Quantifying the Effects of 1,1,1-Trichloroethane and 1,1-Dichloroethane on Chlorinated Ethene Reductive Dehalogenases
Bibliographic record
Abstract
Mixtures of chlorinated ethenes and ethanes are often found at contaminated sites. In this study, we undertook a systematic investigation of the inhibitory effects of 1,1,1-trichloroethane (1,1,1-TCA) and 1,1-dichloroethane (1,1-DCA) on chlorinated ethene dechlorination in three distinct Dehalococcoides-containing consortia. To focus on inhibition acting directly on the reductive dehalogenases, dechlorination assays used cell-free extracts prepared from cultures actively dechlorinating trichloroethene (TCE) to ethene. The dechlorination assays were initiated with TCE, cis-1,2-dichloroethene (cDCE), or vinyl chloride (VC) as substrates and either 1,1,1-TCA or 1,1-DCA as potential inhibitors. 1,1,1-TCA inhibited VC dechlorination similarly in cell suspension and cell-free extract assays, implicating an effect on the VC reductases associated with the dechlorination of VC to nontoxic ethene. Concentrations of 1,1,1-TCA in the range of 30-270 μg/L reduced VC dechlorination rates by approximately 50% relative to conditions without 1,1,1-TCA. 1,1,1-TCA also inhibited reductive dehalogenases involved in TCE and cDCE dechlorination. In contrast, 1,1-DCA had no pronounced inhibitory effects on chlorinated ethene reductive dehalogenases, indicating that removal of 1,1,1-TCA via reductive dechlorination to 1,1-DCA is a viable strategy to relieve inhibition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".