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At least 343 records · Page 19Linked to original sources
PubMed2026

BACH1 orchestrates macrophage state transitions to coordinate regenerative inflammation.

Efficient tissue regeneration requires the precise coordination of inflammatory and regenerative programs, principally mediated by monocyte-derived macrophages. However, the transcriptional wiring and epigenomic processes behind complex macrophage subtype specification and transition between the different states are not known. Here we have identified the transcriptional repressor BACH1 as a critical, cell-intrinsic regulator of monocyte-derived macrophage specification during skeletal muscle regeneration. Using a myeloid-specific BACH1 knockout mouse model, we demonstrate that BACH1 deficiency disrupts the temporal coordination of monocyte-to-macrophage differentiation, leading to aberrant macrophage subsets with concurrent opposing pro- and anti-inflammatory features. Single-cell RNA-sequencing profiling reveals that BACH1 controls a core transcriptional network, including Nfkb1, Cebpb, and interferon signaling, governing inflammatory resolution and functional macrophage specialization. Mechanistically, BACH1 loss accelerates macrophage differentiation but also affects its core cellular identity, resulting in sustained, rather than declining inflammatory programs including upregulation of Il1b and thus, defective tissue remodeling. These immune alterations compromise the paracrine landscape during regenerative inflammation and impair muscle stem cell differentiation. Our findings establish BACH1 as a molecular tuner or controller that integrates early innate immune signaling with regenerative output, positioning it as a central node linking transcriptional control, immune fate decisions, and tissue repair.

Animals↗
PubMed2002

Diabetes: a candidate disease for efficient DNA methylation profiling.

Methylation has been implied in a number of biological processes and has been shown to vary under environmental influences as well as in age. Most results on the correlation of methylation patterns with phenotypic characteristics of cells have been obtained by analysis of very few or even single genomic fragments for methylation. However, variation of methylation may more often than not be a phenomenon that affects multiple genomic loci. The role of methylation has been most conclusively demonstrated in complex disease, with cancer being the most prominent example. The influence of aging and environmental influences such as diet seems to be on global methylation patterns, in turn exerting local effects on groups of genes. Hence, methylation seems literally to be orchestrating complex genetic systems. It could, therefore, be considered an archetypal "genomics" parameter. In consequence, technologies used to analyze methylation patterns should be as industrialized as possible to capture the local events across the entire genome. Epigenomics' research team is the first to have achieved the industrialized production of genome sequence-specific wide methylation data. Our microarray and mass-spectrometry-based detection platform currently allow the analysis of up to 50,000 methylation positions per day, for the first time making methylation data amenable to sophisticated information mining. The information content of methylation position has never been analyzed using the high-dimensional statistical methods that are recognized to be required for the analysis of, for example, mRNA expression profiles or proteomic data. As methylation patterns are nothing but a quasi-digital form of expression data, their information content must be evaluated using similar but adapted algorithms. This article presents a broad set of studies that demonstrate that methylation yields information that is comparable or even superior to the current state of the art, namely, mRNA profiling. We argue that the resulting robust, digital and-because of the highly stable nature of DNA as the analyte-more reproducible information could become the "gold standard" for clinical diagnostics and disease gene identification in age-related, environmentally influenced and epigenetic disease in general, substituting for mRNA expression.

DNA Methylation↗
PubMed2005

Nutritional "omics" technologies for elucidating the role(s) of bioactive food components in colon cancer prevention.

