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Increasing concerns about public health and safety after covid-19 have raised pathogen studies, especially in aquatic environments. However, the extent to how different location and human activities affect geographic occurrence and distribution of pathogens in response to agricultural pollution, boat tourism disturbances and municipal wastewater inflow in a degraded lake remains unclear. Since the surrounding residents depend on the lake for their livelihood, understanding the pathogens reserved in lake sediment and the regulation possibility by environmental factors are challenges with far-reaching significance. Results showed that 187 pathogens were concurrently shared by the nine sediment samples, with Salmonella enterica and Pseudomonas aeruginosa being the most abundant. The similar composition of the pathogens suggests that lake sediment may act as reservoirs of generalist pathogens which may pose infection risk to a wide range of host species. Of the four virulence factors (VFs) types analyzed, offensive VFs were dominant (>46 % on average) in all samples, with dominant subtypes including adherence, secretion systems and toxins. Notably, the lake sediments under the impact of agricultural use (g1) showed significantly higher diversity and abundance of pathogen species and VFs than those under the impact of boat tourism (g2) and/or municipal wastewater inflow with reed marshes filtration (g3). From the co-occurrence networks, pathogens and pesticides, aggregate fractions, EC, pH, phosphatase have strong correlations. Strong positive correlations between pathogens and diazinon in g1 and ppDDT in g2 and g3 suggest higher pesticide-pathogen co-exposure risk. These findings highlight the need to explore pathogen - environmental factor interaction mechanisms in the human-impacted water environments where the control of pathogen invasion by environmental factors may accessible.

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The quantity and complexity of data being generated and published in biology has increased substantially, but few methods exist for capturing knowledge about phenotypes derived from molecular interactions between diverse groups of species, in such a way that is amenable to data-driven biology and research. To improve access to this knowledge, we have constructed a framework for the curation of the scientific literature studying interspecies interactions, using data curated for the Pathogen-Host Interactions database (PHI-base) as a case study. The framework provides a curation tool, phenotype ontology, and controlled vocabularies to curate pathogen-host interaction data, at the level of the host, pathogen, strain, gene, and genotype. The concept of a multispecies genotype, the 'metagenotype,' is introduced to facilitate capturing changes in the disease-causing abilities of pathogens, and host resistance or susceptibility, observed by gene alterations. We report on this framework and describe PHI-Canto, a community curation tool for use by publication authors.

The increasingly vast amount of data being produced in research communities can be difficult to manage, making it challenging for both humans and computers to organise and connect information from different sources. Currently, software tools that allow authors to curate peer-reviewed life science publications are designed solely for single species, or closely related species that do not interact. Although most research communities are striving to make their data FAIR (Findable, Accessible, Interoperable and Reusable), it is particularly difficult to curate detailed information based on interactions between two or more species (interspecies), such as pathogen-host interactions. As a result, there was a lack of tools to support multi-species interaction databases, leading to a reliance on labour-intensive curation methods. To address this problem, Cuzick et al. used the Pathogen-Host Interactions database (PHI-base), which curates knowledge from the text, tables and figures published in over 200 journals, as a case study. A framework was developed that could capture the many observable traits (phenotype annotations) for interactions and link them directly to the combination of genotypes involved in those interactions across multiple scales ­ ranging from microscopic to macroscopic. This demonstrated that it was possible to build a framework of software tools to enable curation of interactions between species in more detail than had been done before. Cuzick et al. developed an online tool called PHI-Canto that allows any researcher to curate published pathogen-host interactions between almost any known species. An ontology ­ a collection of concepts and their relations ­ was created to describe the outcomes of pathogen-host interactions in a standardised way. Additionally, a new concept called the 'metagenotype' was developed which represents the combination of a pathogen and a host genotype and can be easily annotated with the phenotypes arising from each interaction. The newly curated multi-species FAIR data on pathogen-host interactions will enable researchers in different disciplines to compare and contrast interactions across species and scales. Ultimately, this will assist the development of new approaches to reduce the impact of pathogens on humans, livestock, crops and ecosystems with the aim of decreasing disease while increasing food security and biodiversity. The framework is potentially adoptable by any research community investigating interactions between species and could be adapted to explore other harmful and beneficial interspecies interactions.

