Genome sequencing, phylogenomics, and population analyses of <i>Tilletia</i> , with recognition of one common bunt species, <i>T. caries</i> (synonym <i>T. laevis</i> ), distinct from dwarf bunt, <i>T. controversa</i>
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".