A Naturally Occurring Single Amino Acid Replacement in Multiple Gene Regulator of Group A Streptococcus Significantly Increases Virulence
Bibliographic record
Abstract
Single-nucleotide polymorphisms (SNPs) are the most common source of genetic variation within a species; however, few investigations demonstrate how naturally occurring SNPs may increase strain virulence. We recently used group A Streptococcus as a model pathogen to study bacteria strain genotype-patient disease phenotype relationships. Whole-genome sequencing of approximately 800 serotype M59 group A Streptococcus strains, recovered during an outbreak of severe invasive infections across North America, identified a disproportionate number of SNPs in the gene encoding multiple gene regulator of group A Streptococcus (mga). Herein, we report results of studies designed to test the hypothesis that the most commonly occurring SNP, encoding a replacement of arginine for histidine at codon 201 of Mga (H201R), significantly increases virulence. Whole transcriptome analysis revealed that the H201R replacement significantly increased expression of mga and 54 other genes, including many proven virulence factors. Compared to the wild-type strain, a H201R isogenic mutant strain caused significantly larger skin lesions in mice. Serial quantitative bacterial culture and noninvasive magnetic resonance imaging also demonstrated that the isogenic H201R strain was significantly more virulent in a nonhuman primate model of joint infection. These findings show that the H201R replacement in Mga increases the virulence of M59 group A Streptococcus and provide new insight to how a naturally occurring SNP in bacteria contributes to human disease phenotypes.
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 | 0.000 |
| 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".