Thomas HR et al. (2014), High-throughput genome editing and phenotyping ...

ECB-ART-48856

PLoS One 2014 Dec 01;912:e114632. doi: 10.1371/journal.pone.0114632.

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High-throughput genome editing and phenotyping facilitated by high resolution melting curve analysis.

Thomas HR , Percival SM , Yoder BK , Parant JM .

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With the goal to generate and characterize the phenotypes of null alleles in all genes within an organism and the recent advances in custom nucleases, genome editing limitations have moved from mutation generation to mutation detection. We previously demonstrated that High Resolution Melting (HRM) analysis is a rapid and efficient means of genotyping known zebrafish mutants. Here we establish optimized conditions for HRM based detection of novel mutant alleles. Using these conditions, we demonstrate that HRM is highly efficient at mutation detection across multiple genome editing platforms (ZFNs, TALENs, and CRISPRs); we observed nuclease generated HRM positive targeting in 1 of 6 (16%) open pool derived ZFNs, 14 of 23 (60%) TALENs, and 58 of 77 (75%) CRISPR nucleases. Successful targeting, based on HRM of G0 embryos correlates well with successful germline transmission (46 of 47 nucleases); yet, surprisingly mutations in the somatic tail DNA weakly correlate with mutations in the germline F1 progeny DNA. This suggests that analysis of G0 tail DNA is a good indicator of the efficiency of the nuclease, but not necessarily a good indicator of germline alleles that will be present in the F1s. However, we demonstrate that small amplicon HRM curve profiles of F1 progeny DNA can be used to differentiate between specific mutant alleles, facilitating rare allele identification and isolation; and that HRM is a powerful technique for screening possible off-target mutations that may be generated by the nucleases. Our data suggest that micro-homology based alternative NHEJ repair is primarily utilized in the generation of CRISPR mutant alleles and allows us to predict likelihood of generating a null allele. Lastly, we demonstrate that HRM can be used to quickly distinguish genotype-phenotype correlations within F1 embryos derived from G0 intercrosses. Together these data indicate that custom nucleases, in conjunction with the ease and speed of HRM, will facilitate future high-throughput mutation generation and analysis needed to establish mutants in all genes of an organism.

