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PubMed2026

Stable simulations do not guarantee functional engagement: a case study of off-target prediction for Seladelpar and Zanamivir.

Identifying off-target interactions of approved drugs is important to anticipate side effects and uncover repurposing opportunities. Computational pipelines combining structural homology, structure prediction, and molecular dynamics (MD) simulations offer a promising strategy, but it remains unclear whether stable, control-like MD trajectories reliably indicate functional engagement. We examined this in a case study of two approved drugs. Using the Evolutionary Classification of Protein Domains (ECOD) framework to select candidate off-targets, we modeled each drug-protein complex as two independent AlphaFold3 models and simulated both by MD, for Seladelpar (a PPARδ agonist) and Zanamivir, an influenza neuraminidase inhibitor that also inhibits human Sialidase-2 (NEU2). Candidates were ranked by the similarity of global MD descriptors to the on-target control. For Seladelpar, the three top-ranked candidates (FXR, RARγ, ERRγ) were tested experimentally; the Zanamivir set was analyzed computationally only. None showed measurable activity in reporter or thermal shift assays, despite stable simulations and descriptor values comparable to the control. Including PPARα and PPARγ as weak-positive comparators, these descriptors did not rank genuine interactions closer to the control than inactive candidates. Residue-level comparison with experimental structures showed the predicted poses reproduced only part of the canonical contacts. Where experimental drug-bound structures existed, AlphaFold3 reproduced the pose for PPARα but not PPARγ, and its per-model confidence did not track pose accuracy. Within this case study, the specific global descriptors examined reflect complex stability rather than functional engagement, which does not mean MD-based approaches cannot make this distinction.

Zanamivir↗

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↗

PubMed2026

Homology-directed CRISPR-Cas9 correction of the KRT5 p.E475G mutation in human iPSC line from a patient with severe epidermolysis bullosa simplex.

Severe epidermolysis bullosa simplex is a skin fragility disorder characterized by blistering caused by cytolysis within basal keratinocytes, resulting in compromised epidermal integrity. Here we report the generation of the human induced pluripotent stem cell (hiPSC) line MLi002-A-1, an isogenic control derived from patient-specific MLi002-A line carrying the KRT5 c.1424A > G (p.E475G) mutation. Genome editing restored the wild-type sequence without detectable changes at top-predicted off-target sites. The edited line exhibits a normal karyotype, typical pluripotent morphology, robust pluripotency marker expression, and trilineage differentiation potential. This genetically matched control enables mutation-specific studies and in vitro modeling of epidermolysis bullosa simplex.

CRISPR-Cas9↗

PubMed2026

ModiCal: A Targeted Calibration Workflow for Site-Specific m5C Validation by Nanopore Direct RNA Sequencing.

Accurate identification of RNA 5-methylcytidine (m5C) at the single-nucleotide resolution remains a central challenge in nanopore direct RNA sequencing (DRS). Current global scanning and modification-aware basecalling methods enable transcriptome-wide profiling but often yield high false-positive rates and lack site-specific accuracy. To address this, we repurposed ModiDeC, originally a de novo multimodification classifier, into a targeted, high-precision validation tool for RNA modification sites with prior biochemical knowledge. This was implemented through a three-step calibration workflow that alternates between biochemical and computational modules using the well-characterized m5C2278 site in 25S rRNA as a starting point. Baseline training uses short synthetic RNAs carrying either a methylated or unmodified C2278 as ground truth, followed by IVT-derived calibration and validation in methyltransferase knockout yeast. The baseline model accurately detected the bona fide m5C2278 site but initially produced off-target predictions. Iterative retraining with unmodified IVT signals progressively reduced and ultimately eliminated false positives while maintaining a strong signal at the bona fide site. The final model retained enzyme-dependent detection in wild-type versus knockout yeast and, when explicitly targeted, was also able to detect the second rRNA site, C2870, which remained invisible in the initial analysis. Application to native human prerRNA processing intermediates further resolved two distinct m5C deposition regimes on 28S rRNA, while generalization to dengue virus genomic RNA confirmed that the same calibration logic transfers across diverse RNA contexts. Together, this study establishes a reproducible and transferable framework that integrates biochemical validation with iterative neural network refinement, providing a route toward reliable site-specific m5C confirmation by nanopore direct RNA sequencing.

