Paraburkholderia tropica Primes a Multilayered Transcriptional Defense Response to the Nematode Meloidogyne spp. in Tomato
Bibliographic record
Abstract
Meloidogyne causes a devastating disease known as root-knot that affects tomatoes and other cash crops worldwide. Conversely, Paraburkholderia tropica has proven beneficial in mitigating the effects of various pathogens in plants. We aimed to unravel the molecular events that underlie the beneficial effects of the bacterium and the detrimental impacts of the nematode when inoculated separately or together in tomato plants. The transcriptional responses induced by P. tropica (TB group (tomato-bacteria group)), Meloidogyne spp. (TN group (tomato-nematode group)) or by the two agents (TBN group (tomato-bacteria-nematode group)) in tomato were assessed by RNA-seq. We implemented a transcript discovery pipeline which allowed the identification of 2283 putative novel transcripts. Differential expression analysis revealed that upregulated transcripts were much more numerous than downregulated ones. At the gene ontology level, the most activated term was ‘hydrolase activity acting on ester bonds’ in all groups. In addition, when both microbes were inoculated together, ‘hydrolase activity acting on O-glycosyl compounds’ was activated. This finding suggests defense responses related to lipid and carbohydrate metabolism, membrane remodeling and signal transduction. Notably, defense genes, transcription factors and protein kinases stood out. Differentially expressed transcripts suggest the activation of a multifaceted plant defense response against the nematode occurred, which was exacerbated by pre-inoculation of P. tropica.
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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 | 0.000 |
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".