Wheat root transcriptional responses against Gaeumannomyces graminis var. tritici
Bibliographic record
Abstract
Abstract Wheat root rot caused by Gaeumannomyces graminis var. tritici (Ggt) results in severe yield losses in wheat production worldwide. However, little is known about the molecular mechanism that regulates systemic symptom development in infected wheat. Fluorescent microscopy observation of the stained wheat roots infected by Ggt showed that lesions were visible when the fungus could be detected in the endodermis, pericycle and phloem at 5 days post inoculation (dpi), and rust symptoms were visible when there was extensive fungal colonization in the root cortex at 6 dpi. Transcriptome sequencing of Ggt-inoculated wheat roots and healthy control root samples was performed at 5 dpi to identify Ggt-induced gene expression changes in wheat roots at the time of lesion formation. A total of 3973 differentially expressed genes (DEGs) were identified, of which 1004 (25.27%) were up-regulated and 2969 (74.73%) were down-regulated in Ggt-inoculated wheat roots compared with those in control roots. GO annotation and KEGG pathway analysis of these DEGs revealed that many of them were associated with pathogen resistance, such as those involved in oxidation-reduction process, tryptophan biosynthesis process, and phenylpropanoid biosynthesis process. Analysis of DEGs revealed that 15 DEGs were involved in cellular regulation, 57 DEGs in signal transduction pathways, and 75 DEGs in cell wall reorganization, and 23 DEGs are pathogenesis-related proteins. Reverse transcription quantitative PCR (RT-qPCR) of 13 of those DEGs showed that these genes may play roles in wheat resistance against Ggt. Overall, this study represents the first transcriptional profiling of wheat roots in response to Ggt infection and further characterization of DEGs identified in this study may lead to better understanding of resistance against take-all in wheat.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".