The identification of a novel regulator of «Mycobacterium tuberculosis» Sigma Factor K through the use of transposon mutagenesis
Bibliographic record
Abstract
Mycobacterium tuberculosis, M. bovis and the Oryx bacillus are genetically similar bacteria which cause tuberculosis in distinct host species. The ability of these bacteria to survive and cause disease depends on their capacity to adapt to the various stresses that they are exposed to within their respective hosts. A key mechanism of bacterial adaptation to changing conditions involves alteration of gene expression through the use of alternative sigma factors. SigK is an alternative sigma factor shared by these three bacteria that is responsible for the regulation of a small regulon of 11 genes, including those coding for the antigenic proteins, MPT83 and MPT70. In previously published work, it has been shown that the activity of SigK is controlled by the anti-sigma factor, RskA, and that RskA has undergone independent mutations in M. bovis and the Oryx bacillus, resulting in constitutive SigK activity. To further investigate the regulation of SigK across these three bacteria, a transposon mutant library was created in M. smegmatis to search for additional regulatory partners. Through the use of luciferase reporter plasmids, 1700 transposon mutants were screened for impaired SigK activity. The lipoprotein LppZ was identified as an essential partner for SigK regulation in the context of the Oryx bacillus. Further luciferase assays revealed that LppZ functions through amino acids 122-193 of RskA from M. tuberculosis. LppZ has been mutated in M. bovis and appears to be non-essential in terms of SigK regulation within this bacteria, a result of the mutations in RskAbovis which have created an independent SigK-RskA system which requires no additional partners. Lastly, complementation studies revealed that the full length LppZ including the lipoprotein anchor is required for proper SigK activity. These results further elucidate the complex regulation of SigK between these three bacteria and in the future may help to understand the induction and function of this regulon.
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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) | 0.001 | 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".