Capturing disease severity in LIS1-lissencephaly reveals proteostasis dysregulation in patient-derived forebrain organoids
Bibliographic record
Abstract
LIS1-lissencephaly is a neurodevelopmental disorder marked by reduced cortical folding and severe neurological impairment. Although all cases result from heterozygous mutations in the LIS1 gene, patients present a broad spectrum of severity. Here, we use patient-derived forebrain organoids representing mild, moderate, and severe LIS1-lissencephaly to uncover mechanisms underlying this variability. We show that LIS1 protein levels vary across patient lines and partly correlate with clinical severity, indicating mutation-specific effects on protein function. Integrated morphological, transcriptomic, and proteomic analyses reveal progressive changes in neural progenitor homeostasis and neurogenesis that scale with severity. Mechanistically, microtubule destabilization disrupts cell–cell junctions and impairs WNT signaling, and defects in protein homeostasis, causing stress from misfolded proteins, emerge as key severity-linked pathways. Pharmacological inhibition of mTORC1 partially rescues these defects. Our findings demonstrate that patient-derived organoids can model disease severity, enabling mechanistic dissection and guiding targeted strategies in neurodevelopmental disorders. Here authors used patient-derived forebrain organoids to reveal how LIS1 mutations cause varying severity in a neurodevelopmental disorder, uncovering links between cytoskeletal defects, protein stress, and potential therapeutic strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".