Transcriptome Profiling Revealed Stress-Induced and Disease Resistance Genes Up-Regulated in PRSV Resistant Transgenic Papaya
Bibliographic record
Abstract
Papaya is a productive and nutritious tropical fruit. Papaya Ringspot Virus (PRSV) is the most devastating pathogen threatening papaya production worldwide. Development of transgenic resistant varieties is the most effective strategy to control this disease. However, little is known about the genome-wide functional changes induced by particle bombardment transformation. We conducted transcriptome sequencing of PRSV resistant transgenic papaya SunUp and its PRSV susceptible progenitor Sunset to compare the transcriptional changes in young healthy leaves prior to infection with PRSV. In total, 20,700 transcripts were identified, and 842 differentially expressed genes (DEGs) randomly distributed among papaya chromosomes. Gene ontology (GO) category analysis revealed that microtubule-related categories were highly enriched among these DEGs. Numerous DEGs related to various transcription factors, transporters and hormone biosynthesis showed clear differences between the two cultivars, and most were up-regulated in transgenic papaya. Many known and novel stress-induced and disease-resistance genes were most highly expressed in SunUp, including MYB, WRKY, ERF, NAC, nitrate and zinc transporters, and genes involved in the abscisic acid, salicylic acid, and ethylene signaling pathways. We also identified 67,686 alternative splicing (AS) events in Sunset and 68,455 AS events in SunUp, mapping to 10,994 and 10,995 papaya annotated genes, respectively. GO enrichment for the genes displaying AS events exclusively in Sunset was significantly different from those in SunUp. Transcriptomes in Sunset and transgenic SunUp are very similar with noteworthy differences, which increased PRSV-resistance in transgenic papaya. No detrimental pathways and allergenic or toxic proteins were induced on a genome-wide scale in transgenic SunUp. Our results provide a foundation for unraveling the mechanism of PRSV resistance in transgenic papaya.
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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".