Meeting the increasing food and energy demands of a growing population will require the development of ground-breaking strategies that promote sustainable plant production. Host-induced gene silencing has shown great potential for controlling pest and diseases in crop plants. However, while delivery of inhibitory noncoding double-stranded (ds)RNA by transgenic expression is a promising concept, it requires the generation of transgenic crop plants which may cause substantial delay for application strategies depending on the transformability and genetic stability of the crop plant species. Using the agronomically important barley—Fusarium graminearum pathosystem, we alternatively demonstrate that a spray application of a long noncoding dsRNA (791 nt CYP3-dsRNA), which targets the three fungal cytochrome P450 lanosterol C-14α-demethylases, required for biosynthesis of fungal ergosterol, inhibits fungal growth in the directly sprayed (local) as well as the non-sprayed (distal) parts of detached leaves. Unexpectedly, efficient spray-induced control of fungal infections in the distal tissue involved passage of CYP3-dsRNA via the plant vascular system and processing into small interfering (si)RNAs by fungal DICER-LIKE 1 (FgDCL-1) after uptake by the pathogen. We discuss important consequences of this new finding on future RNA-based disease control strategies. Given the ease of design, high specificity, and applicability to diverse pathogens, the use of target-specific dsRNA as an anti-fungal agent offers unprecedented potential as a new plant protection strategy.

Command-line annotation software tools have continuously gained popularity compared to centralized online services due to the worldwide increase of sequenced bacterial genomes. However, results of existing command-line software pipelines heavily depend on taxon-specific databases or sufficiently well annotated reference genomes. Here, we introduce Bakta, a new command-line software tool for the robust, taxon-independent, thorough and, nonetheless, fast annotation of bacterial genomes. Bakta conducts a comprehensive annotation workflow including the detection of small proteins taking into account replicon metadata. The annotation of coding sequences is accelerated via an alignment-free sequence identification approach that in addition facilitates the precise assignment of public database cross-references. Annotation results are exported in GFF3 and International Nucleotide Sequence Database Collaboration (INSDC)-compliant flat files, as well as comprehensive JSON files, facilitating automated downstream analysis. We compared Bakta to other rapid contemporary command-line annotation software tools in both targeted and taxonomically broad benchmarks including isolates and metagenomic-assembled genomes. We demonstrated that Bakta outperforms other tools in terms of functional annotations, the assignment of functional categories and database cross-references, whilst providing comparable wall-clock runtimes. Bakta is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a GPLv3 license at https://github.com/oschwengers/bakta. An accompanying web version is available at https://bakta.computational.bio.

BackgroundSugar beet (Beta vulgaris) is a crop cultivated for its high content in sugar, but it is vulnerable to many soil-borne pathogens. One of them is the basidiomycete Rhizoctonia solani. This fungal species has a compatibility system regulating hyphal fusions (anastomosis). Consequently, R. solani species are categorized in anastomosis groups (AGs). AG2-2IIIB isolates are most aggressive on sugar beet. In the present study, we report on the draft genome of R. solani AG2-2IIIB using the Illumina technology. Genome analysis, interpretation and comparative genomics of five sequenced R. solani isolates were carried out.ResultsThe draft genome of R. solani AG2-2IIIB has an estimated size of 56.02 Mb. In addition, two normalized EST libraries were sequenced. In total 20,790 of 21,980 AG2-2IIIB isotigs (transcript isoforms) were mapped on the genome with more than 95 % sequence identity. The genome of R. solani AG2-2IIIB was predicted to harbor 11,897 genes and 4908 were found to be isolate-specific. R. solani AG2-2IIIB was predicted to contain 1142 putatively secreted proteins and 473 of them were found to be unique for this isolate. The R. solani AG2-2IIIB genome encodes a high number of carbohydrate active enzymes. The highest numbers were observed for the polysaccharide lyases family 1 (PL-1), glycoside hydrolase family 43 (GH-43) and carbohydrate estarase family 12 (CE-12). Transcription analysis of selected genes representing different enzyme clades revealed a mixed pattern of up- and down-regulation six days after infection on sugar beets featuring variable levels of resistance compared to mycelia of the fungus grown in vitro.ConclusionsThe established R. solani AG2-2IIIB genome and EST sequences provide important information on the gene content, gene structure and transcriptional activity for this sugar beet pathogen. The enriched genomic platform provides an important platform to enhance our understanding of R. solani biology.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2561-1) contains supplementary material, which is available to authorized users.

