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Biology subjects

Leigh, F.

Publications and source records attributed to Leigh, F..

2 recordsLinked to original sources

Wheat responds to lack of root hairs with recruitment of bacteria harboring P mineralization genes and increased arbuscular mycorrhizal fungi arbuscularisation

Understanding how crops recruit bacterial and fungal partners for phosphorus (P) nutrition remains incomplete, particularly regarding temporal shifts between P-cycling bacteria and arbuscular mycorrhizal fungi (AMF). We investigated these dynamics using the wheat root hair mutant short root hair 1 (srh1), which is impaired in root hair elongation and may be more dependent on microbial P acquisition compared to the wildtype. We profiled the P-cycling bacterial microbiome and AMF colonization across development and at booting under P-depleted conditions. Before AMF establishment, the root hair mutant rhizosphere was enriched in bacterial organic P-mineralization genes. After AMF colonization increased in both genotypes, this enrichment disappeared, indicating a temporal shift from bacterial to fungal P acquisition. Growing the root hair mutant in P-depleted conditions to booting, when P uptake is highest in wheat, did not induce the mutant to recruit its bacterial P-cycling microbiome. Instead, AMF arbuscule formation was enriched. Collectively, our results reveal a temporal transition in wheat from bacterial to AMF-mediated P nutrition and highlight the compensatory role of the microbiome in plants lacking root hair elongation.

microbiology↗

pyRootHair: Machine Learning Accelerated Software for High-Throughput Phenotyping of Plant Root Hair Traits

1Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have been largely quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Here, we present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under two distinct shape categories, and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including arabidopsis (Arabidopsis thaliana), brachypodium (Brachypodium distachyon), medicago (Medicago truncatula), oat (Avena sativa), rice (Oryza sativa), teff (Eragostis tef) and tomato (Solanum lycopersicum). The application of pyRootHair enables users to rapidly screen large numbers of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variaton on plant performance.

bioinformatics↗