Mechanistic insight into acrylate metabolism and detoxification in marine dimethylsulfoniopropionate‐catabolizing bacteria
Bibliographic record
Abstract
Summary Dimethylsulfoniopropionate (DMSP) cleavage, yielding dimethyl sulfide (DMS) and acrylate, provides vital carbon sources to marine bacteria, is a key component of the global sulfur cycle and effects atmospheric chemistry and potentially climate. Acrylate and its metabolite acryloyl‐CoA are toxic if allowed to accumulate within cells. Thus, organisms cleaving DMSP require effective systems for both the utilization and detoxification of acrylate. Here, we examine the mechanism of acrylate utilization and detoxification in Roseobacters. We propose propionate‐CoA ligase (PrpE) and acryloyl‐CoA reductase (AcuI) as the key enzymes involved and through structural and mutagenesis analyses, provide explanations of their catalytic mechanisms. In most cases, DMSP lyases and DMSP demethylases (DmdAs) have low substrate affinities, but AcuIs have very high substrate affinities, suggesting that an effective detoxification system for acylate catabolism exists in DMSP‐catabolizing Roseobacters. This study provides insight on acrylate metabolism and detoxification and a possible explanation for the high Km values that have been noted for some DMSP lyases. Since acrylate/acryloyl‐CoA is probably produced by other metabolism, and AcuI and PrpE are conserved in many organisms across all domains of life, the detoxification system is likely relevant to many metabolic processes and environments beyond DMSP catabolism.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".