Microbial-type terpene synthase genes occur widely in nonseed land plants, but not in seed plants
Bibliographic record
Abstract
The vast abundance of terpene natural products in nature is due to enzymes known as terpene synthases (TPSs) that convert acyclic prenyl diphosphate precursors into a multitude of cyclic and acyclic carbon skeletons. Yet the evolution of TPSs is not well understood at higher levels of classification. Microbial TPSs from bacteria and fungi are only distantly related to typical plant TPSs, whereas genes similar to microbial TPS genes have been recently identified in the lycophyte Selaginella moellendorffii The goal of this study was to investigate the distribution, evolution, and biochemical functions of microbial terpene synthase-like (MTPSL) genes in other plants. By analyzing the transcriptomes of 1,103 plant species ranging from green algae to flowering plants, putative MTPSL genes were identified predominantly from nonseed plants, including liverworts, mosses, hornworts, lycophytes, and monilophytes. Directed searching for MTPSL genes in the sequenced genomes of a wide range of seed plants confirmed their general absence in this group. Among themselves, MTPSL proteins from nonseed plants form four major groups, with two of these more closely related to bacterial TPSs and the other two to fungal TPSs. Two of the four groups contain a canonical aspartate-rich "DDxxD" motif. The third group has a "DDxxxD" motif, and the fourth group has only the first two "DD" conserved in this motif. Upon heterologous expression, representative members from each of the four groups displayed diverse catalytic functions as monoterpene and sesquiterpene synthases, suggesting these are important for terpene formation in nonseed plants.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".