Transcriptome analysis of the white pine blister rust pathogen Cronartium ribicola: de novo assembly, expression profiling, and identification of candidate effectors
Bibliographic record
Abstract
BACKGROUND: The fungus Cronartium ribicola (Cri) is an economically and ecologically important forest pathogen that causes white pine blister rust (WPBR) disease on five-needle pines. To cause stem cankers and kill white pine trees the fungus elaborates a life cycle with five stages of spore development on five-needle pines and the alternate host Ribes plants. To increase our understanding of molecular WP-BR interactions, here we report genome-wide transcriptional profile analysis of C. ribicola using RNA-seq. RESULTS: cDNA libraries were constructed from aeciospore, urediniospore, and western white pine (Pinus monticola) tissues post Cri infection. Over 200 million RNA-seq 100-bp paired-end (PE) reads from rust fungal spores were de novo assembled and a reference transcriptome was generated with 17,880 transcripts that were expressed from 13,629 unigenes. A total of 734 unique proteins were predicted as a part of the Cri secretome from complete open reading frames (ORFs), and 41 % of them were Cronartium-specific. This study further identified a repertoire of candidate effectors and other pathogenicity determinants. Differentially expressed genes (DEGs) were identified to gain an understanding of molecular events important during the WPBR fungus life cycle by comparing Cri transcriptomes at different infection stages. Large-scale changes of in planta gene expression profiles were observed, revealing that multiple fungal biosynthetic pathways were enhanced during mycelium growth inside infected pine stem tissues. Conversely, many fungal genes that were up-regulated at the urediniospore stage appeared to be signalling components and transporters. The secreted fungal protein genes that were up-regulated in pine needle tissues during early infection were primarily associated with cell wall modifications, possibly to mask the rust pathogen from plant defenses. CONCLUSION: This comprehensive transcriptome profiling substantially improves our current understanding of molecular WP-BR interactions. The repertoire of candidate effectors and other putative pathogenicity determinants identified here are valuable for future functional analysis of Cri virulence and pathogenicity.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".