Features and protective efficacy of human mAbs targeting Mycobacterium tuberculosis arabinomannan
Bibliographic record
Abstract
A better understanding of the epitopes most relevant for antibody-mediated protection against tuberculosis (TB) remains a major knowledge gap. We have shown that human polyclonal IgG against the Mycobacterium tuberculosis (M. tuberculosis) surface glycan arabinomannan (AM) and related lipoarabinomannan (LAM) is protective against TB. To investigate the impact of AM epitope recognition and Fcγ receptor (FcγR) binding on antibody functions against M. tuberculosis, we isolated a high-affinity human monoclonal antibody (mAb; P1AM25) against AM and showed its binding to oligosaccharide (OS) motifs we previously found to be associated with in vitro functions of human polyclonal anti-AM IgG. Human IgG1 P1AM25, but not 2 other high-affinity human IgG1 anti-AM mAbs reactive with different AM OS motifs, enhanced M. tuberculosis phagocytosis by macrophages and reduced intracellular growth in an FcγR-dependent manner. P1AM25 in murine IgG2a, but neither murine IgG1 nor a non-FcγR-binding IgG, given intraperitoneally prior to and after aerosolized M. tuberculosis infection, was protective in C57BL/6 mice. Moreover, we demonstrated the protective efficacy of human IgG1 P1AM25 in passive transfer with M. tuberculosis-infected FcγR-humanized mice. These data enhance our knowledge of the important interplay between both antibody epitope specificity and Fc effector functions in the defense against M. tuberculosis and could inform development of vaccines against TB.
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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".