Antigenic cross‐reactivity and sequence homology between <i>Actinobacillus actinomycetemcomitans</i> GroEL protein and human fibronectin
Bibliographic record
Abstract
The immunologic cross-reactivity between human fibronectin and Actinobacillus actinomycetemcomitans GroEL was examined. Analyses by SDS-PAGE/Western immunoblotting and ELISA showed that a polyclonal antibody directed against the purified GroEL protein of A. actinomycetemcomitans, but not against the Escherichia coli GroEL, cross-reacts with human fibronectin. No antigenic cross-reactivity was observed between anti-A. actinomycetemcomitans GroEL antibody and type IV collagen, another important constituent of the basement membrane. A comparative analysis of the amino acid sequences of A. actinomycetemcomitans GroEL and human fibronectin revealed eight instances of four-amino acid sequence homology between the two proteins. Six of these tetrapeptide sequences were also shared with E. coli GroEL, suggesting that the remaining two tetrapeptides, GQLI (Glycine-Glutamine-Leucine-Isoleucine) and TGLE (Threonine-Glycine-Leucine-Glutamic acid), may be associated with the epitope that the anti-A. actinomycetemcomitans GroEL antibody specifically recognizes. Reactivity between TGLE, but not GQLI, with anti-A. actinomycetemcomitans GroEL antibody was confirmed by a biospecific interaction analysis using a biosensor technology. Although additional investigations are required, the observed phenomenon may lead to an autoimmune response and thus contribute to tissue destruction during periodontitis.
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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".