'; ?> geneimprint : Hot off the Press http://www.geneimprint.com/site/hot-off-the-press Daily listing of the most recent articles in epigenetics and imprinting, collected from the PubMed database. en-us Thu, 15 Jan 2026 02:23:38 EST Thu, 15 Jan 2026 02:23:38 EST jirtle@radonc.duke.edu james001@jirtle.com Copy Number Variants in the 11p15.5 Associated Imprinting Disorders: An Attempt to Establish a Genotype-Phenotype Correlation. Licata AM, Botzenhart E, Kloth-Stachnau K, Eggermann T
Clin Genet (Jan 2026)

Copy number variations (CNVs) affecting the imprinted regions in 11p15.5 (imprinting centre 1 and 2/IC1, IC2) account for more than 2% of the molecular disturbances in Beckwith-Wiedemann and Silver-Russell syndrome (BWS, SRS) and are associated with a recurrence probability of up to 50%. However, their clinical impact can be challenging to estimate, as it depends on the type of imbalance, the parental origin of the affected allele, its size and genomic content. As a result, a genotype-phenotype correlation of 11p15.5 alterations is still missing, at least for CNVs affecting only parts of the IC1 or IC2. By comprehensively summarising all published CNVs within 11p15.5 and the available clinical data of their carriers, we aim to further delineate a correlation of these disturbances with BWS and SRS features. In fact, consistent correlations could be delineated only for duplications including either both the telomeric and centromeric regions or complete gains of one of them. In contrast, CNVs encompassing only parts of these regions lead to heterogeneous phenotypes. In summary, our literature review provides support for pathogenicity assessment of CNVs in 11p15.5 as basis for genetic counselling. However, this dataset underlines the need for further research to enlighten the molecular complexity of this region and to better understand the regulation of genomic imprinting mechanisms in 11p15.5.]]> Wed, 31 Dec 1969 19:00:00 EST Comprehensive epigenomic and transcriptomic analysis identifies FABP7 and CLIC6 as methylation-driven prognostic biomarker for a novel breast cancer subtype. Fahima K, Hosen MR, Mahmud Z
Comput Biol Med (Jan 2026)

Breast cancer (BRCA) is the most prevalent malignancy among women and exhibits significant molecular and clinical heterogeneity. To improve risk stratification and identify novel molecular subtypes, we employed integrative analysis on DNA CpG methylation and transcriptomic data to construct a methylation-driven prognostic model for BRCA. Using LASSO, we identified a 10-gene prognostic signature that effectively stratified patients into two groups designated as high-risk and low-risk groups. Kaplan-Mayer survival analysis revealed worse overall survival of the high-risk patients in the TCGA cohort (p < 0.0001). The risk model was independently validated in two external GEO datasets GSE86166 (p = 0.00011) and GSE42568 (p = 0.00013) demonstrating its resilience and clinical relevance. In addition, the risk groups were not associated with any canonical molecular subtypes of breast cancer. Among the 10 genes, FABP7 and CLIC6 were differentially expressed between the risk groups. FABP7 had the highest negative LASSO coefficients followed by CLIC6. In further analysis, FABP7 (R = 0.42, p = 3e-04) and CLIC6 (R = 0.48, P < 0.001) both showed a strong inverse correlation between CpG methylation and expression, with more than two-fold higher expression in low-risk group and linked to improved survival in all three independent cohort. Functional enrichment analysis identified that genes overexpressed in the low-risk subtype were significantly enriched in immune-related pathways. Immunological analysis indicated a more immunogenic tumor microenvironment in the FABP7 and CLIC6 positive, low-risk group, with significantly higher infiltration of CD8 T cells (p = 0.047) and resting NK cells (p = 0.0391), while FABP7 and CLIC6 negative, high-risk tumors had increased M2 macrophages (p = 9.19 × 10) and Tregs (p = 0.0122). To summarize, this integrative model identified a novel methylation-based risk classifier/molecular subtype for BRCA, highlighting FABP7 and CLIC6 as a key prognostic biomarker with potential utility for risk stratification for strategic treatment. These findings require further validation through wet-lab experiments and prospective clinical studies to support clinical translation.]]> Wed, 31 Dec 1969 19:00:00 EST CRISPR 2.0: Expanding the genome engineering Toolbox for epigenetics, RNA editing, and molecular diagnostics. Pradhan K, Anoop S
Gene (Feb 2026)

