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Can genetic engineering-based methods for gene function identification be eclipsed by genome editing in plants? A comparison of methodologies.
Finding and explaining the functions of genes in plants have promising applications in crop improvement and bioprospecting and hence, it is important to compare various techniques available for gene function identification in plants. Today, the most popular technology among researchers to identify the functions of genes is the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein 9 (Cas9)-based genome editing method. But by no means can we say that CRISPR/Cas9 is the go-to method for all purposes. It comes with its own baggage. Researchers will agree and have lived through at least seven more technologies deployed to find the functions of genes, which come under three umbrellas: 1. genetic engineering, 2. transient expression, and 3. chemical/physical mutagenesis. Each of the methods evolved when the previous one ran into an insurmountable problem. In this review, we compare the eight technologies against one another on 14 parameters. This review lays bare the pros and cons, and similarities and dissimilarities of various methods. Every method comes with its advantages and disadvantages. For example, the CRISPR/Cas9-based genome editing is an excellent method for modifying gene sequences, creating allelic versions of genes, thereby aiding the understanding of gene function. But it comes with the baggage of unwanted or off-target mutations. Then, we have methods based on random or targeted knockout of the gene, knockdown, and overexpression of the gene. Targeted disruption of genes is required for complete knockout of gene function, which may not be accomplished by editing. We have also discussed the strategies to overcome the shortcomings of the targeted gene-knockout and the CRISPR/Cas9-based methods. This review serves as a comprehensive guide towards the understanding and comparison of various technologies available for gene function identification in plants and hence, it will find application for crop improvement and bioprospecting related research.
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Identifying genome-wide off-target sites of CRISPR RNA-guided nucleases and deaminases with Digenome-seq.
Digested genome sequencing (Digenome-seq) is a highly sensitive, easy-to-carry-out, cell-free method for experimentally identifying genome-wide off-target sites of programmable nucleases and deaminases (also known as base editors). Genomic DNA is digested in vitro using clustered regularly interspaced short palindromic repeats ribonucleoproteins (RNPs; plus DNA-modifying enzymes to cleave both strands of DNA at sites containing deaminated base products, in the case of base editors) and subjected to whole-genome sequencing (WGS) with a typical sequencing depth of 30×. A web-based program is available to map in vitro cleavage sites corresponding to on- and off-target sites. Chromatin DNA, in parallel with histone-free genomic DNA, can also be used to account for the effects of chromatin structure on off-target nuclease activity. Digenome-seq is more sensitive and comprehensive than cell-based methods for identifying off-target sites. Unlike other cell-free methods, Digenome-seq does not involve enrichment of DNA ends through PCR amplification. The entire process other than WGS, which takes ~1-2 weeks, including purification and preparation of RNPs, digestion of genomic DNA and bioinformatic analysis after WGS, takes about several weeks.
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Prediction of on-target and off-target activity of CRISPR-Cas13d guide RNAs using deep learning.
Transcriptome engineering applications in living cells with RNA-targeting CRISPR effectors depend on accurate prediction of on-target activity and off-target avoidance. Here we design and test ~200,000 RfxCas13d guide RNAs targeting essential genes in human cells with systematically designed mismatches and insertions and deletions (indels). We find that mismatches and indels have a position- and context-dependent impact on Cas13d activity, and mismatches that result in G-U wobble pairings are better tolerated than other single-base mismatches. Using this large-scale dataset, we train a convolutional neural network that we term targeted inhibition of gene expression via gRNA design (TIGER) to predict efficacy from guide sequence and context. TIGER outperforms the existing models at predicting on-target and off-target activity on our dataset and published datasets. We show that TIGER scoring combined with specific mismatches yields the first general framework to modulate transcript expression, enabling the use of RNA-targeting CRISPRs to precisely control gene dosage.
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CRISPR might be the banana's only hope against a deadly fungus.
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A path to efficient gene editing.
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SeqCor: correct the effect of guide RNA sequences in clustered regularly interspaced short palindromic repeats/Cas9 screening by machine learning algorithm.
Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-based screening using various guide RNA (gRNA) libraries has been executed to identify functional components for a wide range of phenotypes with regard to numerous cell types and organisms. Using data from public CRISPR/Cas9-based screening experiments, we found that the sequences of gRNAs in the library influence CRISPR/Cas9-based screening. As building a standard strategy for correcting results of all gRNA libraries is impractical, we developed SeqCor, an open-source programming bundle that enables researchers to address the result bias potentially triggered by the composition of gRNA sequences via the organization of gRNA in the library used in CRISPR/Cas9-based screening. Furthermore, SeqCor completely computerizes the extraction of sequence features that may influence single-guide RNA knockout efficiency using a machine learning approach. Taken together, we have developed a software program bundle that ought to be beneficial to the CRISPR/Cas9-based screening platform.
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Introduction to the Special Issue on CRISPR.
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How Scientists Perceive CRISPR's Translational Promise and the Implications for Individuals with Genetic Conditions.
CRISPR (clustered regularly interspaced short palindromic repeats) somatic genome editing, an important promissory technology, presents a case study of the movement of basic scientists into translational research. In this paper, we explore how scientists experience the pulls of CRISPR's power and the pushes of economic and societal pressures in adopting new translational roles. Given basic scientists' emerging contact with and influence upon individuals with genetic conditions, we also examine how scientists understand the perspectives of affected populations, both as potential subjects of early experiments and as the patients who could receive future treatments. Finally, we consider the ethical implications of our findings and call for innovative approaches to translational research that help scientists engage with people with genetic conditions in early translational research.
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Clipping cancer with CRISPR: The precise gene-editing tool is showing promise in early cancer studies, although obstacles remain.
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[Research advances on the development and application of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein system].
Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) protein system, as an emerging gene editing system, can be divided into class 1 and class 2 systems according to the number of Cas protein. The CRISPR/Cas9 in class 2 system can cleave target nucleic acid only with the help of Cas9 protein and single-stranded guide RNA, which is currently the most widely used CRISPR/Cas system. In addition to gene editing in the treatment of genetic diseases, a variety of CRISPR/Cas system derived technologies have vast application prospect in the fields of disease-related gene screening, gene expression regulation, and rapid detection, prevention, and control of pathogens. This article summarizes the discovery process of CRISPR/Cas system and applications of several major CRISPR/Cas derived technologies, aiming to provide a reference for researchers in the field of life science.
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