Multi-continental detection of <i>Streptococcus pyogenes</i> M1 <sub>UK</sub> : Impact of ssrA SNP on SpeA expression in ancestral and M1 <sub>UK</sub> isolates
Bibliographic record
Abstract
Abstract The Streptococcus pyogenes M1 UK lineage, characterised by an intrinsic ability to express SpeA toxin and defined by 27 single nucleotide polymorphisms in the core genome, dominates the population of emm 1 S. pyogenes isolates throughout Europe, Canada, South America, Japan and Oceania. While not the sole deterministic factor, enhanced SpeA expression is likely to have contributed to M1 UK lineage expansion, but was not sufficient to support expansion of intermediate lineage M1 23SNP , that expresses SpeA at a similar level to M1 UK . A single nucleotide polymorphism (SNP) in the ssrA leader sequence upstream of speA is one of a limited number of SNPs that distinguish intermediate sublineages that differ in SpeA production. It was recently shown that this SNP leads to increased ssrA terminator read-through, and consequent increased transcription of speA , which lies downstream of ssrA . In this work, introduction of the ssrA SNP into representative isolates of the widely disseminated M1 global clone and the intermediate M1 13SNP lineage, that cannot otherwise produce readily-detectable SpeA in culture, resulted in SpeA expression, confirming the importance of the ssrA SNP to SpeA phenotype. Consistent with this, correction of the ssrA SNP in M1 UK abrogated SpeA expression. However, RNAseq analysis of 8 emm 1 clinical pharyngitis strains showed that presence of the SNP was not invariably linked to read-through from the ssrA leader sequence or SpeA expression. Read-through was observed in isolates that did not possess the ssrA SNP. Critical review of existing data suggests that speA mRNA transcript length may be impacted by the two-component regulator CovRS, pointing to a complex regulatory network interaction between the bacterial chromosome and phage-encoded superantigens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".