CRISPR/Cas9-Mediated Gene Editing of ARG1 in Cell Lines and Primary Cells

Arginase 1 deficiency, a urea cycle disorder resulting from an inability of the body to convert arginine into urea, results in hyperargininemia and sporadic episodes of hyperammonemia. Arginase 1 deficiency can lead to a range of developmental disorders and progressive spastic diplegia in children, and current therapeutic options are limited. Clustered regularly interspaced short palindromic repeat (CRISPR) /CRISPR associated protein (Cas) 9 gene editing systems serve as a novel means of treating genetic disorders such as Arginase 1 (ARG1) deficiency, and must be thoroughly examined to determine their curative capabilities. In these experiments numerous guide RNAs and CRISPR/Cas9 systems targeting the ARG1 gene were designed and observed by heteroduplex assay for their targeting capabilities and cleavage efficiencies in multiple cell lines. The CRISPR/Cas9 system utilized in these experiments, along with a panel of guide RNAs targeting various locations in the arginase 1 gene, successfully produced targeted cleavage in HEK293, MCF7, A549, K562, HeLa, and HepG2 cells; however, targeted cleavage in human dermal fibroblasts, blood outgrowth endothelial cells, and induced pluripotent stem cells was not observed. Additionally, a CRISPR/Cas system involving partially inactivated Cas9 was capable of producing targeted DNA cleavage in intron 1 of ARG1, while a Cas protein termed Cpf1 was incapable of producing targeted cleavage. These results indicate a complex set of variables determining the CRISPR/Cas9 systems’ capabilities in the cell lines and primary cells tested. By examining epigenetic factors and alternative CRISPR/Cas9 gene targeting systems, the CRISPR/Cas9 system can be more thoroughly considered in its ability to act as a means towards editing the genome of arginase 1-deficient individuals.

On The Dirichlet Distribution

The Dirichlet distribution is a multivariate generalization of the Beta distribution. It is an important multivariate continuous distribution in probability and statistics. In this report, we review the Dirichlet distribution and study its properties, including statistical and information-theoretic quantities involving this distribution. Also, relationships between the Dirichlet distribution and other distributions are discussed. There are some different ways to think about generating random variables with a Dirichlet distribution. The stick-breaking approach and the Pólya urn method are discussed. In Bayesian statistics, the Dirichlet distribution and the generalized Dirichlet distribution can both be a conjugate prior for the Multinomial distribution. The Dirichlet distribution has many applications in different fields. We focus on the unsupervised learning of a finite mixture model based on the Dirichlet distribution. The Initialization Algorithm and Dirichlet Mixture Estimation Algorithm are both reviewed for estimating the parameters of a Dirichlet mixture. Three experimental results are shown for the estimation of artificial histograms, summarization of image databases and human skin detection.