Friends and foes: symbiotic and algicidal bacterial influence on <i>Karenia brevis</i> blooms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Harmful Algal Blooms (HABs) of the toxigenic dinoflagellate Karenia brevis (KB) are pivotal in structuring the ecosystem of the Gulf of Mexico (GoM), decimating coastal ecology, local economies, and human health. Bacterial communities associated with toxigenic phytoplankton species play an important role in influencing toxin production in the laboratory, supplying essential factors to phytoplankton and even killing blooming species. However, our knowledge of the prevalence of these mechanisms during HAB events is limited, especially for KB blooms. Here, we introduced native microbial communities from the GoM, collected during two phases of a Karenia bloom, into KB laboratory cultures. Using bacterial isolation, physiological experiments, and shotgun metagenomic sequencing, we identified both putative enhancers and mitigators of KB blooms. Metagenome-assembled genomes from the Roseobacter clade showed strong correlations with KB populations during HABs, akin to symbionts. A bacterial isolate from this group of metagenome-assembled genomes, Mameliella alba, alleviated vitamin limitations of KB by providing it with vitamins B1, B7 and B12. Conversely, bacterial isolates belonging to Bacteroidetes and Gammaproteobacteria, Croceibacter atlanticus, and Pseudoalteromonas spongiae, respectively, exhibited strong algicidal properties against KB. We identified a serine protease homolog in P. spongiae that putatively drives the algicidal activity in this isolate. While the algicidal mechanism in C. atlanticus is unknown, we demonstrated the efficiency of C. atlanticus to mitigate KB growth in blooms from the GoM. Our results highlight the importance of specific bacteria in influencing the dynamics of HABs and suggest strategies for future HAB management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it