Using Network Extracted Ontologies to Identify Novel Genes with Roles in Appressorium Development in the Rice Blast Fungus Magnaporthe oryzae

PHI-base in 2022: a multi-species phenotype database for Pathogen-Host Interactions

Since 2005, the Pathogen–Host Interactions Database (PHI-base) has manually curated experimentally verified pathogenicity, virulence and effector genes from fungal, bacterial and protist pathogens, which infect animal, plant, fish, insect and/or fungal hosts. PHI-base (www.phi-base.org) is devoted to the identification and presentation of phenotype information on pathogenicity and effector genes and their host interactions. Specific gene alterations that did not alter the in host interaction phenotype are also presented. PHI-base is invaluable for comparative analyses and for the discovery of candidate targets in medically and agronomically important species for intervention. Version 4.12 (September 2021) contains 4387 references, and provides information on 8411 genes from 279 pathogens, tested on 228 hosts in 18, 190 interactions. This provides a 24% increase in gene content since Version 4.8 (September 2019). Bacterial and fungal pathogens represent the majority of the interaction data, with a 54:46 split of entries, whilst protists, protozoa, nematodes and insects represent 3.6% of entries. Host species consist of approximately 54% plants and 46% others of medical, veterinary and/or environmental importance. PHI-base data is disseminated to UniProtKB, FungiDB and Ensembl Genomes. PHI-base will migrate to a new gene-centric version (version 5.0) in early 2022. This major development is briefly described.

Mapping the multiscale structure of biological systems

Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.

Identifying Candida albicans Gene Networks Involved in Pathogenicity

Candida albicans is a normal member of the human microbiome. It is also an opportunistic pathogen, which can cause life-threatening systemic infections in severely immunocompromized individuals. Despite the availability of antifungal drugs, mortality rates of systemic infections are high and new drugs are needed to overcome therapeutic challenges including the emergence of drug resistance. Targeting known disease pathways has been suggested as a promising avenue for the development of new antifungals. However, <30% of C. albicans genes are verified with experimental evidence of a gene product, and the full complement of genes involved in important disease processes is currently unknown. Tools to predict the function of partially or uncharacterized genes and generate testable hypotheses will, therefore, help to identify potential targets for new antifungal development. Here, we employ a network-extracted ontology to leverage publicly available transcriptomics data and identify potential candidate genes involved in disease processes. A subset of these genes has been phenotypically screened using available deletion strains and we present preliminary data that one candidate, PEP8, is involved in hyphal development and immune evasion. This work demonstrates the utility of network-extracted ontologies in predicting gene function to generate testable hypotheses that can be applied to pathogenic systems. This could represent a novel first step to identifying targets for new antifungal therapies.

DDOT: A Swiss Army Knife for Investigating Data-Driven Biological Ontologies

Systems biology requires not only genome-scale data but also methods to integrate these data into interpretable models. Previously, we developed approaches that organize omics data into a structured hierarchy of cellular components and pathways, called a "data-driven ontology." Such hierarchies recapitulate known cellular subsystems and discover new ones. To broadly facilitate this type of modeling, we report the development of a software library called the Data-Driven Ontology Toolkit (DDOT), consisting of a Python package (https://github.com/idekerlab/ddot) to assemble and analyze ontologies and a web application (http://hiview.ucsd.edu) to visualize them. Using DDOT, we programmatically assemble a compendium of ontologies for 652 diseases by integrating gene-disease mappings with a gene similarity network derived from omics data. For example, the ontology for Fanconi anemia describes known and novel disease mechanisms in its hierarchy of 194 genes and 74 subsystems. DDOT provides an easy interface to share ontologies online at the Network Data Exchange.

Genome-Wide Transcriptome Analysis Reveals the Comprehensive Response of Two Susceptible Poplar Sections to Marssonina brunnea Infection

Marssonina leaf spot disease of poplar (MLDP), caused by the hemibiotrophic pathogen Marssonina brunnea, frequently results in damage to many poplar species. In nature, two formae speciales of M. brunnea exist that are susceptible to different poplar subgenera. Marssonina brunnea f. sp. monogermtubi infects poplar hosts from Populus sect. Aigeiros (Aig), while M. brunnea f. sp. multigermtubi always infects poplar hosts from Populus sect. Leuce Duby (Leu). Based on the fungal penetration structures, a comprehensive transcriptomic approach was used to investigate the gene expression patterns of these two poplar subgenera at three crucial infection stages. MLDP significantly altered the expression patterns of many genes involved in mitogen activated protein kinase (MAPKs) and calcium signaling, transcription factors, primary and secondary metabolism, and other processes in both poplar subgenera. However, major differences in gene expression were also observed between the two poplar subgenera. Aig was most responsive at the initial infection stage, while Leu largely interacted with M. brunnea at the necrotrophic phase. Furthermore, the differentially expressed genes (DEGs) involved in pathways related to biotic stress also differed substantially between the two poplar subgenera. Further analysis indicated that the genes involved in cell wall metabolism and phenylpropanoid metabolism were differentially expressed in the progression of the disease. By examining the expression patterns of genes related to the defense against disease, we found that several genes annotated with causing hypersensitive cell death were upregulated at the necrotrophic phase of MLDP, inferring that plant immune response potentially happened at this infection stage. The present research elucidated the potential molecular differences between the two susceptible interaction systems in MLDP and provided novel insight into the temporal regulation of genes during the susceptible response. To the best of our knowledge, this study also constitutes the first to reveal the molecular mechanisms of poplar in response to the transition of hemibiotrophic fungal pathogens from the biotrophic phase to the necrotrophic phase.