Contrasting activity patterns determined by BrdU incorporation in bacterial ribotypes from the Arctic Ocean in winter
Bibliographic record
Abstract
The winter Arctic Ocean is one of the most unexplored marine environments from a microbiological perspective. Heterotrophic bacteria maintain their activity at a baseline level during the extremely low-energy conditions of the winter, but little is known about the specific phylotypes that have the potential to survive and grow in such harsh environment. In this study, we aimed at identifying actively growing ribotypes in winter Arctic Ocean seawater cultures by experimental incubations with the thymidine analog bromodeoxyuridine (BrdU), followed by immunocapturing, terminal restriction fragment length polymorphism fingerprinting, cloning, and sequencing the 16S rRNA gene. We incubated water collected at different months over the Arctic winter and showed that the actively growing bacterial fraction, taking up BrdU, represented only a subset of the total community. Among the BrdU-labeled bacterial taxa we identified the Flavobacteria Polaribacter, the Alphaproteobacteria SAR11, the Gammaproteobacteria Arctic 96B-16 cluster and, predominately, members of Colwellia spp. Interestingly, Colwellia sequences formed three clusters (93 and 97% pairwise 16S rRNA identity) that contributed in contrasting ways to the active communities in the incubations. Polaribacter, Arctic 96B-16 and one cluster of Colwellia were more abundant in the active community represented by the BrdU-labeled DNA. In contrast, SAR11 and two other Colwellia clusters were underrepresented in the BrdU-labeled community compared to total communities. Despite the limitation of the long incubations needed to label slow growing arctic communities, the BrdU approach revealed the potential for active growth in low-energy conditions in some relevant groups of polar bacteria, including Polaribacter and Arctic 96B-16. Moreover, under similar incubation conditions, the growth of different Colwellia ribotypes varied, suggesting that related clusters of Colwellia may have distinct metabolic features.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".