The tRNA-degradation pathway impacts the phenotype and metabolome of Arabidopsis thaliana: evidence from atipt2 and atipt9 knockout mutants
Bibliographic record
Abstract
Abstract Isopentenyltransferases (IPTs), including adenosine phosphate-isopentenyltransferases (ATP/ADP-IPTs and AMP-IPTs) and tRNA‐isopentenyltransferases (tRNA-IPTs), are responsible for a rate-limiting step of cytokinin (CK) biosynthesis. tRNA-IPTs, which account for the synthesis of cis-zeatin (cZ)-type CKs, are less understood and often thought to play a housekeeping role or have low activity during plant growth and development. Here, two Arabidopsis tRNA-IPT knockout mutants, atipt2 and atipt9, with independent disturbance of the pathway leading to cisCKs were investigated at the phenotype and metabolite levels at four stages of plant development: first leaf, inflorescence, siliques, and mature seed. Phenotypic deviations were noted in rosette diameter, number of non-rosette leaves, shoot height, flowering time, flower number, carotenoid content, trichome development, and above-ground fresh mass. Hormone profiling by high-performance liquid chromatography - high resolution tandem mass spectrometry (HPLC-HRMS/MS) showed that the atipt2 mutant accumulates lower total cisCKs in the first leaves and in siliques. The atipt9 mutant showed reduced total cisCKs in first leaves, but, during silique development, it had higher levels of cisCKs in than those of the wild type (WT) plants. Additionally, metabolite detection was performed via an untargeted approach using HPLC-HRMS. A total of 33 significant features differing in abundance between ipt mutants and the WT were putatively identified based on database search. Matched metabolites included those that participate in hormone cross-talk, fatty acid synthesis, seed set and germination, and in stress acclimation. Evidence indicates that cisCK production is important for plant growth and development, in ways distinct from CKs produced from de novo pathway.
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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) | 0.001 | 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 | 0.001 |
| 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".