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The substitution rates of transitions are higher than expected by chance relative to those of transversions. Many have argued that selection disfavors transversions, as nonsynonymous transversions are less likely to conserve biochemical properties of the original amino acid. Only recently has it become feasible to directly test this selective hypothesis by comparing the fitness effects of a large number of transition and transversion mutations. For example, a recent study of six viruses and one beta-lactamase gene did not find evidence supporting the selective hypothesis. Here, we analyze the relative fitness effects of transition and transversion mutations from our recently published genome-wide study of mutational fitness effects in influenza virus. In contrast to prior work, we find that transversions are significantly more detrimental than transitions. Using what we believe to be an improved statistical framework, we also identify a similar trend in two HIV data sets. We further demonstrate a fitness difference in transition and transversion mutations using four deep mutational scanning data sets of influenza virus and HIV, which provided adequate statistical power. We find that three of the most commonly cited radical/conservative amino acid categories are predictive of fitness, supporting their utility in studies of positive selection and codon usage bias. We conclude that selection is a major contributor to the transition:transversion substitution bias in viruses and that this effect is only partially explained by the greater likelihood of transversion mutations to cause radical as opposed to conservative amino acid changes.

Here we developed an adenine transversion base editor, AYBE, for A-to-C and A-to-T transversion editing in mammalian cells by fusing an adenine base editor (ABE) with hypoxanthine excision protein N-methylpurine DNA glycosylase (MPG). We also engineered AYBE variants enabling targeted editing at genomic loci with higher transversion editing activity (up to 72% for A-to-C or A-to-T editing). A base editor is engineered for A-to-T and A-to-C transversion editing.

Most genetic variants that contribute to disease 1 are challenging to correct efficiently and without excess byproducts 2 , 3 , 4 – 5 . Here we describe prime editing, a versatile and precise genome editing method that directly writes new genetic information into a specified DNA site using a catalytically impaired Cas9 endonuclease fused to an engineered reverse transcriptase, programmed with a prime editing guide RNA (pegRNA) that both specifies the target site and encodes the desired edit. We performed more than 175 edits in human cells, including targeted insertions, deletions, and all 12 types of point mutation, without requiring double-strand breaks or donor DNA templates. We used prime editing in human cells to correct, efficiently and with few byproducts, the primary genetic causes of sickle cell disease (requiring a transversion in HBB ) and Tay–Sachs disease (requiring a deletion in HEXA ); to install a protective transversion in PRNP ; and to insert various tags and epitopes precisely into target loci. Four human cell lines and primary post-mitotic mouse cortical neurons support prime editing with varying efficiencies. Prime editing shows higher or similar efficiency and fewer byproducts than homology-directed repair, has complementary strengths and weaknesses compared to base editing, and induces much lower off-target editing than Cas9 nuclease at known Cas9 off-target sites. Prime editing substantially expands the scope and capabilities of genome editing, and in principle could correct up to 89% of known genetic variants associated with human diseases. A new DNA-editing technique called prime editing offers improved versatility and efficiency with reduced byproducts compared with existing techniques, and shows potential for correcting disease-associated mutations.

Base editors have substantial promise in basic research and as therapeutic agents for the correction of pathogenic mutations. The development of adenine transversion editors has posed a particular challenge. Here we report a class of base editors that enable efficient adenine transversion, including precise A•T-to-C•G editing. We found that a fusion of mouse alkyladenine DNA glycosylase (mAAG) with nickase Cas9 and deaminase TadA-8e catalyzed adenosine transversion in specific sequence contexts. Laboratory evolution of mAAG significantly increased A-to-C/T conversion efficiency up to 73% and expanded the targeting scope. Further engineering yielded adenine-to-cytosine base editors (ACBEs), including a high-accuracy ACBE-Q variant, that precisely install A-to-C transversions with minimal Cas9-independent off-targeting effects. ACBEs mediated high-efficiency installation or correction of five pathogenic mutations in mouse embryos and human cell lines. Founder mice showed 44–56% average A-to-C edits and allelic frequencies of up to 100%. Adenosine transversion editors substantially expand the capabilities and possible applications of base editing technology. A base editor for precise adenine transversions is demonstrated in mouse embryos.

