SEARCH · PubMed Health

Results for “Guide RNA (gRNA) design”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

10 recordsLinked to original sources

PubMed2025

Genome editing with programmable base editors in human cells.

Genome editing has garnered significant attention over the last decade, resulting in a massive expansion of the genome engineering toolbox. Base editors encompass a class of tools that enable installing single-nucleotide changes in genomic DNA without the use of double-strand breaks. With the ever-increasing development of new and/or improved base editor systems, it is easy to be overwhelmed by the abundance of options. Here, we provide clear guidance to facilitate the selection of a base editor and to design guide RNAs (gRNAs) to suit various needs. Additionally, we describe in detail how to generate gRNA plasmids, transfect various mammalian cell types, and evaluate editing efficiencies. Finally, we give alternative methods and troubleshooting tips for some common pitfalls encountered during base editing.

Humans↗

PubMed2002

A hammerhead ribozyme substrate and reporter for in vitro kinetoplastid RNA editing.

Current in vitro assays for RNA editing in kinetoplastids directly examine the products generated by incubation of pre-mRNA substrate with guide RNA (gRNA) and mitochondrial (mt) extract. RNA editing substrates that are modeled on hammerhead ribozymes were designed with catalytic cores that contained or lacked additional uridylates (Us). They proved to be sensitive reporters of editing activity when used for in vitro assays. A deletion editing substrate that is based on A6 pre-mRNA had no ribozyme activity, but its incubation with gRNA and mt extract resulted in its deletion editing and production of a catalytically active ribozyme. Hammerhead ribozymes are thus sensitive tools to assay in vitro RNA editing.

Animals↗

PubMed2006

Reconstitution of full-round uridine-deletion RNA editing with three recombinant proteins.

Uridine (U)-insertion/deletion RNA editing in trypanosome mitochondria involves an initial cleavage of the preedited mRNA at specific sites determined by the annealing of partially complementary guide RNAs. An involvement of two RNase III-containing core editing complex (L-complex) proteins, MP90 (KREPB1) and MP61 (KREPB3) in, respectively, U-deletion and U-insertion editing, has been suggested, but these putative enzymes have not been characterized or expressed in active form. Recombinant MP90 proteins from Trypanosoma brucei and Leishmania major were expressed in insect cells and cytosol of Leishmania tarentolae, respectively. These proteins were active in specifically cleaving a model U-deletion site and not a U-insertion site. Deletion or mutation of the RNase III motif abolished this activity. Full-round guide RNA (gRNA)-mediated in vitro U-deletion editing was reconstituted by a mixture of recombinant MP90 and recombinant RNA editing exonuclease I from L. major, and recombinant RNA editing RNA ligase 1 from L. tarentolae. MP90 is designated REN1, for RNA-editing nuclease 1.

Amino Acid Sequence↗

PubMed2026

Minimizing Off-Target Effects of CRISPR-Cas9 With Optimized sgRNA: Evaluation of Efficiency and Specificity in the Tumor Protein 53 (TP53) Region.

CRISPR-Cas9 is a widely used genetic tool with therapeutic potential in molecular biology. CRISPR-Cas9 enables precise genome editing by its ability to target specific DNA sequence. After off-target and on-target regions are identified, CRISPR-Cas9 is applied to these regions based on the match between the guide RNA (gRNA) and target DNA sequence. This study points to the off-target impact of mismatches between the gRNA and target DNA on exon regions of the TP53 gene, which are involved in regulating multiple genes and cellular functions. Off-target positions are typically evaluated using scoring methods. In this study, we have used latent class analysis to reveal subclasses of off-target positions. Thus, we have created the levels of off-target positions and evaluated the effects of mismatching positions within these classes using machine learning classifiers. The results revealed that mismatching positions could be categorized into three levels: low, middle, and high off-target positions. We have improved a computational framework to minimize off-target effects and to identify the PAM sequences in the gRNA design. Thus, carefully designed gRNAs will ensure that desired genetic edits are performed and target variants are achieved. This work will avail the future research aimed at optimizing genome editing by customizing CRISPR-Cas9 to target specific protospacer DNA through gRNA.

CRISPR-Cas Systems↗

PubMed2026

Towards efficient perturbation for the noncoding genome.

Deciphering the functionality of the noncoding genome, which includes important cis-regulatory elements (CREs) and transcribed noncoding RNA genes, remains technically challenging. Here, using massively parallel genetic screening, we systematically benchmark the performance of five representative loss-of-function perturbation tools, including single-guide RNA (gRNA) mediated SpCas9 cleavage or CRISPR interference, and paired gRNA (pgRNA) involved dual-SpCas9, Big Papi (paired SpCas9 and SaCas9) or dual-enAsCas12a fragment deletion methods, in decoding the roles of the noncoding genome. For targeting CREs such as enhancers, dual-SpCas9 outperforms other methods with superior efficiency in destroying functional genomic regions. For perturbing noncoding RNA genes, in addition to dual-SpCas9, other RNA-targeting methods such as RNA interference are recommended to discriminate transcript-dependent or -independent roles. A deep learning model, DeepDC, with an associated web server, is built to facilitate optimal dual-SpCas9 pgRNA design for efficiently deleting a genomic fragment. Together, our work provides practical guidance on selecting appropriate loss-of-function tools to resolve the functional complexity of the noncoding genome.

