Publisher Correction: Pairwise library screen systematically interrogates Staphylococcus aureus Cas9 specificity in human cells

The original HTML version of this Article incorrectly listed an affiliation of Josh Tycko as ‘Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA’, instead of the correct ‘Present address: Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA’. It also incorrectly listed an affiliation of this author as ‘Present address: Arrakis Therapeutics, 35 Gatehouse Dr., Waltham, MA, 02451, USA’.The original HTML version incorrectly listed an affiliation of Luis A. Barrera as ‘Present address: Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06511, USA’, instead of the correct ‘Present address: Arrakis Therapeutics, 35 Gatehouse Dr., Waltham, MA 02451, USA’.Finally, the original HTML version incorrectly omitted an affiliation of Nicholas C. Huston: ‘Present address: Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA’.This has been corrected in the HTML version of the Article. The PDF version was correct from the time of publication

SMOOT libraries and phage-induced directed evolution of Cas9 to engineer reduced off-target activity.

RNA-guided endonucleases such as Cas9 provide efficient on-target genome editing in cells but may also cleave at off-target loci throughout the genome. Engineered variants of Streptococcus pyogenes Cas9 (SpCas9) have been developed to globally reduce off-target activity, but individual off-targets may remain, or on-target activity may be compromised. In order to evolve against activity at specific off-targets while maintaining strong on-target editing, we developed a novel M13 bacteriophage-mediated selection method. Using this method, sequential rounds of positive and negative selection are used to identify mutations to Cas9 that enhance or diminish editing activity at particular genomic sequences. We also introduce scanning mutagenesis of oligo-directed targets (SMOOT), a comprehensive mutagenesis method to create highly diverse libraries of Cas9 variants that can be challenged with phage-based selection. Our platform identifies novel SpCas9 mutants which mitigate cleavage against off-targets both in biochemical assays and in T-cells while maintaining higher on-target activity than previously described variants. We describe an evolved variant, S. pyogenes Adapted to Reduce Target Ambiguity Cas9 (SpartaCas), composed of the most enriched mutations, each of unknown function. This evolved Cas9 mutant reduces off-target cleavage while preserving efficient editing at multiple therapeutically relevant targets. Directed evolution of Cas9 using our system demonstrates an improved structure-independent methodology to effectively engineer nuclease activity

Pairwise library screen systematically interrogates Staphylococcus aureus Cas9 specificity in human cells

A rigorous understanding of off-target effects is necessary for SaCas9 to be used in therapeutic genome editing. Here the authors measure SaCas9 mismatch tolerance across a pairwise library screen of 88,000 guides and targets in human cells and develop a model which ranks off-target sites

UDiTaS™, a genome editing detection method for indels and genome rearrangements

Abstract Background Understanding the diversity of repair outcomes after introducing a genomic cut is essential for realizing the therapeutic potential of genomic editing technologies. Targeted PCR amplification combined with Next Generation Sequencing (NGS) or enzymatic digestion, while broadly used in the genome editing field, has critical limitations for detecting and quantifying structural variants such as large deletions (greater than approximately 100 base pairs), inversions, and translocations. Results To overcome these limitations, we have developed a Uni-Directional Targeted Sequencing methodology, UDiTaS, that is quantitative, removes biases associated with variable-length PCR amplification, and can measure structural changes in addition to small insertion and deletion events (indels), all in a single reaction. We have applied UDiTaS to a variety of samples, including those treated with a clinically relevant pair of S. aureus Cas9 single guide RNAs (sgRNAs) targeting CEP290, and a pair of S. pyogenes Cas9 sgRNAs at T-cell relevant loci. In both cases, we have simultaneously measured small and large edits, including inversions and translocations, exemplifying UDiTaS as a valuable tool for the analysis of genome editing outcomes. Conclusions UDiTaS is a robust and streamlined sequencing method useful for measuring small indels as well as structural rearrangements, like translocations, in a single reaction. UDiTaS is especially useful for pre-clinical and clinical application of gene editing to measure on- and off-target editing, large and small

Class III Phosphatidylinositol 4-Kinase Alpha and Beta Are Novel Host Factor Regulators of Hepatitis C Virus Replication▿ †

Host factor pathways are known to be essential for hepatitis C virus (HCV) infection and replication in human liver cells. To search for novel host factor proteins required for HCV replication, we screened a subgenomic genotype 1b replicon cell line (Luc-1b) with a kinome and druggable collection of 20,779 siRNAs. We identified and validated several enzymes required for HCV replication, including class III phosphatidylinositol 4-kinases (PI4KA and PI4KB), carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase (CAD), and mevalonate (diphospho) decarboxylase. Knockdown of PI4KA could inhibit the replication and/or HCV RNA levels of the two subgenomic genotype 1b clones (SG-1b and Luc-1b), two subgenomic genotype 1a clones (SG-1a and Luc-1a), JFH-1 genotype 2a infectious virus (JFH1-2a), and the genomic genotype 1a (FL-1a) replicon. In contrast, PI4KB knockdown inhibited replication and/or HCV RNA levels of Luc-1b, SG-1b, and Luc-1a replicons. The small molecule inhibitor, PIK93, was found to block subgenomic genotype 1b (Luc-1b), subgenomic genotype 1a (Luc-1a), and genomic genotype 2a (JFH1-2a) infectious virus replication in the nanomolar range. PIK93 was characterized by using quantitative chemical proteomics and in vitro biochemical assays to demonstrate PIK93 is a bone fide PI4KA and PI4KB inhibitor. Our data demonstrate that genetic or pharmacological modulation of PI4KA and PI4KB inhibits multiple genotypes of HCV and represents a novel druggable class of therapeutic targets for HCV infection

Correction: Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages

<p>Correction: Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages</p

GREP improves predictions and translatability over standard approaches for feature selection and classification.

<p>(<i>A</i>) GREP^DR5 compared to ratio classifiers with random gene sets (N = 100) of the same size as the hypothesis gene set. 90% of the models were not significantly better than random (Fisher’s test p-value>0.05). Examination of some random models that performed significantly showed that they included DR5-related genes. (<i>B</i>) Challenging the GREP modeling assumptions in predicting <i>in vitro</i> response. GREP^DR5 was compared to four models, each built without one or more of its key assumptions (error bars show 95% confidence from cross-validation). GREP^DR5 outperforms random and 2-gene classifiers, but a standard gene expression predictor that used ratios for feature selection performed just as well in cell lines. (<i>C</i>) Challenging the GREP modeling assumptions in predicting <i>in vivo</i> response. Validation of the classifier predictions in pancreatic patient-derived tumor xenograft models (PTX) compared to classifiers built with single genes. Assuming a 30% margin of error on the PPV calculation for 11 samples (95% confidence), GREP outperforms both the 2-gene classifier and a standard gene expression predictor that used ratios for feature selection. Vertical dotted line denotes AUC of random classifier (0.5).</p