Biology subjects

Nakaki, R.

Publications and source records attributed to Nakaki, R..

9 recordsLinked to original sources

bioRxiv2026

A Pseudo-Longitudinal Methylome Projection Framework Defines a Buccal PACE-like Aging-Rate Score from Cross-Sectional DNA Methylation Data

BackgroundDNA methylation-based biomarkers have enabled robust estimation of biological age across tissues, and longitudinally trained measures such as DunedinPACE provide estimates of the pace of aging from blood methylomes. However, longitudinal methylation data are often unavailable, particularly for minimally invasive tissues such as buccal mucosa. Here, we developed a pseudo-longitudinal framework to estimate a buccal mucosa-derived PACE-like aging-rate score from cross-sectional methylome data. MethodsWe used a buccal biological age estimator as an internal pseudo-time axis. Methylation beta-values were transformed to M-values, and CpG-specific smooth functions of biological age were fitted in cross-validation. Local derivatives of these functions were used to project each individuals buccal methylome forward by a small time step. The projected methylome was converted back to beta-values, biological age was recalculated, and the change in biological age per unit time was defined as a pseudo-aging velocity. This raw velocity was transformed to a non-negative PACE-like score centered at 1.0. We then trained cross-fitted models to predict the derived score from buccal CpG methylation profiles. ResultsIn 151 individuals, the proposed score was reproducibly predicted from buccal methylomes in out-of-fold analysis, with a Pearson correlation of 0.706 and Spearman correlation of 0.710 between observed and predicted PACE-like scores. Sensitivity analyses across CpG selection size and regression models showed broadly consistent performance. In contrast, the proposed buccal PACE-like score showed only modest association with measured DunedinPACE, and alternative attempts to reconstruct DunedinPACE from buccal methylomes, including supervised proxy modeling and buccal-to-blood CpG imputation, showed limited sample-level performance. ConclusionsThese results support the feasibility of deriving a tissue-specific PACE-like aging-rate score from cross-sectional buccal methylome data by treating biological age as a pseudo-time axis. The proposed score should not be interpreted as a replacement for blood-derived DunedinPACE, but rather as an exploratory buccal methylome dynamics index that may capture tissue-specific aging-related variation.

bioinformatics↗

bioRxiv2026

Predicting Biological Age and Clinical Biomarkers from DNA Methylation Profiles of Cheek Mucosa

BackgroundDNA methylation-based biomarkers have been widely used to predict biological age; however, most blood-derived data have been used in most existing models, and whether cheek mucosa can serve as an alternative indicator for methylation-based estimation of aging-related and clinical phenotypes is unclear. MethodsDNA methylation profiles from cheek mucosa and whole blood of 186 Japanese adults were analyzed using Illumina Infinium Methylation Screening Array (MSA). Models were constructed to predict chronological age, phenotypic age, and clinical laboratory biomarkers from cheek mucosa- and blood-derived methylation data. In addition to applying the ordinary elastic net method, a two-stage residual learning method incorporating existing blood-based epigenetic clocks was applied for more accurate prediction of biological age. Sex-stratified analyses and comparisons of selected CpG features across sexes and tissues were performed. ResultsCheek mucosa-derived MSA methylation data enabled accurate prediction of chronological age (R = 0.965) and phenotypic age (R = 0.964) using the two-stage method. The performance gain achieved by the two-stage approach was greater for phenotypic age than for chronological age. Multiple clinical laboratory biomarkers could be predicted using cheek mucosa-derived methylation data, particularly after sex stratification, including inflammatory, metabolic, thyroid-related, and sex hormone-related markers. Most biomarkers that could be predicted using blood-derived methylation data were also predicted using cheek mucosa-derived methylation data. However, the CpG sites selected for prediction showed minimal overlap across sexes and tissues despite overlap in the corresponding predictable phenotypes. ConclusionsCheek mucosa-derived DNA methylation profiles measured using the MSA can predict chronological age, phenotypic age, and multiple clinically relevant laboratory biomarkers, supporting the utility of cheek mucosa as a less invasive alternative for methylation-based assessment of biological aging and systemic physiological state.

bioinformatics↗

bioRxiv2026

Dynamical instability measured by temporal entropy improves psychiatric classification across cohorts

