Non-targeted metabolomics aids in sex pheromone identification: a proof-of-concept study with the triangulate cobweb spider, Steatoda triangulosa
Bibliographic record
Abstract
Targeted metabolomics has been widely used in pheromone research but may miss pheromone components in study organisms that produce pheromones in trace amount and/or lack bio-detectors (e.g., antennae) to readily locate them in complex samples. Here, we used non-targeted metabolomics-together with high-performance liquid chromatography-mass spectrometry (HPLC-MS), gas chromatography-MS, and behavioral bioassays-to unravel the sex pheromone of the triangulate cobweb spider, Steatoda triangulosa. A ternary blend of three contact pheromone components [N-4-methylvaleroyl-O-isobutyroyl-L-serine (5), N-3-methylbutyryl-O-isobutyroyl-L-serine (11), and N-3-methylbutyryl-O-butyroyl-L-serine (12)] elicited courtship by S. triangulosa males as effectively as female web extract. Hydrolysis of 5, 11 and 12 at the ester bond gave rise to two mate-attractant pheromone components [butyric acid (7) and isobutyric acid (8)] which attracted S. triangulosa males as effectively as female webs. Pheromone components 11 and 12 are reported in spiders for the first time, and were discovered only through the use of non-targeted metabolomics and GC-MS. All compounds resemble pheromone components previously identified in widow spiders. Our study provides impetus to apply non-targeted metabolomics for pheromone research in a wide range of animal taxa.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".