Penfield, S. | Search bioRxiv

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↗