CRISPR-Cas Systems↗

PubMed2025

RAPID-DASH: Fast and Efficient Assembly of Guide RNA Arrays for Multiplexed CRISPR-Cas9 Applications.

Guide RNA (gRNA) arrays can enable targeting multiple genomic loci simultaneously using CRISPR-Cas9. In this study, we present a streamlined and efficient method to rapidly construct gRNA arrays with up to 10 gRNA units in a single day. We demonstrate that gRNA arrays maintain robust functional activity across all positions, and can incorporate libraries of gRNAs, combining scalability and multiplexing. Our approach will streamline combinatorial perturbation research by enabling the economical and rapid construction, testing, and iteration of gRNA arrays. To facilitate the adaptation of this approach, we have made a web tool to design oligo sequences necessary to assemble gRNA arrays.

CRISPR-Cas9↗

PubMed2000

RNA-binding properties of the mitochondrial Y-box protein RBP16.

We have previously identified a mitochondrial Y-box protein in Trypanosoma brucei that we designated RBP16. The predicted RBP16 amino acid sequence revealed the presence of a cold-shock domain at its N-terminus and a glycine- and arginine-rich C-terminus reminiscent of an RGG RNA-binding motif. Since RBP16 is capable of interacting with different guide RNAs (gRNAs) in vitro and in vivo primarily via the oligo(U) tail, as well as with ribosomal RNAs, possible functions of RBP16 may be in kinetoplastid RNA editing and/or translation. Herein, we report experiments that further define the RNA-binding properties of RBP16. RBP16 forms a single stable complex with the gRNA gA6[14] at low protein concentration, while at higher protein concentration two stable complexes that possibly represent two different conformations are observed. Both complexes are stable at relatively high salt and moderate heparin concentrations indicating that the binding of RBP16 to gA6[14] does not rely primarily on ionic interactions. Phenylglyoxal treatment of the protein indicates that arginine residues are important in RNA binding. The minimal length of RNA sequence necessary for the binding of RBP16 was assessed by gel retardation and UV cross-linking competition assays using oligo(U) ribonucleotides of varying lengths (4-40 nt). Although RBP16 can bind to oligonucleotides as small as U(4), its affinity increases with the length of the oligo(U) ribonucleotide, with a dramatic increase in binding efficiency observed when the length is increased to 10 nt. Gel retardation assays employing T.brucei mRNAs demonstrated that, although it acts as a major binding determinant, a 3' U tail is not an absolute requirement for efficient RBP16-RNA binding. Experiments with oligonucleotides containing U stretches embedded at different positions in oligo(dC) indicated that high-affinity binding requires both a uridine stretch, as well as 5' and 3' non-specific sequences. These results suggest a model for the molecular interactions involved in RBP16-RNA binding.

Animals↗

PubMed2026

Simulation of CRISPR/Cas9-mediated gene editing for the Vitellogenin gene in Apis mellifera.

CRISPR/Cas9 genome editing provides a powerful framework for interrogating gene function in Apis mellifera. Yet, empirical application remains challenging due to biological constraints, including haplodiploid genetics, narrow embryonic injection window, and the social rearing requirements that complicate functional validation. These constraints necessitate in silico pre-screening to maximize editing success before resource-intensive wet-lab implementation. Within the omnigenic framework, which distinguishes core regulatory genes from peripheral loci buffered by network effects, vitellogenin (Vg) represents an optimal target which is ancestrally dedicated to yolk provisioning; it has been co-opted to orchestrate diverse non-reproductive functions including longevity, stress resistance, immunity, and social behavior. We developed a computational pipeline to design a list of 57 and 56 candidate guide RNAs (gRNA) for targeted Vg knockout, evaluating candidate sites in both functional exons 2 and 3 based on structural accessibility and frameshift efficiency. Comparative analysis revealed complementary strengths in two top-best candidates from initial target pool of predicted gRNAs. The gRNA targeting exon 2 exhibits weaker secondary structure (ΔG = -0.25 kcal/mol versus -2.10 kcal/mol for exon 3), aligning with empirical evidence that sites with ΔG > -1.0 kcal/mol achieve 2-5 × higher Cas9 binding efficiency. This site yielded moderate frameshift frequency (77.8%; 61.9 percentile). Conversely, the predicted editing outcome for the gRNA targeting exon 3, despite stronger structural constraints, demonstrated superior functional disruption metrics demonstrating very high frameshift frequency (88.3%; 95.2 percentile), high in silico editing precision, minimal microhomology-mediated repair bias, and reproducible outcomes wherein nearly all predicted indels disrupt the coding sequence. Protein structure and domain analyses further predict that frameshift edits will generate a truncated protein missing all downstream functional domains. We recommend parallel empirical validation of both exon 2 and exon 3 targets to resolve the trade-off between structural accessibility (favoring higher editing rates) and frameshift efficacy (favoring complete loss-of-function). This dual-target strategy accommodates uncertainty in in vivo performance while maximizing the probability of generating informative phenotypes. Our in silico framework enables rational CRISPR design in non-model organisms by computationally balancing biophysical accessibility with functional impact, accelerating functional genomics in species where empirical optimization faces substantial biological constraints.

