Transcriptional Analysis of a Dehalococcoides-Containing Microbial Consortium Reveals Prophage Activation
Bibliographic record
Abstract
Chlorinated solvents are among the most prevalent groundwater contaminants in the industrialized world. Biodegradation with Dehalococcoides-containing mixed cultures is an effective remediation technology. To elucidate transcribed genes in a Dehalococcoides-containing mixed culture, a shotgun metagenome microarray was created and used to investigate gene transcription during vinyl chloride (VC) dechlorination and during starvation (no chlorinated compounds) by a microbial enrichment culture called KB-1. In both treatment conditions, methanol was amended as an electron donor. Subsequently, spots were sequenced that contained the genes most differentially transcribed between the VC-degrading and methanol-only conditions, as well as spots with the highest intensities. Sequencing revealed that during VC degradation Dehalococcoides genes involved in transcription, translation, metabolic energy generation, and amino acid and lipid metabolism and transport were overrepresented in the transcripts compared to the average Dehalococcoides genome. KB-1 rdhA14 (vcrA) was the only reductive dehalogenase homologous (RDH) gene with higher transcript levels during VC degradation, while multiple RDH genes had higher transcript levels in the absence of VC. Numerous hypothetical genes from Dehalococcoides also had higher transcript levels in methanol-only treatments, indicating that many uncharacterized proteins are involved in cell maintenance in the absence of chlorinated substrates. In addition, microarray results prompted biological experiments confirming that electron acceptor limiting conditions activated a Dehalococcoides prophage. Transcripts from Spirochaetes, Chloroflexi, Geobacter, and methanogens demonstrate the importance of non-Dehalococcoides organisms to the culture, and sequencing of identified shotgun clones of interest provided information for follow-on targeted studies.
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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) | 0.000 | 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) | 0.000 | 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".