RNA Methylation↗

PubMed2025

Stacked mutations in multi-copy AHAS genes enhance sulfonylurea herbicide resistance in soybean.

Weeds are a major factor that negatively impact crop yields. Developing herbicide-resistant germlines is crucial for efficient weed control. Sulfonylurea- and pyrimidinyl benzoate-based herbicides inhibit the function of acetohydroxyacid synthase (AHAS), a key enzyme in the biosynthesis of branched-chain amino acids in plants. To create soybean plants resistant to these classes of herbicides, we performed base editing of AHAS genes in Glycine max. A guide RNA was designed to target the codon for proline-182 in GmAHAS2, with the prediction that off-target base editing might also occur in the GmAHAS3 and GmAHAS4 genes. We selected six genome-edited soybean lines, each carrying distinct mutations in GmAHAS2, GmAHAS3, or GmAHAS4. These lines were treated with three different AHAS-targeting herbicides to evaluate resistance. The results show that the number of mutated GmAHAS genes and the mutation patterns significantly influence herbicide resistance.

Herbicide Resistance↗

PubMed2026

SpacerScope: binary-vectorized, genome-wide off-target profiling for RNA-guided nucleases without prior candidate-site bias.

The precision of CRISPR/Cas systems is fundamental to their application in plant and animal biotechnology. However, comprehensive sequence-based off-target candidate discovery remains a computational bottleneck, particularly in large and complex genomes. Here we developed SpacerScope, an off-target candidate discovery framework that enables unbiased, genome-wide discovery by leveraging binary vectorization, bitwise filtering, and right-end-anchored alignment. Benchmarking against human CIRCLE-seq data demonstrated that SpacerScope recovered 100% of validated off-target sites (6142/6142), matching the sensitivity of exhaustive algorithms. Crucially, SpacerScope achieved this maximum candidate recovery while substantially reducing computational overhead. In large-genome evaluations, SpacerScope maintained low peak memory usage of 2.20 GiB and achieved substantial runtime improvements over indel-aware comparator tools, including more than 50-fold speedup relative to Cas-OFFinder 3 (544 s versus 29 185 s). Furthermore, comparative analyses in polyploid species, such as the octoploid strawberry, revealed that SpacerScope identified larger sequence-compatible candidate burdens than standard web-based design platforms. Our results establish SpacerScope as a high-speed framework for sequence-based genome-wide off-target candidate discovery across diverse and highly repetitive genomic landscapes. The source code and program was publicly available at https://github.com/charlesqu666/SpacerScope. Short Abstract CRISPR/Cas sequence-based off-target candidate discovery remains computationally challenging in large, repetitive, and polyploid genomes. Existing tools either miss indel-containing candidate sites or incur prohibitive runtime and memory costs. We developed SpacerScope, a binary-vectorized framework that enables unbiased, genome-wide off-target candidate discovery without pre-selected candidate sites. By integrating bitwise filtering with right-end-anchored alignment, SpacerScope recovered 100% of validated off-target sites in human CIRCLE-seq data while using only 2.20 GiB of memory and achieving more than 10-fold speedup over indel-aware alternatives. Evaluation in plant genomes, including rice and octoploid strawberry, further demonstrated SpacerScope's capacity to identify larger sequence-compatible candidate burdens overlooked by standard tools. SpacerScope thus provides a high-speed framework for sequence-based genome-wide off-target candidate discovery across diverse and highly repetitive genomic landscapes, supporting downstream prioritization.

CRISPR-Cas Systems↗

PubMed2024

AAV-mediated genome editing is influenced by the formation of R-loops.

