Global DNA hypomethylation of Alu and LINE-1 transposable elements as an epigenetic biomarker of anti-tuberculosis drug-induced liver injury
Bibliographic record
Abstract
Despite being highly effective, anti-tuberculosis (TB) drugs often induce adverse liver injury, anti-TB drug-induced liver injury (ATDILI), leading to treatment failure given no sensitive and selective ATDILI markers. Herein, we conducted a case-control association study to determine whether global DNA methylation of Alu and LINE-1 transposable elements responsible for genomic stability and transcriptional regulation was correlated with clinical parameters indicating ATDILI in TB patients and might serve as an ATDILI biomarker. Alu and LINE-1 methylation levels in blood leukocyte of 130 TB patients (80 ATDILI cases and 50 non-ATDILI cases) and 100 healthy controls were quantified using quantitative combined bisulfite restriction analysis. Both TB patients with and without ATDILI had significantly lower methylation levels of Alu and LINE-1 elements than healthy controls. Compared with non-ATDILI patients, Alu methylation levels were significantly decreased in ATDILI patients, commensurate with LINE-1 methylation analysis. Hypomethylation of Alu and LINE-1 measured within 1-7 days of TB treatment was independently associated with raised levels of serum aminotransferases assessed within 8-60 days of TB treatment. Receiver operating characteristic curve analysis uncovered that Alu and LINE-1 methylation levels were both more sensitive and specific for differentiating ATDILI cases from non-ATDILI cases than serum aminotransferases after starting TB treatment within 1-7 days. Kaplan-Meier analysis displayed a significant association between hypomethylation of Alu and LINE-1 elements and an increased rate of ATDILI occurrence in TB patients. Collectively, global DNA hypomethylation of Alu and LINE-1 elements would reflect ATDILI progression and might serve as novel sensitive and specific ATDILI biomarkers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".