Ribosomal frameshifting and dual-target antiactivation restrict quorum-sensing–activated transfer of a mobile genetic element
Bibliographic record
Abstract
Symbiosis islands are integrative and conjugative mobile genetic elements that convert nonsymbiotic rhizobia into nitrogen-fixing symbionts of leguminous plants. Excision of the Mesorhizobium loti symbiosis island ICEMlSym(R7A) is indirectly activated by quorum sensing through TraR-dependent activation of the excisionase gene rdfS. Here we show that a +1 programmed ribosomal frameshift (PRF) fuses the coding sequences of two TraR-activated genes, msi172 and msi171, producing an activator of rdfS expression named Frameshifted excision activator (FseA). Mass-spectrometry and mutational analyses indicated that the PRF occurred through +1 slippage of the tRNA(phe) from UUU to UUC within a conserved msi172-encoded motif. FseA activated rdfS expression in the absence of ICEMlSym(R7A), suggesting that it directly activated rdfS transcription, despite being unrelated to any characterized DNA-binding proteins. Bacterial two-hybrid and gene-reporter assays demonstrated that FseA was also bound and inhibited by the ICEMlSym(R7A)-encoded quorum-sensing antiactivator QseM. Thus, activation of ICEMlSym(R7A) excision is counteracted by TraR antiactivation, ribosomal frameshifting, and FseA antiactivation. This robust suppression likely dampens the inherent biological noise present in the quorum-sensing autoinduction circuit and ensures that ICEMlSym(R7A) transfer only occurs in a subpopulation of cells in which both qseM expression is repressed and FseA is translated. The architecture of the ICEMlSym(R7A) transfer regulatory system provides an example of how a set of modular components have assembled through evolution to form a robust genetic toggle that regulates gene transcription and translation at both single-cell and cell-population levels.
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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 | 0.000 |
| 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) | 0.001 | 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".