Selective Glycan Labeling of Mannose-Containing Glycolipids in Mycobacteria
Bibliographic record
Abstract
Mycobacterium tuberculosis ( Mtb ) is one of history’s most successful human pathogens. By subverting typical immune responses, Mtb can persist within a host until conditions become favorable for growth and proliferation. Virulence factors that enable mycobacteria to modulate host immune systems include a suite of mannose-containing glycolipids: phosphatidylinositol mannosides, lipomannan, and lipoarabinomannan (LAM). Despite their importance, tools for their covalent capture, modification, and imaging are limited. Here, we describe a chemical biology strategy to detect and visualize these glycans. Our approach, biosynthetic incorporation, is to synthesize a lipid-glycan precursor that can be incorporated at a late-stage step in glycolipid biosynthesis. We previously demonstrated selective mycobacterial arabinan modification by biosynthetic incorporation using an exogenous donor. This report reveals that biosynthetic labeling is general and selective: it allows for cell surface mannose-containing glycolipid modification without nonspecific labeling of mannosylated glycoproteins. Specifically, we employed azido-( Z,Z )-farnesyl phosphoryl-β- d -mannose probes and took advantage of the strain-promoted azide–alkyne cycloaddition to label and directly visualize the localization and dynamics of mycobacterial mannose-containing glycolipids. Our studies highlight the generality and utility of biosynthetic incorporation as the probe structure directs the selective labeling of distinct glycans. The disclosed agents allowed for direct tracking of the target immunomodulatory glycolipid dynamics in cellulo. We anticipate that these probes will facilitate investigating the diverse biological roles of these glycans.
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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".