Strigolactone insensitivity affects the hormonal homeostasis in barley
Bibliographic record
Abstract
In response to environmental changes, plants continuously make architectural changes in order to optimize their growth and development. The regulation of plant branching, influenced by environmental conditions and affecting hormone balance and gene expression, is crucial for agronomic purposes due to its direct correlation with yield. Strigolactones (SL), the youngest class of phytohormones, function to shape the architecture of plants by inhibiting axillary outgrowth. Barley plants harboring the mutation in the HvDWARF14 (HvD14) gene, which encodes the SL-specific receptor, produce almost twice as many tillers as wild-type (WT) Sebastian plants. Here, through hormone profiling and comparison of transcriptomic and proteomic changes between 2- and 4-week-old plants of WT and hvd14 genotypes, we elucidate a regulatory mechanism that might affect the tillering of SL-insensitive plants. The analysis showed statistically significant increased cytokinin content and decreased auxin and abscisic acid content in 'bushy' hvd14 compared to WT, which aligns with the commonly known actions of these hormones regarding branching regulation. Further, transcriptomic and proteomic analysis revealed a set of differentially expressed genes (DEG) and abundant proteins (DAP), among which 11.6% and 14.6% were associated with phytohormone-related processes, respectively. Bioinformatics analyses then identified a series of potential SL-dependent transcription factors (TF), which may control the differences observed in the hvd14 transcriptome and proteome. Comparison to available Arabidopsis thaliana data implicates a sub-selection of these TF as being involved in the transduction of SL signal in both monocotyledonous and dicotyledonous plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".