Highly parallel genome variant engineering with CRISPR–Cas9

Abstract

Understanding the functional effects of DNA sequence variants is of critical importance for studies of basic biology, evolution, and medical genetics; however, measuring these effects in a high-throughput manner is a major challenge. One promising avenue is precise editing with the CRISPR–Cas9 system, which allows for generation of DNA double-strand breaks (DSBs) at genomic sites matching the targeting sequence of a guide RNA (gRNA). Recent studies have used CRISPR libraries to generate many frameshift mutations genome wide through faulty repair of CRISPR-directed breaks by nonhomologous end joining (NHEJ)1. Here, we developed a CRISPR-library-based approach for highly efficient and precise genome-wide variant engineering. We used our method to examine the functional consequences of premature-termination codons (PTCs) at different locations within all annotated essential genes in yeast. We found that most PTCs were highly deleterious unless they occurred close to the 3′ end of the gene and did not affect an annotated protein domain. Unexpectedly, we discovered that some putatively essential genes are dispensable, whereas others have large dispensable regions. This approach can be used to profile the effects of large classes of variants in a high-throughput manner.

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Fig. 1: Measuring the effects of engineered PTCs in essential genes.
Fig. 2: PTC tolerance of genes.
Fig. 3: Selected truncatable essential genes.

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Acknowledgements

We thank members of the laboratory of L.K., and F. Albert, M. P. Hughes, and J. Rine for helpful discussions; R. Cheung and E. Pham for technical assistance; and G. Church (Harvard Medical School) for plasmids. Funding was provided by the Howard Hughes Medical Institute and NIH grants R01 GM102308 (L.K.) and F32 GM116318 (M.J.S.).

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Contributions

Experiments were designed by M.J.S., J.S.B., J.J.S., S.K., and L.K. Experiments were performed by M.J.S. and L.D. Data were analyzed by M.J.S., J.S.B., and L.K. The manuscript was written by M.J.S., J.S.B., and L.K., and incorporates comments from all other authors.

Corresponding authors

Correspondence to Meru J. Sadhu or Joshua S. Bloom or Leonid Kruglyak.

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The authors declare no competing financial interests.

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Integrated supplementary information

Supplementary Figure 1 Design for making large pools of barcoded edit-directing plasmids

Further details on the BstEII and SphI cloning sites are shown in Supplementary figure 12.

Supplementary Figure 2 Histograms of the positions of chosen PTCs

Histograms of the positions of chosen PTCs, represented either as (a) the number of codons from gene ends, or (b) the fraction of the ORF length.

Supplementary Figure 3 Edit-directing plasmid effects are replicable and depend on the repair template.

a, Scatterplot of PTC tolerance scores in nej1∆ cells calculated independently for each replicate experiment (n = 3132 independently targeted PTCs that were observed in both replicates, Pearson's r = 0.6, p < 2 × 10-16). b, Scatterplot of gene PTC tolerance scores calculated independently for each replicate experiment (n = 1140 independently targeted genes, Pearson's r = 0.7, p < 2 × 10-16). c, Repair templates introducing essential gene PTCs were less tolerated than repair templates introducing dubious ORF PTCs. The experiment included 72 cases of pairs of edit-directing plasmids that both used the same gRNA. In each pair, one plasmid introduced a PTC in an essential gene, and the other introduced a PTC in a dubious ORF overlapping the same essential gene; the latter had the effect of introducing a synonymous or nonsynonymous substitution in the essential gene. Lines correspond to effects of the same gRNA targeting either a PTC to an essential gene or an edit to an overlapping dubious ORF. The edit-directing plasmids targeting PTCs to essential genes were less tolerated than their partners targeting PTCs to the overlapping dubious ORFs (Student's two-tailed paired t-test n = 72, t = 6.5, P = 8 × 10−9). The outer boxplots show the distribution of edit tolerance scores. The centerline of each box corresponds to the data's median value; the top and bottom of the box span from the first quartile to the third quartile of the data; and the whiskers reach to either the data's most extreme values or 1.5 times the interquartile range.

