A. Murat Maga

Recent studies have shown that exposure to some nutritional supplements and chemicals in utero can affect the epigenome of the developing mouse embryo, resulting in adult disease. Our hypothesis is that epigenetics is also involved in the gestational programming of adult phenotype by alcohol. We have developed a model of gestational ethanol exposure in the mouse based on maternal ad libitum ingestion of 10% (v/v) ethanol between gestational days 0.5-8.5 and observed changes in the expression of an epigenetically-sensitive allele, Agouti viable yellow (A(vy)), in the offspring. We found that exposure to ethanol increases the probability of transcriptional silencing at this locus, resulting in more mice with an agouti-colored coat. As expected, transcriptional silencing correlated with hypermethylation at A(vy). This demonstrates, for the first time, that ethanol can affect adult phenotype by altering the epigenotype of the early embryo. Interestingly, we also detected postnatal growth restriction and craniofacial dysmorphology reminiscent of fetal alcohol syndrome, in congenic a/a siblings of the A(vy) mice. These findings suggest that moderate ethanol exposure in utero is capable of inducing changes in the expression of genes other than A(vy), a conclusion supported by our genome-wide analysis of gene expression in these mice. In addition, offspring of female mice given free access to 10% (v/v) ethanol for four days per week for ten weeks prior to conception also showed increased transcriptional silencing of the A(vy) allele. Our work raises the possibility of a role for epigenetics in the etiology of fetal alcohol spectrum disorders, and it provides a mouse model that will be a useful resource in the continued efforts to understand the consequences of gestational alcohol exposure at the molecular level.

are generating high-resolution microCT scans of vertebrates by the thousands. Data from these projects are shared with the community using aggregate 3D specimen repositories like MorphoSource through various open licenses. We anticipate an explosion of quantitative research in organismal biology with the convergence of available data and the methodologies to analyze them. 2. Though the data are available, the road from a series of images to analysis is fraught with challenges for most biologists. It involves tedious tasks of data format conversions, preserving spatial scale of the data accurately, 3D visualization and segmentations, acquiring measurements and annotations. When scientists use commercial software with proprietary formats, a roadblock for data exchange, collaboration, and reproducibility is erected that hurts the efforts of the scientific community to broaden participation in research. 3. We developed SlicerMorph as an extension of 3D Slicer, a biomedical visualization and analysis ecosystem with extensive visualization and segmentation capabilities built on proven python-scriptable open-source libraries such as Visualization Toolkit and Insight Toolkit. In addition to the core functionalities of Slicer, SlicerMorph provides users with modules to conveniently retrieve open-access 3D models or import users own 3D volumes, to annotate 3D curve and patch-based landmarks, generate landmark templates, conduct geometric morphometric analyses of 3D organismal form using both landmark-driven and landmark-free approaches, and create 3D animations from their results. We highlight how these individual modules can be tied together to establish complete workflow(s) from image sequence to morphospace. Our software development efforts were supplemented with short courses and workshops that cover the fundamentals of 3D imaging and morphometric analyses as it applies to study of organismal form and shape in evolutionary biology. 4. Our goal is to establish a community of organismal biologists centered around Slicer and SlicerMorph to facilitate easy exchange of data and results and collaborations using 3D specimens. Our proposition to our colleagues is that using a common open platform supported by a large user and developer community ensures the longevity and sustainability of the tools beyond the initial development effort.

Landmark-based geometric morphometrics has emerged as an essential discipline for the quantitative analysis of size and shape in ecology and evolution. With the ever-increasing density of digitized landmarks, the possible development of a fully automated method of landmark placement has attracted considerable attention. Despite the recent progress in image registration techniques, which could provide a pathway to automation, three-dimensional (3D) morphometric data are still mainly gathered by trained experts. For the most part, the large infrastructure requirements necessary to perform image-based registration, together with its system specificity and its overall speed, have prevented its wide dissemination.Here, we propose and implement a general and lightweight point cloud-based approach to automatically collect high-dimensional landmark data in 3D surfaces (Automated Landmarking through Point cloud Alignment and Correspondence Analysis). Our framework possesses several advantages compared with image-based approaches. First, it presents comparable landmarking accuracy, despite relying on a single, random reference specimen and much sparser sampling of the structure's surface. Second, it can be efficiently run on consumer-grade personal computers. Finally, it is general and can be applied at the intraspecific level to any biological structure of interest, regardless of whether anatomical atlases are available.Our validation procedures indicate that the method can recover intraspecific patterns of morphological variation that are largely comparable to those obtained by manual digitization, indicating that the use of an automated landmarking approach should not result in different conclusions regarding the nature of multivariate patterns of morphological variation.The proposed point cloud-based approach has the potential to increase the scale and reproducibility of morphometrics research. To allow ALPACA to be used out-of-the-box by users with no prior programming experience, we implemented it as a SlicerMorph module. SlicerMorph is an extension that enables geometric morphometrics data collection and 3D specimen analysis within the open-source 3D Slicer biomedical visualization ecosystem. We expect that convenient access to this platform will make ALPACA broadly applicable within ecology and evolution.

The premature fusion of the paired frontal bones results in metopic craniosynostosis (MC) and gives rise to the clinical phenotype of trigonocephaly. Deletions of chromosome 9p22.3 are well described as a cause of MC with variably penetrant midface hypoplasia. In order to identify the gene responsible for the trigonocephaly component of the 9p22.3 syndrome, a cohort of 109 patients were assessed by high-resolution arrays and MLPA for copy number variations (CNVs) involving 9p22. Five CNVs involving FREM1, all of which were de novo variants, were identified by array-based analyses. The remaining 104 patients with MC were then subjected to targeted FREM1 gene re-sequencing, which identified 3 further mutant alleles, one of which was de novo. Consistent with a pathogenic role, mouse Frem1 mRNA and protein expression was demonstrated in the metopic suture as well as in the pericranium and dura mater. Micro-computed tomography based analyses of the mouse posterior frontal (PF) suture, the human metopic suture equivalent, revealed advanced fusion in all mice homozygous for either of two different Frem1 mutant alleles, while heterozygotes exhibited variably penetrant PF suture anomalies. Gene dosage-related penetrance of midfacial hypoplasia was also evident in the Frem1 mutants. These data suggest that CNVs and mutations involving FREM1 can be identified in a significant percentage of people with MC with or without midface hypoplasia. Furthermore, we present Frem1 mutant mice as the first bona fide mouse model of human metopic craniosynostosis and a new model for midfacial hypoplasia.