The Msx1 Homeoprotein Recruits G9a Methyltransferase to Repressed Target Genes in Myoblast Cells

Although the significance of lysine modifications of core histones for regulating gene expression is widely appreciated, the mechanisms by which these modifications are incorporated at specific regulatory elements during cellular differentiation remains largely unknown. In our previous studies, we have shown that in developing myoblasts the Msx1 homeoprotein represses gene expression by influencing the modification status of chromatin at its target genes. We now show that genomic binding by Msx1 promotes enrichment of the H3K9me2 mark on repressed target genes via recruitment of G9a histone methyltransferase, the enzyme responsible for catalyzing this histone mark. Interaction of Msx1 with G9a is mediated via the homeodomain and is required for transcriptional repression and regulation of cellular differentiation, as well as enrichment of the H3K9me2 mark in proximity to Msx1 binding sites on repressed target genes in myoblast cells as well as the developing limb. We propose that regulation of chromatin status by Msx1 recruitment of G9a and other histone modifying enzymes to regulatory regions of target genes represents an important means of regulating the gene expression during development

Automated image segmentation of haematoxylin and eosin stained skeletal muscle cross-sections

The ability to accurately and efficiently quantify muscle morphology is essential to determine the physiological relevance of a variety of muscle conditions including growth, atrophy and repair. There is agreement across the muscle biology community that important morphological of characteristics of muscle fibres, such as cross-sectional area, are critical factors that determine the health and function (e.g. quality) of the muscle. However, at this time, quantification of muscle characteristics, especially from haematoxylin and eosin stained slides, is still a manual or semi-automatic process. This procedure is labour-intensive and time-consuming. In this paper, we have developed and validated an automatic image segmentation algorithm that is not only efficient but also accurate. Our proposed automatic segmentation algorithm for haematoxylin and eosin stained skeletal muscle cross-sections consists of two major steps: (1) A learning-based seed detection method to find the geometric centres of the muscle fibres, and (2) a colour gradient repulsive balloon snake deformable model that adopts colour gradient in Luv colour space. Automatic quantification of muscle fibre cross-sectional areas using the proposed method is accurate and efficient, providing a powerful automatic quantification tool that can increase sensitivity, objectivity and efficiency in measuring the morphometric features of the haematoxylin and eosin stained muscle cross-sections