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Interactions between proteins underlie all aspects of complex biological mechanisms. Therefore, methodologies based on complex network analyses can facilitate identification of promising candidate genes involved in phenotypes of interest and put this information into appropriate contexts. To facilitate discovery and gain additional insights into globally important pathogenic fungi, we have reconstructed computationally inferred interactomes using an interolog and domain-based approach for 15 diverse Ascomycete fungal species, across nine orders, specifically Aspergillus fumigatus, Bipolaris sorokiniana, Blumeria graminis f. sp. hordei, Botrytis cinerea, Colletotrichum gloeosporioides, Colletotrichum graminicola, Fusarium graminearum, Fusarium oxysporum f. sp. lycopersici, Fusarium verticillioides, Leptosphaeria maculans, Magnaporthe oryzae, Saccharomyces cerevisiae, Sclerotinia sclerotiorum, Verticillium dahliae, and Zymoseptoria tritici. Network cartography analysis was associated with functional patterns of annotated genes linked to the disease-causing ability of each pathogen. In addition, for the best annotated organism, namely F. graminearum, the distribution of annotated genes with respect to network structure was profiled using a random walk with restart algorithm, which suggested possible co-location of virulence-related genes in the protein-protein interaction network. In a second 'use case' study involving two networks, namely B. cinerea and F. graminearum, previously identified small silencing plant RNAs were mapped to their targets. The F. graminearum phenotypic network analysis implicates eight B. cinerea targets and 35 F. graminearum predicted interacting proteins as prime candidate virulence genes for further testing. All 15 networks have been made accessible for download at www.phi-base.org providing a rich resource for major crop plant pathogens.

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The Rhizoctonia solani species complex is comprised of strains belonging to different anastomosis groups and causes diseases in several economically important crops, including rice. However, individuals within same anastomosis group exhibit distinct morphological and pathological differences on the same host. In this study, we have sequenced the genome of two aggressive Indian strains (BRS11 and BRS13) belonging to AG1-IA anastomosis group and compared them with the available genome of R. solani AG1-IA. We identified several SNPs and Indels in both of these genomes, in comparison to the AG1-IA genome. Furthermore, we observed expansion and emergence of orthogroups in these Indian strains and identified those potentially associated with pathogenesis. Amongst them, transposable elements, cell wall degrading enzymes, transcription factors, and oxalate decarboxylase were noteworthy. The current study unravels genetic variations and identifies genes that might account for pathogenicity variations amongst R. solani strains.

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BACKGROUND: The soil dwelling saprotrophic non-pathogenic fungus Fusarium venenatum, routinely used in the commercial fermentation industry, is phylogenetically closely related to the globally important cereal and non-cereal infecting pathogen F. graminearum. This study aimed to sequence, assemble and annotate the F. venenatum (strain A3/5) genome, and compare this genome with F. graminearum. RESULTS: Using shotgun sequencing, a 38,660,329 bp F. venenatum genome was assembled into four chromosomes, and a 78,618 bp mitochondrial genome. In comparison to F. graminearum, the predicted gene count of 13,946 was slightly lower. The F. venenatum centromeres were found to be 25% smaller compared to F. graminearum. Chromosome length was 2.8% greater in F. venenatum, primarily due to an increased abundance of repetitive elements and transposons, but not transposon diversity. On chromosome 3 a major sequence rearrangement was found, but its overall gene content was relatively unchanged. Unlike homothallic F. graminearum, heterothallic F. venenatum possessed the MAT1-1 type locus, but lacked the MAT1-2 locus. The F. venenatum genome has the type A trichothecene mycotoxin TRI5 cluster, whereas F. graminearum has type B. From the F. venenatum gene set, 786 predicted proteins were species-specific versus NCBI. The annotated F. venenatum genome was predicted to possess more genes coding for hydrolytic enzymes and species-specific genes involved in the breakdown of polysaccharides than F. graminearum. Comparison of the two genomes reduced the previously defined F. graminearum-specific gene set from 741 to 692 genes. A comparison of the F. graminearum versus F. venenatum proteomes identified 15 putative secondary metabolite gene clusters (SMC), 109 secreted proteins and 38 candidate effectors not found in F. venenatum. Five of the 15 F. graminearum-specific SMCs that were either absent or highly divergent in the F. venenatum genome showed increased in planta expression. In addition, two predicted F. graminearum transcription factors previously shown to be required for fungal virulence on wheat plants were absent or exhibited high sequence divergence. CONCLUSIONS: This study identifies differences between the F. venenatum and F. graminearum genomes that may contribute to contrasting lifestyles, and highlights the repertoire of F. graminearum-specific candidate genes and SMCs potentially required for pathogenesis.

