Identification of taxa-specific responses to bioremediation treatments in hydrocarbon-contaminated Arctic soils
Why this work is in the frame
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Bibliographic record
Abstract
A warming climate and improved technology have allowed northern countries to more thoroughly explore and exploit Arctic resources. This increased activity has led to an elevated risk of petroleum contamination, and consequently, there is a need to develop strategies to effectively and efficiently degrade these contaminants on site. While many Arctic soil microorganisms are known to naturally metabolize petroleum hydrocarbons in contaminated sites, a process known as bioremediation, treatments directed at stimulating the hydrocarbon-degrading activity of these microbes (e.g. nutrient amendments) have varied in effectiveness.The objective of this study was to determine whether microbial taxa respond equally to disturbances of the soil environment by hydrocarbon contaminants and nutrient amendments, and whether the most efficient hydrocarbon degraders are naturally stimulated. To determine whether the bacteria inhabiting contaminated Arctic soils assimilate added nitrogen equally, a novel 15N-stable isotope probing approach was developed. After a month of in situ incubation, it was determined that many hydrocarbon-degrading bacteria had incorporated the added nitrogen, but to varying extents. The Alphaproteobacteria most effectively used the added nitrogen, as determined by both 16S rRNA and alkB gene enrichment, and this was noteworthy given that they were not expected to be the most effective hydrocarbon-degrading group.To assess whether the relative abundance of bacterial taxa in hydrocarbon-contaminated soils was determined by soil characteristics as opposed to hydrocarbon-degrading ability, 18 soils from across the Arctic were collected and treated with diesel and monoammonium phosphate. Bacterial diversity and community composition were determined through 16S rRNA gene sequencing on the Ion Torrent platform, while hydrocarbon degradation was measured using gas chromatography. It was found that Actinobacteria dominated soils with low organic matter, while Proteobacteria dominated those with high organic matter. In addition, the extent of bacterial diversity and the relative abundance of specific assemblages of Betaproteobacteria in uncontaminated soils were predictive of hydrocarbon degradation with and without nutrient amendments, respectively. Relative abundance of Betaproteobacteria was associated with efficient hydrocarbon degradation in the presence of added nutrients, suggesting that this may be an important group to target.Finally, to determine whether modifying the microbial community within a given soil would impact rates of hydrocarbon degradation, gentamicin and vancomycin were used to inhibit specific portions of the bacterial community. Bacterial 16S rRNA gene diversity and community composition were again determined using the Ion Torrent platform, qPCR was used to quantify bacterial and fungal populations within each treatment, and GC analysis was used to determine hydrocarbon degradation. Bacterial 16S rRNA gene abundance declined in soils treated with gentamicin, but diesel degradation was highest in the presence of both gentamicin and vancomycin. Bacterial community composition shifted under all treatments, and Xanthomonadaceae (Gammaproteobacteria) and Micrococcaceae (Actinobacteria) dominated soils treated with both antibiotics. Diesel degradation was much less effective when nutrients were also added to soils treated with gentamicin and vancomycin, possibly due to competition from a larger fungal population.Overall, these results suggest that more effective in situ treatments of hydrocarbon-contaminated Arctic soils are possible through selective targeting of efficient hydrocarbon-degrading consortia. Future research should aim to understand which soil microorganisms most quickly degrade various contaminants in situ, as well as the main biotic and abiotic factors that limit their activity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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