Non-canonical CRISPR systems adaptation has led to genome editing through nucleases, and the development of transcriptional and epigenetic regulation, transcriptome editing, and molecular diagnostics has resulted in a diversified set of tools-CRISPR 2.0. In this review, the author summarizes the mechanisms and recent engineering advances of (i) dCas9-based epigenetic effectors, (ii) RNA-targeting Cas13 systems and engineered RNA editors, (iii) DNA base editors and prime editors, and (iv) CRISPR-powered diagnostic platforms and their translational readiness. There is a critical comparison of the various approaches (e.g., RNAi/ASO versus Cas13-based methods; base editing versus prime editing) along with practical translational considerations such as delivery technologies, safety (off-target/edit windows, mosaicism), and regulatory pathways which are evaluated. Three concise case studies refer to map laboratory evidence to clinical or near-clinical outcomes and the ethical and governance discussion is widened to include global access, intellectual property and equity in deployment. Finally, the authors classify technologies according to their level of readiness - diagnostics and some ex-vivo therapeutic approaches are already in or very close to clinical use, chosen in-vivo editing methods are undergoing early trials, and AI-assisted nuclease design is still mostly theoretical but is getting better fast. This comprehensive viewpoint is intended to help researchers and physicians understand which CRISPR tools are most likely to be translated soon and where more validation is required.]]> Wed, 31 Dec 1969 19:00:00 EST Integrative spatial omics and artificial intelligence: transforming cancer research with omics data and AI. McKenzie M, Irac SE, Chen Z, Moradi A, Jenner A, Nguyen Q, Rashidieh B
Semin Cancer Biol (Jan 2026)

The integration of multi-omics data, including genomics, transcriptomics, proteomics, epigenomics, and metabolomics, coupled with histological spatial data has transformed biomedical research, offering unprecedented insights into cellular functions and disease mechanisms. However, the sheer volume and complexity of these datasets present a significant challenge in terms of interpretation and clinical translation. Artificial intelligence (AI) and machine learning (ML) are transforming data analysis, enabling the extraction of meaningful patterns from high-dimensional datasets and facilitating the development of predictive models. This shift is particularly transformative in cancer research, where understanding the tumor microenvironment (TME) and its spatial dynamics is crucial for improving therapeutic outcomes. This review explores recent advancements in spatial omics (SO) including spatial transcriptomics (ST) and spatial proteomics (SP), and AI-driven computational models, focusing on their applications in oncology. We discuss key methodologies, including spatial barcoding, in situ sequencing, and digital spatial profiling, and highlight major platforms. AI-powered tools, including deep learning models and spatial graph-based analyses, enhance data interpretation, allowing for robust predictive modeling, biomarker discovery, and personalized therapeutic strategies. Despite their transformative potential, ST and AI-driven approaches face challenges, including high-dimensional data complexity, computational constraints, and standardization of analytical pipelines. Addressing these challenges requires advanced mathematical frameworks such as spatial graph theory, topological data analysis, and agent-based modeling, which refine data integration and improve biological insights. Future research should focus on enhancing spatial resolution, cross-platform data harmonization, and AI-driven predictive models to advance precision oncology. By integrating ST, SP, and AI, researchers can develop dynamic, patient-specific treatment strategies, ultimately improving clinical outcomes and deepening our understanding of cancer progression and immune system interactions.]]> Wed, 31 Dec 1969 19:00:00 EST Multiomics Data Synthesis of FAM83H in Amelogenesis Imperfecta. Leban T, Kunej T
Int Dent J (Feb 2026)

