Learning Consistency and Specificity of Cells From Single-Cell Multi-Omic Data.

Advancements in single-cell technologies concomitantly develop the epigenomic and transcriptomic profiles at the cell levels, providing opportunities to explore the potential biological mechanisms. Even though significant efforts have been dedicated to them, it remains challenging for the integration analysis of multi-omic data of single-cell because of the heterogeneity, complicated coupling and interpretability of data. To handle these issues, we propose a novel self-representation Learning-based Multi-omics data Integrative Clustering algorithm (sLMIC) for the integration of single-cell epigenomic profiles (DNA methylation or scATAC-seq) and transcriptomic (scRNA-seq), which the consistent and specific features of cells are explicitly extracted facilitating the cell clustering. Specifically, sLMIC constructs a graph for each type of single-cell data, thereby transforming omics data into multi-layer networks, which effectively removes heterogeneity of omic data. Then, sLMIC employs the low-rank and exclusivity constraints to separate the self-representation of cells into two parts, i.e., the shared and specific features, which explicitly characterize the consistency and diversity of omic data, providing an effective strategy to model the structure of cell types. Feature extraction and cell clustering are jointly formulated as an overall objective function, where latent features of data are obtained under the guidance of cell clustering. The extensive experimental results on 13 multi-omics datasets of single-cell from diverse organisms and tissues indicate that sLMIC observably exceeds the advanced algorithms regarding various measurements.

Disease related changes in ATAC-seq of iPSC-derived motor neuron lines from ALS patients and controls.

Amyotrophic Lateral Sclerosis (ALS), like many other neurodegenerative diseases, is highly heritable, but with only a small fraction of cases explained by monogenic disease alleles. To better understand sporadic ALS, we report epigenomic profiles, as measured by ATAC-seq, of motor neuron cultures derived from a diverse group of 380 ALS patients and 80 healthy controls. We find that chromatin accessibility is heavily influenced by sex, the iPSC cell type of origin, ancestry, and the inherent variance arising from sequencing. Once these covariates are corrected for, we are able to identify ALS-specific signals in the data. Additionally, we find that the ATAC-seq data is able to predict ALS disease progression rates with similar accuracy to methods based on biomarkers and clinical status. These results suggest that iPSC-derived motor neurons recapitulate important disease-relevant epigenomic changes.

Kernel-based testing for single-cell differential analysis.

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

Age Prediction Using DNA Methylation Heterogeneity Metrics.

Dynamic changes in genomic DNA methylation patterns govern the epigenetic developmental programs and accompany the organism's aging. Epigenetic clock (eAge) algorithms utilize DNA methylation to estimate the age and risk factors for diseases as well as analyze the impact of various interventions. High-throughput bisulfite sequencing methods, such as reduced-representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS), provide an opportunity to identify the genomic regions of disordered or heterogeneous DNA methylation, which might be associated with cell-type heterogeneity, DNA methylation erosion, and allele-specific methylation. We systematically evaluated the applicability of five scores assessing the variability of methylation patterns by evaluating within-sample heterogeneity (WSH) to construct human blood epigenetic clock models using RRBS data. The best performance was demonstrated by the model based on a metric designed to assess DNA methylation erosion with an MAE of 3.686 years. We also trained a prediction model that uses the average methylation level over genomic regions. Although this region-based model was relatively more efficient than the WSH-based model, the latter required the analysis of just a few short genomic regions and, therefore, could be a useful tool to design a reduced epigenetic clock that is analyzed by targeted next-generation sequencing.

Associations in cell type-specific hydroxymethylation and transcriptional alterations of pediatric central nervous system tumors.

Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors, we utilize a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identify a preponderance differential Cytosine-phosphate-Guanine site hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like histone deacetylase 4 and insulin-like growth factor 1 receptor, are associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric central nervous system tumors.

Single-cell multiomics guided mechanistic understanding of Fontan-associated liver disease.

