Tracking genome engineering outcome at individual DNA breakpoints

Abstract

Site-specific genome engineering technologies are increasingly important tools in the postgenomic era, where biotechnological objectives often require organisms with precisely modified genomes. Rare-cutting endonucleases, through their capacity to create a targeted DNA strand break, are one of the most promising of these technologies. However, realizing the full potential of nuclease-induced genome engineering requires a detailed understanding of the variables that influence resolution of nuclease-induced DNA breaks. Here we present a genome engineering reporter system, designated 'traffic light', that supports rapid flow-cytometric analysis of repair pathway choice at individual DNA breaks, quantitative tracking of nuclease expression and donor template delivery, and high-throughput screens for factors that bias the engineering outcome. We applied the traffic light system to evaluate the efficiency and outcome of nuclease-induced genome engineering in human cell lines and identified strategies to facilitate isolation of cells in which a desired engineering outcome has occurred.

Figure 1. The Traffic light reporter

(a) Diagram of the TLR. Arrow represents promoter and initial GFP start codon. Reading frames relative to the initial GFP start codon are indicated in superscript. (b) Schematic depicting different engineering outcomes following the induction of a site specific double stranded break (DSB). If the break is resolved through the HDR pathway the full GFP sequence will be reconstituted and cells will fluoresce green; if the break undergoes mutagenic non-homologous end joining (mutNHEJ), GFP will be translated out of frame (GibberishFP⁺³) and the T2A and mCherry sequences are rendered in frame to produce red fluorescent cells. (c) Flow cytometric analysis of HEK293 TLR^sce cells 3 days post-transduction with the indicated lentiviral constructs. “FI” = relative fluorescence intensity.

Figure 2. Titration of nuclease and donor template

(a) Representative flow plot following transduction of HEK293 TLR^sce cells with increasing amounts of I-SceI + Donor LV (b) Quantification of data from panel a. Bar graphs represent a minimum of 3 independent experiments performed in duplicate, with standard error shown. (c) Ratio of HDR to mutNHEJ based on data in panel b. (d) Representative flow plot following titration of Donor LV in 5 fold increments while holding I-SceI LV dose constant on HEK293 TLR^sce cells. (e) Quantification of data from panel d. Bars represent a minimum of 3 independent experiments performed in duplicate, with standard error shown. (f) Ratio of HDR to mutNHEJ in panel e.

Figure 3. Four-color system to track nuclease and donor template delivery simultaneously with the TLR

(a) Representative flow plot 3 days post transduction of HEK293 TLR^sce cells with I-SceI-T2A-IFP LV and Donor-T2A-BFP IDLV. (b) Control gating analysis of HEK293 TLR^sce cells co-transduced with I-SceI-T2A-IFP and Donor-T2A-BFP donor template. Inset flow plots show nuclease vs. donor template expression levels. Large plots show readout from the TLR as a function of the gate shown in the inset. (c) Quantification of TLR readout when applying a nuclease titration gating analysis in cells co-transduced with I-SceI-T2A-IFP and Donor-T2A-BFP. Bars represent the amount of gene targeting and mutNHEJ present in the indicated inset gates (lowest to highest MFI) normalized to the HDR and mutNHEJ values for the total ungated population (see methods). Average data of 3 independent experiments are shown with standard error. (d) Quantification of TLR readout when applying donor template titration gating analysis as indicated above.

Figure 4. Effect of single vs. double-strand DNA breaks on engineering outcome

(a) Representative flow plots showing TLR readout of HEK293 TLR^ani cells transduced with either the I-AniY2-T2A-BFP LV (cleavase) or I-AniIK227M-T2A-BFP (nickase). Inset plots show gating for nuclease expression to control for transduction levels. (b) Quantification of panel a from 3 independent experiments in duplicate. Percent measured events have had the background rates from cells transduced with donor alone subtracted out to control for the low numbers. (c) Comparison of the ratio of HDR to mutNHEJ between cleavase and nickase induced engineering. (d) Gating analysis showing TLR readout across nickase expression levels. Inset flow plot shows BFP histogram gated for relative nickase expression.

Figure 5. High-throughput siRNA kinome screen to identify modifiers of engineering outcome

(a) Scatter plot of gene targeting and mutNHEJ Z scores obtained from the siRNA screen. Library in gray, Control siRNAs in black. Control siRNA values are average of at least 3 independent transfections. (b) Gating analysis comparing TLR readout from control and DNA-PKcs siRNA treatment as a function of nuclease expression 72hrs after transduction of HEK293 TLR^sce I-SceI-T2A-IFP LV. Inset histogram shows nuclease expression gates. Data are derived from 3 independent experiments performed in duplicate.