«In vitro» modelling of Lesch-Nyhan Disease
Bibliographic record
Abstract
Lesch-Nyhan Disease (LND) is a rare neurodevelopmental disorder characterized by metabolic symptoms including the accumulation of uric acid crystals in the urine, hyperuricemia, and gout, and neurological symptoms including severe dystonia, intellectual disability, and chronic selfharming behaviours.The causal gene, HPRT1, has been known since 1967 but, despite 50 years of research, the mechanisms and pathways through which HPRT1 mutations cause the neurological symptoms of LND remain unknown.A primary challenge hindering progress in LND research is that traditional approaches to disease modelling have not been very effective.Many in vitro and in vivo models have been used to study LND, but each comes with substantial limitations and studies in different models have yielded at times contradictory results.This work presents the development and transcriptome profiling of three novel models of LND using short hairpin RNA knockdowns (shHPRT) in immortalized human midbrain progenitors, and patient induced pluripotent stem cell-derived forebrain-like neural (fNPCs) and midbrainlike neural progenitors (mdNPCs).These are the first human neuronal models of LND and the largest and most comprehensive transcriptomic datasets available for LND research.Using a combination of bioinformatics, targeted validation, and functional assessments, we have shown cell-type specific alterations to adenosine neurotransmission and increases of the expression of mitochondrial genes.These changes are not found in the brains of HPRT knockout mice, and emphasize the need for species and cell-type accurate models of neurodevelopmental disorders. Contributions of authorsMy contributions to this thesis include designing, performing, and analyzing all of the experiments presented, with the exceptions outlined below.In cases where data was collected by a collaborator, I prepared the samples and analyzed, interpreted and presented the results.I also developed and refined the iPSC culture and differentiation protocols used throughout.Finally, I have prepared the text and figures that make up this thesis.Science is a collaborative work, and many exceptional researchers have lent their talents to my project.Dr. Alpha Diallo and Dr. Jean Francois Theroux assisted with bioinformatic processing (chapters 2-4) Dr.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".