Recombinant adeno-associated viral vectors (rAAV) hold an intrinsic ability to stimulate homologous recombination (AAV-HR) and are the most used in clinical settings for in vivo gene therapy. However, rAAVs also integrate throughout the genome. Here, we describe DNA-RNA immunoprecipitation sequencing (DRIP-seq) in murine HEPA1-6 hepatoma cells and whole murine liver to establish the similarities and differences in genomic R-loop formation in a transformed cell line and intact tissue. We show enhanced AAV-HR in mice upon genetic and pharmacological upregulation of R-loops. Selecting the highly expressed Albumin gene as a model locus for genome editing in both in vitro and in vivo experiments showed that the R-loop prone, 3' end of Albumin was efficiently edited by AAV-HR, whereas the upstream R-loop-deficient region did not result in detectable vector integration. In addition, we found a positive correlation between previously reported off-target rAAV integration sites and R-loop enriched genomic regions. Thus, we conclude that high levels of R-loops, present in highly transcribed genes, promote rAAV vector genome integration. These findings may shed light on potential mechanisms for improving the safety and efficacy of genome editing by modulating R-loops and may enhance our ability to predict regions most susceptible to off-target insertional mutagenesis by rAAV vectors.

Preprint↗

PubMed2024

AAV-mediated genome editing is influenced by the formation of R-loops.

Recombinant adeno-associated viral vectors (rAAV) hold an intrinsic ability to stimulate homologous recombination (AAV-HR) and are the most used in clinical settings for in vivo gene therapy. However, rAAVs also integrate throughout the genome. Here, we describe DNA-RNA immunoprecipitation sequencing (DRIP-seq) in murine HEPA1-6 hepatoma cells and whole murine liver to establish the similarities and differences in genomic R-loop formation in a transformed cell line and intact tissue. We show enhanced AAV-HR in mice upon genetic and pharmacological upregulation of R-loops. Selecting the highly expressed Albumin gene as a model locus for genome editing in both in vitro and in vivo experiments showed that the R-loop prone 3' end of Albumin was efficiently edited by AAV-HR, whereas the upstream R-loop-deficient region did not result in detectable vector integration. In addition, we found a positive correlation between previously reported off-target rAAV integration sites and R-loop enriched genomic regions. Thus, we conclude that high levels of R-loops, present in highly transcribed genes, may promote rAAV vector genome integration. These findings may shed light on potential mechanisms for improving the safety and efficacy of genome editing by modulating R-loops and may enhance our ability to predict regions most susceptible to off-target insertional mutagenesis by rAAV vectors.

Dependovirus↗

PubMed2026

Integrative cross-tissue transcriptome-wide association and metabolomic analysis reveals novel genetic risk loci for aortic aneurysm.

BACKGROUND: Aortic aneurysm (AA) is a life-threatening cardiovascular condition with a strong genetic component, however, its molecular mechanisms remain poorly understood. Although genome-wide association studies (GWAS) have identified numerous risk loci, most prior studies have investigated genetic and metabolic factors separately, leaving the causal pathways from genetic variants to disease largely unexplored. METHODS: We established an integrative framework combining cross-tissue transcriptome-wide association studies (TWAS) with metabolomic mediation analysis. First, we integrated GWAS data from FinnGen R12 with multi-tissue expression quantitative trait loci (eQTL) data from Genotype-Tissue Expression Project (GTEx) V8, then performed cross-tissue TWAS using the Unified Test for MOlecular SignaTures (UTMOST) and single-tissue validation with the Functional Summary-based Imputation (FUSION) to prioritize susceptibility genes. Second, we applied Mendelian randomization (MR), colocalization, and Fine-mapping Of CaUsal gene Sets (FOCUS) to assess causality and identify high-confidence genes. Third, we performed metabolite mediation analysis to uncover metabolic pathways linking genetic variants to disease risk. Finally, we validated key findings in mouse models of thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) using Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) and Western blotting. RESULTS: We identified multiple novel susceptibility genes for AA and its subtypes. Key genes included ADH family members (ADH1A, ADH1B, ADH4, ADH6) and ZNF827, which showed cross-subtype associations with strong colocalization evidence in vascular tissues. Metabolite mediation analysis revealed significant pathways involving N-acetylphenylalanine and methionine sulfoxide. Functional enrichment revealed distinct biological mechanisms: AA and AAA were primarily associated with metabolic pathways, whereas TAA-related genes were enriched in developmental and contractile processes. PheWAS indicated no significant off-target associations. Critically, experimental validation in mouse models confirmed significant upregulation of ZNF827 in TAA and ADH6 in AAA at both mRNA and protein levels, corroborating the genetic predictions. CONCLUSION: This integrated cross-omics analysis identifies novel genetic loci and, crucially, uncovers specific nutrient-related metabolic pathways that mediate genetic risk. These findings provide a mechanistic basis for future nutritional and metabolic intervention studies in AA and its subtypes.

