Biology subjects

Percival-Alwyn, L.

Publications and source records attributed to Percival-Alwyn, L..

7 recordsLinked to original sources

bioRxiv2025

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↗

bioRxiv2022

Genomic erosion in the assessment of species extinction risk and recovery potential

Many species are undergoing rapid population declines and environmental deterioration, leading to genomic erosion, the loss of genetic diversity, accumulation of deleterious mutations, maladaptation, and introgression, that undermines fitness and long-term viability. Critically, this process continues even after demographic recovery due to a time-lagged genetic burden known as drift debt. Current conservation assessments, such as the IUCN Red List, focus on short-term extinction risk and do not capture the long-term consequences of genomic erosion. Likewise, the longer-term assessments of the IUCN Green Status may overestimate population recovery by failing to account for the lasting effects of genomic erosion. As genome sequencing becomes increasingly accessible, there is a growing opportunity to quantify genomic erosion and integrate it into conservation planning. Here, we use genomic simulations to illustrate how how different genomic metrics are sensitive to the drift debt. We test how ancestral effective population size (Ne) and bottleneck history influence the tempo and severity of genomic erosion, and demonstrate how these dynamics shape genetic load and additive genetic variation, key indicators of long-term evolutionary potential. Finally, we present a proof-of-concept for a Genomic Green Status framework that aligns genomic metrics with conservation impact assessments, laying the foundation for genomics-informed strategies to support species recovery.

genomics↗

bioRxiv2021

Analysis of wild plant pathogen populations reveals a signal of adaptation in genes evolving for survival in agriculture in the beet rust pathogen (Uromyces beticola)

Improvements in crop resistance to pathogens can reduce yield losses and address global malnourishment today. Gene-for-gene -type interactions can identify new sources of resistance but genetic resistance is often short lived. Ultimately an understanding of how pathogens rapidly adapt will allow us to both increase resistance gene durability and more effectively target chemical treatments. Until recently all agricultural pathogens were living on wild hosts. To understand crop pathogen evolution, we compared genetic diversity in agricultural and wild populations. Wild reservoirs may be the source of emergent pathogen lineages, but here we outline a strategy for comparison of wild and agricultural pathogen populations to highlight genes adapting to agriculture. To address this, we have selected and developed the beet rust system (Beta vulgaris, Uromyces beticola, respectively) as our wild-agricultural model. Our hypothesis is that pathogen adaptation to agricultural crops will be evident as divergence in comparisons of wild and agricultural plant pathogen populations. We sampled isolates in both the wild and agriculture, sequenced and assembled and annotated a large fungal genome and analysed genetic diversity in 42 re-sequenced rust isolates. We found population differentiation between isolates in the wild compared to a predominantly agricultural group. Fungal effector genes are co-evolving with host resistance and are important for successful colonisation. We predicted (and found) that these exhibit a greater signal of diversification and adaptation and more importantly displayed increased wild agricultural divergence. Finding a signal of adaptation in these genes highlights this as an important strategy to identify genes which are key to pathogen success, that analysis of agricultural isolates alone cannot. Author SummaryAs quickly as we develop new strategies for crop defence, pathogens evolve to circumvent them. Novel crop pathogen strains emerge periodically and sweep through the agricultural system. However, because of the (often) clonal nature of these crop pathogens it is difficult to identify the trait that is key to their success. In other words, if there is a trait that is key for success in agriculture, all agricultural isolates will have it (or die without it). What we need is a case and control system where we identify genes important to pathogen success in agricultural by comparing them to pathogens that live in the wild. Here we exemplify this strategy by focussing on genes already known to specifically adapt for the successful colonisation of the host, the fungal effector genes. We find that these genes appear to be evolving quickly and that they are more different between the wild and agriculture than other non-effector genes. These differences between wild and agricultural pathogens suggest we are observing adaptation to agriculture. We do this work in the sugar beet rust system because of its tractability to sample but this understanding about how to identify genetic variation that is key to pathogen success in agriculture is applicable to crop systems where pathogen reservoirs exist as well as other pathogen reservoir systems (e.g. zoonoses).

evolutionary biology↗

bioRxiv2019

Semi-quantitative characterisation of mixed pollen samples using MinION sequencing and Reverse Metagenomics (RevMet)