The Fontan operation is the current standard of care for single-ventricle congenital heart disease. Individuals with a Fontan circulation (FC) exhibit central venous hypertension and face life-threatening complications of hepatic fibrosis, known as Fontan-associated liver disease (FALD). The fundamental biology and mechanisms of FALD are little understood. Here, we generated a transcriptomic and epigenomic atlas of human FALD at single-cell resolution using multiomic snRNA-ATAC-seq. We found profound cell type-specific transcriptomic and epigenomic changes in FC livers. Central hepatocytes (cHep) exhibited the most substantial changes, featuring profound metabolic reprogramming. These cHep changes preceded substantial activation of hepatic stellate cells and liver fibrosis, suggesting cHep as a potential first "responder" in the pathogenesis of FALD. We also identified a network of ligand-receptor pairs that transmit signals from cHep to hepatic stellate cells, which may promote their activation and liver fibrosis. We further experimentally demonstrated that activins A and B promote fibrotic activation in vitro and identified mechanisms of activin A's transcriptional activation in FALD. Together, our single-cell transcriptomic and epigenomic atlas revealed mechanistic insights into the pathogenesis of FALD and may aid identification of potential therapeutic targets.

Moving toward the Inclusion of Epigenomics in Bacterial Genome Evolution: Perspectives and Challenges.

The universality of DNA methylation as an epigenetic regulatory mechanism belongs to all biological kingdoms. However, while eukaryotic systems have been the primary focus of DNA methylation studies, the molecular mechanisms in prokaryotes are less known. Nevertheless, DNA methylation in prokaryotes plays a pivotal role in many cellular processes such as defense systems against exogenous DNA, cell cycle dynamics, and gene expression, including virulence. Thanks to single-molecule DNA sequencing technologies, genome-wide identification of methylated DNA is becoming feasible on a large scale, providing the possibility to investigate more deeply the presence, variability, and roles of DNA methylation. Here, we present an overview of the multifaceted roles of DNA methylation in prokaryotes and suggest research directions and tools which can enable us to better understand the contribution of DNA methylation to prokaryotic genome evolution and adaptation. In particular, we emphasize the need to understand the presence and role of transgenerational inheritance, as well as the impact of epigenomic signatures on adaptation and genome evolution. Research directions and the importance of novel computational tools are underlined.

MammalMethylClock R package: software for DNA methylation-based epigenetic clocks in mammals.

MOTIVATION: Epigenetic clocks are prediction methods based on DNA methylation levels in a given species or set of species. Defined as multivariate regression models, these DNA methylation-based biomarkers of age or mortality risk are useful in species conservation efforts and in preclinical studies. RESULTS: We present an R package called MammalMethylClock for the construction, assessment, and application of epigenetic clocks in different mammalian species. The R package includes the utility for implementing pre-existing mammalian clocks from the Mammalian Methylation Consortium. AVAILABILITY AND IMPLEMENTATION: The source code and documentation manual for MammalMethylClock, and clock coefficient .csv files that are included within this software package, can be found on Zenodo at https://doi.org/10.5281/zenodo.10971037.

Special Issue "Epigenetics in Neurodegenerative Diseases".

Epigenetic mechanisms inducing phenotypic changes without altering the DNA genome are increasingly recognized as key factors modulating gene expression and, consequently, cell functions [...].

The Role of Epigenetic Factors in the Pathogenesis of Psoriasis.

Psoriasis is a chronic inflammatory skin disease, the prevalence of which is increasing. Genetic, genomic, and epigenetic changes play a significant role in the pathogenesis of psoriasis. This review summarizes the impact of epigenetics on the development of psoriasis and highlights challenges for the future. The development of epigenetics provides a basis for the search for genetic markers associated with the major histocompatibility complex. Genome-wide association studies have made it possible to link psoriasis to genes and therefore to epigenetics. The acquired knowledge may in the future serve as a solid foundation for developing newer, increasingly effective methods of treating psoriasis. In this narrative review, we discuss the role of epigenetic factors in the pathogenesis of psoriasis.

DNA Methylation in Ctenophores.