Psychiatric disorders, such as attention-deficit/hyperactivity disorder, autism spectrum disorder, and schizophrenia, are clinically heterogeneous and lack objective biomarkers for reliable diagnosis. Although blood transcriptomic data have been proposed as a potential source of diagnostic information, their generalizability across independent cohorts remains unclear. This study aimed to assess whether biologically informed measures of dynamic instability enhance the reproducibility and generalizability of psychiatric classifications based on peripheral blood data by integrating publicly available blood transcriptomic datasets from multiple cohorts and evaluating classification performance using individual-level cross-validation and study-level holdout validation. To investigate the underlying biological structure, we applied a dynamic systems framework, including pseudotime-based vector field inference and attractor analysis. Additionally, we introduced temporal entropy as a measure of dynamic instability in the inferred transcriptomic trajectories. High classification performance was observed in individual-level cross-validation (area under the receiver operating characteristic [AUROC] > 0.8 across several comparisons); however, performance decreased substantially in study-level validation (AUROC {approx} 0.5-0.7), indicating limited generalizability. Attractor analysis revealed that transcriptomic states formed continuous and overlapping structures rather than distinct diagnostic clusters. Stratification based on temporal entropy identified a subset of individuals with unstable transcriptomic dynamics, and excluding these individuals improved the classification performance across most diagnostic pairs (AUROC > 0.7). These findings suggest that transcriptomic variability and dynamic instability contribute to the limited reproducibility of psychiatric classifications. Incorporating temporal entropy as a measure of system-level instability may enhance the robustness and interpretability of biomarker-based models and provide a new perspective on psychiatric disorders as dynamic systems.

bioinformatics↗

bioRxiv2026

Prediction of Biological Age and Blood Biomarkers from DNA Methylation Profiles Measured by the Methylation Screening Array: Development and Validation of Models on Japanese Data

BackgroundEpigenetic clocks based on DNA methylation (DNAm) are widely used indicators of biological aging; however, most established models have been developed using EPIC arrays and non-Japanese populations. The Methylation Screening Array (MSA), a cost-efficient platform with reduced CpG content, has not been evaluated for its capacity to support biological age estimation and biomarker prediction in Japanese cohorts. MethodsDNAm profiles and clinical laboratory measurements were obtained from 166 Japanese participants for model development; an independent cohort of 48 individuals processed at a separate institute was used for validation. A linear regression model was trained using the Elastic Net method to predict phenotypic age from MSA-derived methylation data, and a two-stage modeling (residual learning) framework integrating EPIC-based clock predictions with MSA-specific residual predictions was evaluated. Additional models were constructed to examine the predictability of 59 clinical biomarkers and their log-transformed variants, including sex-stratified analyses. ResultsThe MSA-based model accurately predicted phenotypic age in the validation dataset; prediction performance improved when the EPIC-based estimates were incorporated through the residual learning framework. Several clinical biomarkers, particularly those related to leukocyte composition and sex hormone regulation, were also predicted from the MSA data, although some markers were strongly affected by sex. Some of the nine constituent phenotypic age biomarkers were not individually predicted. ConclusionsMSA methylation profiles contain sufficient biological information for reliable prediction of epigenetic aging markers in Japanese individuals. These findings demonstrate the feasibility of applying cost-efficient MSA-based DNAm profiling for biological age prediction and provide a methodological foundation for expanding epigenetic biomarker applications in Japan.

bioinformatics↗

bioRxiv2025

Cross-Platform Calibration of Epigenetic Age Between the EPICv2 and MSA Arrays

Epigenetic clocks are regression models that predict biological age based on the DNA methylation patterns. They are developed using DNA methylation array data; however, systematic differences in the predicted values between the Infinium Methylation Screening Array (MSA) and Infinium MethylationEPIC v2.0 BeadChip (EPICv2) platforms have not been evaluated. We quantified the systematic differences between the MSA and EPICv2 arrays for six major epigenetic clocks (Horvath, Hannum, PhenoAge, GrimAge, GrimAge v2, and DunedinPACE) using 166 human blood samples measured on both platforms. For the five clocks expressed in years, the mean differences (MSA - EPICv2) ranged from -7.67 years for the Horvath clock to -0.634 years for GrimAge v2, despite strong correlations between platforms (r = 0.946-0.991). For DunedinPACE, which predicts the pace of aging, the mean difference was -0.117, with a correlation coefficient of 0.855 between the platforms. Based on these training data, we developed three linear regression models to map the MSA-based epigenetic ages and pace of aging to the EPICv2-based values, which were used as a reference: Model 1 (offset location calibration), Model 2 (slope and location calibration), and Model 3 (slope and location calibration with covariates). Validation using 48 independent samples measured at separate institutions showed that the calibration models reduced cross-platform differences for Horvath, Hannum, PhenoAge, GrimAge, and DunedinPACE. Although Model 3 yielded mean differences closest to zero for the Horvath clock (-0.227 years) and PhenoAge (-0.510 years), Model 1, the simplest specification, also reduced the mean cross-platform differences to -1.03 years for the Horvath clock and -1.37 years for PhenoAge. For DunedinPACE, Model 1 reduced the mean difference from -0.092 to 0.025 and the RMSE from 0.103 to 0.052. These methods are expected to enable reliable epigenetic age comparisons across platforms and facilitate large-scale epigenetic studies.

bioinformatics↗

bioRxiv2025

iComBat: An Incremental Framework for Batch Effect Correction in DNA Methylation Array Data

