A gene cluster encoding cholesterol catabolism in a soil actinomycete provides insight into <i>Mycobacterium tuberculosis</i> survival in macrophages
Bibliographic record
Abstract
Rhodococcus sp. strain RHA1, a soil bacterium related to Mycobacterium tuberculosis, degrades an exceptionally broad range of organic compounds. Transcriptomic analysis of cholesterol-grown RHA1 revealed a catabolic pathway predicted to proceed via 4-androstene-3,17-dione and 3,4-dihydroxy-9,10-seconandrost-1,3,5(10)-triene-9,17-dione (3,4-DHSA). Inactivation of each of the hsaC, supAB, and mce4 genes in RHA1 substantiated their roles in cholesterol catabolism. Moreover, the hsaC(-) mutant accumulated 3,4-DHSA, indicating that HsaC(RHA1), formerly annotated as a biphenyl-degrading dioxygenase, catalyzes the oxygenolytic cleavage of steroid ring A. Bioinformatic analyses revealed that 51 rhodococcal genes specifically expressed during growth on cholesterol, including all predicted to specify the catabolism of rings A and B, are conserved within an 82-gene cluster in M. tuberculosis H37Rv and Mycobacterium bovis bacillus Calmette-Guérin. M. bovis bacillus Calmette-Guérin grew on cholesterol, and hsaC and kshA were up-regulated under these conditions. Heterologously produced HsaC(H37Rv) and HsaD(H37Rv) transformed 3,4-DHSA and its ring-cleaved product, respectively, with apparent specificities approximately 40-fold higher than for the corresponding biphenyl metabolites. Overall, we annotated 28 RHA1 genes and proposed physiological roles for a similar number of mycobacterial genes. During survival of M. tuberculosis in the macrophage, these genes are specifically expressed, and many appear to be essential. We have delineated a complete suite of genes necessary for microbial steroid degradation, and pathogenic mycobacteria have been shown to catabolize cholesterol. The results suggest that cholesterol metabolism is central to M. tuberculosis's unusual ability to survive in macrophages and provide insights into potential targets for novel therapeutics.
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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 | 0.001 |
| 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) | 0.001 | 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".