From <i>mec</i> cassette to <i>rdhA</i> : a key <i>Dehalobacter</i> genomic neighborhood in a chloroform and dichloromethane–transforming microbial consortium
Bibliographic record
Abstract
Abstract Chloroform (CF) and dichloromethane (DCM) are groundwater contaminants of concern due to their high toxicity and inhibition of important biogeochemical processes such as methanogenesis. Anaerobic biotransformation of CF and DCM has been well documented but typically independently of one another. CF is the electron acceptor for certain organohalide-respiring bacteria that use reductive dehalogenases (RDases) to dechlorinate CF to DCM. In contrast, known DCM-degraders use DCM as their electron donor, which is oxidized using a series of methyltransferases and associated proteins encoded by the mec cassette to facilitate the entry of DCM to the Wood-Ljungdahl pathway. The SC05 culture is an enrichment culture sold commercially for bioaugmentation, that transforms CF via DCM to CO 2 . This culture has the unique ability to dechlorinate CF to DCM using electron equivalents provided by the oxidation of DCM to CO 2 . Here we use metagenomic and metaproteomic analysis to identify the functional genes involved in each of these transformations. Though 91 metagenome-assembled genomes were assembled, the genes for an RDase—named acdA —and a complete mec cassette were found to be encoded on a single contig belonging to Dehalobacter . AcdA and critical Mec proteins were also highly expressed by the culture. Heterologously-expressed AcdA dechlorinated CF and other chloroalkanes but had 100-fold lower activity on DCM. Overall, the high expression of Mec proteins and the activity of AcdA suggest a Dehalobacter capable of dechlorination of CF to DCM, and subsequent mineralization of DCM using the mec cassette. Importance Chloroform (CF) and dichloromethane (DCM) are regulated groundwater contaminants. A cost-effective approach to remove these pollutants from contaminated groundwater is to employ microbes that transform CF and DCM as part of their metabolism, thus depleting the contamination as the microbes continue to grow. In this work, we investigate bioaugmentation culture SC05, a mixed microbial consortium that effectively and simultaneously degrades both CF and DCM coupled to the growth of Dehalobacter . We identified the functional genes responsible for the transformation of CF and DCM in SC05. These genetic biomarkers provide a means to monitor the remediation process in the field.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".