Foreign peptide triggers boost in pneumococcal metabolism and growth.
Bibliographic record
Abstract
BACKGROUND Nonencapsulated Streptococcus pneumoniae bacteria are successful colonizers of the human nasopharynx and often possess genes aliB-like ORF 1 and 2 in place of capsule genes. AliB-like ORF 2 binds peptide FPPQSV, found in Prevotella species, resulting in enhanced colonization. How this response is mediated is so far unknown. RESULTS Here we show that the peptide increases expression of genes involved in release of host carbohydrates, carbohydrate uptake and carbohydrate metabolism. In particular, the peptide increased expression of 1,5-anhydro-D-fructose reductase, a metabolic enzyme of an alternative starch and glycogen degrading pathway found in many organisms, in both transcriptomic and proteomic data. The peptide enhanced pneumococcal growth giving a competitive advantage to a strain with aliB-like ORF 2, over its mutant lacking the gene. Possession of aliB-like ORF 2 did not affect release of inflammatory cytokine CXCL8 from epithelial cells in culture and the nonencapsulated wild type strain was not able to establish disease or inflammation in an infant rat model of meningitis. CONCLUSIONS We propose that AliB-like ORF 2 confers an advantage in colonization by enhancing carbohydrate metabolism resulting in a boost in growth. This may explain the widespread presence of aliB-like ORF 2 in the nonencapsulated pneumococcal population in the human nasopharynx.
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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) | 0.002 | 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".