Impact of Salinity on Cell Surface Chemistry of Cyanobacteria From Freshwater, Marine and Alkaline Environments: A Hidden Phosphorus Engine
Bibliographic record
Abstract
Climate change is altering ocean salinity, impacting cyanobacteria, key primary producers with vital ecological roles. While cyanobacterial adaptations to salinity are well studied, molecular cell surface chemistry changes remain underexplored. This study examines the impact of salinity on surface properties of freshwater Synechocystis sp. PCC6803, marine Synechococcus sp. PCC8806 and alkaliphilic Spirulina platensis. Species were cultured under salinities of 2‰, 6‰, 10‰, 30‰ and 60‰. Surface chemical composition and content were characterised using infrared and x-ray photoelectron spectroscopy and potentiometric titration. All strains exhibited salinity-dependent changes in surface charge and functional group expression, reflecting distinct adaptation strategies. In non-marine strains, salinity stress led to decreased phosphoryl signals and increased lipid saturation, consistent with phospholipid replacement and reduced membrane fluidity. We propose membrane phospholipids as a phosphorus reservoir, mobilised to support biosynthesis and ion homeostasis. In contrast, Syn. PCC8806 increased phosphoryl-associated signals and membrane fluidity across salinity conditions, consistent with phosphatidylglycerol enrichment to maintain photosynthetic function. Surface chemical shifts support a model where membrane remodelling is central to species-specific acclimatisation, balancing nutrient conservation with functional integrity. This work enhances understanding of microbial adaptation under osmotic stress and provides insight for predicting cyanobacterial blooms, designing biotechnological systems.
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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".