Identification of Mycobacterial σ Factor Binding Sites by Chromatin Immunoprecipitation Assays
Bibliographic record
Abstract
Mycobacterium tuberculosis and Mycobacterium bovis are responsible for infections that cause a substantial amount of death, suffering, and loss around the world. Still, relatively little is known about the mechanisms of gene expression in these bacteria. Here, we used genome-wide location assays to identify direct target genes for mycobacterial sigma factors. Chromatin immunoprecipitation assays were performed with M. bovis BCG for Myc-tagged proteins expressed using an anhydrotetracycline-inducible promoter, and enriched DNA fragments were hybridized to a microarray representing intergenic regions from the M. tuberculosis H37Rv genome. Several putative target genes were validated by quantitative PCR. The corresponding transcriptional start sites were identified for sigma(F), sigma(C), and sigma(K), and consensus promoter sequences are proposed. Our conclusions were supported by the results of in vitro transcription assays. We also examined the role of each holoenzyme in the expression of sigma factor genes. Our results revealed that many sigma factors are expressed from autoregulated promoters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".