Dynamic polyphosphate metabolism in cyanobacteria responding to phosphorus availability
Bibliographic record
Abstract
Despite the crucial role of polyphosphate (polyP) in aquatic environments, its metabolism in cyanobacteria responding to nutrients is poorly understood. We investigate polyP in three cyanobacteria species, specifically unicellular picocyanobacteria, under various nutritional conditions. Our experiments show that the accumulation of polyP in cyanobacteria is strongly dynamic, depending on phosphate levels and growth stages. 'Overplus' uptake of phosphorus (P) during the lag phase leads to the rapid accumulation of polyP, followed by lower polyP quotas during the exponential growth stage as a result of competing 'luxury' P uptake and polyP utilization to support rapid cell division. Cyanobacteria are capable of P deficiency responses that preferentially maintain polyP. However, preferential utilization of polyP occurs under severe P stress, suggesting the crucial role of polyP as P reserve to support cellular survival. Strong variability was observed among different species of cyanobacteria in their ability to accumulate polyP, and likely in the threshold P levels at which preferential polyP degradation occurs. This suggests that some cyanobacteria may be more adaptive to P-stressed or P-fluctuating conditions. Our results explain and provide important insights into the variability of polyP observed in aquatic environments where picocyanobacteria are the dominant primary producers.
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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) | 0.000 | 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".