Modulation of AAV transduction and integration targeting by topoisomerase poisons

Abstract

Adeno-associated virus (AAV) is a widely used vehicle for gene delivery, lending interest to developing methods for enhancing AAV transduction and transgene expression. Here, we profile the function of several topoisomerase poisons, which are small molecules that stabilize topoisomerase enzymatic intermediates, where topoisomerase enzymes are covalently bound at chromosomal DNA breaks. As previously observed, we found that the topoisomerase poisons camptothecin (CPT), doxorubicin (DOX), and etoposide (ETO) increased AAV transduction in cultured cell models. DOX and ETO, small molecules that specifically inhibit type II topoisomerases and so stabilize double-strand breaks, were found to boost integration of AAV DNA into the host cell chromosome. Analysis of integration site distributions showed that integration targeting was altered, so that integration in the presence of DOX or ETO was favored near actively transcribed regions. Locations of topoisomerase II binding sites were inferred from genomic data using a novel machine learning platform, and integration in the presence of DOX or ETO was found to be selectively favored near inferred topoisomerase II binding sites. These data help guide development of improved transduction protocols using these reagents and establish that DOX and ETO can control AAV integration targeting.

Keywords: AAV gene therapy; AAV integration; AAV vectors; adriamycin; camptothecin; doxorubicin; etoposide; machine learning; short-read sequencing; topoisomerase.

Graphical abstract

Figure 1

CPT, DOX, and ETO increase AAV transduction in HeLa cells HeLa cells were treated overnight with either medium only (no drug control, green circles), 62.5 nM of CPT (blue squares), 50 nM DOX (maroon upward triangles), or 3.13 μM of ETO (pink downward triangles) before drug washout. Treated cells were transduced with AAV-GFP and subsequently cultured in the absence of topoisomerase poisons. GFP fluorescence was measured to track AAV gene expression. (A) Over the first 96 h post transduction, phase and green fluorescence live cell images were taken every 12 h using the Incucyte S3 and 25 images were taken per well. The Incucyte analysis software was used to quantify GFP-positive foci, with the reported count taking an average across the 25 images. Data displayed are the mean value and standard deviation of quadruplicate biological replicates (n = 4) per group. Highlighted time points (24, 36, 48, 60, 72, and 84 h) were analyzed using one-way ANOVA with Dunnett’s multiple comparison test of each drug against the control cells. (B and C) As cells were further passaged, harvested HeLa cells were stained with LIVE/DEAD dye, fixed and run on a flow cytometer to determine the percentage of cell population expressing GFP expression. Cells were analyzed at (B) 6 days post transduction and (C) 19 days post transduction. Data displayed are the percent GFP-positive values for each biological replicate with the mean displayed as a horizontal bar. (B) n = 4 and (C) n = 8. Data were analyzed using one-way ANOVA with Dunnett’s multiple comparison test of each drug against the control cells. In all graphs, significance is displayed as such (ns, no significance; ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001).

Figure 2

CPT, DOX, and ETO augment AAV transduction in IMR90 cells Contact-inhibited IMR90 cells were treated overnight with either medium (no drug control, green circles), 0.1 μM CPT (blue squares), 300 μM DOX (maroon upward triangles), or 100 μM of ETO (pink downward triangles) before drug washout. Treated cells were transduced with AAV-GFP and cultured in the absence of topoisomerase drugs. Over the first 120 h post transduction, phase and green fluorescence live cell images were taken every 12 h using the Incucyte S3 and 25 images were taken per well. The Incucyte analysis software was used to quantify GFP-positive foci, with the reported count taking an average across the 25 images. Data displayed are the values for each biological replicate (n = 4) at 96 h post transduction with the mean displayed as a horizontal bar. Fold changes were calculated by comparing the means of each drug condition. Data were analyzed using one-way ANOVA with Dunnett’s multiple comparison test for each drug against the control cells. ns, no significance; ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001.

Figure 3

CPT, DOX, and ETO have different effects on AAV vector copy numbers in HeLa cells HeLa cells were harvested during each passage, genomic DNA was extracted and subjected to vector copy number analysis using GFP-targeted primers with three technical replicates per sample. Vector copy number was quantified from cells harvested at (A) 6 days post transduction and (B) 19 days post transduction. Data displayed are the values for each biological replicate (n = 4) with the mean displayed as a horizontal bar. Medium only (no drug control, green circles), 62.5 nM of CPT (blue squares), 50 nM DOX (maroon upward triangles), or 3.13 μM of ETO (pink downward triangles). Fold changes were calculated by comparing the means of each drug condition. Data were analyzed using one-way ANOVA with Dunnett’s multiple comparison test of each drug against the control cells. ns, no significance; ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001.