MAGMA↗

PubMed2026

From scissors to editors: how the evolution of precision is redefining therapeutic genome editing.

Since its introduction as a genome-editing tool, CRISPR-based technology has undergone rapid refinement, with precision emerging as a central focus of development. Early CRISPR-Cas9 systems demonstrated unprecedented ease and efficiency in targeting specific DNA sequences, but concerns over off-target effects and variable editing outcomes limited their broader application. This review outlines the progression of CRISPR from its discovery in prokaryotes to its application as a versatile tool in precision medicine, where it supports targeted therapies for genetic disorders in various ways. Although technical challenges, including off-target editing and delivery inefficiencies, persist alongside ethical considerations of accessibility and long-term consequences, CRISPR's ongoing refinements and innovations reflect a clear trajectory toward greater specificity, safety, and predictability, positioning CRISPR as an increasingly precise platform for both fundamental research and therapeutic use.

Gene Editing↗

PubMed2026

Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.

We present Tahoe-100M, a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions. This parallel profiling of thousands of perturbations at single-cell resolution with minimal batch effects is enabled by the Mosaic platform, which multiplexes genetically distinct cell models into balanced "cell villages." Beyond cataloging transcriptomic shifts, Tahoe-100M systematically quantifies cellular phenotypes, including proliferation, cytotoxicity, lineage-specific vulnerabilities, and cell-cycle changes. It captures population-level transcriptomic heterogeneity, characterizing whether drug responses drive cells toward divergent fates or convergent states. Pathway-based signatures define drug-induced expression programs, classify mechanisms of action, reveal off-target activities, and expose adaptive stress responses associated with resistance. By unifying cellular and molecular readouts, this broadly applicable perturbation atlas advances our ability to model gene regulation, drug response, and network dynamics. Its public release enables the training of AI frameworks to advance predictive models of cell behavior.

Humans↗

PubMed2026

Selective Macrocyclic WEE1 Kinase Inhibitors with Strong Efficacy against Patient-Derived Colorectal Cancer Organoids.

Macrocyclization can enhance the selectivity of acyclic compounds toward structurally similar biological targets such as kinases. WEE1 regulates cellular homeostasis and is a promising target in oncology. The clinical candidate AZD1775 (1) failed to progress past Phase II trials because of patient tolerability issues, likely due to off-target inhibition of polo-like kinase 1 (PLK1). Herein, a computer-aided drug design approach was conducted to develop a macrocycle based on the 1-WEE1 X-ray cocrystal structure. Significantly enhanced WEE1 inhibitory selectivity over PLK1 was determined for leading macrocycle 2, which also demonstrated broader kinome-wide selectivity. Patient-derived organoids from colorectal cancer (CRC) peritoneal and liver metastases, treated with 2, demonstrated comparably strong or enhanced anticancer efficacy compared to that of 1. Against patient-matched normal colon vs primary CRC organoids, 2 potently and selectively treated CRC, as well as enhanced DNA damage compared to 1. Finally, the X-ray cocrystal structure of 2 bound to WEE1 validated its computationally predicted bioactive binding mode.

Humans↗

PubMed2026

Discovery of NAT-6-321056 as a novel modulator of VEGFR2 signaling to suppress tumor angiogenesis.

