Metagenomic analysis of an anaerobic alkane-degrading microbial culture: potential hydrocarbon-activating pathways and inferred roles of community members
Bibliographic record
Abstract
A microbial community (short-chain alkane-degrading culture, SCADC) enriched from an oil sands tailings pond was shown to degrade C6-C10 alkanes under methanogenic conditions. Total genomic DNA from SCADC was subjected to 454 pyrosequencing, Illumina paired-end sequencing, and 16S rRNA amplicon pyrotag sequencing; the latter revealed 320 operational taxonomic units at 5% distance. Metagenomic sequences were subjected to in-house quality control and co-assembly, yielding 984 086 contigs, and annotation using MG-Rast and IMG. Substantial nucleotide and protein recruitment to Methanosaeta concilii, Syntrophus aciditrophicus, and Desulfobulbus propionicus reference genomes suggested the presence of closely related strains in SCADC; other genomes were not well mapped, reflecting the paucity of suitable reference sequences for such communities. Nonetheless, we detected numerous homologues of putative hydrocarbon succinate synthase genes (e.g., assA, bssA, and nmsA) implicated in anaerobic hydrocarbon degradation, suggesting the ability of the SCADC microbial community to initiate methanogenic alkane degradation by addition to fumarate. Annotation of a large contig revealed analogues of the ass operon 1 in the alkane-degrading sulphate-reducing bacterium Desulfatibacillum alkenivorans AK-01. Despite being enriched under methanogenic-fermentative conditions, additional metabolic functions inferred by COG profiling indicated multiple CO(2) fixation pathways, organic acid utilization, hydrogenase activity, and sulphate reduction.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".