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CRISPR-Cas9 has been widely adopted as the basic toolkit for precise genome-editing and engineering in various organisms. Alternative to Cas9, Cas12 or Cpf1 uses a simple crRNA as a guide and expands the protospacer adjacent motif (PAM) sequence to TTTN. This unique PAM sequence of Cpf1 may significantly increase the on-target editing efficiency due to lower chance of Cpf1 misreading the PAMs on a high GC genome. To demonstrate the utility of CRISPR-Cpf1, we have optimized the CRISPR-Cpf1 system and achieved high-editing efficiency for two counter-selectable markers in the industrially-relevant oleaginous yeast Yarrowia lipolytica: arginine permease (93% for CAN1) and orotidine 5′-phosphate decarboxylase (~96% for URA3). Both mutations were validated by indel mutation sequencing. For the first time, we further expanded this toolkit to edit three sulfur house-keeping genetic markers (40%–75% for MET2, MET6 and MET25), which confers yeast distinct colony color changes due to the formation of PbS (lead sulfide) precipitates. Different from Cas9, we demonstrated that the crRNA transcribed from a standard type II RNA promoter was sufficient to guide Cpf1 endonuclease activity. Furthermore, modification of the crRNA with 3′ polyUs facilitates the faster maturation and folding of crRNA and improve the genome editing efficiency. We also achieved multiplexed genome editing, and the editing efficiency reached 75%–83% for duplex genomic targets (CAN1-URA3 and CAN1-MET25) and 41.7% for triplex genomic targets (CAN1-URA3-MET25). Taken together, this work expands the genome-editing toolbox for oleaginous yeast species and may accelerate our ability to engineer oleaginous yeast for both biotechnological and biomedical applications.

Targeted genome editing by clustered regularly interspaced short palindromic repeats (CRISPR-Cas9) raised concerns over off-target effects. The use of double-nicking strategy using paired Cas9 nickase has been developed to minimize off-target effects. However, it was reported that the efficiency of paired nickases were comparable or lower than that of either corresponding nuclease alone. Recently, we conducted a systematic comparison of the efficiencies of several paired Cas9 with their corresponding Cas9 nucleases and showed that paired D10A Cas9 nickases are sometimes more efficient than individual nucleases for gene disruption. However, sometimes the designed paired Cas9 nickases exhibited significantly lower mutation frequencies than nucleases, hampering the generation of cells containing paired Cas9 nickase-induced mutations. Here we implemented IRES peptide-conjugation of fluorescent protein to Cas9 nickase and subjected for fluorescence-activated cell sorting. The sorted cell populations are highly enriched with cells containing paired Cas9 nickase-induced mutations, by a factor of up to 40-fold as compared with the unsorted population. Furthermore, gene-disrupted single cell clones using paired nickases followed by FACS sorting strategy were generated highly efficiently, without compromising with its low off-target effects. We envision that our fluorescent protein coupled paired nickase-mediated gene disruption, facilitating efficient and highly specific genome editing in medical research. [ABSTRACT FROM AUTHOR]

The adaptive immunity system in bacteria and archaea, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-associate (CRISPR/Cas), has been adapted as a powerful gene editing tool and got a broad application in genome research field due to its ease of use and cost-effectiveness. The performance of CRISPR/Cas relies on well-designed single-guide RNA (sgRNA), so a lot of bioinformatic tools have been developed to assist the design of highly active and specific sgRNA. These tools vary in design specifications, parameters, genomes and so on. To help researchers to choose their proper tools, we reviewed various sgRNA design tools, mainly focusing on their on-target efficiency prediction model and off-target detection algorithm. [ABSTRACT FROM AUTHOR]

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a powerful genome editing technology. Guide RNA (gRNA) plays an essential guiding role in the CRISPR system by complementary base pairing with target DNA. Since the CRISPR targeting mechanism problem has not yet been fully resolved, it remains a challenge to predict gRNA on-target efficiency. Current gRNA design tools often lack efficient information extraction and cannot learn the target efficiency patterns thoroughly. In this study, CRISPR-OTE is proposed to consider both multi-dimensional sequence information and important complementary prior knowledge based on a simple but effective framework. CRISPR-OTE consists of the local-contextual information branch and the prior knowledge branch. The local-contextual information branch extracts multi-dimensional sequence features from the DNA primary sequence by a parallel framework of Convolutional Neural Networks (CNN) and bidirectional Long Short-Term Memory networks (biLSTM). The prior knowledge branch selects the optimal subset of physicochemical features to provide the neural network with complementary knowledge, such as complex secondary structures. A simple feature fusion strategy is also adopted to fully utilize multi-modal data from the two branches. The experimental results show that the optimal subset of physicochemical features (RNA secondary structure and melting temperature of 34nt target) can effectively improve the prediction performance. Additionally, combining multi-dimensional sequence features and multi-modal features can extract information more comprehensively. Through transfer learning, CRISPR-OTE trained on the CRISPR-Cpf1 system can also be successfully applied to the CRISPR-Cas9 system. The performance of CRISPR-OTE is superior to other methods in different CRISPR systems and species. Therefore, CRISPR-OTE is a simple on-target efficiency prediction framework with better accuracy and generalization performance. • CRISPR-OTE extracts multi-dimensional sequence features. • The optimal subset of physicochemical features is selected as prior knowledge. • Transfer learning enables CRISPR-OTE to be successfully applied to different CRISPR systems and species. [ABSTRACT FROM AUTHOR]