Vascular endothelial growth factor receptor 2 (VEGFR2) is a master regulator of angiogenesis and cancer progression. However, current VEGFR2 modulators face significant challenges, including off-target toxicity and acquired resistance, underscoring the urgent need for novel therapeutic agents with improved efficacy and safety profiles. Here, we reported that virtual screening of 39,442 natural products from the ZINC natural products-derived library, coupled with molecular docking and molecular dynamics (MD) simulations to evaluate the binding stability of candidate compounds, identified NAT-6-321056 as a highly promising modulator of VEGFR2 signaling. Biological evaluations demonstrated that NAT-6-321056 exerted potent inhibition on the growth of a broad spectrum of cancer cells, including both solid tumors and hematological malignancies. In EA.hy 926 endothelial cells and SK-N-DZ neuroblast cells, the compound significantly suppressed proliferation, migration, and invasion. Microscale thermophoresis (MST) confirmed direct binding of NAT-6-321056 to VEGFR2 with favorable affinity. Kinase profiling against a panel of 33 kinases indicated that NAT-6-321056 exhibited a multi-kinase modulation profile. Mechanistic studies revealed that NAT-6-321056 suppressed the expression of hypoxia-inducible factor 1-alpha (HIF-1α) and was associated with reduced VEGFR2 phosphorylation and attenuation of the downstream ERK/JNK/AKT signaling pathways. Moreover, NAT-6-321056 exhibited robust in vivo anti-angiogenic effects in both the chick chorioallantoic membrane (CAM) assay and transgenic zebrafish vascular fluorescence imaging models. Computational absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction suggested acceptable drug-like properties. Collectively, these findings demonstrated that NAT-6-321056 is a promising modulator of VEGFR2 signaling with potent anti-angiogenic activity and represents a viable candidate for cancer therapy.

Vascular Endothelial Growth Factor Receptor-2↗

PubMed2026

Spatial Multiomics Reveal Insights Into ADC Efficacy.

Antibody-drug conjugates (ADCs) have transformed the therapeutic landscape of solid tumors; however, responses remain heterogeneous and complex to predict. In addition, a growing number of multiple ADC targets are either approved or in late-stage clinical development, such as NECTIN-4, HER2, or TROP2 for metastatic urothelial cancer. Spatial multiomics-representing next-generation methods that couple high-plex RNA sequencing and multiplex protein imaging with precise x-y-z coordinates within tissues-offer a direct way to correlate (ADC) antigen expression, cell state information, and micro-anatomical context with patient treatment outcomes. In this review, we highlight suitability and technological advancements in current spatial transcriptomics and proteomics approaches to decode modes of action and resistance to ADCs and extract biological insights, particularly in metastatic urothelial cancer-and propose an integrative framework that combines spatial readouts with machine and/or deep learning-driven analytics to stratify patients, forecast on- and off-target toxicities, and guide next-generation linker-payload designs or combination therapies.

Humans↗

PubMed2026

Immune cell-specific genetic architecture of Alzheimer's disease revealed by multi-omics analysis for therapeutic target discovery and prioritization.

Alzheimer's disease (AD) is a multifactorial neurodegenerative condition in which accumulating genetic and molecular evidence implicates dysregulation of peripheral immune processes in disease pathogenesis. Nevertheless, the contribution of distinct peripheral immune cell subsets and associated gene regulatory landscapes to AD risk remains incompletely defined. To address this gap, we integrated single-cell expression quantitative trait loci (sc‑eQTL) data from the OneK1K cohort with AD GWAS summary statistics. We systematically interrogated immune cell-specific genes for their contributions to AD risk by integrating genetic causal inference with Bayesian colocalization analyses, and identified 24 eGenes that passed both the MR significance threshold (P < 0.05) and the criterion for strong shared genetic signals (PP.H4 > 0.8). Notable candidates included GATS, HLA-DOB, HLA-DQA1, PM20D1, and others, with each gene demonstrating a cell-type-specific association restricted to its corresponding immune cell type, such as monocytes, CD8 + T cells, or B cells. Independent peripheral blood single-cell transcriptomic data further supported disease-associated shifts in cell-type-specific expression patterns in AD. Phenome-wide association studies (PheWAS) indicated limited associations with off-target traits, indicating a favorable safety profile for therapeutic intervention, with the exceptions of B4GALNT3, PM20D1, and CNN2. Integration of immune gene targets with pharmacological databases yielded three candidate compound, including NSC321521 (targeting HLA-DQA1), phenoxybenzamine (targeting GSTP1), and rimexolone (targeting BIN1). Among these compounds, Predicted blood-brain barrier permeability was observed only for phenoxybenzamine and rimexolone, with docking studies indicating stable interactions, such as those between NSC321521 and HLA-DQA1, phenoxybenzamine and GSTP1, and rimexolone and BIN1. This integrative approach highlights key immune‑cell‑specific genes involved in AD and proposes repurposable drugs with central nervous system potential, paving the way for more targeted immunomodulatory strategies in AD.

