Exploring Binding and Effector Functions of Natural Human Antibodies Using Synthetic Immunomodulators
Bibliographic record
Abstract
The ability to profile the prevalence and functional activity of endogenous antibodies is of vast clinical and diagnostic importance. Serum antibodies are an important class of biomarkers and are also crucial elements of immune responses elicited by natural disease-causing agents as well as vaccines. In particular, materials for manipulating and/or enhancing immune responses toward disease-causing cells or viruses have exhibited significant promise for therapeutic applications. Antibody-recruiting molecules (ARMs), bifunctional organic molecules that redirect endogenous antibodies to pathological targets, thereby increasing their recognition and clearance by the immune system, have proven particularly interesting. Notably, although ARMs capable of hijacking antibodies against oligosaccharides and electron-poor aromatics have proven efficacious, systematic comparisons of the prevalence and effectiveness of natural anti-hapten antibody populations have not appeared in the literature. Herein we report head-to-head comparisons of three chemically simple antigens, which are known ligands for endogenous antibodies. Thus, we have chemically synthesized bifunctional molecules containing 2,4-dinitrophenyl (DNP), phosphorylcholine (PC), and rhamnose. We have then used a combination of ELISA, flow cytometry, and cell-viability assays to compare these antigens in terms of their abilities both to recruit natural antibody from human serum and also to direct serum-dependent cytotoxicity against target cells. These studies have revealed rhamnose to be the most efficacious of the synthetic antigens examined. Furthermore, analysis of 122 individual serum samples has afforded comprehensive insights into population-wide prevalence and isotype distributions of distinct anti-hapten antibody populations. In addition to providing a general platform for comparing and studying anti-hapten antibodies, these studies serve as a useful starting point for the optimization of antibody-recruiting molecules and other synthetic strategies for modulating human immunity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".