The Expression of Cytoplasmic Effectors by <i>Phytophthora infestans</i> in Potato Leaves and Tubers Is Organ-Biased
Bibliographic record
Abstract
Potato late blight is a devastating disease caused by Phytophthora infestans that affects organs, such as leaves and tubers. Previous studies of organ specificity in this interaction have mainly addressed the plant side (i.e., screening for resistance to late blight in potato leaves versus tubers). However, the extent of the organ specificity of P. infestans virulence mechanisms remains understudied. Here, we investigated the extent of organ-specific expression of effector genes using RNA-seq by contrasting the infection of leaves and tubers of potato (cultivar Russet Burbank) by the P. infestans strain 1306. We focused on RxLR and Crinkler (CRN) effectors to obtain insight on genes putatively involved in P. infestans virulence exhibiting differential expression during leaf versus tuber infection. Our results indicated that (i) the proportion of differentially expressed genes (DEGs) increased over time; (ii) a high proportion of effector genes was differentially expressed at one or more time points (34% of RxLRs and 58% of CRNs); and (iii) some RxLR and CRN families appeared to host more DEGs than others. Quantitative reverse transcription PCR (RT-qPCR) was also used to assess the expression of seven RxLR effectors in a time-course experiment, allowing us to validate their organ-biased profiles. Organ-specific effector genes may perform distinct virulence roles at the organ level, which should be considered when performing effectoromics or any infection study. These results bring new insights into the organ specificity of the potato- P. infestans interaction that could lead to improved potato-breeding programs using effectoromics-based approaches. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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 | 0.000 |
| 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".