What can I say, we have a serious weakness for a tight small pumpkin shaped ass! It laterally makes us weak in the knees. Mac has a perfect pump-kin butt, truth be told, we invited him over for a solo stroke video and it quickly turned into more (cock sucking ass eating and facials, all on MMDirects) Butt as soon as we saw that pumpkin we knew we had to have him back for a full on fuck flick. When we see a sweet little pumpkin butt like Macs, it makes us crazy. As soon as we met Mack for the first video, we realized he was going to be fun, he is so sexy cute, has a great sense of humor and he was so comfortable with us and then when we saw that PHAT ASS we fell in lust lol when the video was over I put my camera down and jumped in the shower with Hunter, and much to our surprise Mack picked up the camera when we were out of the room and recorded us a little message telling us how much fun he had, so sexy and so sweet lol. You will love this deep ass fuck session
We present a simple CRISPR-Cas9 method to generate zebrafish F0 knockouts suitable for studying behaviour and other continuous traits. The protocol uses a set of three synthetic gRNAs per gene, combining multi-locus targeting with high mutagenesis at each locus. The method consistently converts > 90% of injected embryos into biallelic knockouts that show fully penetrant pigmentation phenotypes and near complete absence of wild-type alleles in deep sequencing data. In parallel, we developed a quick and cheap PCR-based tool to validate gRNAs whatever the nature of the mutant alleles. The F0 knockout protocol is easily adapted to generate biallelic mutations in up to three genes in individual animals. The populations of F0 knockout animals generated by the method are suitable for quantitative analysis of complex phenotypes, as demonstrated by mutation of a circadian clock component and by meticulous replication of multi-parameter behavioural phenotypes of a genetic model of epilepsy.
(A) The headloop score (HL score) for a given sample (here, tyr locus A larva 1) was calculated as the ratio between the headloop PCR band intensity (HL) and the standard PCR band intensity (std). As the standard PCR band intensity represents the sum of wild-type and mutated alleles, and the headloop PCR band intensity represents the mutated alleles only, the ratio approximates the proportion of mutated alleles in the sample. (B) Percentage of mutated reads (deep sequencing) as a function of headloop score. Each data point corresponds to one targeted locus in one F0 knockout animal. Some samples were artificially created to simulate mediocre gRNAs by mixing genomic DNA from an injected embryo with genomic DNA from the uninjected control in a 1:1 (diluted , squares) or 1:3 ratio (diluted , triangles). For these, the percentage of mutated reads was not measured by deep sequencing but estimated by dividing the percentage of mutated reads of the original sample by 2 (diluted ) or 4 (diluted ). The dark grey dashed line is the line of best fit by linear regression: proportion of mutated reads = 0.33 + 0.69 headloop score; R2 = 0.44. Headloop score is a significant predictor of the proportion of mutated reads, p
In the first round, each gRNA set is injected followed by deep sequencing or headloop PCR to confirm mutagenesis, thereby controlling the false negative rate of a screen. Headloop PCR is cheap, robust, and requires only a single step, which makes it easily adapted to high-throughput screening. No specialist equipment is required, as opposed to qPCR (Yu et al., 2014), high resolution melting analysis (HRMA) (Samarut et al., 2016), or fluorescent PCR (Carrington et al., 2015). Unlike deep sequencing, qPCR, and HRMA, it is also flexible with respect to the size of amplicons and so is sensitive to a wide range of alleles, from small indels to large deletions between targeted loci. It can be used to assay the efficiency of any gRNA, with no restrictions on target sequence that might be imposed by the use of restriction fragment length polymorphism (Jao et al., 2013), for example. The products of headloop PCR are also compatible with different sequencing methods, should further analysis of mutant haplotypes be required.
It is precisely because discovering novel phenotypes is the main challenge of F0 knockout methods that an essential criterion for success was achieving high phenotypic penetrance and close to complete removal of wild-type alleles for test genes. When studying phenotypes that vary continuously in the population, such as locomotor activity (Figure 6) or circadian clock period (Figure 5B), it is not possible to readily identify animals in the F0 knockout population that display a phenotype, in contrast to situations in which animals exhibit overt morphological differences. Therefore, the majority of the animals in the F0 knockout population need to be bona fide knockouts. Continuous traits often have the added challenge that they are not phenotypes whose spatial variation is evident in individual animals (Watson et al., 2020), in contrast to developmental phenotypes like formation of electrical synapses (Shah et al., 2015) or distribution of microglia (Kuil et al., 2019). Therefore, most cells in each F0 knockout animal must carry biallelic loss-of-function mutations. With the pigmentation phenotypes golden (slc24a5 knockout) and sandy (tyr knockout), and the deep sequencing data at 32 targeted sites, we demonstrate that the F0 knockouts generated by the present method show very little mosaicism, in the sense that most wild-type alleles are removed, and hence can be used to discover novel phenotypes that require most or all the cells of most F0 knockout animals to be mutated.
In the case of the small indels around each targeted site, we discovered through deep sequencing that there was substantial diversity in alleles, including within individual animals. Most mutations at each locus are present at low frequencies in individual animals (> 95% mutations detected at each locus are present at frequencies below 10% in individual animals, Author response image 1). This may be because the mutation happened later in development or in a cell that did not contribute many daughter cells. High coverage is necessary to detect and quantify this diversity. Many of these alleles would likely be missed by the lower coverage of an amplification-free experiment.
To answer specifically the first question raised, the order when targeting gradually more loci (Figure 1E,F) followed the IDT ranking when selecting a single crRNA per exon (see Material and methods, section RNP pooling). Therefore, when targeting a single locus, only the top predicted gRNA is injected; when targeting two loci, the top two predicted gRNAs are injected together; etc. Based on the deep sequencing data (Figure 2A), this amounts to essentially the same as picking the order randomly within the top 3 or 4 gRNAs, as the top predicted gRNA of the set is only rarely the most mutagenic one in practice. Indeed, when we sequenced 10 genes targeted by three or four-gRNA sets, there were only 3 sets in which the top predicted gRNA by IDT (gRNA A) was also the top performing gRNA (slc24a5, tbx5a, scn1lab). This is what is expected by chance alone (if the most mutagenic gRNA of each set was drawn randomly, 31% of gRNA sets would have the top predicted gRNA as most mutagenic gRNA). In other words, it is not possible to guess above chance the IDT ranking from the mutagenesis levels alone. 59ce067264