Deficiency of <i>N</i> ‐Acetyltransferase Potentiates Isoniazid‐Endobiotics Interactions and Contributes to Isoniazid Hepatotoxicity
Bibliographic record
Abstract
Isoniazid (INH) is a first‐line anti‐tuberculosis drug, but it causes liver injury frequently. The biochemical basis of INH toxicity in the liver remains elusive. We used an untargeted metabolomic approach to explore the effects of INH on endobiotic homeostasis in mouse liver. We found that overdose of INH resulted in accumulation of multiple endobiotics and INH‐endobiotics conjugates, including long‐chain acylcarnitines, INH‐fatty acid amides, heme, nicotinamide adenine dinucleotide (NAD), and INH‐NAD adduct. Furthermore, we illustrated that overdose of INH depleted vitamin B6 in the liver and blocked vitamin B6‐dependent cystathionine degradation. These results indicate INH can disrupt multiple endobiotics metabolic pathways, which are all critical for hepatocellular functions. Acetylation is the major metabolic pathway of INH and it is mediated by N‐acetyltransferase (NAT). Previous reports showed that slow acetylators have a higher risk of hepatotoxicity than rapid acetylators during INH chemotherapy. We used Nat1/2 (−/−) mice to mimic NAT slow metabolizers. We found that INH acetylation was significantly suppressed in Nat1/2(−/−) mice, whereas INH‐endobiotic interactions were significantly increased. These data demonstrate that deficiency of NAT increases the interactions of INH with endobiotics in the liver, which may in part explains the high risks of INH hepatotoxicity in NAT slow metabolizers. Support or Funding Information This work was supported in part by the National Institute of Diabetes and Digestive and Kidney Diseases (DK090305), the National Institute of Allergy And Infectious Diseases (R01AI131983), and the National Institute of General Medical Sciences (GM118367). This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".