From Northwest Passage shores to molecular pathways: Comparative transcriptomic responses of a novel Arctic marine fuel-degrading <i>Flavobacterium</i> species
Bibliographic record
Abstract
ABSTRACT Accelerated sea-ice decline is opening the Arctic to increased shipping, elevating the risk of marine fuel spills in fragile ecosystems where extreme cold and remoteness limit cleanup options. While microbial biodegradation is the primary removal mechanism, the metabolic strategies of abundant polar taxa remain poorly understood, particularly those lacking canonical degradation genes. We characterized the hydrocarbon degradation mechanisms of Flavobacterium sp. strain R2B_3I, a psychrotolerant isolate from high Arctic beach sediments in Resolute Bay, Nunavut, Canada. During three-month incubations with ultra-low sulfur fuel oil at 4 °C, R2B_3I mounted a systems-level response involving the upregulation of diverse non-canonical oxidoreductases, membrane remodeling systems, cold-shock, and oxidative stress defenses. Crucially, this strain achieved efficient degradation in the complete absence of alkB alkane hydroxylases, challenging the reliance on alkB as a universal biomarker for hydrocarbon biodegradation. Transcriptomic analysis revealed distinct temporal shifts, linking specific gene clusters to the degradation of complex petroleum mixtures under environmentally relevant conditions. These results demonstrate that Flavobacterium, a dominant genus in polar oceans, utilize a “cryptic” metabolic network to process hydrocarbons, effectively bypassing the pathways typically monitored in environmental surveys. By uncovering alternative mechanisms, our study revises current models of microbial oil degradation, highlighting the overlooked potential of non-canonical degraders in determining the fate of marine fuel spills in a warming Arctic.
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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 | 0.001 |
| 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) | 0.001 | 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".