Effects of Cyanobacterial Lipopolysaccharides from Microcystis on Glutathione-Based Detoxification Pathways in the Zebrafish (Danio rerio) Embryo
Bibliographic record
Abstract
Cyanobacteria ("blue-green algae") are recognized producers of a diverse array of toxic secondary metabolites. Of these, the lipopolysaccharides (LPS), produced by all cyanobacteria, remain to be well investigated. In the current study, we specifically employed the zebrafish (Danio rerio) embryo to investigate the effects of LPS from geographically diverse strains of the widespread cyanobacterial genus, Microcystis, on several detoxifying enzymes/pathways, including glutathione-S-transferase (GST), glutathione peroxidase (GPx)/glutathione reductase (GR), superoxide dismutase (SOD), and catalase (CAT), and compared observed effects to those of heterotrophic bacterial (i.e., E. coli) LPS. In agreement with previous studies, cyanobacterial LPS significantly reduced GST in embryos exposed to LPS in all treatments. In contrast, GPx moderately increased in embryos exposed to LPS, with no effect on reciprocal GR activity. Interestingly, total glutathione levels were elevated in embryos exposed to Microcystis LPS, but the relative levels of reduced and oxidized glutathione (i.e., GSH/GSSG) were, likewise, elevated suggesting that oxidative stress is not involved in the observed effects as typical of heterotrophic bacterial LPS in mammalian systems. In further support of this, no effect was observed with respect to CAT or SOD activity. These findings demonstrate that Microcystis LPS affects glutathione-based detoxification pathways in the zebrafish embryo, and more generally, that this model is well suited for investigating the apparent toxicophore of cyanobacterial LPS, including possible differences in structure-activity relationships between heterotrophic and cyanobacterial LPS, and teleost fish versus mammalian 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 | 0.001 |
| 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".