Genome-scale analyses of transcriptional start sites in Mycobacterium marinum under normoxic and hypoxic conditions
Bibliographic record
Abstract
BACKGROUND: Hypoxic stress plays a critical role in the persistence of Mycobacterium tuberculosis (Mtb) infection, but the mechanisms underlying this adaptive response remain ill defined. MATERIAL AND METHODS: In this study, using M. marinum as a surrogate, we analyzed hypoxic responses at the transcriptional level by Cappable-seq and regular RNA-seq analyses. RESULTS: A total of 6808 transcriptional start sites (TSSs) were identified under normoxic and hypoxic conditions. Among these TSSs, 1112 were upregulated and 1265 were downregulated in response to hypoxic stress. Using SigE-recognized consensus sequence, we identified 59 SigE-dependent promoters and all were upregulated under hypoxic stress, suggesting an important role for SigE in this process. We also compared the performance of Cappable-seq and regular RNA-seq using the same RNA samples collected from normoxic and hypoxic conditions, and confirmed that Cappable-seq is a valuable approach for global transcriptional regulation analyses. CONCLUSIONS: Our results provide insights and information for further characterization of responses to hypoxia in mycobacteria, and prove that Cappable-seq is a valuable approach for global transcriptional studies in mycobacteria.
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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".