Figure 4

Effects of CPT, DOX, and ETO on AAV integration frequency in HeLa cells HeLa DNA harvested 6 days post vector transduction was subject to integration site sequencing. (A) Unique integration sites recovered from each biological replicate, quantified using the AAVengeR pipeline. Integration sites were filtered to exclude sites supported by fewer than three reads. (B) Abundance measures were used to estimate the minimum population size of integrations in each biological replicate (Chao1). Data displayed are the values for each biological replicate (n = 4) with the mean displayed as a horizontal bar. Medium only (no drug control, green circles), 62.5 nM of CPT (blue squares), 50 nM DOX (maroon upward triangles), or 3.13 μM of ETO (pink downward triangles). Fold changes were calculated by comparing the means of each drug condition. Data were analyzed using one-way ANOVA with Dunnett’s multiple comparison test of each drug against the control cells. ns, no significance; ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001.

Figure 5

CPT-, DOX-, and ETO-treated HeLa cells have different patterns of AAV integration relative to genomic features (A) The percentage of integration sites in transcription units was calculated for each biological replicate. Data displayed are the values for each biological replicate (n = 4) with the mean displayed as a horizontal bar. Medium only (no drug control, green circles), 62.5 nM of CPT (blue squares), 50 nM DOX (maroon upward triangles), or 3.13 μM of ETO (pink downward triangles). Data were analyzed using one-way ANOVA with Dunnett’s multiple comparison test of each drug against the control cells. ns, no significance; ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001. (B and C) Distribution of the AAV vector integration sites in the human genome relative to random controls (three times the number of sites as each experimental measure). Tracks are grouped by (B) genomic features or (C) histone markers/chromatin features. All biological replicates are collapsed by treatment condition, and associations were calculated versus random distributions using the ROC area method. Values of the ROC were scaled between 0.4 (negatively associated, (B) blue or (C) aqua) and 0.6 (positively associated, (B) yellow or (C) red). Significance was calculated by the ROC method (∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001).

Figure 6

AAV favors integration near inferred topoisomerase IIB binding sites selectively in the presence of the topoisomerase II poisons DOX and ETO TOP2B binding sites were predicted by machine learning methods for HeLa cells. (A) Distribution of the AAV vector integration sites in the human genome relative to random controls (three times the number of sites as each experimental measure). TOP2B binding sites were predicted by machine learning. All biological replicates are collapsed by treatment condition, and associations were calculated using the ROC area method. Values of the ROC were scaled between 0.4 (negatively associated, green) and 0.6 (positively associated, purple). Significance was calculated by the ROC method (∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001). (B) Percentage of integration sites within 10,000 bp windows of inferred TOP2B binding sites. Random simulations are depicted in black and were generated from 1 million bootstraps of 1,000 randomly selected integration sites. Percent of sites in TOP2B tracks are presented as colored lines. No drug control (green), 62.5 nM of CPT (blue), 50 nM DOX (maroon), or 3.13 μM of ETO (pink).

Figure 7

CPT, DOX, and ETO do not alter AAV ITR-genome junction rearrangement in HeLa cells (A) Percentage of integration sites with at least one rearrangement in the ITR remnant were calculated per biological replicate. Medium only (no drug control, green circles), 62.5 nM of CPT (blue squares), 50 nM DOX (maroon upward triangles), or 3.13 μM of ETO (pink downward triangles). The data displayed are the value for each biological replicate (n = 4) with the mean displayed as a horizontal bar. Data were analyzed using one-way ANOVA with Dunnett’s multiple comparison test for each drug against the control cells. ns, no significance; ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001. (B) Composite visualization of ITR remnants of all biological replicates in each drug condition. Integration sites are mapped by the terminal ITR position of the ITR remnant (x axis) and sites sharing the same terminal position were summed (y axis). Sites with no rearrangements were colored green and sites containing rearrangements were colored according to the number of breaks (1, light green; 2, yellow; 3, orange; 4, red; ≥5, maroon). The dashed lines indicate the tips of the ITR dumbbells.