Cultivating microbial dark matter in benzene‐degrading methanogenic consortia
Bibliographic record
Abstract
The microbes responsible for anaerobic benzene biodegradation remain poorly characterized. In this study, we identified and quantified microbial populations in a series of 16 distinct methanogenic, benzene-degrading enrichment cultures using a combination of traditional 16S rRNA clone libraries (four cultures), pyrotag 16S rRNA amplicon sequencing (11 cultures), metagenome sequencing (1 culture) and quantitative polymerase chain reaction (qPCR; 12 cultures). An operational taxonomic unit (OTU) from the Deltaproteobacteria designated ORM2 that is only 84% to 86% similar to Syntrophus or Desulfobacterium spp. was consistently identified in all enrichment cultures, and typically comprised more than half of the bacterial sequences. In addition to ORM2, a sequence belonging to Parcubacteria (candidate division OD1) identified from the metagenome data was the only other OTU common to all the cultures surveyed. Culture transfers (1% and 0.1%) were made in the presence and absence of benzene, and the abundance of ORM2, OD1 and other OTUs was tracked over 415 days using qPCR. ORM2 sequence abundance increased only when benzene was present, while the abundance of OD1 and other OTUs increased even in the absence of benzene. Deltaproteobacterium ORM2 is unequivocally the benzene-metabolizing population. This study also hints at laboratory cultivation conditions for a member of the widely distributed yet uncultivated Parcubacteria (OD1).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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; both teacher heads agree on what is shown here.
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".