Comparative transcriptomic analysis reveal genes involved in the pathogenicity increase of <i>Streptococcus suis</i> epidemic strains
Bibliographic record
Abstract
Streptococcus suis epidemic strains were responsible for two outbreaks in China and possessed increased pathogenicity which was featured prominently by inducing an excessive inflammatory response at the early phase of infection. To discover the critical genes responsible for the pathogenicity increase of S. suis epidemic strains, the genome-wide transcriptional profiles of epidemic strain SC84 were investigated at the early phase of interaction with BV2 cells. The overall low expression levels of 89K pathogenicity island (PAI) and 129 known virulence genes in the SC84 interaction groups indicated that its pathogenicity increase should be attributed to novel mechanisms. Using highly pathogenic strain P1/7 and intermediately pathogenic strain 89–1591 as controls, 11 pathogenicity increase crucial genes (PICGs) and 38 pathogenicity increase-related genes (PIRGs) were identified in the SC84 incubation groups. The PICGs encoded proteins related to the methionine biosynthesis/uptake pathway and played critical roles in the pathogenicity increase of epidemic strains. A high proportion of PIRGs encoded surface proteins related to host cell adherence and immune escape, which may be conducive to the pathogenicity increase of epidemic strains by rapidly initiating infection. The fact that none of PICGs and PIRGs belonged to epidemic strain-specific gene indicated that the pathogenicity increase of epidemic strain may be determined by the expression level of genes, rather than the presence of them. Our results deepened the understanding on the mechanism of the pathogenicity increase of S. suis epidemic strains and provided novel approaches to control the life-threatening infections of S. suis epidemic strains.
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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 | 0.001 | 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) | 0.002 | 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".