Evidence continues to implicate dietary components and genetic susceptibilities as important determinants of cancer risk and tumor behavior. Variation in cancer incidence among and within populations with similar dietary patterns suggests that an individual's response may reflect interactions with genetic factors, which may modify gene, protein, and metabolite expression patterns. Nutrigenomics, defined as the interaction between nutrition and an individual's genome, will likely provide important clues about responders and nonresponders. In this symposium, the role of bioactive food components in colon cancer susceptibility was used to exemplify the application of "omic" technologies for cancer prevention. Topics that were addressed included dietary changes and gene polymorphisms (nutrigenetics), DNA methylation (nutritional epigenomics), gene expression (nutritional transcriptomics), altered formation or bioactivation of proteins (proteomics), and characterizing how the quantity and timing of exposure influence small molecular weight cellular constituents (metabolomics). The final presentation focused on exfoliated cells as a surrogate sample for the evaluation of bioactive food components in cancer prevention. The goal of the symposium was to provide an example of each of the "omic" technologies as they relate to nutrition, cancer risk, and tumor behavior, and to help the participants understand that an integrated framework that simultaneously examines all of the "omic" technologies is needed.

Colonic Neoplasms↗
PubMed2007

The biological revolution: understanding the impact of SNPs on diet-cancer interrelationships.

Evidence is accumulating that individual risk of neoplasia depends on complex interactions among genetic inheritance, a range of exposures both in utero and in postnatal life, and the play of chance. Knowledge of the portfolio of genetic variants that confer susceptibility or resistance to cancer is limited, and there is potential for genome-wide scans and hypothesis-driven studies to reveal novel polymorphisms and haplotypes that modify risk. There is only fragmentary evidence of the scale and nature of diet-gene interactions that modulate risk of neoplasia, but it seems probable that such interactions will play a significant role as they do in other complex diseases including cardiovascular disease and type 2 diabetes. All existing evidence about diet-gene interactions and cancer risk comes from observational studies, and it will be necessary to undertake intervention studies to test the hypotheses generated by epidemiologic investigations. Because it is very unlikely that primary cancer will be an endpoint in dietary intervention studies in the foreseeable future, development of robust surrogate endpoints is a high priority. Emerging biological science using epigenomics, proteomics, and other molecular technologies appears to offer novel approaches to the discovery and validation of surrogate endpoints.

Diet↗
PubMed2005

Epigenetic changes in prostate cancer: implication for diagnosis and treatment.

Prostate cancer is the most common noncutaneous malignancy and the second leading cause of cancer death among men in the United States. DNA methylation and histone modifications are important epigenetic mechanisms of gene regulation and play essential roles both independently and cooperatively in tumor initiation and progression. Aberrant epigenetic events such as DNA hypo- and hypermethylation and altered histone acetylation have both been observed in prostate cancer, in which they affect a large number of genes. Although the list of aberrantly epigenetically regulated genes continues to grow, only a few genes have, so far, given promising results as potential tumor biomarkers for early diagnosis and risk assessment of prostate cancer. Thus, large-scale screening of aberrant epigenetic events such as DNA hypermethylation is needed to identify prostate cancer-specific epigenetic fingerprints. The reversibility of epigenetic aberrations has made them attractive targets for cancer treatment with modulators that demethylate DNA and inhibit histone deacetylases, leading to reactivation of silenced genes. More studies into the mechanism and consequence of demethylation are required before the cancer epigenome can be safely manipulated with therapeutics as a treatment modality. In this review, we examine the current literature on epigenetic changes in prostate cancer and discuss the clinical potential of cancer epigenetics for the diagnosis and treatment of this disease.

Acyltransferases↗
PubMed2026

Decoding gene regulation in plant genomes with artificial intelligence.

One of the central goals of plant functional genomics is to uncover regulatory mechanisms that shape agriculturally important traits to inform crop improvement. Recent advances in machine learning (ML) and artificial intelligence (AI), especially Large Language Models (LLMs), have greatly transformed our ability to derive regulatory information from complex genomics data. This review starts with a brief introduction of recent advances in AI and ML. We then present a plant-focused synthesis of emerging applications of AI- and LLM tools to: (i) predict epigenomic features, regulatory DNA elements, and gene expressions; (ii) infer gene regulatory network; and (iii) estimate post-transcriptional regulation.

