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Some species of Tilletia are responsible for diseases in economically important crops, such as wheat and rice. In this study, we sequenced, assembled, and annotated 22 new genomes for Tilletia , with a focus on species causing dwarf bunt (DB; T. controversa ), common bunt (CB; T. caries and T. laevis ), and rice kernel smut (RKS; T. horrida ). We present the first genomes for four other species ( T. bromi, T. fusca, T. goloskokovii , and T. rugispora ), resulting in the largest and most diverse sample of Tilletia genomes studied to date. Depending on the species and strain, the assembly size ranged from 24.3 to 30.5 Mb and gene prediction resulted in 7138 to 8261 gene models per genome. Phylogenomic analyses with hundreds to thousands of genes revealed significant support for the relationships among certain Tilletia taxa and validated findings of previous molecular studies that employed a small number of genes. Further population-level analyses showed two distinct populations of DB and CB: T. controversa (DB) as a single population and another intermixed population of T. caries and T. laevis (CB). No evidence of geographic isolation was observed within these populations. Our phylogenomic analyses also supported previous multigene hypotheses that multiple lineages of Tilletia may cause RKS. Collectively, our results suggest that taxonomic revisions are needed for the RKS-causing pathogens and provide convincing evidence for formally recognizing the CB-causing taxa as one species, named T. caries (synonym T. laevis ). Overall, our study significantly enhances genomic resources for Tilletia , offers insights into phylogenetic relationships and population structure, and provides whole genome sequences for future studies.

Fungal and oomycete plant pathogens are responsible for the devastation of various ecosystems such as forest and crop species worldwide. In an effort to protect such natural resources for food, lumber, etc., early detection of non-indigenous phytopathogenic fungi in new areas is a key approach in managing threats at their source of introduction. A workflow was developed using high-throughput sequencing (HTS), more specifically metabarcoding, a method for rapid and higher throughput species screening near high-risk areas, and over larger geographical spaces. Biomonitoring of fungal and oomycete entities of plant pathogens (e.g., airborne spores) regained from environmental samples and their processing by metabarcoding is thoroughly described here. The amplicon-based approach goes from DNA and sequencing library preparation using custom-designed polymerase chain reaction (PCR) fusion primers that target the internal transcribed spacer 1 (ITS1) from fungi and oomycetes and extends to multiplex HTS with the Ion Torrent platform. In addition, a brief and simplified overview of the bioinformatics analysis pipeline and other available tools required to process amplicon sequences is also included. The raw data obtained and processed enable users to select a bioinformatics pipeline in order to directly perform biodiversity, presence/absence, geographical distribution, and abundance analyses through the tools suggested, which allows for accelerated identification of phytopathogens of interest., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Several fungi classified in the genus Tilletia are well-known to infect grass species including wheat ( Triticum ). Tilletia indica is a highly unwanted wheat pathogen causing Karnal bunt, subject to quarantine regulations in many countries. Historically, suspected Karnal bunt infections were identified by morphology, a labour-intensive process to rule out other tuberculate-spored species that may be found as contaminants in grain shipments, and the closely-related pathogen T. walkeri on ryegrass ( Lolium ). Molecular biology advances have brought numerous detection tools to discriminate Tilletia congeners (PCR, qPCR, etc.). While those tests may help to identify T. indica more rapidly, they share weaknesses of targeting insufficiently variable markers or lacking sensitivity in a zero-tolerance context. A recent approach used comparative genomics to identify unique regions within target species, and qPCR assays were designed in silico. This study validated four qPCR tests based on single-copy genomic regions and with highly sensitive limits of detection (~200 fg), two to detect T. indica and T. walkeri separately, and two newly designed, targeting both species as a complex. The assays were challenged with reference DNA of the targets, their close relatives, other crop pathogens, the wheat host, and environmental specimens, ensuring a high level of specificity for accurate discrimination.