Tomato defences modulate not only insect performance but also their gut microbial composition
Bibliographic record
Abstract
Plants protect their tissues from insect herbivory with specialized structures and chemicals, such as cuticles, trichomes, and metabolites contained therein. Bacteria inside the insect gut are also exposed to plant defences and can potentially modify the outcome of plant-insect interactions. To disentangle this complex multi-organism system, we used tomato mutants impaired in the production of plant defences (odorless-2 and jasmonic acid-insensitive1) and two cultivars (Ailsa Craig and Castlemart), exposed them to herbivory by the cabbage looper (Trichoplusia ni H.) and collected the insect frass for bacterial community analysis. While the epicuticular wax and terpene profiles were variable, the leaf fatty acid composition remained consistent among genotypes. Moreover, larval weight confirmed the negative association between plant defences and insect performance. The distinctive frass fatty acid profiles indicated that plant genotype also influences the lipid digestive metabolism of insects. Additionally, comparisons of leaf and insect-gut bacterial communities revealed a limited overlap in bacterial species between the two sample types. Insect bacterial community abundance and diversity were notably reduced in insects fed on the mutants, with Enterobacteriaceae being the predominant group, whereas putatively pathogenic taxa were found in wildtype genotypes. Altogether, these results indicate that plant defences can modulate insect-associated bacterial community composition.
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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".