Animals↗

PubMed2025

Deciphering estrogen receptor alpha-driven transcription in human endometrial stromal cells via transcriptome, cistrome, and integration with chromatin landscape.

OBJECTIVE: To investigate estrogen receptor gene 1 (ESR1) and estrogen-driven transcription in human endometrial stromal cells. DESIGN: RNA sequencing (RNA-seq) and Cleavage Under Targets and Release Using Nuclease (Cut&Run) were performed on telomerase-immortalized human endometrial stromal cells with Clustered Regularly Interspaced Short Palindromic Repeats-mediated ESR1 activation. Hi-C-based chromatin architecture analysis (H3K27ac HiChIP) was conducted in primary endometrial stromal cells. SUBJECTS: Biopsies from two healthy, reproductive-aged volunteers with regular menstrual cycles and no history of gynecological malignancies. EXPOSURE: The ESR1-activated and control endometrial stromal cells were treated with estradiol (E2) or vehicle. Primary endometrial stromal cells were treated with vehicle or a decidualization cocktail. MAIN OUTCOME MEASURES: Differential gene expression analysis (RNA-seq) identified ligand-independent and -dependent ESR1 activity. Cut&Run profiled ESR1 genomic binding in ESR1-activated cells. H3K27ac HiChIP mapped hormone-induced changes in chromatin looping in primary cells. RESULTS: Among seven tested guide RNAs (gRNA), the ESR1-3 gRNA induced robust ESR1 activation and restored E2 responsiveness. Bulk RNA-seq revealed both ligand-dependent and -independent ESR1 transcriptional programs regulating inflammation, proliferation, and cancer-related pathways. Notably, 72% of differentially expressed genes overlapped with genes active in human endometrial tissue during the proliferative estrogen-dominant phase, supporting their physiological relevance. The Cut&Run-seq identified genome-wide ESR1 binding sites, with most binding sites located at distal regulatory elements. Integration of Cut&Run data with H3K27ac HiChIP chromatin loops linked distal ESR1 binding sites to gene promoters, including genes involved in decidualization (e.g., FOXO1) and endometrial cancer (e.g., ERRFI1, NRIP1, and EPAS1). Functional assays showed that ESR1 promotes cell viability and, in the presence of E2, enhances migration. CONCLUSION: The CRISPR-mediated ESR1 activation restores estrogen responsiveness in endometrial stromal cells. Combined transcriptomic, cistromic, and chromatin architecture analyses reveal ESR1's role in regulating decidualization and inflammation-related gene networks, with relevance to endometrial pathologies including endometrial cancer. This model serves as a powerful tool to study estrogen signaling in endometrial stromal cell biology and related pathologies.

Humans↗

PubMed2026

abCRISPR: deep learning-based design of abasic gRNA sequences for specific CRISPR-Cas9 genome editing.

SUMMARY: CRISPR-Cas9 has become a widely used tool for genome editing. However, its off-target cleavage caused by partial sequence matches with guide RNAs (gRNAs) remains a critical limitation. Recently, abasic gRNAs (ØXØ) have been developed to enhance target specificity, but their effects vary depending on the positional sequence context. Here, we present abCRISPR, a deep neural network (DNN) framework for the rational design of ØXØ sequences with minimized off-target activity. abCRISPR leverages informative few-shot training with paired datasets of abasic and unmodified gRNAs, using high-quality random mismatch target libraries, exhaustively sequenced for mismatched off-target substrates (n = 97583) in in vitro CRISPR-Cas9 cleavage experiments. Predicted off-target activities for both abasic and unmodified gRNAs showed strong correlation with experimental data (r ≥ 0.95, 10-fold cross-validation). Notably, these comprehensive training sets provide robust ground-truth negatives, enabling accurate and sensitive prediction of off-targets. For unmodified gRNAs, abCRISPR (AUC = 0.98) was validated to outperform existing deep learning-based methods (AUC = 0.45-0.68). When applied to the human genome, abCRISPR generated ØXØ sequences, covering 58 875 004 potent CRISPR-targetable sites with improved target specificity. Together, this work provides a comprehensive bioinformatics resource for safe and precise CRISPR-Cas9 genome editing. AVAILABILITY AND IMPLEMENTATION: The source code for abCRISPR and training data are available at https://doi.org/10.5281/zenodo.20398246. abCRISPR results for the human genome are available at http://clip.korea.ac.kr/abCRISPR/.

Deep Learning↗