Copy Number Variation and Transcriptional Polymorphisms of Phytophthora sojae RXLR Effector Genes Avr1a and Avr3a
Bibliographic record
Abstract
The importance of segmental duplications and copy number variants as a source of genetic and phenotypic variation is gaining greater appreciation, in a variety of organisms. Now, we have identified the Phytophthora sojae avirulence genes Avr1a and Avr3a and demonstrate how each of these Avr genes display copy number variation in different strains of P. sojae. The Avr1a locus is a tandem array of four near-identical copies of a 5.2 kb DNA segment. Two copies encoding Avr1a are deleted in some P. sojae strains, causing changes in virulence. In other P. sojae strains, differences in transcription of Avr1a result in gain of virulence. For Avr3a, there are four copies or one copy of this gene, depending on the P. sojae strain. In P. sojae strains with multiple copies of Avr3a, this gene occurs within a 10.8 kb segmental duplication that includes four other genes. Transcriptional differences of the Avr3a gene among P. sojae strains cause changes in virulence. To determine the extent of duplication within the superfamily of secreted proteins that includes Avr1a and Avr3a, predicted RXLR effector genes from the P. sojae and the P. ramorum genomes were compared by counting trace file matches from whole genome shotgun sequences. The results indicate that multiple, near-identical copies of RXLR effector genes are prevalent in oomycete genomes. We propose that multiple copies of particular RXLR effectors may contribute to pathogen fitness. However, recognition of these effectors by plant immune systems results in selection for pathogen strains with deleted or transcriptionally silenced gene copies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".