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There is no comprehensive storage for generated mutants of Fusarium graminearum or data associated with these mutants. Instead, researchers relied on several independent and non-integrated databases. FgMutantDb was designed as a simple spreadsheet that is accessible globally on the web that will function as a centralized source of information on F. graminearum mutants. FgMutantDb aids in the maintenance and sharing of mutants within a research community. It will serve also as a platform for disseminating prepublication results as well as negative results that often go unreported. Additionally, the highly curated information on mutants in FgMutantDb will be shared with other databases (FungiDB, Ensembl, PhytoPath, and PHI-base) through updating reports. Here we describe the creation and potential usefulness of FgMutantDb to the F. graminearum research community, and provide a tutorial on its use. This type of database could be easily emulated for other fungal species.

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Deciphering plant-microbe interactions is a promising aspect to understand the benefits and the pathogenic effect of microbes and crop improvement. The advancement in sequencing technologies and various 'omics' tool has impressively accelerated the research in biological sciences in this area. The recent and ongoing developments provide a unique approach to describing these intricate interactions and test hypotheses. In the present review, we discuss the role of plant-pathogen interaction in crop improvement. The plant innate immunity has always been an important aspect of research and leads to some interesting information like the adaptation of unique immune mechanisms of plants against pathogens. The development of new techniques in the post - genomic era has greatly enhanced our understanding of the regulation of plant defense mechanisms against pathogens. The present review also provides an overview of beneficial plant-microbe interactions with special reference to Agrobacterium tumefaciens-plant interactions where plant derived signal molecules and plant immune responses are important in pathogenicity and transformation efficiency. The construction of various Genome-scale metabolic models of microorganisms and plants presented a better understanding of all metabolic interactions activated during the interactions. This review also lists the emerging repertoire of phytopathogens and its impact on plant disease resistance. Outline of different aspects of plant-pathogen interactions is presented in this review to bridge the gap between plant microbial ecology and their immune responses.

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Pathogen-Host interaction data is core to our understanding of disease processes and their molecular/genetic bases. Facile access to such core data is particularly important for the plant sciences, where individual genetic and phenotypic observations have the added complexity of being dispersed over a wide diversity of plant species vs. the relatively fewer host species of interest to biomedical researchers. Recently, an international initiative interested in scholarly data publishing proposed that all scientific data should be "FAIR"-Findable, Accessible, Interoperable, and Reusable. In this work, we describe the process of migrating a database of notable relevance to the plant sciences-the Pathogen-Host Interaction Database (PHI-base)-to a form that conforms to each of the FAIR Principles. We discuss the technical and architectural decisions, and the migration pathway, including observations of the difficulty and/or fidelity of each step. We examine how multiple FAIR principles can be addressed simultaneously through careful design decisions, including making data FAIR for both humans and machines with minimal duplication of effort. We note how FAIR data publishing involves more than data reformatting, requiring features beyond those exhibited by most life science Semantic Web or Linked Data resources. We explore the value-added by completing this FAIR data transformation, and then test the result through integrative questions that could not easily be asked over traditional Web-based data resources. Finally, we demonstrate the utility of providing explicit and reliable access to provenance information, which we argue enhances citation rates by encouraging and facilitating transparent scholarly reuse of these valuable data holdings.