Cellulose Nanocrystals Protect Plants from Pathogen Infection
Bibliographic record
Abstract
Plants are capable of mounting an immune defense in response to infection, wounding, or environmental stress. The immune response of a plant is often mediated through the recognition of conserved molecular motifs associated with stress. Treating plants with these molecular motifs prior to exposure to stress, termed immune priming, can improve plant resilience to subsequent stressors. Here, we demonstrate the use of cellulose nanocrystals (CNCs) as an immune priming tool for Arabidopsis thaliana combining phenotypic and transcriptomic data to validate the efficacy of CNCs as an immune primer. Pretreatment of Arabidopsis with CNCs reduces the level of infection by the pathogen Pseudomonas syringae up to 65%. RNA sequencing shows that treatment of Arabidopsis with CNCs results in deep transcriptional reprogramming, perturbing over 1300 genes, most of which are associated with immune regulation. We hypothesize plants recognize CNCs as cell-wall damage, potentially explaining the observed immune response. Finally, physiological characterization of the plant response to CNCs demonstrates minimal effects on plant growth without inducing callose deposition or generation of reactive oxygen species. This work provides a foundation for future investigation of cellulose-based nanomaterials as immune primers, particularly in crop species. Given their ease of synthesis, low cost, degradability, and bio/environmental compatibility, cellulose-based materials could serve as a complement to conventional biocides for pathogen control.
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".