Mechanism of NanR transcriptional activation of sialic acid metabolism in <i>Streptococcus pneumoniae</i>
Bibliographic record
Abstract
Abstract In Streptococcus pneumoniae , the RpiR transcriptional regulator NanR ( Sp NanR) senses sialic acid in the environment and upregulates transcription of the nan and sia A operons to increase uptake and metabolism of sialic acid. The molecular basis of this activation is unknown. Here, we demonstrate that Sp NanR binds N -acetylmannosamine-6-phosphate, a metabolite of sialic acid catabolism. Sp NanR exists in a dimer-tetramer equilibrium, and N -acetylmannosamine-6-phosphate binding strongly stabilizes the tetramer. Crystal structures and site-specific substitutions demonstrate that N -acetylmannosamine-6-phosphate bridges and stabilizes the Sp NanR tetramer. Sp NanR binds its DNA recognition sequence with nanomolar affinity. Notably, the effector N -acetylmannosamine-6-phosphate does not affect the affinity of Sp NanR for DNA. The DNA binding domains are not structurally coupled to the sugar isomerase domains, explaining why N -acetylmannosamine-6-phosphate binding does not affect DNA binding. Structural analysis reveals that sequence specificity arises through distortion of B-DNA and an unusual π-stack formed by two arginine residues in the minor groove, while affinity is driven by backbone contacts. We propose a mechanism by which S. pneumoniae regulates sialic acid metabolism, consistent with our biophysical experiments and in vivo regulatory behavior. These findings define a unique activation mechanism for an RpiR regulator and provide new insights into carbohydrate-responsive gene regulation in pneumococci.
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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) | 0.001 | 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".