Navigating the rare diseases landscape

Описание: Title:
Navigating the rare diseases landscape: a comprehensive approach to identify gene therapy targets based on cell type-phenotype associations

Abstract:
Rare diseases (RDs) are individually uncommon, but collectively they contribute to an enormous global disease burden. Yet we still do not understand the biological mechanisms by which most of these diseases act. Therefore, we utilised the gene Human Phenotype Ontology gene annotations and single-cell transcriptomic atlases to identify the cell types underlying 6,000+ phenotypes associated with 8,000+ RDs. Our results both confirm well-known cell type-phenotype relationships and reveal previously unknown connections. We also demonstrate that the particular cell types underlying phenotypes (e.g. neonatal hypotonia, brachydactyly) predict differential clinical outcomes (age of death, severity) across diseases, opening avenues for mechanism-driven differential diagnosis in the clinic. Next, we identified candidate gene therapy targets based on phenotype severity, onset, and viral vector compatibility. Top candidates included respiratory failure (alveolar cells via CCNO), mental deterioration (neurons via APOE/CSTB), and coma (islet endocrine cells via INS/KCNJ11). Finally, we provide a user-friendly web app to enable clinicians, researchers, and patients to trace disease mechanisms down to the level of symptoms, cell types and genes. In summary, our findings have important implications for understanding disease biology at multi-scale resolution, and for the development of gene therapies to treat patients in a more targeted, mechanism-driven manner.

Presented by Brian Schilder.(  / brian-schilder  .

Linked Resources:
Rare Disease Celltyping Portal (https://neurogenomics.github.io/rare_... )
HPOExplorer (https://github.com/neurogenomics/HPOE...)
MultiEWCE (https://github.com/neurogenomics/Mult...)

Linked Publications:
Identification of cell type-specific gene targets underlying thousands of rare diseases and subtraits
(https://doi.org/10.1101/2023.02.13.23...)