Your browser does not allow saving data (e.g. private mode): bookmarks will be lost when the tab closes.
Search PubMed
Keywords, author, journal, years — results from PubMed, abstracts with Russian translation, bookmarks in your browser.
Found: 26 147
Page 545 of 1000-
Profiling Genome-Wide Specificity of dCpf1 Cytidine Base Editors Using Digenome-Seq.
Digenome-seq is a powerful approach for determining the genome-wide specificity of programmable nuclease including CRISPR-Cas9 and CRISPR-Cpf1 (also known as Cas12a) and programmable deaminase including cytosine base editors (CBEs) and adenine base editors (ABEs). To define the genome-wide specificity of dLbCpf1-BE (also known as dLbCas12a-BE), genomic DNA is first incubated with dLbCpf1-BE, which induces C-to-U conversion at on-target and off-target sites, and then treated with a mixture of E. coli uracil DNA glycosylase (UDG) and Endonuclease VIII, which creates single-strand breaks (SSBs) by removing uracil in vitro. Digested genomic DNA is subjected to WGS, and then sequencing reads are aligned to the reference genome, resulting in straight alignments at on-target and off-target sites. The in vitro cleavage sites related to the straight alignments can be identified using the Digenome-seq computer tool.
-
Editing base in mouse model.
-
Gene Therapy and Gene Editing in Type 1 Diabetes: CRISPR-Based β-Cell Replacement and Treg Immune Modulation Approaches.
BACKGROUND: Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease marked by the destruction of pancreatic β-cells, resulting in lifelong dependence on exogenous insulin. Despite advances in insulin delivery and glucose monitoring technologies, patients remain at risk for acute and long-term complications, underscoring the need for curative strategies. Gene therapy and gene-editing technologies are emerging as transformative approaches capable of restoring β-cell function, modulating immune responses and potentially achieving durable remission. METHOD: This review synthesizes basic science foundations and clinical trial evidence, focusing on five key protocols (NCT03162237, NCT05210530, NCT05241444, NCT05565248 and NCT06938334). RESULTS: Strategies include immune modulation (PD-L1, FOXP3), β-cell replacement (CRISPR-edited progenitors, xenotransplantation) and combination approaches. Early-phase clinical trials have demonstrated feasibility and safety; however, long-term efficacy, durability and scalability remain uncertain. Critical challenges include potential off-target effects in CRISPR editing, risks of insertional mutagenesis, safety concerns in xenotransplantation and achieving a balance between immune tolerance and protective immunity. Future directions emphasize combination therapies, personalized medicine and next-generation editing tools such as base and prime editing. CONCLUSION: Together, these efforts represent a paradigm shift from symptomatic insulin replacement toward curative interventions, while highlighting the considerable translational hurdles that must be overcome before routine clinical application.
-
Off-target RNA editing hotspots caused by base editors.
Base editors, composed of engineered deaminases fused with Cas proteins and a guide RNA, enable precise, programmable alteration of single nucleotides within the genome and transcriptome. This innovative technology holds promising therapeutic potential for correcting disease-causing point mutations. However, its clinical translation hinges on both high efficacy and accuracy. Non-specific unintended edits by base editors remain a critical challenge. Efforts to mitigate off-target activity have focused mostly on detecting recurrent RNA deaminations at specific sites. Complementarily, our methodology quantifies the total burden of RNA alterations, which is particularly effective for capturing stochastic off-target edits that evade conventional detection. Here, we applied the RNA editing index algorithm to quantify off-target levels across individual genes and identified 2,844 adenine base editors and 1,253 cytosine base editor hotspot genes susceptible to aberrant editing. Exon-level analysis revealed localized regions within genes that are particularly prone to off-target editing, including regions where edits introduce premature stop codons, a critical risk for therapeutic applications. By uncovering these previously unrecognized off-target landscapes, our study deepens our understanding of base editor specificity and provides a framework for optimizing their precision, accelerating the development of safer next-generation editing tools.
-
Artificial Intelligence-Based Genome Editing in CRISPR/Cas9.
