Whole exome sequencing in children with autoimmune hepatitis identified mutations in genes involved in the mTORC1 signaling pathway
Bibliographic record
Abstract
Background & Aims: Autoimmune hepatitis (AIH), a severe immune-mediated liver disease resulting from defective immune tolerance, was thought to be caused by monogenic predisposition with possible external triggers. The development of autoimmune hepatitis in monogenic primary immune deficiencies prompted us to search for causative genetic defects in AIH. Methods: Twenty-two children with AIH were included for whole exome sequencing analysis. Data were analyzed with an in-house software, combined with in-silico tools and confirmed by Sanger sequencing. mTOR pathway activation was assessed by phosphoS6-RP expression by FACS. Results: In six children with type 1 or type 2 autoimmune hepatitis, seven rare missense candidate-variants with damaging predictive scores were identified in five genes involved in the regulation of the mTORC1 signaling pathway (RRAGC, LAMTOR3, MTOR, TSC2 and PRKAG1). Excess of phospho-S6RP expression both ex vivo by monocytes and in vitro by activated T cells demonstrated an abnormal activation of mTORC1 in five out of six tested patients. Persistent mTORC1 activation in starved activated T cells could be abolished by mTOR inhibitor. Conclusion: These data showed that pediatric patients with AIH-1 or -2, especially when burdened with poly-autoimmunity, may carry mutations in key partners of mTORC1 signalingpathway. Moreover, even without identified genetic defect, we observed a deregulation of the mTOR pathway in five out of six tested patients. Impact and implications: These results shed new light on the pathogenesis of AIH. Moreover, they suggested that the patients could benefit from specific targeted agents such as mTOR inhibitors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".