Spatial and Temporal Analysis of Gene Expression during Growth and Fusion of the Mouse Facial Prominences

Abstract

Orofacial malformations resulting from genetic and/or environmental causes are frequent human birth defects yet their etiology is often unclear because of insufficient information concerning the molecular, cellular and morphogenetic processes responsible for normal facial development. We have, therefore, derived a comprehensive expression dataset for mouse orofacial development, interrogating three distinct regions – the mandibular, maxillary and frontonasal prominences. To capture the dynamic changes in the transcriptome during face formation, we sampled five time points between E10.5–E12.5, spanning the developmental period from establishment of the prominences to their fusion to form the mature facial platform. Seven independent biological replicates were used for each sample ensuring robustness and quality of the dataset. Here, we provide a general overview of the dataset, characterizing aspects of gene expression changes at both the spatial and temporal level. Considerable coordinate regulation occurs across the three prominences during this period of facial growth and morphogenesis, with a switch from expression of genes involved in cell proliferation to those associated with differentiation. An accompanying shift in the expression of polycomb and trithorax genes presumably maintains appropriate patterns of gene expression in precursor or differentiated cells, respectively. Superimposed on the many coordinated changes are prominence-specific differences in the expression of genes encoding transcription factors, extracellular matrix components, and signaling molecules. Thus, the elaboration of each prominence will be driven by particular combinations of transcription factors coupled with specific cell:cell and cell:matrix interactions. The dataset also reveals several prominence-specific genes not previously associated with orofacial development, a subset of which we externally validate. Several of these latter genes are components of bidirectional transcription units that likely share cis-acting sequences with well-characterized genes. Overall, our studies provide a valuable resource for probing orofacial development and a robust dataset for bioinformatic analysis of spatial and temporal gene expression changes during embryogenesis.

Gene and metabolite time-course response to cigarette smoking in mouse lung and plasma

“…This analysis identified a novel association of CS exposure with Fam162b expression, a gene predicted to encode an integral membrane protein [35]. Slc22a12 , a urate transporter has not been previously associated with COPD.…”

MicroRNA expression profiling of the developing murine upper lip

“…However, there are no current datasets of the proteome in the developing facial processes. There have been a few studies systematically analyzing the expression of mRNAs in the developing mouse face (Feng et al ., 2009). However, one must be careful in merging miRNA expression and mRNA expression profiles because the mechanism of miRNA silencing of mRNA expression involves not only mRNA destabilization, but also inhibition of translation (Djuranovic et al ., 2012).…”

“…By focusing on a limited number of miRNAs with experimentally validated targets using the Tarbase 6.0 database (Vergoulis et al ., 2012), we can propose that mRNAs previously identified in the facial processes, and predicted to be important for mid-facial development, are regulated by miRNAs identified in the current study. For example, the expression of Zeb2 (a regulator of epithelial to mesenchymal transdifferentiation), Dkk1 (a Wnt inhibitor), and Mylip (myosin light chain interacting protein) were all increased in the maxillary process on GD11.5 when compared to the expression level of GD10.5 (Feng et al ., 2009). These mRNAs are known to be targeted by miR-200c ( Zeb2 ), miRs-292-3p and 291a-3p ( Dkk1 ), and miRs-92a and -20b ( Mylip ).…”

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

“…For a gene, this may be through expression profiling (e.g. microarray or RNAseq) of relevant tissues [see (Bhattacherjee et al, 2007; Brunskill et al, 2014; Feng et al, 2009; Mukhopadhyay et al, 2010)], co-localization in a shared structure or pathway, or human disease mapping approaches (see Section II). For a cis-regulatory enhancer element, identification may be though a variety of means, including evolutionary conservation, distinct localization of chromatin marks in the genome, or reporter based functional assays.…”