Protective Enterotoxigenic Escherichia coli Antigens in a Murine Intranasal Challenge Model
Bibliographic record
Abstract
Enterotoxigenic Escherichia coli (ETEC) is an endemic health threat in underdeveloped nations. Despite the significant effort extended to vaccine trials using ETEC colonization factors, these approaches have generally not been especially effective in mediating cross-protective immunity. We used quantitative proteomics to identify 24 proteins that differed in abundance in membrane protein preparations derived from wild-type vs. a type II secretion system mutant of ETEC. We expressed and purified a subset of these proteins and identified nine antigens that generated significant immune responses in mice. Sera from mice immunized with either the MltA-interacting protein MipA, the periplasmic chaperone seventeen kilodalton protein, Skp, or a long-chain fatty acid outer membrane transporter, ETEC_2479, reduced the adherence of multiple ETEC strains differing in colonization factor expression to human intestinal epithelial cells. In intranasal challenge assays of mice, immunization with ETEC_2479 protected 88% of mice from an otherwise lethal challenge with ETEC H10407. Immunization with either Skp or MipA provided an intermediate degree of protection, 68 and 64%, respectively. Protection was significantly correlated with the induction of a secretory immunoglobulin A response. This study has identified several proteins that are conserved among heterologous ETEC strains and may thus potentially improve cross-protective efficacy if incorporated into future vaccine designs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".