Mucosal and systemic antigen-specific antibody responses correlate with protection against active tuberculosis in nonhuman primates
Bibliographic record
Abstract
BACKGROUND: Increasing evidence supports that antibodies can protect against active tuberculosis (TB) but knowledge of potentially protective antigens, especially in the airways, is limited. The main objective of this study was to identify antigen-specific airway and systemic immunoglobulin isotype responses associated with the outcome of controlled latent Mycobacterium tuberculosis (Mtb) infection (LTBI) versus uncontrolled infection (TB) in nonhuman primates. METHODS: In a case-control design, using non-parametric group comparisons with false discovery rate adjustments, we assessed antibodies in 57 cynomolgus macaques which, following low-dose airway Mtb infection, developed either LTBI or TB. We investigated airway and systemic IgG, IgA, and IgM responses in paired bronchoalveolar lavage and plasma samples prior to, two-, and 5-6-months post Mtb infection using an antigen-unbiased approach with Mtb glycan and proteome-wide microarrays. FINDINGS: Macaques that developed LTBI (n = 36) had significantly increased airway and plasma IgA reactivities to specific arabinomannan (AM) motifs prior to Mtb infection compared to those that developed TB (n = 21; p < 0.01, q < 0.05). Furthermore, LTBI macaques had higher plasma IgG reactivity to protein MTB32A (Rv0125) early post Mtb infection (p < 0.05) and increasing airway IgG responses to some proteins over time. INTERPRETATION: Our results support a protective role of pre-existing mucosal (lung) and systemic IgA to specific Mtb glycan motifs, suggesting that prior exposure to nontuberculous mycobacteria could be protective against TB. They further suggest that IgG to Mtb proteins early post infection could provide an additional protective mechanism. These findings could inform TB vaccine development strategies. FUNDING: NIH/NIAID AI117927, AI146329, and AI127173 to JMA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".