Molecular profiling of <i>Mycobacterium tuberculosis</i> identifies tuberculosinyl nucleoside products of the virulence-associated enzyme Rv3378c
Bibliographic record
Abstract
To identify lipids with roles in tuberculosis disease, we systematically compared the lipid content of virulent Mycobacterium tuberculosis with the attenuated vaccine strain Mycobacterium bovis bacillus Calmette-Guérin. Comparative lipidomics analysis identified more than 1,000 molecular differences, including a previously unknown, Mycobacterium tuberculosis-specific lipid that is composed of a diterpene unit linked to adenosine. We established the complete structure of the natural product as 1-tuberculosinyladenosine (1-TbAd) using mass spectrometry and NMR spectroscopy. A screen for 1-TbAd mutants, complementation studies, and gene transfer identified Rv3378c as necessary for 1-TbAd biosynthesis. Whereas Rv3378c was previously thought to function as a phosphatase, these studies establish its role as a tuberculosinyl transferase and suggest a revised biosynthetic pathway for the sequential action of Rv3377c-Rv3378c. In agreement with this model, recombinant Rv3378c protein produced 1-TbAd, and its crystal structure revealed a cis-prenyl transferase fold with hydrophobic residues for isoprenoid binding and a second binding pocket suitable for the nucleoside substrate. The dual-substrate pocket distinguishes Rv3378c from classical cis-prenyl transferases, providing a unique model for the prenylation of diverse metabolites. Terpene nucleosides are rare in nature, and 1-TbAd is known only in Mycobacterium tuberculosis. Thus, this intersection of nucleoside and terpene pathways likely arose late in the evolution of the Mycobacterium tuberculosis complex; 1-TbAd serves as an abundant chemical marker of Mycobacterium tuberculosis, and the extracellular export of this amphipathic molecule likely accounts for the known virulence-promoting effects of the Rv3378c enzyme.
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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".