1. The ability to identify and quantify the constituent plant species that make up a mixed-species sample of pollen has important applications in ecology, conservation, and agriculture. Recently, metabarcoding protocols have been developed for pollen that can identify constituent plant species, but there are strong reasons to doubt that metabarcoding can accurately quantify their relative abundances. A PCR-free, shotgun metagenomics approach has greater potential for accurately quantifying species relative abundances, but applying metagenomics to eukaryotes is challenging due to low numbers of reference genomes. 2. We have developed a pipeline, RevMet (Reverse Metagenomics), that allows reliable and semi-quantitative characterization of the species composition of mixed-species eukaryote samples, such as bee-collected pollen, without requiring reference genomes. Instead, reference species are represented only by genome skims: low-cost, low-coverage, short-read sequence datasets. The skims are mapped to individual long reads sequenced from mixed-species samples using the MinION, a portable nanopore sequencing device, and each long read is uniquely assigned to a plant species. 3. We genome-skimmed 49 wild UK plant species, validated our pipeline with mock DNA mixtures of known composition, and then applied RevMet to pollen loads collected from wild bees. We demonstrate that RevMet can identify plant species present in mixed-species samples at proportions of DNA [≥]1%, with few false positives and false negatives, and reliably differentiate species represented by high versus low amounts of DNA in a sample. 4. The RevMet pipeline could readily be adapted to generate semi-quantitative datasets for a wide range of mixed eukaryote samples, which could include characterising diets, quantifying allergenic pollen from air samples, quantifying soil fauna, and identifying the compositions of algal and diatom communities. Our per-sample costs were {pound}90 per genome skim and {pound}60 per pollen sample, and new versions of sequencers available now will further reduce these costs.

ecology↗

bioRxiv2018

Sex-specific changes in the aphid DNA methylation landscape

Aphids present an ideal system to study epigenetics as they can produce diverse, but genetically identical, morphs in response to environmental stimuli. Here, using whole genome bisulphite sequencing and transcriptome sequencing of the green peach aphid (Myzus persicae), we present the first detailed analysis of cytosine methylation in an aphid and investigate differences in the methylation and transcriptional landscapes of male and asexual female morphs. We find that methylation primarily occurs in a CG dinucleotide (CpG) context and that exons are highly enriched for methylated CpGs, particularly at the 3 end of genes. Methylation is positively associated with gene expression, and methylated genes are more stably expressed than un-methylated genes. Male and asexual female morphs have distinct methylation profiles. Strikingly, these profiles are divergent between the sex chromosome and the autosomes; autosomal genes are hypo-methylated in males compared to asexual females, whereas genes belonging to the sex chromosome, which is haploid in males, are hyper-methylated. Overall, we find correlated changes in methylation and gene expression between males and asexual females, and this correlation is particularly strong for genes located on the sex chromosome. Our results suggest that differential methylation of sex-biased genes plays a role in M. persicae sexual differentiation.

genomics↗

bioRxiv2017

The ash dieback invasion of Europe was founded by two individuals from a native population with huge adaptive potential

Accelerating international trade and climate change make pathogen spread an increasing concern. Hymenoscyphus fraxineus, the causal agent of ash dieback is one such pathogen, moving across continents and hosts from Asian to European ash. Most European common ash (Fraxinus excelsior) trees are highly susceptible to H. fraxineus although a small minority (~5%) evidently have partial resistance to dieback. We have assembled and annotated a draft of the H. fraxineus genome which approaches chromosome scale. Pathogen genetic diversity across Europe, and in Japan, reveals a tight bottleneck into Europe, though a signal of adaptive diversity remains in key host interaction genes (effectors). We find that the European population was founded by two divergent haploid individuals. Divergence between these haplotypes represents the 'shadow' of a large source population and subsequent introduction would greatly increase adaptive potential and the pathogen's threat. Thus, EU wide biological security measures remain an important part of the strategy to manage this disease.

genomics↗

bioRxiv2016

An improved assembly and annotation of the allohexaploid wheat genome identifies complete families of agronomic genes and provides genomic evidence for chromosomal translocations.

Advances in genome sequencing and assembly technologies are generating many high quality genome sequences, but assemblies of large, repeat-rich polyploid genomes, such as that of bread wheat, remain fragmented and incomplete. We have generated a new wheat whole-genome shotgun sequence assembly using a combination of optimised data types and an assembly algorithm designed to deal with large and complex genomes. The new assembly represents more than 78% of the genome with a scaffold N50 of 88.8kbp that has a high fidelity to the input data. Our new annotation combines strand-specific Illumina RNAseq and PacBio full-length cDNAs to identify 104,091 high confidence protein-coding genes and 10,156 non-coding RNA genes. We confirmed three known and identified one novel genome rearrangements. Our approach enables the rapid and scalable assembly of wheat genomes, the identification of structural variants, and the definition of complete gene models, all powerful resources for trait analysis and breeding of this key global crop. [Supplemental material is available for this article.]

genomics↗