Eric Vallabh Minikel

This study demonstrates that deep PrP lowering with a potent siRNA prevents prion disease symptoms in mice and extends survival in symptomatic animals.

The paper describes BI-TfR1 CapX, a gene delivery vector that efficiently crosses the blood-brain barrier, transduces CNS neurons and astrocytes, and reduces Prnp mRNA with low doses.

The paper measures PrP half-life in vivo and shows it limits the time to effect of PrP-lowering therapies, with implications for drug development and clinical trial design.

Divalent siRNA 2439-s4 lowers human PrP to 17% residual in transgenic mouse brain after one dose, and a separate mouse Prnp-targeting siRNA extends survival in prion-infected mice.

The paper assesses the cell-type-specific activity of intrathecally delivered antisense oligonucleotides in deep brain regions of non-human primates, finding broad target engagement across neuronal and non-neuronal cells.

The paper shows that human genetic evidence can identify drug side effects by linking drug targets to traits similar to the side effects.

This study demonstrates that Zinc Finger Repressors (ZFRs) reduce prion protein (PrP) expression in nonhuman primates and extend survival in prion disease mice.

This study identifies a cryptic exon in the human PRNP gene that can reduce prion protein expression when included in mRNA, offering a new therapeutic target for prion disease.

The paper maps antisense oligonucleotide activity in the brain using single-cell transcriptomics in mice and macaques, showing widespread target suppression across cell types with varying durations.

This study tests anle138b in mouse models of prion disease, finding improved survival in RML strain models but no clear endpoints in genetic mutation models.

The paper evaluates the feasibility of using non-human primate models to assess prion disease therapeutic efficacy by analyzing incubation times and model characteristics.

This study shows that lowering prion protein expression with ASOs is effective in treating prion disease across different strains, treatment times, and endpoints.

The paper evaluates potential drug targets using loss-of-function genetic variants from gnomAD, highlighting the importance of deep curation and targeted recall studies for drug discovery.

From 125,748 exomes and 15,708 genomes, 443,769 high-confidence predicted loss-of-function variants yield LOEUF, a continuous gene intolerance score tracking mouse lethality, cell essentiality, and disease associations.

The paper develops an expression-based annotation metric to improve rare variant interpretation by filtering false-positive loss-of-function variants and identifying pathogenic variants in highly expressed exons.

Short-read genome sequencing of 14,891 diverse genomes yields 433,371 structural variants (SVs) for gnomAD; loss-constrained genes also resist duplication, and clinical SV carrier rates are benchmarked.

The paper analyzes loss-of-function variants in LRRK2 across large genomic datasets to assess the safety of partial LRRK2 inhibition as a Parkinson's disease therapy.

The paper systematically studies uORF variants in 15,708 genomes, showing they are under strong negative selection and contribute to disease.

The paper analyzes multi-nucleotide variants (MNVs) in 125,748 exomes and 15,708 genomes, identifying their distribution, mutational origins, and functional implications.

The paper develops a targeted mass spectrometry method to quantify prion protein in CSF, confirming that reduced PrP levels in prion disease are due to the disease process, not ELISA artifacts.

This paper quantifies the challenges of designing preventive clinical trials for genetic prion disease due to high variability in age of onset, suggesting the need for biomarker-based trials.

This study evaluates ELISA-based quantification of PrP in CSF as a biomarker for PrP-reducing therapeutics, showing its reliability and CNS origin.

The paper responds to a critique of their variant filtering method, demonstrating its robustness and highlighting the benefits of using high-resolution variant frequencies for clinical genome interpretation.

The paper presents a statistical framework to filter candidate disease-causing variants using high-resolution variant frequencies, reducing the number of variants under consideration and reclassifying some as pathogenic.