The Genetics of Tuberous Sclerosis Complex and Related mTORopathies: Current Understanding and Future Directions
Bibliographic record
Abstract
The mechanistic target of rapamycin (mTOR) pathway serves as a master regulator of cell growth, proliferation, and survival. Upregulation of the mTOR pathway has been shown to cause malformations of cortical development, medically refractory epilepsies, and neurodevelopmental disorders, collectively described as mTORopathies. Tuberous sclerosis complex (TSC) serves as the prototypical mTORopathy. Characterized by the development of benign tumors in multiple organs, pathogenic variants in TSC1 or TSC2 disrupt the TSC protein complex, a negative regulator of the mTOR pathway. Variants in critical domains of the TSC complex, especially in the catalytic TSC2 subunit, correlate with increased disease severity. Variants in less crucial exons and non-coding regions, as well as those undetectable with conventional testing, may lead to milder phenotypes. Despite the assumption of complete penetrance, expressivity varies within families, and certain variants delay disease onset with milder neurological effects. Understanding these genotype–phenotype correlations is crucial for effective clinical management. Notably, 15% of patients have no mutation identified by conventional genetic testing, with the majority of cases postulated to be caused by somatic TSC1/TSC2 variants which present complex diagnostic challenges. Advancements in genetic testing, prenatal screening, and precision medicine hold promise for changing the diagnostic and treatment paradigm for TSC and related mTORopathies. Herein, we explore the genetic and molecular mechanisms of TSC and other mTORopathies, emphasizing contemporary genetic methods in understanding and diagnosing the condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".