Hutton Highlights, February 2019

16 Hutton Highlights 4 An international team of scientists from the University of Dundee, Huazhong Agricultural University, Heilongjiang Academy of Agricultural Sciences (both China) and Wageningen University (Netherlands), has shed further light into the mechanisms through which the potato blight pathogen interacts with plant cells to promote disease. Late blight played a major role in the historical Irish potato famine, and is still a huge problem for farmers today, causing massive crop losses and proving difficult to manage by chemical control and traditional breeding methods. This new research is another milestone in the quest to provide potato breeders with the knowledge needed to develop disease-resistant varieties suited to the requirements of consumers and industry. The potato blight pathogen delivers proteins called effectors into plant cells to manipulate host processes and promote disease. Knowledge of where they localise inside host cells is important in understanding their functions. Dr Petra Boevink, lead author of the research, explained: “We examined 52 effectors and found they were mostly located in the cytoplasm, nucleus, or cell membrane but some were at the endoplasmic reticulum, mitochondria, Research shines a light into mechanisms of potato late blight infection peroxisomes or microtubules, suggesting they function in diverse locations. “Forty-five effectors enhanced the pathogen’s ability to colonise leaves when expressed inside the plant cells, revealing that they can indeed assist infection. Mixing effectors that are known to suppress different plant immune system pathways gave an additive boost to infection. Given that the pathogen produces many effectors this indicates that these effectors work in combination to suppress the many different strategies the plants use to defend themselves. The paper is in the latest issue of Journal of Experimental Botany. Dr Jonathan Hopkins said: “The size of the ‘evidence base’ required for place- based policy in Scotland could be very large, as a wide range of issues affect people and society. “Researchers need to consult practitioners, policymakers, community groups and other stakeholders to identify the most important issues which affect Scotland’s rural areas and small towns” To that end, a survey was carried out covering the following topics: • What stakeholders perceive to be the most important issues and subjects which affect Scotland’s rural areas and small towns, and whether data is available to measure them at the local level. • How stakeholders use and analyse data about places and people, and how existing online resources for viewing and accessing data and maps can be improved. The wider research project on place- based policy and rural Scotland aims to Views sought on local issues and quality of life in Scottish communities improve current understanding of (i) the main reasons for differences in economic performance and social outcomes across rural areas and small towns of Scotland, and (ii) how policies can help to deliver positive outcomes and address these disparities. The work is funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government, within the RESAS Strategic Research Programme 2016-2021. Social scientists from the Institute and Scotland’s Rural College carried out a survey about wellbeing, local issues and quality of life in Scottish communities, in a drive to improve currently-available data and inform policy development and implementation. Soil X-ray signatures contribute to vision of digital mineralogy Soils are a complex mixture of minerals, water, air, organic matter, and home to a huge variety of organisms. Minerals are the major component of many soils, made up from a combination of primary minerals derived from the soils geological “parent materials”, along with secondary minerals such as clay and iron oxides. Soils are responsible for many functions relating to biodiversity and are vital to life on earth, but their complexity has made it notoriously challenging to systematise soil property – mineralogy relationships. Conventional approaches for the assessment of soil mineralogy are primarily based on X-ray diffraction (XRD) measurements. Modern instrumentation and precision sample preparation methods allow for the collection of remarkably reproducible XRD patterns. In recent years, this has resulted in databases containing thousands of reproducible, geo-referenced, XRD measurements and associated soil properties. Such datasets present unique opportunities to advance the understanding of how soil minerals govern or influence soil properties, processes, and functions. Each XRD pattern within these databases can be treated as a detailed mineral signature of a soil, from which minerals can be identified and quantified. The steps of mineral identification and quantification are notoriously time consuming, and thus not suitable for datasets of this size. If instead the XRD patterns are treated simply as digital signatures, which encode information on both soil mineral (peaks) and amorphous (background) components, then computational, data-driven, approaches such as data mining and cluster analysis become possible. The project is supported by the Macaulay Development Trust and led by its fellow Dr Benjamin Butler, digital mineralogist at the Institute. “We applied machine learning to a dataset of Scottish soil XRD patterns to predict and interpret a range of soil properties. The X-ray diffraction data can be considered a mineralogical signature of each soil sample; therefore, this approach gave us the opportunity to investigate relationships between soil mineralogy and soil properties using data-driven technology for the first time,” Dr Butler explained. “Not only was the machine learning algorithm able to predict several soil properties accurately, but the way in which it selected mineral variables from the data allowed us to evaluate specific mineral contributions to the soil properties. Since minerals are the major component of most soils, this study ultimately showed the utility of machine learning to investigate soil mineralogy – soil property relationships in greater detail. Further application will help us better understand the role of soil minerals in Scotland and beyond.” The departure from conventional methods of analysing soil XRD data contributes to the emerging field of “digital soil mineralogy.” More information on the study and the full paper is available on sciencedirect.com. A new research study aims to demonstrate how the combination of digital X-ray signatures with data-driven approaches can replace the time-consuming elements of expert led mineral identification and allow for new insights into the role of soil minerals in the environment. February 2019 15 Comments?