Discovery of thiophen-2-ylmethylene bis-dimedone derivatives as novel WRN inhibitors for treating cancers with microsatellite instability.

Microsatellite instability (MSI) is a hypermutable condition caused by DNA mismatch repair system defects, contributing to the development of various cancer types. Recent research has identified Werner syndrome ATP-dependent helicase (WRN) as a promising synthetic lethal target for MSI cancers. Herein, we report the first discovery of thiophen-2-ylmethylene bis-dimedone derivatives as novel WRN inhibitors for MSI cancer therapy. Initial computational analysis and biological evaluation identified a new scaffold for a WRN inhibitor. Subsequent SAR study led to the discovery of a highly potent WRN inhibitor. Furthermore, we demonstrated that the optimal compound induced DNA damage and apoptotic cell death in MSI cancer cells by inhibiting WRN. This study provides a new pharmacophore for WRN inhibitors, emphasizing their therapeutic potential for MSI cancers.

Intercellular cross-talk through lineage-specific gap junction of cancer-associated fibroblasts related to stromal fibrosis and prognosis.

Stromal fibrosis in cancer is usually associated with poor prognosis and chemotherapy resistance. It is thought to be caused by fibroblasts; however, the exact mechanism is not yet well understood. The study aimed to identify lineage-specific cancer-associated fibroblast (CAF) subgroup and their associations with extracellular matrix remodeling and clinical significances in various tumor types using single-cell and bulk RNA sequencing data. Through unsupervised clustering, six subclusters of CAFs were identified, including a cluster with exclusively high gap junction protein beta-2 (GJB2) expression. This cluster was named GJB2-positive CAF. It was found to be a unique subgroup of terminally differentiated CAFs associated with collagen gene expression and extracellular matrix remodeling. GJB2-positive CAFs showed higher communication frequency with vascular endothelial cells and cancer cells than GJB2-negative CAFs. Moreover, GJB2 was poorly expressed in normal tissues, indicating that its expression is dependent on interaction with other cells, including vascular endothelial cells and cancer cells. Finally, the study investigated the clinical significance of GJB2 signature score for GJB2-positive CAFs in cancer and found a correlation with poor prognosis. These results suggest that GJB2-positive CAF is a unique fibroblast subtype involved in extracellular matrix remodeling, with significant clinical implications in cancer.

Zika Virus Infection Induces Interleukin-1ß-Mediated Inflammatory Responses by Macrophages in the Brain of an Adult Mouse Model.

During the 2015-2016 Zika virus (ZIKV) epidemic, ZIKV-associated neurological diseases were reported in adults, including microcephaly, Guillain-Barre syndrome, myelitis, meningoencephalitis, and fatal encephalitis. However, the mechanisms underlying the neuropathogenesis of ZIKV infection are not yet fully understood. In this study, we used an adult ZIKV infection mouse model (Ifnar1-/-) to investigate the mechanisms underlying neuroinflammation and neuropathogenesis. ZIKV infection induced the expression of proinflammatory cytokines, including interleukin-1ß (IL-1ß), IL-6, gamma interferon, and tumor necrosis factor alpha, in the brains of Ifnar1-/- mice. RNA-seq analysis of the infected mouse brain also revealed that genes involved in innate immune responses and cytokine-mediated signaling pathways were significantly upregulated at 6 days postinfection. Furthermore, ZIKV infection induced macrophage infiltration and activation and augmented IL-1ß expression, whereas microgliosis was not observed in the brain. Using human monocyte THP-1 cells, we confirmed that ZIKV infection promotes inflammatory cell death and increases IL-1ß secretion. In addition, expression of the complement component C3, which is associated with neurodegenerative diseases and known to be upregulated by proinflammatory cytokines, was induced by ZIKV infection through the IL-1ß-mediated pathway. An increase in C5a produced by complement activation in the brains of ZIKV-infected mice was also verified. Taken together, our results suggest that ZIKV infection in the brain of this animal model augments IL-1ß expression in infiltrating macrophages and elicits IL-1ß-mediated inflammation, which can lead to the destructive consequences of neuroinflammation. IMPORTANCE Zika virus (ZIKV) associated neurological impairments are an important global health problem. Our results suggest that ZIKV infection in the mouse brain can induce IL-1ß-mediated inflammation and complement activation, thereby contributing to the development of neurological disorders. Thus, our findings reveal a mechanism by which ZIKV induces neuroinflammation in the mouse brain. Although we used adult type I interferon receptor IFNAR knockout (Ifnar1-/-) mice owing to the limited mouse models of ZIKV pathogenesis, our conclusions contributed to the understanding ZIKV-associated neurological diseases to develop treatment strategies for patients with ZIKV infection based on these findings.