FAM83H is a critical gene implicated in amelogenesis imperfecta type IIIA (AI type IIIA), but its precise role in enamel formation remains poorly understood. Fragmented datasets, inconsistent terminology, and limited integrative analyses hinder functional interpretation. This study presents a comprehensive multi-omics analysis of FAM83H-associated AI type IIIA.]]> Wed, 31 Dec 1969 19:00:00 EST A hitchhiker's guide to single-cell epigenomics: Methods and applications for cancer research. Moreno-Gonzalez M, Sierra I, Kind J
Int J Cancer (Jan 2026)

Genetic mutations are well known to influence tumorigenesis, tumor progression, treatment response and relapse, but the role of epigenetic variation in cancer progression is still largely unexplored. The lack of epigenetic understanding in cancer evolution is in part due to the limited availability of methods to examine such a heterogeneous disease. However, in the last decade the development of several single-cell methods to profile diverse chromatin features (chromatin accessibility, histone modifications, DNA methylation, etc.) has propelled the study of cancer epigenomics. In this review, we detail the current landscape of single-omic and multi-omic single-cell methods with a particular focus on the examination of histone modifications. Furthermore, we provide recommendations on both the application of these methods to cancer research and how to perform initial computational analyses. Together, this review serves as a referential framework for incorporating single-cell methods as an important tool for tumor biology.]]> Wed, 31 Dec 1969 19:00:00 EST Multi-omic biomarker detection in ovarian cancer. Abuhassan Q, Al-Assi G, Rekha MM, Chanania K, Bavanilatha M, Arora V, Sinha A, Hayitova M
Clin Chim Acta (Feb 2026)

Ovarian cancer remains one of the most lethal gynecologic malignancies, largely because of late-stage diagnosis and the absence of reliable biomarkers for early detection and therapeutic stratification. Recent advances in high-throughput technologies have enabled multi-omics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics to elucidate the comprehensive molecular landscape of ovarian cancer. This narrative review synthesizes current progress in applying multi-omics strategies to biomarker discovery, highlighting how integrative analyses uncover novel diagnostic, prognostic, and predictive candidates beyond the limitations of single-omics studies. We discuss methodological frameworks, computational pipelines and translational challenges in harmonizing heterogeneous datasets, as well as the potential of systems biology and machine learning to improve biomarker validation. Particular emphasis is placed on the identification of noncoding RNAs, protein signatures, and metabolic alterations as promising biomarker classes. Finally, we outline future directions for clinical implementation, including the development of multiparameter biomarker panels and precision medicine applications. By bridging molecular complexity with translational utility, multi-omics approaches hold transformative potential to advance biomarker identification and improve patient outcomes in ovarian cancer.]]> Wed, 31 Dec 1969 19:00:00 EST Single-cell multiome and enhancer connectome of human retinal pigment epithelium and choroid nominate causal variants in macular degeneration. Wang SK, Li J, Nair S, Kosaraju R, Chen Y, Zhang Y, Kundaje A, Liu Y, Wang N, Chang HY
Cell Rep (Jan 2026)

Age-related macular degeneration (AMD) is a leading cause of vision loss worldwide. Genome-wide association studies (GWASs) of AMD have identified dozens of risk loci that may house disease targets. However, variants at these loci are largely noncoding, making it difficult to assess their function and whether they are causal. Here, we present a single-cell gene expression and chromatin accessibility atlas of human retinal pigment epithelium (RPE) and choroid to systematically analyze both coding and noncoding variants implicated in AMD. We employ HiChIP and activity-by-contact modeling to map enhancers in these tissues and predict cell and gene targets of risk variants. We further perform allele-specific self-transcribing active regulatory region sequencing (STARR-seq) to functionally test variant activity in RPE cells, including in the context of complement activation. Our work nominates pathogenic variants and mechanisms in AMD and offers a rich and accessible resource for studying diseases of the RPE and choroid.]]> Wed, 31 Dec 1969 19:00:00 EST A smoothing method for DNA methylome analysis to enhance epigenomic signature detection in epigenome-wide association studies. Oussalah A, Mousel L, Trégouët DA, Guéant JL
Methods (Feb 2026)

