sRNA23, a novel small RNA, regulates to the pathogenesis of <i>Streptococcus suis</i> serotype 2
Bibliographic record
Abstract
Streptococcus suis serotype 2 (S. suis 2) is an important ubiquitous zoonotic pathogen. To date, regulatory factors and their implication in S. suis pathogenesis are not fully understood. Small non-coding RNAs (sRNAs) have been proven to function as important regulatory factors in bacterial pathogenesis and stress adaptation. Here, we identified a differentially downregulated S. suis 05ZYH33 sRNA after iron starvation by RNA-seq, which we named sRNA23. The presence of sRNA23 was further confirmed by RACE and Northern blot. Expression of sRNA23 was significantly altered under different environmental stresses such as nutritional starvation, osmotic pressure, oxidative stress, and lysozymal exposure. A sRNA23-deleted mutant exhibited relatively shorter streptococcal chains and weakened biofilm-forming ability. The mutation also resulted in decreased adherence of the S. suis 05ZYH33 to human laryngeal epidermoid carcinoma (HEp-2) cells, increased sensitivity to phagocytosis by RAW264.7 macrophages, and significantly reduced hemolytic activity. Furthermore, we observed that a sRNA23-deleted mutant had a low survival rate in pig whole blood and attenuated virulence in a mouse model. Moreover, based on RNA pull-down and electrophoretic mobility shift assay, we found that sRNA23 can directly bind to two proteins involved in adhesion and biofilm formation, namely, moonlighting protein FBA (fructose diphosphate aldolase) and rplB (50S ribosomal protein L2), respectively. Collectively, sRNA23 enhances S. suis 2 pathogenicity and the binding between sRNA23 and FBA/rplB might play an essential role in the adherence and biofilm-forming ability of S. suis 2. sRNA: small noncoding RNA; FBA: fructose diphosphate aldolase; rplB: 50S ribosomal protein L2; RACE: rapid amplification of cDNA ends; EMSA: electrophoretic mobility shift assay; THB: Todd-Hewitt broth; FBS: fetal bovine serum; BIP: 2,2ʹ-Bipyridine
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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 | 0.001 |
| 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".