Integrated Glycosylation Analysis of Immunoglobulin Isotypes Reveals Expanded Humoral Remodeling in Elderly Tuberculosis Infection
Bibliographic record
Abstract
Antibody fragment crystallizable region (Fc) glycosylation critically modulates immune signaling, yet characterization of glycosylation beyond the immunoglobulin G (IgG) isotype remains limited. Here, we present the first site-specific glycoprofiling of immunoglobulin A (IgA) and immunoglobulin M (IgM) in elderly individuals with tuberculosis (TB), a population particularly susceptible to disease reactivation. Using dual-enzyme digestion and targeted LC-MS/MS analysis, we quantified Fc glycosylation of IgG, IgA, and IgM in plasma from 20 patients with active TB (ATB), 18 with latent TB infection (LTBI), and 20 controls. Consistent with previous studies, IgG1 and IgG2 in ATB displayed reduced galactosylation and elevated fucosylation compared with LTBI. Extending the analysis to other isotypes, we identified analogous alterations in IgA and IgM. ATB samples showed reduced digalactosylation and increased monogalactosylation at IgA1/2-N144/131, indicating a shift toward agalactosylation. In IgM, decreased galactosylation at N171, N332, and N395, increased agalactosylation at N563, and increased fucosylation and sialylation at N71 were observed in ATB relative to LTBI and controls. Integrating 18 significantly altered glycosylation traits across all three Ig isotypes revealed coordinated humoral remodeling associated with active disease. Collectively, these findings indicate that IgA and IgM, like IgG, undergo infection-associated proinflammatory glycan remodeling, underscoring their overlooked roles in antibody-mediated immune modulation and providing a broader framework for understanding humoral responses in aging and chronic infection.
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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) | 0.000 | 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 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".