Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning

Arbab, Mandana; Shen, Max W.; Mok, Beverly; Wilson, Christopher G.; Matuszek, Żaneta; Cassa, Christopher A.; Liu, David R.

doi:10.1016/j.cell.2020.05.037

Cited by 210 publications

(221 citation statements)

References 84 publications

(152 reference statements)

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“…Recently, novel forms of DNA base editing such as a C>G editing [15][16][17] , and simultaneous C and A editing [18][19][20][21] have been suggested, which would substantially expand the utilities of DNA base editors. Along with the intense efforts to improve DNA base editors, our suggested tools, high-delity ABE variants that exhibit minimized cytosine catalysis and reduced off-target RNA editing, and TC-speci c base editors with negligible bystander effects, should make DNA base editing tools a more attractive alternative for gene editing in many research areas, such as disease therapy development, gene regulation, and plant transformation.…”

mentioning

confidence: 99%

Precise adenine base editors that exhibit minimized cytosine catalysis

Jeong

Lee

Hwang

et al. 2020

Preprint

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Adenine base editors (ABEs) promise specific A-to-G conversions at genomic sites of interest. However, ABEs also induce cytosine deamination at the target DNA site and exhibit transcriptome-wide off-target RNA editing. To alleviate the ABE-mediated cytosine editing activity, here we engineered the commonly-used version of adenosine deaminase, TadA7.10, to contain rationally designed mutations. We ultimately found that ABE7.10 with a D108Q mutation in TadA7.10 exhibited greatly reduced cytosine deamination activity, and conversely, ABE7.10 containing a P48R mutation displayed increased cytosine deamination activity rather than adenine editing. We found that the D108Q mutation also reduces cytosine deamination activity in two recently-developed versions of ABE, ABE8e and ABE8s, and has a synergistic effect with V106W, a key mutation that reduces off-target RNA editing. On the other hand, by incorporating the P48R mutation into ABE7.10, we demonstrated TC-specific base editing tools that enable either TC-to-TT or TC-to-TG conversions, broadening the utility of base editors.

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mentioning

confidence: 99%

Precise adenine base editors that exhibit minimized cytosine catalysis

Jeong

Lee

Hwang

et al. 2020

Preprint

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“…At the time we prepared this manuscript, a deep conditional autoregressive machine learning model (BE-Hive) capable of predicting editing outcomes and efficiencies of several base editors including ABEmax and CBE4max was published [20]. To compare the performance of both models, we applied BE-Hive to the same set of endogenous loci tested in our study ( Fig.…”

Section: Comparison Between Be-dict and Be-hivementioning

confidence: 99%

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

Marquart

Allam

Janjuha

et al. 2020

Preprint

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AbstractBase editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable conversion of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have vast potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on thousands of lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy.

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“…Annotate the protospacer. Alternately, free software programs such as the Benchling wizard (Komor, 2016), BE-designer (Hwang et al, 2018), or BE-Hive (Arbab et al, 2020) can be used for automated gRNA design (see Critical Parameters). As most automated programs lack flexibility in deaminase and…”

Section: Of 38mentioning

confidence: 99%

“…Current Protocols in Molecular Biology Cas variants, we recommend manually designing your protospacers (Fig. 3A) and checking predicted editing efficiencies in BE-Hive (Arbab et al, 2020).…”

Section: Of 38mentioning

confidence: 99%

Base Editing in Human Cells to Produce Single‐Nucleotide‐Variant Clonal Cell Lines

Vasquez

Cowan

Komor

2020

CP Molecular Biology

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Base-editing technologies enable the introduction of point mutations at targeted genomic sites in mammalian cells, with higher efficiency and precision than traditional genome-editing methods that use DNA double-strand breaks, such as zinc finger nucleases (ZFNs), transcription-activator-like effector nucleases (TALENs), and the clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (CRISPR-Cas9) system. This allows the generation of single-nucleotide-variant isogenic cell lines (i.e., cell lines whose genomic sequences differ from each other only at a single, edited nucleotide) in a more time-and resource-effective manner. These single-nucleotide-variant clonal cell lines represent a powerful tool with which to assess the functional role of genetic variants in a native cellular context. Base editing can therefore facilitate genotype-to-phenotype studies in a controlled laboratory setting, with applications in both basic research and clinical applications. Here, we provide optimized protocols (including experimental design, methods, and analyses) to design base-editing constructs, transfect adherent cells, quantify base-editing efficiencies in bulk, and generate single-nucleotidevariant clonal cell lines.

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Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning

Cited by 210 publications

References 84 publications

Precise adenine base editors that exhibit minimized cytosine catalysis

Precise adenine base editors that exhibit minimized cytosine catalysis

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

Base Editing in Human Cells to Produce Single‐Nucleotide‐Variant Clonal Cell Lines

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