Programmable C•G-to-G•C base editors (CGBEs) have broad scientific and therapeutic potential, but their editing outcomes have proved difficult to predict and their editing efficiency and product purity are often low. We describe a suite of engineered CGBEs paired with machine learning models to enable efficient, high-purity C•G-to-G•C base editing. We performed a CRISPR interference (CRISPRi) screen targeting DNA repair genes to identify factors that affect C•G-to-G•C editing outcomes and used these insights to develop CGBEs with diverse editing profiles. We characterized ten promising CGBEs on a library of 10,638 genomically integrated target sites in mammalian cells and trained machine learning models that accurately predict the purity and yield of editing outcomes ( R  = 0.90) using these data. These CGBEs enable correction to the wild-type coding sequence of 546 disease-related transversion single-nucleotide variants (SNVs) with >90% precision (mean 96%) and up to 70% efficiency (mean 14%). Computational prediction of optimal CGBE–single-guide RNA pairs enables high-purity transversion base editing at over fourfold more target sites than achieved using any single CGBE variant. Efficiency of transversion base editing is increased by matching a set of base editors to target sequences.

Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the sequence context predictable via machine-learning methods. By changing the species origin and relative position of uracil-DNA glycosylase and deaminase, together with codon optimization, we obtain optimized C-to-G BEs (OPTI-CGBEs) for efficient C-to-G transversion. The motif preference of OPTI-CGBEs for editing 100 endogenous sites is determined in HEK293T cells. Using a sgRNA library comprising 41,388 sequences, we develop a deep-learning model that accurately predicts the OPTI-CGBE editing outcome for targeted sites with specific sequence context. These OPTI-CGBEs are further shown to be capable of efficient base editing in mouse embryos for generating Tyr -edited offspring. Thus, these engineered CGBEs are useful for efficient and precise base editing, with outcome predictable based on sequence context of targeted sites. C->G transversions can be highly desirable editing outcomes. Here the authors optimise CGBEs and provide a deep learning model for predicting editing outcomes based on sequence context.

We announce the release of the fourth version of MEGA software, which expands on the existing facilities for editing DNA sequence data from autosequencers, mining Web-databases, performing automatic and manual sequence alignment, analyzing sequence alignments to estimate evolutionary distances, inferring phylogenetic trees, and testing evolutionary hypotheses. Version 4 includes a unique facility to generate captions, written in figure legend format, in order to provide natural language descriptions of the models and methods used in the analyses. This facility aims to promote a better understanding of the underlying assumptions used in analyses, and of the results generated. Another new feature is the Maximum Composite Likelihood (MCL) method for estimating evolutionary distances between all pairs of sequences simultaneously, with and without incorporating rate variation among sites and substitution pattern heterogeneities among lineages. This MCL method also can be used to estimate transition/transversion bias and nucleotide substitution pattern without knowledge of the phylogenetic tree. This new version is a native 32-bit Windows application with multi-threading and multi-user supports, and it is also available to run in a Linux desktop environment (via the Wine compatibility layer) and on Intel-based Macintosh computers under the Parallels program. The current version of MEGA is available free of charge at http://www.megasoftware.net. [PUBLICATION ABSTRACT]

