A processive CRISPR-guided base editing system
Ruth Groza
Efficient and precise in vivo mutagenesis of nucleic acids has been a longstanding objective of genetic engineering. Clustered regularly interspersed short palindromic repeat (CRISPR) guided base editing, technology that couples Cas9 nucleases to nucleotide-modifying enzymes, offers a promising method to achieve this goal. However, current CRISPR-guided editors are limited to targeting regions within 350 nucleotides of protospacer adjacent motifs (PAMs), hampering their ability to diversify full-length genes (e.g. Halperin et al., 2018 Nature 560, 248). We are developing a base editing system in S. cerevisiae that addresses this issue by creating a fusion of Cas9 with enzymes that can move along DNA, similar to work with a Cas9 fusion to T7 RNA polymerase (Moore et al., 2018 JACS 140, 11560) from the Shoulders lab at MIT.
Empirical Estimation of Robustness from Deep Mutational Scans
Bryan Andrews (Collaboration with Peter Conlin and Ben Kerr)
Are genes more robust to errors than expected by chance?
Because of the way the genetic code is structured, different codons for the same amino acid have different ‘mutational neighborhoods.’ That is, if a single-base substitution occurs at an arginine codon, for instance, the set of possible new amino acids is very different if the original codon was an ‘cgg’ versus an ‘agg’, and the same is true for half of the amino acids: A, G, I, K, L, P, R, S, T & V.
Because of the way the genetic code is structured, different codons for the same amino acid have different ‘mutational neighborhoods.’ That is, if a single-base substitution occurs at an arginine codon, for instance, the set of possible new amino acids is very different if the original codon was an ‘cgg’ versus an ‘agg’, and the same is true for half of the amino acids: A, G, I, K, L, P, R, S, T & V.
As a result, the potential effects of a single-base substitution on a protein depends on what codons are used to encode that protein. In this project, in collaboration with Peter Conlin and Ben Kerr, we computationally reanalyzed published datasets in which proteins were subjected to Deep Mutational Scans, in order to test whether naturally occurring gene sequences are more robust to errors than would be expected by chance, a property we are calling ‘genetic robustness.’
In the above figure, we compare the robustness of the wild type sequences (green lines) to a distribution of 100,000 sequences encoding the same protein with randomly selected codons. With a few exceptions, we see broad evidence that actual genes are more robust than synonymous random encodings. Amino acids that are buried, that are empirically sensitive to mutation, and that are evolutionarily conserved all appear to be more robust than sites that tolerate mutations. Furthermore, gene-wide codon frequencies are not sufficient to explain the degree of robustness observed; most of the signal comes from where specific codons are used rather than how frequently they are used. We posit the existence of an evolutionary mechanism to select for sequences that are robust to errors, but cannot distinguish with our data whether selection acts to remove sequences that give rise to inherited errors (i.e. rare low-fitness offspring), or non-inherited errors such as mistranslation events.
Uncovering gene regulatory elements in the plant genome
Tobias Jores, Mike Dorrity, and Josh Cuperus
With climate change threatening global food security, crop plants with higher yields and improved response to abiotic stresses are required to satisfy the needs of our rapidly increasing population. Many beneficial traits in domesticated crops arose through mutations in enhancers and promoters, cis-regulatory elements that govern tissue- and condition-specific expression. Genetic engineering of such elements is a promising strategy for future crop improvement. However, our knowledge of plant cis-regulatory elements and their influence on gene expression is limited. To facilitate the genome-wide identification of active plant regulatory elements, we adapted STARR-seq – a technology for the high-throughput identification of enhancers (Arnold et al., 2013, Science 339, 1074) – for use in plants.
In a classical STARR-seq approach, DNA fragments are inserted in the 3'-UTR of a gene under control of a minimal promoter. If the DNA fragment has enhancer activity, it can interact with the minimal promoter and thereby increase its own transcription. The amount of mRNA produced is, therefore, a direct readout of the enhancer activity of the inserted DNA fragment. In initial experiments, we tested whether the cauliflower mosaic virus 35S core enhancer can stimulate the activity of the 35S minimal promoter. Surprisingly, when the 35S enhancer was inserted into the 3'-UTR of the reporter gene, it did not increase the activity of the minimal promoter. In contrast, when cloned upstream of the minimal promoter, the 35S enhancer did lead to increased transcription of the reporter gene.
In a classical STARR-seq approach, DNA fragments are inserted in the 3'-UTR of a gene under control of a minimal promoter. If the DNA fragment has enhancer activity, it can interact with the minimal promoter and thereby increase its own transcription. The amount of mRNA produced is, therefore, a direct readout of the enhancer activity of the inserted DNA fragment. In initial experiments, we tested whether the cauliflower mosaic virus 35S core enhancer can stimulate the activity of the 35S minimal promoter. Surprisingly, when the 35S enhancer was inserted into the 3'-UTR of the reporter gene, it did not increase the activity of the minimal promoter. In contrast, when cloned upstream of the minimal promoter, the 35S enhancer did lead to increased transcription of the reporter gene.
Therefore, we used a modified version of STARR-seq in which DNA fragments were inserted upstream of the 35S minimal promoter with a barcode inside the reporter gene that is linked to the individual fragments and reports on their enhancer activity. To test if this approach can identify the 35S enhancer and distinguish it from non-regulatory DNA, we fragmented a plasmid harboring the enhancer and inserted the fragments into the plant STARR vector. This fragment library was then subjected to STARR-seq in transiently transfected tobacco leaves. Almost all the most enriched fragments overlapped at least partially with the 35S enhancer region.
We have begun using Arabidopsis thaliana genomic DNA in the STARR-seq assay. One example (below) shows enrichment of intergenic regions. Some of these enrichment peaks overlap with known chromatin-accessible regions. Overall, peaks in our STARR-seq library have enrichment of regions that are classified in genomic categories similar to those of hypersensitive sites, with the notable exception of transposons. This outcome is expected, given that transposons are heavily methylated in planta but are not methylated in STARR-seq.
In addition to our efforts to identify enhancers in the genome of A. thaliana, we are also interested in characterizing minimal promotors. We array-synthesized the putative promoter regions of all Arabidopsis, maize and sorghum genes and have begun testing their transcriptional activity in an assay similar to the one used for enhancers. The majority of maize promoters have basal transcriptional activity that can be further increased by the 35S core enhancer.