Artificial intelligence↗
PubMed2005

IVF results in de novo DNA methylation and histone methylation at an Igf2-H19 imprinting epigenetic switch.

Recent studies suggest that IVF and assisted reproduction technologies (ART) may result in abnormal genomic imprinting, leading to an increased frequency of Angelman syndrome (AS) and Beckwith-Weidemann syndrome (BWS) in IVF children. To learn how ART might alter the epigenome, we examined morulas and blastocysts derived from C57BL/6J X M. spretus F1 mice conceived in vivo and in vitro and determined the allelic expression of four imprinted genes: Igf2, H19, Cdkn1c and Slc221L. IVF-derived mouse embryos that were cultured in human tubal fluid (HTF) (Quinn's advantage) media displayed a high frequency of aberrant H19 imprinting, whereas in vivo and IVF embryos showed normal maternal expression of Cdkn1c and normal biallelic expression of Igf2 and Slc221L. Embryonic stem (ES) cells derived from IVF blastocysts also showed abnormal Igf2/H19 imprinting. Allele-specific bisulphite PCR reveals abnormal DNA methylation at a CCCTC-binding factor (CTCF) site in the imprinting control region (ICR), as the normally unmethylated maternal allele acquired a paternal methylation pattern. Chromatin immunoprecipitation (ChIP) assays indicate an increase of lysine 4 methylation (dimethyl Lys4-H3) on the paternal chromatin and a gain in lysine 9 methylation (trimethyl Lys9-H3) on the maternal chromatin at the same CTCF-binding site. Our results indicate that de novo DNA methylation on the maternal allele and allele-specific acquisition of histone methylation lead to aberrant Igf2/H19 imprinting in IVF-derived ES cells. We suggest that ART, which includes IVF and various culture media, might cause imprinting errors that involve both aberrant DNA methylation and histone methylation at an epigenetic switch of the Igf2-H19 gene region.

Animals↗
PubMed2025

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.

Deep Learning↗
PubMed2006

Microarray-based DNA methylation profiling: technology and applications.

This work is dedicated to the development of a technology for unbiased, high-throughput DNA methylation profiling of large genomic regions. In this method, unmethylated and methylated DNA fractions are enriched using a series of treatments with methylation sensitive restriction enzymes, and interrogated on microarrays. We have investigated various aspects of the technology including its replicability, informativeness, sensitivity and optimal PCR conditions using microarrays containing oligonucleotides representing 100 kb of genomic DNA derived from the chromosome 22 COMT region in addition to 12 192 element CpG island microarrays. Several new aspects of methylation profiling are provided, including the parallel identification of confounding effects of DNA sequence variation, the description of the principles of microarray design for epigenomic studies and the optimal choice of methylation sensitive restriction enzymes. We also demonstrate the advantages of using the unmethylated DNA fraction versus the methylated one, which substantially improve the chances of detecting DNA methylation differences. We applied this methodology for fine-mapping of methylation patterns of chromosomes 21 and 22 in eight individuals using tiling microarrays consisting of over 340 000 oligonucleotide probe pairs. The principles developed in this work will help to make epigenetic profiling of the entire human genome a routine procedure.

Chromosome Mapping↗
PubMed2026

Unlocking the Full Potential of Spatial Omics in Plants: Practical Challenges, Solutions, and a Path Forward.

Spatial omics technologies are providing new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organised cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls, and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalise. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.

Journal Article↗
PubMed2006

Altered methylation in gene-specific and GC-rich regions of DNA is progressive and nonrandom during promotion of skin tumorigenesis.

