Dr Claire Smith

Post-Doctoral Researcher

0113 343 8455

Location: Room 8.17, Wellcome Trust Brenner Building


After completing a degree in biology at the University of Nottingham in 2004, I accepted a position as a research technician with the Faculty of Biological Sciences, at the University of Leeds. I studied the epigenetic regulation of transgene expression in various plant species and on the effects of mutation in the moss, Physcomitrella patens.

In 2009 I became a research assistant within the Section of Genetics, LIMM, Faculty of Medicine and Health studying the expression of PLAGL1, a gene implicated in cancer, transient neonatal diabetes mellitis and intrauterine growth restriction. I then worked as a research technician within Molecular Epidemiology, LICAMM, Faculty of Medicine and Health where I studied the detection of aflatoxin-albumin adducts as an epidemiological marker of aflatoxin exposure.

From 2010 to 2013 I worked as a research assistant within the Section of Genetics on the EU FP7 project; Nuclease Immune Mediated Brain and Lupus-like conditions: natural history, pathophysiology, diagnostic and therapeutic modalities with application to other disorders of autoimmunity. My work specifically focused on producing various cell and animal models of Aicardi Goutières Syndrome and analysing them by RNA sequencing, co-immunoprecipitation, immunofluorescence, immunohistochemistry and western blotting.

In 2013, I began my PhD, studying amelogenesis imperfecta (AI), a genetic condition resulting in defective tooth enamel. My studies focused on a syndromic form of AI, Heimler syndrome. This condition also encompasses sensorineural hearing loss and often also retinal dystrophy. By whole exome sequencing of affected individuals, I identified mutations in the peroxisomal biogenesis factor genes, PEX1 and PEX6, as the cause of disease. I also studied a non-syndromic hypomineralised form of AI and identified a heterozygous multi-exon deletion in the amelotin gene in affected individuals. My thesis was entitled “Blindness, hearing loss and brown crumbly teeth; determining the molecular basis of Heimler and Heimler plus syndromes and other related conditions” and my PhD was awarded in November 2016.

In April 2016 I was awarded Wellcome Trust Institutional Strategic Support funding for 1 year to complete research that followed on from my PhD. During my PhD, I identified AI-causing mutations in over 45 families. Where teeth were available from these families, enamel phenotyping of genotyped AI teeth and matched controls was carried out so that the effects of the mutations on the resulting enamel could be determined. Teeth analysed included those from patients with ACPT, KLK4 and LAMB3 mutations.

With an aim to cataloguing all published AI mutations, I established an Leiden Open Variant Database for AI which can be access here: http://dna2.leeds.ac.uk/LOVD/ I continue to curate published AI variants to provide a useful resource for fellow researchers and clinicians around the world. This database forms the basis of a review on the genetics of AI: https://doi.org/10.3389/fphys.2017.00435

My current research is as part of the UK Inherited Retinal Dystrophies Consortium team, jointly funded by charities RP Fighting Blindness and Fight For Sight. This project aims to identify the human gene variants that underlie Inherited Retinal Dystrophies (IRDs). Methods used include targetted sequencing via capture by use of molecular inversion probe, whole exome sequencing, whole genome sequencing, transcriptomics and metabolomics. This research is a UK wide collaboration to study a cohort of over 500 families and ultimately aims to translate diagnostic protocols developed from this research to an NHS clinical setting. The research will better catalogue the scope of variants (including structural, copy number and intronic variants) within known disease genes but also aims to identify new genes for IRDs through the use of large pre-screened cohorts. I use bioinformatics analyses including developing alignment, variant calling and filtering pipelines to identify variants and assess their pathogenicity by in silico and in vitro analysis. The large amount of sequencing data are processed using the Medical Advanced Research Computing 1 (http://arc.leeds.ac.uk/systems/marc1/) large memory node cluster at the University of Leeds.