The old and new face of craniofacial research: How animal models inform human craniofacial genetic and clinical data

Otterloo, Eric Van; Williams, Trevor; Artinger, Kristin

doi:10.1016/j.ydbio.2016.01.017

Cited by 64 publications

(63 citation statements)

References 204 publications

(187 reference statements)

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“…The ectoderm also receives critical signaling input from mesenchyme to regulate its growth, to maintain its competence to drive face formation and to diversify its derivatives. Human clinical analyses and animal model studies have shown that normal face formation requires the integration of multiple signals between the ectoderm and mesenchyme (reviewed in Van Otterloo et al, 2016; Yuan et al, 2016). Some of these – including Fibroblast Growth Factors (Fgfs), Bone Morphogenetic proteins (BMPs), Wnts, Hedgehogs (Hhs), Platelet Derived Growth Factors (PDGFs), Retinoic Acid (RA), and endothelin – are well known, but others remain to be identified.…”

Section: Introductionmentioning

confidence: 99%

Systems biology of facial development: contributions of ectoderm and mesenchyme

Hooper

Feng

et al. 2017

Developmental Biology

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The rapid increase in gene-centric biological knowledge coupled with analytic approaches for genomewide data integration provides an opportunity to develop systems-level understanding of facial development. Experimental analyses have demonstrated the importance of signaling between the surface ectoderm and the underlying mesenchyme in coordinating facial patterning. However, current transcriptome data from the developing vertebrate face is dominated by the mesenchymal component, and the contributions of the ectoderm are not easily identified. We have generated transcriptome datasets from critical periods of mouse face formation that enable gene expression to be analyzed with respect to time, prominence, and tissue layer. Notably, by separating the ectoderm and mesenchyme we considerably improved the sensitivity compared to data obtained from whole prominences, with more genes detected over a wider dynamic range. From these data we generated a detailed description of ectoderm-specific developmental programs, including pan-ectodermal programs, prominence- specific programs and their temporal dynamics. The genes and pathways represented in these programs provide mechanistic insights into several aspects of ectodermal development. We also used these data to identify co-expression modules specific to facial development. We then used 14 co-expression modules enriched for genes involved in orofacial clefts to make specific mechanistic predictions about genes involved in tongue specification, in nasal process patterning and in jaw development. Our multidimensional gene expression dataset is a unique resource for systems analysis of the developing face; our co-expression modules are a resource for predicting functions of poorly annotated genes, or for predicting roles for genes that have yet to be studied in the context of facial development; and our analytic approaches provide a paradigm for analysis of other complex developmental programs.

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Section: Introductionmentioning

confidence: 99%

Systems biology of facial development: contributions of ectoderm and mesenchyme

Hooper

Feng

et al. 2017

Developmental Biology

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“…Animal models have been extensively and successfully used to study human diseases (Khokha et al, ; Lovely et al, ; van Otterloo, Williams, & Artinger, ). Animal models provide researchers with a consistent, reproducible, genetically manipulatable, platform in which to study the underlying causes of birth defects.…”

Section: The Utility Of Animal Modelsmentioning

confidence: 99%

“…Animal models provide researchers with a consistent, reproducible, genetically manipulatable, platform in which to study the underlying causes of birth defects. Animal models have also proven critical in driving our understanding of the mechanisms underlying gene–environment interactions and birth defects in general and this has been reviewed extensively elsewhere (Khokha et al, ; Lovely et al, 2016; van Otterloo et al, ). In this review, we will focus on the use of animal models to identify and characterize gene–environment interactions.…”

Section: The Utility Of Animal Modelsmentioning

confidence: 99%

Animal models of gene–alcohol interactions

Lovely

2019

Birth Defects Research

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Most birth defects arise from complex interactions between multiple genetic and environmental factors. However, our current understanding of how these interactions and their contributions affect birth defects remains incomplete. Human studies are limited in their ability to identify the fundamental causes of birth defects due to ethical and practical limitations. Animal models provide a great number of resources not available to human studies and they have been critical in advancing our understanding of birth defects and the complex interactions that underlie them. In this review, we discuss the use of animal models in the context of gene–environment interactions that underlie birth defects. We focus on alcohol which is the most common environmental factor associated with birth defects. Prenatal alcohol exposure leads to a wide range of cognitive impairments and structural deficits broadly termed fetal alcohol spectrum disorders (FASD). We discuss the broad impact of prenatal alcohol exposure on the developing embryo and elaborate on the current state of gene–alcohol interactions. Additionally, we discuss how animal models have informed our understanding of the genetics of FASD. Ultimately, these topics will provide insight into the use of animal models in understanding gene–environment interactions and their subsequent impact on birth defects.

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“…Specialized databases cataloguing expression of genes in specific murine tissues such as the ABA, the Gene Expression Nervous System Atlas (GENSAT; the nervous system; http://www.gensat.org/; [Gong et al, 2003]), the GenitoUrinary Development Molecular Anatomy Project (GUDMAP; the gonads, reproductive tract, kidney and urinary tract; http://www.gudmap.org/; [Brunskill et al, 2008]) and FaceBase (curated craniofacial data from mouse, human and zebrafish; https://www.facebase.org; [Van Otterloo et al, 2016]) have now been integrated into broader databases. These collaborations bring together specialist expression data with that from projects on the whole embryo such as the Edinburgh Mouse Atlas of Gene Expression (EMAGE; [Hill et al, 2004]; http://www.emouseatlas.org/emage/) and the Mouse Genome Informatics (MGI) Gene Expression Database (GXD; [Christiansen et al, 2006]; http://www.informatics.jax.org/expression.shtml).…”

Section: Online Tools For Generating and Analyzing The Genes-of-intermentioning

confidence: 99%

Leveraging Online Resources to Prioritize Candidate Genes for Functional Analyses: Using the Fetal Testis as a Test Case

McClelland

Yao²

2017

Sex Dev

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With each new microarray or RNA-seq experiment, massive quantities of transcriptomic information are generated with the purpose to produce a list of candidate genes for functional analyses. Yet an effective strategy remains elusive to prioritize the genes on these candidate lists. In this review, we outline a prioritizing strategy by taking a step back from the bench and leveraging the rich range of public databases. This in silico approach provides an economical, less biased, and more effective solution. We discuss the publicly available online resources that can be used to answer a range of questions about a gene. Is the gene of interest expressed in the system of interest (using expression databases)? Where else is this gene expressed (using added-value transcriptomic resources)? What pathways and processes is the gene involved in (using enriched gene pathway analysis and mouse knockout databases)? Is this gene correlated with human diseases (using human disease variant databases)? Using mouse fetal testis as an example, our strategies identified 298 genes annotated as expressed in the fetal testis. We cross-referenced these genes to existing microarray data and narrowed the list down to cell-type-specific candidates (35 for Sertoli cells, 11 for Leydig cells, and 25 for germ cells). Our strategies can be customized so that they allow researchers to effectively and confidently prioritize genes for functional analysis.

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The old and new face of craniofacial research: How animal models inform human craniofacial genetic and clinical data

Cited by 64 publications

References 204 publications

Systems biology of facial development: contributions of ectoderm and mesenchyme

Systems biology of facial development: contributions of ectoderm and mesenchyme

Animal models of gene–alcohol interactions

Leveraging Online Resources to Prioritize Candidate Genes for Functional Analyses: Using the Fetal Testis as a Test Case

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