The evaluation of biogenic silica in brackish and freshwater strains reveals links between phylogeny and silica accumulation in picocyanobacteria
Bibliographic record
Abstract
ABSTRACT Through biosilicification, organisms incorporate dissolved silica (dSi) and deposit it as biogenic silica (bSi), driving the silicon (Si) cycle in aquatic systems. While Si accumulation in marine picocyanobacteria has been recently observed, its mechanisms and ecological implications remain unclear. This study investigates biosilicification in marine and brackish picocyanobacteria of the Synechococcus clade and two model freshwater coccoid cyanobacteria. Brackish strains showed significantly higher Si quotas when supplemented with external dSi (100 µM) compared to controls (up to 60.0 ± 7.3 amol Si.cell-1 versus 9.2 to 16.3 ± 2.9 amol Si.cell-1). Conversely, freshwater strains displayed no significant differences in Si quotas between dSi-enriched treatments and controls, emphasizing that not all phytoplanktons without an obligate Si requirement accumulate this element. The Si-accumulating marine and brackish picocyanobacteria clustered within the Synechococcus clade, whereas their freshwater counterparts formed a distinct sister group, suggesting a link between phylogeny and silicification. Rapid culture growth caused increased pH and led to dSi precipitation, influencing apparent dSi uptake; this was mitigated by pH control through bubbling. This phenomenon has significant implications for natural systems affected by phytoplankton blooms. In such environments, pH-induced silicon precipitation may reduce dSi availability impacting Si-dependent populations like diatoms. Our findings suggest brackish picocyanobacteria could significantly influence the Si cycle through at least two mechanisms: cellular Si accumulation and biologically induced changes in dSi concentrations. IMPORTANCE This work provides the first evidence of biogenic silica accumulation in brackish picocyanobacteria and uncovers a link between phylogeny and biosilicification patterns. Our findings demonstrate that picocyanobacterial growth induces pH-dependent silica precipitation, which could lead to overestimations of cellular Si quotas by up to 85%. This process may drive substantial silica precipitation in highly productive freshwater and coastal marine systems, with potential effects on silica cycling and the population dynamics of Si-dependent phytoplankton. The extent of biosilicification in modern picocyanobacteria offers insights into the rock record, shedding light on the evolutionary and ecological dynamics that influence sedimentary processes and the preservation of biosilicification signatures in geological formations. Overall, this research adds to the significant impact that microorganisms lacking an obligate silica requirement may have on silica dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".