlock the genetic and breeding potential of S. pimpinellifolium, and to discover the genomic basis underlying its environmental robustness.

plant biology↗