Biology subjects

Sturrock, C. J.

Publications and source records attributed to Sturrock, C. J..

3 recordsLinked to original sources

bioRxiv2026

Water stress adaptive responses in plants require movement of ABA and AB-aldehyde from vascular to target tissues

Vascular plants rapidly coordinate root and shoot responses to water stress. Abscisic acid (ABA) mediates these adaptations; however, it remains unclear which cells produce ABA, whether ABA synthesis shifts during stress, and whether ABA movement is required for its adaptive functions. Here, we map ABA biosynthesis at cellular resolution in Arabidopsis and report that water-stress adaptive responses in roots and shoots require movement of ABA and its precursor AB-aldehyde from vascular tissues to target cells. We suggest that ABA accumulation arises from two parallel routes: (i) ABA synthesized in the vasculature via ABA2 and AAO3, then moving to guard cells, and (ii) phloem-derived AB-aldehyde being converted to ABA in the epidermis or bundle sheath by AAO1 and AAO2. Finally, we predict that tightly packed cells beneath leaf veins facilitate efficient ABA delivery to guard cells, an anatomical arrangement that has enabled angiosperms to evolve the use of ABA to rapidly close stomata.

plant biology↗

bioRxiv2024

Maize genetic diversity identifies moisture-dependent root-branch signaling pathways

Plants grow complex root systems to extract unevenly distributed resources from soils. Spatial differences in soil moisture are perceived by root tips leading to the patterning of new root branches towards available water, a process called hydropatterning. Little is known about hydropatterning behavior and its genetic basis in crops plants. Here, we develop an assay to measure hydropatterning in maize and reveal substantial differences between tropical/subtropical and temperate maize breeding germplasm that likely resulted from divergent selection. Genetic dissection of hydropatterning confirmed the regulatory role of auxin and revealed that the gaseous hormone ethylene acts to locally inhibit root branching from air-exposed tissues. These findings demonstrate the crop relevance of hydropatterning and establish its genetic basis.

plant biology↗

bioRxiv2019

Three Dimensional Root CT Segmentation using Multi-Resolution Encoder-Decoder Networks

We address the complex problem of reliably segmenting root structure from soil in X-ray Computed Tomography (CT) images. We utilise a deep learning approach, and propose a state-of-the-art multi-resolution architecture based on encoder-decoders. While previous work in encoder-decoders implies the use of multiple resolutions simply by downsampling and upsampling images, we make this process explicit, with branches of the network tasked separately with obtaining local high-resolution segmentation, and wider low-resolution contextual information. The complete network is a memory efficient implementation that is still able to resolve small root detail in large volumetric images. We evaluate our approach by comparing against a number of different encoder-decoder based architectures from the literature, as well as a popular existing image analysis tool designed for root CT segmentation. We show qualitatively and quantitatively that a multi-resolution approach offers substantial accuracy improvements over a both a small receptive field size in a deep network, or a larger receptive field in a shallower network. We obtain a Dice score of 0.59 compared with 0.41 for the closest competing method. We then further improve performance using an incremental learning approach, in which failures in the original network are used to generate harder negative training examples. Results of this process raise the precision of the network, and improve the Dice score to 0.66. Our proposed method requires no user interaction, is fully automatic, and identifies large and fine root material throughout the whole volume. The 3D segmented output of our method is well-connected, allowing the recovery of structured representations of root system architecture, and so may be successfully utilised in root phenotyping.

bioinformatics↗