A Vaccine Approach for the Prevention of Infections by Multidrug-resistant Enterococcus faecium
Bibliographic record
Abstract
The incidence of multidrug-resistant Enterococcus faecium hospital infections has been steadily increasing. With the goal of discovering new vaccine antigens, we systematically fractionated and purified four distinct surface carbohydrates from E. faecium endocarditis isolate Tx16, shown previously to be resistant to phagocytosis in the presence of human serum. The two most abundant polysaccharides consist of novel branched heteroglycan repeating units that include signature sugars altruronic acid and legionaminic acid, respectively. A minor high molecular weight polysaccharide component was recognized as the fructose homopolymer levan, and a glucosylated lipoteichoic acid (LTA) was identified in a micellar fraction. The polysaccharides were conjugated to the CRM197 carrier protein, and the resulting glycoconjugates were used to immunize rabbits. Rabbit immune sera were evaluated for their ability to kill Tx16 in opsonophagocytic assays and in a mouse passive protection infection model. Although antibodies raised against levan failed to mediate opsonophagocytic killing, the other glycoconjugates induced effective opsonic antibodies, with the altruronic acid-containing polysaccharide antisera showing the greatest opsonophagocytic assay activity. Antibodies directed against either novel heteroglycan or the LTA reduced bacterial load in mouse liver or kidney tissue. To assess antigen prevalence, we screened a diverse collection of blood isolates (n = 101) with antibodies to the polysaccharides. LTA was detected on the surface of 80% of the strains, and antigens recognized by antibodies to the two major heteroglycans were co-expressed on 63% of these clinical isolates. Collectively, these results represent the first steps toward identifying components of a glycoconjugate vaccine to prevent E. faecium infection.
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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) | 0.001 | 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".