Detection of Complement Activation on Antigen Microarrays Generates Functional Antibody Profiles and Helps Characterization of Disease-Associated Changes of the Antibody Repertoire
Bibliographic record
Abstract
Humoral immune responses are traditionally characterized by determining the presence and quality of Abs specific for certain Ags. Arraying of large numbers of Ags allows the parallel measurement of Abs, generating patterns called Ab profiles. Functional characterization of these Abs could help draw an even more informative map of an immune response. To generate functional Ab profiles we simultaneously tested not only IgM, IgG, and IgA binding to, but also complement activation by, a panel of endogenous and exogenous Ags printed as microarrays, using normal and autoimmune human sera. We show that complement activation by a particular Ag in a given individual cannot be predicted by the measurement of Ag-specific Abs, despite a general correlation between the amount of Ag-bound Ab and the deposited C3 fragments. This is due to both differences in the isotypes that dominate in the recognition of an Ag and individual variations for a given isotype, resulting in altered complement activation potential. Thus, Ag-specific C3 deposition can be used as an additional parameter in immune response monitoring. This is exemplified by comparing the coordinates of Ags, used for the diagnosis of systemic lupus erythematosus, of normal and autoimmune serum samples in a two-dimensional space derived from C3 deposition and Ab binding. Since cleavage fragments of C3 mediate important immunological processes, we propose that measurement of their deposition on Ag microarrays, in addition to Ab profiling, can provide useful functional signature about the tested serum.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".