Composition and pathogenic potential of a microbial bioremediation product used for crude oil degradation
Bibliographic record
Abstract
A microbial bioremediation product (MBP) used for large-scale oil degradation was investigated for microbial constituents and possible pathogenicity. Aerobic growth on various media yielded >108 colonies mL-1. Full-length 16S rDNA sequencing and fatty acid profiling from morphologically distinct colonies revealed ≥13 distinct genera. Full-length 16S rDNA library sequencing, by either Sanger or long-read PacBio technology, suggested that up to 21% of the MBP was composed of Arcobacter. Other high abundance microbial constituents (>6%) included the genera Proteus, Enterococcus, Dysgonomonas and several genera in the order Bacteroidales. The MBP was most susceptible to ciprofloxacin, doxycycline, gentamicin, and meropenam. MBP exposure of human HT29 and A549 cells caused significant cytotoxicity, and bacterial growth and adherence. An acellular MBP filtrate was also cytotoxic to HT29, but not A549. Both MBP and filtrate exposures elevated the neutrophil chemoattractant IL-8. In endotracheal murine exposures, bacterial pulmonary clearance was complete after one-week. Elevation of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α, and chemokines KC and MCP-1 occurred between 2h and 48h post-exposure, followed by restoration to baseline levels at 96h. Cytokine/chemokine signalling was accompanied by elevated blood neutrophils and monocytes at 4h and 48h, respectively. Peripheral acute phase response markers were maximal at 24h. All indicators examined returned to baseline values by 168h. In contrast to HT29, but similar to A549 observations, MBP filtrate did not induce significant murine effects with the indicators examined. The results demonstrated the potentially complex nature of MBPs and transient immunological effects during exposure. Products containing microbes should be scrutinized for pathogenic components and subjected to characterisation and quality validation prior to commercial release.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".