DNA Methylation impairs monocyte function in tuberculosis leading to disease progression
Bibliographic record
Abstract
Abstract Despite treatment and cure, Tuberculosis (TB) is still considered a world health public problem. It is known that interaction between bacteria and host immune mechanisms results in changes in gene expression, which could be a result of epigenetic changes, as DNA methylation. Recent studies suggest that alterations in host epigenome could be promoted by Mtb infection and influence immune cells function. In this context, we aimed to determine the methylation profile of monocytes from tuberculosis patients and correlate with immune function of these cells. Monocytes were isolated from active TB patients (APTB) with treatment-time less than one month (n=10) and non-disease controls (CTRL) (n=16) - Ethics Committee approval FMRP-USP (Protocol #6481/2013). The monocytes were used to evaluate the global DNA methylation content and immune function, through cytokine and ROS production after heat killed Mtb challenge. When we evaluated the group of tuberculosis patients according to their lung injury degree, we found an increased methylation in those with more severe disease. Also, it was possible to observe that monocytes from patients with increased methylation profile, presented a reduction in secretion of anti-inflammatory cytokine, IL-10 and increased pro-inflammatory IL-12, indicating impairment in monocytes ability to regulate the excess of inflammation, which mediates the lung injury often observed in these patients. Hence, we suggested that global DNA methylation content might act as a clinic prognostic toll for active tuberculosis disease.
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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) | 0.002 | 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".