Craniosynostosis, the premature fusion of the cranial sutures, is a heterogeneous disorder with a prevalence of ~1 in 2,200 (refs. 1,2). A specific genetic etiology can be identified in ~21% of cases3, including mutations of TWIST1, which encodes a class II basic helix-loop-helix (bHLH) transcription factor, and causes Saethre-Chotzen syndrome, typically associated with coronal synostosis4-6. Starting with an exome sequencing screen, we identified 38 heterozygous TCF12 mutations in 347 samples from unrelated individuals with craniosynostosis. The mutations predominantly occurred in patients with coronal synostosis and accounted for 32% and 10% of subjects with bilateral and unilateral pathology, respectively. TCF12 encodes one of three class I E-proteins that heterodimerize with class II bHLH proteins such as TWIST1. We show that TCF12 and TWIST1 act synergistically in a transactivation assay, and that mice doubly heterozygous for loss-of-function mutations in Tcf12 and Twist1 exhibit severe coronal synostosis. Hence, the dosage of TCF12/TWIST1 heterodimers is critical for coronal suture development.

No abstract

Craniosynostosis, the premature fusion of the cranial sutures, is a heterogeneous disorder with a prevalence of ~1 in 2, 200 (refs. 1,2). A specific genetic etiology can be identified in ~21% of cases 3 , including mutations of TWIST1, which encodes a class II basic helix-loop-helix (bHLH) transcription factor, and causes Saethre-Chotzen syndrome, typically associated with coronal URLs. ANNOVAR, http://www.openbioinformatics.org/annovar; GBrowse2, http://gmod.org/wiki/GBrowse; MRC-Holland, www.mrc-holland.com/pages/indexpag.html; PolyPhen-2, http://genetics.bwh.harvard.edu/pph2; SAMtools, http:// samtools.sourceforge.net. Accession codes.All cDNA numbering of TCF12 follows NCBI reference NM_207037.1, starting with A of the ATG initiation codon (=1). We used NM_207040.1 to design primers to the alternatively spliced first exon (9A). The genomic reference sequence is available from NC_000015.9. Europe PMC Funders GroupAuthor Manuscript Nat Genet. Author manuscript; available in PMC 2013 September 01. [4][5][6] . Starting with an exome sequencing screen, we identified 38 heterozygous TCF12 mutations in 347 samples from unrelated individuals with craniosynostosis. The mutations predominantly occurred in patients with coronal synostosis and accounted for 32% and 10% of subjects with bilateral and unilateral pathology, respectively. TCF12 encodes one of three class I E-proteins that heterodimerize with class II bHLH proteins such as TWIST1. We show that TCF12 and TWIST1 act synergistically in a transactivation assay, and that mice doubly heterozygous for loss-of-function mutations in Tcf12 and Twist1 exhibit severe coronal synostosis. Hence, the dosage of TCF12/TWIST1 heterodimers is critical for coronal suture development.Our exome sequencing approach focused on bilateral coronal craniosynostosis (Fig. 1a) because of previous evidence that this pathological group is loaded with cases of monogenic etiology 3 . We examined the variant lists from whole exome data 7 of seven unrelated patients with bilateral coronal synostosis, negative for previously described mutations 8 , for genes showing non-synonymous changes in two or more samples. Two samples had different heterozygous frameshift mutations in TCF12 (transcription factor 12; also known as HEB, HTF4 and ALF1) 9-11 ; one (Family #19) had a single nucleotide deletion and the other (#30) a 4-nucleotide deletion (details in Supplementary Table 1). The mutations were confirmed by dideoxy-sequencing of the original DNA samples (primary sequence results for all families are shown in Supplementary Fig. 1). In addition a text search of the exome sequence data revealed a novel T>A variant in TCF12 in a third sample (#22), located 20 nucleotides upstream of the start of exon 17. This variant was predicted to generate a cryptic splice acceptor site that was confirmed experimentally ( Supplementary Fig. 2a). Thus, we had identified different heterozygous disruptive mutations of TCF12 in 3 of 7 samples from patients with bilateral coronal synostosis. We interrogated oth...

Three-dimensional surgical planning is used widely in orthognathic surgery. Although numerous computer programs exist, the accuracy of soft tissue prediction remains uncertain. The purpose of this study was to compare the prediction accuracy of Dolphin, ProPlan CMF, and a probabilistic finite element method (PFEM). Seven patients (mean age 18 years; five female) who had undergone Le Fort I osteotomy with preoperative and 1-year postoperative cone beam computed tomography (CBCT) were included. The three programs were used for soft tissue prediction using planned and postoperative maxillary position, and these were compared to postoperative CBCT. Accurate predictions were obtained with each program, indicated by root mean square distances: RMS Dolphin = 1.8 AE 0.8 mm, RMS ProPlan = 1.2 AE 0.4 mm, and RMS PFEM = 1.3 AE 0.4 mm. Dolphin utilizes a landmark-based algorithm allowing for patient-specific bone-to-soft tissue ratios, which works well for cephalometric radiographs but has limited three-dimensional accuracy, whilst ProPlan and PFEM provide better three-dimensional predictions with continuous displacements. Patient or population-specific material properties can be defined in PFEM, while no soft tissue parameters are adjustable in ProPlan. Important clinical considerations are the topological differences between predictions due to the three algorithms, the

Current computational tools for planning and simulation in plastic and reconstructive surgery lack sufficient precision and are time-consuming, thus resulting in limited adoption. Although computer-assisted surgical planning systems help to improve clinical outcomes, shorten operation time and reduce cost, they are often too complex and require extensive manual input, which ultimately limits their use in doctor-patient communication and clinical decision making. Here, we present the first large-scale clinical 3D morphable model, a machine-learning-based framework involving supervised learning for diagnostics, risk stratification, and treatment simulation. The model, trained and validated with 4,261 faces of healthy volunteers and orthognathic (jaw) surgery patients, diagnoses patients with 95.5% sensitivity and 95.2% specificity, and simulates surgical outcomes with a mean accuracy of 1.1 ± 0.3 mm. We demonstrate how this model could fully-automatically aid diagnosis and provide patient-specific treatment plans from a 3D scan alone, to help efficient clinical decision making and improve clinical understanding of face shape as a marker for primary and secondary surgery.