ORCID Profile
0000-0002-8392-2822
Current Organisation
University Of Strathclyde
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Springer Science and Business Media LLC
Date: 09-2009
DOI: 10.1038/NATURE08358
Abstract: Phytophthora infestans is the most destructive pathogen of potato and a model organism for the oomycetes, a distinct lineage of fungus-like eukaryotes that are related to organisms such as brown algae and diatoms. As the agent of the Irish potato famine in the mid-nineteenth century, P. infestans has had a tremendous effect on human history, resulting in famine and population displacement. To this day, it affects world agriculture by causing the most destructive disease of potato, the fourth largest food crop and a critical alternative to the major cereal crops for feeding the world's population. Current annual worldwide potato crop losses due to late blight are conservatively estimated at $6.7 billion. Management of this devastating pathogen is challenged by its remarkable speed of adaptation to control strategies such as genetically resistant cultivars. Here we report the sequence of the P. infestans genome, which at approximately 240 megabases (Mb) is by far the largest and most complex genome sequenced so far in the chromalveolates. Its expansion results from a proliferation of repetitive DNA accounting for approximately 74% of the genome. Comparison with two other Phytophthora genomes showed rapid turnover and extensive expansion of specific families of secreted disease effector proteins, including many genes that are induced during infection or are predicted to have activities that alter host physiology. These fast-evolving effector genes are localized to highly dynamic and expanded regions of the P. infestans genome. This probably plays a crucial part in the rapid adaptability of the pathogen to host plants and underpins its evolutionary potential.
Publisher: Wiley
Date: 03-05-2000
DOI: 10.1111/J.1469-8137.2011.03736.X
Abstract: • A detailed molecular understanding of how oomycete plant pathogens evade disease resistance is essential to inform the deployment of durable resistance (R) genes. • Map-based cloning, transient expression in planta, pathogen transformation and DNA sequence variation across erse isolates were used to identify and characterize PiAVR2 from potato late blight pathogen Phytophthora infestans. • PiAVR2 is an RXLR-EER effector that is up-regulated during infection, accumulates at the site of haustoria formation, and is recognized inside host cells by potato protein R2. Expression of PiAVR2 in a virulent P. infestans isolate conveys a gain-of-avirulence phenotype, indicating that this is a dominant gene triggering R2-dependent disease resistance. PiAVR2 presence/absence polymorphisms and differential transcription explain virulence on R2 plants. Isolates infecting R2 plants express PiAVR2-like, which evades recognition by R2. PiAVR2 and PiAVR2-like differ in 13 amino acids, eight of which are in the C-terminal effector domain one or more of these determines recognition by R2. Nevertheless, few polymorphisms were observed within each gene in pathogen isolates, suggesting limited selection pressure for change within PiAVR2 and PiAVR2-like. • Our results direct a search for R genes recognizing PiAVR2-like, which, deployed with R2, may exert strong selection pressure against the P. infestans population.
Publisher: Springer Science and Business Media LLC
Date: 14-09-2019
DOI: 10.1007/S11306-019-1588-0
Abstract: A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike. To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science. This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform.
Publisher: Humana Press
Date: 2014
DOI: 10.1007/978-1-62703-986-4_4
Abstract: High-throughput sequencing is an increasingly accessible tool for cataloging gene complements of plant pathogens and their hosts. It has had great impact in plant pathology, enabling rapid acquisition of data for a wide range of pathogens and hosts, leading to the selection of novel candidate effector proteins, and/or associated host targets (Bart et al., Proc Nat Acad Sci U S A doi:10.1073 nas.1208003109, 2012 Agbor and McCormick, Cell Microbiol 13:1858-1869, 2011 Fabro et al., PLoS Pathog 7:e1002348, 2011 Kim et al., Mol Plant Pathol 2:715-730, 2011 Kimbrel et al., Mol Plant Pathol 12:580-594, 2011 O'Brien et al., Curr Opin Microbiol 14:24-30, 2011 Vleeshouwers et al., Annu Rev Phytopathol 49:507-531, 2011 Sarris et al., Mol Plant Pathol 11:795-804, 2010 Boch and Bonas, Annu Rev Phytopathol 48:419-436, 2010 Mcdermott et al., Infect Immun 79:23-32, 2011).Identification of candidate effectors from genome data is not different from classification in any other high-content or high-throughput experiment. The primary aim is to discover a set of qualitative or quantitative sequence characteristics that discriminate, with a defined level of certainty, between proteins that have previously been identified as being either "effector" (positive) or "not effector" (negative). Combination of these characteristics in a mathematical model, or classifier, enables prediction of whether a protein is or is not an effector, with a defined level of certainty. High-throughput screening of the gene complement is then performed to identify candidate effectors this may seem straightforward, but it is unfortunately very easy to identify seemingly persuasive candidate effectors that are, in fact, entirely spurious.The main sources of danger in this area of statistical modeling are not entirely independent of each other, and include: inappropriate choice of classifier model poor selection of reference sequences (known positive and negative ex les) poor definition of classes (what is, and what is not, an effector) inadequate training s le size poor model validation and lack of adequate model performance metrics (Xia et al., Metabolomics doi:10.1007/s11306-012-0482-9, 2012). Many studies fail to take these issues into account, and thereby fail to discover anything of true significance or, worse, report spurious findings that are impossible to validate. Here we summarize the impact of these issues and present strategies to assist in improving design and evaluation of effector classifiers, enabling robust scientific conclusions to be drawn from the available data.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Leighton Pritchard.