The EDGAR platform, a web server providing databases of precomputed orthology data for thousands of microbial genomes, is one of the most established tools in the field of comparative genomics and phylogenomics. Based on precomputed gene alignments, EDGAR allows quick identification of the differential gene content, i.e. the pan genome, the core genome, or singleton genes. Furthermore, EDGAR features a wide range of analyses and visualizations like Venn diagrams, synteny plots, phylogenetic trees, as well as Amino Acid Identity (AAI) and Average Nucleotide Identity (ANI) matrices. During the last few years, the average number of genomes analyzed in an EDGAR project increased by two orders of magnitude. To handle this massive increase, a completely new technical backend infrastructure for the EDGAR platform was designed and launched as EDGAR3.0. For the calculation of new EDGAR3.0 projects, we are now using a scalable Kubernetes cluster running in a cloud environment. A new storage infrastructure was developed using a file-based high-performance storage backend which ensures timely data handling and efficient access. The new data backend guarantees a memory efficient calculation of orthologs, and parallelization has led to drastically reduced processing times. Based on the advanced technical infrastructure new analysis features could be implemented including POCP and FastANI genomes similarity indices, UpSet intersecting set visualization, and circular genome plots. Also the public database section of EDGAR was largely updated and now offers access to 24,317 genomes in 749 free-to-use projects. In summary, EDGAR 3.0 provides a new, scalable infrastructure for comprehensive microbial comparative gene content analysis. The web server is accessible at http://edgar3.computational.bio.

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Summary CYP3RNA, a double‐stranded (ds)RNA designed to concomitantly target the two sterol 14α‐demethylase genes FgCYP51A and FgCYP51B and the fungal virulence factor FgCYP51C, inhibits the growth of the ascomycete fungus Fusarium graminearum (Fg) in vitro and in planta. Here we compare two different methods (setups) of dsRNA delivery, viz. transgene expression (host‐induced gene silencing, HIGS) and spray application (spray‐induced gene silencing, SIGS), to assess the activity of CYP3RNA and novel dsRNA species designed to target one or two FgCYP51 genes. Using Arabidopsis and barley, we found that dsRNA designed to target two FgCYP51 genes inhibited fungal growth more efficiently than dsRNA targeting a single gene, although both dsRNA species reduced fungal infection. Either dsRNA delivery method reduced fungal growth stronger than anticipated from previous mutational knock‐out (KO) strategies, where single gene KO had no significant effect on fungal viability. Consistent with the strong inhibitory effects of the dsRNAs on fungal development in both setups, we detected to a large extent dsRNA‐mediated co‐silencing of respective non‐target FgCYP51 genes. Together, our data further support the valuation that dsRNA applications have an interesting potential for pesticide target validation and gene function studies, apart from their potential for crop protection.

One of the emerging systems in plant–microbe interaction is the study of proteins, referred to as effectors, secreted by microbes in order to modulate host cells function and structure and to promote microbial growth on plant tissue. Current knowledge on fungal effectors derives mainly from biotrophic and hemibiotrophic plant fungal pathogens that have a limited host range. Here, we focus on effectors of Piriformospora indica, a soil borne endophyte forming intimate associations with roots of a wide range of plant species. Complete genome sequencing provides an opportunity to investigate the role of effectors during the interaction of this mutualistic fungus with plants. We describe in silico analyses to predict effectors of P. indica and we explore effector features considered here to mine a high priority protein list for functional analysis.

Command line annotation software tools have continuously gained popularity compared to centralized online services due to the worldwide increase of sequenced bacterial genomes. However, results of existing command line software pipelines heavily depend on taxon specific databases or sufficiently well annotated reference genomes. Here, we introduce Bakta, a new command line software tool for the robust, taxon-independent, thorough and nonetheless fast annotation of bacterial genomes. Bakta conducts a comprehensive annotation workflow including the detection of small proteins taking into account replicon metadata. The annotation of coding sequences is accelerated via an alignment-free sequence identification approach that in addition facilitates the precise assignment of public database cross references. Annotation results are exported in GFF3 and INSDC-compliant flat files as well as comprehensive JSON files facilitating automated downstream analysis. We compared Bakta to other rapid contemporary command line annotation software tools in both targeted and taxonomically broad benchmarks including isolates and metagenomic-assembled genomes. We demonstrated that Bakta outperforms other tools in terms of functional annotations, the assignment of functional categories and database cross-references whilst providing comparable wall clock runtimes. Bakta is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a GPLv3 license at https://github.com/oschwengers/bakta. An accompanying web version is available at https://bakta.computational.bio.