S-Pred: protein structural property prediction using MSA transformer.

Predicting the local structural features of a protein from its amino acid sequence helps its function prediction to be revealed and assists in three-dimensional structural modeling. As the sequence-structure gap increases, prediction methods have been developed to bridge this gap. Additionally, as the size of the structural database and computing power increase, the performance of these methods have also significantly improved. Herein, we present a powerful new tool called S-Pred, which can predict eight-state secondary structures (SS8), accessible surface areas (ASAs), and intrinsically disordered regions (IDRs) from a given sequence. For feature prediction, S-Pred uses multiple sequence alignment (MSA) of a query sequence as an input. The MSA input is converted to features by the MSA Transformer, which is a protein language model that uses an attention mechanism. A long short-term memory (LSTM) was employed to produce the final prediction. The performance of S-Pred was evaluated on several test sets, and the program consistently provided accurate predictions. The accuracy of the SS8 prediction was approximately 76%, and the Pearson's correlation between the experimental and predicted ASAs was 0.84. Additionally, an IDR could be accurately predicted with an F1-score of 0.514. The program is freely available at https://github.com/arontier/S_Pred_Paper and https://ad3.io as a code and a web server.

SARS-CoV-2 Infection of Microglia Elicits Proinflammatory Activation and Apoptotic Cell Death.

Accumulating evidence suggests that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes various neurological symptoms in patients with coronavirus disease 2019 (COVID-19). The most dominant immune cells in the brain are microglia. Yet, the relationship between neurological manifestations, neuroinflammation, and host immune response of microglia to SARS-CoV-2 has not been well characterized. Here, we reported that SARS-CoV-2 can directly infect human microglia, eliciting M1-like proinflammatory responses, followed by cytopathic effects. Specifically, SARS-CoV-2 infected human microglial clone 3 (HMC3), leading to inflammatory activation and cell death. RNA sequencing (RNA-seq) analysis also revealed that endoplasmic reticulum (ER) stress and immune responses were induced in the early, and apoptotic processes in the late phases of viral infection. SARS-CoV-2-infected HMC3 showed the M1 phenotype and produced proinflammatory cytokines, such as interleukin (IL)-1ß, IL-6, and tumor necrosis factor α (TNF-α), but not the anti-inflammatory cytokine IL-10. After this proinflammatory activation, SARS-CoV-2 infection promoted both intrinsic and extrinsic death receptor-mediated apoptosis in HMC3. Using K18-hACE2 transgenic mice, murine microglia were also infected by intranasal inoculation of SARS-CoV-2. This infection induced the acute production of proinflammatory microglial IL-6 and TNF-α and provoked a chronic loss of microglia. Our findings suggest that microglia are potential mediators of SARS-CoV-2-induced neurological problems and, consequently, can be targets of therapeutic strategies against neurological diseases in patients with COVID-19. IMPORTANCE Recent studies reported neurological and cognitive sequelae in patients with COVID-19 months after the viral infection with several symptoms, including ageusia, anosmia, asthenia, headache, and brain fog. Our conclusions raise awareness of COVID-19-related microglia-mediated neurological disorders to develop treatment strategies for the affected patients. We also indicated that HMC3 was a novel human cell line susceptible to SARS-CoV-2 infection that exhibited cytopathic effects, which could be further used to investigate cellular and molecular mechanisms of neurological manifestations of patients with COVID-19.

A-Prot: protein structure modeling using MSA transformer.

