Methods in molecular biology
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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.
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The adaptation of the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR associated endonuclease 9 (CRISPR-Cas9) machinery from prokaryotic organisms has resulted in a gene editing system that is highly versatile, easily constructed, and can be leveraged to generate human cells knocked out (KO) for a specific gene. While standard transfection techniques can be used for the introduction of CRISPR-Cas9 expression cassettes to many cell types, delivery by this method is not efficient in many primary cell types, including primary human airway epithelial cells (AECs). More efficient delivery in AECs can be achieved through lentiviral-mediated transduction, allowing the CRISPR-Cas9 system to be integrated into the genome of the cell, resulting in stable expression of the nuclease machinery and increasing editing rates. ⋯ Applying these methods, we detail here our latest protocol to generate mucociliary epithelial cultures knocked out for a specific gene from donor-isolated primary human basal airway epithelial cells. This protocol includes methods to: (1) design and generate lentivirus which targets a specific gene for KO with CRISPR-Cas9 machinery, (2) efficiently transduce AECs, (3) culture and select for a bulk edited AEC population, (4) molecularly screen AECs for Cas9 cutting and specific sequence edits, and (5) further expand and differentiate edited cells to a mucociliary airway epithelial culture. The AEC knockouts generated using this protocol provide an excellent primary cell model system with which to characterize the function of genes involved in airway dysfunction and disease.
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Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.
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In the World Health Organization (WHO) classification, adenocarcinoma of esophagus comprises preinvasive type (dysplasia), adenocarcinoma, adenoid cystic carcinoma, adenosquamous carcinoma, and mucoepidermoid carcinoma. For adenocarcinoma, it is important to determine the grading of the cancer and histological variants such as signet ring adenocarcinoma. In the current day management of esophageal adenocarcinoma by neoadjuvant therapy, the histology of the cancer and the lymph nodal status may change after the therapy. Tumor regression grading systems could be used to assess the response to the neoadjuvant therapy in esophageal adenocarcinoma.