Transcriptomic RNAseq drug screen in cerebrocortical cultures: toward novel neurogenetic disease therapies
Bibliographic record
Abstract
Rare monogenic diseases affect millions worldwide; although over 4500 rare disease genotypes are known, disease-modifying drugs are available for only 5% of them. The sheer number of these conditions combined with their rarity precludes traditional costly drug discovery programs. An economically viable alternative is to repurpose established drugs for rare diseases. Many genetic diseases result from increased or decreased protein activity and identification of clinically approved drugs which moderate this pathogenic dosage holds therapeutic potential. To identify such agents for neurogenetic diseases, we have generated genome-wide transcriptome profiles of mouse primary cerebrocortical cultures grown in the presence of 218 blood-brain barrier (BBB) penetrant clinic-tested drugs. RNAseq and differential expression analyses were used to generate transcriptomic profiles; therapeutically relevant drug-gene interactions related to rare neurogenetic diseases identified in this fashion were further analyzed by quantitative reverse transcriptase-polymerase chain reaction, western blot and immunofluorescence. We have created a transcriptome-wide searchable database for easy access to the gene expression data resulting from the cerebrocortical drug screen (Neuron Screen) and have mined this data to identify a novel link between thyroid hormone and expression of the peripheral neuropathy associated gene Pmp22. Our results demonstrate the utility of cerebrocortical cultures for transcriptomic drug screening, and the database we have created will foster further discovery of novel links between over 200 clinic-tested BBB penetrant drugs and genes related to diverse neurologic conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".