BACKGROUND: The accuracy of protein 3D structure prediction has been dramatically improved with the help of advances in deep learning. In the recent CASP14, Deepmind demonstrated that their new version of AlphaFold (AF) produces highly accurate 3D models almost close to experimental structures. The success of AF shows that the multiple sequence alignment of a sequence contains rich evolutionary information, leading to accurate 3D models. Despite the success of AF, only the prediction code is open, and training a similar model requires a vast amount of computational resources. Thus, developing a lighter prediction model is still necessary. RESULTS: In this study, we propose a new protein 3D structure modeling method, A-Prot, using MSA Transformer, one of the state-of-the-art protein language models. An MSA feature tensor and row attention maps are extracted and converted into 2D residue-residue distance and dihedral angle predictions for a given MSA. We demonstrated that A-Prot predicts long-range contacts better than the existing methods. Additionally, we modeled the 3D structures of the free modeling and hard template-based modeling targets of CASP14. The assessment shows that the A-Prot models are more accurate than most top server groups of CASP14. CONCLUSION: These results imply that A-Prot accurately captures the evolutionary and structural information of proteins with relatively low computational cost. Thus, A-Prot can provide a clue for the development of other protein property prediction methods.

Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments.

Designing efficient synthetic routes for a target molecule remains a major challenge in organic synthesis. Atom environments are ideal, stand-alone, chemically meaningful building blocks providing a high-resolution molecular representation. Our approach mimics chemical reasoning, and predicts reactant candidates by learning the changes of atom environments associated with the chemical reaction. Through careful inspection of reactant candidates, we demonstrate atom environments as promising descriptors for studying reaction route prediction and discovery. Here, we present a new single-step retrosynthesis prediction method, viz. RetroTRAE, being free from all SMILES-based translation issues, yields a top-1 accuracy of 58.3% on the USPTO test dataset, and top-1 accuracy reaches to 61.6% with the inclusion of highly similar analogs, outperforming other state-of-the-art neural machine translation-based methods. Our methodology introduces a novel scheme for fragmental and topological descriptors to be used as natural inputs for retrosynthetic prediction tasks.

Substructure-based neural machine translation for retrosynthetic prediction.

With the rapid improvement of machine translation approaches, neural machine translation has started to play an important role in retrosynthesis planning, which finds reasonable synthetic pathways for a target molecule. Previous studies showed that utilizing the sequence-to-sequence frameworks of neural machine translation is a promising approach to tackle the retrosynthetic planning problem. In this work, we recast the retrosynthetic planning problem as a language translation problem using a template-free sequence-to-sequence model. The model is trained in an end-to-end and a fully data-driven fashion. Unlike previous models translating the SMILES strings of reactants and products, we introduced a new way of representing a chemical reaction based on molecular fragments. It is demonstrated that the new approach yields better prediction results than current state-of-the-art computational methods. The new approach resolves the major drawbacks of existing retrosynthetic methods such as generating invalid SMILES strings. Specifically, our approach predicts highly similar reactant molecules with an accuracy of 57.7%. In addition, our method yields more robust predictions than existing methods.

Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images-The ACDC@LungHP Challenge 2019.

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using metrics using the precision, accuracy, sensitivity, specificity, and DICE coefficient (DC). The DC ranged from 0.7354 ±0.1149 to 0.8372 ±0.0858. The DC of the best method was close to the inter-observer agreement (0.8398 ±0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better (p 0.01) than single model methods, with mean DC of 0.7966 and 0.7544, respectively. Deep learning based methods could potentially help pathologists find suspicious regions for further analysis of lung cancer in WSI.

Deep learning method for comet segmentation and comet assay image analysis.

