Biology subjects

Wells, D. M.

Publications and source records attributed to Wells, D. M..

9 recordsLinked to original sources

bioRxiv2025

The Rapid Anatomics Tool (RAT): A low-cost root anatomical phenotyping pipeline reveals changes in root anatomy along the root axis

Root anatomical phenotyping has become a demonstrably essential part of investigating root physiology and in acquiring a holistic understanding of plant development. However, accessible high throughput methods for root anatomical analysis are still lacking. Here, we present the Rapid Anatomics Tool (RAT), a novel, low-cost system for high throughput root anatomical imaging with a shallow learning curve for obtaining high quality images suitable for comparative analysis across a number of plant species. Its efficiency comes from combining blockface-like imaging and stain-free imaging using near-ultraviolet (nUV) autofluorescence utilising a combination of low-cost commercial equipment, readily available mechanical components, and custom designed and 3D printed tools. Using this system, we investigated the anatomy of mature tissue along the axis of wheat crown roots, revealing a tendency of reduction in vascular complexity (expressed through a reduction in metaxylem number, area, and mean area per metaxylem file) from the basal to the distal region of the root. This study highlights the importance of thorough sampling strategies for investigating root anatomy in relation to organ function and introduces an accessible, relatively high-throughput method to support such research.

plant biology↗

bioRxiv2024

Root metaxylem area influences drought tolerance and transpiration in pearl millet in a soil texture dependent manner

O_LIPearl millet is a key cereal for food security in drylands but its yield is strongly impacted by drought. We investigated how root anatomical traits contribute to mitigating the effects of vegetative drought stress in pearl millet. C_LIO_LIWe examined associations between root anatomical traits and agronomical performance in a pearl millet diversity panel under irrigated and vegetative drought stress treatments in field trials. The impact of associated anatomical traits on transpiration was assessed using subpanels grown in different soil within a greenhouse. C_LIO_LIIn the field, total metaxylem area was positively correlated with grain weight and its maintenance under drought. In the greenhouse, genotypes with larger metaxylem area grown in sandy soil exhibited a consumerist water use strategy under irrigation, which shifted to a conservative strategy under drought. Water savings was mediated by transpiration restriction under high evaporative demand. This mechanism was dependent on soil hydraulics as it was not observed in peat soil with higher hydraulic conductivity upon soil drying. C_LIO_LIWe propose that water savings under drought, mediated by large metaxylem area and its interaction with soil hydraulics, help mitigate vegetative drought stress. Our findings highlight the role of soil hydraulic properties in shaping plant hydraulics and drought tolerance. C_LI

physiology↗

bioRxiv2024

Persistent legacy effects on soil metagenomes facilitate plant adaptive responses to drought

Both chronic and acute drought alter the composition and physiology of soil microbiota by selecting for functional traits that preserve fitness in dry conditions. Currently, little is known about how the resulting precipitation legacy effects manifest at the molecular and physiological levels and how they influence neighboring plants, especially in the context of subsequent drought. We characterized metagenomes of six prairie soils spanning a steep precipitation gradient in Kansas, USA. By statistically controlling for variation in soil porosity and elemental profiles, we identified bacterial taxa and functional gene categories associated with precipitation. This microbial precipitation legacy persisted through a 5-month-long experimental drought and mitigated the negative physiological effects of acute drought for a wild grass species that is native to the precipitation gradient, but not for the domesticated crop species maize. In particular, microbiota with a low-precipitation legacy altered transcription of a subset of host genes that mediate transpiration and intrinsic water use efficiency during drought. Our results show how long-term exposure to water stress alters soil microbial communities with consequences for the drought responses of neighboring plants.

microbiology↗

bioRxiv2023

Glutaredoxin regulation of primary root growth confers early drought stress tolerance in pearl millet

Seedling root traits impact plant establishment under challenging environments. Pearl millet is one of the most heat and drought tolerant cereal crops that provides a vital food source across the sub-Saharan Sahel region. Pearl millets early root system features a single fast-growing primary root which we hypothesize is an adaptation to the Sahelian climate. Using crop modelling, we demonstrate that early drought stress is an important constraint in agrosystems in the Sahel where pearl millet was domesticated. Furthermore, we show that increased pearl millet primary root growth is correlated with increased early water stress tolerance in field conditions. Genetics including GWAS and QTL approaches identify genomic regions controlling this key root trait. Combining gene expression data, re-sequencing and re-annotation of one of these genomic regions identified a glutaredoxin-encoding gene PgGRXC9 as the candidate stress resilience root growth regulator. Functional characterization of its closest Arabidopsis homolog AtROXY19 revealed a novel role for this glutaredoxin (GRX) gene clade in regulating cell elongation. In summary, our study suggests a conserved function for GRX genes in conferring root cell elongation and enhancing resilience of pearl millet to its Sahelian environment.

