A comparative gene expression analysis of Emery-Dreifuss muscular dystrophy using a cDNA microarray.

Abstract

AbstractThe muscular dystrophies are a genetically heterogeneous group of disorders characterized by progressive wasting, weakness, and degeneration of the skeletal muscle. The types of muscular dystrophy have been classified according to clinical symptoms, disease progression, inheritance pattern, and genetic loci. Pathological changes, such as necrotic and regenerating fibers and inflammatory cells, present in biopsied muscle samples from muscular dystrophy cases, are caused by abnormalities in the muscle membrane. Defects in the plasma membrane or in extracellular matrix-associated proteins have been identified in muscular dystrophy cases, which leads to a fragile sarco-lemma. In fact, most of the genetic defects responsible for muscular dystrophy pathology reside in genes that encode plasma membrane or extracellular matrix-associated proteins (1–4). However, there are some muscular dystrophy genes that encode products not associated with the plasma membrane. In particular, deficiencies in two nuclear membrane associated proteins; emerin and lamin A/C, result in Emery-Dreifuss muscular dystrophy (EDMD) (5–8). EDMD is an inherited muscular disorder that displays three characteristics: early contracture of the elbows, Achilles’ tendons, and postcervical muscles; slowly progressive muscle wasting and weakness; and cardiomy-opathy with severe conduction block. Most families with EDMD show an X-linked recessive inheritance, but a few autosomal dominant forms have been reported. Therefore, EDMD is a genetically heterogeneous disorder. The product of the gene responsible for the X-linked recessive EDMD is emerin, whereas that for the autosomal dominant form of EDMD is lamin A/C.KeywordsMuscular DystrophycDNA MicroarrayDuchenne Muscular DystrophyAutosomal Dominant FormMolecular PortraitThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.