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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".