Epigenome-wide association studies (EWAS) are instrumental for mapping DNA methylation changes in human traits and diseases but often suffer from low statistical power and false positives, especially in small cohorts. We developed an EWAS smoothing method that exploits co-methylation of adjacent CpG probes within CpG islands via a sliding-window average and generalized it using Savitzky-Golay filtering. We applied the smoothing approach-with window widths of 1-3 CpGs and, for generalization, Savitzky-Golay filters of varying polynomial orders and window sizes-across five distinct EWAS settings. Performance was quantified by signal-to-noise ratio (SNR), noise-variance reduction, variance ratio (VR), Bayes factors, and sample-size sensitivity. In the MMACHC epimutation dataset, a 5-CpG window (width, w = 2) increased SNR by 90 %, reduced noise variance by 80 %, and elevated VR by 176 % at the target CpG island, with no genome-wide false positives. For MLH1, smoothing preserved the top association and suppressed background signals. In the aging EWAS, a "Polyepigenetic CpG aging score" was derived following smoothing. This score correlated strongly with chronological age in the discovery cohort (Spearman's ρ = 0.89; P = 3.0 × 10) and was independently validated in a separate dataset, significantly distinguishing newborns from nonagenarians (P = 3.4 × 10). Savitzky-Golay filtering of order 0 with a 5-CpG window yielded optimal SNR across bootstrap iterations, supporting this configuration as a robust choice for methylation array smoothing. As an extension of the Savitzky-Golay-based smoothing framework, reanalysis of a liver cancer dataset identified five top loci surpassing a smoothed P-value threshold of 1 × 10. Among these, MIR10A within the HOXB3 locus was the only previously reported functionally relevant site. In conclusion, the smoothing method improves EWAS performance by enhancing SNR, enabling detection of meaningful associations even in small cohorts, and offers a valuable tool for reanalyzing existing Infinium methylation array datasets to uncover previously undetected epigenomic signatures.]]> Wed, 31 Dec 1969 19:00:00 EST A cell type enrichment analysis tool for brain DNA methylation data (CEAM). Müller J, Laroche VT, Imm J, Weymouth L, Harvey J, Reijnders RA, Smith AR, van den Hove D, Lunnon K, Cavill R, Pishva E
Epigenetics (Dec 2026)

DNA methylation (DNAm) signatures are highly cell type-specific, yet most epigenome-wide association studies (EWAS) are performed on bulk tissue, potentially obscuring critical cell type-specific patterns. Existing computational tools for detecting cell type-specific DNAm changes are often limited by the accuracy of cell type deconvolution algorithms. Here, we introduce CEAM (Cell-type Enrichment Analysis for Methylation), a robust and interpretable framework for cell type enrichment analysis in DNA methylation data. CEAM applies over-representation analysis with cell type-specific CpG panels from Illumina EPIC arrays derived from nuclei-sorted cortical post-mortem brains from neurologically healthy aged individuals. The constructed CpG panels were systematically evaluated using both simulated datasets and published EWAS results from Alzheimer's disease, Lewy body disease, and multiple sclerosis. CEAM demonstrated resilience to shifts in cell type composition, a common confounder in EWAS, and remained robust across a wide range of differentially methylated positions, when upstream modeling of cell type composition was modeled with sufficient accuracy. Application to existing EWAS findings generated in neurodegenerative diseases revealed enrichment patterns concordant with established disease biology, confirming CEAM's biological relevance. The workflow is publicly available as an interactive Shiny app (https://um-dementia-systems-biology.shinyapps.io/CEAM/) enabling rapid, interpretable analysis of cell type-specific DNAm changes from bulk EWAS.]]> Wed, 31 Dec 1969 19:00:00 EST Advancements in Pathogenic Genes and Biomarkers for Non-syndromic Cleft Lip With or Without Cleft Palate Via Multiomics. Yang C, Ding L, Dong Y, Wang Y, Cao S, Yuan Z, Jia S
Int Dent J (Feb 2026)

