RNA-Seq Reveals Infection-Related Gene Expression Changes in Phytophthora capsici
Bibliographic record
Abstract
Phytophthora capsici is a soilborne plant pathogen capable of infecting a wide range of plants, including many solanaceous crops. However, genetic resistance and fungicides often fail to manage P. capsici due to limited knowledge on the molecular biology and basis of P. capsici pathogenicity. To begin to rectify this situation, Illumina RNA-Seq was used to perform massively parallel sequencing of three cDNA samples derived from P. capsici mycelia (MY), zoospores (ZO) and germinating cysts with germ tubes (GC). Over 11 million reads were generated for each cDNA library analyzed. After read mapping to the gene models of P. capsici reference genome, 13,901, 14,633 and 14,695 putative genes were identified from the reads of the MY, ZO and GC libraries, respectively. Comparative analysis between two of samples showed major differences between the expressed gene content of MY, ZO and GC stages. A large number of genes associated with specific stages and pathogenicity were identified, including 98 predicted effector genes. The transcriptional levels of 19 effector genes during the developmental and host infection stages of P. capsici were validated by RT-PCR. Ectopic expression in Nicotiana benthamiana showed that P. capsici RXLR and Crinkler effectors can suppress host cell death triggered by diverse elicitors including P. capsici elicitin and NLP effectors. This study provides a first look at the transcriptome and effector arsenal of P. capsici during the important pre-infection stages.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".