Epigenomic regulation and dynamic DNA methylation, in particular, are widespread mechanisms orchestrating the genome operation across time and species. Whole-genome bisulfite sequencing (WGBS) is currently the only method for unbiasedly capturing the presence of 5-methylcytosine (5-mC) DNA methylation patterns across an entire genome with single-nucleotide resolution. Bisulfite treatment converts unmethylated cytosines to uracils but leaves methylated cytosines intact, thereby creating a map of all methylated cytosines across a genome also known as a methylome. These epigenomic patterns of DNA methylation have been found to regulate gene expression and influence gene evolution rates between species. While protocols have been optimized for vertebrate methylome production, little adaptation has been done for invertebrates. Creating a methylome reference allows comparisons to be made between rates of transcription and epigenomic patterning in animals. Here we present a method of library construction for bisulfite sequencing optimized for non-bilateral metazoans such as the ctenophore, Mnemiopsis leidyi. We have improved upon our previously published method by including spike-in genomic DNA controls to measure methylation conversion rates. By pooling two bisulfite conversion reactions from the same individual, we also produced sequencing libraries that yielded a higher percentage of sequenced reads uniquely mapping to the reference genome. We successfully detected 5-mC in whole-animal methylomes at CpG, CHG, and CHH sites and visualized datasets using circos diagrams. The proof-of-concept tests were performed both under control conditions and following injury tests with changes in methylation patterns of genes encoding innexins, toxins and neuropeptides. Our approach can be easily adapted to produce epigenomes from other fragile marine animals.

MicroRNAs: A novel signature in the metastasis of esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a malignant epithelial tumor, characterized by squamous cell differentiation, it is the sixth leading cause of cancer-related deaths globally. The increased mortality rate of ESCC patients is predominantly due to the advanced stage of the disease when discovered, coupled with higher risk of metastasis, which is an exceedingly malignant characteristic of cancer, frequently leading to a high mortality rate. Unfortunately, there is currently no specific and effective marker to predict and treat metastasis in ESCC. MicroRNAs (miRNAs) are a class of small non-coding RNA molecules, approximately 22 nucleotides in length. miRNAs are vital in modulating gene expression and serve pivotal regulatory roles in the occurrence, progression, and prognosis of cancer. Here, we have examined the literature to highlight the intimate correlations between miRNAs and ESCC metastasis, and show that ESCC metastasis is predominantly regulated or regulated by genetic and epigenetic factors. This review proposes a potential role for miRNAs as diagnostic and therapeutic biomarkers for metastasis in ESCC metastasis, with the ultimate aim of reducing the mortality rate among patients with ESCC.

Epigenomic mechanism regulating the quality and ripeness of apple fruit with differing harvest maturity.

Harvest maturity significantly affects the quality of apple fruit in post-harvest storage process. Although the regulatory mechanisms underlying fruit ripening have been studied, the associated epigenetic modifications remain unclear. Thus, we compared the DNA methylation changes and the transcriptional responses of mature fruit (MF) and immature fruit (NF). There were significant correlations between DNA methylation and gene expression. Moreover, the sugar contents (sucrose, glucose, and fructose) were higher in MF than in NF, whereas the opposite pattern was detected for the starch content. The expression-level differences were due to DNA methylations and ultimately resulted in diverse fruit textures and ripeness. Furthermore, the higher ethylene, auxin, and abscisic acid levels in MF than in NF, which influenced the fruit texture and ripening, were associated with multiple differentially expressed genes in hormone synthesis, signaling, and response pathways (ACS, ACO, ZEP, NCED, and ABA2) that were regulated by DNA methylations. Multiple transcription factor genes involved in regulating fruit ripening and quality via changes in DNA methylation were identified, including MIKCC-type MADS-box genes and fruit ripening-related genes (NAP, SPL, WRKY, and NAC genes). These findings reflect the diversity in the epigenetic regulation of gene expression and may be relevant for elucidating the epigenetic regulatory mechanism underlying the ripening and quality of apple fruit with differing harvest maturity.

Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation.

BACKGROUND: Regulation of transcription is central to the emergence of new cell types during development, and it often involves activation of genes via proximal and distal regulatory regions. The activity of regulatory elements is determined by transcription factors (TFs) and epigenetic marks, but despite extensive mapping of such patterns, the extraction of regulatory principles remains challenging. RESULTS: Here we study differentially and similarly expressed genes along with their associated epigenomic profiles, chromatin accessibility and DNA methylation, during lineage specification at gastrulation in mice. Comparison of the three lineages allows us to identify genomic and epigenomic features that distinguish the two classes of genes. We show that differentially expressed genes are primarily regulated by distal elements, while similarly expressed genes are controlled by proximal housekeeping regulatory programs. Differentially expressed genes are relatively isolated within topologically associated domains, while similarly expressed genes tend to be located in gene clusters. Transcription of differentially expressed genes is associated with differentially open chromatin at distal elements including enhancers, while that of similarly expressed genes is associated with ubiquitously accessible chromatin at promoters. CONCLUSION: Based on these associations of (linearly) distal genes' transcription start sites (TSSs) and putative enhancers for developmental genes, our findings allow us to link putative enhancers to their target promoters and to infer lineage-specific repertoires of putative driver transcription factors, within which we define subgroups of pioneers and co-operators.

Epigenomic insights into common human disease pathology.

The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.

FGFR1 fusions as a novel molecular driver in rhabdomyosarcoma.

The wide application of RNA sequencing in clinical practice has allowed the discovery of novel fusion genes, which have contributed to a refined molecular classification of rhabdomyosarcoma (RMS). Most fusions in RMS result in aberrant transcription factors, such as PAX3/7::FOXO1 in alveolar RMS (ARMS) and fusions involving VGLL2 or NCOA2 in infantile spindle cell RMS. However, recurrent fusions driving oncogenic kinase activation have not been reported in RMS. Triggered by an index case of an unclassified RMS (overlapping features between ARMS and sclerosing RMS) with a novel FGFR1::ANK1 fusion, we reviewed our molecular files for cases harboring FGFR1-related fusions. One additional case with an FGFR1::TACC1 fusion was identified in a tumor resembling embryonal RMS (ERMS) with anaplasia, but with no pathogenic variants in TP53 or DICER1 on germline testing. Both cases occurred in males, aged 7 and 24, and in the pelvis. The 2nd case also harbored additional alterations, including somatic TP53 and TET2 mutations. Two additional RMS cases (one unclassified, one ERMS) with FGFR1 overexpression but lacking FGFR1 fusions were identified by RNA sequencing. These two cases and the FGFR1::TACC1-positive case clustered together with the ERMS group by RNAseq. This is the first report of RMS harboring recurrent FGFR1 fusions. However, it remains unclear if FGFR1 fusions define a novel subset of RMS or alternatively, whether this alteration can sporadically drive the pathogenesis of known RMS subtypes, such as ERMS. Additional larger series with integrated genomic and epigenetic datasets are needed for better subclassification, as the resulting oncogenic kinase activation underscores the potential for targeted therapy.

The genetic regulatory architecture and epigenomic basis for age-related changes in rattlesnake venom.

Developmental phenotypic changes can evolve under selection imposed by age- and size-related ecological differences. Many of these changes occur through programmed alterations to gene expression patterns, but the molecular mechanisms and gene-regulatory networks underlying these adaptive changes remain poorly understood. Many venomous snakes, including the eastern diamondback rattlesnake (Crotalus adamanteus), undergo correlated changes in diet and venom expression as snakes grow larger with age, providing models for identifying mechanisms of timed expression changes that underlie adaptive life history traits. By combining a highly contiguous, chromosome-level genome assembly with measures of expression, chromatin accessibility, and histone modifications, we identified cis-regulatory elements and trans-regulatory factors controlling venom ontogeny in the venom glands of C. adamanteus. Ontogenetic expression changes were significantly correlated with epigenomic changes within genes, immediately adjacent to genes (e.g., promoters), and more distant from genes (e.g., enhancers). We identified 37 candidate transcription factors (TFs), with the vast majority being up-regulated in adults. The ontogenetic change is largely driven by an increase in the expression of TFs associated with growth signaling, transcriptional activation, and circadian rhythm/biological timing systems in adults with corresponding epigenomic changes near the differentially expressed venom genes. However, both expression activation and repression contributed to the composition of both adult and juvenile venoms, demonstrating the complexity and potential evolvability of gene regulation for this trait. Overall, given that age-based trait variation is common across the tree of life, we provide a framework for understanding gene-regulatory-network-driven life-history evolution more broadly.

