Time Course-Dependent Methanogenic Crude Oil Biodegradation: Dynamics of Fumarate Addition Metabolites, Biodegradative Genes, and Microbial Community Composition
Bibliographic record
Abstract
Biodegradation of crude oil in subsurface petroleum reservoirs has adversely impacted most of the world’s oil, converting this resource to heavier forms that are of lower quality and more challenging to recover. Oil degradation in deep reservoir environments has been attributed to methanogenesis over geological time, yet our understanding of the processes and organisms mediating oil transformation in the absence of electron electrons remains incomplete. Here, we sought to identify hydrocarbon activation mechanisms and reservoir-associated microorganisms that may have helped shape the formation of biodegraded oil by incubating oilfield produced water in the presence of light (°API = 32) or heavy crude oil (°API = 16). Over the course of 17 months, we conducted routine analytical (GC, GC-MS) and molecular (PCR/qPCR of assA and bssA genes, 16S rRNA gene sequencing) surveys to assess microbial community composition and activity changes over time. Over the incubation period, we detected the formation of transient hydrocarbon metabolites indicative of alkane and alkylbenzene addition to fumarate, corresponding with increases in methane production and fumarate addition gene abundance. Chemical and gene-based evidence of hydrocarbon biodegradation under methanogenic conditions was supported by the enrichment of hydrocarbon fermenters known to catalyze fumarate addition reactions (e.g. Desulfotomaculum, Smithella), along with syntrophic bacteria (Syntrophus), methanogenic archaea, and several candidate phyla (e.g. ‘Atribacteria’, ‘Cloacimonetes’). Our results reveal that fumarate addition is a possible mechanism for catalyzing the methanogenic biodegradation of susceptible saturates and aromatic hydrocarbons in crude oil, and we propose the roles of community members and candidate phyla in our cultures that may be involved in hydrocarbon transformation to methane in crude oil systems.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".