Exploration of the presence and abundance of multidrug resistance efflux genes in oil and gas environments
Bibliographic record
Abstract
As sequencing technology improves and the cost of metagenome sequencing decreases, the number of sequenced environments increases. These metagenomes provide a wealth of data in the form of annotated and unannotated genes. The role of multidrug resistance efflux pumps (MDREPs) is the removal of antibiotics, biocides and toxic metabolites created during aromatic hydrocarbon metabolism. Due to their naturally occurring role in hydrocarbon metabolism and their role in biocide tolerance, MDREP genes are of particular importance for the protection of pipeline assets. However, the heterogeneity of MDREP genes creates a challenge during annotation and detection. Here we use a selection of primers designed to target MDREPs in six pure species and apply them to publicly available metagenomes associated with oil and gas environments. Using in silico PCR with relaxed primer binding conditions we probed the metagenomes of a shale reservoir, a heavy oil tailings pond, a civil wastewater treatment, two marine sediments exposed to hydrocarbons following the Deepwater Horizon oil spill and a non-exposed marine sediment to assess the presence and abundance of MDREP genes. Through relaxed primer binding conditions during in silico PCR, the prevalence of MDREPs was determined. The percentage of nucleotide sequences identified by the MDREP primers was partially augmented by exposure to hydrocarbons in marine sediment and in shale reservoir compared to hydrocarbon-free marine sediments while tailings ponds and wastewater had the highest percentages. We believe this approach lays the groundwork for a supervised method of identifying poorly conserved genes within metagenomes.
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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".