Evidence for a Common Toolbox Based on Necrotrophy in a Fungal Lineage Spanning Necrotrophs, Biotrophs, Endophytes, Host Generalists and Specialists

The Sclerotiniaceae (Ascomycotina, Leotiomycetes) is a relatively recently evolved lineage of necrotrophic host generalists, and necrotrophic or biotrophic host specialists, some latent or symptomless. We hypothesized that they inherited a basic toolbox of genes for plant symbiosis from their common ancestor. Maintenance and evolutionary diversification of symbiosis could require selection on toolbox genes or on timing and magnitude of gene expression. The genes studied were chosen because their products have been previously investigated as pathogenicity factors in the Sclerotiniaceae. They encode proteins associated with cell wall degradation: acid protease 1 (acp1), aspartyl protease (asps), and polygalacturonases (pg1, pg3, pg5, pg6), and the oxalic acid (OA) pathway: a zinc finger transcription factor (pac1), and oxaloacetate acetylhydrolase (oah), catalyst in OA production, essential for full symptom production in Sclerotinia sclerotiorum. Site-specific likelihood analyses provided evidence for purifying selection in all 8 pathogenicity-related genes. Consistent with an evolutionary arms race model, positive selection was detected in 5 of 8 genes. Only generalists produced large, proliferating disease lesions on excised Arabidopsis thaliana leaves and oxalic acid by 72 hours in vitro. In planta expression of oah was 10–300 times greater among the necrotrophic host generalists than necrotrophic and biotrophic host specialists; pac1 was not differentially expressed. Ability to amplify 6/8 pathogenicity related genes and produce oxalic acid in all genera are consistent with the common toolbox hypothesis for this gene sample. That our data did not distinguish biotrophs from necrotrophs is consistent with 1) a common toolbox based on necrotrophy and 2) the most conservative interpretation of the 3-locus housekeeping gene phylogeny – a baseline of necrotrophy from which forms of biotrophy emerged at least twice. Early oah overexpression likely expands the host range of necrotrophic generalists in the Sclerotiniaceae, while specialists and biotrophs deploy oah, or other as-yet-unknown toolbox genes, differently

VoIDext: Vocabulary and Patterns for Enhancing Interoperable Datasets with Virtual Links

Semantic heterogeneity remains a problem when interoperating with data from sources of different scopes and knowledge domains. Causes for this challenge are context-specific requirements (i.e. no “one model fits all”), different data modelling decisions, domain-specific purposes, and technical constraints. Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is solved, querying and accessing the data of distributed RDF datasets on the Web is not straightforward. This is because of the complex and fastidious process needed to understand how these datasets can be related or linked, and consequently, queried. To address this issue, we propose to extend the existing Vocabulary of Interlinked Datasets (VoID) by introducing new terms such as the Virtual Link Set concept and data model patterns. A virtual link is a connection between resources such as literals and IRIs (Internationalized Resource Identifier) with some commonality where each of these resources is from a different RDF dataset. The links are required in order to understand how to semantically relate datasets. In addition, we describe several benefits of using virtual links to improve interoperability between heterogenous and independent datasets. Finally, we exemplify and apply our approach to multiple world-wide used RDF datasets

How to Develop a Drug Target Ontology: KNowledge Acquisition and Representation Methodology (KNARM)

Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. This era of "big data" provides unprecedented opportunities for data-driven research and "big picture" models. However, in-depth analyses-making use of various data types and data sources and extracting knowledge-have become a more daunting task. This is especially the case in life sciences where simplification and flattening of diverse data types often lead to incorrect predictions. Effective applications of big data approaches in life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding oversimplifications, such as reducing various biological data types to the gene level. A huge hurdle in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited, and significant human expertise is required. In this chapter, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We then describe application of the methodology while implementing the Drug Target Ontology (DTO). We aimed to create an approach, involving domain experts and knowledge engineers, to build useful, comprehensive, consistent ontologies that will enable big data approaches in the domain of drug discovery, without the currently common simplifications