Inherent Anti-amyloidogenic Activity of Human Immunoglobulin γ Heavy Chains
Bibliographic record
Abstract
We have previously shown that a subpopulation of naturally occurring human IgGs were cross-reactive against conformational epitopes on pathologic aggregates of Abeta, a peptide that forms amyloid fibrils in the brains of patients with Alzheimer disease, inhibited amyloid fibril growth, and dissociated amyloid in vivo. Here, we describe similar anti-amyloidogenic activity that is a general property of free human Ig gamma heavy chains. A gamma(1) heavy chain, F1, had nanomolar binding to an amyloid fibril-related conformational epitope on synthetic oligomers and fibrils as well as on amyloid-laden tissue sections. F1 did not bind to native Abeta monomers, further indicating the conformational nature of its binding site. The inherent anti-amyloidogenic activity of Ig gamma heavy chains was demonstrated by nanomolar amyloid fibril and oligomer binding by polyclonal and monoclonal human heavy chains that were isolated from inert or weakly reactive antibodies. Most importantly, the F1 heavy chain prevented in vitro fibril growth and reduced in vivo soluble Abeta oligomer-induced impairment of rodent hippocampal long term potentiation, a cellular mechanism of learning and memory. These findings demonstrate that free human Ig gamma heavy chains comprise a novel class of molecules for developing potential therapeutics for Alzheimer disease and other amyloid disorders. Moreover, establishing the molecular basis for heavy chain-amyloidogenic conformer interactions should advance understanding on the types of interactions that these pathologic assemblies have with biological molecules.
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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".