Supplementary Figure 4 PTCs were similarly deleterious in nej1∆ nmd2∆ and nej1∆ NMD2 cells

a, Tolerance score for each tested PTC in essential genes and dubious ORFs in nej1∆ nmd2∆ cells, with overlaid boxplots (n = 8,346 PTCs in essential genes and 695 PTCs in dubious ORFs). The centerline of each box corresponds to the data's median value; the top and bottom of the box span from the first quartile to the third quartile of the data; and the whiskers reach to either the data's most extreme values or 1.5 times the interquartile range. P < 2 × 10−16, two-sided Wilcoxon rank test. b, Scatterplot of PTC tolerance score versus distance in codons from the 3′ ends of essential genes in nej1∆ nmd2∆ cells (top) and in nej1∆ NMD2 cells (bottom; same as Fig. 1d). As in Fig. 1d, the thick blue line shows a two-segment regression fit, and the 95% confidence interval for the boundary between the segments is shown by the vertical blue lines. The segmented regression was fit on PTC tolerance scores for n = 7,583 PTCs and n = 7,561 PTCs that were within 500 codons of the 3′ end of a gene, for the top and bottom panels, respectively.

Supplementary Figure 5 Truncations that disrupt domains are less tolerated

For each PTC, the fraction of amino acid residues perfectly conserved downstream of the PTC among S. cerevisiae, S. paradoxus, S. mikatae, S. kudriavzevii, and S. bayanus v. uvarum 12 was calculated. PTCs were then binned by whether their affected sequence was more or less conserved than the median truncated sequence, as well as by whether or not they disrupted a Pfam-annotated domain. Scatterplots show the PTC tolerance scores versus the distance in codons from the 3′ end of essential genes, as in Fig. 1d, with the blue line showing a two-segment regression fit on PTCs within 500 codons of the 3′ end of the gene. a, PTCs affecting less conserved sequence and disrupting an annotated protein domain. n = 2,228 PTCs. b, PTCs affecting more conserved sequence and disrupting an annotated protein domain. n = 3,233 PTCs. c, PTCs affecting less conserved sequence and not disrupting an annotated protein domain. n = 1,754 PTCs. d, PTCs affecting more conserved sequence and not disrupting an annotated protein domain. n = 854 PTCs.

Supplementary Figure 6 Tolerance of end truncation

a, Histogram of the number of genes tolerating a given number of tested PTCs. For each gene, the number of tolerated PTCs was determined by a Hidden Markov Model analysis. b, The 3′-most tested PTC in POB3, at position 551, had a tolerance score of -1.2. It was also called deleterious by the HMM analysis. We confirmed that POB3 does not tolerate a deletion spanning its terminal two codons (n = 6 tetrads analyzed), and found that it tolerates deletion of its last codon (n = 8 tetrads analyzed). Tetrad dissections were done as in Fig. 2, from a diploid yeast heterozygous for a truncation mutation of interest. c, The 3′-most tested PTC in PCNA, at position 251, had a tolerance score of -0.59, and was called deleterious by the HMM analysis. We confirmed that PCNA does not tolerate a deletion spanning its terminal eight codons, and discovered that terminal deletions of five or more codons were lethal. n = 6 tetrads were analyzed for each truncation mutant.

Supplementary Figure 7 Gene Ontology enrichment

Data is shown as dot-plots overlaid with box-and-whisker plots. The centerline of each box corresponds to the data's median value; the top and bottom of the box span from the first quartile to the third quartile of the data; and the whiskers reach to either the data's most extreme values or 1.5 times the interquartile range. See also Supplementary Table 4. a, Gene tolerance scores for genes in the biological process category of “RNA splicing, via spliceosome,” compared to the remaining tested genes (n = 69 and 965, respectively; Kolmogorov-Smirnov test, Bonferroni corrected P = 0.0017) b, Gene tolerance scores for genes in the molecular function category of “catalytic activity,” compared to the remaining tested genes (n = 477 and 557, respectively; Kolmogorov-Smirnov test, Bonferroni corrected P = 0.0024).

Supplementary Figure 8 YJR012C and UTR5 are not essential

a, RNA-seq and ribosome footprinting read depth by position (Albert, F. W., Muzzey, D., Weissman, J. S. & Kruglyak, L. Genetic influences on translation in yeast. PLoS Genet. 10, e1004692; 2014) in the vicinity of YJR012C and GPI14. We mark the position of YJR012C(M76), which we propose to be the actual start of YJR012C. b, Alignment of the annotated YJR012C protein sequence from related yeast species. The proposed start position at M76 is highlighted with a red box. The 48 codons of S. mikatae YJR012C not shown include two additional stop codons. c, Tetrad dissections of a yjr012c(76-207Δ)/YJR012C diploid strain, as in Fig. 2. n = 12 tetrads were analyzed. d The positions of UTR5, HYP2, and the TATA box of HYP2 (left) (Rhee, H. S. & Pugh, B. F. Genome-wide structure and organization of eukaryotic pre-initiation complexes. Nature 483, 295–301; 2012). Tetrad dissections of a utr5(34-166Δ)/UTR5 diploid strain; n = 4 tetrads were analyzed (right).