Comet assay is a widely used method, especially in the field of genotoxicity, to quantify and measure DNA damage visually at the level of individual cells with high sensitivity and efficiency. Generally, computer programs are used to analyze comet assay output images following two main steps. First, each comet region must be located and segmented, and next, it is scored using common metrics (e.g., tail length and tail moment). Currently, most studies on comet assay image analysis have adopted hand-crafted features rather than the recent and effective deep learning (DL) methods. In this paper, however, we propose a DL-based baseline method, called DeepComet, for comet segmentation. Furthermore, we created a trainable and testable comet assay image dataset that contains 1037 comet assay images with 8271 manually annotated comet objects. From the comet segmentation test results with the proposed dataset, the DeepComet achieves high average precision (AP), which is an essential metric in image segmentation and detection tasks. A comparative analysis was performed between the DeepComet and the state-of-the-arts automatic comet segmentation programs on the dataset. Besides, we found that the DeepComet records high correlations with a commercial comet analysis tool, which suggests that the DeepComet is suitable for practical application.

AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks.

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.

Comparative transcriptome analysis identified candidate genes involved in mycelium browning in Lentinula edodes.

BACKGROUND: Lentinula edodes is one of the most popular edible mushroom species in the world and contains useful medicinal components, such as lentinan. The light-induced formation of brown film on the vegetative mycelial tissues of L. edodes is an important process for ensuring the quantity and quality of this edible mushroom. To understand the molecular mechanisms underlying this critical developmental process in L. edodes, we characterized the morphological phenotypic changes in a strain, Chamaram, associated with abnormal brown film formation and compared its genome-wide transcriptional features. RESULTS: In the present study, we performed genome-wide transcriptome analyses of different vegetative mycelium growth phenotypes, namely, early white, normal brown, and defective dark yellow partial brown films phenotypes which were exposed to different light conditions. The analysis revealed the identification of clusters of genes specific to the light-induced brown film phenotypes. These genes were significantly associated with light sensing via photoreceptors such as FMN- and FAD-bindings, signal transduction by kinases and GPCRs, melanogenesis via activation of tyrosinases, and cell wall degradation by glucanases, chitinases, and laccases, which suggests these processes are involved in the formation of mycelial browning in L. edodes. Interestingly, hydrophobin genes such as SC1 and SC3 exhibited divergent expression levels in the normal and abnormal brown mycelial films, indicating the ability of these genes to act in fruiting body initiation and formation of dikaryotic mycelia. Furthermore, we identified the up-regulation of glycoside hydrolase domain-containing genes in the normal brown film but not in the abnormal film phenotype, suggesting that cell wall degradation in the normal brown film phenotype is crucial in the developmental processes related to the initiation and formation of fruiting bodies. CONCLUSIONS: This study systematically analysed the expression patterns of light-induced browning-related genes in L. edodes. Our findings provide information for further investigations of browning formation mechanisms in L. edodes and a foundation for future L. edodes breeding.

TaF: a web platform for taxonomic profile-based fungal gene prediction.

INTRODUCTION: The accurate prediction and annotation of gene structures from the genome sequence of an organism enable genome-wide functional analyses to obtain insight into the biological properties of an organism. OBJECTIVES: We recently developed a highly accurate filamentous fungal gene prediction pipeline and web platform called TaF. TaF is a homology-based gene predictor employing large-scale taxonomic profiling to search for close relatives in genome queries. METHODS: TaF pipeline consists of four processing steps; (1) taxonomic profiling to search for close relatives to query, (2) generation of hints for determining exon-intron boundaries from orthologous protein sequence data of the profiled species, (3) gene prediction by combination of ab inito and evidence-based prediction methods, and (4) homology search for gene models. RESULTS: TaF generates extrinsic evidence that suggests possible exon-intron boundaries based on orthologous protein sequence data, thus reducing false-positive predictions of gene structure based on distantly related orthologs data. In particular, the gene prediction method using taxonomic profiling shows very high accuracy, including high sensitivity and specificity for gene models, suggesting a new approach for homology-based gene prediction from newly sequenced or uncharacterized fungal genomes, with the potential to improve the quality of gene prediction. CONCLUSION: TaF will be a useful tool for fungal genome-wide analyses, including the identification of targeted genes associated with a trait, transcriptome profiling, comparative genomics, and evolutionary analysis.

RNA seq- and DEG-based comparison of developmental toxicity in fish embryos of two species exposed to Iranian heavy crude oil.