plant biology↗

bioRxiv2022

Duckweed roots are dispensable and are on a trajectory toward vestigiality

Duckweeds are morphologically simplified, free floating aquatic monocots comprising both rooted and rootless genera. This has led to the idea that roots in these species may be vestigial, but empirical evidence supporting this is lacking. Here we show that duckweed roots are no longer required for their ancestral role of nutrient uptake. Comparative analyses of nearly all rooted duckweed species revealed a highly reduced anatomy, with greater simplification in the more recently diverged genus Lemna. A series of root excision experiments demonstrated that roots are dispensable for normal growth in Spirodela polyrhiza and Lemna minor. Furthermore, ionomic analyses of fronds in these two species showed little difference in the elemental composition of plants in rooted versus root-excised samples. In comparison, another free-floating member of the Araceae, Pistia stratiotes, which colonized the aquatic environment independently of duckweeds, has retained a more complex root anatomy. Whilst Pistia roots were not absolutely required for growth, their removal inhibited plant growth and resulted in a broad change in the mineral profile of aerial tissues. Collectively, these observations suggest that duckweeds and Pistia may be different stages along a trajectory towards root vestigialization Given this, along with the striking diversity of root phenotypes, culminating in total loss in the most derived species, we propose that duckweed roots are a powerful system with which to understand organ loss and vestigiality. One sentence summaryThrough their adaption to the aquatic environment, duckweed roots have progressively become structurally reduced making them an ideal plant model with which to study vestigiality.

evolutionary biology↗

bioRxiv2019

RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures

We present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task Convolutional Neural Network architecture. The network has been designed to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. In addition, the network simultaneously locates seeds, and first and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. The proposed method is evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. The results are compared with semi-automatic analysis via the original RootNav tool, demonstrating comparable accuracy, with a 10-fold increase in speed. We then demonstrate the ability of the network to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. We transfer for a final time to images of Brassica napus from a hydroponic assay, and still demonstrate good accuracy despite many fewer training images. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools.

bioinformatics↗

bioRxiv2019

Identification of nitrogen-dependent QTL and underlying genes for root system architecture in hexaploid wheat

The root system architecture (RSA) of a crop has a profound effect on the uptake of nutrients and consequently the potential yield. However, little is known about the genetic basis of RSA and resource adaptive responses in wheat (Triticum aestivum L.). Here, a high-throughput germination paper plant phenotyping system was used to identify seedling traits in a wheat doubled haploid mapping population, Savannah x Rialto. Significant genotypic and nitrate-N treatment variation was found across the population for seedling traits with distinct trait grouping for root size-related traits and root distribution-related traits. Quantitative trait locus (QTL) analysis identified a total of 59 seedling trait QTLs. Across two nitrate treatments, 27 root QTLs were specific to the nitrate treatment. Transcriptomic analyses for one of the QTLs on chromosome 2D found under low nitrate conditions was pursued revealing gene enrichment in N-related biological processes and 17 candidate up-regulated genes with possible involvement in a root angle response. Together, these findings provide genetic insight into root system architecture and plant adaptive responses to nitrate and provide targets that could help improve N capture in wheat.

plant biology↗

bioRxiv2017

Deep Learning for Multi-task Plant Phenotyping

Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recently, machine learning approaches have shown impressive results in many areas of computer vision, but these rely on large datasets that are at present not available for crops. We present a new dataset, called ACID, that provides hundreds of accurately annotated images of wheat spikes and spikelets, along with image level class annotation. We then present a deep learning approach capable of accurately localising wheat spikes and spikelets, despite the varied nature of this dataset. As well as locating features, our network offers near perfect counting accuracy for spikes (95.91%) and spikelets (99.66%). We also extend the network to perform simultaneous classification of images, demonstrating the power of multi-task deep architectures for plant phenotyping. We hope that our dataset will be useful to researchers in continued improvement of plant and crop phenotyping. With this in mind, alongside the dataset we will make all code and trained models available online.

plant biology↗

bioRxiv2017

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies

BackgroundGenetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming).\n\nFindingsWe trained a Random Forest algorithm to infer architectural traits from automatically-extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify Quantitative Trait Loci that had previously been discovered using a semi-automated method.\n\nConclusionsWe have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput in large scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other area of plant phenotyping.

plant biology↗