Intercepting a Mycobacterial Biosynthetic Pathway with Covalent Labeling
Bibliographic record
Abstract
The mycobacterial cell envelope plays both infectious and protective roles. Understanding its structure is crucial for unlocking the molecular basis underlying these functions. Studying glycans, the primary components of the cell envelope, is challenging due to their limited native functional handles for chemoselective modification. New labeling methods exploit biorthogonal chemistry, using small molecule mimics that intercept cellular metabolism or late-stage glycan biosynthesis. However, these strategies can have practical limitations, including probe delivery and effectiveness. An ideal small molecule probe should be easily deployed and exploit the critical enzyme–substrate relationships of natural substrates. To this end, we developed a “probegenic” strategy to label mycobacteria. Our approach eliminates the need for explicit substrate mimicry, as the relevant functionality is revealed by a target enzyme. Specifically, we synthesized an azide-substituted trans- β-lactone probe (AzLac), which adopts a substrate-like structure upon covalent enzyme labeling. This probe is incorporated by mycolyltransferases into a core mycobacterial cell envelope glycan, including in the pathogen Mycobacterium tuberculosis . Unlike other probes of the cell envelope, AzLac facilitates selective covalent labeling of the inner leaflet of the mycomembrane. Using Corynebacterium glutamicum mycolyltransferase deletion strains, we implicated Cmt2 as the primary mycolyltransferase target. We leveraged the ability to modify the cell envelope by demonstrating that AzLac could be used to attach a DNA barcode to mycobacteria, which would help track infection dynamics. Thus, we expect AzLac will be a valuable means of monitoring and tracking the mycobacterial cell envelope. Moreover, we anticipate masking and revealing recognition motifs in probes can be applied to diverse cellular targets.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".