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Multiple Reductive-Dehalogenase-Homologous Genes Are Simultaneously Transcribed during Dechlorination by <i>Dehalococcoides-</i> Containing Cultures

2005· article· en· W2137347189 on OpenAlexaff
Alison S. Waller, Rosa Krajmalnik‐Brown, Frank E. Löffler, Elizabeth A. Edwards

Bibliographic record

VenueApplied and Environmental Microbiology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDehalococcoidesDehalogenaseGeneReductive dechlorinationBiologyGeneticsChemistryBiochemistryVinyl chlorideBiodegradation

Abstract

fetched live from OpenAlex

Degenerate primers were used to amplify 14 distinct reductive-dehalogenase-homologous (RDH) genes from the Dehalococcoides-containing mixed culture KB1. Most of the corresponding predicted proteins were highly similar (97 to >99% amino acid identity) to previously reported Dehalococcoides reductive dehalogenases. To examine the differential transcription of these RDH genes, KB1 was split into five subcultures amended with either trichloroethene, cis-1,2-dichloroethene, vinyl chloride, 1,2-dichlorethane, or no chlorinated electron acceptor. Total RNA was extracted following the onset of reductive dechlorination, and RDH transcripts were reverse transcribed and amplified using degenerate primers. The results indicate that the transcription of RDH genes requires the presence of a chlorinated electron acceptor, and for all treatments, multiple RDH genes were simultaneously transcribed, with transcripts of two of the genes being present under all four electron-accepting conditions. Two of the transcribed sequences were highly similar to reported vinyl chloride reductase genes, namely, vcrA from Dehalococcoides sp. strain VS and bvcA from Dehalococcoides sp. strain BAV1. These findings suggest that multiple RDH genes are induced by a single chlorinated substrate and that multiple reductive dehalogenases contribute to chloroethene degradation in KB1.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.179
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations126
Published2005
Admission routes1
Has abstractyes

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