Methods in molecular biology
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Duchenne muscular dystrophy (DMD) is a severe muscle wasting X-linked genetic disease caused by dystrophin gene mutations. Gene replacement therapy aims to transfer a functional full-length dystrophin cDNA or a quasi micro/mini-gene into the muscle. ⋯ Further modification/optimization of these microgene vectors may improve the therapeutic potency. In this chapter, we describe a species-specific, codon optimization protocol to improve microdystrophin gene therapy in the mdx model.
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Duchenne muscular dystrophy (DMD) is caused by mutations that disrupt the reading frame of the human DMD gene. Selective removal of exons flanking an out-of-frame DMD mutation can result in an in-frame mRNA transcript that may be translated into an internally deleted, Becker muscular dystrophy (BMD)-like, but functionally active dystrophin protein with therapeutic activity. Antisense oligonucleotides (AOs) can be designed to bind to complementary sequences in the targeted mRNA and modify pre-mRNA splicing to correct the reading frame of a mutated transcript so that gene expression is restored. ⋯ However, it should be noted that personalized molecular medicine may be necessary, since the various reading frame-disrupting mutations are spread across the DMD gene. The different deletions that cause DMD would require skipping of different exons, which would require the optimization and clinical trial workup of many specific AOs. This chapter describes the methodologies available for the optimization of AOs, and in particular phosphorodiamidate morpholino oligomers (PMOs), for the targeted skipping of specific exons on the DMD gene.
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Presence of foreign tissue in a host's body would immediately lead to a strong immune response directed to destroy the alloantigens present in fetus and placenta. However, during pregnancy, the semiallogeneic fetus is allowed to grow within the maternal uterus due to multiple mechanisms of immune tolerance, which are discussed in this chapter.
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This chapter gives a brief overview of text-mining techniques to extract knowledge from large text collections. It describes the basis pipeline of how to come from text to relationships between biological concepts and the problems that are encountered at each step in the pipeline. We first explain how words in text are recognized as concepts. ⋯ This we call implicit information extraction. Fourth, the validation techniques to evaluate a text-mining system such as ROC curves and retrospective studies are discussed. We conclude by examining how text information can be combined with other non-textual data sources such as microarray expression data and what the future directions are for text-mining within the Internet.
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Human embryonic stem cells (hESCs) are pluripotent cells derived from the embryo at the blastocyst stage. Their embryonic origin confers upon them the capacity to proliferate indefinitely in vitro while maintaining the capacity to differentiate into a large variety of cell types. ⋯ Consequently, the possibility to expand hESCs in serum-free and in feeder-free culture conditions is becoming a major challenge to deliver the clinical promises of hESCs. Here, we describe the basic principles of growing hESCs in a chemically defined medium (CDM) devoid of serum and feeders.