Building Minimized Epigenetic Clock by iPlex MassARRAY Platform.

Epigenetic clocks are valuable tools for estimating both chronological and biological age by assessing DNA methylation levels at specific CpG dinucleotides. While conventional epigenetic clocks rely on genome-wide methylation data, targeted approaches offer a more efficient alternative. In this study, we explored the feasibility of constructing a minimized epigenetic clock utilizing data acquired through the iPlex MassARRAY technology. The study enrolled a cohort of relatively healthy individuals, and their methylation levels of eight specific CpG dinucleotides in genes SLC12A5, LDB2, FIGN, ACSS3, FHL2, and EPHX3 were evaluated using the iPlex MassARRAY system and the Illumina EPIC array. The methylation level of five studied CpG sites demonstrated significant correlations with chronological age and an acceptable convergence of data obtained by the iPlex MassARRAY and Illumina EPIC array. At the same time, the methylation level of three CpG sites showed a weak relationship with age and exhibited a low concordance between the data obtained from the two technologies. The construction of the epigenetic clock involved the utilization of different machine-learning models, including linear models, deep neural networks (DNN), and gradient-boosted decision trees (GBDT). The results obtained from these models were compared with each other and with the outcomes generated by other well-established epigenetic clocks. In our study, the TabNet architecture (deep tabular data learning architecture) exhibited the best performance (best MAE = 5.99). Although our minimized epigenetic clock yielded slightly higher age prediction errors compared to other epigenetic clocks, it still represents a viable alternative to the genome-wide epigenotyping array.

Antipsychotic-induced epigenomic reorganization in frontal cortex of individuals with schizophrenia.

Genome-wide association studies have revealed >270 loci associated with schizophrenia risk, yet these genetic factors do not seem to be sufficient to fully explain the molecular determinants behind this psychiatric condition. Epigenetic marks such as post-translational histone modifications remain largely plastic during development and adulthood, allowing a dynamic impact of environmental factors, including antipsychotic medications, on access to genes and regulatory elements. However, few studies so far have profiled cell-specific genome-wide histone modifications in postmortem brain samples from schizophrenia subjects, or the effect of antipsychotic treatment on such epigenetic marks. Here, we conducted ChIP-seq analyses focusing on histone marks indicative of active enhancers (H3K27ac) and active promoters (H3K4me3), alongside RNA-seq, using frontal cortex samples from antipsychotic-free (AF) and antipsychotic-treated (AT) individuals with schizophrenia, as well as individually matched controls (n=58). Schizophrenia subjects exhibited thousands of neuronal and non-neuronal epigenetic differences at regions that included several susceptibility genetic loci, such as NRG1, DISC1, and DRD3. By analyzing the AF and AT cohorts separately, we identified schizophrenia-associated alterations in specific transcription factors, their regulatees, and epigenomic and transcriptomic features that were reversed by antipsychotic treatment; as well as those that represented a consequence of antipsychotic medication rather than a hallmark of schizophrenia in postmortem human brain samples. Notably, we also found that the effect of age on epigenomic landscapes was more pronounced in frontal cortex of AT-schizophrenics, as compared to AF-schizophrenics and controls. Together, these data provide important evidence of epigenetic alterations in the frontal cortex of individuals with schizophrenia, and remark for the first time on the impact of age and antipsychotic treatment on chromatin organization.