Base editors (BEs) introduce base substitutions without double-strand DNA cleavage. Besides precise substitutions, BEs generate low-frequency ‘stochastic’ byproducts through unclear mechanisms. Here, we performed in-depth outcome profiling and genetic dissection, revealing that C-to-G BEs (CGBEs) generate substantial amounts of intermediate double-strand breaks (DSBs), which are at the centre of several byproducts. Imperfect DSB end-joining leads to small deletions via end-resection, templated insertions or aberrant transversions during end fill-in. Chromosomal translocations were detected between the editing target and off-targets of Cas9/deaminase origin. Genetic screenings of DNA repair factors disclosed a central role of abasic site processing in DSB formation. Shielding of abasic sites by the suicide enzyme HMCES reduced CGBE-initiated DSBs, providing an effective way to minimize DSB-triggered events without affecting substitutions. This work demonstrates that CGBEs can initiate deleterious intermediate DSBs and therefore require careful consideration for therapeutic applications, and that HMCES-aided CGBEs hold promise as safer tools. Huang, Qin, Shang et al. profile double-strand breaks (DSBs) generated by C-to-G base editors (CGBEs) and find that HMCES protects abasic sites and reduces CGBE-triggered DSBs.

The year 2020 marks a decade since the first gene-edited plants were generated using homing endonucleases and zinc finger nucleases. The advent of CRISPR/Cas9 for gene-editing in 2012 was a major science breakthrough that revolutionized both basic and applied research in various organisms including plants and consequently honored with “The Nobel Prize in Chemistry, 2020.” CRISPR technology is a rapidly evolving field and multiple CRISPR-Cas derived reagents collectively offer a wide range of applications for gene-editing and beyond. While most of these technological advances are successfully adopted in plants to advance functional genomics research and development of innovative crops, others await optimization. One of the biggest bottlenecks in plant gene-editing has been the delivery of gene-editing reagents, since genetic transformation methods are only established in a limited number of species. Recently, alternative methods of delivering CRISPR reagents to plants are being explored. This review mainly focuses on the most recent advances in plant gene-editing including (1) the current Cas effectors and Cas variants with a wide target range, reduced size and increased specificity along with tissue specific genome editing tool kit (2) cytosine, adenine, and glycosylase base editors that can precisely install all possible transition and transversion mutations in target sites (3) prime editing that can directly copy the desired edit into target DNA by search and replace method and (4) CRISPR delivery mechanisms for plant gene-editing that bypass tissue culture and regeneration procedures including de novo meristem induction, delivery using viral vectors and prospects of nanotechnology-based approaches.

While next-generation sequencing (NGS) has transformed genetic testing, it generates large quantities of noisy data that require a significant amount of bioinformatics to generate useful interpretation. The accuracy of variant calling is therefore critical. Although GATK HaplotypeCaller is a widely used tool for this purpose, newer methods such as DeepVariant have shown higher accuracy in assessments of gold-standard samples for whole-genome sequencing (WGS) and whole-exome sequencing (WES), but a side-by-side comparison on clinical samples has not been performed. Trio WES was used to compare GATK (4.1.2.0) HaplotypeCaller and DeepVariant (v0.8.0). The performance of the two pipelines was evaluated according to the Mendelian error rate, transition-to-transversion (Ti/Tv) ratio, concordance rate, and pathological variant detection rate. Data from 80 trios were analyzed. The Mendelian error rate of the 77 biological trios calculated from the data by DeepVariant (3.09 ± 0.83%) was lower than that calculated from the data by GATK (5.25 ± 0.91%) (p < 0.001). DeepVariant also yielded a higher Ti/Tv ratio (2.38 ± 0.02) than GATK (2.04 ± 0.07) (p < 0.001), suggesting that DeepVariant proportionally called more true positives. The concordance rate between the 2 pipelines was 88.73%. Sixty-three disease-causing variants were detected in the 80 trios. Among them, DeepVariant detected 62 variants, and GATK detected 61 variants. The one variant called by DeepVariant but not GATK HaplotypeCaller might have been missed by GATK HaplotypeCaller due to low coverage. OTC exon 2 (139 bp) deletion was not detected by either method. Mendelian error rate calculation is an effective way to evaluate variant callers. By this method, DeepVariant outperformed GATK, while the two pipelines performed equally in other parameters.