Convergence of Ser/Thr and Two-component Signaling to Coordinate Expression of the Dormancy Regulon in Mycobacterium tuberculosis*
Bibliographic record
Abstract
Signal transduction in Mycobacterium tuberculosis is mediated primarily by the Ser/Thr protein kinases and the two-component systems. The Ser/Thr kinase PknH has been shown to regulate growth of M. tuberculosis in a mouse model and in response to NO stress in vitro. Comparison of a pknH deletion mutant (ΔpknH) with its parental M. tuberculosis H37Rv strain using iTRAQ enabled us to quantify >700 mycobacterial proteins. Among these, members of the hypoxia- and NO-inducible dormancy (DosR) regulon were disregulated in the ΔpknH mutant. Using kinase assays, protein-protein interactions, and mass spectrometry analysis, we demonstrated that the two-component response regulator DosR is a substrate of PknH. PknH phosphorylation of DosR mapped to Thr(198) and Thr(205) on the key regulatory helix α10 involved in activation and dimerization of DosR. PknH Thr phosphorylation and DosS Asp phosphorylation of DosR cooperatively enhanced DosR binding to cognate DNA sequences. Transcriptional analysis comparing ΔpknH and parental M. tuberculosis revealed that induction of the DosR regulon was subdued in the ΔpknH mutant in response to NO. Together, these results indicate that PknH phosphorylation of DosR is required for full induction of the DosR regulon and demonstrate convergence of the two major signal transduction systems for the first time in M. tuberculosis.
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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".