Humans↗

PubMed2025

Multi-omics Mendelian randomization integrating RNA-seq, eQTL and pQTL data revealed CPXM1 as a potential drug target for osteoporosis.

Osteoporosis, a prevalent skeletal disorder characterized by decreased bone mineral density and increased fracture risk, continues to be a major global health concern. Traditional treatments for osteoporosis have limited efficacy and safety profiles, highlighting the need for novel therapeutic targets. This study integrates multi-omics data, including RNA-seq, expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data, through Mendelian randomization (MR) to identify potential drug targets for osteoporosis. By leveraging bidirectional two-sample MR analysis, we identified CPXM1 (Carboxypeptidase X, M14 family member 1) as a novel gene that is causally linked to osteoporosis risk. Through transcriptomic and proteomic validation, we demonstrate that CPXM1 was upregulated in aged bone tissues and osteoporotic conditions in both human and murine models. Gene set enrichment analysis (GSEA) revealed significant dysregulation of bone homeostasis pathways, including increased extracellular matrix degradation and suppression of osteoblast differentiation in aged mice. Furthermore, phenome-wide association studies (PheWAS) confirmed minimal off-target effects of CPXM1, reinforcing its potential as a therapeutic target. Finally, computational drug repurposing predicted several promising drug candidates, including Doxorubicin, 5-Fluorouracil, and 2-Methylcholine, which may target CPXM1 pathways for osteoporosis treatment. These findings highlight CPXM1 as a potential biomarker and therapeutic target, offering new avenues for osteoporosis therapy.

Osteoporosis↗

PubMed2026

[Research Advances on Mechanisms and Interventions of DNA Methylation-Regulated Aging-Related Imbalance in Bone Metabolism].

Aging can induce age-related bone diseases such as osteoporosis. DNA methylation, a core epigenetic regulatory mechanism, participate in the pathological process of aging-induced bone metabolism imbalance by modulating gene expression at the epigenetic level. Using S-adenosylmethionine as a methyl donor, it exhibits characteristics of hypomethylation in genomic repetitive regions and abnormal methylation in CpG islands of promoters of key bone metabolism genes with advancing age. The "epigenetic clock" constructed based on these features can accurately predict an individual's biological age. In bone metabolism, DNA methylation disrupts the osteoblast-osteoclast balance by targeting key factors. Such abnormalities are driven by aging-related inflammation and oxidative stress, while bone loss feedback exacerbates epigenetic disorders, forming a vicious cycle. Targeted intervention strategies have demonstrated significant potential in addressing bone metabolism-related issues. Low-dose DNA methyltransferase inhibitors can improve bone metabolism; nutrients such as folate and cobalamin maintain methylation homeostasis by optimizing one-carbon metabolism pathways; while CRISPR/dCas technology enables precise regulation in the cellular and animal levels, thereby affecting bone metabolism. However, existing strategies still face challenges such as off-target effects and low delivery efficiency. Future research needs to deepen mechanistic studies, optimize intervention methods, and promote their translation into clinical prevention and treatment of osteoporosis.

DNA Methylation↗