DNA methylation is associated with various diseases and aging; thus, longitudinal and repeated assessments of methylation patterns are crucial for revealing the mechanisms of disease onset and identifying factors associated with aging. The presence of batch effects influences the analysis of DNA methylation array data. Since existing methods for correcting batch effects are designed to correct all samples simultaneously, when data are incrementally measured and included, the correction of newly added data affects previous data. In this study, we propose an incremental framework for batch-effect correction based on ComBat, a location/scale adjustment approach using a Bayesian hierarchical model, and empirical Bayes estimation. Using numerical experiments and application to actual data, we demonstrate that the proposed method can correct newly included data without re-correcting the old data. The proposed method is expected to be useful for studies involving repeated measurements of DNA methylation, such as clinical trials of anti-aging interventions.

bioinformatics↗

bioRxiv2024

Transfer Elastic Net for Developing Epigenetic Clocks for the Japanese Population

MotivationThe epigenetic clock evaluates human biological age based on DNA methylation patterns. It takes the form of a regression model where the methylation ratio at CpG sites serves as the predictor, and chronological or adjusted age as the response variable. Due to the large number of CpG sites considered as candidate explanatory variables and their potential correlation, Elastic Net is commonly used to train the regression models. However, existing standard epigenetic clocks, trained on multiracial data, may exhibit biases due to genetic and environmental differences among specific racial groups. The development of epigenetic clocks suitable for a single-race population typically necessitates the collection of hundreds to thousands of samples to measure DNA methylation and other biomarkers, which costs a lot of time and money. Consequently, a method for developing accurate epigenetic clocks with relatively small sample sizes is needed. ResultsWe propose Transfer Elastic Net, a transfer learning approach that uses the parameter information from a linear regression model trained with the Elastic Net to estimate another model. Using this method, we constructed Horvaths, Hannums, and Levines types of epigenetic clocks using DNA methylation data from blood samples of 143 Japanese subjects. The data were transformed through principal component analysis to obtain more reliable clocks. The developed clocks demonstrated the smallest prediction errors compared to both the original clocks and those trained with the Elastic Net on the same Japanese data. Furthermore, the bias relative to the original clocks was reduced. Thus, we successfully developed epigenetic clocks that are well-suited for the Japanese population. Transfer Elastic Net can also be applied to develop epigenetic clocks for other specific populations, and is expected to be applied in various fields. Availabilityhttps://github.com/t-yui/TransferENet-EpigeneticClock

bioinformatics↗

bioRxiv2019

Comprehensive epigenome characterization reveals diverse transcriptional regulation across human vascular endothelial cells

BackgroundEndothelial cells (ECs) make up the innermost layer throughout the entire vasculature. Their phenotypes and physiological functions are initially regulated by developmental signals and extracellular stimuli. The underlying molecular mechanisms responsible for the diverse phenotypes of ECs from different organs are not well understood.\n\nResultsTo characterize the transcriptomic and epigenomic landscape in the vascular system, we cataloged gene expression and active histone marks in nine types of human ECs (generating 148 genome-wide datasets) and carried out a comprehensive analysis with chromatin interaction data. We identified 3,765 EC-specific enhancers, some of which were associated with disease-associated genetic variations. We also identified various candidate marker genes for each EC type. Notably, reflecting the developmental origins of ECs and their roles in angiogenesis, vasculogenesis and wound healing.\n\nConclusionsWhile the importance of several HOX genes for early vascular development and adult angiogenesis in pathological conditions has been reported, a systematic analysis of the regulation and roles of HOX genes in mature tissue cells has been lacking. These datasets provide a valuable resource for understanding the vascular system and associated diseases.

genomics↗

bioRxiv2018

Integrative analysis of transcription factor occupancy at enhancers and disease risk loci in noncoding genomic regions

Noncoding regions of the human genome possess enhancer activity and harbor risk loci for heritable diseases. Whereas the binding profiles of multiple transcription factors (TFs) have been investigated, integrative analysis with the large body of public data available so as to provide an overview of the function of such noncoding regions has remained a challenge. Here we have fully integrated public ChIP-seq and DNase-seq data (n ~ 70,000), including those for 743 human transcription factors (TFs) with 97 million binding sites, and have devised a data- mining platform --designated ChIP-Atlas--to identify significant TF-genome, TF-gene, and TF-TF interactions. Using this platform, we found that TFs enriched at macrophage or T-cell enhancers also accumulated around risk loci for autoimmune diseases, whereas those enriched at hepatocyte or macrophage enhancers were preferentially detected at loci associated with HDL-cholesterol levels. Of note, we identified \"hotspots\" around such risk loci that accumulated multiple TFs and are therefore candidates for causal variants. Integrative analysis of public chromatin-profiling data is thus able to identify TFs and tissues associated with heritable disorders.

genomics↗