RNA-seq discovery, functional characterization, and comparison of sesquiterpene synthases from Solanum lycopersicum and Solanum habrochaites trichomes
Bibliographic record
Abstract
Solanum lycopersicum and Solanum habrochaites (f. typicum) accession PI127826 emit a variety of sesquiterpenes. To identify terpene synthases involved in the production of these volatile sesquiterpenes, we used massive parallel pyrosequencing (RNA-seq) to obtain the transcriptome of the stem trichomes from these plants. This approach resulted initially in the discovery of six sesquiterpene synthase cDNAs from S. lycopersicum and five from S. habrochaites. Searches of other databases and the S. lycopersicum genome resulted in the discovery of two additional sesquiterpene synthases expressed in trichomes. The sesquiterpene synthases from S. lycopersicum and S. habrochaites have high levels of protein identity. Several of them appeared to encode for non-functional proteins. Functional recombinant proteins produced germacrenes, β-caryophyllene/α-humulene, viridiflorene and valencene from (E,E)-farnesyl diphosphate. However, the activities of these enzymes do not completely explain the differences in sesquiterpene production between the two tomato plants. RT-qPCR confirmed high levels of expression of most of the S. lycopersicum sesquiterpene synthases in stem trichomes. In addition, one sesquiterpene synthase was induced by jasmonic acid, while another appeared to be slightly repressed by the treatment. Our data provide a foundation to study the evolution of terpene synthases in cultivated and wild tomato.
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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".