Artificial intelligence (AI) plays a critical role in predicting and improving genome editing methods, including CRISPR/Cas9. Recently, several AI models, such as DeepCRISPR, CRISTA, and Deep High Fidelity (DeepHF), have been utilized to design guide RNAs (gRNAs) for CRISPR-Cas systems. These models assess genomic context, desired mutation type, on-target and off-target scores, and potential off-target locations. AI models help improve various genome editing methods, such as base, prime, and epigenome editing, which allow for precise and intentional changes to DNA sequences without the need for donor DNA templates. Furthermore, integrating AI with genome editing and precision medicine enables the creation of personalized treatments tailored to each individual's unique genetic profile. Examining genomic data enables the identification of mutations, variations, and biomarkers linked to diseases like cancer, diabetes, and Alzheimer's disease. Integrating AI with genome editing can potentially enhance genetic modification techniques' precision, efficiency, and cost-effectiveness. Furthermore, it presents novel prospects for fields like genetics, biomedicine, and healthcare, which could significantly impact human health. However, several challenges still exist, including high costs, inaccurate edits, and effective delivery methods for CRISPR components, improved editing performance, and safety in clinical applications.
-
CRISPR/Cas9-mediated editing of the GhJAZ2 gene improves fiber length and lint percentage in Gossypium hirsutum L.
Cotton is regarded as a strategic agricultural commodity owing to its renewable and naturally derived fiber. With the escalating global demand for high-quality fiber, genetic improvement of fiber traits is a critical focus for sustaining and advancing the textile industry standards. The cotton GhJAZ2 gene encodes the Jasmonate ZIM-domain 2 protein, a known repressor in the jasmonic acid signaling pathway and negatively regulates fiber initiation. In this study, we designed a gRNA that simultaneously targets GhJAZ2 homologs and assembled it into the CRISPR vector (pHSE401). Subsequently, the construct (pHSE401-gRNA) was transformed into cotton (Gossypium hirsutum L.) using an Agrobacterium-mediated in planta transformation strategy, targeting the shoot apical meristem as the primary site of transformation. Sanger sequencing analysis revealed consistent single-base pair indels at the targeted site across both A and D sub-genomes, with edited T1 progenies showing both inherited and newly introduced indels at the targeted loci. Fiber analysis of edited lines compared to the control revealed a significant (p < .05) enhancement in lint percentage (≤13.74%) and fiber length (≤16.91%). This study demonstrated the effective application of CRISPR/Cas9 for targeted trait improvement in cotton, offering GhJAZ2-edited lines that can be advanced to develop transgene-free cultivars with improved fiber traits.
-
Base Editors and Off-Targeting: The Deaminase Matters.
-
Phage-Encoded Anti-CRISPR Defenses.
The battle for survival between bacteria and bacteriophages (phages) is an arms race where bacteria develop defenses to protect themselves from phages and phages evolve counterstrategies to bypass these defenses. CRISPR-Cas adaptive immune systems represent a widespread mechanism by which bacteria protect themselves from phage infection. In response to CRISPR-Cas, phages have evolved protein inhibitors known as anti-CRISPRs. Here, we describe the discovery and mechanisms of action of anti-CRISPR proteins. We discuss the potential impact of anti-CRISPRs on bacterial evolution, speculate on their evolutionary origins, and contemplate the possible next steps in the CRISPR-Cas evolutionary arms race. We also touch on the impact of anti-CRISPRs on the development of CRISPR-Cas-based biotechnological tools.
-
Developing guanine base editors for G-to-T editing in rice.
Two guanine base editors created using an engineered N-methylpurine DNA glycosylase with CRISPR systems achieved targeted G-to-T editing with 4.94-12.50% efficiency in rice (Oryza sativa). The combined use of the DNA glycosylase and deaminases enabled co-editing of target guanines with adenines or cytosines.
-
The Regulatory Status of Genome-edited Crops.
Genome editing with engineered nucleases (GEEN) represents a highly specific and efficient tool for crop improvement with the potential to rapidly generate useful novel phenotypes/traits. Genome editing techniques initiate specifically targeted double strand breaks facilitating DNA-repair pathways that lead to base additions or deletions by non-homologous end joining as well as targeted gene replacements or transgene insertions involving homology-directed repair mechanisms. Many of these techniques and the ancillary processes they employ generate phenotypic variation that is indistinguishable from that obtained through natural means or conventional mutagenesis; and therefore, they do not readily fit current definitions of genetically engineered or genetically modified used within most regulatory regimes. Addressing ambiguities regarding the regulatory status of genome editing techniques is critical to their application for development of economically useful crop traits. Continued regulatory focus on the process used, rather than the nature of the novel phenotype developed, results in confusion on the part of regulators, product developers, and the public alike and creates uncertainty as of the use of genome engineering tools for crop improvement.
PubMed returns only the first 10,000 results — refine the query to see the rest.