A Diverse Soil Microbiome Degrades More Crude Oil than Specialized Bacterial Assemblages Obtained in Culture
Bibliographic record
Abstract
UNLABELLED: Soil microbiome modification may alter system function, which may enhance processes like bioremediation. In this study, we filled microcosms with gamma-irradiated soil that was reinoculated with the initial soil or cultivated bacterial subsets obtained on regular media (REG-M) or media containing crude oil (CO-M). We allowed 8 weeks for microbiome stabilization, added crude oil and monoammonium phosphate, incubated the microcosms for another 6 weeks, and then measured the biodegradation of crude oil components, bacterial taxonomy, and functional gene composition. We hypothesized that the biodegradation of targeted crude oil components would be enhanced by limiting the microbial taxa competing for resources and by specifically selecting bacteria involved in crude oil biodegradation (i.e., CO-M). Postincubation, large differences in taxonomy and functional gene composition between the three microbiome types remained, indicating that purposeful soil microbiome structuring is feasible. Although phylum-level bacterial taxonomy was constrained, operational taxonomic unit composition varied between microbiome types. Contrary to our hypothesis, the biodegradation of C10 to C50 hydrocarbons was highest when the original microbiome was reinoculated, despite a higher relative abundance of alkane hydroxylase genes in the CO-M microbiomes and of carbon-processing genes in the REG-M microbiomes. Despite increases in the relative abundances of genes potentially linked to hydrocarbon processing in cultivated subsets of the microbiome, reinoculation of the initial microbiome led to maximum biodegradation. IMPORTANCE: In this study, we show that it is possible to sustainably modify microbial assemblages in soil. This has implications for biotechnology, as modification of gut microbial assemblages has led to improved treatments for diseases like Clostridium difficile infection. Although the soil environment determined which major phylogenetic groups of bacteria would dominate the assemblage, we saw differences at lower levels of taxonomy and in functional gene composition (e.g., genes related to hydrocarbon degradation). Further studies are needed to determine the success of such an approach in nonsterile environments. Although the biodegradation of certain crude oil fractions was still the highest when we inoculated with the diverse initial microbiome, the possibility of discovering and establishing microbiomes that are more efficient in crude oil degradation is not precluded.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".