A steady-state physiological model for intracellular dimethylsulfoxide in marine phytoplankton
Bibliographic record
Abstract
Environmental context Dimethylsulfoxide (DMSO) is important in the biogeochemical cycle of sulfur. Using a mathematical flux model of DMSO production and loss rates, we find that the high intracellular DMSO concentrations measured in phytoplankton cannot be produced without invoking unrealistically high intracellular concentrations of the precursor dimethylsulfoniopropionate, or much lower phytoplankton cellular efflux rates than currently reported. Our study emphasises the need for further investigations of DMSO fluxes across intracellular and outer cell membranes. Abstract Despite 20+ years of research, the mechanisms whereby marine phytoplankton accumulate high dimethylsulfoxide (DMSO) concentrations (up to 1–70 mmol per litre of cell volume) are still puzzling. In order to evaluate reported intracellular DMSO concentrations, we constructed a kinetic steady-state rate model of intracellular DMSO concentrations in microalgae based on reported DMSO production from the oxidation of dimethylsulfoniopropionate (DMSP) and loss by diffusion out of the cell. Based on measured rates of DMSO diffusion across the outer cell membrane of model algal species, the steady-state model indicates that sustaining intracellular DMSO concentrations in the millimolar range by the oxidation of intracellular DMSP would require steady-state intracellular DMSP concentrations that are 40 to 10 000 times higher than values measured in prymnesiophytes and diatoms, high- and low-DMSP algal groups. However, if DMSO is mainly produced within the chloroplast by the oxidation of DMSP by photosynthetically produced reactive oxygen species, it would have to diffuse through multiple chloroplast membranes before being lost from the cell across the outer membrane. Consequently, its loss rate might be considerably slower than our model predicts, allowing the build-up of higher intracellular DMSO concentrations. Possible biases in sample handling and DMSO analyses could also explain the discrepancy between modelled and measured intracellular DMSO.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".