Mn <sup>2+</sup> -Dependent Regulation of Multiple Genes in <i>Streptococcus pneumoniae</i> through PsaR and the Resultant Impact on Virulence
Bibliographic record
Abstract
The concentration of Mn2+ is 1,000-fold higher in secretions than it is at internal sites of the body, making it a potential signal by which bacteria can sense a shift from a mucosal environment to a more invasive site. PsaR, a metal-dependent regulator in Streptococcus pneumoniae, was found to negatively affect the transcription of psaBCA, pcpA, rrgA, rrgB, rrgC, srtBCD, and rlrA in the presence of Mn2+. psaBCA encode an ABC-type transporter for Mn2+. pcpA, rrgA, rrgB, and rrgC encode several outer surface proteins. srtBCD encode a cluster of sortase enzymes, and rlrA encodes a transcriptional regulator. Steady-state RNA levels are high under low Mn2+ concentrations in the wild-type strain and are elevated under both high and low Mn2+ concentrations in a psaR mutant strain. RlrA is an activator of rrgA, rrgB, rrgC, and srtBCD (D. Hava and A. Camilli, Mol. Microbiol. 45:1389-1406, 2002), suggesting that PsaR may indirectly control these genes through rlrA, while PsaR-dependent repression of psaBCA, pcpA, and rlrA transcription is direct. The impact of Mn2+-dependent regulation on virulence was further examined in mouse models of pneumonia and nasopharyngeal carriage. The abilities of DeltapsaR, pcpA, and DeltapsaR DeltapcpA mutant strains to colonize the lung were reduced compared to those of the wild type, confirming that both PcpA-mediated gene regulation and PsaR-mediated gene regulation are required for full virulence in the establishment of pneumonia. Neither PcpA nor PsaR was found to be required for colonization of the nasopharynx in a carriage model. This is the first demonstration of Mn2+ acting as a signal for the expression of virulence factors within different host sites.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".