Genome‐wide binding analysis of AtGNC and AtCGA1 demonstrates their cross‐regulation and common and specific functions
Bibliographic record
Abstract
), are important regulators in greening, flowering, senescence, and hormone signaling. However, their direct target genes related to these biological processes are poorly characterized. Here, GNC and CGA1 are shown to be transcription activators and by using chromatin immunoprecipitation sequencing (ChIP-seq), 1475 and 638 genes are identified to be associated with GNC and CGA1 binding, respectively. Enrichment of diverse motifs in the peak binding regions for GNC and CGA1 suggests the possibility that these two transcription factors also interact with other transcription factors and in addition genes coding for DNA-binding proteins are highly enriched among GNC- and CGA1-associated genes. Despite the fact that these two GATA factors are known to share a large portion of co-expressed genes, our analysis revealed a low percentage of overlapping binding-associated genes for these two homologues. This suggests a possible cross-regulation between these, which is verified using ChIP-qPCR. The common and specific biological processes regulated by GNC and CGA1 also support this notion. Functional analysis of the binding-associated genes revealed that those encoding transcription factors, E3 ligase, as well as genes with roles in plant development are highly enriched, indicating that GNC and CGA1 mediate complex genetic networks in regulating different aspects of plant growth and development.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".