Using transcription of six Puccinia triticina races to identify the effective secretome during infection of wheat
Bibliographic record
Abstract
Wheat leaf rust, caused by the basidiomycete Puccinia triticina, can cause yield losses of up to 20% in wheat producing regions. During infection, the fungus forms haustoria that secrete proteins into the plant cell and effect changes in plant transcription, metabolism, and defense. It is hypothesized that new races emerge as a result of overcoming plant resistance via changes in the secreted effector proteins. To understand gene expression during infection and find genetic differences associated with races, RNA from wheat leaves infected with six different rust races, at 6 days post inoculation, was sequenced using Illumina. As P. triticina is an obligate biotroph, RNA from both the host and fungi were present and separated by alignment to the P. triticina genome and a wheat EST reference. A total of 222,571 rust contigs were assembled from 165 million reads. An examination of the resulting contigs revealed 532 predicted secreted proteins among the transcripts. Of these, 456 were found in all races. Fifteen genes were found with amino acid changes, corresponding to putative avirulence effectors potentially recognized by 11 different leaf rust resistance (Lr) genes. Twelve of the potential avirulence effectors have no homology to known genes. One gene had significant similarity to cerato-platanin, a known fungal elicitor, and another showed similarity to fungal tyrosinase, an enzyme involved in melanin synthesis. Temporal expression profiles were developed for these genes by qRT-PCR and show that the genes expression patterns were consistent between races from infection initiation to just prior to spore eruption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".