Transcriptomic Analyses Unveil Hydrocarbon Degradation Mechanisms in a Novel Polar <i>Rhodococcus</i> sp. Strain R1B_2T From a High Arctic Intertidal Zone Exposed to Ultra‐Low Sulphur Fuel Oil
Bibliographic record
Abstract
As Arctic shipping increases due to climate change, characterised by rising temperatures and decreasing sea-ice coverage, the risk of oil spills through the Northwest Passage in this fragile ecosystem grows, necessitating effective bioremediation strategies. Research on bioremediation using Arctic coastal sediment bacteria has gained attention, particularly Rhodococcus species that play key roles in hydrocarbon degradation under extreme conditions. This study investigates the hydrocarbon degradation capabilities of a novel cryophilic Arctic Rhodococcus sp. strain R1B_2T isolated from Canadian high Arctic beach sediment in Resolute Bay, exposed to ultra-low sulphur fuel oil for 3 months at 5°C. Comparative transcriptomics analyses revealed dynamic responses and metabolic plasticity, with upregulation of genes for aliphatic, aromatic, and polycyclic aromatic hydrocarbons, biosurfactant production (e.g., rhamnolipid), cold adaptation, and stress responses. The strain possesses several key alkane degradation genes (alkB, almA, CYP153, ladA), with co-expression network analysis highlighting synergistic mechanisms between alkB and CYP153 that target different chain-length alkanes (alkB: ~C5-C20; CYP153: ~C5-C12 and > C30), demonstrating complementary degradation strategies. The findings reveal adaptive mechanisms and degradation kinetics of native Arctic bacteria, highlighting the potential of Arctic cryophilic and halotolerant Rhodococcus species for oil spill remediation in polar marine environments.
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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) | 0.001 | 0.001 |
| 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) | 0.000 | 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".