Microarray-guided evaluation of the frequency, B-cell origins, and selectivity of human glycan-binding antibodies reveals new insights and novel antibodies
Bibliographic record
Abstract
The immune system produces a diverse collection of antiglycan antibodies that are critical for host defense. At present, however, we know very little about the binding properties, origins, and sequences of these antibodies because of a lack of access to a variety of defined individual antibodies. To address this challenge, we used a glycan microarray with over 800 different components to screen a panel of 516 human monoclonal antibodies that had been randomly cloned from different B-cell subsets originating from healthy human subjects. We obtained 26 antiglycan antibodies, most of which bound microbial carbohydrates. The majority of the antiglycan antibodies identified in the screen displayed selective binding for specific glycan motifs on our array and lacked polyreactivity. We found that antiglycan antibodies were about twice as likely than expected to originate from IgG + memory B cells, whereas none were isolated from naïve, early emigrant, or immature B cells. Therefore, our results indicate that certain B-cell subsets in our panel are enriched in antiglycan antibodies, and IgG + memory B cells may be a promising source of such antibodies. Furthermore, some of the newly identified antibodies bound glycans for which there are no reported monoclonal antibodies available, and these may be useful as research tools, diagnostics, or therapeutic agents. Overall, the results provide insight into the types and properties of antiglycan antibodies produced by the human immune system and a framework for the identification of novel antiglycan antibodies in the future.
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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) | 0.002 | 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".