Protein Targets of Isoniazid-Reactive Metabolites in Mouse Liver<i>in Vivo</i>
Bibliographic record
Abstract
Isoniazid (INH) has been a first-line drug for the treatment of tuberculosis for more than 40 years. INH is well-tolerated by most patients, but some patients develop hepatitis that can be severe in rare cases or after overdose. The mechanisms underlying the hepatotoxicity of INH are not known, but covalent binding of reactive metabolites is known to occur in animals and is suspected in human cases. A major unresolved question is the identity of the liver proteins that are modified by INH metabolites. Treating mice with INH leads to accumulation of isonicotinoyl-lysine residues on numerous proteins in the hepatic S9 fraction. Analysis of this fraction by SDS-PAGE followed by tryptic digestion of bands and LC-MS/MS revealed a single adducted peptide derived from d-dopachrome decarboxylase. When a tryptic digest of whole S9 was applied to anti-INH antibody immobilized on beads, only 12 peptides were retained, 5 of which clearly contained isonicotinoyl-lysine adducts and could be confidently assigned to 5 liver proteins. In another experiment, undigested S9 fractions from INA-treated and untreated (UT) mice were adsorbed in parallel on anti-INA beads and the retained proteins were digested and analyzed by LC-MS/MS. The INA-S9 digest showed 1 adducted peptide that was associated with a unique protein whose identity was corroborated by numerous nonadducted peptides in the digest and 13 other proteins identified only by multiple nonadducted peptides. None of these 14 proteins was associated with any peptides present in the UT-S9 fraction. Overall, we identified 7 mouse liver proteins that became adducted by INH metabolites in vivo. Of these 7 INH target proteins, only 2 have been previously reported as targets of any reactive metabolite in vivo.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".