Non-syndromic cleft lip with or without cleft palate (nsCL/P) is a common congenital malformation influenced by a combination of environmental and genetic factors. nsCL/P is usually diagnosed using fetal ultrasound during the late second trimester; however, these results are often affected by factors such as instruments, fetal position, and maternal obesity. Moreover, by this time, structural anomalies in the fetuses are already formed and missed optimal time for intervention. Therefore, identifying more efficient and non-invasive biomarkers before fetal ultrasound is essential. In recent years, rapidly evolving omics technologies, including genomics, transcriptomics, proteomics, lipidomics, epigenomics, and single-cell omics, have been used to identify several nsCL/P-associated risk genes. Additionally, omics technologies have proven invaluable for investigating non-invasive biomarkers for prenatal diagnosis of nsCL/P. Therefore, this article reviews the current applications of multi-omics technologies in nsCL/P research, focusing on their use to identify pathogenic genes and the research advances in prenatal diagnosis. We highlighted the technological landscape and applications of multi-omics in nsCL/P, and explored the potential opportunities and challenges for future clinical practice.]]> Wed, 31 Dec 1969 19:00:00 EST Epigenomics-guided precision oncology: Chromatin variants in prostate tumor evolution. Furlano K, Keshavarzian T, Biernath N, Fendler A, de Santis M, Weischenfeldt J, Lupien M
Int J Cancer (Jan 2026)

Prostate cancer is a common malignancy that in 5%-30% leads to treatment-resistant and highly aggressive disease. Metastasis-potential and treatment-resistance is thought to rely on increased plasticity of the cancer cells-a mechanism whereby cancer cells alter their identity to adapt to changing environments or therapeutic pressures to create cellular heterogeneity. To understand the molecular basis of this plasticity, genomic studies have uncovered genetic variants to capture clonal heterogeneity of primary tumors and metastases. As cellular plasticity is largely driven by non-genetic events, complementary studies in cancer epigenomics are now being conducted to identify chromatin variants. These variants, defined as genomic loci in cancer cells that show changes in chromatin state due to the loss or gain of epigenomic marks, inclusive of histone post-translational modifications, DNA methylation and histone variants, are considered the fundamental units of epigenomic heterogeneity. In prostate cancer chromatin variants hold the promise of guiding the new era of precision oncology. In this review, we explore the role of epigenomic heterogeneity in prostate cancer, focusing on how chromatin variants contribute to tumor evolution and therapy resistance. We therefore discuss their impact on cellular plasticity and stochastic events, highlighting the value of single-cell sequencing and liquid biopsy epigenomic assays to uncover new therapeutic targets and biomarkers. Ultimately, this review aims to support a new era of precision oncology, utilizing insights from epigenomics to improve prostate cancer patient outcomes.]]> Wed, 31 Dec 1969 19:00:00 EST TALE Homeodomain Proteins in Plant Reproductive Development and Environmental Stress Resilience. Niu X, Jiang X, Li H, Qin R, Qin Y
Plant Cell Environ (Feb 2026)

TALE (Three Amino acid Loop Extension) homeodomain transcription factors are key conserved elements in eukaryotic developmental patterning. In plants, this superclass divides into the KNOX and BELL families, which are essential for regulating meristem maintenance, organogenesis, and tissue identity. Recent advances show that TALE proteins are intricately involved in plant reproductive processes, including gametophyte differentiation, embryonic axis formation, and floral organogenesis. They function as molecular scaffolds, integrating spatiotemporal signals and hormonal signaling like auxin, cytokinin, and gibberellin to control phase transitions and reproductive cell fate determination. The lineage-specific expansions and domain rearrangements of TALE genes across bryophytes, gymnosperms, and angiosperms indicate repeated co-option and neofunctionalization throughout land plant evolution. Emerging insights from epigenomics and protein interactomes reveal that TALE complexes modulate cell type-specific transcriptional responses. This review synthesizes current understanding of TALE-mediated regulatory networks during plant reproductive development and presents a conceptual framework for investigating their roles in developmental plasticity and stress-responsive fertility. We also highlight opportunities to utilize TALE-based regulatory modules to develop climate-resilient crops through multi-omics and genome editing approaches. Decoding the reproductive logic embedded in TALE networks offers transformative potential for reprogramming plant development in an era of agricultural and ecological uncertainty.]]> Wed, 31 Dec 1969 19:00:00 EST Denoising spatial epigenomic data via deep matrix factorization. Wang S, Xu H, Wang J, Xiao Y, Dai S, Lu J, Cao R, Chen X, Qu K
Nat Comput Sci (Jan 2026)