Altered DNA methylation, an epigenetic mechanism, likely contributes to tumorigenesis, with an inverse relationship existing between methylation in a promoter region and transcription. Using the SENCAR two-stage mouse skin tumorigenesis model, altered methylation was characterized in precancerous tissue and in tumor tissue. Mouse skin was initiated with 7,12-dimethylbenz[a]anthracene and promoted three times a week with 3, 9, 18, or 27 mg cigarette smoke condensate (CSC) for 4, 8, or 29 weeks; tumors were collected at 29 weeks. In addition, reversibility of changes in methylation was assessed following cessation of the promoting stimulus. DNA was isolated, and GC-rich methylation was assessed quantitatively via methylation-sensitive restriction digestion, arbitrarily primed PCR, and electrophoretic separation of PCR products. Analysis focused on regions of altered methylation (RAMs), which persisted from 4 to 8 weeks and from 8 weeks to tumor tissue. Persistent RAMs (i.e., seen in precancerous tissue and carried forward to tumors) are likely to play a key role in tumorigenesis. Twenty-two CpG sites in the upstream region of the Ha-ras promoter were unmethylated in control skin, 27 mg CSC, and tumor tissue. At two CpG sites closer to the transcriptional start site the incidence of hypomethylation increased with the dose of CSC. Hypomethylation was detected in all tumor samples. Expression of Ha-ras increased with 18 and 27 mg CSC promotion and more so in tumor tissue. These data support our hypothesis that tumor promotion involves instability of the epigenome, providing an environment where changes in the methylation status of specific regions of the genome accumulate progressively and contribute to the clonal expansion of initiated cells that leads to tumor formation.

9,10-Dimethyl-1,2-benzanthracene↗
PubMed2023

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning.

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

Preprint↗
PubMed2024

Rhinovirus infection of airway epithelial cells uncovers the non-ciliated subset as a likely driver of genetic susceptibility to childhood-onset asthma.

Asthma is a complex disease caused by genetic and environmental factors. Epidemiological studies have shown that in children, wheezing during rhinovirus infection (a cause of the common cold) is associated with asthma development during childhood. This has led scientists to hypothesize there could be a causal relationship between rhinovirus infection and asthma or that RV-induced wheezing identifies individuals at increased risk for asthma development. However, not all children who wheeze when they have a cold develop asthma. Genome-wide association studies (GWAS) have identified hundreds of genetic variants contributing to asthma susceptibility, with the vast majority of likely causal variants being non-coding. Integrative analyses with transcriptomic and epigenomic datasets have indicated that T cells drive asthma risk, which has been supported by mouse studies. However, the datasets ascertained in these integrative analyses lack airway epithelial cells. Furthermore, large-scale transcriptomic T cell studies have not identified the regulatory effects of most non-coding risk variants in asthma GWAS, indicating there could be additional cell types harboring these "missing regulatory effects". Given that airway epithelial cells are the first line of defense against rhinovirus, we hypothesized they could be mediators of genetic susceptibility to asthma. Here we integrate GWAS data with transcriptomic datasets of airway epithelial cells subject to stimuli that could induce activation states relevant to asthma. We demonstrate that epithelial cultures infected with rhinovirus significantly upregulate childhood-onset asthma-associated genes. We show that this upregulation occurs specifically in non-ciliated epithelial cells. This enrichment for genes in asthma risk loci, or 'asthma heritability enrichment' is also significant for epithelial genes upregulated with influenza infection, but not with SARS-CoV-2 infection or cytokine activation. Additionally, cells from patients with asthma showed a stronger heritability enrichment compared to cells from healthy individuals. Overall, our results suggest that rhinovirus infection is an environmental factor that interacts with genetic risk factors through non-ciliated airway epithelial cells to drive childhood-onset asthma.

Preprint↗
PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g., associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available at https://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.

Journal Article↗
PubMed2025

scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution.

