Genome-wide survey of the F-box/Kelch (FBK) members and molecular identification of a novel FBK gene TaAFR in wheat
Bibliographic record
Abstract
F-box proteins play critical roles in plant responses to biotic/abiotic stresses. In the present study, a total of 68 wheat F-box/Kelch (TaFBK) genes, unevenly distributed across 21 chromosomes and encoding 74 proteins, were identified in EnsemblPlants. Protein sequences were compared with those of Arabidopsis and three cereal species by phylogenetic and domain analyses, where the wheat sequences were resolved into 6 clades. In silico analysis of a digital PCR dataset revealed that TaFBKs were expressed at multiple developmental stages and tissues, and in response to drought and/or heat stresses. The TaFBK19 gene, a homolog of the Attenuated Far-Red Response (AFR) genes in other plant species, and hence named TaAFR, was selected for further analysis. Reverse-transcription quantitative real-time PCR (RT-qPCR) was carried out to determine tissue-specific, hormone and stress (abiotic/biotic) responsive expression patterns. Of interest, TaAFR was expressed most abundantly in the leaves, and its expression in response to leaf rust variants suggests a potential role in compatible vs incompatible rust responses. The protein was predicted to localize in cytosol, but it was shown experimentally to localize in both the cytosol and the nucleus of tobacco. A series of protein interaction studies, starting with a yeast-2-hybrid (Y2H) library screen (wheat leaf infected with incompatible leaf rust pathogens), led to the identification of three TaAFR interacting proteins. Skp1/ASK1-like protein (Skp1) was found to interact with the F-box domain of TaAFR, while ADP-ribosylation factor 2-like isoform X1 (ARL2) and phenylalanine ammonia-lyase (PAL) were shown to interact with its Kelch domain. The data presented herein provides a solid foundation from which the function and metabolic network of TaAFR and other wheat FBKs can be further explored.
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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 | 0.001 | 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) | 0.001 | 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".