Supplementary Figure 9 MMF1 is conditionally essential

Tetrad dissections of an mmf1Δ/MMF1 strain were done as in Fig. 2, with pictures taken after four days of growth. Top two panels show tetrads dissected on rich medium; bottom two panels show tetrads dissected on defined (CSM) medium. Left two panels show tetrads dissected on 2% glucose medium; right two panels show tetrads dissected on 2% galactose medium. The original annotation of essential genes was done in rich glucose medium (top left), whereas our PTC tolerance experiment was done in defined galactose medium (bottom right). n = 6 tetrads were analyzed for each condition tested.

Supplementary Figure 10 Comparison of gene PTC tolerance and SATAY-determined gene-transposon tolerance

Transposon tolerance was determined as the logarithm of the number of transposons tolerated per kilobase of gene length. Black points correspond to essential genes; red points correspond to dubious ORFs.

Supplementary Figure 11 Predicted effects of human NMD on PTCs, for human genes that are homologs of essential yeast genes

For human genes, we plot the fraction of PTCs escaping NMD regulation according to the 50-bp rule (Nagy, E. & Maquat, L. E. A rule for termination-codon position within intron-containing genes: when nonsense affects RNA abundance. Trends Biochem. Sci. 23, 198–199; 1998) versus distance from gene ends. Note that the majority of PTCs within the last 27 codons are predicted to escape NMD regulation.

Supplementary Figure 12 Modifications to gRNA sequence to enable bulk cloning

Sequence numbering corresponds to the nucleotide position in the gRNA sequence. (a) The end of the SNR52 promoter sequence and start of the gRNA. The upper sequence is what was used for S. cerevisiae sgRNA expression by DiCarlo, et al. (2); the lower sequence shows the modifications, in red, that incorporated a BstEII cloning site. (b) The first gRNA hairpin. The left sequence is what DiCarlo, et al., used; the right sequence shows the modifications, in red, that incorporated an SphI cloning site.

Supplementary Figure 13 Barcodes per targeted site

(a) The number of uniquely barcoded edit-directing plasmids tracked per PTC in nej1Δ cells. (b) The number of uniquely barcoded edit-directing plasmids tracked per PTC in nej1Δ nmd2Δ cells. (c) The number of uniquely barcoded edit-directing plasmids tracked per PTC, combined across nej1Δ and nej1Δ nmd2Δ cells.

Supplementary Figure 14 Histogram of barcoded-plasmid persistence

We fit a generalized linear model to the read count data for each tracked barcoded plasmid. This histogram shows the resulting slopes (thetas) for the barcoded plasmids. The vertical red line demarcates our “persisting” versus “depleted” binarization threshold of −0.025.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14, Supplementary Notes 1 and 2, and Supplementary Tables 1, 2 and 5–10

Reporting Summary

Supplementary Table 3: Effects of biological and technical covariates on PTC tolerance.

Effects were determined using a generalized linear mixed model (n = 84,284 barcoded variant-engineering plasmids). Coefficients were obtained from the glmer function. Type III analysis-of-variance tables were computed for the fixed effect terms in the model with the Anova() function in the car R package. Likelihood ratio chisquare values and p-values for the fixed-effect terms in the model were also computed using this function. Tjur's D was used to calculate a pseudo R2 statistic for overall model fit (Tjur's D = 0.39).

Supplementary Table 4: Gene Ontology (GO) enrichment results for gene tolerance of PTCs (n = 1,034 genes)

Significance was determined with a non-parametric Kolmogorov-Smirnov test.

Supplementary Table 11

PTC tolerance scores for each directed PTC (n = 10,971 PTCs).

Supplementary Table 12

Gene PTC tolerance scores for each targeted gene (n = 1,140 genes).

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Sadhu, M.J., Bloom, J.S., Day, L. et al. Highly parallel genome variant engineering with CRISPR–Cas9. Nat Genet 50, 510–514 (2018). https://doi.org/10.1038/s41588-018-0087-y

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