Comprehensive transcriptomics analysis reveals the molecular resistance mechanisms of Guanggan (Citrus reticulata) against Phytophthora parasitica infection
Bibliographic record
Abstract
Phytophthora spp. are devastating oomycete pathogens cause severe diseases such as root rot and gummosis, posing a significant threat to global citrus production. Despite their economic impact, the molecular mechanisms underlying citrus- Phytophthora interactions remain poorly understood, limiting the development of resistant cultivars. Citrus reticulata var. Guanggan (GG), a semi-wild citrus genotype, has demonstrated remarkable resistance to Phytophthora infection, making it a valuable resource for disease resistance research. In this study, we performed a comparative transcriptomic analysis of the resistant GG and a susceptible citrus variety, C. sunki var. Ziyang’xiangcheng (XC), following P. parasitica infection. Our results revealed distinct genotype-specific responses, with dynamic differentially expressed genes (DEGs) enriched in key defense pathways, including plant-pathogen interaction, hormone signaling, phenylpropanoid biosynthesis, and endoplasmic reticulum (ER) protein processing. Notably, GG exhibited earlier and stronger induction of defense-related genes, such as chitinase, CaMs / CMLs, PR1 and EDS1 . Functional validation via transient overexpression of eight candidate DEGs (e.g., Ciclev10029431m ( PR1 ), Ciclev10028964m ( chitinase ), and phenylpropanoid biosynthesis related genes) in Nicotiana benthamiana significantly enhanced resistance to P. parasitica , confirming their role in P. parasitica defense. These findings demonstrate the GG’s multi-layered defense strategy, integrating early pathogen recognition, phytohormone signaling, secondary metabolite production, and protein homeostasis. This study identifies key genetic targets for breeding Phytophthora -resistant citrus varieties.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".