Spatial epigenomics (SE) technologies profile epigenomic landscapes within intact tissues, preserving spatial context and enabling the study of gene regulatory mechanisms in situ. However, current SE datasets typically suffer from low signal detection, substantial noise and extremely sparse peak matrices, which pose considerable challenges for downstream analysis. Here we introduce SPEED (spatial epigenomic data denoising), a deep matrix factorization framework that leverages atlas-level single-cell epigenomic data and spatial context to impute and denoise SE data. In comprehensive benchmarks on both simulated data and real SE tissue datasets, SPEED outperformed five state-of-the-art methods across diverse tissues and technologies. Moreover, SPEED's denoised outputs facilitated downstream analyses such as differential chromatin accessibility analysis, epigenomic spatial domain identification and gene activity inference. Collectively, our results indicate that SPEED is a generalizable tool for improving data quality and biological insights in SE.]]> Wed, 31 Dec 1969 19:00:00 EST Forensic genetics in the omics era. Kayser M
Nat Rev Genet (Feb 2026)

Recent advances in forensic genetics, driven by technological innovation coupled with the use of an expanding range of nucleic acid markers, have markedly improved the scope, accuracy and reliability of evidential information obtainable from human biological traces recovered at crime scenes. The majority of these biomarkers have been identified using non-targeted omics approaches, including genomics, transcriptomics, epigenomics and microbiome profiling. Moreover, targeted massively parallel sequencing, in some cases non-targeted whole-genome sequencing, are being applied to the analyses of biological trace material. These approaches and methods are being used for the identification of perpetrators (including monozygotic twins), their relatives or victims of criminal activities; the prediction of phenotypic and behavioural traits of unknown individuals; and the determination of trace characteristics, including tissue type and time of deposition.]]> Wed, 31 Dec 1969 19:00:00 EST Ferroptosis in renal cell carcinoma: integrative multi-omics insights and therapeutic perspectives. Wang X, Li J, Zhang Y, Huang R, Zhang P, Hu H
Int J Surg (Jan 2026)

Renal cell carcinoma (RCC) exhibits marked heterogeneity in its molecular landscape and clinical behavior. Ferroptosis, an iron-dependent and lipid peroxidation-driven form of cell death, has emerged as a biologically relevant process in RCC pathogenesis. This review summarizes recent advances in the multi-omics dissection of ferroptosis in RCC, including findings from genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics. Key molecular regulators such as VHL, SLC7A11, GPX4, and ACSL4 are highlighted for their roles in ferroptosis sensitivity or resistance. In parallel, insights from single-cell and spatial omics offer new perspectives on cell-type specificity and microenvironmental context. We also discuss the implications of ferroptosis in therapeutic modulation, including potential integration with immune checkpoint inhibitors and metabolic interventions. This review aims to provide a coherent overview of ferroptosis in RCC and inform future mechanistic studies and translational strategies.]]> Wed, 31 Dec 1969 19:00:00 EST Detection of Isodisomy Utilizing SNP Microarray: Frequency, Ascertainment, and Implications. Molinari S, Williams N, Haskell G, Penton A, Arreola A, Gadi I, Phillips K, Tepperberg J, Schwartz S
Am J Med Genet A (Feb 2026)

