Complex of GM1- and GD1a-Like Lipo-Oligosaccharide Mimics GM1b, Inducing Anti-GM1b Antibodies
Bibliographic record
Abstract
OBJECTIVE: Molecular mimicry between Campylobacter jejuni lipo-oligosaccharides (LOSs) and human gangliosides GM1 and GD1a induces the production of anti-GM1 and anti-GD1a antibodies, and the development of Guillain-Barré syndrome. Complexes of two different gangliosides form new molecular shapes capable of enhancing recognition by anti-ganglioside antibodies. To test the hypothesis that the complex of GM1-like and GD1a-like LOSs of C. jejuni induces the development of anti-GM1b antibodies in Guillain-Barré syndrome patients. METHODS: Mass spectrometry analysis determined the LOS outer core structures, with which mice were immunized. IgG antibodies to single gangliosides and complex of gangliosides were tested in sera from Guillain-Barré syndrome patients from whom C. jejuni LOS had been isolated. RESULTS: Two isolates from GBS patients who had anti-GM1b antibodies, but neither anti-GM1 nor -GD1a antibodies, expressed both GM1-like and GD1a-like LOSs, but not GM1b-like LOS. Anti-GM1b antibodies were induced in one of the mice immunized with the C. jejuni bearing GM1-like and GD1a-like LOS. Sera from 20 patients had antibodies to the complex of GM1 and GD1a, all of which carried anti-GM1b reactivity. Five of these sera harbored neither anti-GM1 nor anti-GD1a antibodies. IgG antibodies to the complex were absorbed by GM1b, but by neither GM1 nor GD1a. CONCLUSIONS: GM1-like and GD1a-like LOSs form a GM1b epitope, inducing the development of anti-GM1b antibodies in patients with Guillain-Barré syndrome subsequent to C. jejuni enteritis. Here, we present a new paradigm that the complex of two different structures forms a new molecular mimicry, inducing the production of autoantibodies.
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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".