Serological Response of Shiga Toxin-Producing Escherichia coli Type III Secreted Proteins in Sera from Vaccinated Rabbits, Naturally Infected Cattle, and Humans
Bibliographic record
Abstract
Escherichia coli O157:H7 is an important zoonotic pathogen, causing hemolytic uremic syndrome (HUS). The colonization of cattle and human hosts is mediated through the action of effectors secreted via a type III secretion system (T3SS). The structural genes for the T3SS and many of the secreted effectors are located on a pathogenicity island called the locus of enterocyte effacement (LEE). We cloned and expressed the genes coding for 66 effectors and purified each to measure the cross-reactivity of type III secreted proteins from Shiga toxin-producing Escherichia coli (STEC) serotypes. These included 37 LEE-encoded proteins and 29 non-LEE effectors. The serological response against each protein was measured by Western blot analysis and enzyme-linked immunosorbent assay (ELISA) using sera from rabbits immunized with type III secreted proteins (T3SPs) from four STEC serotypes, experimentally infected cattle, and human sera from six HUS patients. Twenty proteins were recognized by at least one of the STEC T3SP-vaccinated rabbits by Western blotting. Several structural proteins (EspA, EspB, and EspD) and a number of effectors (Tir, NleA, and TccP) were recognized by O26-, O103-, O111-, and O157-specific sera. Sera from experimentally infected cattle and HUS patients were tested using an ELISA against each of the proteins. Tir, EspB, EspD, EspA, and NleA were recognized by the majority of the samples tested. A number of other proteins also were recognized by individual serum samples. Overall, proteins such as Tir, EspB, EspD, NleA, and EspA were highly immunogenic in vaccinated and naturally infected subjects and could be candidates for a cross-protective STEC vaccine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".