Defective Neuronal Differentiation in Lowe Syndrome is Associated with Mitochondrial Dysfunction and Impaired Cilia-related Sonic Hedgehog Signaling
Bibliographic record
Abstract
Human brain development is a complex process that requires intricate coordination of multiple cellular and developmental events. Dysfunction of lipid metabolism can lead to neurodevelopmental disorders. Lowe syndrome (LS) is a recessive X-linked disorder associated with proximal tubular renal disease, congenital cataracts and glaucoma, and central nervous system developmental delays. Mutations in OCRL, which encodes an inositol polyphosphate 5-phosphatase, lead to the development of LS. The cellular mechanism responsible for neuronal dysfunction in LS is unknown. Here we show depletion of mitochondrial DNA and decrease in mitochondrial activities result in neuronal differentiation defects. Increased astrocytes, which are secondary responders to neurodegeneration, are observed in neuronal (iN) cells differentiated from Lowe patient-derived iPSCs and an LS mouse model. Inactivation of cilia-related sonic hedgehog signaling, which organizes the pattern of cellular neuronal differentiation, is observed in an OCRL knockout, iN cells differentiated from Lowe patient-derived iPSCs, and an LS mouse model. Taken together, our findings indicate that mitochondrial dysfunction and impairment of the ciliary sonic hedgehog signaling pathway represent a novel pathogenic mechanism underlying the disrupted neuronal differentiation observed in LS.
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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 | 0.000 |
| 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".