Biology subjects

Penfield, S.

Publications and source records attributed to Penfield, S..

6 recordsLinked to original sources

bioRxiv2026

Larval antibiosis to cabbage stem flea beetle (Psylliodes chrysocephala) is absent within oilseed rape (Brassica napus)

BACKGROUNDInsect pests present a global threat to crops, with plant resistance representing a key breeding goal. The cabbage stem flea beetle (Psylliodes chrysocephala; CSFB) is a key pest of oilseed rape (Brassica napus; OSR) within Europe; however, CSFB resistance is yet to be found within B. napus. To address this, we examine CSFB larval development over time, explore antibiosis across a genetically diverse B. napus panel, and test whether larvae can develop within model Brassicaceae species (Brassica rapa and Arabidopsis thaliana). RESULTSCSFB larvae completed development from four-weeks post-infestation, undergoing a 20-fold size increase, with larval recovery after two weeks allowing semi-high throughput resistance phenotyping. Applying this method to 98 Brassicaceae genotypes (97 B. napus and a single Sinapis alba), we found weak evidence for genotype effects on larval survival. However, phenotype validation with resistant and susceptible B. napus genotypes showed no differences in larval survival or adult emergence. Larval antibiosis was consistently observed in S. alba. Finally, we showed that model B. rapa and A. thaliana genotypes represent suitable hosts for CSFB, with larvae increasing 8-10x in size after two weeks. CONCLUSIONCSFB larval antibiosis appears absent within B. napus, possibly due to bottlenecks experienced during domestication. However, larval antibiosis is present within S. alba, and future work should study the basis of this resistance. Further, CSFB larval screening in Brassicaceae model species presents an opportunity to explore CSFB resistance genetics, informing breeding progress for insect resistance in B. napus.

plant biology↗

bioRxiv2024

The paternally derived genome opposes seed dormancy induction by the mother plant in Arabidopsis

Seed dormancy in Arabidopsis is known to be mediated by the interaction of maternal and zygotic genomes during seed maturation. While studies have revealed the extensive influence of maternal processes on dormancy and germination, less is known about the influence of the father. Here we exploit differences in ploidy to explore the role of the paternal genome on progeny seed dormancy. We show that paternal genome acts to reduce seed dormancy regardless of maternal genome dose, resulting in lower dormancy in tetraploid Arabidopsis versus genetically identical diploids. We show that this paternal effect requires synthesis of RNA Polymerase IV-dependent RNAs in the male gametophyte which oppose the dormancy-inducing effects of maternal siRNAs on seed coat and endosperm development. We conclude that the paternal genome has evolved to subvert the dormancy-inducing role of the mother plant in progeny seeds.

plant biology↗

bioRxiv2024

Trans-generational adaptation to maternal climate through hormone transport in plants

Whether organisms can inherit parental adaptations to the environment is a major question in evolutionary biology. Plant development is highly plastic and dependent on the seasonal cues which are used to control growth and reproduction. Seed dormancy and germination are key traits which respond strongly to temperature during seed development and here we show that progeny adaptation to seasonal climate is inherited from the mother plant. Loss of maternal LIKE HETEROCHROMATIN PROTEIN 1 (LHP1) causes an inability of progeny seeds to sense temperature and this is linked mechanistically to reduced ABA levels in seeds and activation of the primary nitrate response. At the single cell level, small changes in temperature activate nitrate signalling specifically in the mother, and ABA biosensor imaging reveals temperature-dependent fluxes of ABA into seeds necessary for dormancy induction. Thus, we reveal that progeny seeds inherit the climate adaptation of mother plants via active hormone transport during seed set.

plant biology↗

bioRxiv2022

Combining historical agricultural and climate datasets sheds new light on early 20th century barley performance

Barley (Hordeum vulgare ssp. vulgare) is cultivated globally across a wide range of environments, both in highly productive agricultural systems and in subsistence agriculture and provides valuable feedstock for the animal feed and malting industries. However, as the climate changes there is an urgent need to identify adapted spring barley varieties that will consistently yield highly under increased environmental stresses. In this research we combined recently released historical weather data with published early 20th century Irish spring barley trials data for two heritage varieties: Archer and Goldthorpe, following an analysis first published by Student in 1923. Using linear mixed models, we show that interannual variation in observed spring barley yields can be partially explained by recorded weather variability. We find that whilst Archer largely yields more highly, Goldthorpe is more stable under wetter growing conditions, highlighting the importance of considering growing climate in variety selection. Furthermore, this study demonstrates the benefits of access to historical trials and climatic data and the importance of incorporating climate data in modern day breeding programmes to improve climate resilience of future varieties.

plant biology↗

bioRxiv2022

A histone deacetylase complex establishes a transgenerationally inherited maternal epigenetic state controlling progeny seed dormancy

Mother plants play an important role in the control of dormancy and dispersal characters of their progeny. In Arabidopsis seed dormancy is imposed by the embryo-surrounding tissues of the endosperm and seed coat. Here we show that the VERNALIZATION5/VIN3-LIKE 3 (VEL3) gene maintains maternal control over progeny seed dormancy by establishing an epigenetic state early in endosperm development that primes the depth of primary seed dormancy later established during seed maturation. VEL3 relocates MSI1 to the nucleolus and associates with other components of the histone deacetylase complex (HDAC), and is required for gene repression via PRC2 and HDAC at pericentromeric regions established in the central cell. Interestingly, the epigenetic state established by the maternal VEL3 is retained trans-generationally in mature seeds, and controls seed dormancy through the repression of programmed cell death-associated gene ORE1. Our data demonstrates a novel mechanism by which maternal control of progeny seed physiology persists post-shedding, maintaining parental control of seed behaviour.

developmental biology↗

bioRxiv2017

Leaf-GP: An Open and Automated Software Application for Measuring Growth Phenotypes for Arabidopsis and Wheat

BackgroundPlants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch processing of image datasets, multiple trait analysis, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic.\n\nResultsHere, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different platforms. To facilitate diverse scientific user communities, we provide three versions of the software, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and an interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat (Triticum aestivum) growth studies at the Norwich Research Park (NRP, UK). By quantifying growth phenotypes over time, we are able to identify diverse plant growth patterns based on a variety of key growth-related phenotypes under varied experimental conditions.\n\nAs Leaf-GP has been evaluated with noisy image series acquired by different imaging devices and still produced reliable biologically relevant outputs, we believe that our automated analysis workflow and customised computer vision based feature extraction algorithms can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, our software can not only contribute to biological research, but also exhibit how to utilise existing open numeric and scientific libraries (including Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, efficiently and effectively.\n\nConclusionsLeaf-GP is a comprehensive software application that provides three approaches to quantify multiple growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and high-performance computing clusters (HPC), with open Python-based scientific libraries preinstalled. We share our modulated source code and executables (.exe for Windows; .app for Mac) together with this paper to serve the plant research community. The software, source code and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

plant biology↗