The unusual convergence of steroid catabolic pathways in <i>Mycobacterium abscessus</i>
Bibliographic record
Abstract
Mycobacterium abscessus , an opportunistic pathogen responsible for pulmonary infections, contains genes predicted to encode two steroid catabolic pathways: a cholesterol catabolic pathway similar to that of Mycobacterium tuberculosis and a 4-androstenedione (4-AD) catabolic pathway. Consistent with this prediction, M. abscessus grew on both steroids. In contrast to M. tuberculosis , Rhodococcus jostii RHA1, and other Actinobacteria, the cholesterol and 4-AD catabolic gene clusters of the M. abscessus complex lack genes encoding HsaD, the meta -cleavage product (MCP) hydrolase. However, M. abscessus ATCC 19977 harbors two hsaD homologs elsewhere in its genome. Only one of the encoded enzymes detectably transformed steroid metabolites. Among tested substrates, HsaD Mab and HsaD Mtb of M. tuberculosis had highest substrate specificities for MCPs with partially degraded side chains thioesterified with coenzyme A ( k cat / K M = 1.9 × 10 4 and 5.7 × 10 3 mM −1 s −1 , respectively). Consistent with a dual role in cholesterol and 4-AD catabolism, HsaD Mab also transformed nonthioesterified substrates efficiently, and a Δ hsaD mutant of M. abscessus grew on neither steroid. Interestingly, both steroids prevented growth of the mutant on acetate. The Δ hsaD mutant of M. abscessus excreted cholesterol metabolites with a fully degraded side chain, while the corresponding RHA1 mutant excreted metabolites with partially degraded side chains. Finally, the Δ hsaD mutant was not viable in macrophages. Overall, our data establish that the cholesterol and 4-AD catabolic pathways of M. abscessus are unique in that they converge upstream of where this occurs in characterized steroid-catabolizing bacteria. The data further indicate that cholesterol is a substrate for intracellular bacteria and that cholesterol-dependent toxicity is not strictly dependent on coenzyme A sequestration.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".