DNA packaging bias and differential expression of gene transfer agent genes within a population during production and release of the <i>Rhodobacter capsulatus</i> gene transfer agent, RcGTA
Bibliographic record
Abstract
Rhodobacter capsulatus produces a gene transfer agent (GTA) called RcGTA. RcGTA is a phage-like particle that packages R. capsulatus DNA and transfers it to other R. capsulatus cells. We quantified the relative frequency of packaging for each gene in the genome by hybridization of DNA from RcGTA particles to an R. capsulatus microarray. All genes were found within the RcGTA particles. However, the genes encoding the RcGTA particle were under-packaged compared with other regions. Gene transfer bioassays confirmed that the transfer of genes within the RcGTA structural cluster is reduced relative to those of other genes. Single-cell expression analysis, by flow cytometry analysis of cells containing RcGTA-reporter gene fusion constructs, demonstrated that RcGTA gene expression is not uniform within a culture. This phenomenon was accentuated when the constructs were placed in a strain lacking a putative lysis gene involved in RcGTA release; a small subpopulation was found to be responsible for ∼ 95% of RcGTA activity. We propose a mechanism whereby high levels of RcGTA gene transcription in the most active RcGTA-producing cells cause a reduction in their packaging frequency. This subpopulation's role in producing and releasing the RcGTA particles explains the lack of observed cell lysis in cultures.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".