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Jenkins, D.

Publications and source records attributed to Jenkins, D..

2 recordsLinked to original sources

A CRISPR/Cas9-generated zebrafish mutant implicates PPP2R3B loss in idiopathic scoliosis pathogenesis in Turner syndrome.

Idiopathic scoliosis (IS) is the deformation and/or abnormal curvature of the spine that develops progressively after birth. It is a very common condition, affecting approximately 4% of the general population, yet the genetic and mechanistic causes of IS are poorly understood. Here, we focus on PPP2R3B, which encodes a protein phosphatase 2A regulatory subunit that has been linked to regulation of autophagy. We found that PPP2R3B is expressed at sites of chondrogenesis within human foetuses, including the vertebrae. We also demonstrated prominent expression in myotome and muscle fibres in human foetuses, and zebrafish embryos and adolescents. As there is no rodent orthologue of PPP2R3B, we used CRIPSR/Cas9-mediated gene-editing to generate a series of frameshift mutations in zebrafish ppp2r3b. Adolescent zebrafish that were homozygous for this mutation exhibited a fully penetrant kyphoscoliosis phenotype which became progressively worse over time, mirroring IS in humans. These defects were associated with reduced mineralisation of vertebrae, resembling osteoporosis. Electron microscopy demonstrated abnormal mitochondria adjacent to muscle fibres, suggestive of abnormal mitophagy.

genetics

Prioritisation of Structural Variant Calls in Cancer Genomes

Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants.

genomics