To determine and compare the toxic effects of Iranian heavy crude oil (IHCO) on the embryonic development of two fish species, we examined transcriptome profiles using RNA-seq. The assembled contigs were 66,070 unigenes in olive flounder embryos and 76,498 unigenes in spotted seabass embryos. In the differential gene expression (DEG) profiles, olive flounder embryos showed different up- and down-regulated patterns than spotted seabass embryos in response to fresh IHCO (FIHCO) and weathered IHCO (WIHCO). In this work, we categorized DEG profiles into six pathways: ribosome, oxidative phosphorylation, Parkinson's disease, Alzheimer's disease, Huntington's disease, and cardiac muscle contraction, validating the expression patterns of 13 DEGs using real-time quantitative RT-PCR. The expression of the CYP1A, CYP1B1, and CYP1C1 genes in spotted seabass embryos was higher than in olive flounder embryos, whereas genes related to cell processing, development, and the immune system showed the opposite trend. Orthologous gene cluster analysis showed that olive flounder embryos were sensitive (fold change of genes with cutoff P<0.05) to both FIHCO and WIHCO, but spotted seabass embryos exhibited higher sensitivity to WIHCO than FIHCO, indicating that species-specific differences are likely to be reflected in population levels after oil spills. Overall, our study provides new insight on the different embryonic susceptibilities of two marine fish species to FIHCO and WIHCO and a better understanding of the underlying molecular mechanisms via RNA-seq and DEGs.

Long-read transcriptome data for improved gene prediction in Lentinula edodes.

Lentinula edodes is one of the most popular edible mushrooms in the world and contains useful medicinal components such as lentinan. The whole-genome sequence of L. edodes has been determined with the objective of discovering candidate genes associated with agronomic traits, but experimental verification of gene models with correction of gene prediction errors is lacking. To improve the accuracy of gene prediction, we produced 12.6 Gb of long-read transcriptome data of variable lengths using PacBio single-molecule real-time (SMRT) sequencing and generated 36,946 transcript clusters with an average length of 2.2 kb. Evidence-driven gene prediction on the basis of long- and short-read RNA sequencing data was performed; a total of 16,610 protein-coding genes were predicted with error correction. Of the predicted genes, 42.2% were verified to be covered by full-length transcript clusters. The raw reads have been deposited in the NCBI SRA database under accession number PRJNA396788.

The first whole genome and transcriptome of the cinereous vulture reveals adaptation in the gastric and immune defense systems and possible convergent evolution between the Old and New World vultures.

BACKGROUND: The cinereous vulture, Aegypius monachus, is the largest bird of prey and plays a key role in the ecosystem by removing carcasses, thus preventing the spread of diseases. Its feeding habits force it to cope with constant exposure to pathogens, making this species an interesting target for discovering functionally selected genetic variants. Furthermore, the presence of two independently evolved vulture groups, Old World and New World vultures, provides a natural experiment in which to investigate convergent evolution due to obligate scavenging. RESULTS: We sequenced the genome of a cinereous vulture, and mapped it to the bald eagle reference genome, a close relative with a divergence time of 18 million years. By comparing the cinereous vulture to other avian genomes, we find positively selected genetic variations in this species associated with respiration, likely linked to their ability of immune defense responses and gastric acid secretion, consistent with their ability to digest carcasses. Comparisons between the Old World and New World vulture groups suggest convergent gene evolution. We assemble the cinereous vulture blood transcriptome from a second individual, and annotate genes. Finally, we infer the demographic history of the cinereous vulture which shows marked fluctuations in effective population size during the late Pleistocene. CONCLUSIONS: We present the first genome and transcriptome analyses of the cinereous vulture compared to other avian genomes and transcriptomes, revealing genetic signatures of dietary and environmental adaptations accompanied by possible convergent evolution between the Old World and New World vultures.

Whole genome sequencing of an ethnic Pathan (Pakhtun) from the north-west of Pakistan.

