Methods in molecular biology
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DNA methylation is a covalent modification of DNA that plays important roles in processes such as the regulation of gene expression, transcription factor binding, and suppression of transposable elements. The use of whole genome bisulfite sequencing (WGBS) enables the genome-wide identification and quantification of DNA methylation patterns at single-base resolution and is the gold standard for analysis of DNA methylation. Computational analysis of WGBS data can be particularly challenging, as many computationally intensive steps are required. ⋯ Second, DNA methylation levels are estimated at each cytosine position using the aligned sequence reads of the bisulfite treated DNA. Third, regions of differential cytosine methylation between samples can be identified. Finally, these data need to be visualized and interpreted in the context of the biological question at hand.
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Exon skipping is an emerging approach to treating Duchenne muscular dystrophy (DMD), one of the most common lethal genetic disorders. Exon skipping uses synthetic antisense oligonucleotides (AONs) to splice out frame-disrupting exon(s) of DMD mRNA to restore the reading frame of the gene products and produce truncated yet functional proteins. ⋯ Although the success of multiple exon skipping in a DMD dog model has made a significant impact on the development of therapeutics for DMD, unmodified AONs such as phosphorodiamidate morpholino oligomers (PMOs) have little efficacy in cardiac muscles. Here, we describe our technique of intravenous injection of a cocktail of peptide-conjugated PMOs (PPMOs) to skip multiple exons, exons 6 and 8, in both skeletal and cardiac muscles in dystrophic dogs and the evaluation of the efficacy and toxicity.
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Studies in psychoneuroimmunology (PNI) would provide better insights into the "whole mind-body system." Systems biology models of the complex adaptive systems (CASs), such as a conceptual framework of "Yin-Yang dynamics," may be helpful for identifying systems-based biomarkers and targets for more effective prevention and treatment. The disturbances in the Yin-Yang dynamical balance may result in stress, inflammation, and various disorders including insomnia, Alzheimer's disease, obesity, diabetes, cardiovascular diseases, skin disorders, and cancer. At the molecular and cellular levels, the imbalances in the cytokine pathways, mitochondria networks, redox systems, and various signaling pathways may contribute to systemic inflammation. ⋯ The studies of cancer have revealed the importance of the Yin-Yang dynamics in the tumoricidal and tumorigenic activities of the immune system. Stress-induced neuroimmune imbalances are also essential in chronic skin disorders including atopic dermatitis and psoriasis. With the integrative framework, the restoration of the Yin-Yang dynamics can become the objective of dynamical systems medicine.
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Spinal muscular atrophy (SMA) is one of the most common genetic causes of infantile death arising due to mutations in the SMN1 gene and the subsequent loss of motor neurons. With the discovery of the intronic splicing silencer N1 (ISS-N1) as a potential target for antisense therapy, several antisense oligonucleotides (ASOs) are being developed to include exon 7 in the final mRNA transcript of the SMN2 gene and thereby increasing the production of spinal motor neuron (SMN) proteins. Nusinersen (spinraza), a modified 2'-O-methoxyethyl (MOE) antisense oligonucleotide is the first drug to be approved by Food and Drug Agency (FDA) in December of 2016. Here we briefly review the pharmacological relevance of the drug, clinical trials, toxicity, and future directions following the approval of nusinersen.
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Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. ⋯ Those additional drug properties can aid in gaining higher accuracy for the identification of drug target and mechanism of action. We then progress to discuss using these targets in combination with disease-specific expression patterns, known pathways, and genetic interaction networks to aid drug choice. Finally, we conclude this chapter with a general overview of machine learning methods that can integrate multiple pieces of sequencing data along with prior drug or biological knowledge to drastically improve response prediction.