An RNase III–processed sRNA coordinates sialic acid metabolism of <i>Salmonella enterica</i> during gut colonization
Bibliographic record
Abstract
Sialic acids derived from colonic mucin glycans are crucial nutrients for enteric bacterial pathogens like Salmonella . The uptake and utilization of sialic acid in Salmonella depend on coordinated regulons, each activated by specific metabolites at the transcriptional level. However, the mechanisms enabling crosstalk among these regulatory circuits to synchronize gene expression remain poorly understood. Here, we identify ManS, a small noncoding RNA derived from the 3’ UTR of STM1128 mRNA transcribed from a Salmonella enterica –specific genetic locus, as an important posttranscriptional regulator coordinating sialic acid metabolism regulons. ManS is primarily processed by RNase III and, along with its parental transcripts, is specifically activated by N-acetylmannosamine (ManNAc), the initial degradation product of sialic acid. We found that the imperfect stem-loop structure at the 5’ end of ManS allows RNase III to cleave in a noncanonical manner, generating two functional types of ManS with the assistance of RNase E and other RNases: short isoforms with a single seed region that regulate the uptake of N-acetylglucosamine, an essential intermediate in sialic acid metabolism; and long isoforms with an additional seed region that regulate multiple genes involved in central and secondary metabolism. This sophisticated regulation by ManS significantly impacts ManNAc metabolism and S. enterica ’s competitive behavior during infection. Our findings highlight the role of sRNA in coordinating transcriptional circuits and advance our understanding of RNase III-mediated processing of 3’ UTR-derived sRNAs, underscoring the important role of ManNAc in Salmonella adaptation within host environments.
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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 | 0.000 |
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".