Improved efficiency of genome editing by constitutive expression of Cas9 endonuclease in genetically-modified mice

Despite its convenience and precision, CRISPR-based gene editing approaches still suffer from off-target effects and low efficiencies, which are partially rooted in Cas9, the nuclease component of the CRISPR/Cas9 system. In this study, we showed how mouse genome editing efficiency can be improved by constitutive and inheritable expression of Cas9 nuclease. For this goal, a transgenic mouse line expressing the Cas9 protein (Cas9-mouse) was generated. For in vitro assessment of gene editing efficiency, the Cas9-mice were crossed with the EGFP-mice to obtain mouse embryonic fibroblasts (MEF) expressing both EGFP and Cas9 (MEFCas9-EGFP). Transfection of these cells with in vitro transcribed (IVT) EGFP sgRNA or phU6-EGFPsgRNA plasmid led to robust decrease of Mean Fluorescent Intensity (MFI) to 8500 ± 1025 a.u. and 13,200 ± 1006 a.u. respectively. However, in the control group, in which the MEFEGFP cells were transfected with a pX330-EGFPsgRNA plasmid, the measured MFI was 16,800 ± 2254 a.u. For in vivo assessment, the Cas9-zygotes at two pronuclei stage (2PN) were microinjected with a phU6-HhexsgRNA vector and the gene mutation efficiency was compared with the wild-type (WT) zygotes microinjected with a pX330-HhexsgRNA plasmid. The analysis of born mice showed that while the injection of Cas9-zygotes resulted in 43.75 Hhex gene mutated mice, it was just 15.79 for the WT zygotes. In conclusion, the inheritable and constitutive expression of Cas9 in mice provides an efficient platform for gene editing, which can facilitate the production of genetically-modified cells and animals. © 2021, King Abdulaziz City for Science and Technology

Data for: Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: the role of SPAG1 and PLEKHF2

The present study aimed to identify the genes associated with the involvement of adjunct lymph nodes of patients with prostate cancer (PCa) and to provide valuable information for the identification of potential diagnostic biomarkers and pathological genes in PCa metastasis. The most important candidate genes were identified through several machine learning approaches including K-means clustering, neural network, Naïve Bayesian classifications and PCA with or without downsampling. In total, 21 genes positively associated with lymph nodes involvement were identified. Among them, nine genes have been identified in metastatic prostate cancer, six have been found in other metastatic cancers and four in other local cancers. The amplification of the candidate genes was evaluated in the other PCa data sets. Besides, we identified a validated set of genes involved in the PCa metastasis. The amplification of SPAG1 and PLEKHF2 genes were associated with decreased survival in patients with PCa.A TCGA dataset of Prostate Adenocarcinoma (TCGA, PanCancer Atlas) was retrieved from cBioPortal [7, 8]. RNA expression values had been standardized against the gene's expression distribution in a reference population and had been reported as log2 values. CNA data had been reported as +2 , +1, 0, -1 or -2. We initially performed the analyses on the RNA data and then used the CNA data for further validation. The samples had been assigned as either N1 or N0 groups (Figure 2). The N1 group included the samples from the patients with PCa with the involvement of lymph nodes whereas N0 group included the samples from the patients with PCa without the involvement of any lymph nodes. The NA samples were removed from the study

Data for: A machine learning approach to discriminate MR1 binders: the importance of the phenol and carbonyl fragments

A machine learning approach to discriminate MR1 binders: the importance of the phenol and carbonyl fragmentsAn excel file includes descriptor table, the 2D images of the entire dataset, name of the descriptors, descriptor importance suggested by the models, details of the LR model, and model performance metrics are provided as Supplementary Material.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Data for: Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: the role of SPAG1 and PLEKHF2

The present study aimed to identify the genes associated with the involvement of adjunct lymph nodes of patients with prostate cancer (PCa) and to provide valuable information for the identification of potential diagnostic biomarkers and pathological genes in PCa metastasis. The most important candidate genes were identified through several machine learning approaches including K-means clustering, neural network, Naïve Bayesian classifications and PCA with or without downsampling. In total, 21 genes positively associated with lymph nodes involvement were identified. Among them, nine genes have been identified in metastatic prostate cancer, six have been found in other metastatic cancers and four in other local cancers. The amplification of the candidate genes was evaluated in the other PCa data sets. Besides, we identified a validated set of genes involved in the PCa metastasis. The amplification of SPAG1 and PLEKHF2 genes were associated with decreased survival in patients with PCa.A TCGA dataset of Prostate Adenocarcinoma (TCGA, PanCancer Atlas) was retrieved from cBioPortal [7, 8]. RNA expression values had been standardized against the gene's expression distribution in a reference population and had been reported as log2 values. CNA data had been reported as +2 , +1, 0, -1 or -2. We initially performed the analyses on the RNA data and then used the CNA data for further validation. The samples had been assigned as either N1 or N0 groups (Figure 2). The N1 group included the samples from the patients with PCa with the involvement of lymph nodes whereas N0 group included the samples from the patients with PCa without the involvement of any lymph nodes. The NA samples were removed from the study.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Pipeline for the generation of gene knockout mice using dual sgRNA CRISPR/Cas9-mediated gene editing

Animal models possess undeniable utility for progress on biomedical research projects and developmental and disease studies. Transgenic mouse models recreating specific disease phenotypes associated with β-hemoglobinopathies have been developed previously. However, traditional methods for gene targeting in mouse using embryonic stem cells (ESCs) are laborious and time consuming. Recently, CRISPR has been developed to facilitate and improve genomic modifications in mouse or isogenic cell lines. Applying CRISPR to gene modification eliminates the time consuming steps of traditional approach including selection of targeted ESC clones and production of chimeric mouse. This study shows that microinjection of a plasmid DNA encoding Cas9 protein along with dual sgRNAs specific to Hbb-bs gene (hemoglobin, beta adult s chain) enables breaking target sequences at exons 2 and 3 positions. The injections led to a knockout allele with efficiency around 10 for deletion of exons 2 and 3 and 20 for indel mutation. © 2018 Elsevier Inc