J Genomics 2014; 2:121-130. doi:10.7150/jgen.8833 This volume
1. Center for Integrative Genomics, School of Biology, Georgia Institute of Technology, Atlanta, GA, USA.
2. Department of Biomedical Engineering, School of Engineering, Virginia Commonwealth University, Richmond, VA.
3. Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA.
4. Center for Craniofacial Disorders, Scottish Rite Hospital and Children's Healthcare of Atlanta.
Craniosynostosis, the premature fusion of one or more skull sutures, occurs in approximately 1 in 2500 infants, with the majority of cases non-syndromic and of unknown etiology. Two common reasons proposed for premature suture fusion are abnormal compression forces on the skull and rare genetic abnormalities. Our goal was to evaluate whether different sub-classes of disease can be identified based on total gene expression profiles. RNA-Seq data were obtained from 31 human osteoblast cultures derived from bone biopsy samples collected between 2009 and 2011, representing 23 craniosynostosis fusions and 8 normal cranial bones or long bones. No differentiation between regions of the skull was detected, but variance component analysis of gene expression patterns nevertheless supports transcriptome-based classification of craniosynostosis. Cluster analysis showed 4 distinct groups of samples; 1 predominantly normal and 3 craniosynostosis subtypes. Similar constellations of sub-types were also observed upon re-analysis of a similar dataset of 199 calvarial osteoblast cultures. Annotation of gene function of differentially expressed transcripts strongly implicates physiological differences with respect to cell cycle and cell death, stromal cell differentiation, extracellular matrix (ECM) components, and ribosomal activity. Based on these results, we propose non-syndromic craniosynostosis cases can be classified by differences in their gene expression patterns and that these may provide targets for future clinical intervention.
Keywords: Non-syndromic craniosynostosis, RNA-Seq, Transcriptome profile, Personalized medicine, Biomarkers.