Understanding how regulatory DNA elements shape gene expression across individual cells is a fundamental challenge in genomics. Joint RNA-seq and epigenomic profiling provides opportunities to build unifying models of gene regulation capturing sequence determinants across steps of gene expression. However, current models, developed primarily for bulk omics data, fail to capture the cellular heterogeneity and dynamic processes revealed by single-cell multi-modal technologies. Here, we introduce scooby, the first framework to model scRNA-seq coverage and scATAC-seq insertion profiles along the genome from sequence at single-cell resolution. For this, we leverage the pre-trained multi-omics profile predictor Borzoi as a foundation model, equip it with a cell-specific decoder, and fine-tune its sequence embeddings. Specifically, we condition the decoder on the cell position in a precomputed single-cell embedding resulting in strong generalization capability. Applied to a hematopoiesis dataset, scooby recapitulates cell-specific expression levels of held-out genes, and identifies regulators and their putative target genes through in silico motif deletion. Moreover, accurate variant effect prediction with scooby allows for breaking down bulk eQTL effects into single-cell effects and delineating their impact on chromatin accessibility and gene expression. We anticipate scooby to aid unraveling the complexities of gene regulation at the resolution of individual cells.

Journal Article↗
PubMed2025

Free energy spectroscopy reveals the mechanistic landscape of chromatin compaction.

Eukaryotic genomic DNA is repeatedly wrapped into nucleosome spools: the basic building block of chromatin. This organization regulates the physical accessibility of the genome to gene transcription, replication, and repair regulatory factors. Chromatin compaction is controlled by multivalent weak interactions, resulting in a complicated conformational landscape that remains challenging to characterize. This work reports a method for characterizing chromatin compaction, Free Energy Spectroscopy (FES), which is based on DNA nanotechnology and transmission electron microscopy. This method experimentally determines the chromatin compaction free energy landscape in terms of end-to-end distance and nucleosome stacking interactions. By deconvolving the free energy landscapes of partially and fully compact tetranucleosomes, FES revealed three separate mechanisms by which linker histones reshape the compaction energetics to condense chromatin. This study establishes FES as a method with the potential to help answer a broad range of mechanistic questions about genome and epigenome function.

DNA nanotechnology↗
PubMed2025

PARTAGE: Parallel analysis of replication timing and gene expression.

The human genome is partitioned into functional compartments that replicate at specific times during the S-phase. This temporal program, referred to as replication timing (RT), is co-regulated with the 3D genome organization, is cell type-specific, and changes during development in coordination with gene expression. Moreover, RT alterations are linked to abnormal gene expression, genome instability, and structural variation in multiple diseases, including cancer. However, mechanistic links between RT, large-scale 3D genome architecture, and transcriptional regulation remain poorly understood. A major limitation is that current approaches require the separate profiling of RT and transcriptomes from independent batches of samples, obscuring the complex co-regulation between the epigenome and transcriptome. Here, we developed PARTAGE, a multiomics approach that enables joint profiling of copy number variation (CNV), RT, and gene expression from the same sample, providing a more accurate integrative view of the complex relationships between RT and gene regulation.

Journal Article↗
PubMed2025

Sex differences in the developing human cortex intersect with genetic risk of neurodevelopmental disorders.

Autism is highly heritable and diagnosed more frequently in males than females. To identify neurodevelopmental processes that might present sex-biased vulnerability, we generated transcriptomic and epigenomic profiles of cell types present in the prenatally developing human cerebral cortex of 27 males and 21 females. By intersecting sex-biased molecular signatures and genes with de novo mutations in male and female autistic probands, we reveal two points of vulnerability contributing to the sex-biased penetrance in neurodevelopmental disorders (NDDs). First, we show that NDD risk genes are biased towards higher expression in females, identifying the NDD gene MEF2C as a critical transcription factor for female-biased expression. Second, we identify a significant contribution of X chromosome genes to NDD pathobiology. We construct a gene regulatory map of X-linked risk genes to enable functional studies of genetic variants that likely disrupt gene expression in the developing brains of autistic males. Together, these results point towards an outsized contribution of the X-chromosome to both the origin of sex differences in the developing human cortex and NDD vulnerability. We propose a model where female-biased vulnerability is driven by coding variation within genes while male-biased vulnerability is driven by noncoding variation in regulatory elements that affect gene expression.

Sex differences↗