BACKGROUND: Pakistan covers a key geographic area in human history, being both part of the Indus River region that acted as one of the cradles of civilization and as a link between Western Eurasia and Eastern Asia. This region is inhabited by a number of distinct ethnic groups, the largest being the Punjabi, Pathan (Pakhtuns), Sindhi, and Baloch. RESULTS: We analyzed the first ethnic male Pathan genome by sequencing it to 29.7-fold coverage using the Illumina HiSeq2000 platform. A total of 3.8 million single nucleotide variations (SNVs) and 0.5 million small indels were identified by comparing with the human reference genome. Among the SNVs, 129,441 were novel, and 10,315 nonsynonymous SNVs were found in 5,344 genes. SNVs were annotated for health consequences and high risk diseases, as well as possible influences on drug efficacy. We confirmed that the Pathan genome presented here is representative of this ethnic group by comparing it to a panel of Central Asians from the HGDP-CEPH panels typed for ~650 k SNPs. The mtDNA (H2) and Y haplogroup (L1) of this individual were also typical of his geographic region of origin. Finally, we reconstruct the demographic history by PSMC, which highlights a recent increase in effective population size compatible with admixture between European and Asian lineages expected in this geographic region. CONCLUSIONS: We present a whole-genome sequence and analyses of an ethnic Pathan from the north-west province of Pakistan. It is a useful resource to understand genetic variation and human migration across the whole Asian continent.

Genome-wide analysis of DNA methylation before-and after exercise in the thoroughbred horse with MeDIP-Seq.

Athletic performance is an important criteria used for the selection of superior horses. However, little is known about exercise-related epigenetic processes in the horse. DNA methylation is a key mechanism for regulating gene expression in response to environmental changes. We carried out comparative genomic analysis of genome-wide DNA methylation profiles in the blood samples of two different thoroughbred horses before and after exercise by methylated-DNA immunoprecipitation sequencing (MeDIP-Seq). Differentially methylated regions (DMRs) in the pre-and post-exercise blood samples of superior and inferior horses were identified. Exercise altered the methylation patterns. After 30 min of exercise, 596 genes were hypomethylated and 715 genes were hypermethylated in the superior horse, whereas in the inferior horse, 868 genes were hypomethylated and 794 genes were hypermethylated. These genes were analyzed based on gene ontology (GO) annotations and the exercise-related pathway patterns in the two horses were compared. After exercise, gene regions related to cell division and adhesion were hypermethylated in the superior horse, whereas regions related to cell signaling and transport were hypermethylated in the inferior horse. Analysis of the distribution of methylated CpG islands confirmed the hypomethylation in the gene-body methylation regions after exercise. The methylation patterns of transposable elements also changed after exercise. Long interspersed nuclear elements (LINEs) showed abundance of DMRs. Collectively, our results serve as a basis to study exercise-based reprogramming of epigenetic traits.

Genome-wide analysis of DNA methylation patterns in horse.

BACKGROUND: DNA methylation is an epigenetic regulatory mechanism that plays an essential role in mediating biological processes and determining phenotypic plasticity in organisms. Although the horse reference genome and whole transcriptome data are publically available the global DNA methylation data are yet to be known. RESULTS: We report the first genome-wide DNA methylation characteristics data from skeletal muscle, heart, lung, and cerebrum tissues of thoroughbred (TH) and Jeju (JH) horses, an indigenous Korea breed, respectively by methyl-DNA immunoprecipitation sequencing. The analysis of the DNA methylation patterns indicated that the average methylation density was the lowest in the promoter region, while the density in the coding DNA sequence region was the highest. Among repeat elements, a relatively high density of methylation was observed in long interspersed nuclear elements compared to short interspersed nuclear elements or long terminal repeat elements. We also successfully identified differential methylated regions through a comparative analysis of corresponding tissues from TH and JH, indicating that the gene body regions showed a high methylation density. CONCLUSIONS: We provide report the first DNA methylation landscape and differentially methylated genomic regions (DMRs) of thoroughbred and Jeju horses, providing comprehensive DMRs maps of the DNA methylome. These data are invaluable resource to better understanding of epigenetics in the horse providing information for the further biological function analyses.