This study investigates the frequency, ascertainment, and clinical implications of whole chromosomal isodisomy using a database of over 415,000 chromosomal microarray (CMA) tests conducted since 2008 across prenatal, postnatal, and products of conception specimens. In this cohort, 0.04% of cases exhibited the rare chromosomal phenomenon of isodisomy. Analysis of these cases revealed distinct patterns in frequency, chromosome involvement, and parent of origin related to specimen type. Isodisomy 14 was most frequent in prenatal samples, while chromosomes 6, 7, and 15 were more common in postnatal cases. The involvement of imprinted and non-imprinted chromosomes was equivalent for prenatal cases, while imprinted chromosomes consisted of two-thirds of postnatal cases, with paternal uniparental isodisomy more prevalent than maternal across all specimen types. Several cases demonstrated unmasking of pathogenic variants in recessive genes, and findings support prior studies of associations between isodisomy 11 and prenatal or neonatal lethality. These results underscore the diagnostic value of CMA and contribute to an extended understanding of isodisomy's clinical relevance.]]> Wed, 31 Dec 1969 19:00:00 EST Predicting the effect of CRISPR-Cas9-based epigenome editing. Batra SS, Cabrera A, Spence JP, Goell J, Anand SS, Hilton IB, Song YS
Elife (Jan 2026)

Epigenetic regulation orchestrates mammalian transcription, but functional links between them remain elusive. To tackle this problem, we use epigenomic and transcriptomic data from 13 ENCODE cell types to train machine learning models to predict gene expression from histone post-translational modifications (PTMs), achieving transcriptome-wide correlations of ∼0.70-0.79 for most cell types. Our models recapitulate known associations between histone PTMs and expression patterns, including predicting that acetylation of histone subunit H3 lysine residue 27 (H3K27ac) near the transcription start site (TSS) significantly increases expression levels. To validate this prediction experimentally and investigate how natural vs. engineered deposition of H3K27ac might differentially affect expression, we apply the synthetic dCas9-p300 histone acetyltransferase system to 8 genes in the HEK293T cell line and to 5 genes in the K562 cell line. Further, to facilitate model building, we perform MNase-seq to map genome-wide nucleosome occupancy levels in HEK293T. We observe that our models perform well in accurately ranking relative fold-changes among genes in response to the dCas9-p300 system; however, their ability to rank fold-changes within individual genes is noticeably diminished compared to predicting expression across cell types from their native epigenetic signatures. Our findings highlight the need for more comprehensive genome-scale epigenome editing datasets, better understanding of the actual modifications made by epigenome editing tools, and improved causal models that transfer better from endogenous cellular measurements to perturbation experiments. Together, these improvements would facilitate the ability to understand and predictably control the dynamic human epigenome with consequences for human health.]]> Wed, 31 Dec 1969 19:00:00 EST Maternal UPD(20) Leading to Mulchandani-Bhoj-Conlin Syndrome: A Rare Neonatal Case With Additional TRPS1 Deletion. Zhang J, Chen X, Chen M, Wu S, Huang F, Pan R, Chen G
Am J Med Genet A (Feb 2026)

Mulchandani-Bhoj-Conlin syndrome is an extremely rare imprinting disorder caused by maternal uniparental disomy of chromosome 20, primarily characterized by intrauterine growth restriction, severe postnatal growth failure, and feeding difficulties. Here, we report a neonate diagnosed with Mulchandani-Bhoj-Conlin syndrome via whole exome sequencing and copy number variation analysis, which also identified a 0.26 Mb deletion on chromosome 8q23.3 affecting the TRPS1 gene, associated with Trichorhinophalangeal syndrome. We describe the clinical features and genetic findings of this infant, with the aim of contributing to a better understanding of these two rare diseases.]]> Wed, 31 Dec 1969 19:00:00 EST Cross-species prediction of histone modifications in plants via deep learning. Lv T, Han Q, Li Y, Liang C, Ruan Z, Chao H, Chen M, Chen D
Genome Biol (Jan 2026)

The regulation of gene expression in plants is governed by complex interactions between cis-regulatory elements and epigenetic modifications such as histone marks. While deep learning models have achieved success in predicting regulatory features from DNA sequence, their cross-species generalizability in plants remains largely unexplored.]]> Wed, 31 Dec 1969 19:00:00 EST