Ecological, functional, and genomic characterization of the bacterial endophytes in plants growing in a hydrocarbon contaminated oil field site
Bibliographic record
Abstract
In the last decade, there has been an increasing awareness of the plant microbiota’s role in plant growth and fitness. Of particular interest is the plant-bacterial endophytes’ partnership to degrade organic contaminants. This thesis assessed the ecological diversity and function of bacterial endophytes (BEs) in pioneer plants growing in highly contaminated soil around the vicinity of a crude oil pump in Oil Springs, Ontario, Canada. This study focused on the bacteria in the stem endosphere, an understudied ecological niche in the plant-bacterial system. The plants are not only exposed to both soluble petroleum hydrocarbons from the soil, but also to volatile atmospheric hydrocarbons in the ambient air. BEs were isolated and analyzed through culture-dependent and -independent means. 16S-based terminal restriction fragment length polymorphism analysis revealed significant differences between the endophytic bacterial communities, showing them to be plant host specific. Culture-dependent analyses revealed predominance of Actinobacteria and Gammaproteobacteria in the spring and summer seasons, respectively. Notably, there was a >50% taxonomic overlap (genus level) of 16s rRNA high-throughput amplicon sequences with cultivable endophytes. BEs demonstrated hydrocarbon degrading and plant growth promoting potential. Interestingly, our findings revealed that functional capabilities of bacterial isolates being examined were not influenced by the presence of contamination, and that the stem endosphere supports the same predominant BEs in all plant hosts in both contaminated and noncontaminated sites. Genomic analyses of BEs that were present in all the plants sampled in Spring–Plantibacter flavus 251 and Microbacterium foliorum 122 revealed absence of known hydrocarbon degrading genes despite demonstrated utilization of hydrocarbon substrates, indicating potentially novel hydrocarbon degrading genes.
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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 | 0.001 | 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".