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	Figure 1. 100 bp amplicons are optimal for HRM detection of nuclease induced small indels.A&B) General depiction of HRM analysis. A homozygous wild-type sample (double blue lines in A) will only produce one species following PCR, which has a specific HRM curve (blue curve in B). A heterozygous sample with a novel mutation (double red lines in A) will produce 2 homoduplex species following PCR (double blue and double red lines in A), which each have a unique HRM curve that can be separated if the Tm difference are great enough. Importantly for novel mutation detection, if the PCR samples are heated to 95°C and then rapidly cooled, heteroduplex products (red over blue and blue over red in A) will also be generated, which due to decreased complementarity (represented by bubble in lines in A) will have a much lower Tm and therefore a curve profile represented to the left of the homoduplex (see blue over red in B). C) HRM curves, of wild-type (grey curves) and either p53 (T to C missense mutation), RBΔ2, or RBΔ13 heterozygous genomic DNA samples (red curves), generated from 3 different sized PCR amplicons that surround the mutation.
	Figure 2. HRM can efficiently detect low level chimeric mutations.A) Depicts different degrees of chimerism (amount red colored cells) that could occur due to custom nuclease injection. B) HRM curves of TA cloned 300 bp amplicons (100 ng/ul) or genomic DNA (100 ng/ul) of wild-type (grey curves) and either RBΔ2 or RBΔ13 mutations at different allele ratios with wild-type DNA.
	Figure 3. HRM can efficiently detect compound mutations.A) Depicts compound mutation (different colored cells) in a chimeric animal that could occur due to custom nuclease injection. B) HRM curves of RBΔ2, RBΔ5 and a 1∶1 mix of RBΔ2 + RBΔ5 genomic DNA; C) HRM curves of RBΔ2, RBΔ13 and a 1∶1 mix of RBΔ2 + RBΔ13 genomic DNA; D) HRM curves of RBΔ2, RBΔ5, RBΔ13 and a 1∶1∶1 mix of RBΔ2 + RBΔ5 + RBΔ13 genomic DNA.
	Figure 4. Nucleases cleavage in G0 embryo strongly correlates with germline transmission.A) HRM analysis of genomic DNA from G0 injected embryos (n = 24) and subsequent F1 progeny (n = 48) for 6 CRISPR targeted genes. Esco1, CDCa5, & Esco21 (1 denotes a guide species that targets exon 6) were injected as pooled guide RNA (pool 1); CHEK2, Ptgs2b and IFT881 (1 denotes the guide that targets intron 15) were injected as pooled guides (pool 2). B) Summary of HRM analysis of ZFN, TALEN, and CRISPR injected G0 and subsequent germline F1's. HRM positive G0 represent the number of different nuclease, within each category, that when injected into embryos produce at least 1 HRM positive embryo amongst 24 injected embryos; germline transmission (G.T.) from HRM+ G0 represents the number of nucleases, within each category, that were positive for HRM in injected embryos (G0) that also produce F1 progeny that contain HRM positive mutations.
	Figure 5. Small amplicon HRM can distinguish distinct mutant alleles.A) 100 bp amplicon of different PUMA alleles in F1 progeny of TALEN derived G0's fish; each curve denotes a different mutation. B) HRM of the same genomic DNA as in A) with a 50 bp amplicon across the PUMA target site enhanced curve separation. Identification of 5 different HRM curves in F1 progeny genomic DNA from a (C) Gas8 CRISPR G0'sor (D) AKAP8I CRISPR G0's. Subsequent sequencing revealed that each curve is the result of a unique mutation.
	Figure 6. Micro-homology directed repair is the prevalent repair mechanism of CAS9 induced cleavage.From sequence data obtained in Table 3 three dominant alleles were observed (Δ71, Δ72, and Δ121). Alignment of wild-type and mutant sequences (yellow highlight denotes PAM, blue denotes target sequence, and red arrow denotes cleavage site) are displayed, and potential micro-homologies are underlined. G0 frequency represents how many of the G0 analyzed produced at least one embryo with this allele; F1 frequency represent how often we observed this allele in total F1 analyzed; and F1 Frequency/mutant allele represents how often we observed this allele amongst all mutant allele discovered. For BARD1, p107, and Atad5b predicted mutations are shown based on underlined micro-homologies. Actual observed frequencies are displayed to right.
	Figure 7. HRM analysis reveals low frequency of off-target cleavage by the CRISPR system.Using the CRISPR Design (http://crispr.mit.edu/) web tool we identified the most likely 4 off-target sites in the zebrafish genome for 5 different guides with different overall scores (in parenthesis next to gene name). The sequences are depicted with the PAM site in green and the nucleotide different from the guide sequence in red. To discern SNP's within the Oft amplicons we performed HRM analysis of genomic DNA from 24 wild-type adults. The HRM positive frequency within genomic DNA obtained from 24 G0 embryos injected with the guide/Cas9 RNAs is displayed. Also the HRM positive frequency within genomic DNA obtained from 12 F1 embryos derived from 4 different G0's is displayed. Select HRM curves that display unique features are displayed for G0 embryos as well as F1 embryos. For Ptgs1 Oft 1 is displayed to depict no off-target hits. For Esco2 Oft 3 is displayed to depict a common SNP derived from the wild-type AB. For Wapal1 Oft 1 is displayed to depict no off-target hits. For P107 Oft 2 is displayed to: 1) depict a SNP found in wild-type AB strain, G0 and F1 animals; and 2) novel CRISPR derived off-target hits in some of the F1 progeny (orange and green curves). For Bub1bb, Oft 1 is displayed to depict a low frequency off-target hit (red curve) in the F1 progeny. * in frequencies denotes that SNP curves were not considered a HRM positive.
	Figure 8. GC content impacts effectiveness of guides.A) denotes the percent G, A, T, or C content by position among all Bad or Good perfect match guides, based on if they produce an HRM positive curve in G0 embryos. B) Dot plot of GC content of BAD and GOOD guides. C) Success of guides that have either position 1 or/and 2 changed to a G (non-perfect match guides).
	Figure 9. HRM established genotype-phenotype correlation within Esco2 mutant embryos from a G0 intercross.A) wild-type and mutant phenotypes with Mendelian frequencies in embryos derived from heterozygous intercross of the Esco2 retroviral insertion mutant hi2865. Note the head necrosis in the mutant embryos. B) HRM genotyping of wild-type (grey and blue curves) and mutant (red curves) embryos display perfect genotype-phenotype correlation. C) wild-type and mutant phenotypes in embryos derived from intercross of G0 Esco2 CRISPR injected fish. Note the head necrosis in mutant embryos. D) Select HRM curves of 6 mutant and 6 wild-type embryos that were subsequently sequences to reveal specific alleles result in specific curves. All Wild-type animals (beyond the 6 displayed here) make up the green and grey curves; while 5 of 6 mutant animals make up the unique red and blue curves establishing a genotype phenotype correlation. E&G) are wild-type and mutant phenotypes and frequencies of G0#9 (E) or G0#10 (G) crossed to Esco2 hi2865 heterozygous animals. F&H) are HRM curves of mutant and wild-type embryos (from E&G) that are Esco2 hi2865 heterozygous. All heterozygous curves (red) are mutants, and all grey curves are normal phenotypically.

References [+] :

Amsterdam, Identification of 315 genes essential for early zebrafish development. 2004, Pubmed