ABSTRACT The bones of the skull are important for protection of the brain. In the embryo, they form separately and expand at their growing margins, the “osteogenic fronts” (OFs). Eventually, neighboring OFs closely approximate, but remain separated by mesenchyme in a complex known as a “suture”. The bones generally do not fuse until later in human adult life. In craniosynostosis (CS), bones fuse prematurely. This can result in raised intracranial pressure and neurologic deficits including mental retardation if not surgically treated. Mutations in ~70 genes have been found that cause CS in humans, particularly for syndromic forms. Mutations in some of these genes also are found in other craniofacial malformations such as enlarged parietal foramina, frontonasal dysostosis, and craniofrontonasal syndrome. However, the genetic etiology is unknown for most CS cases, especially sporadic and non-syndromic forms. The “Transcriptome Atlases of the Craniofacial Sutures” (NIH/NIDCR U01 DE024448) grant was awarded to generate 635 RNAseq datasets of 11 different sutures, 2 to 3 subregions (OFs and mesenchyme) per suture, and 5 replicates per subregion for 2 to 3 stages (E14.5, 16.5, and/or E18.5) in wild-type mice and two CS mouse models for the FaceBase2 consortium. In this application for secondary bioinformatics analyses, the goal is to use an integrative multiscale network biology approach to elucidate the molecular processes and their key drivers in craniofacial suture formation and CS. The aims are: 1) to assemble and perform quality control on large scale datasets generated through the above grant and relevant datasets in the literature; 2) to perform differential gene, non-coding RNA (ncRNA) and splicing analyses and identify molecular changes in suture and CS development; 3) to integrate suture-related molecular data and build multiscale gene, ncRNA and splicing network models of suture and CS development. The diverse and complementary modeling techniques proposed with the uniquely large dataset of suture- related expression will generate unprecedentedly high-resolution signaling maps and identify robust key causal regulators for craniofacial suture and CS development, with implications for other craniofacial disorders and bone formation in general. Multi-scale network modeling provides an unbiased and hypothesis-free approach toward mechanism discovery at the network level. Testable hypotheses through mechanistic network models and predicted novel key regulators will be developed for future functional validations in cellular and ultimately, animal model systems by the scientific community. Thus, our network analysis will have a significant impact on the field of craniofacial and bone biology.

NARRATIVE Craniosynostosis (CS) is a common birth defect of premature fusion of skull bones that can adversely affect brain growth and lead to neurologic deficits. Our current knowledge is limited to causative mutations in ~70 different genes in humans, accounting for less than a quarter of all CS cases. Expression datasets from the developing skull bones of wild-type mice and CS mouse models are available from the FaceBase consortium. This project will apply a multiscale network biology approach to these and related datasets to elucidate the molecular processes controlling skull development & the pathogenesis of CS and other craniofacial disorders.