Cell type identity determines transcriptomic immune responses in <i>Arabidopsis thaliana</i> roots
Bibliographic record
Abstract
Abstract Root pathogens are a major threat in global crop production and protection strategies are required to sustainably enhance the efficiency of root immunity. Our understanding of root immunity is still limited relative to our knowledge of immune responses in leaves. In an effort to reveal the organisation of immunity in roots, we undertook a cell type-specific transcriptome analysis to identify gene networks activated in epidermis, cortex and pericycle cells of Arabidopsis roots upon treatment with two immunity elicitors, the bacterial microbe-associated molecular pattern flagellin, and the endogenous damage-associated molecular pattern Pep1. Our analyses revealed that both elicitors induced immunity gene networks in a cell type-specific manner. Interestingly, both elicitors did not alter cell identity-determining gene networks. Using sophisticated paired motif promoter analyses, we identified key transcription factor pairs involved in the regulation of cell type-specific immunity networks. In addition, our data show that cell identity networks integrate with cell immunity networks to activate cell type-specific immune response according to the functional capabilities of each cell type. Material Distribution Footnote The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors ( www.plantcell.org ) is: Patrick Schäfer ( p.schafer@warwick.ac.uk ).
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) | 0.002 | 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".